From d144bae058919f4cbb0a1fa0458014efe5ad86c8 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Tue, 10 Feb 2026 08:34:10 -0600 Subject: [PATCH 01/25] 1st crf api --- docs/source/reference/main.rst | 106 ---------------- src/crf/api.py | 107 +++++++++++++++++ src/crf/io/excel_inputs.py | 39 ++++++ src/crf/io/excel_levers.py | 13 ++ src/crf/model/avg_runner.py | 54 +++++++++ src/crf/model/core_accounts.py | 164 +++++++++++++++++++++++++ src/crf/model/direct_cost.py | 199 +++++++++++++++++++++++++++++++ src/crf/model/finance.py | 112 +++++++++++++++++ src/crf/model/indirect_cost.py | 50 ++++++++ src/crf/model/learning.py | 98 +++++++++++++++ src/crf/model/pipeline.py | 62 ++++++++++ src/crf/sampling/lever_schema.py | 28 +++++ src/crf/sampling/postprocess.py | 6 + src/crf/sampling/sampler.py | 62 ++++++++++ src/crf/types.py | 42 +++++++ src/crf/utils/df_ops.py | 16 +++ src/crf/utils/serialize.py | 8 ++ 17 files changed, 1060 insertions(+), 106 deletions(-) delete mode 100644 docs/source/reference/main.rst create mode 100644 src/crf/api.py create mode 100644 src/crf/io/excel_inputs.py create mode 100644 src/crf/io/excel_levers.py create mode 100644 src/crf/model/avg_runner.py create mode 100644 src/crf/model/core_accounts.py create mode 100644 src/crf/model/direct_cost.py create mode 100644 src/crf/model/finance.py create mode 100644 src/crf/model/indirect_cost.py create mode 100644 src/crf/model/learning.py create mode 100644 src/crf/model/pipeline.py create mode 100644 src/crf/sampling/lever_schema.py create mode 100644 src/crf/sampling/postprocess.py create mode 100644 src/crf/sampling/sampler.py create mode 100644 src/crf/types.py create mode 100644 src/crf/utils/df_ops.py create mode 100644 src/crf/utils/serialize.py diff --git a/docs/source/reference/main.rst b/docs/source/reference/main.rst deleted file mode 100644 index 7b06104..0000000 --- a/docs/source/reference/main.rst +++ /dev/null @@ -1,106 +0,0 @@ - - -Accert Code Reference -========================== -This section provides detailed information on the Accert codebase. The Accert codebase is divided into two main sections: the main Accert class and utility functions. The main Accert class contains the main functions and methods for the Accert model, while the utility functions contain helper functions for the Accert model. - -.. toctree:: - :maxdepth: 1 - :caption: Contents: - -Accert Class --------------------- - -.. autosummary:: - :toctree: main/ - :template: function.rst - - - Main.Accert.__init__ - Main.Accert.setup_table_names - Main.Accert.load_obj - Main.Accert.get_current_COAs - Main.Accert.update_account_before_insert - Main.Accert.insert_new_COA - Main.Accert.insert_COA - Main.Accert.extract_variable_info_on_name - Main.Accert.extract_super_val - Main.Accert.update_input_variable - Main.Accert.update_variable_info_on_name - Main.Accert.update_super_variable - Main.Accert.extract_total_cost_on_name - Main.Accert.check_unit_conversion - Main.Accert.convert_unit - Main.Accert.convert_unit_scale - Main.Accert.update_total_cost - Main.Accert.update_total_cost_on_name - Main.Accert.get_var_value_by_name - Main.Accert.run_pre_alg - Main.Accert.update_account_value - Main.Accert.update_cost_element_on_name - Main.Accert.update_new_cost_elements - Main.Accert.update_new_accounts - Main.Accert.update_account_table_by_cost_elements - Main.Accert.roll_up_cost_elements - Main.Accert.roll_up_cost_elements_by_level - Main.Accert.roll_up_account_table - Main.Accert.roll_up_account_table_by_level - Main.Accert.roll_up_account_table_GNCOA - Main.Accert.sum_cost_elements_2C - Main.Accert.fetch_sum_and_update - Main.Accert.roll_up_lmt_account_2C - Main.Accert.roll_up_lmt_direct_cost - Main.Accert.cal_direct_cost_elements - Main.Accert.roll_up_lmt_account_table - Main.Accert.print_logo - Main.Accert.execute_accert - Main.Accert.process_reference_model - Main.Accert.process_power_inputs - Main.Accert.process_variables - Main.Accert.process_super_values - Main.Accert.process_COA - Main.Accert.process_level_accounts - Main.Accert.process_ce - Main.Accert.process_var - Main.Accert.process_alg - Main.Accert.check_and_process_total_cost - Main.Accert.check_total_cost_changed - Main.Accert.check_total_cost_accounts - Main.Accert.process_total_cost - Main.Accert.process_total_cost_accounts - Main.Accert.exit_with_error - Main.Accert.finalize_process - Main.Accert.generate_results - Main.Accert._generate_common_results - Main.Accert._common_cost_processing - Main.Accert._print_results - Main.Accert._pwr12be_processing - Main.Accert._no_cost_element_processing - Main.Accert.generate_results_table_with_cost_elements - Main.Accert._generate_excel - Main.Accert.generate_results_table - - -Accert Utility Functions --------------------------- -.. autosummary:: - :toctree: utility/ - :template: function.rst - - utility_accert.Utility_methods.__init__ - utility_accert.Utility_methods.setup_table_names - utility_accert.Utility_methods.print_table - utility_accert.Utility_methods.print_account - utility_accert.Utility_methods.print_leveled_accounts - utility_accert.Utility_methods.print_leveled_accounts_gncoa - utility_accert.Utility_methods.print_algorithm - utility_accert.Utility_methods.print_cost_element - utility_accert.Utility_methods.print_facility - utility_accert.Utility_methods.print_escalation - utility_accert.Utility_methods.print_variable - utility_accert.Utility_methods.print_user_request_parameter - utility_accert.Utility_methods.print_updated_cost_elements - utility_accert.Utility_methods.extract_affected_cost_elements - utility_accert.Utility_methods.extract_affected_accounts - utility_accert.Utility_methods.extract_user_changed_variables - utility_accert.Utility_methods.extract_changed_cost_elements diff --git a/src/crf/api.py b/src/crf/api.py new file mode 100644 index 0000000..5723ea4 --- /dev/null +++ b/src/crf/api.py @@ -0,0 +1,107 @@ +import csv +import pickle +import numpy as np + +from .io.excel_inputs import InputStore +from .io.excel_levers import read_lever_sheet +from .sampling.sampler import sample_levers +from .sampling.lever_schema import unpack_sample_column, STATIC_KEYS +from .sampling.postprocess import apply_itc_rounding +from .model.avg_runner import run_avg_all_units +from .utils.serialize import write_csv_row, stream_pickle_dump + + +def normalize_levers(levers: dict) -> dict: + """Convert raw lever dict to model-ready normalized values.""" + def label01(x, zero_label, one_label): + if x == 0: return zero_label + if x == 1: return one_label + return x + + return { + "num_orders": int(levers["num_orders"]), + "ITC_0": float(levers["itc_percent"]) / 100.0, + "n_ITC": levers["n_itc"], + "interest_rate_0": float(levers["interest_percent"]) / 100.0, + "design_completion_0": float(levers["design_completion_percent"]) / 100.0, + "Design_Maturity_0": levers["design_maturity"], + "proc_exp_0": levers["proc_exp"], + "N_proc": levers["N_proc"], + "ce_exp_0": levers["ce_exp"], + "N_cons": levers["N_cons"], + "ae_exp_0": levers["ae_exp"], + "N_AE": levers["N_AE"], + "standardization_0": float(levers["standardization_percent"]) / 100.0, + "mod_0": label01(levers["modularity_code"], "stick_built", "modularized"), + "BOP_grade_0": label01(levers["bop_grade_code"], "nuclear", "non_nuclear"), + "RB_grade_0": label01(levers["rb_grade_code"], "nuclear", "non_nuclear"), + } + + +def run_one_scenario(config: dict, levers: dict) -> dict: + store = InputStore(config["inputs_xlsx"]) + levers = apply_itc_rounding(levers) + inp = normalize_levers(levers) + + result = run_avg_all_units(config=config, inp=inp, store=store) + + # attach static input columns (raw sampled values, matching your previous CSV meaning) + # IMPORTANT: keep the same static key ordering as before + static_vals = { + "Num_orders": levers["num_orders"], + "ITC": levers["itc_percent"], + "n_ITC": levers["n_itc"], + "interest rate": levers["interest_percent"], + "Design completion": levers["design_completion_percent"], + "Design_Maturity_0": levers["design_maturity"], + "supply chain exp_0": levers["proc_exp"], + "N supply chain": levers["N_proc"], + "Const Proficiency": levers["ce_exp"], + "N const prof": levers["N_cons"], + "AE": levers["ae_exp"], + "N AE prof": levers["N_AE"], + "standardization": levers["standardization_percent"], + "modularity": levers["modularity_code"], + "BOP commercial": levers["bop_grade_code"], + "RB Safety Related": levers["rb_grade_code"], + } + + return {**static_vals, **result} + + +def run_sampling_from_excel( + config: dict, + levers_xlsx: str, + n_samples: int, + out_csv: str, + out_pkl: str, + seed: int | None = None +): + levers_df = read_lever_sheet(levers_xlsx, sheet_name="Levers") + samples = sample_levers(n_samples, levers_df, seed=seed) # shape (n_levers, n_samples) + + # headers: build from max num_orders in sampled set + max_orders = int(np.max(samples[0, :])) + + headers = ( + STATIC_KEYS + + [f"OCC_{i}" for i in range(max_orders)] + + [f"TCI_{i}" for i in range(max_orders)] + + [f"duration_{i}" for i in range(max_orders)] + + [ + "cons_duration_cumulative_wz_startup", + "occLastUnit", "TCILastUnit", "durationsLastUnit", + "avg_OCC", "avg_TCI", "avg_duration", + ] + ) + + with open(out_csv, "w", newline="", encoding="utf-8") as csv_f, open(out_pkl, "wb") as pkl_f: + writer = csv.DictWriter(csv_f, fieldnames=headers) + writer.writeheader() + + for i in range(n_samples): + levers = unpack_sample_column(samples[:, i]) + row = run_one_scenario(config, levers) + + write_csv_row(writer, row, headers) + stream_pickle_dump(row, pkl_f) diff --git a/src/crf/io/excel_inputs.py b/src/crf/io/excel_inputs.py new file mode 100644 index 0000000..7c39f59 --- /dev/null +++ b/src/crf/io/excel_inputs.py @@ -0,0 +1,39 @@ +import pandas as pd + +COLS = [ + "Account", "Title", "Total Cost (USD)", + "Factory Equipment Cost", "Site Labor Hours", + "Site Labor Cost", "Site Material Cost" +] + +class InputStore: + def __init__(self, inputs_xlsx: str): + self.inputs_xlsx = inputs_xlsx + self._baseline = {} + self._spending = None + + def get_baseline(self, reactor_type: str): + if reactor_type in self._baseline: + df, power = self._baseline[reactor_type] + return df.copy(), power + + if reactor_type == "Concept A": + df = pd.read_excel(self.inputs_xlsx, sheet_name="Concept_A", nrows=69)[COLS].copy() + power = 1056 * 1000 + elif reactor_type == "Concept B": + df = pd.read_excel(self.inputs_xlsx, sheet_name="Concept_B", nrows=69)[COLS].copy() + power = 310.8 * 1000 + else: + raise ValueError(f"Unknown reactor_type: {reactor_type}") + + self._baseline[reactor_type] = (df, power) + return df.copy(), power + + def get_spending_curve(self): + if self._spending is not None: + return self._spending + sp = pd.read_excel(self.inputs_xlsx, sheet_name="Ref Spending Curve", nrows=104, usecols="A:D") + months = sp["Month"].to_numpy(dtype=float) + cdfs = sp["CDF"].to_numpy(dtype=float) + self._spending = (months, cdfs) + return self._spending diff --git a/src/crf/io/excel_levers.py b/src/crf/io/excel_levers.py new file mode 100644 index 0000000..d17d281 --- /dev/null +++ b/src/crf/io/excel_levers.py @@ -0,0 +1,13 @@ +import pandas as pd + +REQUIRED_COLS = { + "Distribution", "Min", "Max", "Median", "Low", "High", + "Set", "Probabilities", "Type" +} + +def read_lever_sheet(levers_xlsx: str, sheet_name="Levers") -> pd.DataFrame: + df = pd.read_excel(levers_xlsx, sheet_name=sheet_name) + missing = REQUIRED_COLS - set(df.columns) + if missing: + raise ValueError(f"Levers sheet missing columns: {sorted(missing)}") + return df diff --git a/src/crf/model/avg_runner.py b/src/crf/model/avg_runner.py new file mode 100644 index 0000000..c8dc0b7 --- /dev/null +++ b/src/crf/model/avg_runner.py @@ -0,0 +1,54 @@ +import numpy as np +from .pipeline import calculate_final_result + +def run_avg_all_units(config: dict, inp: dict, store): + """ + Runs n_th=1..num_orders and returns a CSV-friendly dict: + OCC_i, TCI_i, duration_i plus summary metrics. + """ + num_orders = int(inp["num_orders"]) + OCC, TCI, DUR = [], [], [] + + for n_th in range(1, num_orders + 1): + _, occ, tci, dur = calculate_final_result(config, inp, store, n_th=n_th) + OCC.append(occ) + TCI.append(tci) + DUR.append(dur) + + OCC = np.array(OCC, dtype=float) + TCI = np.array(TCI, dtype=float) + DUR = np.array(DUR, dtype=float) + + # summaries + occLastUnit = float(OCC[-1]) + TCILastUnit = float(TCI[-1]) + durationsLastUnit = float(DUR[-1]) + + avg_OCC = float(np.mean(OCC)) + avg_TCI = float(np.mean(TCI)) + avg_duration = float(np.mean(DUR)) + + final_startup_duration = max(7, config["startup_0"] * (1 - 0.3) ** np.log2(num_orders)) + cons_duration_cumulative_wz_startup = ( + (1 - config["staggering_ratio"]) * np.sum(DUR[:-1]) + DUR[-1] + final_startup_duration + ) + + # expand arrays to OCC_i / TCI_i / duration_i + out = {} + for i, v in enumerate(OCC): + out[f"OCC_{i}"] = float(v) + for i, v in enumerate(TCI): + out[f"TCI_{i}"] = float(v) + for i, v in enumerate(DUR): + out[f"duration_{i}"] = float(v) + + out.update({ + "cons_duration_cumulative_wz_startup": float(cons_duration_cumulative_wz_startup), + "occLastUnit": occLastUnit, + "TCILastUnit": TCILastUnit, + "durationsLastUnit": durationsLastUnit, + "avg_OCC": avg_OCC, + "avg_TCI": avg_TCI, + "avg_duration": avg_duration, + }) + return out diff --git a/src/crf/model/core_accounts.py b/src/crf/model/core_accounts.py new file mode 100644 index 0000000..4b89d31 --- /dev/null +++ b/src/crf/model/core_accounts.py @@ -0,0 +1,164 @@ +import numpy as np +import pandas as pd + +def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFrame: + # account 21 + db.loc[db.Account == 21, "Factory Equipment Cost"] = ( + db.loc[db.Account == 212, "Factory Equipment Cost"].values + + db.loc[db.Account == 213, "Factory Equipment Cost"].values + + db.loc[db.Account == "211 plus 214 to 219", "Factory Equipment Cost"].values + ) + db.loc[db.Account == 21, "Site Material Cost"] = ( + db.loc[db.Account == 212, "Site Material Cost"].values + + db.loc[db.Account == 213, "Site Material Cost"].values + + db.loc[db.Account == "211 plus 214 to 219", "Site Material Cost"].values + ) + db.loc[db.Account == 21, "Site Labor Cost"] = ( + db.loc[db.Account == 212, "Site Labor Cost"].values + + db.loc[db.Account == 213, "Site Labor Cost"].values + + db.loc[db.Account == "211 plus 214 to 219", "Site Labor Cost"].values + ) + db.loc[db.Account == 21, "Site Labor Hours"] = ( + db.loc[db.Account == 212, "Site Labor Hours"].values + + db.loc[db.Account == 213, "Site Labor Hours"].values + + db.loc[db.Account == "211 plus 214 to 219", "Site Labor Hours"].values + ) + + # account 23 + db.loc[db.Account == 23, "Factory Equipment Cost"] = ( + db.loc[db.Account == "232.1", "Factory Equipment Cost"].values + + db.loc[db.Account == 233, "Factory Equipment Cost"].values + ) + db.loc[db.Account == 23, "Site Material Cost"] = ( + db.loc[db.Account == "232.1", "Site Material Cost"].values + + db.loc[db.Account == 233, "Site Material Cost"].values + ) + db.loc[db.Account == 23, "Site Labor Cost"] = ( + db.loc[db.Account == "232.1", "Site Labor Cost"].values + + db.loc[db.Account == 233, "Site Labor Cost"].values + ) + db.loc[db.Account == 23, "Site Labor Hours"] = ( + db.loc[db.Account == "232.1", "Site Labor Hours"].values + + db.loc[db.Account == 233, "Site Labor Hours"].values + ) + + # total costs for 21..26 components + for x in [21, 212, 213, "211 plus 214 to 219", 22, 23, "232.1", 233, 24, 26]: + db.loc[db["Account"] == x, "Total Cost (USD)"] = ( + db.loc[db["Account"] == x, "Factory Equipment Cost"] + + db.loc[db["Account"] == x, "Site Labor Cost"] + + db.loc[db["Account"] == x, "Site Material Cost"] + ) + + # subtotals + db.loc[db["Title"] == "10s - Subtotal", "Total Cost (USD)"] = db.loc[ + db["Account"].isin([11, 12, 13, 14, 15, 16, 18]), "Total Cost (USD)" + ].sum() + + db.loc[db["Title"] == "20s - Subtotal", "Total Cost (USD)"] = db.loc[ + db["Account"].isin([21, 22, 23, 24, 25, 26, 28]), "Total Cost (USD)" + ].sum() + + db.loc[db["Title"] == "30s - Subtotal", "Total Cost (USD)"] = db.loc[ + db["Account"].isin([31, 32, 33, 34, 35]), "Total Cost (USD)" + ].sum() + + db.loc[db["Title"] == "50s - Subtotal", "Total Cost (USD)"] = db.loc[ + db["Account"].isin([51, 52, 54]), "Total Cost (USD)" + ].sum() + + db.loc[db["Title"] == "60s - Subtotal", "Total Cost (USD)"] = db.loc[ + db["Account"].isin([62]), "Total Cost (USD)" + ].sum() + + # $/kWe lines + for t in ["10s", "20s", "30s", "40s", "50s", "60s"]: + db.loc[db["Title"] == f"{t} - $/kWe", "Total Cost (USD)"] = ( + db.loc[db["Title"] == f"{t} - Subtotal", "Total Cost (USD)"].values / reactor_power + ) + + # final rollups + db.loc[db["Title"] == "Total Direct Capital Cost (Accounts 10 to 20)", "Total Cost (USD)"] = ( + db.loc[db["Title"] == "10s - Subtotal", "Total Cost (USD)"].values + + db.loc[db["Title"] == "20s - Subtotal", "Total Cost (USD)"].values + ) + + db.loc[db["Title"] == "Base Construction Cost (Accounts 10 to 30)", "Total Cost (USD)"] = ( + db.loc[db["Title"] == "Total Direct Capital Cost (Accounts 10 to 20)", "Total Cost (USD)"].values + + db.loc[db["Title"] == "30s - Subtotal", "Total Cost (USD)"].values + ) + + db.loc[db["Title"] == "Total Overnight Cost (Accounts 10 to 50)", "Total Cost (USD)"] = ( + db.loc[db["Title"] == "Base Construction Cost (Accounts 10 to 30)", "Total Cost (USD)"].values + + db.loc[db["Title"] == "50s - Subtotal", "Total Cost (USD)"].values + ) + + db.loc[db["Title"] == "Total Capital Investment Cost (All Accounts)", "Total Cost (USD)"] = ( + db.loc[db["Title"] == "Total Overnight Cost (Accounts 10 to 50)", "Total Cost (USD)"].values + + db.loc[db["Title"] == "60s - Subtotal", "Total Cost (USD)"].values + ) + + # final $/kWe + db.loc[db["Title"] == "(Accounts 10 to 20) US$/kWe", "Total Cost (USD)"] = ( + db.loc[db["Title"] == "Total Direct Capital Cost (Accounts 10 to 20)", "Total Cost (USD)"].values / reactor_power + ) + db.loc[db["Title"] == "(Accounts 10 to 30) US$/kWe", "Total Cost (USD)"] = ( + db.loc[db["Title"] == "Base Construction Cost (Accounts 10 to 30)", "Total Cost (USD)"].values / reactor_power + ) + db.loc[db["Title"] == "(Accounts 10 to 50) US$/kWe", "Total Cost (USD)"] = ( + db.loc[db["Title"] == "Total Overnight Cost (Accounts 10 to 50)", "Total Cost (USD)"].values / reactor_power + ) + db.loc[db["Title"] == "(Accounts 10 to 60) US$/kWe", "Total Cost (USD)"] = ( + db.loc[db["Title"] == "Total Capital Investment Cost (All Accounts)", "Total Cost (USD)"].values / reactor_power + ) + return db + +def ITC_reduction_factor(itc_level: float) -> float: + itc_values = [0, 0.06, 0.3, 0.4, 0.5] + factors = [1, 0.95, 0.73, 0.63, 0.53] + return float(np.interp(itc_level, itc_values, factors)) + +def sum_lab_hrs(db: pd.DataFrame): + return ( + db.loc[db.Account == 21, "Site Labor Hours"].values + + db.loc[db.Account == 22, "Site Labor Hours"].values + + db.loc[db.Account == 23, "Site Labor Hours"].values + + db.loc[db.Account == 24, "Site Labor Hours"].values + + db.loc[db.Account == 26, "Site Labor Hours"].values + ) + +def update_cons_duration(db0: pd.DataFrame, db1: pd.DataFrame, ref_duration: float): + sum_old = ( + db0.loc[db0.Account == 21, "Site Labor Hours"].values + + db0.loc[db0.Account == 22, "Site Labor Hours"].values + + db0.loc[db0.Account == 23, "Site Labor Hours"].values + + db0.loc[db0.Account == 24, "Site Labor Hours"].values + + db0.loc[db0.Account == 26, "Site Labor Hours"].values + ) + sum_new = ( + db1.loc[db1.Account == 21, "Site Labor Hours"].values + + db1.loc[db1.Account == 22, "Site Labor Hours"].values + + db1.loc[db1.Account == 23, "Site Labor Hours"].values + + db1.loc[db1.Account == 24, "Site Labor Hours"].values + + db1.loc[db1.Account == 26, "Site Labor Hours"].values + ) + lab_delta = (sum_new - sum_old) / sum_old + return 0.3 * lab_delta * ref_duration + ref_duration + +def update_cons_duration_2(db0, db1, ref_duration, prev_cons_duration, baseline_lab_hours): + sum_old = ( + db0.loc[db0.Account == 21, "Site Labor Hours"].values + + db0.loc[db0.Account == 22, "Site Labor Hours"].values + + db0.loc[db0.Account == 23, "Site Labor Hours"].values + + db0.loc[db0.Account == 24, "Site Labor Hours"].values + + db0.loc[db0.Account == 26, "Site Labor Hours"].values + ) + sum_new = ( + db1.loc[db1.Account == 21, "Site Labor Hours"].values + + db1.loc[db1.Account == 22, "Site Labor Hours"].values + + db1.loc[db1.Account == 23, "Site Labor Hours"].values + + db1.loc[db1.Account == 24, "Site Labor Hours"].values + + db1.loc[db1.Account == 26, "Site Labor Hours"].values + ) + lab_delta = (sum_new - sum_old) / baseline_lab_hours + return 0.3 * lab_delta * ref_duration + prev_cons_duration diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py new file mode 100644 index 0000000..1e68631 --- /dev/null +++ b/src/crf/model/direct_cost.py @@ -0,0 +1,199 @@ +# src/crf/model/direct_cost.py +# Direct-cost pipeline: +# add_factory_cost -> add_land_cost -> add_BOP_RP_grades -> add_bulk_ordering -> add_reworking_productivity +# Returns updated df and construction duration (no supply-chain delay yet) + +import numpy as np +import pandas as pd + +from ..utils.df_ops import getv, setv, mulv +from .core_accounts import ( + update_high_level_costs, + sum_lab_hrs, + update_cons_duration, + update_cons_duration_2, +) + +COLS = [ + "Account", "Title", "Total Cost (USD)", + "Factory Equipment Cost", "Site Labor Hours", + "Site Labor Cost", "Site Material Cost" +] + +ACCT_DIRECT = [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26] + + +def add_factory_cost(df: pd.DataFrame, power: float, f_22: float, f_2321: float, num_orders: int): + db = df.copy() + + # Add factory-building shares; clear old values first (keep numeric dtype) + setv(db, 22, "Factory Equipment Cost", np.nan) + setv(db, "232.1", "Factory Equipment Cost", np.nan) + + setv(db, 22, "Factory Equipment Cost", float(getv(df, 22, "Factory Equipment Cost")) + f_22 / num_orders) + setv(db, "232.1", "Factory Equipment Cost", float(getv(df, "232.1", "Factory Equipment Cost")) + f_2321 / num_orders) + + db = update_high_level_costs(db, power)[COLS].copy() + baseline_lab_hours = sum_lab_hrs(db) + return db, baseline_lab_hours + + +def add_land_cost(df: pd.DataFrame, land_cost_per_acre: float, power: float): + db = df.copy() + factor = land_cost_per_acre / 22000.0 + for acct in [11, 12, 51]: + setv(db, acct, "Total Cost (USD)", np.nan) + setv(db, acct, "Total Cost (USD)", float(getv(df, acct, "Total Cost (USD)")) * factor) + return update_high_level_costs(db, power)[COLS].copy() + + +def add_BOP_RP_grades( + df: pd.DataFrame, + RB_grade_0: str, + BOP_grade_0: str, + power: float, + reactor_type: str, + n_th: int, + mod_0: str +): + # RB grade does not change; BOP becomes non_nuclear after FOAK + RB_grade = RB_grade_0 + BOP_grade = BOP_grade_0 if n_th == 1 else "non_nuclear" + + db = df.copy() + + if RB_grade == "non_nuclear": + mulv( + db, [212], + ["Site Material Cost", "Site Labor Cost", "Site Labor Hours", "Factory Equipment Cost"], + 0.6 + ) + + if BOP_grade == "non_nuclear": + mulv( + db, [213], + ["Site Material Cost", "Site Labor Cost", "Site Labor Hours", "Factory Equipment Cost"], + 0.6 + ) + # for 232.1 apply to factory+labor but NOT material (matches your original) + mulv( + db, ["232.1"], + ["Factory Equipment Cost", "Site Labor Cost", "Site Labor Hours"], + 0.6 + ) + + db2 = update_high_level_costs(db, power)[COLS].copy() + + # duration update from grade change + duration_ref = 125 if reactor_type == "Concept A" else 80 + new_dur = float(update_cons_duration(df, db2, duration_ref)) + + # modularity factor on duration; for n>=2 assume modularized + mod = mod_0 if n_th == 1 else "modularized" + mod_factor = 0.8 if mod == "modularized" else 1.0 + + return db2, new_dur * mod_factor + + +def add_bulk_ordering(df: pd.DataFrame, num_orders: int, f_22: float, f_2321: float, power: float): + """ + Applies average learning reduction to factory equipment cost of 22 and 232.1, + but keeps factory-building portion intact. + """ + db = df.copy() + + lr22 = 0.1802341659291420 + lr2321 = 0.2607462372040820 + + red22 = 0.0 + red2321 = 0.0 + for ith in range(1, num_orders + 1): + red22 += ((1 - lr22) ** np.log2(ith)) / num_orders + red2321 += ((1 - lr2321) ** np.log2(ith)) / num_orders + + old22 = float(getv(df, 22, "Factory Equipment Cost")) + new22 = red22 * (old22 - (f_22 / num_orders)) + (f_22 / num_orders) + setv(db, 22, "Factory Equipment Cost", new22) + + old2321 = float(getv(df, "232.1", "Factory Equipment Cost")) + new2321 = red2321 * (old2321 - (f_2321 / num_orders)) + (f_2321 / num_orders) + setv(db, "232.1", "Factory Equipment Cost", new2321) + + return update_high_level_costs(db, power)[COLS].copy() + + +def add_reworking_productivity( + df: pd.DataFrame, + reactor_type: str, + n_th: int, + design_completion_0: float, + ae_exp_0: float, + N_AE: float, + ce_exp_0: float, + N_cons: float, + power: float, + prev_cons_duration: float, + baseline_lab_hours +): + if n_th == 1: + design_completion = design_completion_0 + ae_exp = ae_exp_0 + ce_exp = ce_exp_0 + else: + design_completion = 1.0 + ae_exp = min(ae_exp_0 + (2 / N_AE) * (n_th - 1), 2) + ce_exp = min(ce_exp_0 + (2 / N_cons) * (n_th - 1), 2) + + productivity = 0.145 * ce_exp + 0.71 + + if reactor_type == "Concept B": + rework = (-0.9 * design_completion + 1.9) * (-0.15 * ae_exp + 1.3) * (-0.15 * ce_exp + 1.3) + ref_duration = 80 + else: + rework = (-0.69 * design_completion + 1.69) * (-0.125 * ae_exp + 1.25) * (-0.125 * ce_exp + 1.25) + ref_duration = 125 + + db = df.copy() + + for acct in ACCT_DIRECT: + setv(db, acct, "Factory Equipment Cost", float(getv(df, acct, "Factory Equipment Cost")) * rework) + setv(db, acct, "Site Material Cost", float(getv(df, acct, "Site Material Cost")) * rework) + setv(db, acct, "Site Labor Hours", float(getv(df, acct, "Site Labor Hours")) * rework / productivity) + setv(db, acct, "Site Labor Cost", float(getv(df, acct, "Site Labor Cost")) * rework / productivity) + + db2 = update_high_level_costs(db, power)[COLS].copy() + + new_dur = float(update_cons_duration_2(df, db2, ref_duration, prev_cons_duration, baseline_lab_hours)) + return db2, new_dur + + +def update_direct_cost( + store, + reactor_type: str, + n_th: int, + f_22: float, + f_2321: float, + land_cost_per_acre_0: float, + RB_grade_0: str, + BOP_grade_0: str, + num_orders: int, + design_completion_0: float, + ae_exp_0: float, + N_AE: float, + ce_exp_0: float, + N_cons: float, + mod_0: str +): + reactor_df, power = store.get_baseline(reactor_type) + + db, baseline_lab_hours = add_factory_cost(reactor_df, power, f_22, f_2321, num_orders) + db = add_land_cost(db, land_cost_per_acre_0, power) + db, prev_dur = add_BOP_RP_grades(db, RB_grade_0, BOP_grade_0, power, reactor_type, n_th, mod_0) + db = add_bulk_ordering(db, num_orders, f_22, f_2321, power) + db, dur_no_delay = add_reworking_productivity( + db, reactor_type, n_th, + design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, + power, prev_dur, baseline_lab_hours + ) + + return db, dur_no_delay diff --git a/src/crf/model/finance.py b/src/crf/model/finance.py new file mode 100644 index 0000000..6191352 --- /dev/null +++ b/src/crf/model/finance.py @@ -0,0 +1,112 @@ +# src/crf/model/finance.py +# Insurance, interest, ITC application + +import numpy as np +import pandas as pd + +from .core_accounts import update_high_level_costs, ITC_reduction_factor + +COLS = [ + "Account", "Title", "Total Cost (USD)", + "Factory Equipment Cost", "Site Labor Hours", + "Site Labor Cost", "Site Material Cost" +] + + +def insurance_cost_update(base_df: pd.DataFrame, df: pd.DataFrame, power: float): + db = df.copy() + + new_tot = float(db.loc[db["Title"].eq("20s - Subtotal"), "Total Cost (USD)"].iloc[0]) + \ + float(db.loc[db["Title"].eq("30s - Subtotal"), "Total Cost (USD)"].iloc[0]) + + base_tot = float(base_df.loc[base_df["Title"].eq("20s - Subtotal"), "Total Cost (USD)"].iloc[0]) + \ + float(base_df.loc[base_df["Title"].eq("30s - Subtotal"), "Total Cost (USD)"].iloc[0]) + + factor = new_tot / base_tot + + old_52 = float(df.loc[df["Account"].eq(52), "Total Cost (USD)"].iloc[0]) + db.loc[db["Account"].eq(52), "Total Cost (USD)"] = old_52 * factor + + return update_high_level_costs(db, power)[COLS].copy() + + +def update_interest_cost( + store, + df: pd.DataFrame, + final_construction_duration: float, + interest_rate: float, + startup_0: float, + n_th: int, + power: float +): + Months, CDFs = store.get_spending_curve() + dur = float(final_construction_duration) + + n_years = int(dur / 12) + if n_years <= 0: + annual_periods = np.array([dur - 1]) + else: + annual_periods = np.linspace(12, 12 * n_years, n_years) + if max(annual_periods) < int(dur) - 1: + annual_periods = np.append(annual_periods, dur - 1) + + new_period = 103 * annual_periods / dur + annual_cum_spend = np.interp(new_period, Months, CDFs) + annual_spend = np.append(annual_cum_spend[0], np.diff(annual_cum_spend)) + + tot_overnight_cost = float( + df.loc[df["Title"].eq("Total Overnight Cost (Accounts 10 to 50)"), "Total Cost (USD)"].iloc[0] + ) + + annual_loan_add = annual_spend * tot_overnight_cost + interest_exp = ((1 + interest_rate) ** ((dur - annual_periods) / 12)) * annual_loan_add - annual_loan_add + tot_int_exp_construction = float(np.sum(interest_exp)) + + if n_th == 1: + startup = startup_0 + else: + startup = max(7, startup_0 * (1 - 0.3) ** np.log2(n_th)) + + int_exp_startup = (tot_int_exp_construction + tot_overnight_cost) * ((1 + interest_rate) ** (startup / 12)) \ + - (tot_int_exp_construction + tot_overnight_cost) + + db = df.copy() + db.loc[db["Account"].eq(62), "Total Cost (USD)"] = float(int_exp_startup + tot_int_exp_construction) + + db2 = update_high_level_costs(db, power)[COLS].copy() + + tot_cap_investment = float( + db2.loc[db2["Title"].eq("Total Capital Investment Cost (All Accounts)"), "Total Cost (USD)"].iloc[0] + ) + return db2, tot_overnight_cost, tot_cap_investment + + +def update_itc( + df: pd.DataFrame, + tot_overnight_cost: float, + tot_cap_investment: float, + n_th: int, + ITC_0: float, + n_ITC: int, + reactor_power: float +): + ITC = ITC_0 if n_th <= n_ITC else 0.0 + + db = df.copy() + + itc_factor = ITC_reduction_factor(ITC) + itc_reduced_occ = tot_overnight_cost * itc_factor + occ_reduction = tot_overnight_cost - itc_reduced_occ + + # Titles must exist (same as your original) + db.loc[db["Title"].eq("Total Overnight Cost - ITC reduced"), "Total Cost (USD)"] = itc_reduced_occ + db.loc[db["Title"].eq("Total Overnight Cost -ITC reduced (US$/kWe)"), "Total Cost (USD)"] = itc_reduced_occ / reactor_power + db.loc[db["Title"].eq("Total Capital Investment Cost - ITC reduced"), "Total Cost (USD)"] = tot_cap_investment - occ_reduction + + levelized_NCI = float( + db.loc[db["Title"].eq("Total Capital Investment Cost - ITC reduced"), "Total Cost (USD)"].iloc[0] / reactor_power + ) + db.loc[db["Title"].eq("Total Capital Investment Cost - ITC reduced (US$/kWe)"), "Total Cost (USD)"] = levelized_NCI + + db2 = update_high_level_costs(db, reactor_power)[COLS].copy() + return db2, itc_reduced_occ / reactor_power, levelized_NCI diff --git a/src/crf/model/indirect_cost.py b/src/crf/model/indirect_cost.py new file mode 100644 index 0000000..939cf5f --- /dev/null +++ b/src/crf/model/indirect_cost.py @@ -0,0 +1,50 @@ +# src/crf/model/indirect_cost.py +# Indirect costs accounts 31–35, unchanged equations + +import pandas as pd + +from ..utils.df_ops import setv +from .core_accounts import update_high_level_costs + +COLS = [ + "Account", "Title", "Total Cost (USD)", + "Factory Equipment Cost", "Site Labor Hours", + "Site Labor Cost", "Site Material Cost" +] + + +def update_indirect_cost( + n_th: int, + standardization_0: float, + df: pd.DataFrame, + final_construction_duration: float, + power: float +): + standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 + factor_35 = (10 / 3) * (1 - standardization) + + db = df.copy() + + sum_new_mat_cost = 0.0 + sum_new_lab_cost = 0.0 + sum_new_lab_hrs = 0.0 + for acct in [21, 22, 23, 24, 26]: + sum_new_mat_cost += float(db.loc[db["Account"].eq(acct), "Site Material Cost"].iloc[0]) + sum_new_lab_cost += float(db.loc[db["Account"].eq(acct), "Site Labor Cost"].iloc[0]) + sum_new_lab_hrs += float(db.loc[db["Account"].eq(acct), "Site Labor Hours"].iloc[0]) + + dur = float(final_construction_duration) + + val31 = (sum_new_mat_cost * 0.785 * sum_new_lab_hrs / dur / 160 / 1058) + sum_new_lab_cost * 0.36 + val32 = sum_new_lab_cost * 0.36 * 3.661 * dur / 72 + val33 = 0.04207006 * val32 + val34 = 0.00354234616938 * val32 + val35 = (0.27017603 * val32) * factor_35 + + setv(db, 31, "Total Cost (USD)", val31) + setv(db, 32, "Total Cost (USD)", val32) + setv(db, 33, "Total Cost (USD)", val33) + setv(db, 34, "Total Cost (USD)", val34) + setv(db, 35, "Total Cost (USD)", val35) + + return update_high_level_costs(db, power)[COLS].copy() diff --git a/src/crf/model/learning.py b/src/crf/model/learning.py new file mode 100644 index 0000000..413f9c8 --- /dev/null +++ b/src/crf/model/learning.py @@ -0,0 +1,98 @@ +# src/crf/model/learning.py +# Learning effects: +# - learning_effect (cost learning by doing on direct accounts) +# - act_cons_duration_plus_delay (supply chain delay model) +# - duration_learning_effect (duration learning by doing) + +import numpy as np +import pandas as pd + +from ..utils.df_ops import getv, setv +from .core_accounts import update_high_level_costs + +COLS = [ + "Account", "Title", "Total Cost (USD)", + "Factory Equipment Cost", "Site Labor Hours", + "Site Labor Cost", "Site Material Cost" +] + +ACCT_DIRECT = [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26] + + +def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power: float): + # standardization cap for FOAK + standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 + + # fitted learning rates (same order as ACCT_DIRECT) + mat_lr = np.array([ + 0.099588665391, 0.099588665391, 0.099588665391, 0.080817992281, + 0.0, 0.099588665391, 0.099588665391, 0.099588665391 + ]) * standardization / 0.7 + + lab_lr = np.array([ + 0.180678729399, 0.180678729399, 0.180678729399, 0.146555539499, + 0.137148574884, 0.180678729399, 0.180678729399, 0.180678729399 + ]) * standardization / 0.7 + + db = df.copy() + + for idx, acct in enumerate(ACCT_DIRECT): + mat_mult = (1 - float(mat_lr[idx])) ** np.log2(n_th) + lab_mult = (1 - float(lab_lr[idx])) ** np.log2(n_th) + + setv(db, acct, "Site Material Cost", float(getv(df, acct, "Site Material Cost")) * mat_mult) + setv(db, acct, "Site Labor Hours", float(getv(df, acct, "Site Labor Hours")) * lab_mult) + setv(db, acct, "Site Labor Cost", float(getv(df, acct, "Site Labor Cost")) * lab_mult) + + return update_high_level_costs(db, power)[COLS].copy() + + +def act_cons_duration_plus_delay( + reactor_type: str, + n_th: int, + Design_Maturity_0, + proc_exp_0, + N_proc, + cons_duration_no_delay: float +): + if n_th == 1: + Design_Maturity = Design_Maturity_0 + proc_exp = proc_exp_0 + else: + Design_Maturity = 2 + proc_exp = min(proc_exp_0 + (2 / N_proc) * (n_th - 1), 2) + + if reactor_type == "Concept B": + task_length_multiplier = 1.0 + ref_construction_duration = 64 + else: # Concept A + task_length_multiplier = 100 / 64 + ref_construction_duration = 100 + + B_21 = 42.1 * task_length_multiplier + B_22 = 60.2 * task_length_multiplier + B_23 = 14.8 * task_length_multiplier + B_24 = 3.6 * task_length_multiplier + B_25 = 10.1 * task_length_multiplier + B_26 = 43.9 * task_length_multiplier + + D = -6 * Design_Maturity - 3 * proc_exp + 18 + + T_21 = B_21 + D + T_22 = 0.09 * (B_21 + D) + B_22 + D + T_23 = 0.24 * (B_21 + D) + B_23 + D + T_24 = 0.24 * (B_21 + D) + 0.34 * (B_23 + D) + B_24 + D + T_25 = 0.18 * (B_21 + D) + B_25 + D + T_26 = 0.21 * (B_21 + D) + B_26 + D + + T_end = max(T_21, T_22, T_23, T_24, T_25, T_26) + + supply_chain_delay = max(T_end - ref_construction_duration, 0) + return float(cons_duration_no_delay) + float(supply_chain_delay) + + +def duration_learning_effect(n_th: int, standardization_0: float, actual_construction_duration_plus_delay: float): + standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 + fitted_LR_duration = 0.103719051 * standardization / 0.7 + duration_multiplier = (1 - fitted_LR_duration) ** np.log2(n_th) + return float(duration_multiplier) * float(actual_construction_duration_plus_delay) diff --git a/src/crf/model/pipeline.py b/src/crf/model/pipeline.py new file mode 100644 index 0000000..139b288 --- /dev/null +++ b/src/crf/model/pipeline.py @@ -0,0 +1,62 @@ +from .direct_cost import update_direct_cost +from .learning import learning_effect, act_cons_duration_plus_delay, duration_learning_effect +from .indirect_cost import update_indirect_cost +from .finance import insurance_cost_update, update_interest_cost, update_itc + +def calculate_final_result(config: dict, inp: dict, store, n_th: int): + """ + One unit (n_th) calculation. + Returns: (final_df, levelized_net_OCC, levelized_NCI, final_construction_duration_scalar) + """ + base0, power = store.get_baseline(config["reactor_type"]) + + # direct updates (returns df + duration without supply chain delay) + direct_df, dur_no_delay = update_direct_cost( + store=store, + reactor_type=config["reactor_type"], + n_th=n_th, + f_22=config["f_22"], + f_2321=config["f_2321"], + land_cost_per_acre_0=config["land_cost_per_acre_0"], + RB_grade_0=inp["RB_grade_0"], + BOP_grade_0=inp["BOP_grade_0"], + num_orders=inp["num_orders"], + design_completion_0=inp["design_completion_0"], + ae_exp_0=inp["ae_exp_0"], + N_AE=inp["N_AE"], + ce_exp_0=inp["ce_exp_0"], + N_cons=inp["N_cons"], + mod_0=inp["mod_0"], + ) + + # duration: add delay + learning + dur_plus_delay = act_cons_duration_plus_delay( + reactor_type=config["reactor_type"], + n_th=n_th, + Design_Maturity_0=inp["Design_Maturity_0"], + proc_exp_0=inp["proc_exp_0"], + N_proc=inp["N_proc"], + cons_duration_no_delay=dur_no_delay, + ) + final_dur = duration_learning_effect(n_th, inp["standardization_0"], dur_plus_delay) + + # learning on direct costs + direct_plus_learning = learning_effect(direct_df, n_th, inp["standardization_0"], power) + + # indirect costs + with_indirect = update_indirect_cost(n_th, inp["standardization_0"], direct_plus_learning, final_dur, power) + + # insurance + interest + ITC + with_insurance = insurance_cost_update(base0, with_indirect, power) + with_interest, tot_occ, tot_cap = update_interest_cost( + store=store, + df=with_insurance, + final_construction_duration=final_dur, + interest_rate=inp["interest_rate_0"], + startup_0=config["startup_0"], + n_th=n_th, + power=power, + ) + final_df, net_occ, nci = update_itc(with_interest, tot_occ, tot_cap, n_th, inp["ITC_0"], inp["n_ITC"], power) + + return final_df, net_occ, nci, float(final_dur) diff --git a/src/crf/sampling/lever_schema.py b/src/crf/sampling/lever_schema.py new file mode 100644 index 0000000..81b7df2 --- /dev/null +++ b/src/crf/sampling/lever_schema.py @@ -0,0 +1,28 @@ +LEVER_ORDER = [ + "num_orders", + "itc_percent", + "n_itc", + "interest_percent", + "design_completion_percent", + "design_maturity", + "proc_exp", + "N_proc", + "ce_exp", + "N_cons", + "ae_exp", + "N_AE", + "standardization_percent", + "modularity_code", + "bop_grade_code", + "rb_grade_code", +] + +STATIC_KEYS = [ + "Num_orders", "ITC", "n_ITC", "interest rate", "Design completion", "Design_Maturity_0", + "supply chain exp_0", "N supply chain", "Const Proficiency", "N const prof", "AE", + "N AE prof", "standardization", "modularity", "BOP commercial", "RB Safety Related", +] + +def unpack_sample_column(col_vec): + # col_vec length must match LEVER_ORDER + return dict(zip(LEVER_ORDER, col_vec)) diff --git a/src/crf/sampling/postprocess.py b/src/crf/sampling/postprocess.py new file mode 100644 index 0000000..0f21292 --- /dev/null +++ b/src/crf/sampling/postprocess.py @@ -0,0 +1,6 @@ +def closest(value, options): + return min(options, key=lambda x: abs(x - value)) + +def apply_itc_rounding(lever_dict, allowed=(6, 30, 40, 50)): + lever_dict["itc_percent"] = closest(lever_dict["itc_percent"], allowed) + return lever_dict diff --git a/src/crf/sampling/sampler.py b/src/crf/sampling/sampler.py new file mode 100644 index 0000000..7a080c6 --- /dev/null +++ b/src/crf/sampling/sampler.py @@ -0,0 +1,62 @@ +# src/crf/sampling/sampler.py +import numpy as np +import scipy.stats as stats + +def trunc_normal(min_, low_, med_, high_, max_, n): + std = (high_ - low_) / 4 + mean = med_ + a, b = (min_ - mean) / std, (max_ - mean) / std + return stats.truncnorm.rvs(a=a, b=b, loc=mean, scale=std, size=n) + +def truncate_shift_lognormal(min_, low_, med_, high_, max_, n): + std = (np.log(high_) - np.log(low_)) / 4 + mean = np.log(med_) + a, b = (np.log(min_) - mean) / std, (np.log(max_) - mean) / std + x = stats.truncnorm.rvs(a=a, b=b, loc=mean, scale=std, size=n) + return np.exp(x) + +def sample_levers(n_samples: int, levers_df, seed: int | None = None) -> np.ndarray: + if seed is not None: + np.random.seed(seed) + + n_var = levers_df.shape[0] + out = np.empty((n_var, n_samples), dtype=float) + + dist = levers_df["Distribution"] + min_ = levers_df["Min"]; max_ = levers_df["Max"]; med_ = levers_df["Median"] + low_ = levers_df["Low"]; high_ = levers_df["High"] + set_ = levers_df["Set"]; p_ = levers_df["Probabilities"] + typ = levers_df["Type"] + + for i in range(n_var): + d = str(dist.iloc[i]).lower() + + if d in ["normal", "gaussian"]: + out[i, :] = trunc_normal(min_.iloc[i], low_.iloc[i], med_.iloc[i], high_.iloc[i], max_.iloc[i], n_samples) + if str(typ.iloc[i]).lower() in ["discrete", "integer"]: + out[i, :] = np.round(out[i, :]) + + elif d in ["lognormal"]: + out[i, :] = truncate_shift_lognormal(min_.iloc[i], low_.iloc[i], med_.iloc[i], high_.iloc[i], max_.iloc[i], n_samples) + if str(typ.iloc[i]).lower() in ["discrete", "integer"]: + out[i, :] = np.round(out[i, :]) + + elif d in ["triangular"]: + out[i, :] = np.random.triangular(left=min_.iloc[i], mode=med_.iloc[i], right=max_.iloc[i], size=n_samples) + if str(typ.iloc[i]).lower() in ["discrete", "integer"]: + out[i, :] = np.round(out[i, :]) + + elif d in ["uniform"]: + out[i, :] = np.random.uniform(left=min_.iloc[i], right=max_.iloc[i], size=n_samples) + if str(typ.iloc[i]).lower() in ["discrete", "integer"]: + out[i, :] = np.round(out[i, :]) + + elif d in ["binary", "boolean", "set"]: + choices = eval("[" + str(set_.iloc[i]) + "]") + probs = eval("[" + str(p_.iloc[i]) + "]") + out[i, :] = np.random.choice(choices, size=n_samples, p=probs) + + else: + raise ValueError(f"Invalid Distribution='{dist.iloc[i]}' at row {i}") + + return out diff --git a/src/crf/types.py b/src/crf/types.py new file mode 100644 index 0000000..752f253 --- /dev/null +++ b/src/crf/types.py @@ -0,0 +1,42 @@ +# src/crf/types.py + +STATIC_KEYS = [ + "Num_orders", "ITC", "n_ITC", "interest rate", "Design completion", "Design_Maturity_0", + "supply chain exp_0", "N supply chain", "Const Proficiency", "N const prof", "AE", + "N AE prof", "standardization", "modularity", "BOP commercial", "RB Safety Related" +] + +SUMMARY_KEYS = [ + "cons_duration_cumulative_wz_startup", + "occLastUnit", "TCILastUnit", "durationsLastUnit", + "avg_OCC", "avg_TCI", "avg_duration", +] + +def build_headers(max_orders: int) -> list[str]: + return ( + STATIC_KEYS + + [f"OCC_{i}" for i in range(max_orders)] + + [f"TCI_{i}" for i in range(max_orders)] + + [f"duration_{i}" for i in range(max_orders)] + + SUMMARY_KEYS + ) + +def validate_config(config: dict): + required = { + "inputs_xlsx", "reactor_type", "f_22", "f_2321", + "land_cost_per_acre_0", "startup_0", "staggering_ratio", + } + missing = required - set(config) + if missing: + raise ValueError(f"Missing config keys: {sorted(missing)}") + +def validate_levers(levers: dict): + required = { + "num_orders", "itc_percent", "n_itc", "interest_percent", + "design_completion_percent", "design_maturity", "proc_exp", "N_proc", + "ce_exp", "N_cons", "ae_exp", "N_AE", + "standardization_percent", "modularity_code", "bop_grade_code", "rb_grade_code", + } + missing = required - set(levers) + if missing: + raise ValueError(f"Missing lever keys: {sorted(missing)}") diff --git a/src/crf/utils/df_ops.py b/src/crf/utils/df_ops.py new file mode 100644 index 0000000..da441ae --- /dev/null +++ b/src/crf/utils/df_ops.py @@ -0,0 +1,16 @@ +import numpy as np +import pandas as pd + +def getv(df: pd.DataFrame, account, col: str): + return df.loc[df["Account"].eq(account), col].iloc[0] + +def setv(df: pd.DataFrame, account, col: str, value): + df.loc[df["Account"].eq(account), col] = value + +def mulv(df: pd.DataFrame, accounts, cols, factor: float): + m = df["Account"].isin(accounts) + df.loc[m, cols] = df.loc[m, cols].to_numpy() * factor + +def resetv(df: pd.DataFrame, accounts, cols): + m = df["Account"].isin(accounts) + df.loc[m, cols] = np.nan diff --git a/src/crf/utils/serialize.py b/src/crf/utils/serialize.py new file mode 100644 index 0000000..726ddd7 --- /dev/null +++ b/src/crf/utils/serialize.py @@ -0,0 +1,8 @@ +import csv +import pickle + +def stream_pickle_dump(obj, file_handle): + pickle.dump(obj, file_handle) + +def write_csv_row(writer: csv.DictWriter, row: dict, headers: list[str]): + writer.writerow({h: row.get(h, None) for h in headers}) From b36ce97d00dec04a020cde780c4be6e53b8ebca9 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Thu, 12 Feb 2026 16:21:18 -0600 Subject: [PATCH 02/25] first test pass --- .../CostReduction_exploration_mode.ipynb | 2 +- Cost_Reduction/scenarios.ipynb | 23 +-- Cost_Reduction/sensitivity_analysis.ipynb | 2 +- requirements.txt | 1 + src/__init__.py | 5 +- src/crf/__init__.py | 14 ++ src/crf/api.py | 44 +++--- src/crf/io/__init__.py | 12 ++ src/crf/io/excel_levers.py | 18 ++- src/crf/model/__init__.py | 7 + src/crf/sampling/__init__.py | 17 +++ src/crf/sampling/lever_schema.py | 139 +++++++++++++++--- src/crf/types.py | 34 ++--- src/crf/utils/__init__.py | 9 ++ test/test_crf_smoke.py | 52 +++++++ 15 files changed, 282 insertions(+), 97 deletions(-) create mode 100644 src/crf/__init__.py create mode 100644 src/crf/io/__init__.py create mode 100644 src/crf/model/__init__.py create mode 100644 src/crf/sampling/__init__.py create mode 100644 src/crf/utils/__init__.py create mode 100644 test/test_crf_smoke.py diff --git a/Cost_Reduction/CostReduction_exploration_mode.ipynb b/Cost_Reduction/CostReduction_exploration_mode.ipynb index f6800e6..1c2cb30 100644 --- a/Cost_Reduction/CostReduction_exploration_mode.ipynb +++ b/Cost_Reduction/CostReduction_exploration_mode.ipynb @@ -5734,7 +5734,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.10.18" } }, "nbformat": 4, diff --git a/Cost_Reduction/scenarios.ipynb b/Cost_Reduction/scenarios.ipynb index 5bb5cfd..50f3a0e 100644 --- a/Cost_Reduction/scenarios.ipynb +++ b/Cost_Reduction/scenarios.ipynb @@ -44,6 +44,7 @@ "\n", "import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", + "warnings.simplefilter(action='ignore', category=DeprecationWarning)\n", "\n", "pd.set_option('display.max_rows', None)" ] @@ -113,23 +114,7 @@ "execution_count": 4, "id": "6a4a23ef", "metadata": {}, - "outputs": [ - { - "ename": "UnboundLocalError", - "evalue": "cannot access local variable 'task_length_multiplier' where it is not associated with a value", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 57\u001b[0m\n\u001b[1;32m 54\u001b[0m ITC_0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.4\u001b[39m\n\u001b[1;32m 55\u001b[0m n_ITC \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m \n\u001b[0;32m---> 57\u001b[0m avg_results \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_final_result_avg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreactor_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_22\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_2321\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mland_cost_per_acre_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mRB_grade_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBOP_grade_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[43m\u001b[49m\u001b[43mnum_orders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdesign_completion_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mae_exp_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN_AE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mce_exp_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN_cons\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmod_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDesign_Maturity_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproc_exp_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN_proc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstandardization_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterest_rate_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstartup_0\u001b[49m\u001b[43m)\u001b[49m \n", - "Cell \u001b[0;32mIn[3], line 11\u001b[0m, in \u001b[0;36mcalculate_final_result_avg\u001b[0;34m(reactor_type, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0, interest_rate_0, startup_0)\u001b[0m\n\u001b[1;32m 8\u001b[0m durations_list \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m n_th \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, num_orders\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m---> 11\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_final_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreactor_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_th\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_22\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_2321\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mland_cost_per_acre_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mRB_grade_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBOP_grade_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_orders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdesign_completion_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mae_exp_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN_AE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mce_exp_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN_cons\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmod_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDesign_Maturity_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproc_exp_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN_proc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstandardization_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterest_rate_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstartup_0\u001b[49m\u001b[43m)\u001b[49m \n\u001b[1;32m 14\u001b[0m OCC_result \u001b[38;5;241m=\u001b[39m results[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;66;03m# The ITC_reduced OCC (levelized)\u001b[39;00m\n\u001b[1;32m 15\u001b[0m TCI_result \u001b[38;5;241m=\u001b[39m results[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;66;03m# The ITC_reduced TCI(levelized)\u001b[39;00m\n", - "File \u001b[0;32m/var/folders/fn/9991pz_174vgdscjw2zf2tbxtn5546/T/ipykernel_63785/3212539402.py:6\u001b[0m, in \u001b[0;36mcalculate_final_result\u001b[0;34m(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0, interest_rate_0, startup_0)\u001b[0m\n\u001b[1;32m 4\u001b[0m reactor_data \u001b[38;5;241m=\u001b[39m (reactor_data_read(reactor_type ))[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 5\u001b[0m reactor_power \u001b[38;5;241m=\u001b[39m (reactor_data_read(reactor_type ))[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m----> 6\u001b[0m tot_base_cost_results \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_base_cost\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreactor_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_th\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_22\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_2321\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mland_cost_per_acre_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mRB_grade_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBOP_grade_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_orders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdesign_completion_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mae_exp_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN_AE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mce_exp_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN_cons\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmod_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDesign_Maturity_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproc_exp_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN_proc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstandardization_0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m tot_base_cost \u001b[38;5;241m=\u001b[39m tot_base_cost_results[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 8\u001b[0m final_construction_duration \u001b[38;5;241m=\u001b[39m tot_base_cost_results[\u001b[38;5;241m1\u001b[39m]\n", - "File \u001b[0;32m/var/folders/fn/9991pz_174vgdscjw2zf2tbxtn5546/T/ipykernel_63785/896935451.py:7\u001b[0m, in \u001b[0;36mcalculate_base_cost\u001b[0;34m(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0)\u001b[0m\n\u001b[1;32m 4\u001b[0m direct_cost_updated \u001b[38;5;241m=\u001b[39m update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons)\n\u001b[1;32m 6\u001b[0m act_con_duration \u001b[38;5;241m=\u001b[39m update_cons_dur(reactor_data, direct_cost_updated , mod_0)\n\u001b[0;32m----> 7\u001b[0m cons_duration_plus_delay \u001b[38;5;241m=\u001b[39m \u001b[43mact_cons_duration_plus_delay\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreactor_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_th\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDesign_Maturity_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproc_exp_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN_proc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mact_con_duration\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m final_con_duration \u001b[38;5;241m=\u001b[39m duration_learning_effect(n_th, standardization_0, cons_duration_plus_delay)\n\u001b[1;32m 10\u001b[0m direct_cost_updated_plus_learning \u001b[38;5;241m=\u001b[39m learning_effect(direct_cost_updated , n_th, standardization_0, reactor_power)\n", - "File \u001b[0;32m/var/folders/fn/9991pz_174vgdscjw2zf2tbxtn5546/T/ipykernel_63785/1160249579.py:25\u001b[0m, in \u001b[0;36mact_cons_duration_plus_delay\u001b[0;34m(reactor_type, n_th, Design_Maturity_0, proc_exp_0, N_proc, cons_duration_no_delay)\u001b[0m\n\u001b[1;32m 22\u001b[0m task_length_multiplier \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m64\u001b[39m\n\u001b[1;32m 23\u001b[0m ref_construction_duration \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m\n\u001b[0;32m---> 25\u001b[0m B_21 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m42.1\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mtask_length_multiplier\u001b[49m \u001b[38;5;66;03m# months \u001b[39;00m\n\u001b[1;32m 26\u001b[0m B_22 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m60.2\u001b[39m \u001b[38;5;241m*\u001b[39m task_length_multiplier\n\u001b[1;32m 27\u001b[0m B_23 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m14.8\u001b[39m \u001b[38;5;241m*\u001b[39m task_length_multiplier\n", - "\u001b[0;31mUnboundLocalError\u001b[0m: cannot access local variable 'task_length_multiplier' where it is not associated with a value" - ] - } - ], + "outputs": [], "source": [ "reactor_type = \"Concept A\"\n", "\n", @@ -193,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "8187e003", "metadata": {}, "outputs": [], @@ -219,7 +204,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.10.18" } }, "nbformat": 4, diff --git a/Cost_Reduction/sensitivity_analysis.ipynb b/Cost_Reduction/sensitivity_analysis.ipynb index 7c9c070..22b49e4 100644 --- a/Cost_Reduction/sensitivity_analysis.ipynb +++ b/Cost_Reduction/sensitivity_analysis.ipynb @@ -943,7 +943,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.1.undefined" + "version": "3.10.18" } }, "nbformat": 4, diff --git a/requirements.txt b/requirements.txt index 7a996e3..1b979ca 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,3 +7,4 @@ pytest pandas numpy==1.26.4 matplotlib +scipy diff --git a/src/__init__.py b/src/__init__.py index c229391..f2e8526 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -5,5 +5,8 @@ from .utility_accert import Utility_methods from .Algorithm import Algorithm import necost +from .crf import run_one_scenario, run_sampling_from_excel -__all__ = ['Accert', 'xml2obj', 'Utility_methods', 'Algorithm', 'necost'] + +__all__ = ['Accert', 'xml2obj', 'Utility_methods', 'Algorithm', + 'necost', "run_one_scenario", "run_sampling_from_excel"] diff --git a/src/crf/__init__.py b/src/crf/__init__.py new file mode 100644 index 0000000..38163fc --- /dev/null +++ b/src/crf/__init__.py @@ -0,0 +1,14 @@ +""" +Cost Reduction Framework (CRF) +ACCERT Submodule +""" + +from .api import ( + run_one_scenario, + run_sampling_from_excel, +) + +__all__ = [ + "run_one_scenario", + "run_sampling_from_excel", +] diff --git a/src/crf/api.py b/src/crf/api.py index 5723ea4..0d4a147 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -3,9 +3,14 @@ import numpy as np from .io.excel_inputs import InputStore -from .io.excel_levers import read_lever_sheet +from .io.excel_levers import read_levers_sheet from .sampling.sampler import sample_levers -from .sampling.lever_schema import unpack_sample_column, STATIC_KEYS +from .sampling.lever_schema import ( + STATIC_KEYS, + attach_internal_ids, + sample_column_to_levers, + static_row_from_levers, +) from .sampling.postprocess import apply_itc_rounding from .model.avg_runner import run_avg_all_units from .utils.serialize import write_csv_row, stream_pickle_dump @@ -44,28 +49,7 @@ def run_one_scenario(config: dict, levers: dict) -> dict: inp = normalize_levers(levers) result = run_avg_all_units(config=config, inp=inp, store=store) - - # attach static input columns (raw sampled values, matching your previous CSV meaning) - # IMPORTANT: keep the same static key ordering as before - static_vals = { - "Num_orders": levers["num_orders"], - "ITC": levers["itc_percent"], - "n_ITC": levers["n_itc"], - "interest rate": levers["interest_percent"], - "Design completion": levers["design_completion_percent"], - "Design_Maturity_0": levers["design_maturity"], - "supply chain exp_0": levers["proc_exp"], - "N supply chain": levers["N_proc"], - "Const Proficiency": levers["ce_exp"], - "N const prof": levers["N_cons"], - "AE": levers["ae_exp"], - "N AE prof": levers["N_AE"], - "standardization": levers["standardization_percent"], - "modularity": levers["modularity_code"], - "BOP commercial": levers["bop_grade_code"], - "RB Safety Related": levers["rb_grade_code"], - } - + static_vals = static_row_from_levers(levers) return {**static_vals, **result} @@ -77,7 +61,9 @@ def run_sampling_from_excel( out_pkl: str, seed: int | None = None ): - levers_df = read_lever_sheet(levers_xlsx, sheet_name="Levers") + levers_df = read_levers_sheet(levers_xlsx, sheet_name="Levers") + levers_df = attach_internal_ids(levers_df) + samples = sample_levers(n_samples, levers_df, seed=seed) # shape (n_levers, n_samples) # headers: build from max num_orders in sampled set @@ -100,8 +86,12 @@ def run_sampling_from_excel( writer.writeheader() for i in range(n_samples): - levers = unpack_sample_column(samples[:, i]) - row = run_one_scenario(config, levers) + # levers = unpack_sample_column(samples[:, i]) + # row = run_one_scenario(config, levers) + + + levers_raw = sample_column_to_levers(samples, levers_df, i) + row = run_one_scenario(config, levers_raw) write_csv_row(writer, row, headers) stream_pickle_dump(row, pkl_f) diff --git a/src/crf/io/__init__.py b/src/crf/io/__init__.py new file mode 100644 index 0000000..06cfa5f --- /dev/null +++ b/src/crf/io/__init__.py @@ -0,0 +1,12 @@ +""" +CRF Excel I/O +""" + +from .excel_inputs import InputStore +from .excel_levers import read_levers_sheet, read_baseline_levers + +__all__ = [ + "InputStore", + "read_levers_sheet", + "read_baseline_levers", +] diff --git a/src/crf/io/excel_levers.py b/src/crf/io/excel_levers.py index d17d281..53b97f0 100644 --- a/src/crf/io/excel_levers.py +++ b/src/crf/io/excel_levers.py @@ -1,13 +1,25 @@ import pandas as pd REQUIRED_COLS = { - "Distribution", "Min", "Max", "Median", "Low", "High", - "Set", "Probabilities", "Type" + "Levers", "Min", "Low", "Median", "High", "Max", + "Distribution", "Type", "Set", "Probabilities" } -def read_lever_sheet(levers_xlsx: str, sheet_name="Levers") -> pd.DataFrame: +def read_levers_sheet(levers_xlsx: str, sheet_name="Levers") -> pd.DataFrame: df = pd.read_excel(levers_xlsx, sheet_name=sheet_name) + missing = REQUIRED_COLS - set(df.columns) if missing: raise ValueError(f"Levers sheet missing columns: {sorted(missing)}") + + return df + +def read_baseline_levers(levers_xlsx: str, sheet_name="baseline levers") -> pd.DataFrame: + """ + Returns the baseline levers sheet as a dataframe with columns: Levers, Baseline + Keep it as df for now (you can convert to dict later). + """ + df = pd.read_excel(levers_xlsx, sheet_name=sheet_name) + if "Levers" not in df.columns or "Baseline" not in df.columns: + raise ValueError("Baseline levers sheet must have columns: 'Levers' and 'Baseline'") return df diff --git a/src/crf/model/__init__.py b/src/crf/model/__init__.py new file mode 100644 index 0000000..3d91ef3 --- /dev/null +++ b/src/crf/model/__init__.py @@ -0,0 +1,7 @@ +""" +CRF Model Core +""" + +from .pipeline import calculate_final_result + +__all__ = ["calculate_final_result"] diff --git a/src/crf/sampling/__init__.py b/src/crf/sampling/__init__.py new file mode 100644 index 0000000..7c03971 --- /dev/null +++ b/src/crf/sampling/__init__.py @@ -0,0 +1,17 @@ +""" +CRF Sampling Module +""" + +from .sampler import sample_levers +from .lever_schema import ( + attach_internal_ids, + sample_column_to_levers, + static_row_from_levers, +) + +__all__ = [ + "sample_levers", + "attach_internal_ids", + "sample_column_to_levers", + "static_row_from_levers", +] diff --git a/src/crf/sampling/lever_schema.py b/src/crf/sampling/lever_schema.py index 81b7df2..e82630f 100644 --- a/src/crf/sampling/lever_schema.py +++ b/src/crf/sampling/lever_schema.py @@ -1,28 +1,121 @@ -LEVER_ORDER = [ - "num_orders", - "itc_percent", - "n_itc", - "interest_percent", - "design_completion_percent", - "design_maturity", - "proc_exp", - "N_proc", - "ce_exp", - "N_cons", - "ae_exp", - "N_AE", - "standardization_percent", - "modularity_code", - "bop_grade_code", - "rb_grade_code", +import pandas as pd +# --- Unique internal IDs (these are the keys everywhere else) --- +LEVER_IDS = [ + "num_orders", + "itc_percent", + "n_itc", + "interest_percent", + "design_completion_percent", + "design_maturity", + "proc_exp", + "N_proc", + "ce_exp", + "N_cons", + "ae_exp", + "N_AE", + "standardization_percent", + "modularity_code", + "bop_grade_code", + "rb_grade_code", +] + +# --- Map Excel lever names to internal IDs --- +EXCEL_NAME_TO_ID_ORDERED = [ + ("Number of firm orders", "num_orders"), + ("ITC amount", "itc_percent"), + ("Number of plants claiming ITC", "n_itc"), + ("Interest rate", "interest_percent"), + ("Design completion", "design_completion_percent"), + ("Design maturity (technology maturity)", "design_maturity"), + ("Supply chain proficiency", "proc_exp"), + ("Number of plants to achieve best proficiency", "N_proc"), # first occurrence + ("Construction proficiency", "ce_exp"), + ("Number of plants to achieve best proficiency", "N_cons"), # second occurrence + ("A/E proficiency", "ae_exp"), + ("Number of plants to achieve best proficiency", "N_AE"), # third occurrence + ("Cross-site standardization", "standardization_percent"), + ("Modular civil construction", "modularity_code"), + ("Commercial BOP", "bop_grade_code"), + ("Non-safety-related RB", "rb_grade_code"), ] STATIC_KEYS = [ - "Num_orders", "ITC", "n_ITC", "interest rate", "Design completion", "Design_Maturity_0", - "supply chain exp_0", "N supply chain", "Const Proficiency", "N const prof", "AE", - "N AE prof", "standardization", "modularity", "BOP commercial", "RB Safety Related", + "Num_orders", "ITC", "n_ITC", "interest rate", "Design completion", "Design_Maturity_0", + "supply chain exp_0", "N supply chain", "Const Proficiency", "N const prof", "AE", + "N AE prof", "standardization", "modularity", "BOP commercial", "RB Safety Related", ] -def unpack_sample_column(col_vec): - # col_vec length must match LEVER_ORDER - return dict(zip(LEVER_ORDER, col_vec)) + +def _normalize_excel_name(s: str) -> str: + # make matching robust to extra spaces + return " ".join(str(s).strip().split()) + + +def attach_internal_ids(levers_df: pd.DataFrame) -> pd.DataFrame: + """ + Adds a 'lever_id' column to the Levers dataframe using ordered matching. + This is required because Excel contains repeated lever names. + """ + df = levers_df.copy() + df["Levers_norm"] = df["Levers"].apply(_normalize_excel_name) + + ordered = [( _normalize_excel_name(n), i ) for n, i in EXCEL_NAME_TO_ID_ORDERED] + + ids = [] + cursor = 0 + for row_name in df["Levers_norm"].tolist(): + if cursor >= len(ordered): + raise ValueError("Levers sheet has more rows than expected mapping.") + + expected_name, lever_id = ordered[cursor] + if row_name != expected_name: + raise ValueError( + f"Levers sheet row mismatch at position {cursor}:\n" + f" expected: '{expected_name}'\n" + f" found: '{row_name}'\n" + f"Fix the Excel row order OR update EXCEL_NAME_TO_ID_ORDERED." + ) + ids.append(lever_id) + cursor += 1 + + df["lever_id"] = ids + df.drop(columns=["Levers_norm"], inplace=True) + + # final validation + if df["lever_id"].tolist() != LEVER_IDS: + raise ValueError("Internal lever_id order does not match LEVER_IDS. Check mapping.") + return df + + +def sample_column_to_levers(samples_matrix, levers_df_with_ids: pd.DataFrame, sample_idx: int) -> dict: + """ + samples_matrix shape: (n_levers, n_samples) + returns levers_raw dict keyed by internal lever ids. + """ + vec = samples_matrix[:, sample_idx] + ids = levers_df_with_ids["lever_id"].tolist() + return {ids[i]: vec[i] for i in range(len(ids))} + + +def static_row_from_levers(levers_raw: dict) -> dict: + """ + Convert internal lever dict into your legacy CSV static columns. + """ + return { + "Num_orders": levers_raw["num_orders"], + "ITC": levers_raw["itc_percent"], + "n_ITC": levers_raw["n_itc"], + "interest rate": levers_raw["interest_percent"], + "Design completion": levers_raw["design_completion_percent"], + "Design_Maturity_0": levers_raw["design_maturity"], + "supply chain exp_0": levers_raw["proc_exp"], + "N supply chain": levers_raw["N_proc"], + "Const Proficiency": levers_raw["ce_exp"], + "N const prof": levers_raw["N_cons"], + "AE": levers_raw["ae_exp"], + "N AE prof": levers_raw["N_AE"], + "standardization": levers_raw["standardization_percent"], + "modularity": levers_raw["modularity_code"], + "BOP commercial": levers_raw["bop_grade_code"], + "RB Safety Related": levers_raw["rb_grade_code"], + } diff --git a/src/crf/types.py b/src/crf/types.py index 752f253..00f9f7a 100644 --- a/src/crf/types.py +++ b/src/crf/types.py @@ -1,15 +1,13 @@ -# src/crf/types.py - STATIC_KEYS = [ - "Num_orders", "ITC", "n_ITC", "interest rate", "Design completion", "Design_Maturity_0", - "supply chain exp_0", "N supply chain", "Const Proficiency", "N const prof", "AE", - "N AE prof", "standardization", "modularity", "BOP commercial", "RB Safety Related" + "Num_orders", "ITC", "n_ITC", "interest rate", "Design completion", "Design_Maturity_0", + "supply chain exp_0", "N supply chain", "Const Proficiency", "N const prof", "AE", + "N AE prof", "standardization", "modularity", "BOP commercial", "RB Safety Related", ] SUMMARY_KEYS = [ - "cons_duration_cumulative_wz_startup", - "occLastUnit", "TCILastUnit", "durationsLastUnit", - "avg_OCC", "avg_TCI", "avg_duration", + "cons_duration_cumulative_wz_startup", + "occLastUnit", "TCILastUnit", "durationsLastUnit", + "avg_OCC", "avg_TCI", "avg_duration", ] def build_headers(max_orders: int) -> list[str]: @@ -23,20 +21,12 @@ def build_headers(max_orders: int) -> list[str]: def validate_config(config: dict): required = { - "inputs_xlsx", "reactor_type", "f_22", "f_2321", - "land_cost_per_acre_0", "startup_0", "staggering_ratio", + "inputs_xlsx", "reactor_type", + "f_22", "f_2321", + "land_cost_per_acre_0", + "startup_0", + "staggering_ratio", } missing = required - set(config) if missing: - raise ValueError(f"Missing config keys: {sorted(missing)}") - -def validate_levers(levers: dict): - required = { - "num_orders", "itc_percent", "n_itc", "interest_percent", - "design_completion_percent", "design_maturity", "proc_exp", "N_proc", - "ce_exp", "N_cons", "ae_exp", "N_AE", - "standardization_percent", "modularity_code", "bop_grade_code", "rb_grade_code", - } - missing = required - set(levers) - if missing: - raise ValueError(f"Missing lever keys: {sorted(missing)}") + raise ValueError(f"Missing config keys: {sorted(missing)}") \ No newline at end of file diff --git a/src/crf/utils/__init__.py b/src/crf/utils/__init__.py new file mode 100644 index 0000000..d4ea010 --- /dev/null +++ b/src/crf/utils/__init__.py @@ -0,0 +1,9 @@ +""" +CRF Utilities +""" + +from .df_ops import getv, setv, mulv, resetv + +__all__ = ["getv", "setv", "mulv", "resetv"] + + diff --git a/test/test_crf_smoke.py b/test/test_crf_smoke.py new file mode 100644 index 0000000..4306003 --- /dev/null +++ b/test/test_crf_smoke.py @@ -0,0 +1,52 @@ + + +import os +import sys + +src_path = os.path.abspath(os.path.join(os.pardir, 'src')) +sys.path.insert(0, src_path) +from crf import run_one_scenario + + +def main(): + + config = { + "inputs_xlsx": "Inputs.xlsx", + "reactor_type": "Concept A", + "f_22": 250_000_000, + "f_2321": 150_000_000, + "land_cost_per_acre_0": 22000, + "startup_0": 16, + "staggering_ratio": 0.75, + } + + # baseline-style levers (raw form, like from Excel) + levers = { + "num_orders": 3, + "itc_percent": 30, + "n_itc": 1, + "interest_percent": 6.5, + "design_completion_percent": 90, + "design_maturity": 2, + "proc_exp": 0.6, + "N_proc": 3, + "ce_exp": 0.6, + "N_cons": 5, + "ae_exp": 0.6, + "N_AE": 4, + "standardization_percent": 70, + "modularity_code": 1, + "bop_grade_code": 1, + "rb_grade_code": 0, + } + + result = run_one_scenario(config, levers) + + print("\nTest Successful") + print("avg_OCC:", result["avg_OCC"]) + print("avg_TCI:", result["avg_TCI"]) + print("avg_duration:", result["avg_duration"]) + + +if __name__ == "__main__": + main() From 0e7239434ab9eb5068d3c6a94eb3600dd2dbf5fe Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Tue, 17 Feb 2026 14:58:28 -0600 Subject: [PATCH 03/25] simplified input --- src/crf/api.py | 98 ++++++++++++++- src/crf/data/HTGR_baseline.csv | 34 +++++ src/crf/data/SFR_baseline.csv | 70 +++++++++++ src/crf/data/ref_spending_curve.csv | 105 ++++++++++++++++ src/crf/io/excel_inputs.py | 37 ++++-- src/crf/model/core_accounts.py | 187 +++++++++++++++++++--------- src/crf/model/direct_cost.py | 2 +- src/crf/model/finance.py | 30 +++-- src/crf/model/indirect_cost.py | 10 +- src/crf/utils/df_ops.py | 43 +++++-- test/test_crf_smoke.py | 24 ++-- 11 files changed, 545 insertions(+), 95 deletions(-) create mode 100644 src/crf/data/HTGR_baseline.csv create mode 100644 src/crf/data/SFR_baseline.csv create mode 100644 src/crf/data/ref_spending_curve.csv diff --git a/src/crf/api.py b/src/crf/api.py index 0d4a147..db91a33 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -16,6 +16,51 @@ from .utils.serialize import write_csv_row, stream_pickle_dump +def normalize_levers(levers: dict) -> dict: + """Convert raw lever dict to model-ready normalized values.""" + def label01(x, zero_label, one_label): + if x == 0: return zero_label + if x == 1: return one_label + return x + + return { + "num_orders": int(levers["num_orders"]), + "ITC_0": float(levers["itc_percent"]) / 100.0, + "n_ITC": levers["n_itc"], + "interest_rate_0": float(levers["interest_percent"]) / 100.0, + "design_completion_0": float(levers["design_completion_percent"]) / 100.0, + "Design_Maturity_0": levers["design_maturity"], + "proc_exp_0": levers["proc_exp"], + "N_proc": levers["N_proc"], + "ce_exp_0": levers["ce_exp"], + "N_cons": levers["N_cons"], + "ae_exp_0": levers["ae_exp"], + "N_AE": levers["N_AE"], + "standardization_0": float(levers["standardization_percent"]) / 100.0, + "mod_0": label01(levers["modularity_code"], "stick_built", "modularized"), + "BOP_grade_0": label01(levers["bop_grade_code"], "nuclear", "non_nuclear"), + "RB_grade_0": label01(levers["rb_grade_code"], "nuclear", "non_nuclear"), + } + + +import csv +import pickle +import numpy as np + +from .io.excel_inputs import InputStore +from .io.excel_levers import read_levers_sheet +from .sampling.sampler import sample_levers +from .sampling.lever_schema import ( + STATIC_KEYS, + attach_internal_ids, + sample_column_to_levers, + static_row_from_levers, +) +from .sampling.postprocess import apply_itc_rounding +from .model.avg_runner import run_avg_all_units +from .utils.serialize import write_csv_row, stream_pickle_dump + + def normalize_levers(levers: dict) -> dict: """Convert raw lever dict to model-ready normalized values.""" def label01(x, zero_label, one_label): @@ -44,7 +89,58 @@ def label01(x, zero_label, one_label): def run_one_scenario(config: dict, levers: dict) -> dict: - store = InputStore(config["inputs_xlsx"]) + store = InputStore(config.get("data_dir")) + levers = apply_itc_rounding(levers) + inp = normalize_levers(levers) + + result = run_avg_all_units(config=config, inp=inp, store=store) + static_vals = static_row_from_levers(levers) + return {**static_vals, **result} + + +def run_sampling_from_excel( + config: dict, + levers_xlsx: str, + n_samples: int, + out_csv: str, + out_pkl: str, + seed: int | None = None +): + levers_df = read_levers_sheet(levers_xlsx, sheet_name="Levers") + levers_df = attach_internal_ids(levers_df) + + samples = sample_levers(n_samples, levers_df, seed=seed) # shape (n_levers, n_samples) + + # headers: build from max num_orders in sampled set + max_orders = int(np.max(samples[0, :])) + + headers = ( + STATIC_KEYS + + [f"OCC_{i}" for i in range(max_orders)] + + [f"TCI_{i}" for i in range(max_orders)] + + [f"duration_{i}" for i in range(max_orders)] + + [ + "cons_duration_cumulative_wz_startup", + "occLastUnit", "TCILastUnit", "durationsLastUnit", + "avg_OCC", "avg_TCI", "avg_duration", + ] + ) + + with open(out_csv, "w", newline="", encoding="utf-8") as csv_f, open(out_pkl, "wb") as pkl_f: + writer = csv.DictWriter(csv_f, fieldnames=headers) + writer.writeheader() + + for i in range(n_samples): + # levers = unpack_sample_column(samples[:, i]) + # row = run_one_scenario(config, levers) + + + levers_raw = sample_column_to_levers(samples, levers_df, i) + row = run_one_scenario(config, levers_raw) + + write_csv_row(writer, row, headers) + stream_pickle_dump(row, pkl_f) + levers = apply_itc_rounding(levers) inp = normalize_levers(levers) diff --git a/src/crf/data/HTGR_baseline.csv b/src/crf/data/HTGR_baseline.csv new file mode 100644 index 0000000..08bcf24 --- /dev/null +++ b/src/crf/data/HTGR_baseline.csv @@ -0,0 +1,34 @@ +Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labor Cost,Site Material Cost +10,Capitalized Pre-Construction Costs,,,,, +11,Land & Land Rights,15000000,,,, +12,Site Permits,0,,,, +13,Plant Licensing,107009771.98697068,,,, +14,Plant Permits,4721019.352366353,,,, +15,Plant Studies,0,,,, +16,Plant Reports,0,,,, +18,Other Pre-Construction Costs,36194481.701475374,,,, +20,Capitalized Direct Costs,,,,, +21,Structures & Improvements,914902741.8258162,90263521.11,11824706.17,591583080.9,233056139.8 +212,Reactor Containment Building,413396747.7312481,65728880.25,5420558.48,264358228.1,83309639.36 +213,Turbine Room and Heater Bay,15257054.820128009,1136504.843,163947.4218,8458991.226,5661558.752 +211 plus 214 to 219,Othe Structures & Improvements,486248939.27444005,23398136.024113387,6240200.264892557,318765861.5921357,144084941.65819097 +22,Reactor System,1813536678,1530752484,4903887.695,261886915.4,20897278.33 +23,Energy Conversion System,665574715.5139421,534245006.41216296,2364645.320437796,125502354.2820378,5827354.82 +232.1,Electricity Generation Systems,576019409.4786329,486396295.4,1682189.488,89623114.06,0 +233,Ultimate Heat Sink,89555306.03530921,47848710.99,682455.832,35879240.23,5827354.82 +24,Electrical Equipment,140508400.4910087,28461247.42,1597897.041,86126547.25,25920605.82 +25,Initial fuel inventory,451686470.5748004,,0,0,0 +26,Miscellaneous Equipment,214388490.9241457,65401342.47,2408453.335,129937709.9,19049438.52 +28,Simulator,0,,,, +30,Capitalized Indirect Services Costs,,,,, +31,Factory & Field Indirect Costs,691371059.4821017,,,, +32,Factory and construction supervision,2734393138.1498117,,,, +33,Start-Up Costs,115036083.38555087,,,, +34,Shipping & Transportation Costs,9686167.142,,,, +35,Engineering Services,738767482.8,,,, +50,Capitalized Supplementary Costs,,,,, +51,Taxes,187500,,,, +52,Insurance,38204331.42647941,,,, +54,Decommissioning ,8141760,,,, +60,Capitalized Financial Costs,,,,, +62,Interest ,3202161760.899656,,,, \ No newline at end of file diff --git a/src/crf/data/SFR_baseline.csv b/src/crf/data/SFR_baseline.csv new file mode 100644 index 0000000..e8e39b4 --- /dev/null +++ b/src/crf/data/SFR_baseline.csv @@ -0,0 +1,70 @@ +Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labor Cost,Site Material Cost +10,Capitalized Pre-Construction Costs,,,,, +11,Land & Land Rights,11000000.0,,,, +12,Site Permits,1598890.7521620637,,,, +13,Plant Licensing,24382988.437500004,,,, +14,Plant Permits,12679166.666666668,,,, +15,Plant Studies,12679166.666666668,,,, +16,Plant Reports,3972185.8507187515,,,, +18,Other Pre-Construction Costs,12679166.666666668,,,, +,,,,,, +,10s - Subtotal,78991565.04038084,,,, +,10s - $/kWe,254.15561467304,,,, +,,,,,, +20,Capitalized Direct Costs,,,,, +21,Structures & Improvements,244678343.4084428,64350800.8593666,2899406.53716477,145691461.90599,34636080.6430862 +212,Reactor Containment Building,145108927.86932483,57583605.3695275,1605670.3085077,81082169.3410215,6443153.15877585 +213,Turbine Room and Heater Bay,9518417.644642511,620963.974754291,102399.473629672,5280509.16508102,3616944.5048072 +211 plus 214 to 219,Othe Structures & Improvements,90050997.89447546,6146231.515084816,1191336.755027398,59328783.39988749,24575982.97950315 +22,Reactor System,661334796.7533815,559862843.635677,1809703.51462689,97213738.5700561,4258214.54764847 +23,Energy Conversion System,238844021.25290728,189802947.4112764,874095.8194883969,46348086.0900651,2692987.75156579 +232.1,Electricity Generation Systems,197558120.73250762,167738159.268431,559708.577958467,29819961.4640766,0.0 +233,Ultimate Heat Sink,41285900.52039969,22064788.1428454,314387.24152993,16528124.6259885,2692987.75156579 +24,Electrical Equipment,67451644.91245879,13662940.7771874,767077.153200817,41345408.9752344,12443295.160037 +25,Initial fuel inventory,279724434.0,,,, +26,Miscellaneous Equipment,25655315.754125603,8372438.25664057,278805.251378048,15110212.4141026,2172665.08338243 +28,Simulator,78200.0,,,, +,,,,,, +,20s - Subtotal,1517766756.081316,,,, +,20s - $/kWe,4883.419421111054,,,, +,,,,,, +30,Capitalized Indirect Services Costs,,,,, +31,Factory & Field Indirect Costs,146051996.53692532,,,, +32,Factory and construction supervision,506256124.80995834,,,, +33,Start-Up Costs,21298225.546122435,,,, +34,Shipping & Transportation Costs,1793334.4599472817,,,, +35,Engineering Services,136778270.01496467,,,, +,,,,,, +,30s - Subtotal,812177951.367918,,,, +,30s - $/kWe,2613.1851717114478,,,, +,,,,,, +50,Capitalized Supplementary Costs,,,,, +51,Taxes,3510602.4683867483,,,, +52,Insurance,10484751.183521552,,,, +54,Decommissioning ,14606673.443824586,,,, +,,,,,, +,50s - Subtotal,28602027.095732886,,,, +,50s - $/kWe,92.02711420763477,,,, +,,,,,, +60,Capitalized Financial Costs,,,,, +62,Interest ,592300799.7656411,,,, +-,60s - Subtotal,592300799.7656411,,,, +,60s - $/kWe,1905.729728975679,,,, +,,,,,, +,Total Direct Capital Cost (Accounts 10 to 20),1596758321.121697,,,, +,(Accounts 10 to 20) US$/kWe,5137.575035784095,,,, +,,,,,, +,Base Construction Cost (Accounts 10 to 30),2408936272.489615,,,, +,(Accounts 10 to 30) US$/kWe,7750.760207495543,,,, +,,,,,, +,Total Overnight Cost (Accounts 10 to 50),2437538299.5853477,,,, +,(Accounts 10 to 50) US$/kWe,7842.787321703177,,,, +,,,,,, +,Total Capital Investment Cost (All Accounts),3029839099.350989,,,, +,(Accounts 10 to 60) US$/kWe,9748.517050678856,,,, +,,,,,, +,Total Overnight Cost - ITC reduced,2437538299.5853477,,,, +,Total Overnight Cost -ITC reduced (US$/kWe),7842.787321703177,,,, +,,,,,, +,Total Capital Investment Cost - ITC reduced,3029839099.350989,,,, +,Total Capital Investment Cost - ITC reduced (US$/kWe),9748.517050678856,,,, diff --git a/src/crf/data/ref_spending_curve.csv b/src/crf/data/ref_spending_curve.csv new file mode 100644 index 0000000..6ac193d --- /dev/null +++ b/src/crf/data/ref_spending_curve.csv @@ -0,0 +1,105 @@ +Month,CDF +0,0.0032 +1,0.0064 +2,0.009600000000000001 +3,0.0128 +4,0.016 +5,0.019200000000000002 +6,0.022400000000000003 +7,0.029100000000000004 +8,0.035800000000000005 +9,0.0425 +10,0.0492 +11,0.0559 +12,0.0681 +13,0.08349999999999999 +14,0.10289999999999999 +15,0.11699999999999999 +16,0.1309 +17,0.1404 +18,0.1499 +19,0.1602 +20,0.1711 +21,0.1838 +22,0.19799999999999998 +23,0.21209999999999998 +24,0.22709999999999997 +25,0.24129999999999996 +26,0.25479999999999997 +27,0.2689 +28,0.282 +29,0.2934 +30,0.3039 +31,0.3147 +32,0.32539999999999997 +33,0.33519999999999994 +34,0.34589999999999993 +35,0.3616999999999999 +36,0.3773999999999999 +37,0.3928999999999999 +38,0.4094999999999999 +39,0.4265999999999999 +40,0.4444999999999999 +41,0.4596999999999999 +42,0.4701999999999999 +43,0.48049999999999987 +44,0.49119999999999986 +45,0.5020999999999999 +46,0.5130999999999999 +47,0.5240999999999999 +48,0.5370999999999999 +49,0.5481999999999999 +50,0.5592999999999999 +51,0.5703999999999999 +52,0.5813999999999999 +53,0.5923999999999999 +54,0.6006999999999999 +55,0.6089999999999999 +56,0.6185999999999999 +57,0.6268999999999999 +58,0.6350999999999999 +59,0.6432999999999999 +60,0.6528999999999999 +61,0.6621999999999999 +62,0.6713999999999999 +63,0.6808999999999998 +64,0.6904999999999999 +65,0.6989999999999998 +66,0.7073999999999998 +67,0.7174999999999998 +68,0.7263999999999998 +69,0.7352999999999998 +70,0.7441999999999999 +71,0.7528999999999999 +72,0.7613999999999999 +73,0.7697999999999998 +74,0.7780999999999998 +75,0.7866999999999998 +76,0.7952999999999999 +77,0.8039 +78,0.8123999999999999 +79,0.8204999999999999 +80,0.8285999999999999 +81,0.8366999999999999 +82,0.8447999999999999 +83,0.8528999999999999 +84,0.8600999999999999 +85,0.8674999999999998 +86,0.8748999999999998 +87,0.8822999999999998 +88,0.8896999999999997 +89,0.8973999999999998 +90,0.9050999999999998 +91,0.9127999999999998 +92,0.9203999999999999 +93,0.9279999999999999 +94,0.9356 +95,0.9432 +96,0.9507 +97,0.9581999999999999 +98,0.9656999999999999 +99,0.9731999999999998 +100,0.9806999999999998 +101,0.9880999999999998 +102,0.9943999999999997 +103,0.9998999999999997 diff --git a/src/crf/io/excel_inputs.py b/src/crf/io/excel_inputs.py index 7c39f59..7ba59db 100644 --- a/src/crf/io/excel_inputs.py +++ b/src/crf/io/excel_inputs.py @@ -1,3 +1,4 @@ +from pathlib import Path import pandas as pd COLS = [ @@ -7,33 +8,53 @@ ] class InputStore: - def __init__(self, inputs_xlsx: str): - self.inputs_xlsx = inputs_xlsx + """ + Minimal store: + - baseline csv per reactor_type + - one spending curve csv shared + """ + def __init__(self, data_dir: str = None): + if data_dir is None: + self.data_dir = Path(__file__).resolve().parents[1] / "data" + else: + self.data_dir = Path(data_dir) self._baseline = {} self._spending = None def get_baseline(self, reactor_type: str): + if reactor_type in self._baseline: df, power = self._baseline[reactor_type] return df.copy(), power - - if reactor_type == "Concept A": - df = pd.read_excel(self.inputs_xlsx, sheet_name="Concept_A", nrows=69)[COLS].copy() + if reactor_type == "HTGR": + # path = f"{self.data_dir}/HTGR_baseline.csv" + path = self.data_dir / "HTGR_baseline.csv" power = 1056 * 1000 - elif reactor_type == "Concept B": - df = pd.read_excel(self.inputs_xlsx, sheet_name="Concept_B", nrows=69)[COLS].copy() + elif reactor_type == "SFR": + path = self.data_dir / "SFR_baseline.csv" power = 310.8 * 1000 else: raise ValueError(f"Unknown reactor_type: {reactor_type}") + # print current running path for debugging + print(f"Loading baseline from: {path}") + df = pd.read_csv(path) + missing = set(COLS) - set(df.columns) + if missing: + raise ValueError(f"{path} missing columns: {sorted(missing)}") + df = df[COLS].copy() self._baseline[reactor_type] = (df, power) return df.copy(), power def get_spending_curve(self): if self._spending is not None: return self._spending - sp = pd.read_excel(self.inputs_xlsx, sheet_name="Ref Spending Curve", nrows=104, usecols="A:D") + sp_path = path = self.data_dir / "ref_spending_curve.csv" + sp = pd.read_csv(sp_path) + if "Month" not in sp.columns or "CDF" not in sp.columns: + raise ValueError(f"{sp_path} must contain Month and CDF") months = sp["Month"].to_numpy(dtype=float) cdfs = sp["CDF"].to_numpy(dtype=float) self._spending = (months, cdfs) return self._spending + diff --git a/src/crf/model/core_accounts.py b/src/crf/model/core_accounts.py index 4b89d31..790a8cd 100644 --- a/src/crf/model/core_accounts.py +++ b/src/crf/model/core_accounts.py @@ -1,7 +1,79 @@ -import numpy as np import pandas as pd +import numpy as np + +REQUIRED_DERIVED_ROWS = [ + # Subtotals + $/kWe rows + ("10s - Subtotal", None), + ("10s - $/kWe", None), + ("20s - Subtotal", None), + ("20s - $/kWe", None), + ("30s - Subtotal", None), + ("30s - $/kWe", None), + ("50s - Subtotal", None), + ("50s - $/kWe", None), + ("60s - Subtotal", None), + ("60s - $/kWe", None), + + # Final results + ("Total Direct Capital Cost (Accounts 10 to 20)", None), + ("(Accounts 10 to 20) US$/kWe", None), + ("Base Construction Cost (Accounts 10 to 30)", None), + ("(Accounts 10 to 30) US$/kWe", None), + ("Total Overnight Cost (Accounts 10 to 50)", None), + ("(Accounts 10 to 50) US$/kWe", None), + ("Total Capital Investment Cost (All Accounts)", None), + ("(Accounts 10 to 60) US$/kWe", None), + + # ITC reduced outputs (updated later, but create placeholders now) + ("Total Overnight Cost - ITC reduced", None), + ("Total Overnight Cost -ITC reduced (US$/kWe)", None), + ("Total Capital Investment Cost - ITC reduced", None), + ("Total Capital Investment Cost - ITC reduced (US$/kWe)", None), +] + +COST_COLS = [ + "Total Cost (USD)", + "Factory Equipment Cost", + "Site Labor Hours", + "Site Labor Cost", + "Site Material Cost", +] + +ALL_COLS = ["Account", "Title"] + COST_COLS + + +def _blank_row(title: str, account=None) -> dict: + row = {c: np.nan for c in ALL_COLS} + row["Title"] = title + row["Account"] = account + return row + + +def ensure_rows_exist(db: pd.DataFrame) -> pd.DataFrame: + """ + Ensure all derived rows exist (by Title). If missing, append them. + """ + if not set(ALL_COLS).issubset(db.columns): + missing = set(ALL_COLS) - set(db.columns) + raise ValueError(f"DB missing columns: {sorted(missing)}") + + existing_titles = set(db["Title"].astype(str).tolist()) + rows_to_add = [] + for title, acct in REQUIRED_DERIVED_ROWS: + if title not in existing_titles: + rows_to_add.append(_blank_row(title, acct)) + + if rows_to_add: + db = pd.concat([db, pd.DataFrame(rows_to_add)], ignore_index=True) + + return db + def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFrame: + db = ensure_rows_exist(db) + # change all nan to 0 for cost calculations (but keep original db unchanged for later use) + db = db.copy() + db[COST_COLS] = db[COST_COLS].fillna(0.0) # account 21 db.loc[db.Account == 21, "Factory Equipment Cost"] = ( db.loc[db.Account == 212, "Factory Equipment Cost"].values @@ -23,7 +95,6 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr + db.loc[db.Account == 213, "Site Labor Hours"].values + db.loc[db.Account == "211 plus 214 to 219", "Site Labor Hours"].values ) - # account 23 db.loc[db.Account == 23, "Factory Equipment Cost"] = ( db.loc[db.Account == "232.1", "Factory Equipment Cost"].values @@ -43,36 +114,37 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr ) # total costs for 21..26 components - for x in [21, 212, 213, "211 plus 214 to 219", 22, 23, "232.1", 233, 24, 26]: + for x in [21, 22, 23, 24, 25, 26]: db.loc[db["Account"] == x, "Total Cost (USD)"] = ( db.loc[db["Account"] == x, "Factory Equipment Cost"] + db.loc[db["Account"] == x, "Site Labor Cost"] + db.loc[db["Account"] == x, "Site Material Cost"] ) + # subtotals db.loc[db["Title"] == "10s - Subtotal", "Total Cost (USD)"] = db.loc[ - db["Account"].isin([11, 12, 13, 14, 15, 16, 18]), "Total Cost (USD)" - ].sum() + db["Account"].isin(["11", "12", "13", "14", "15", "16", "18"]), "Total Cost (USD)" + ].fillna(0.0).sum() db.loc[db["Title"] == "20s - Subtotal", "Total Cost (USD)"] = db.loc[ - db["Account"].isin([21, 22, 23, 24, 25, 26, 28]), "Total Cost (USD)" - ].sum() + db["Account"].isin(["21", "22", "23", "24", "25", "26", "28"]), "Total Cost (USD)" + ].fillna(0.0).sum() db.loc[db["Title"] == "30s - Subtotal", "Total Cost (USD)"] = db.loc[ - db["Account"].isin([31, 32, 33, 34, 35]), "Total Cost (USD)" - ].sum() + db["Account"].isin(["31", "32", "33", "34", "35"]), "Total Cost (USD)" + ].fillna(0.0).sum() db.loc[db["Title"] == "50s - Subtotal", "Total Cost (USD)"] = db.loc[ - db["Account"].isin([51, 52, 54]), "Total Cost (USD)" - ].sum() + db["Account"].isin(["51", "52", "54"]), "Total Cost (USD)" + ].fillna(0.0).sum() db.loc[db["Title"] == "60s - Subtotal", "Total Cost (USD)"] = db.loc[ - db["Account"].isin([62]), "Total Cost (USD)" - ].sum() + db["Account"].isin(["62"]), "Total Cost (USD)" + ].fillna(0.0).sum() # $/kWe lines - for t in ["10s", "20s", "30s", "40s", "50s", "60s"]: + for t in ["10s", "20s", "30s", "50s", "60s"]: db.loc[db["Title"] == f"{t} - $/kWe", "Total Cost (USD)"] = ( db.loc[db["Title"] == f"{t} - Subtotal", "Total Cost (USD)"].values / reactor_power ) @@ -118,47 +190,50 @@ def ITC_reduction_factor(itc_level: float) -> float: factors = [1, 0.95, 0.73, 0.63, 0.53] return float(np.interp(itc_level, itc_values, factors)) -def sum_lab_hrs(db: pd.DataFrame): - return ( - db.loc[db.Account == 21, "Site Labor Hours"].values - + db.loc[db.Account == 22, "Site Labor Hours"].values - + db.loc[db.Account == 23, "Site Labor Hours"].values - + db.loc[db.Account == 24, "Site Labor Hours"].values - + db.loc[db.Account == 26, "Site Labor Hours"].values - ) - -def update_cons_duration(db0: pd.DataFrame, db1: pd.DataFrame, ref_duration: float): - sum_old = ( - db0.loc[db0.Account == 21, "Site Labor Hours"].values - + db0.loc[db0.Account == 22, "Site Labor Hours"].values - + db0.loc[db0.Account == 23, "Site Labor Hours"].values - + db0.loc[db0.Account == 24, "Site Labor Hours"].values - + db0.loc[db0.Account == 26, "Site Labor Hours"].values - ) - sum_new = ( - db1.loc[db1.Account == 21, "Site Labor Hours"].values - + db1.loc[db1.Account == 22, "Site Labor Hours"].values - + db1.loc[db1.Account == 23, "Site Labor Hours"].values - + db1.loc[db1.Account == 24, "Site Labor Hours"].values - + db1.loc[db1.Account == 26, "Site Labor Hours"].values +def sum_lab_hrs(db: pd.DataFrame) -> float: + return float( + db.loc[db["Account"].astype(str).str.strip().isin(["21","22","23","24","26"]), "Site Labor Hours"] + .fillna(0.0) + .sum() ) + +def update_cons_duration(db0: pd.DataFrame, db1: pd.DataFrame, ref_duration: float) -> float: + def _sum_hours(db): + return float( + db.loc[db["Account"].astype(str).str.strip().isin(["21","22","23","24","26"]), "Site Labor Hours"] + .fillna(0.0) + .sum() + ) + + sum_old = _sum_hours(db0) + sum_new = _sum_hours(db1) + + if sum_old == 0: + return float(ref_duration) + lab_delta = (sum_new - sum_old) / sum_old - return 0.3 * lab_delta * ref_duration + ref_duration - -def update_cons_duration_2(db0, db1, ref_duration, prev_cons_duration, baseline_lab_hours): - sum_old = ( - db0.loc[db0.Account == 21, "Site Labor Hours"].values - + db0.loc[db0.Account == 22, "Site Labor Hours"].values - + db0.loc[db0.Account == 23, "Site Labor Hours"].values - + db0.loc[db0.Account == 24, "Site Labor Hours"].values - + db0.loc[db0.Account == 26, "Site Labor Hours"].values - ) - sum_new = ( - db1.loc[db1.Account == 21, "Site Labor Hours"].values - + db1.loc[db1.Account == 22, "Site Labor Hours"].values - + db1.loc[db1.Account == 23, "Site Labor Hours"].values - + db1.loc[db1.Account == 24, "Site Labor Hours"].values - + db1.loc[db1.Account == 26, "Site Labor Hours"].values - ) - lab_delta = (sum_new - sum_old) / baseline_lab_hours - return 0.3 * lab_delta * ref_duration + prev_cons_duration + return float(0.3 * lab_delta * ref_duration + ref_duration) + +def update_cons_duration_2( + db0: pd.DataFrame, + db1: pd.DataFrame, + ref_duration: float, + prev_cons_duration: float, + baseline_lab_hours: float +) -> float: + def _sum_hours(db): + return float( + db.loc[db["Account"].astype(str).str.strip().isin(["21","22","23","24","26"]), "Site Labor Hours"] + .fillna(0.0) + .sum() + ) + + sum_old = _sum_hours(db0) + sum_new = _sum_hours(db1) + + if baseline_lab_hours == 0: + return float(prev_cons_duration) + + lab_delta = (sum_new - sum_old) / float(baseline_lab_hours) + return float(0.3 * lab_delta * ref_duration + float(prev_cons_duration)) + diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index 1e68631..c23eaa3 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -86,7 +86,7 @@ def add_BOP_RP_grades( # duration update from grade change duration_ref = 125 if reactor_type == "Concept A" else 80 - new_dur = float(update_cons_duration(df, db2, duration_ref)) + new_dur = update_cons_duration(df, db2, duration_ref) # modularity factor on duration; for n>=2 assume modularized mod = mod_0 if n_th == 1 else "modularized" diff --git a/src/crf/model/finance.py b/src/crf/model/finance.py index 6191352..7848e68 100644 --- a/src/crf/model/finance.py +++ b/src/crf/model/finance.py @@ -13,21 +13,31 @@ ] -def insurance_cost_update(base_df: pd.DataFrame, df: pd.DataFrame, power: float): - db = df.copy() +def insurance_cost_update(base_df, updated_df, power): + # Ensure derived rows (subtotals/totals) exist and are up-to-date + base_df = update_high_level_costs(base_df.copy(), power) + updated_df = update_high_level_costs(updated_df.copy(), power) + + ref_20 = float(base_df.loc[base_df["Title"].eq("20s - Subtotal"), "Total Cost (USD)"].iloc[0]) + ref_30 = float(base_df.loc[base_df["Title"].eq("30s - Subtotal"), "Total Cost (USD)"].iloc[0]) - new_tot = float(db.loc[db["Title"].eq("20s - Subtotal"), "Total Cost (USD)"].iloc[0]) + \ - float(db.loc[db["Title"].eq("30s - Subtotal"), "Total Cost (USD)"].iloc[0]) + new_20 = float(updated_df.loc[updated_df["Title"].eq("20s - Subtotal"), "Total Cost (USD)"].iloc[0]) + new_30 = float(updated_df.loc[updated_df["Title"].eq("30s - Subtotal"), "Total Cost (USD)"].iloc[0]) - base_tot = float(base_df.loc[base_df["Title"].eq("20s - Subtotal"), "Total Cost (USD)"].iloc[0]) + \ - float(base_df.loc[base_df["Title"].eq("30s - Subtotal"), "Total Cost (USD)"].iloc[0]) + denom = ref_20 + ref_30 + change_factor = 1.0 if denom == 0 else (new_20 + new_30) / denom - factor = new_tot / base_tot + # Update Account 52 (Insurance) + mask52 = updated_df["Account"].astype(str).str.strip().eq("52") + if not mask52.any(): + raise KeyError("Account 52 (Insurance) not found in dataframe.") + ins0 = float(np.nan_to_num(updated_df.loc[mask52, "Total Cost (USD)"].iloc[0], nan=0.0)) + updated_df.loc[mask52, "Total Cost (USD)"] = ins0 * change_factor - old_52 = float(df.loc[df["Account"].eq(52), "Total Cost (USD)"].iloc[0]) - db.loc[db["Account"].eq(52), "Total Cost (USD)"] = old_52 * factor + # Recompute derived totals after change + updated_df = update_high_level_costs(updated_df, power) + return updated_df - return update_high_level_costs(db, power)[COLS].copy() def update_interest_cost( diff --git a/src/crf/model/indirect_cost.py b/src/crf/model/indirect_cost.py index 939cf5f..9ab9956 100644 --- a/src/crf/model/indirect_cost.py +++ b/src/crf/model/indirect_cost.py @@ -28,10 +28,12 @@ def update_indirect_cost( sum_new_mat_cost = 0.0 sum_new_lab_cost = 0.0 sum_new_lab_hrs = 0.0 - for acct in [21, 22, 23, 24, 26]: - sum_new_mat_cost += float(db.loc[db["Account"].eq(acct), "Site Material Cost"].iloc[0]) - sum_new_lab_cost += float(db.loc[db["Account"].eq(acct), "Site Labor Cost"].iloc[0]) - sum_new_lab_hrs += float(db.loc[db["Account"].eq(acct), "Site Labor Hours"].iloc[0]) + mask = db["Account"].astype(str).str.strip().isin(["21","22","23","24","26"]) + sum_new_mat_cost = float(db.loc[mask, "Site Material Cost"].fillna(0.0).sum()) + sum_new_lab_cost = float(db.loc[mask, "Site Labor Cost"].fillna(0.0).sum()) + sum_new_lab_hrs = float(db.loc[mask, "Site Labor Hours"].fillna(0.0).sum()) + + dur = float(final_construction_duration) diff --git a/src/crf/utils/df_ops.py b/src/crf/utils/df_ops.py index da441ae..b3c0a52 100644 --- a/src/crf/utils/df_ops.py +++ b/src/crf/utils/df_ops.py @@ -1,16 +1,43 @@ import numpy as np import pandas as pd -def getv(df: pd.DataFrame, account, col: str): - return df.loc[df["Account"].eq(account), col].iloc[0] +def _acc_str(x) -> str: + return str(x).strip() -def setv(df: pd.DataFrame, account, col: str, value): - df.loc[df["Account"].eq(account), col] = value +def _mask_accounts(df: pd.DataFrame, accounts) -> pd.Series: + """ + Robust account matcher: compares Account values as stripped strings. + accounts can be a scalar or iterable of scalars. + """ + if isinstance(accounts, (str, int, float)): + accounts = [accounts] + targets = {_acc_str(a) for a in accounts} + return df["Account"].astype(str).str.strip().isin(targets) + +def getv(df: pd.DataFrame, account, col): + a = _acc_str(account) + s = df.loc[df["Account"].astype(str).str.strip().eq(a), col] + if s.empty: + raise KeyError( + f"Account '{account}' not found in dataframe (column='{col}'). " + f"Available sample accounts: {df['Account'].astype(str).str.strip().head(30).tolist()}" + ) + return s.iloc[0] + +def setv(df: pd.DataFrame, account, col, value): + mask = _mask_accounts(df, account) + if not mask.any(): + raise KeyError(f"Account '{account}' not found for setv (column='{col}')") + df.loc[mask, col] = value def mulv(df: pd.DataFrame, accounts, cols, factor: float): - m = df["Account"].isin(accounts) - df.loc[m, cols] = df.loc[m, cols].to_numpy() * factor + mask = _mask_accounts(df, accounts) + if not mask.any(): + raise KeyError(f"None of accounts {accounts} found for mulv") + df.loc[mask, cols] = df.loc[mask, cols].to_numpy() * factor def resetv(df: pd.DataFrame, accounts, cols): - m = df["Account"].isin(accounts) - df.loc[m, cols] = np.nan + mask = _mask_accounts(df, accounts) + if not mask.any(): + raise KeyError(f"None of accounts {accounts} found for resetv") + df.loc[mask, cols] = np.nan diff --git a/test/test_crf_smoke.py b/test/test_crf_smoke.py index 4306003..7eded09 100644 --- a/test/test_crf_smoke.py +++ b/test/test_crf_smoke.py @@ -10,16 +10,26 @@ def main(): + # config = { + # "inputs_xlsx": "Inputs.xlsx", + # "reactor_type": "Concept A", + # "f_22": 250_000_000, + # "f_2321": 150_000_000, + # "land_cost_per_acre_0": 22000, + # "startup_0": 16, + # "staggering_ratio": 0.75, + # } config = { - "inputs_xlsx": "Inputs.xlsx", - "reactor_type": "Concept A", - "f_22": 250_000_000, - "f_2321": 150_000_000, - "land_cost_per_acre_0": 22000, - "startup_0": 16, - "staggering_ratio": 0.75, + "reactor_type": "HTGR", # or "SFR" + # "data_dir": "src/crf/data", # + "f_22": 250_000_000, + "f_2321": 150_000_000, + "land_cost_per_acre_0": 22000, + "startup_0": 16, + "staggering_ratio": 0.75, } + # baseline-style levers (raw form, like from Excel) levers = { "num_orders": 3, From 7ee6e615bb2d5819076b09d8e9bbf16d19453de4 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Thu, 19 Feb 2026 14:59:34 -0600 Subject: [PATCH 04/25] GNCOA25 exclude --- src/crf/data/HTGR_baseline.csv | 2 +- src/crf/model/core_accounts.py | 68 ++++++++++++++++++++-------------- src/crf/model/direct_cost.py | 22 +++++++++-- 3 files changed, 61 insertions(+), 31 deletions(-) diff --git a/src/crf/data/HTGR_baseline.csv b/src/crf/data/HTGR_baseline.csv index 08bcf24..fb432e8 100644 --- a/src/crf/data/HTGR_baseline.csv +++ b/src/crf/data/HTGR_baseline.csv @@ -17,7 +17,7 @@ Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labo 232.1,Electricity Generation Systems,576019409.4786329,486396295.4,1682189.488,89623114.06,0 233,Ultimate Heat Sink,89555306.03530921,47848710.99,682455.832,35879240.23,5827354.82 24,Electrical Equipment,140508400.4910087,28461247.42,1597897.041,86126547.25,25920605.82 -25,Initial fuel inventory,451686470.5748004,,0,0,0 +25,Initial fuel inventory,451686470.5748004,451686470.5748004,0,0,0 26,Miscellaneous Equipment,214388490.9241457,65401342.47,2408453.335,129937709.9,19049438.52 28,Simulator,0,,,, 30,Capitalized Indirect Services Costs,,,,, diff --git a/src/crf/model/core_accounts.py b/src/crf/model/core_accounts.py index 790a8cd..6979bc8 100644 --- a/src/crf/model/core_accounts.py +++ b/src/crf/model/core_accounts.py @@ -75,51 +75,63 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr db = db.copy() db[COST_COLS] = db[COST_COLS].fillna(0.0) # account 21 - db.loc[db.Account == 21, "Factory Equipment Cost"] = ( - db.loc[db.Account == 212, "Factory Equipment Cost"].values - + db.loc[db.Account == 213, "Factory Equipment Cost"].values + db.loc[db.Account == "21", "Factory Equipment Cost"] = ( + db.loc[db.Account == "212", "Factory Equipment Cost"].values + + db.loc[db.Account == "213", "Factory Equipment Cost"].values + db.loc[db.Account == "211 plus 214 to 219", "Factory Equipment Cost"].values ) - db.loc[db.Account == 21, "Site Material Cost"] = ( - db.loc[db.Account == 212, "Site Material Cost"].values - + db.loc[db.Account == 213, "Site Material Cost"].values + db.loc[db.Account == "21", "Site Material Cost"] = ( + db.loc[db.Account == "212", "Site Material Cost"].values + + db.loc[db.Account == "213", "Site Material Cost"].values + db.loc[db.Account == "211 plus 214 to 219", "Site Material Cost"].values ) - db.loc[db.Account == 21, "Site Labor Cost"] = ( - db.loc[db.Account == 212, "Site Labor Cost"].values - + db.loc[db.Account == 213, "Site Labor Cost"].values + db.loc[db.Account == "21", "Site Labor Cost"] = ( + db.loc[db.Account == "212", "Site Labor Cost"].values + + db.loc[db.Account == "213", "Site Labor Cost"].values + db.loc[db.Account == "211 plus 214 to 219", "Site Labor Cost"].values ) - db.loc[db.Account == 21, "Site Labor Hours"] = ( - db.loc[db.Account == 212, "Site Labor Hours"].values - + db.loc[db.Account == 213, "Site Labor Hours"].values + db.loc[db.Account == "21", "Site Labor Hours"] = ( + db.loc[db.Account == "212", "Site Labor Hours"].values + + db.loc[db.Account == "213", "Site Labor Hours"].values + db.loc[db.Account == "211 plus 214 to 219", "Site Labor Hours"].values ) # account 23 - db.loc[db.Account == 23, "Factory Equipment Cost"] = ( + db.loc[db.Account == "23", "Factory Equipment Cost"] = ( db.loc[db.Account == "232.1", "Factory Equipment Cost"].values - + db.loc[db.Account == 233, "Factory Equipment Cost"].values + + db.loc[db.Account == "233", "Factory Equipment Cost"].values ) - db.loc[db.Account == 23, "Site Material Cost"] = ( + db.loc[db.Account == "23", "Site Material Cost"] = ( db.loc[db.Account == "232.1", "Site Material Cost"].values - + db.loc[db.Account == 233, "Site Material Cost"].values + + db.loc[db.Account == "233", "Site Material Cost"].values ) - db.loc[db.Account == 23, "Site Labor Cost"] = ( + db.loc[db.Account == "23", "Site Labor Cost"] = ( db.loc[db.Account == "232.1", "Site Labor Cost"].values - + db.loc[db.Account == 233, "Site Labor Cost"].values + + db.loc[db.Account == "233", "Site Labor Cost"].values ) - db.loc[db.Account == 23, "Site Labor Hours"] = ( + db.loc[db.Account == "23", "Site Labor Hours"] = ( db.loc[db.Account == "232.1", "Site Labor Hours"].values - + db.loc[db.Account == 233, "Site Labor Hours"].values + + db.loc[db.Account == "233", "Site Labor Hours"].values ) - # total costs for 21..26 components - for x in [21, 22, 23, 24, 25, 26]: - db.loc[db["Account"] == x, "Total Cost (USD)"] = ( - db.loc[db["Account"] == x, "Factory Equipment Cost"] - + db.loc[db["Account"] == x, "Site Labor Cost"] - + db.loc[db["Account"] == x, "Site Material Cost"] - ) + # total costs for all accounts should be updated except the lines + # with no Account (subtotals, $/kWe, and final results) + db.loc[db["Account"].notna(), "Total Cost (USD)"] = ( + db.loc[db["Account"].notna(), "Factory Equipment Cost"].values + + db.loc[db["Account"].notna(), "Site Material Cost"].values + + db.loc[db["Account"].notna(), "Site Labor Cost"].values + ) + + + + + + + # for x in [21, 22, 23, 24, 25, 26]: + # db.loc[db["Account"] == x, "Total Cost (USD)"] = ( + # db.loc[db["Account"] == x, "Factory Equipment Cost"] + # + db.loc[db["Account"] == x, "Site Labor Cost"] + # + db.loc[db["Account"] == x, "Site Material Cost"] + # ) # subtotals @@ -183,6 +195,8 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr db.loc[db["Title"] == "(Accounts 10 to 60) US$/kWe", "Total Cost (USD)"] = ( db.loc[db["Title"] == "Total Capital Investment Cost (All Accounts)", "Total Cost (USD)"].values / reactor_power ) + # print(db[8:21]) + # print(db[35:37]) # debug print return db def ITC_reduction_factor(itc_level: float) -> float: diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index c23eaa3..d463965 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -19,7 +19,7 @@ "Factory Equipment Cost", "Site Labor Hours", "Site Labor Cost", "Site Material Cost" ] - +# exclude account 25 because it is the initial fuel cost and should not be affected by rework ACCT_DIRECT = [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26] @@ -185,15 +185,31 @@ def update_direct_cost( mod_0: str ): reactor_df, power = store.get_baseline(reactor_type) - db, baseline_lab_hours = add_factory_cost(reactor_df, power, f_22, f_2321, num_orders) + # all Total Cost (USD) columns should be 0 at this point + db["Total Cost (USD)"] = 0.0 + db = update_high_level_costs(db, power)[COLS].copy() + # print("After factory cost update:") + # print(db[8:21]) + # print(db[35:37]) # debug print db = add_land_cost(db, land_cost_per_acre_0, power) + # print("After land cost update:") + # print(db[8:21]) + # print(db[35:37]) # debug print db, prev_dur = add_BOP_RP_grades(db, RB_grade_0, BOP_grade_0, power, reactor_type, n_th, mod_0) + # print("After BOP/RP grade update:") + # print(db[8:21]) + # print(db[35:37]) # debug print db = add_bulk_ordering(db, num_orders, f_22, f_2321, power) + # print("After bulk ordering update:") + # print(db[8:21]) + # print(db[35:37]) # debug print db, dur_no_delay = add_reworking_productivity( db, reactor_type, n_th, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, power, prev_dur, baseline_lab_hours ) - + # print("After direct cost updates:") + # print(db[8:21]) + # print(db[35:37]) # debug print return db, dur_no_delay From 1f4ce52fd735b4380627a8b78aa3106dcf9adef1 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Thu, 19 Feb 2026 22:26:01 -0600 Subject: [PATCH 05/25] update total cost on 20s --- src/crf/model/core_accounts.py | 33 +++++++++++++-------------------- src/crf/model/direct_cost.py | 5 +++-- 2 files changed, 16 insertions(+), 22 deletions(-) diff --git a/src/crf/model/core_accounts.py b/src/crf/model/core_accounts.py index 6979bc8..afb1857 100644 --- a/src/crf/model/core_accounts.py +++ b/src/crf/model/core_accounts.py @@ -95,6 +95,8 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr + db.loc[db.Account == "213", "Site Labor Hours"].values + db.loc[db.Account == "211 plus 214 to 219", "Site Labor Hours"].values ) + + # account 23 db.loc[db.Account == "23", "Factory Equipment Cost"] = ( db.loc[db.Account == "232.1", "Factory Equipment Cost"].values @@ -114,24 +116,16 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr ) # total costs for all accounts should be updated except the lines - # with no Account (subtotals, $/kWe, and final results) - db.loc[db["Account"].notna(), "Total Cost (USD)"] = ( - db.loc[db["Account"].notna(), "Factory Equipment Cost"].values - + db.loc[db["Account"].notna(), "Site Material Cost"].values - + db.loc[db["Account"].notna(), "Site Labor Cost"].values - ) - - - - - - - # for x in [21, 22, 23, 24, 25, 26]: - # db.loc[db["Account"] == x, "Total Cost (USD)"] = ( - # db.loc[db["Account"] == x, "Factory Equipment Cost"] - # + db.loc[db["Account"] == x, "Site Labor Cost"] - # + db.loc[db["Account"] == x, "Site Material Cost"] - # ) + # with no Account (subtotals, $/kWe, and final results) but only + # accounts under 20s has factory equipment costs, labor hours, and + # labor costs, so we can skip accounts 10s, 30s 50s and 60s + + for x in ['21', '211 plus 214 to 219', '212', '213', '22', '23', '232.1', '233', '24', '25', '26']: + db.loc[db["Account"] == x, "Total Cost (USD)"] = ( + db.loc[db["Account"] == x, "Factory Equipment Cost"] + + db.loc[db["Account"] == x, "Site Labor Cost"] + + db.loc[db["Account"] == x, "Site Material Cost"] + ) # subtotals @@ -195,8 +189,7 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr db.loc[db["Title"] == "(Accounts 10 to 60) US$/kWe", "Total Cost (USD)"] = ( db.loc[db["Title"] == "Total Capital Investment Cost (All Accounts)", "Total Cost (USD)"].values / reactor_power ) - # print(db[8:21]) - # print(db[35:37]) # debug print + # print(db[["Account", "Title", "Total Cost (USD)"]]) return db def ITC_reduction_factor(itc_level: float) -> float: diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index d463965..c6fe749 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -186,8 +186,9 @@ def update_direct_cost( ): reactor_df, power = store.get_baseline(reactor_type) db, baseline_lab_hours = add_factory_cost(reactor_df, power, f_22, f_2321, num_orders) - # all Total Cost (USD) columns should be 0 at this point - db["Total Cost (USD)"] = 0.0 + # all Total Cost (USD) columns should be 0 + # when accounts are in 20s + db.loc[db["Account"].isin(['21', '211 plus 214 to 219', '212', '213', '22', '23', '232.1', '233', '24', '25', '26']), "Total Cost (USD)"] = 0.0 db = update_high_level_costs(db, power)[COLS].copy() # print("After factory cost update:") # print(db[8:21]) From 566322724a17048acb8135b3b2e0c4fe1c07497c Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Thu, 26 Feb 2026 18:32:32 -0600 Subject: [PATCH 06/25] construction duration has not fixed also the concept name --- .../CostReduction_exploration_mode.ipynb | 8 +- Cost_Reduction/scenarios.ipynb | 282 +++++++++++++++++- src/crf/data/HTGR_baseline.csv | 2 +- src/crf/model/core_accounts.py | 1 - src/crf/model/direct_cost.py | 26 +- src/crf/model/finance.py | 5 + src/crf/model/indirect_cost.py | 6 +- src/crf/model/learning.py | 4 +- test/test_crf_smoke.py | 43 ++- 9 files changed, 314 insertions(+), 63 deletions(-) diff --git a/Cost_Reduction/CostReduction_exploration_mode.ipynb b/Cost_Reduction/CostReduction_exploration_mode.ipynb index 1c2cb30..70af906 100644 --- a/Cost_Reduction/CostReduction_exploration_mode.ipynb +++ b/Cost_Reduction/CostReduction_exploration_mode.ipynb @@ -3676,7 +3676,7 @@ ], "source": [ "def update_indirect_cost(n_th, standardization_0, Reactor_data_updated_6, final_construction_duration, power ):\n", - " \n", + " print(\"in function update indirect\")\n", " if n_th == 1:\n", " standardization = 0.7\n", " elif n_th >1:\n", @@ -3709,8 +3709,7 @@ " sum_new_mat_cost += (db.loc[db.Account == x, 'Site Material Cost']).values\n", " sum_new_lab_cost += (db.loc[db.Account == x, 'Site Labor Cost']).values\n", " sum_new_lab_hrs += (db.loc[db.Account == x, 'Site Labor Hours']).values\n", - "\n", - "\n", + " \n", " # The new indirect costs \n", " db.loc[db.Account == 31, 'Total Cost (USD)'] = (sum_new_mat_cost*0.785* sum_new_lab_hrs/final_construction_duration/160/1058)\\\n", " + sum_new_lab_cost *0.36\n", @@ -3728,7 +3727,6 @@ " Reactor_data_updated_7_ = pd.DataFrame()\n", " Reactor_data_updated_7_ = Reactor_data_updated_7[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", " 'Site Material Cost']].copy()\n", - " \n", " return Reactor_data_updated_7_ \n", "\n", "\n", @@ -5734,7 +5732,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/Cost_Reduction/scenarios.ipynb b/Cost_Reduction/scenarios.ipynb index 50f3a0e..6d38278 100644 --- a/Cost_Reduction/scenarios.ipynb +++ b/Cost_Reduction/scenarios.ipynb @@ -54,7 +54,30 @@ "execution_count": 2, "id": "7b9a01a8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", + "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", + "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", + "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", + "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", + "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", + "in function update indirect\n", + "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", + "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", + "in function update indirect\n", + "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", + "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", + "in function update indirect\n", + "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", + "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", + "in function update indirect\n" + ] + } + ], "source": [ "# Just running the other jupyter notebook to bring all the functions from there\n", "%run CostReduction_exploration_mode.ipynb" @@ -172,8 +195,8 @@ " ITC_0 = 0.4\n", " n_ITC = 4 \n", " \n", - " avg_results = calculate_final_result_avg(reactor_type, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0,\\\n", - " num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0, interest_rate_0, startup_0) " + " #avg_results = calculate_final_result_avg(reactor_type, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0,\\\n", + " #num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0, interest_rate_0, startup_0) " ] }, { @@ -181,11 +204,256 @@ "execution_count": 5, "id": "8187e003", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", + "111.4278125 100 [121.63844064] 11.427812500000002 [133.06625314]\n", + "in function update indirect\n" + ] + }, + { + "data": { + "text/plain": [ + "( Account Title \\\n", + " 0 10 Capitalized Pre-Construction Costs \n", + " 1 11 Land & Land Rights \n", + " 2 12 Site Permits \n", + " 3 13 Plant Licensing \n", + " 4 14 Plant Permits \n", + " 5 15 Plant Studies \n", + " 6 16 Plant Reports \n", + " 7 18 Other Pre-Construction Costs \n", + " 8 NaN NaN \n", + " 9 NaN 10s - Subtotal \n", + " 10 NaN 10s - $/kWe \n", + " 11 NaN NaN \n", + " 12 20 Capitalized Direct Costs \n", + " 13 21 Structures & Improvements \n", + " 14 212 Reactor Containment Building \n", + " 15 213 Turbine Room and Heater Bay \n", + " 16 211 plus 214 to 219 Othe Structures & Improvements \n", + " 17 22 Reactor System \n", + " 18 23 Energy Conversion System \n", + " 19 232.1 Electricity Generation Systems \n", + " 20 233 Ultimate Heat Sink \n", + " 21 24 Electrical Equipment \n", + " 22 25 Initial fuel inventory \n", + " 23 26 Miscellaneous Equipment \n", + " 24 28 Simulator \n", + " 25 NaN NaN \n", + " 26 NaN 20s - Subtotal \n", + " 27 NaN 20s - $/kWe \n", + " 28 NaN NaN \n", + " 29 30 Capitalized Indirect Services Costs \n", + " 30 31 Factory & Field Indirect Costs \n", + " 31 32 Factory and construction supervision \n", + " 32 33 Start-Up Costs \n", + " 33 34 Shipping & Transportation Costs \n", + " 34 35 Engineering Services \n", + " 35 NaN NaN \n", + " 36 NaN 30s - Subtotal \n", + " 37 NaN 30s - $/kWe \n", + " 38 NaN NaN \n", + " 39 50 Capitalized Supplementary Costs \n", + " 40 51 Taxes \n", + " 41 52 Insurance \n", + " 42 54 Decommissioning \n", + " 43 NaN NaN \n", + " 44 NaN 50s - Subtotal \n", + " 45 NaN 50s - $/kWe \n", + " 46 NaN NaN \n", + " 47 60 Capitalized Financial Costs \n", + " 48 62 Interest \n", + " 49 - 60s - Subtotal \n", + " 50 NaN 60s - $/kWe \n", + " 51 NaN NaN \n", + " 52 NaN Total Direct Capital Cost (Accounts 10 to 20) \n", + " 53 NaN (Accounts 10 to 20) US$/kWe \n", + " 54 NaN NaN \n", + " 55 NaN Base Construction Cost (Accounts 10 to 30) \n", + " 56 NaN (Accounts 10 to 30) US$/kWe \n", + " 57 NaN NaN \n", + " 58 NaN Total Overnight Cost (Accounts 10 to 50) \n", + " 59 NaN (Accounts 10 to 50) US$/kWe \n", + " 60 NaN NaN \n", + " 61 NaN Total Capital Investment Cost (All Accounts) \n", + " 62 NaN (Accounts 10 to 60) US$/kWe \n", + " 63 NaN NaN \n", + " 64 NaN Total Overnight Cost - ITC reduced \n", + " 65 NaN Total Overnight Cost -ITC reduced (US$/kWe) \n", + " 66 NaN NaN \n", + " 67 NaN Total Capital Investment Cost - ITC reduced \n", + " 68 NaN Total Capital Investment Cost - ITC reduced (U... \n", + " \n", + " Total Cost (USD) Factory Equipment Cost Site Labor Hours \\\n", + " 0 NaN NaN NaN \n", + " 1 1.500000e+07 NaN NaN \n", + " 2 0.000000e+00 NaN NaN \n", + " 3 1.070098e+08 NaN NaN \n", + " 4 4.721019e+06 NaN NaN \n", + " 5 0.000000e+00 NaN NaN \n", + " 6 0.000000e+00 NaN NaN \n", + " 7 3.619448e+07 NaN NaN \n", + " 8 NaN NaN NaN \n", + " 9 1.629253e+08 NaN NaN \n", + " 10 1.542853e+02 NaN NaN \n", + " 11 NaN NaN NaN \n", + " 12 NaN NaN NaN \n", + " 13 1.533300e+09 1.365362e+08 2.090920e+07 \n", + " 14 6.966448e+08 9.992741e+07 9.638431e+06 \n", + " 15 1.522573e+07 1.036695e+06 1.749114e+05 \n", + " 16 8.214295e+08 3.557211e+07 1.109586e+07 \n", + " 17 1.975878e+09 1.478440e+09 8.719725e+06 \n", + " 18 4.755064e+08 3.072326e+08 3.008178e+06 \n", + " 19 3.301050e+08 2.344884e+08 1.794686e+06 \n", + " 20 1.454013e+08 7.274425e+07 1.213492e+06 \n", + " 21 2.358203e+08 4.326955e+07 2.841261e+06 \n", + " 22 4.516865e+08 NaN 0.000000e+00 \n", + " 23 3.594357e+08 9.942946e+07 4.282531e+06 \n", + " 24 0.000000e+00 NaN NaN \n", + " 25 NaN NaN NaN \n", + " 26 5.031627e+09 NaN NaN \n", + " 27 4.764798e+03 NaN NaN \n", + " 28 NaN NaN NaN \n", + " 29 NaN NaN NaN \n", + " 30 1.377074e+09 NaN NaN \n", + " 31 5.005922e+09 NaN NaN \n", + " 32 2.102487e+08 NaN NaN \n", + " 33 1.752073e+07 NaN NaN \n", + " 34 1.351599e+09 NaN NaN \n", + " 35 NaN NaN NaN \n", + " 36 7.962364e+09 NaN NaN \n", + " 37 7.540118e+03 NaN NaN \n", + " 38 NaN NaN NaN \n", + " 39 NaN NaN NaN \n", + " 40 1.875000e+05 NaN NaN \n", + " 41 5.847296e+07 NaN NaN \n", + " 42 8.141760e+06 NaN NaN \n", + " 43 NaN NaN NaN \n", + " 44 6.680222e+07 NaN NaN \n", + " 45 6.325968e+01 NaN NaN \n", + " 46 NaN NaN NaN \n", + " 47 NaN NaN NaN \n", + " 48 6.594528e+09 NaN NaN \n", + " 49 6.594528e+09 NaN NaN \n", + " 50 6.244818e+03 NaN NaN \n", + " 51 NaN NaN NaN \n", + " 52 5.194552e+09 NaN NaN \n", + " 53 4.919084e+03 NaN NaN \n", + " 54 NaN NaN NaN \n", + " 55 1.315692e+10 NaN NaN \n", + " 56 1.245920e+04 NaN NaN \n", + " 57 NaN NaN NaN \n", + " 58 1.322372e+10 NaN NaN \n", + " 59 1.252246e+04 NaN NaN \n", + " 60 NaN NaN NaN \n", + " 61 1.981825e+10 NaN NaN \n", + " 62 1.876728e+04 NaN NaN \n", + " 63 NaN NaN NaN \n", + " 64 8.330943e+09 NaN NaN \n", + " 65 7.889150e+03 NaN NaN \n", + " 66 NaN NaN NaN \n", + " 67 1.492547e+10 NaN NaN \n", + " 68 1.413397e+04 NaN NaN \n", + " \n", + " Site Labor Cost Site Material Cost \n", + " 0 NaN NaN \n", + " 1 NaN NaN \n", + " 2 NaN NaN \n", + " 3 NaN NaN \n", + " 4 NaN NaN \n", + " 5 NaN NaN \n", + " 6 NaN NaN \n", + " 7 NaN NaN \n", + " 8 NaN NaN \n", + " 9 NaN NaN \n", + " 10 NaN NaN \n", + " 11 NaN NaN \n", + " 12 NaN NaN \n", + " 13 1.045892e+09 3.508716e+08 \n", + " 14 4.700620e+08 1.266554e+08 \n", + " 15 9.024686e+06 5.164350e+06 \n", + " 16 5.668055e+08 2.190519e+08 \n", + " 17 4.656677e+08 3.177007e+07 \n", + " 18 1.594144e+08 8.859309e+06 \n", + " 19 9.561666e+07 0.000000e+00 \n", + " 20 6.379777e+07 8.859309e+06 \n", + " 21 1.531438e+08 3.940702e+07 \n", + " 22 0.000000e+00 0.000000e+00 \n", + " 23 2.310455e+08 2.896080e+07 \n", + " 24 NaN NaN \n", + " 25 NaN NaN \n", + " 26 NaN NaN \n", + " 27 NaN NaN \n", + " 28 NaN NaN \n", + " 29 NaN NaN \n", + " 30 NaN NaN \n", + " 31 NaN NaN \n", + " 32 NaN NaN \n", + " 33 NaN NaN \n", + " 34 NaN NaN \n", + " 35 NaN NaN \n", + " 36 NaN NaN \n", + " 37 NaN NaN \n", + " 38 NaN NaN \n", + " 39 NaN NaN \n", + " 40 NaN NaN \n", + " 41 NaN NaN \n", + " 42 NaN NaN \n", + " 43 NaN NaN \n", + " 44 NaN NaN \n", + " 45 NaN NaN \n", + " 46 NaN NaN \n", + " 47 NaN NaN \n", + " 48 NaN NaN \n", + " 49 NaN NaN \n", + " 50 NaN NaN \n", + " 51 NaN NaN \n", + " 52 NaN NaN \n", + " 53 NaN NaN \n", + " 54 NaN NaN \n", + " 55 NaN NaN \n", + " 56 NaN NaN \n", + " 57 NaN NaN \n", + " 58 NaN NaN \n", + " 59 NaN NaN \n", + " 60 NaN NaN \n", + " 61 NaN NaN \n", + " 62 NaN NaN \n", + " 63 NaN NaN \n", + " 64 NaN NaN \n", + " 65 NaN NaN \n", + " 66 NaN NaN \n", + " 67 NaN NaN \n", + " 68 NaN NaN ,\n", + " 7889.150458472184,\n", + " 14133.968613003488,\n", + " array([133.06625314]))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# calculate_final_result(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0,\\\n", - "# num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0, interest_rate_0, startup_0)" + "print(n_ITC)\n", + "calculate_final_result(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0,\\\n", + " num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, \\\n", + " standardization_0, interest_rate_0, startup_0)\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f62c044-f816-49db-8564-5ef81c3221f8", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -204,7 +472,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/src/crf/data/HTGR_baseline.csv b/src/crf/data/HTGR_baseline.csv index fb432e8..501a4d9 100644 --- a/src/crf/data/HTGR_baseline.csv +++ b/src/crf/data/HTGR_baseline.csv @@ -19,7 +19,7 @@ Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labo 24,Electrical Equipment,140508400.4910087,28461247.42,1597897.041,86126547.25,25920605.82 25,Initial fuel inventory,451686470.5748004,451686470.5748004,0,0,0 26,Miscellaneous Equipment,214388490.9241457,65401342.47,2408453.335,129937709.9,19049438.52 -28,Simulator,0,,,, +28,Simulator,0,0,0,0,0 30,Capitalized Indirect Services Costs,,,,, 31,Factory & Field Indirect Costs,691371059.4821017,,,, 32,Factory and construction supervision,2734393138.1498117,,,, diff --git a/src/crf/model/core_accounts.py b/src/crf/model/core_accounts.py index afb1857..d0ced42 100644 --- a/src/crf/model/core_accounts.py +++ b/src/crf/model/core_accounts.py @@ -189,7 +189,6 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr db.loc[db["Title"] == "(Accounts 10 to 60) US$/kWe", "Total Cost (USD)"] = ( db.loc[db["Title"] == "Total Capital Investment Cost (All Accounts)", "Total Cost (USD)"].values / reactor_power ) - # print(db[["Account", "Title", "Total Cost (USD)"]]) return db def ITC_reduction_factor(itc_level: float) -> float: diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index c6fe749..38eb3bf 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -85,9 +85,11 @@ def add_BOP_RP_grades( db2 = update_high_level_costs(db, power)[COLS].copy() # duration update from grade change - duration_ref = 125 if reactor_type == "Concept A" else 80 + duration_ref = 125 if reactor_type == "HTGR" else 80 + # duration_ref = 100 if reactor_type == "HTGR" else 64 + print(f"DEBUG: reactor_type={reactor_type}, duration_ref={duration_ref}") new_dur = update_cons_duration(df, db2, duration_ref) - + print(f"DEBUG: new_dur before modulized change={new_dur}") # modularity factor on duration; for n>=2 assume modularized mod = mod_0 if n_th == 1 else "modularized" mod_factor = 0.8 if mod == "modularized" else 1.0 @@ -146,7 +148,7 @@ def add_reworking_productivity( productivity = 0.145 * ce_exp + 0.71 - if reactor_type == "Concept B": + if reactor_type == "SFR": rework = (-0.9 * design_completion + 1.9) * (-0.15 * ae_exp + 1.3) * (-0.15 * ce_exp + 1.3) ref_duration = 80 else: @@ -162,8 +164,9 @@ def add_reworking_productivity( setv(db, acct, "Site Labor Cost", float(getv(df, acct, "Site Labor Cost")) * rework / productivity) db2 = update_high_level_costs(db, power)[COLS].copy() - + print(f"DEBUG: ref_duration={ref_duration}, prev_cons_duration={prev_cons_duration}, baseline_lab_hours={baseline_lab_hours}") new_dur = float(update_cons_duration_2(df, db2, ref_duration, prev_cons_duration, baseline_lab_hours)) + print(f"DEBUG: new_dur={new_dur}") return db2, new_dur @@ -190,27 +193,12 @@ def update_direct_cost( # when accounts are in 20s db.loc[db["Account"].isin(['21', '211 plus 214 to 219', '212', '213', '22', '23', '232.1', '233', '24', '25', '26']), "Total Cost (USD)"] = 0.0 db = update_high_level_costs(db, power)[COLS].copy() - # print("After factory cost update:") - # print(db[8:21]) - # print(db[35:37]) # debug print db = add_land_cost(db, land_cost_per_acre_0, power) - # print("After land cost update:") - # print(db[8:21]) - # print(db[35:37]) # debug print db, prev_dur = add_BOP_RP_grades(db, RB_grade_0, BOP_grade_0, power, reactor_type, n_th, mod_0) - # print("After BOP/RP grade update:") - # print(db[8:21]) - # print(db[35:37]) # debug print db = add_bulk_ordering(db, num_orders, f_22, f_2321, power) - # print("After bulk ordering update:") - # print(db[8:21]) - # print(db[35:37]) # debug print db, dur_no_delay = add_reworking_productivity( db, reactor_type, n_th, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, power, prev_dur, baseline_lab_hours ) - # print("After direct cost updates:") - # print(db[8:21]) - # print(db[35:37]) # debug print return db, dur_no_delay diff --git a/src/crf/model/finance.py b/src/crf/model/finance.py index 7848e68..07056d8 100644 --- a/src/crf/model/finance.py +++ b/src/crf/model/finance.py @@ -119,4 +119,9 @@ def update_itc( db.loc[db["Title"].eq("Total Capital Investment Cost - ITC reduced (US$/kWe)"), "Total Cost (USD)"] = levelized_NCI db2 = update_high_level_costs(db, reactor_power)[COLS].copy() + print('DEBUG db2') + # print line 22 to 26 and line 37 38 + print(db2[22:27]) + # print line 37 and 38 Title Total Cost (USD) + print(db2.iloc[[37, 38]][["Title", "Total Cost (USD)"]]) return db2, itc_reduced_occ / reactor_power, levelized_NCI diff --git a/src/crf/model/indirect_cost.py b/src/crf/model/indirect_cost.py index 9ab9956..c75930f 100644 --- a/src/crf/model/indirect_cost.py +++ b/src/crf/model/indirect_cost.py @@ -32,17 +32,17 @@ def update_indirect_cost( sum_new_mat_cost = float(db.loc[mask, "Site Material Cost"].fillna(0.0).sum()) sum_new_lab_cost = float(db.loc[mask, "Site Labor Cost"].fillna(0.0).sum()) sum_new_lab_hrs = float(db.loc[mask, "Site Labor Hours"].fillna(0.0).sum()) - + print(f"DEBUG: sum_new_mat_cost={sum_new_mat_cost}, sum_new_lab_cost={sum_new_lab_cost}, sum_new_lab_hrs={sum_new_lab_hrs}") dur = float(final_construction_duration) - + print(f"DEBUG: final_construction_duration={dur}, standardization={standardization}, factor_35={factor_35}") val31 = (sum_new_mat_cost * 0.785 * sum_new_lab_hrs / dur / 160 / 1058) + sum_new_lab_cost * 0.36 val32 = sum_new_lab_cost * 0.36 * 3.661 * dur / 72 val33 = 0.04207006 * val32 val34 = 0.00354234616938 * val32 val35 = (0.27017603 * val32) * factor_35 - + print(f"DEBUG: val31={val31}, val32={val32}, val33={val33}, val34={val34}, val35={val35}") setv(db, 31, "Total Cost (USD)", val31) setv(db, 32, "Total Cost (USD)", val32) setv(db, 33, "Total Cost (USD)", val33) diff --git a/src/crf/model/learning.py b/src/crf/model/learning.py index 413f9c8..89541fa 100644 --- a/src/crf/model/learning.py +++ b/src/crf/model/learning.py @@ -62,7 +62,7 @@ def act_cons_duration_plus_delay( Design_Maturity = 2 proc_exp = min(proc_exp_0 + (2 / N_proc) * (n_th - 1), 2) - if reactor_type == "Concept B": + if reactor_type == "SFR": task_length_multiplier = 1.0 ref_construction_duration = 64 else: # Concept A @@ -88,11 +88,13 @@ def act_cons_duration_plus_delay( T_end = max(T_21, T_22, T_23, T_24, T_25, T_26) supply_chain_delay = max(T_end - ref_construction_duration, 0) + print(f"DEBUG: cons_duration_no_delay={cons_duration_no_delay}") return float(cons_duration_no_delay) + float(supply_chain_delay) def duration_learning_effect(n_th: int, standardization_0: float, actual_construction_duration_plus_delay: float): standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 + # fitted_LR_duration = 0.15 * standardization / 0.7 fitted_LR_duration = 0.103719051 * standardization / 0.7 duration_multiplier = (1 - fitted_LR_duration) ** np.log2(n_th) return float(duration_multiplier) * float(actual_construction_duration_plus_delay) diff --git a/test/test_crf_smoke.py b/test/test_crf_smoke.py index 7eded09..f23e889 100644 --- a/test/test_crf_smoke.py +++ b/test/test_crf_smoke.py @@ -10,18 +10,8 @@ def main(): - # config = { - # "inputs_xlsx": "Inputs.xlsx", - # "reactor_type": "Concept A", - # "f_22": 250_000_000, - # "f_2321": 150_000_000, - # "land_cost_per_acre_0": 22000, - # "startup_0": 16, - # "staggering_ratio": 0.75, - # } config = { "reactor_type": "HTGR", # or "SFR" - # "data_dir": "src/crf/data", # "f_22": 250_000_000, "f_2321": 150_000_000, "land_cost_per_acre_0": 22000, @@ -29,25 +19,24 @@ def main(): "staggering_ratio": 0.75, } - # baseline-style levers (raw form, like from Excel) levers = { - "num_orders": 3, - "itc_percent": 30, - "n_itc": 1, - "interest_percent": 6.5, - "design_completion_percent": 90, - "design_maturity": 2, - "proc_exp": 0.6, + "num_orders": 13, + "itc_percent": 40, + "n_itc": 4, + "interest_percent": 6, + "design_completion_percent": 80, + "design_maturity": 1, + "proc_exp": 0.5, "N_proc": 3, - "ce_exp": 0.6, + "ae_exp": 0.5, + "N_AE": 4, + "ce_exp": 1, "N_cons": 5, - "ae_exp": 0.6, - "N_AE": 4, - "standardization_percent": 70, - "modularity_code": 1, - "bop_grade_code": 1, - "rb_grade_code": 0, + "standardization_percent": 80, # 80% standardized + "modularity_code": 1, # Modularized + "bop_grade_code": 1, # Non-nuclear + "rb_grade_code": 0, # Nuclear } result = run_one_scenario(config, levers) @@ -56,7 +45,9 @@ def main(): print("avg_OCC:", result["avg_OCC"]) print("avg_TCI:", result["avg_TCI"]) print("avg_duration:", result["avg_duration"]) - + for k, v in result.items(): + if k not in ["avg_OCC", "avg_TCI", "avg_duration"]: + print(f"{k}: {v}") if __name__ == "__main__": main() From b3c95f2ef52d856405bf49dcfe17fffe6067c5fa Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Fri, 27 Feb 2026 19:29:17 -0600 Subject: [PATCH 07/25] add OCC NCI pass HTGR scenario 1 and 4 --- .../CostReduction_exploration_mode.ipynb | 10 +- Cost_Reduction/scenarios.ipynb | 902 +++++++++++++++++- src/crf/api.py | 46 - src/crf/model/avg_runner.py | 33 +- src/crf/model/core_accounts.py | 14 +- src/crf/model/direct_cost.py | 9 +- src/crf/model/finance.py | 12 +- src/crf/model/indirect_cost.py | 6 +- src/crf/model/learning.py | 6 +- src/crf/model/pipeline.py | 5 +- test/test_crf_smoke.py | 11 +- 11 files changed, 941 insertions(+), 113 deletions(-) diff --git a/Cost_Reduction/CostReduction_exploration_mode.ipynb b/Cost_Reduction/CostReduction_exploration_mode.ipynb index 70af906..ee05c43 100644 --- a/Cost_Reduction/CostReduction_exploration_mode.ipynb +++ b/Cost_Reduction/CostReduction_exploration_mode.ipynb @@ -2932,7 +2932,8 @@ " (db.loc[db.Account == 24, 'Site Labor Hours']).values+\\\n", " (db.loc[db.Account == 26, 'Site Labor Hours']).values\n", "\n", - "\n", + " print('update_cons_dur')\n", + " print('sum_old_lab_hrs,sum_new_lab_hrs',sum_old_lab_hrs,sum_new_lab_hrs)\n", "\n", " # # # change in labor hours for account 20\n", " labor_hour_ratio = (sum_new_lab_hrs)/sum_old_lab_hrs # note that this number can be positive or negative\n", @@ -2961,7 +2962,8 @@ " baseline_construction_duration = 64/mod_factor # months\n", " elif reactor_type == \"Concept A\": \n", " baseline_construction_duration = 100/mod_factor # months\n", - "\n", + " print('baseline_construction_duration',baseline_construction_duration)\n", + " print('labor_hour_ratio',labor_hour_ratio)\n", " actual_construction_duration = baseline_construction_duration*(0.3*labor_hour_ratio+0.7)\n", " return actual_construction_duration \n", "\n", @@ -3770,9 +3772,9 @@ "def calculate_base_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0):\n", " reactor_data = (reactor_data_read(reactor_type ))[0]\n", " reactor_power = (reactor_data_read(reactor_type ))[1]\n", - " direct_cost_updated = update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons)\n", - "\n", + " direct_cost_updated = update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons) \n", " act_con_duration = update_cons_dur(reactor_data, direct_cost_updated , mod_0)\n", + " \n", " cons_duration_plus_delay = act_cons_duration_plus_delay(reactor_type, n_th, Design_Maturity_0, proc_exp_0, N_proc, act_con_duration)\n", " final_con_duration = duration_learning_effect(n_th, standardization_0, cons_duration_plus_delay)\n", "\n", diff --git a/Cost_Reduction/scenarios.ipynb b/Cost_Reduction/scenarios.ipynb index 6d38278..d594d17 100644 --- a/Cost_Reduction/scenarios.ipynb +++ b/Cost_Reduction/scenarios.ipynb @@ -59,21 +59,673 @@ "name": "stdout", "output_type": "stream", "text": [ - "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", - "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", - "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", - "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", - "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", - "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", "in function update indirect\n", - "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", - "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", + " Account Title \\\n", + "0 10 Capitalized Pre-Construction Costs \n", + "1 11 Land & Land Rights \n", + "2 12 Site Permits \n", + "3 13 Plant Licensing \n", + "4 14 Plant Permits \n", + "5 15 Plant Studies \n", + "6 16 Plant Reports \n", + "7 18 Other Pre-Construction Costs \n", + "8 NaN NaN \n", + "9 NaN 10s - Subtotal \n", + "10 NaN 10s - $/kWe \n", + "11 NaN NaN \n", + "12 20 Capitalized Direct Costs \n", + "13 21 Structures & Improvements \n", + "14 212 Reactor Containment Building \n", + "15 213 Turbine Room and Heater Bay \n", + "16 211 plus 214 to 219 Othe Structures & Improvements \n", + "17 22 Reactor System \n", + "18 23 Energy Conversion System \n", + "19 232.1 Electricity Generation Systems \n", + "20 233 Ultimate Heat Sink \n", + "21 24 Electrical Equipment \n", + "22 25 Initial fuel inventory \n", + "23 26 Miscellaneous Equipment \n", + "24 28 Simulator \n", + "25 NaN NaN \n", + "26 NaN 20s - Subtotal \n", + "27 NaN 20s - $/kWe \n", + "28 NaN NaN \n", + "29 30 Capitalized Indirect Services Costs \n", + "30 31 Factory & Field Indirect Costs \n", + "31 32 Factory and construction supervision \n", + "32 33 Start-Up Costs \n", + "33 34 Shipping & Transportation Costs \n", + "34 35 Engineering Services \n", + "35 NaN NaN \n", + "36 NaN 30s - Subtotal \n", + "37 NaN 30s - $/kWe \n", + "38 NaN NaN \n", + "39 50 Capitalized Supplementary Costs \n", + "40 51 Taxes \n", + "41 52 Insurance \n", + "42 54 Decommissioning \n", + "43 NaN NaN \n", + "44 NaN 50s - Subtotal \n", + "45 NaN 50s - $/kWe \n", + "46 NaN NaN \n", + "47 60 Capitalized Financial Costs \n", + "48 62 Interest \n", + "49 - 60s - Subtotal \n", + "50 NaN 60s - $/kWe \n", + "51 NaN NaN \n", + "52 NaN Total Direct Capital Cost (Accounts 10 to 20) \n", + "53 NaN (Accounts 10 to 20) US$/kWe \n", + "54 NaN NaN \n", + "55 NaN Base Construction Cost (Accounts 10 to 30) \n", + "56 NaN (Accounts 10 to 30) US$/kWe \n", + "57 NaN NaN \n", + "58 NaN Total Overnight Cost (Accounts 10 to 50) \n", + "59 NaN (Accounts 10 to 50) US$/kWe \n", + "60 NaN NaN \n", + "61 NaN Total Capital Investment Cost (All Accounts) \n", + "62 NaN (Accounts 10 to 60) US$/kWe \n", + "63 NaN NaN \n", + "64 NaN Total Overnight Cost - ITC reduced \n", + "65 NaN Total Overnight Cost -ITC reduced (US$/kWe) \n", + "66 NaN NaN \n", + "67 NaN Total Capital Investment Cost - ITC reduced \n", + "68 NaN Total Capital Investment Cost - ITC reduced (U... \n", + "\n", + " Total Cost (USD) Factory Equipment Cost Site Labor Hours \\\n", + "0 NaN NaN NaN \n", + "1 1.100000e+07 NaN NaN \n", + "2 1.598891e+06 NaN NaN \n", + "3 2.438299e+07 NaN NaN \n", + "4 1.267917e+07 NaN NaN \n", + "5 1.267917e+07 NaN NaN \n", + "6 3.972186e+06 NaN NaN \n", + "7 1.267917e+07 NaN NaN \n", + "8 NaN NaN NaN \n", + "9 7.899157e+07 NaN NaN \n", + "10 2.541556e+02 NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 4.408830e+08 1.065590e+08 5.557506e+06 \n", + "14 2.640765e+08 9.572267e+07 3.121808e+06 \n", + "15 1.038681e+07 6.193464e+05 1.194535e+05 \n", + "16 1.664197e+08 1.021703e+07 2.316244e+06 \n", + "17 8.076069e+08 6.115216e+08 3.518498e+06 \n", + "18 2.065652e+08 1.351676e+08 1.264169e+06 \n", + "19 1.332751e+08 9.848878e+07 6.529246e+05 \n", + "20 7.329011e+07 3.667885e+07 6.112442e+05 \n", + "21 1.237824e+08 2.271225e+07 1.491382e+06 \n", + "22 2.797244e+08 NaN NaN \n", + "23 4.690726e+07 1.391771e+07 5.420643e+05 \n", + "24 7.820000e+04 NaN NaN \n", + "25 NaN NaN NaN \n", + "26 1.905547e+09 NaN NaN \n", + "27 6.131105e+03 NaN NaN \n", + "28 NaN NaN NaN \n", + "29 NaN NaN NaN \n", + "30 1.460520e+08 NaN NaN \n", + "31 5.062561e+08 NaN NaN \n", + "32 2.129823e+07 NaN NaN \n", + "33 1.793334e+06 NaN NaN \n", + "34 1.367783e+08 NaN NaN \n", + "35 NaN NaN NaN \n", + "36 8.121780e+08 NaN NaN \n", + "37 2.613185e+03 NaN NaN \n", + "38 NaN NaN NaN \n", + "39 NaN NaN NaN \n", + "40 3.510602e+06 NaN NaN \n", + "41 1.048475e+07 NaN NaN \n", + "42 1.460667e+07 NaN NaN \n", + "43 NaN NaN NaN \n", + "44 2.860203e+07 NaN NaN \n", + "45 9.202711e+01 NaN NaN \n", + "46 NaN NaN NaN \n", + "47 NaN NaN NaN \n", + "48 5.923008e+08 NaN NaN \n", + "49 5.923008e+08 NaN NaN \n", + "50 1.905730e+03 NaN NaN \n", + "51 NaN NaN NaN \n", + "52 1.984539e+09 NaN NaN \n", + "53 6.385260e+03 NaN NaN \n", + "54 NaN NaN NaN \n", + "55 2.796717e+09 NaN NaN \n", + "56 8.998446e+03 NaN NaN \n", + "57 NaN NaN NaN \n", + "58 2.825319e+09 NaN NaN \n", + "59 9.090473e+03 NaN NaN \n", + "60 NaN NaN NaN \n", + "61 3.417620e+09 NaN NaN \n", + "62 1.099620e+04 NaN NaN \n", + "63 NaN NaN NaN \n", + "64 2.437538e+09 NaN NaN \n", + "65 7.842787e+03 NaN NaN \n", + "66 NaN NaN NaN \n", + "67 3.029839e+09 NaN NaN \n", + "68 9.748517e+03 NaN NaN \n", + "\n", + " Site Labor Cost Site Material Cost \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 2.791525e+08 5.517141e+07 \n", + "14 1.576432e+08 1.071061e+07 \n", + "15 6.159946e+06 3.607522e+06 \n", + "16 1.153494e+08 4.085327e+07 \n", + "17 1.890068e+08 7.078536e+06 \n", + "18 6.692093e+07 4.476621e+06 \n", + "19 3.478629e+07 0.000000e+00 \n", + "20 3.213464e+07 4.476621e+06 \n", + "21 8.038539e+07 2.068480e+07 \n", + "22 NaN NaN \n", + "23 2.937788e+07 3.611675e+06 \n", + "24 NaN NaN \n", + "25 NaN NaN \n", + "26 NaN NaN \n", + "27 NaN NaN \n", + "28 NaN NaN \n", + "29 NaN NaN \n", + "30 NaN NaN \n", + "31 NaN NaN \n", + "32 NaN NaN \n", + "33 NaN NaN \n", + "34 NaN NaN \n", + "35 NaN NaN \n", + "36 NaN NaN \n", + "37 NaN NaN \n", + "38 NaN NaN \n", + "39 NaN NaN \n", + "40 NaN NaN \n", + "41 NaN NaN \n", + "42 NaN NaN \n", + "43 NaN NaN \n", + "44 NaN NaN \n", + "45 NaN NaN \n", + "46 NaN NaN \n", + "47 NaN NaN \n", + "48 NaN NaN \n", + "49 NaN NaN \n", + "50 NaN NaN \n", + "51 NaN NaN \n", + "52 NaN NaN \n", + "53 NaN NaN \n", + "54 NaN NaN \n", + "55 NaN NaN \n", + "56 NaN NaN \n", + "57 NaN NaN \n", + "58 NaN NaN \n", + "59 NaN NaN \n", + "60 NaN NaN \n", + "61 NaN NaN \n", + "62 NaN NaN \n", + "63 NaN NaN \n", + "64 NaN NaN \n", + "65 NaN NaN \n", + "66 NaN NaN \n", + "67 NaN NaN \n", + "68 NaN NaN \n", + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", "in function update indirect\n", - "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", - "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", + " Account Title \\\n", + "0 10 Capitalized Pre-Construction Costs \n", + "1 11 Land & Land Rights \n", + "2 12 Site Permits \n", + "3 13 Plant Licensing \n", + "4 14 Plant Permits \n", + "5 15 Plant Studies \n", + "6 16 Plant Reports \n", + "7 18 Other Pre-Construction Costs \n", + "8 NaN NaN \n", + "9 NaN 10s - Subtotal \n", + "10 NaN 10s - $/kWe \n", + "11 NaN NaN \n", + "12 20 Capitalized Direct Costs \n", + "13 21 Structures & Improvements \n", + "14 212 Reactor Containment Building \n", + "15 213 Turbine Room and Heater Bay \n", + "16 211 plus 214 to 219 Othe Structures & Improvements \n", + "17 22 Reactor System \n", + "18 23 Energy Conversion System \n", + "19 232.1 Electricity Generation Systems \n", + "20 233 Ultimate Heat Sink \n", + "21 24 Electrical Equipment \n", + "22 25 Initial fuel inventory \n", + "23 26 Miscellaneous Equipment \n", + "24 28 Simulator \n", + "25 NaN NaN \n", + "26 NaN 20s - Subtotal \n", + "27 NaN 20s - $/kWe \n", + "28 NaN NaN \n", + "29 30 Capitalized Indirect Services Costs \n", + "30 31 Factory & Field Indirect Costs \n", + "31 32 Factory and construction supervision \n", + "32 33 Start-Up Costs \n", + "33 34 Shipping & Transportation Costs \n", + "34 35 Engineering Services \n", + "35 NaN NaN \n", + "36 NaN 30s - Subtotal \n", + "37 NaN 30s - $/kWe \n", + "38 NaN NaN \n", + "39 50 Capitalized Supplementary Costs \n", + "40 51 Taxes \n", + "41 52 Insurance \n", + "42 54 Decommissioning \n", + "43 NaN NaN \n", + "44 NaN 50s - Subtotal \n", + "45 NaN 50s - $/kWe \n", + "46 NaN NaN \n", + "47 60 Capitalized Financial Costs \n", + "48 62 Interest \n", + "49 - 60s - Subtotal \n", + "50 NaN 60s - $/kWe \n", + "51 NaN NaN \n", + "52 NaN Total Direct Capital Cost (Accounts 10 to 20) \n", + "53 NaN (Accounts 10 to 20) US$/kWe \n", + "54 NaN NaN \n", + "55 NaN Base Construction Cost (Accounts 10 to 30) \n", + "56 NaN (Accounts 10 to 30) US$/kWe \n", + "57 NaN NaN \n", + "58 NaN Total Overnight Cost (Accounts 10 to 50) \n", + "59 NaN (Accounts 10 to 50) US$/kWe \n", + "60 NaN NaN \n", + "61 NaN Total Capital Investment Cost (All Accounts) \n", + "62 NaN (Accounts 10 to 60) US$/kWe \n", + "63 NaN NaN \n", + "64 NaN Total Overnight Cost - ITC reduced \n", + "65 NaN Total Overnight Cost -ITC reduced (US$/kWe) \n", + "66 NaN NaN \n", + "67 NaN Total Capital Investment Cost - ITC reduced \n", + "68 NaN Total Capital Investment Cost - ITC reduced (U... \n", + "\n", + " Total Cost (USD) Factory Equipment Cost Site Labor Hours \\\n", + "0 NaN NaN NaN \n", + "1 1.100000e+07 NaN NaN \n", + "2 1.598891e+06 NaN NaN \n", + "3 2.438299e+07 NaN NaN \n", + "4 1.267917e+07 NaN NaN \n", + "5 1.267917e+07 NaN NaN \n", + "6 3.972186e+06 NaN NaN \n", + "7 1.267917e+07 NaN NaN \n", + "8 NaN NaN NaN \n", + "9 7.899157e+07 NaN NaN \n", + "10 2.541556e+02 NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 4.408830e+08 1.065590e+08 5.557506e+06 \n", + "14 2.640765e+08 9.572267e+07 3.121808e+06 \n", + "15 1.038681e+07 6.193464e+05 1.194535e+05 \n", + "16 1.664197e+08 1.021703e+07 2.316244e+06 \n", + "17 8.076069e+08 6.115216e+08 3.518498e+06 \n", + "18 2.065652e+08 1.351676e+08 1.264169e+06 \n", + "19 1.332751e+08 9.848878e+07 6.529246e+05 \n", + "20 7.329011e+07 3.667885e+07 6.112442e+05 \n", + "21 1.237824e+08 2.271225e+07 1.491382e+06 \n", + "22 2.797244e+08 NaN NaN \n", + "23 4.690726e+07 1.391771e+07 5.420643e+05 \n", + "24 7.820000e+04 NaN NaN \n", + "25 NaN NaN NaN \n", + "26 1.905547e+09 NaN NaN \n", + "27 6.131105e+03 NaN NaN \n", + "28 NaN NaN NaN \n", + "29 NaN NaN NaN \n", + "30 1.460520e+08 NaN NaN \n", + "31 5.062561e+08 NaN NaN \n", + "32 2.129823e+07 NaN NaN \n", + "33 1.793334e+06 NaN NaN \n", + "34 1.367783e+08 NaN NaN \n", + "35 NaN NaN NaN \n", + "36 8.121780e+08 NaN NaN \n", + "37 2.613185e+03 NaN NaN \n", + "38 NaN NaN NaN \n", + "39 NaN NaN NaN \n", + "40 3.510602e+06 NaN NaN \n", + "41 1.048475e+07 NaN NaN \n", + "42 1.460667e+07 NaN NaN \n", + "43 NaN NaN NaN \n", + "44 2.860203e+07 NaN NaN \n", + "45 9.202711e+01 NaN NaN \n", + "46 NaN NaN NaN \n", + "47 NaN NaN NaN \n", + "48 5.923008e+08 NaN NaN \n", + "49 5.923008e+08 NaN NaN \n", + "50 1.905730e+03 NaN NaN \n", + "51 NaN NaN NaN \n", + "52 1.984539e+09 NaN NaN \n", + "53 6.385260e+03 NaN NaN \n", + "54 NaN NaN NaN \n", + "55 2.796717e+09 NaN NaN \n", + "56 8.998446e+03 NaN NaN \n", + "57 NaN NaN NaN \n", + "58 2.825319e+09 NaN NaN \n", + "59 9.090473e+03 NaN NaN \n", + "60 NaN NaN NaN \n", + "61 3.417620e+09 NaN NaN \n", + "62 1.099620e+04 NaN NaN \n", + "63 NaN NaN NaN \n", + "64 2.437538e+09 NaN NaN \n", + "65 7.842787e+03 NaN NaN \n", + "66 NaN NaN NaN \n", + "67 3.029839e+09 NaN NaN \n", + "68 9.748517e+03 NaN NaN \n", + "\n", + " Site Labor Cost Site Material Cost \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 2.791525e+08 5.517141e+07 \n", + "14 1.576432e+08 1.071061e+07 \n", + "15 6.159946e+06 3.607522e+06 \n", + "16 1.153494e+08 4.085327e+07 \n", + "17 1.890068e+08 7.078536e+06 \n", + "18 6.692093e+07 4.476621e+06 \n", + "19 3.478629e+07 0.000000e+00 \n", + "20 3.213464e+07 4.476621e+06 \n", + "21 8.038539e+07 2.068480e+07 \n", + "22 NaN NaN \n", + "23 2.937788e+07 3.611675e+06 \n", + "24 NaN NaN \n", + "25 NaN NaN \n", + "26 NaN NaN \n", + "27 NaN NaN \n", + "28 NaN NaN \n", + "29 NaN NaN \n", + "30 NaN NaN \n", + "31 NaN NaN \n", + "32 NaN NaN \n", + "33 NaN NaN \n", + "34 NaN NaN \n", + "35 NaN NaN \n", + "36 NaN NaN \n", + "37 NaN NaN \n", + "38 NaN NaN \n", + "39 NaN NaN \n", + "40 NaN NaN \n", + "41 NaN NaN \n", + "42 NaN NaN \n", + "43 NaN NaN \n", + "44 NaN NaN \n", + "45 NaN NaN \n", + "46 NaN NaN \n", + "47 NaN NaN \n", + "48 NaN NaN \n", + "49 NaN NaN \n", + "50 NaN NaN \n", + "51 NaN NaN \n", + "52 NaN NaN \n", + "53 NaN NaN \n", + "54 NaN NaN \n", + "55 NaN NaN \n", + "56 NaN NaN \n", + "57 NaN NaN \n", + "58 NaN NaN \n", + "59 NaN NaN \n", + "60 NaN NaN \n", + "61 NaN NaN \n", + "62 NaN NaN \n", + "63 NaN NaN \n", + "64 NaN NaN \n", + "65 NaN NaN \n", + "66 NaN NaN \n", + "67 NaN NaN \n", + "68 NaN NaN \n", + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", "in function update indirect\n", - "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", - "75.434 64 [80.63803108] 11.433999999999997 [92.07203108]\n", + " Account Title \\\n", + "0 10 Capitalized Pre-Construction Costs \n", + "1 11 Land & Land Rights \n", + "2 12 Site Permits \n", + "3 13 Plant Licensing \n", + "4 14 Plant Permits \n", + "5 15 Plant Studies \n", + "6 16 Plant Reports \n", + "7 18 Other Pre-Construction Costs \n", + "8 NaN NaN \n", + "9 NaN 10s - Subtotal \n", + "10 NaN 10s - $/kWe \n", + "11 NaN NaN \n", + "12 20 Capitalized Direct Costs \n", + "13 21 Structures & Improvements \n", + "14 212 Reactor Containment Building \n", + "15 213 Turbine Room and Heater Bay \n", + "16 211 plus 214 to 219 Othe Structures & Improvements \n", + "17 22 Reactor System \n", + "18 23 Energy Conversion System \n", + "19 232.1 Electricity Generation Systems \n", + "20 233 Ultimate Heat Sink \n", + "21 24 Electrical Equipment \n", + "22 25 Initial fuel inventory \n", + "23 26 Miscellaneous Equipment \n", + "24 28 Simulator \n", + "25 NaN NaN \n", + "26 NaN 20s - Subtotal \n", + "27 NaN 20s - $/kWe \n", + "28 NaN NaN \n", + "29 30 Capitalized Indirect Services Costs \n", + "30 31 Factory & Field Indirect Costs \n", + "31 32 Factory and construction supervision \n", + "32 33 Start-Up Costs \n", + "33 34 Shipping & Transportation Costs \n", + "34 35 Engineering Services \n", + "35 NaN NaN \n", + "36 NaN 30s - Subtotal \n", + "37 NaN 30s - $/kWe \n", + "38 NaN NaN \n", + "39 50 Capitalized Supplementary Costs \n", + "40 51 Taxes \n", + "41 52 Insurance \n", + "42 54 Decommissioning \n", + "43 NaN NaN \n", + "44 NaN 50s - Subtotal \n", + "45 NaN 50s - $/kWe \n", + "46 NaN NaN \n", + "47 60 Capitalized Financial Costs \n", + "48 62 Interest \n", + "49 - 60s - Subtotal \n", + "50 NaN 60s - $/kWe \n", + "51 NaN NaN \n", + "52 NaN Total Direct Capital Cost (Accounts 10 to 20) \n", + "53 NaN (Accounts 10 to 20) US$/kWe \n", + "54 NaN NaN \n", + "55 NaN Base Construction Cost (Accounts 10 to 30) \n", + "56 NaN (Accounts 10 to 30) US$/kWe \n", + "57 NaN NaN \n", + "58 NaN Total Overnight Cost (Accounts 10 to 50) \n", + "59 NaN (Accounts 10 to 50) US$/kWe \n", + "60 NaN NaN \n", + "61 NaN Total Capital Investment Cost (All Accounts) \n", + "62 NaN (Accounts 10 to 60) US$/kWe \n", + "63 NaN NaN \n", + "64 NaN Total Overnight Cost - ITC reduced \n", + "65 NaN Total Overnight Cost -ITC reduced (US$/kWe) \n", + "66 NaN NaN \n", + "67 NaN Total Capital Investment Cost - ITC reduced \n", + "68 NaN Total Capital Investment Cost - ITC reduced (U... \n", + "\n", + " Total Cost (USD) Factory Equipment Cost Site Labor Hours \\\n", + "0 NaN NaN NaN \n", + "1 1.100000e+07 NaN NaN \n", + "2 1.598891e+06 NaN NaN \n", + "3 2.438299e+07 NaN NaN \n", + "4 1.267917e+07 NaN NaN \n", + "5 1.267917e+07 NaN NaN \n", + "6 3.972186e+06 NaN NaN \n", + "7 1.267917e+07 NaN NaN \n", + "8 NaN NaN NaN \n", + "9 7.899157e+07 NaN NaN \n", + "10 2.541556e+02 NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 4.408830e+08 1.065590e+08 5.557506e+06 \n", + "14 2.640765e+08 9.572267e+07 3.121808e+06 \n", + "15 1.038681e+07 6.193464e+05 1.194535e+05 \n", + "16 1.664197e+08 1.021703e+07 2.316244e+06 \n", + "17 8.076069e+08 6.115216e+08 3.518498e+06 \n", + "18 2.065652e+08 1.351676e+08 1.264169e+06 \n", + "19 1.332751e+08 9.848878e+07 6.529246e+05 \n", + "20 7.329011e+07 3.667885e+07 6.112442e+05 \n", + "21 1.237824e+08 2.271225e+07 1.491382e+06 \n", + "22 2.797244e+08 NaN NaN \n", + "23 4.690726e+07 1.391771e+07 5.420643e+05 \n", + "24 7.820000e+04 NaN NaN \n", + "25 NaN NaN NaN \n", + "26 1.905547e+09 NaN NaN \n", + "27 6.131105e+03 NaN NaN \n", + "28 NaN NaN NaN \n", + "29 NaN NaN NaN \n", + "30 1.460520e+08 NaN NaN \n", + "31 5.062561e+08 NaN NaN \n", + "32 2.129823e+07 NaN NaN \n", + "33 1.793334e+06 NaN NaN \n", + "34 1.367783e+08 NaN NaN \n", + "35 NaN NaN NaN \n", + "36 8.121780e+08 NaN NaN \n", + "37 2.613185e+03 NaN NaN \n", + "38 NaN NaN NaN \n", + "39 NaN NaN NaN \n", + "40 3.510602e+06 NaN NaN \n", + "41 1.048475e+07 NaN NaN \n", + "42 1.460667e+07 NaN NaN \n", + "43 NaN NaN NaN \n", + "44 2.860203e+07 NaN NaN \n", + "45 9.202711e+01 NaN NaN \n", + "46 NaN NaN NaN \n", + "47 NaN NaN NaN \n", + "48 5.923008e+08 NaN NaN \n", + "49 5.923008e+08 NaN NaN \n", + "50 1.905730e+03 NaN NaN \n", + "51 NaN NaN NaN \n", + "52 1.984539e+09 NaN NaN \n", + "53 6.385260e+03 NaN NaN \n", + "54 NaN NaN NaN \n", + "55 2.796717e+09 NaN NaN \n", + "56 8.998446e+03 NaN NaN \n", + "57 NaN NaN NaN \n", + "58 2.825319e+09 NaN NaN \n", + "59 9.090473e+03 NaN NaN \n", + "60 NaN NaN NaN \n", + "61 3.417620e+09 NaN NaN \n", + "62 1.099620e+04 NaN NaN \n", + "63 NaN NaN NaN \n", + "64 2.437538e+09 NaN NaN \n", + "65 7.842787e+03 NaN NaN \n", + "66 NaN NaN NaN \n", + "67 3.029839e+09 NaN NaN \n", + "68 9.748517e+03 NaN NaN \n", + "\n", + " Site Labor Cost Site Material Cost \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 2.791525e+08 5.517141e+07 \n", + "14 1.576432e+08 1.071061e+07 \n", + "15 6.159946e+06 3.607522e+06 \n", + "16 1.153494e+08 4.085327e+07 \n", + "17 1.890068e+08 7.078536e+06 \n", + "18 6.692093e+07 4.476621e+06 \n", + "19 3.478629e+07 0.000000e+00 \n", + "20 3.213464e+07 4.476621e+06 \n", + "21 8.038539e+07 2.068480e+07 \n", + "22 NaN NaN \n", + "23 2.937788e+07 3.611675e+06 \n", + "24 NaN NaN \n", + "25 NaN NaN \n", + "26 NaN NaN \n", + "27 NaN NaN \n", + "28 NaN NaN \n", + "29 NaN NaN \n", + "30 NaN NaN \n", + "31 NaN NaN \n", + "32 NaN NaN \n", + "33 NaN NaN \n", + "34 NaN NaN \n", + "35 NaN NaN \n", + "36 NaN NaN \n", + "37 NaN NaN \n", + "38 NaN NaN \n", + "39 NaN NaN \n", + "40 NaN NaN \n", + "41 NaN NaN \n", + "42 NaN NaN \n", + "43 NaN NaN \n", + "44 NaN NaN \n", + "45 NaN NaN \n", + "46 NaN NaN \n", + "47 NaN NaN \n", + "48 NaN NaN \n", + "49 NaN NaN \n", + "50 NaN NaN \n", + "51 NaN NaN \n", + "52 NaN NaN \n", + "53 NaN NaN \n", + "54 NaN NaN \n", + "55 NaN NaN \n", + "56 NaN NaN \n", + "57 NaN NaN \n", + "58 NaN NaN \n", + "59 NaN NaN \n", + "60 NaN NaN \n", + "61 NaN NaN \n", + "62 NaN NaN \n", + "63 NaN NaN \n", + "64 NaN NaN \n", + "65 NaN NaN \n", + "66 NaN NaN \n", + "67 NaN NaN \n", + "68 NaN NaN \n", + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", "in function update indirect\n" ] } @@ -210,8 +862,222 @@ "output_type": "stream", "text": [ "4\n", - "T_end,ref_construction_duration,cons_duration_no_delay,supply_chain_delay,actual_construction_duration_plus_delay\n", - "111.4278125 100 [121.63844064] 11.427812500000002 [133.06625314]\n", + " Account Title \\\n", + "0 10 Capitalized Pre-Construction Costs \n", + "1 11 Land & Land Rights \n", + "2 12 Site Permits \n", + "3 13 Plant Licensing \n", + "4 14 Plant Permits \n", + "5 15 Plant Studies \n", + "6 16 Plant Reports \n", + "7 18 Other Pre-Construction Costs \n", + "8 NaN NaN \n", + "9 NaN 10s - Subtotal \n", + "10 NaN 10s - $/kWe \n", + "11 NaN NaN \n", + "12 20 Capitalized Direct Costs \n", + "13 21 Structures & Improvements \n", + "14 212 Reactor Containment Building \n", + "15 213 Turbine Room and Heater Bay \n", + "16 211 plus 214 to 219 Othe Structures & Improvements \n", + "17 22 Reactor System \n", + "18 23 Energy Conversion System \n", + "19 232.1 Electricity Generation Systems \n", + "20 233 Ultimate Heat Sink \n", + "21 24 Electrical Equipment \n", + "22 25 Initial fuel inventory \n", + "23 26 Miscellaneous Equipment \n", + "24 28 Simulator \n", + "25 NaN NaN \n", + "26 NaN 20s - Subtotal \n", + "27 NaN 20s - $/kWe \n", + "28 NaN NaN \n", + "29 30 Capitalized Indirect Services Costs \n", + "30 31 Factory & Field Indirect Costs \n", + "31 32 Factory and construction supervision \n", + "32 33 Start-Up Costs \n", + "33 34 Shipping & Transportation Costs \n", + "34 35 Engineering Services \n", + "35 NaN NaN \n", + "36 NaN 30s - Subtotal \n", + "37 NaN 30s - $/kWe \n", + "38 NaN NaN \n", + "39 50 Capitalized Supplementary Costs \n", + "40 51 Taxes \n", + "41 52 Insurance \n", + "42 54 Decommissioning \n", + "43 NaN NaN \n", + "44 NaN 50s - Subtotal \n", + "45 NaN 50s - $/kWe \n", + "46 NaN NaN \n", + "47 60 Capitalized Financial Costs \n", + "48 62 Interest \n", + "49 - 60s - Subtotal \n", + "50 NaN 60s - $/kWe \n", + "51 NaN NaN \n", + "52 NaN Total Direct Capital Cost (Accounts 10 to 20) \n", + "53 NaN (Accounts 10 to 20) US$/kWe \n", + "54 NaN NaN \n", + "55 NaN Base Construction Cost (Accounts 10 to 30) \n", + "56 NaN (Accounts 10 to 30) US$/kWe \n", + "57 NaN NaN \n", + "58 NaN Total Overnight Cost (Accounts 10 to 50) \n", + "59 NaN (Accounts 10 to 50) US$/kWe \n", + "60 NaN NaN \n", + "61 NaN Total Capital Investment Cost (All Accounts) \n", + "62 NaN (Accounts 10 to 60) US$/kWe \n", + "63 NaN NaN \n", + "64 NaN Total Overnight Cost - ITC reduced \n", + "65 NaN Total Overnight Cost -ITC reduced (US$/kWe) \n", + "66 NaN NaN \n", + "67 NaN Total Capital Investment Cost - ITC reduced \n", + "68 NaN Total Capital Investment Cost - ITC reduced (U... \n", + "\n", + " Total Cost (USD) Factory Equipment Cost Site Labor Hours \\\n", + "0 NaN NaN NaN \n", + "1 1.500000e+07 NaN NaN \n", + "2 0.000000e+00 NaN NaN \n", + "3 1.070098e+08 NaN NaN \n", + "4 4.721019e+06 NaN NaN \n", + "5 0.000000e+00 NaN NaN \n", + "6 0.000000e+00 NaN NaN \n", + "7 3.619448e+07 NaN NaN \n", + "8 NaN NaN NaN \n", + "9 1.629253e+08 NaN NaN \n", + "10 1.542853e+02 NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 1.533300e+09 1.365362e+08 2.090920e+07 \n", + "14 6.966448e+08 9.992741e+07 9.638431e+06 \n", + "15 1.522573e+07 1.036695e+06 1.749114e+05 \n", + "16 8.214295e+08 3.557211e+07 1.109586e+07 \n", + "17 1.975878e+09 1.478440e+09 8.719725e+06 \n", + "18 4.755064e+08 3.072326e+08 3.008178e+06 \n", + "19 3.301050e+08 2.344884e+08 1.794686e+06 \n", + "20 1.454013e+08 7.274425e+07 1.213492e+06 \n", + "21 2.358203e+08 4.326955e+07 2.841261e+06 \n", + "22 4.516865e+08 NaN 0.000000e+00 \n", + "23 3.594357e+08 9.942946e+07 4.282531e+06 \n", + "24 0.000000e+00 NaN NaN \n", + "25 NaN NaN NaN \n", + "26 5.031627e+09 NaN NaN \n", + "27 4.764798e+03 NaN NaN \n", + "28 NaN NaN NaN \n", + "29 NaN NaN NaN \n", + "30 6.913711e+08 NaN NaN \n", + "31 2.734393e+09 NaN NaN \n", + "32 1.150361e+08 NaN NaN \n", + "33 9.686167e+06 NaN NaN \n", + "34 7.387675e+08 NaN NaN \n", + "35 NaN NaN NaN \n", + "36 4.289254e+09 NaN NaN \n", + "37 4.061793e+03 NaN NaN \n", + "38 NaN NaN NaN \n", + "39 NaN NaN NaN \n", + "40 1.875000e+05 NaN NaN \n", + "41 3.820433e+07 NaN NaN \n", + "42 8.141760e+06 NaN NaN \n", + "43 NaN NaN NaN \n", + "44 4.653359e+07 NaN NaN \n", + "45 4.406590e+01 NaN NaN \n", + "46 NaN NaN NaN \n", + "47 NaN NaN NaN \n", + "48 3.202162e+09 NaN NaN \n", + "49 3.202162e+09 NaN NaN \n", + "50 3.032350e+03 NaN NaN \n", + "51 NaN NaN NaN \n", + "52 5.194552e+09 NaN NaN \n", + "53 4.919084e+03 NaN NaN \n", + "54 NaN NaN NaN \n", + "55 9.483806e+09 NaN NaN \n", + "56 8.980877e+03 NaN NaN \n", + "57 NaN NaN NaN \n", + "58 9.530340e+09 NaN NaN \n", + "59 9.024943e+03 NaN NaN \n", + "60 NaN NaN NaN \n", + "61 1.273250e+10 NaN NaN \n", + "62 1.205729e+04 NaN NaN \n", + "63 NaN NaN NaN \n", + "64 8.699310e+09 NaN NaN \n", + "65 8.237983e+03 NaN NaN \n", + "66 NaN NaN NaN \n", + "67 1.190147e+10 NaN NaN \n", + "68 1.127033e+04 NaN NaN \n", + "\n", + " Site Labor Cost Site Material Cost \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 1.045892e+09 3.508716e+08 \n", + "14 4.700620e+08 1.266554e+08 \n", + "15 9.024686e+06 5.164350e+06 \n", + "16 5.668055e+08 2.190519e+08 \n", + "17 4.656677e+08 3.177007e+07 \n", + "18 1.594144e+08 8.859309e+06 \n", + "19 9.561666e+07 0.000000e+00 \n", + "20 6.379777e+07 8.859309e+06 \n", + "21 1.531438e+08 3.940702e+07 \n", + "22 0.000000e+00 0.000000e+00 \n", + "23 2.310455e+08 2.896080e+07 \n", + "24 NaN NaN \n", + "25 NaN NaN \n", + "26 NaN NaN \n", + "27 NaN NaN \n", + "28 NaN NaN \n", + "29 NaN NaN \n", + "30 NaN NaN \n", + "31 NaN NaN \n", + "32 NaN NaN \n", + "33 NaN NaN \n", + "34 NaN NaN \n", + "35 NaN NaN \n", + "36 NaN NaN \n", + "37 NaN NaN \n", + "38 NaN NaN \n", + "39 NaN NaN \n", + "40 NaN NaN \n", + "41 NaN NaN \n", + "42 NaN NaN \n", + "43 NaN NaN \n", + "44 NaN NaN \n", + "45 NaN NaN \n", + "46 NaN NaN \n", + "47 NaN NaN \n", + "48 NaN NaN \n", + "49 NaN NaN \n", + "50 NaN NaN \n", + "51 NaN NaN \n", + "52 NaN NaN \n", + "53 NaN NaN \n", + "54 NaN NaN \n", + "55 NaN NaN \n", + "56 NaN NaN \n", + "57 NaN NaN \n", + "58 NaN NaN \n", + "59 NaN NaN \n", + "60 NaN NaN \n", + "61 NaN NaN \n", + "62 NaN NaN \n", + "63 NaN NaN \n", + "64 NaN NaN \n", + "65 NaN NaN \n", + "66 NaN NaN \n", + "67 NaN NaN \n", + "68 NaN NaN \n", + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [23099589.55901049] [39760892.80720583]\n", + "baseline_construction_duration 100.0\n", + "labor_hour_ratio [1.72128135]\n", "in function update indirect\n" ] }, @@ -454,6 +1320,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b4dfbc1-3f1e-48c5-b089-035bb954f4c4", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/crf/api.py b/src/crf/api.py index db91a33..79b1e92 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -1,50 +1,4 @@ import csv -import pickle -import numpy as np - -from .io.excel_inputs import InputStore -from .io.excel_levers import read_levers_sheet -from .sampling.sampler import sample_levers -from .sampling.lever_schema import ( - STATIC_KEYS, - attach_internal_ids, - sample_column_to_levers, - static_row_from_levers, -) -from .sampling.postprocess import apply_itc_rounding -from .model.avg_runner import run_avg_all_units -from .utils.serialize import write_csv_row, stream_pickle_dump - - -def normalize_levers(levers: dict) -> dict: - """Convert raw lever dict to model-ready normalized values.""" - def label01(x, zero_label, one_label): - if x == 0: return zero_label - if x == 1: return one_label - return x - - return { - "num_orders": int(levers["num_orders"]), - "ITC_0": float(levers["itc_percent"]) / 100.0, - "n_ITC": levers["n_itc"], - "interest_rate_0": float(levers["interest_percent"]) / 100.0, - "design_completion_0": float(levers["design_completion_percent"]) / 100.0, - "Design_Maturity_0": levers["design_maturity"], - "proc_exp_0": levers["proc_exp"], - "N_proc": levers["N_proc"], - "ce_exp_0": levers["ce_exp"], - "N_cons": levers["N_cons"], - "ae_exp_0": levers["ae_exp"], - "N_AE": levers["N_AE"], - "standardization_0": float(levers["standardization_percent"]) / 100.0, - "mod_0": label01(levers["modularity_code"], "stick_built", "modularized"), - "BOP_grade_0": label01(levers["bop_grade_code"], "nuclear", "non_nuclear"), - "RB_grade_0": label01(levers["rb_grade_code"], "nuclear", "non_nuclear"), - } - - -import csv -import pickle import numpy as np from .io.excel_inputs import InputStore diff --git a/src/crf/model/avg_runner.py b/src/crf/model/avg_runner.py index c8dc0b7..a0272f3 100644 --- a/src/crf/model/avg_runner.py +++ b/src/crf/model/avg_runner.py @@ -1,5 +1,6 @@ import numpy as np from .pipeline import calculate_final_result +# Ignore runtime warning def run_avg_all_units(config: dict, inp: dict, store): """ @@ -7,25 +8,37 @@ def run_avg_all_units(config: dict, inp: dict, store): OCC_i, TCI_i, duration_i plus summary metrics. """ num_orders = int(inp["num_orders"]) - OCC, TCI, DUR = [], [], [] + n_itc = int(inp["n_ITC"]) + + OCC, NETOCC, TCI, NCI, DUR = [], [], [], [], [] + for n_th in range(1, num_orders + 1): - _, occ, tci, dur = calculate_final_result(config, inp, store, n_th=n_th) + _, occ, netocc, tci, nci, dur = calculate_final_result(config, inp, store, n_th=n_th) OCC.append(occ) TCI.append(tci) DUR.append(dur) + if n_th <= n_itc: + NETOCC.append(netocc) + NCI.append(nci) + OCC = np.array(OCC, dtype=float) + NETOCC = np.array(NETOCC, dtype=float) TCI = np.array(TCI, dtype=float) + NCI = np.array(NCI, dtype=float) DUR = np.array(DUR, dtype=float) # summaries occLastUnit = float(OCC[-1]) TCILastUnit = float(TCI[-1]) durationsLastUnit = float(DUR[-1]) - - avg_OCC = float(np.mean(OCC)) - avg_TCI = float(np.mean(TCI)) + if n_itc > 0: + avg_OCC = float(np.mean(NETOCC)) + avg_TCI = float(np.mean(NCI)) + else: + avg_OCC = float(np.mean(OCC)) + avg_TCI = float(np.mean(TCI)) avg_duration = float(np.mean(DUR)) final_startup_duration = max(7, config["startup_0"] * (1 - 0.3) ** np.log2(num_orders)) @@ -36,11 +49,15 @@ def run_avg_all_units(config: dict, inp: dict, store): # expand arrays to OCC_i / TCI_i / duration_i out = {} for i, v in enumerate(OCC): - out[f"OCC_{i}"] = float(v) + out[f"OCC_{i+1}"] = float(v) + for i, v in enumerate(NETOCC): + out[f"NETOCC_{i+1}"] = float(v) for i, v in enumerate(TCI): - out[f"TCI_{i}"] = float(v) + out[f"TCI_{i+1}"] = float(v) + for i, v in enumerate(NCI): + out[f"NCI_{i+1}"] = float(v) for i, v in enumerate(DUR): - out[f"duration_{i}"] = float(v) + out[f"duration_{i+1}"] = float(v) out.update({ "cons_duration_cumulative_wz_startup": float(cons_duration_cumulative_wz_startup), diff --git a/src/crf/model/core_accounts.py b/src/crf/model/core_accounts.py index d0ced42..adb8e02 100644 --- a/src/crf/model/core_accounts.py +++ b/src/crf/model/core_accounts.py @@ -204,6 +204,10 @@ def sum_lab_hrs(db: pd.DataFrame) -> float: ) def update_cons_duration(db0: pd.DataFrame, db1: pd.DataFrame, ref_duration: float) -> float: + """Calculate new construction duration based on change in labor hours for accounts 21, 22, 23, 24, and 26. + The formula is: new_duration = 0.3 * labor_hours_delta * ref_duration + ref_duration, where labor_hours_delta = (new_hours - old_hours) / old_hours. + this function should be used before the standardization effect is applied. + """ def _sum_hours(db): return float( db.loc[db["Account"].astype(str).str.strip().isin(["21","22","23","24","26"]), "Site Labor Hours"] @@ -213,10 +217,6 @@ def _sum_hours(db): sum_old = _sum_hours(db0) sum_new = _sum_hours(db1) - - if sum_old == 0: - return float(ref_duration) - lab_delta = (sum_new - sum_old) / sum_old return float(0.3 * lab_delta * ref_duration + ref_duration) @@ -227,6 +227,8 @@ def update_cons_duration_2( prev_cons_duration: float, baseline_lab_hours: float ) -> float: + """Calculate new construction duration based on change in labor hours for accounts 21, 22, 23, 24, and 26. This should be used after the standardization effect is applied, so the labor hours change should be compared to the baseline labor hours instead of the previous labor hours. NOTE I think I should merge this with the previous function and just pass in the baseline labor hours as an argument. + """ def _sum_hours(db): return float( db.loc[db["Account"].astype(str).str.strip().isin(["21","22","23","24","26"]), "Site Labor Hours"] @@ -236,10 +238,6 @@ def _sum_hours(db): sum_old = _sum_hours(db0) sum_new = _sum_hours(db1) - - if baseline_lab_hours == 0: - return float(prev_cons_duration) - lab_delta = (sum_new - sum_old) / float(baseline_lab_hours) return float(0.3 * lab_delta * ref_duration + float(prev_cons_duration)) diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index 38eb3bf..cf14a55 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -19,7 +19,9 @@ "Factory Equipment Cost", "Site Labor Hours", "Site Labor Cost", "Site Material Cost" ] -# exclude account 25 because it is the initial fuel cost and should not be affected by rework +# exclude account 25 because it is the initial fuel cost and +# should not be affected by rework. Those accounts are end level accounts +# that do not have sub-accounts ACCT_DIRECT = [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26] @@ -87,13 +89,10 @@ def add_BOP_RP_grades( # duration update from grade change duration_ref = 125 if reactor_type == "HTGR" else 80 # duration_ref = 100 if reactor_type == "HTGR" else 64 - print(f"DEBUG: reactor_type={reactor_type}, duration_ref={duration_ref}") new_dur = update_cons_duration(df, db2, duration_ref) - print(f"DEBUG: new_dur before modulized change={new_dur}") # modularity factor on duration; for n>=2 assume modularized mod = mod_0 if n_th == 1 else "modularized" mod_factor = 0.8 if mod == "modularized" else 1.0 - return db2, new_dur * mod_factor @@ -164,9 +163,7 @@ def add_reworking_productivity( setv(db, acct, "Site Labor Cost", float(getv(df, acct, "Site Labor Cost")) * rework / productivity) db2 = update_high_level_costs(db, power)[COLS].copy() - print(f"DEBUG: ref_duration={ref_duration}, prev_cons_duration={prev_cons_duration}, baseline_lab_hours={baseline_lab_hours}") new_dur = float(update_cons_duration_2(df, db2, ref_duration, prev_cons_duration, baseline_lab_hours)) - print(f"DEBUG: new_dur={new_dur}") return db2, new_dur diff --git a/src/crf/model/finance.py b/src/crf/model/finance.py index 07056d8..fae90ff 100644 --- a/src/crf/model/finance.py +++ b/src/crf/model/finance.py @@ -3,7 +3,7 @@ import numpy as np import pandas as pd - +from ..utils.df_ops import setv from .core_accounts import update_high_level_costs, ITC_reduction_factor COLS = [ @@ -76,12 +76,12 @@ def update_interest_cost( startup = startup_0 else: startup = max(7, startup_0 * (1 - 0.3) ** np.log2(n_th)) - int_exp_startup = (tot_int_exp_construction + tot_overnight_cost) * ((1 + interest_rate) ** (startup / 12)) \ - (tot_int_exp_construction + tot_overnight_cost) db = df.copy() - db.loc[db["Account"].eq(62), "Total Cost (USD)"] = float(int_exp_startup + tot_int_exp_construction) + int_curved = float(tot_int_exp_construction + int_exp_startup) + setv(db, "62", "Total Cost (USD)", int_curved) db2 = update_high_level_costs(db, power)[COLS].copy() @@ -108,7 +108,6 @@ def update_itc( itc_reduced_occ = tot_overnight_cost * itc_factor occ_reduction = tot_overnight_cost - itc_reduced_occ - # Titles must exist (same as your original) db.loc[db["Title"].eq("Total Overnight Cost - ITC reduced"), "Total Cost (USD)"] = itc_reduced_occ db.loc[db["Title"].eq("Total Overnight Cost -ITC reduced (US$/kWe)"), "Total Cost (USD)"] = itc_reduced_occ / reactor_power db.loc[db["Title"].eq("Total Capital Investment Cost - ITC reduced"), "Total Cost (USD)"] = tot_cap_investment - occ_reduction @@ -119,9 +118,4 @@ def update_itc( db.loc[db["Title"].eq("Total Capital Investment Cost - ITC reduced (US$/kWe)"), "Total Cost (USD)"] = levelized_NCI db2 = update_high_level_costs(db, reactor_power)[COLS].copy() - print('DEBUG db2') - # print line 22 to 26 and line 37 38 - print(db2[22:27]) - # print line 37 and 38 Title Total Cost (USD) - print(db2.iloc[[37, 38]][["Title", "Total Cost (USD)"]]) return db2, itc_reduced_occ / reactor_power, levelized_NCI diff --git a/src/crf/model/indirect_cost.py b/src/crf/model/indirect_cost.py index c75930f..738af69 100644 --- a/src/crf/model/indirect_cost.py +++ b/src/crf/model/indirect_cost.py @@ -32,17 +32,17 @@ def update_indirect_cost( sum_new_mat_cost = float(db.loc[mask, "Site Material Cost"].fillna(0.0).sum()) sum_new_lab_cost = float(db.loc[mask, "Site Labor Cost"].fillna(0.0).sum()) sum_new_lab_hrs = float(db.loc[mask, "Site Labor Hours"].fillna(0.0).sum()) - print(f"DEBUG: sum_new_mat_cost={sum_new_mat_cost}, sum_new_lab_cost={sum_new_lab_cost}, sum_new_lab_hrs={sum_new_lab_hrs}") + # print(f"DEBUG: sum_new_mat_cost={sum_new_mat_cost}, sum_new_lab_cost={sum_new_lab_cost}, sum_new_lab_hrs={sum_new_lab_hrs}") dur = float(final_construction_duration) - print(f"DEBUG: final_construction_duration={dur}, standardization={standardization}, factor_35={factor_35}") + # print(f"DEBUG: final_construction_duration={dur}, standardization={standardization}, factor_35={factor_35}") val31 = (sum_new_mat_cost * 0.785 * sum_new_lab_hrs / dur / 160 / 1058) + sum_new_lab_cost * 0.36 val32 = sum_new_lab_cost * 0.36 * 3.661 * dur / 72 val33 = 0.04207006 * val32 val34 = 0.00354234616938 * val32 val35 = (0.27017603 * val32) * factor_35 - print(f"DEBUG: val31={val31}, val32={val32}, val33={val33}, val34={val34}, val35={val35}") + # print(f"DEBUG: val31={val31}, val32={val32}, val33={val33}, val34={val34}, val35={val35}") setv(db, 31, "Total Cost (USD)", val31) setv(db, 32, "Total Cost (USD)", val32) setv(db, 33, "Total Cost (USD)", val33) diff --git a/src/crf/model/learning.py b/src/crf/model/learning.py index 89541fa..1885401 100644 --- a/src/crf/model/learning.py +++ b/src/crf/model/learning.py @@ -86,15 +86,11 @@ def act_cons_duration_plus_delay( T_26 = 0.21 * (B_21 + D) + B_26 + D T_end = max(T_21, T_22, T_23, T_24, T_25, T_26) - - supply_chain_delay = max(T_end - ref_construction_duration, 0) - print(f"DEBUG: cons_duration_no_delay={cons_duration_no_delay}") + supply_chain_delay = max(T_end - ref_construction_duration, 0) return float(cons_duration_no_delay) + float(supply_chain_delay) - def duration_learning_effect(n_th: int, standardization_0: float, actual_construction_duration_plus_delay: float): standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 - # fitted_LR_duration = 0.15 * standardization / 0.7 fitted_LR_duration = 0.103719051 * standardization / 0.7 duration_multiplier = (1 - fitted_LR_duration) ** np.log2(n_th) return float(duration_multiplier) * float(actual_construction_duration_plus_delay) diff --git a/src/crf/model/pipeline.py b/src/crf/model/pipeline.py index 139b288..19acda8 100644 --- a/src/crf/model/pipeline.py +++ b/src/crf/model/pipeline.py @@ -57,6 +57,7 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): n_th=n_th, power=power, ) + org_occ = float(tot_occ/power) + org_tci = float(tot_cap/power) final_df, net_occ, nci = update_itc(with_interest, tot_occ, tot_cap, n_th, inp["ITC_0"], inp["n_ITC"], power) - - return final_df, net_occ, nci, float(final_dur) + return final_df, org_occ, net_occ, org_tci, nci, float(final_dur) diff --git a/test/test_crf_smoke.py b/test/test_crf_smoke.py index f23e889..9845b82 100644 --- a/test/test_crf_smoke.py +++ b/test/test_crf_smoke.py @@ -19,7 +19,6 @@ def main(): "staggering_ratio": 0.75, } - # baseline-style levers (raw form, like from Excel) levers = { "num_orders": 13, "itc_percent": 40, @@ -40,14 +39,10 @@ def main(): } result = run_one_scenario(config, levers) - - print("\nTest Successful") - print("avg_OCC:", result["avg_OCC"]) - print("avg_TCI:", result["avg_TCI"]) - print("avg_duration:", result["avg_duration"]) for k, v in result.items(): - if k not in ["avg_OCC", "avg_TCI", "avg_duration"]: - print(f"{k}: {v}") + print(f"{k}: {v}") + print("\nTest Successful") + if __name__ == "__main__": main() From 91f0809b4936d7cdb2e5fdf8ecf3290328cdde87 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Sat, 28 Feb 2026 18:08:45 -0600 Subject: [PATCH 08/25] HTGR pass --- src/crf/api.py | 45 +++++++++----- src/crf/data/HTGR_baseline.csv | 5 -- src/crf/data/SFR_baseline.csv | 104 +++++++++++---------------------- src/crf/model/avg_runner.py | 49 ++++++++++++++-- src/crf/model/core_accounts.py | 41 ++++++++++--- src/crf/model/pipeline.py | 4 -- test/test_crf_smoke.py | 53 +++++++++++++++-- 7 files changed, 189 insertions(+), 112 deletions(-) diff --git a/src/crf/api.py b/src/crf/api.py index 79b1e92..c08bca0 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -47,7 +47,7 @@ def run_one_scenario(config: dict, levers: dict) -> dict: levers = apply_itc_rounding(levers) inp = normalize_levers(levers) - result = run_avg_all_units(config=config, inp=inp, store=store) + result = run_avg_all_units(config=config, inp=inp, store=store, details=True) static_vals = static_row_from_levers(levers) return {**static_vals, **result} @@ -68,37 +68,49 @@ def run_sampling_from_excel( # headers: build from max num_orders in sampled set max_orders = int(np.max(samples[0, :])) - headers = ( - STATIC_KEYS - + [f"OCC_{i}" for i in range(max_orders)] - + [f"TCI_{i}" for i in range(max_orders)] - + [f"duration_{i}" for i in range(max_orders)] - + [ + headers_wo_itc = ( + STATIC_KEYS + + [f"OCC_{i}" for i in range(max_orders)] + + [f"TCI_{i}" for i in range(max_orders)] + + [f"duration_{i}" for i in range(max_orders)] + + [ "cons_duration_cumulative_wz_startup", "occLastUnit", "TCILastUnit", "durationsLastUnit", "avg_OCC", "avg_TCI", "avg_duration", - ] + ] + ) + headers_w_itc = ( + STATIC_KEYS + + [f"OCC_{i}" for i in range(max_orders)] + + [f"NETOCC_{i}" for i in range(max_orders)] + + [f"TCI_{i}" for i in range(max_orders)] + + [f"NCI_{i}" for i in range(max_orders)] + + [f"duration_{i}" for i in range(max_orders)] + + [ + "cons_duration_cumulative_wz_startup", + "occLastUnit", "TCILastUnit", "durationsLastUnit", + "avg_OCC", "avg_TCI", "avg_duration", + ] ) with open(out_csv, "w", newline="", encoding="utf-8") as csv_f, open(out_pkl, "wb") as pkl_f: writer = csv.DictWriter(csv_f, fieldnames=headers) writer.writeheader() - for i in range(n_samples): - # levers = unpack_sample_column(samples[:, i]) - # row = run_one_scenario(config, levers) - - levers_raw = sample_column_to_levers(samples, levers_df, i) row = run_one_scenario(config, levers_raw) - + n_itc = int(levers_raw["n_itc"]) + if n_itc > 0: + headers = headers_w_itc + else: + headers = headers_wo_itc write_csv_row(writer, row, headers) stream_pickle_dump(row, pkl_f) levers = apply_itc_rounding(levers) inp = normalize_levers(levers) - result = run_avg_all_units(config=config, inp=inp, store=store) + result = run_avg_all_units(config=config, inp=inp, store=store, details=False) static_vals = static_row_from_levers(levers) return {**static_vals, **result} @@ -118,7 +130,8 @@ def run_sampling_from_excel( # headers: build from max num_orders in sampled set max_orders = int(np.max(samples[0, :])) - + + # NOTE if n_itc is not 0 we might need to change this into NETOCC and NCI for the first n_itc units and OCC/TCI for the rest. headers = ( STATIC_KEYS + [f"OCC_{i}" for i in range(max_orders)] diff --git a/src/crf/data/HTGR_baseline.csv b/src/crf/data/HTGR_baseline.csv index 501a4d9..d1e18b1 100644 --- a/src/crf/data/HTGR_baseline.csv +++ b/src/crf/data/HTGR_baseline.csv @@ -1,5 +1,4 @@ Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labor Cost,Site Material Cost -10,Capitalized Pre-Construction Costs,,,,, 11,Land & Land Rights,15000000,,,, 12,Site Permits,0,,,, 13,Plant Licensing,107009771.98697068,,,, @@ -7,7 +6,6 @@ Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labo 15,Plant Studies,0,,,, 16,Plant Reports,0,,,, 18,Other Pre-Construction Costs,36194481.701475374,,,, -20,Capitalized Direct Costs,,,,, 21,Structures & Improvements,914902741.8258162,90263521.11,11824706.17,591583080.9,233056139.8 212,Reactor Containment Building,413396747.7312481,65728880.25,5420558.48,264358228.1,83309639.36 213,Turbine Room and Heater Bay,15257054.820128009,1136504.843,163947.4218,8458991.226,5661558.752 @@ -20,15 +18,12 @@ Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labo 25,Initial fuel inventory,451686470.5748004,451686470.5748004,0,0,0 26,Miscellaneous Equipment,214388490.9241457,65401342.47,2408453.335,129937709.9,19049438.52 28,Simulator,0,0,0,0,0 -30,Capitalized Indirect Services Costs,,,,, 31,Factory & Field Indirect Costs,691371059.4821017,,,, 32,Factory and construction supervision,2734393138.1498117,,,, 33,Start-Up Costs,115036083.38555087,,,, 34,Shipping & Transportation Costs,9686167.142,,,, 35,Engineering Services,738767482.8,,,, -50,Capitalized Supplementary Costs,,,,, 51,Taxes,187500,,,, 52,Insurance,38204331.42647941,,,, 54,Decommissioning ,8141760,,,, -60,Capitalized Financial Costs,,,,, 62,Interest ,3202161760.899656,,,, \ No newline at end of file diff --git a/src/crf/data/SFR_baseline.csv b/src/crf/data/SFR_baseline.csv index e8e39b4..fe23002 100644 --- a/src/crf/data/SFR_baseline.csv +++ b/src/crf/data/SFR_baseline.csv @@ -1,70 +1,34 @@ -Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labor Cost,Site Material Cost -10,Capitalized Pre-Construction Costs,,,,, -11,Land & Land Rights,11000000.0,,,, -12,Site Permits,1598890.7521620637,,,, -13,Plant Licensing,24382988.437500004,,,, -14,Plant Permits,12679166.666666668,,,, -15,Plant Studies,12679166.666666668,,,, -16,Plant Reports,3972185.8507187515,,,, -18,Other Pre-Construction Costs,12679166.666666668,,,, -,,,,,, -,10s - Subtotal,78991565.04038084,,,, -,10s - $/kWe,254.15561467304,,,, -,,,,,, -20,Capitalized Direct Costs,,,,, -21,Structures & Improvements,244678343.4084428,64350800.8593666,2899406.53716477,145691461.90599,34636080.6430862 -212,Reactor Containment Building,145108927.86932483,57583605.3695275,1605670.3085077,81082169.3410215,6443153.15877585 -213,Turbine Room and Heater Bay,9518417.644642511,620963.974754291,102399.473629672,5280509.16508102,3616944.5048072 -211 plus 214 to 219,Othe Structures & Improvements,90050997.89447546,6146231.515084816,1191336.755027398,59328783.39988749,24575982.97950315 -22,Reactor System,661334796.7533815,559862843.635677,1809703.51462689,97213738.5700561,4258214.54764847 -23,Energy Conversion System,238844021.25290728,189802947.4112764,874095.8194883969,46348086.0900651,2692987.75156579 -232.1,Electricity Generation Systems,197558120.73250762,167738159.268431,559708.577958467,29819961.4640766,0.0 -233,Ultimate Heat Sink,41285900.52039969,22064788.1428454,314387.24152993,16528124.6259885,2692987.75156579 -24,Electrical Equipment,67451644.91245879,13662940.7771874,767077.153200817,41345408.9752344,12443295.160037 -25,Initial fuel inventory,279724434.0,,,, -26,Miscellaneous Equipment,25655315.754125603,8372438.25664057,278805.251378048,15110212.4141026,2172665.08338243 -28,Simulator,78200.0,,,, -,,,,,, -,20s - Subtotal,1517766756.081316,,,, -,20s - $/kWe,4883.419421111054,,,, -,,,,,, -30,Capitalized Indirect Services Costs,,,,, -31,Factory & Field Indirect Costs,146051996.53692532,,,, -32,Factory and construction supervision,506256124.80995834,,,, -33,Start-Up Costs,21298225.546122435,,,, -34,Shipping & Transportation Costs,1793334.4599472817,,,, -35,Engineering Services,136778270.01496467,,,, -,,,,,, -,30s - Subtotal,812177951.367918,,,, -,30s - $/kWe,2613.1851717114478,,,, -,,,,,, -50,Capitalized Supplementary Costs,,,,, -51,Taxes,3510602.4683867483,,,, -52,Insurance,10484751.183521552,,,, -54,Decommissioning ,14606673.443824586,,,, -,,,,,, -,50s - Subtotal,28602027.095732886,,,, -,50s - $/kWe,92.02711420763477,,,, -,,,,,, -60,Capitalized Financial Costs,,,,, -62,Interest ,592300799.7656411,,,, --,60s - Subtotal,592300799.7656411,,,, -,60s - $/kWe,1905.729728975679,,,, -,,,,,, -,Total Direct Capital Cost (Accounts 10 to 20),1596758321.121697,,,, -,(Accounts 10 to 20) US$/kWe,5137.575035784095,,,, -,,,,,, -,Base Construction Cost (Accounts 10 to 30),2408936272.489615,,,, -,(Accounts 10 to 30) US$/kWe,7750.760207495543,,,, -,,,,,, -,Total Overnight Cost (Accounts 10 to 50),2437538299.5853477,,,, -,(Accounts 10 to 50) US$/kWe,7842.787321703177,,,, -,,,,,, -,Total Capital Investment Cost (All Accounts),3029839099.350989,,,, -,(Accounts 10 to 60) US$/kWe,9748.517050678856,,,, -,,,,,, -,Total Overnight Cost - ITC reduced,2437538299.5853477,,,, -,Total Overnight Cost -ITC reduced (US$/kWe),7842.787321703177,,,, -,,,,,, -,Total Capital Investment Cost - ITC reduced,3029839099.350989,,,, -,Total Capital Investment Cost - ITC reduced (US$/kWe),9748.517050678856,,,, +Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labor Cost,Site Material Cost +10,Capitalized Pre-Construction Costs,,,,, +11,Land & Land Rights,11000000,,,, +12,Site Permits,1598890.7521620637,,,, +13,Plant Licensing,24382988.437500004,,,, +14,Plant Permits,12679166.666666668,,,, +15,Plant Studies,12679166.666666668,,,, +16,Plant Reports,3972185.8507187515,,,, +18,Other Pre-Construction Costs,12679166.666666668,,,, +20,Capitalized Direct Costs,,,,, +21,Structures & Improvements,244678343.4084428,64350800.86,2899406.537,145691461.9,34636080.64 +212,Reactor Containment Building,145108927.86932483,57583605.37,1605670.309,81082169.34,6443153.159 +213,Turbine Room and Heater Bay,9518417.644642511,620963.9748,102399.4736,5280509.165,3616944.505 +211 plus 214 to 219,Othe Structures & Improvements,90050997.89447546,6146231.515084816,1191336.755027398,59328783.39988749,24575982.97950315 +22,Reactor System,661334796.7533815,559862843.6,1809703.515,97213738.57,4258214.548 +23,Energy Conversion System,238844021.25290728,189802947.4112764,874095.8194883969,46348086.09,2692987.752 +232.1,Electricity Generation Systems,197558120.73250762,167738159.3,559708.578,29819961.46,0 +233,Ultimate Heat Sink,41285900.52039969,22064788.14,314387.2415,16528124.63,2692987.752 +24,Electrical Equipment,67451644.91245879,13662940.78,767077.1532,41345408.98,12443295.16 +25,Initial fuel inventory,279724434,,,, +26,Miscellaneous Equipment,25655315.754125603,8372438.257,278805.2514,15110212.41,2172665.083 +28,Simulator,78200,,,, +30,Capitalized Indirect Services Costs,,,,, +31,Factory & Field Indirect Costs,146051996.53692532,,,, +32,Factory and construction supervision,506256124.80995834,,,, +33,Start-Up Costs,21298225.546122435,,,, +34,Shipping & Transportation Costs,1793334.4599472817,,,, +35,Engineering Services,136778270.01496467,,,, +50,Capitalized Supplementary Costs,,,,, +51,Taxes,3510602.4683867483,,,, +52,Insurance,10484751.183521552,,,, +54,Decommissioning ,14606673.443824586,,,, +60,Capitalized Financial Costs,,,,, +62,Interest ,592300799.7656411,,,, \ No newline at end of file diff --git a/src/crf/model/avg_runner.py b/src/crf/model/avg_runner.py index a0272f3..3203cbc 100644 --- a/src/crf/model/avg_runner.py +++ b/src/crf/model/avg_runner.py @@ -2,7 +2,7 @@ from .pipeline import calculate_final_result # Ignore runtime warning -def run_avg_all_units(config: dict, inp: dict, store): +def run_avg_all_units(config: dict, inp: dict, store, details=False): """ Runs n_th=1..num_orders and returns a CSV-friendly dict: OCC_i, TCI_i, duration_i plus summary metrics. @@ -11,31 +11,53 @@ def run_avg_all_units(config: dict, inp: dict, store): n_itc = int(inp["n_ITC"]) OCC, NETOCC, TCI, NCI, DUR = [], [], [], [], [] + if details: + # Stores the 10s, 20s, 30s, 50s and 60s, with 20s equipment, material, labor costs + D10s, D20s, D30s, D50s, D60s = [], [], [], [], [] + D20_equip, D20_mat, D20_labor = [], [], [] for n_th in range(1, num_orders + 1): - _, occ, netocc, tci, nci, dur = calculate_final_result(config, inp, store, n_th=n_th) + final_df, occ, netocc, tci, nci, dur = calculate_final_result(config, inp, store, n_th=n_th) OCC.append(occ) TCI.append(tci) DUR.append(dur) if n_th <= n_itc: NETOCC.append(netocc) NCI.append(nci) - + if details: + D10s.append(float(final_df.loc[final_df["Title"].eq("10s - $/kWe"), "Total Cost (USD)"].iloc[0])) + D20s.append(float(final_df.loc[final_df["Title"].eq("20s - $/kWe"), "Total Cost (USD)"].iloc[0])) + D30s.append(float(final_df.loc[final_df["Title"].eq("30s - $/kWe"), "Total Cost (USD)"].iloc[0])) + D50s.append(float(final_df.loc[final_df["Title"].eq("50s - $/kWe"), "Total Cost (USD)"].iloc[0])) + D60s.append(float(final_df.loc[final_df["Title"].eq("60s - $/kWe"), "Total Cost (USD)"].iloc[0])) + D20_equip.append(float(final_df.loc[final_df["Title"].eq("20s - $/kWe"), "Factory Equipment Cost"].iloc[0])) + D20_mat.append(float(final_df.loc[final_df["Title"].eq("20s - $/kWe"), "Site Material Cost"].iloc[0])) + D20_labor.append(float(final_df.loc[final_df["Title"].eq("20s - $/kWe"), "Site Labor Cost"].iloc[0])) OCC = np.array(OCC, dtype=float) NETOCC = np.array(NETOCC, dtype=float) TCI = np.array(TCI, dtype=float) NCI = np.array(NCI, dtype=float) DUR = np.array(DUR, dtype=float) + if details: + D10s = np.array(D10s, dtype=float) + D20s = np.array(D20s, dtype=float) + D30s = np.array(D30s, dtype=float) + D50s = np.array(D50s, dtype=float) + D60s = np.array(D60s, dtype=float) + D20_equip = np.array(D20_equip, dtype=float) + D20_mat = np.array(D20_mat, dtype=float) + D20_labor = np.array(D20_labor, dtype=float) # summaries occLastUnit = float(OCC[-1]) TCILastUnit = float(TCI[-1]) durationsLastUnit = float(DUR[-1]) if n_itc > 0: - avg_OCC = float(np.mean(NETOCC)) - avg_TCI = float(np.mean(NCI)) + # from 1st to n_ITC-th unit (inclusive) have ITC, so use NETOCC/NCI for avg; from (n_ITC+1)-th to last unit have no ITC, so use OCC/TCI for avg; + avg_OCC = float(np.mean(NETOCC))*(n_itc/num_orders) + float(np.mean(OCC[n_itc:]))*((num_orders-n_itc)/num_orders) + avg_TCI = float(np.mean(NCI))*(n_itc/num_orders) + float(np.mean(TCI[n_itc:]))*((num_orders-n_itc)/num_orders) else: avg_OCC = float(np.mean(OCC)) avg_TCI = float(np.mean(TCI)) @@ -58,6 +80,23 @@ def run_avg_all_units(config: dict, inp: dict, store): out[f"NCI_{i+1}"] = float(v) for i, v in enumerate(DUR): out[f"duration_{i+1}"] = float(v) + if details: + for i, v in enumerate(D10s): + out[f"D10s_{i+1}"] = float(v) + for i, v in enumerate(D20s): + out[f"D20s_{i+1}"] = float(v) + for i, v in enumerate(D30s): + out[f"D30s_{i+1}"] = float(v) + for i, v in enumerate(D50s): + out[f"D50s_{i+1}"] = float(v) + for i, v in enumerate(D60s): + out[f"D60s_{i+1}"] = float(v) + for i, v in enumerate(D20_equip): + out[f"D20_equip_{i+1}"] = float(v) + for i, v in enumerate(D20_mat): + out[f"D20_mat_{i+1}"] = float(v) + for i, v in enumerate(D20_labor): + out[f"D20_labor_{i+1}"] = float(v) out.update({ "cons_duration_cumulative_wz_startup": float(cons_duration_cumulative_wz_startup), diff --git a/src/crf/model/core_accounts.py b/src/crf/model/core_accounts.py index adb8e02..1d28be2 100644 --- a/src/crf/model/core_accounts.py +++ b/src/crf/model/core_accounts.py @@ -17,8 +17,8 @@ # Final results ("Total Direct Capital Cost (Accounts 10 to 20)", None), ("(Accounts 10 to 20) US$/kWe", None), - ("Base Construction Cost (Accounts 10 to 30)", None), - ("(Accounts 10 to 30) US$/kWe", None), + ("Base Construction Cost (Accounts 20 to 30)", None), + ("(Accounts 20 to 30) US$/kWe", None), ("Total Overnight Cost (Accounts 10 to 50)", None), ("(Accounts 10 to 50) US$/kWe", None), ("Total Capital Investment Cost (All Accounts)", None), @@ -137,6 +137,22 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr db["Account"].isin(["21", "22", "23", "24", "25", "26", "28"]), "Total Cost (USD)" ].fillna(0.0).sum() + db.loc[db["Title"] == "20s - Subtotal", "Factory Equipment Cost"] = db.loc[ + db["Account"].isin(["21", "22", "23", "24", "25", "26", "28"]), "Factory Equipment Cost" + ].fillna(0.0).sum() + + db.loc[db["Title"] == "20s - Subtotal", "Site Material Cost"] = db.loc[ + db["Account"].isin(["21", "22", "23", "24", "25", "26", "28"]), "Site Material Cost" + ].fillna(0.0).sum() + + db.loc[db["Title"] == "20s - Subtotal", "Site Labor Cost"] = db.loc[ + db["Account"].isin(["21", "22", "23", "24", "25", "26", "28"]), "Site Labor Cost" + ].fillna(0.0).sum() + + db.loc[db["Title"] == "20s - Subtotal", "Site Labor Hours"] = db.loc[ + db["Account"].isin(["21", "22", "23", "24", "25", "26", "28"]), "Site Labor Hours" + ].fillna(0.0).sum() + db.loc[db["Title"] == "30s - Subtotal", "Total Cost (USD)"] = db.loc[ db["Account"].isin(["31", "32", "33", "34", "35"]), "Total Cost (USD)" ].fillna(0.0).sum() @@ -154,6 +170,16 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr db.loc[db["Title"] == f"{t} - $/kWe", "Total Cost (USD)"] = ( db.loc[db["Title"] == f"{t} - Subtotal", "Total Cost (USD)"].values / reactor_power ) + # 20s equipment, material, labor costs per kWe + db.loc[db["Title"] == "20s - $/kWe", "Factory Equipment Cost"] = ( + db.loc[db["Title"] == "20s - Subtotal", "Factory Equipment Cost"].values / reactor_power + ) + db.loc[db["Title"] == "20s - $/kWe", "Site Material Cost"] = ( + db.loc[db["Title"] == "20s - Subtotal", "Site Material Cost"].values / reactor_power + ) + db.loc[db["Title"] == "20s - $/kWe", "Site Labor Cost"] = ( + db.loc[db["Title"] == "20s - Subtotal", "Site Labor Cost"].values / reactor_power + ) # final rollups db.loc[db["Title"] == "Total Direct Capital Cost (Accounts 10 to 20)", "Total Cost (USD)"] = ( @@ -161,13 +187,14 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr + db.loc[db["Title"] == "20s - Subtotal", "Total Cost (USD)"].values ) - db.loc[db["Title"] == "Base Construction Cost (Accounts 10 to 30)", "Total Cost (USD)"] = ( - db.loc[db["Title"] == "Total Direct Capital Cost (Accounts 10 to 20)", "Total Cost (USD)"].values + db.loc[db["Title"] == "Base Construction Cost (Accounts 20 to 30)", "Total Cost (USD)"] = ( + + db.loc[db["Title"] == "20s - Subtotal", "Total Cost (USD)"].values + db.loc[db["Title"] == "30s - Subtotal", "Total Cost (USD)"].values ) db.loc[db["Title"] == "Total Overnight Cost (Accounts 10 to 50)", "Total Cost (USD)"] = ( - db.loc[db["Title"] == "Base Construction Cost (Accounts 10 to 30)", "Total Cost (USD)"].values + db.loc[db["Title"] == "10s - Subtotal", "Total Cost (USD)"].values + + db.loc[db["Title"] == "Base Construction Cost (Accounts 20 to 30)", "Total Cost (USD)"].values + db.loc[db["Title"] == "50s - Subtotal", "Total Cost (USD)"].values ) @@ -180,8 +207,8 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr db.loc[db["Title"] == "(Accounts 10 to 20) US$/kWe", "Total Cost (USD)"] = ( db.loc[db["Title"] == "Total Direct Capital Cost (Accounts 10 to 20)", "Total Cost (USD)"].values / reactor_power ) - db.loc[db["Title"] == "(Accounts 10 to 30) US$/kWe", "Total Cost (USD)"] = ( - db.loc[db["Title"] == "Base Construction Cost (Accounts 10 to 30)", "Total Cost (USD)"].values / reactor_power + db.loc[db["Title"] == "(Accounts 20 to 30) US$/kWe", "Total Cost (USD)"] = ( + db.loc[db["Title"] == "Base Construction Cost (Accounts 20 to 30)", "Total Cost (USD)"].values / reactor_power ) db.loc[db["Title"] == "(Accounts 10 to 50) US$/kWe", "Total Cost (USD)"] = ( db.loc[db["Title"] == "Total Overnight Cost (Accounts 10 to 50)", "Total Cost (USD)"].values / reactor_power diff --git a/src/crf/model/pipeline.py b/src/crf/model/pipeline.py index 19acda8..f11e263 100644 --- a/src/crf/model/pipeline.py +++ b/src/crf/model/pipeline.py @@ -9,7 +9,6 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): Returns: (final_df, levelized_net_OCC, levelized_NCI, final_construction_duration_scalar) """ base0, power = store.get_baseline(config["reactor_type"]) - # direct updates (returns df + duration without supply chain delay) direct_df, dur_no_delay = update_direct_cost( store=store, @@ -28,7 +27,6 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): N_cons=inp["N_cons"], mod_0=inp["mod_0"], ) - # duration: add delay + learning dur_plus_delay = act_cons_duration_plus_delay( reactor_type=config["reactor_type"], @@ -42,10 +40,8 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): # learning on direct costs direct_plus_learning = learning_effect(direct_df, n_th, inp["standardization_0"], power) - # indirect costs with_indirect = update_indirect_cost(n_th, inp["standardization_0"], direct_plus_learning, final_dur, power) - # insurance + interest + ITC with_insurance = insurance_cost_update(base0, with_indirect, power) with_interest, tot_occ, tot_cap = update_interest_cost( diff --git a/test/test_crf_smoke.py b/test/test_crf_smoke.py index 9845b82..6c05b36 100644 --- a/test/test_crf_smoke.py +++ b/test/test_crf_smoke.py @@ -2,7 +2,7 @@ import os import sys - +import pandas as pd src_path = os.path.abspath(os.path.join(os.pardir, 'src')) sys.path.insert(0, src_path) from crf import run_one_scenario @@ -11,7 +11,8 @@ def main(): config = { - "reactor_type": "HTGR", # or "SFR" + # "reactor_type": "HTGR", # or "SFR" + "reactor_type": "SFR", # or "SFR" "f_22": 250_000_000, "f_2321": 150_000_000, "land_cost_per_acre_0": 22000, @@ -21,8 +22,8 @@ def main(): levers = { "num_orders": 13, - "itc_percent": 40, - "n_itc": 4, + "itc_percent": 0, + "n_itc": 0, "interest_percent": 6, "design_completion_percent": 80, "design_maturity": 1, @@ -39,8 +40,50 @@ def main(): } result = run_one_scenario(config, levers) + # transfer the result into more readable format + # all OCC, NETOCC, TCI, NCI can be put into a table with columns plant number and metric name, but for simplicity we just print them out here. + results_df = pd.DataFrame({"Plant_number": list(range(1, int(levers["num_orders"])+1))}) for k, v in result.items(): - print(f"{k}: {v}") + if k.startswith("OCC_"): + results_df[f"OCC"] = results_df["Plant_number"].apply(lambda x: result.get(f"OCC_{x}", None)) + elif k.startswith("NETOCC_"): + results_df[f"NETOCC"] = results_df["Plant_number"].apply(lambda x: result.get(f"NETOCC_{x}", None)) + elif k.startswith("TCI_"): + results_df[f"TCI"] = results_df["Plant_number"].apply(lambda x: result.get(f"TCI_{x}", None)) + elif k.startswith("NCI_"): + results_df[f"NCI"] = results_df["Plant_number"].apply(lambda x: result.get(f"NCI_{x}", None)) + elif k.startswith("duration_"): + results_df[f"duration"] = results_df["Plant_number"].apply(lambda x: result.get(f"duration_{x}", None)) + elif k.startswith("D10s_"): + results_df[f"D10s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D10s_{x}", None)) + elif k.startswith("D20s_"): + results_df[f"D20s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20s_{x}", None)) + elif k.startswith("D30s_"): + results_df[f"D30s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D30s_{x}", None)) + elif k.startswith("D50s_"): + results_df[f"D50s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D50s_{x}", None)) + elif k.startswith("D60s_"): + results_df[f"D60s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D60s_{x}", None)) + elif k.startswith("D20_equip_"): + results_df[f"D20_equip"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_equip_{x}", None)) + elif k.startswith("D20_mat_"): + results_df[f"D20_mat"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_mat_{x}", None)) + elif k.startswith("D20_labor_"): + results_df[f"D20_labor"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_labor_{x}", None)) + + + print("Results by plant number:\n") + print(results_df.round(2).fillna("").to_string(index=False)) + + summary_metrics = ["cons_duration_cumulative_wz_startup", "occLastUnit", "TCILastUnit", "durationsLastUnit", "avg_OCC", "avg_TCI", "avg_duration"] + print("\nSummary metrics:\n") + for metric in summary_metrics: + print(f"{metric}: {result[metric]:.2f}") + + + + # for k, v in result.items(): + # print(f"{k}: {v}") print("\nTest Successful") From 62ee25f3bfb499ab4ad31f531fcd480f45d84279 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Wed, 4 Mar 2026 16:19:37 -0600 Subject: [PATCH 09/25] SFR test not pass dur to duration and misinfo in the report --- .../CostReduction_exploration_mode.ipynb | 8087 +++++++++-------- src/crf/data/SFR_baseline.csv | 4 +- src/crf/data/SFR_baselineold.csv | 34 + src/crf/model/core_accounts.py | 7 + src/crf/model/direct_cost.py | 10 +- src/crf/model/learning.py | 24 +- src/crf/model/pipeline.py | 7 +- 7 files changed, 4177 insertions(+), 3996 deletions(-) create mode 100644 src/crf/data/SFR_baselineold.csv diff --git a/Cost_Reduction/CostReduction_exploration_mode.ipynb b/Cost_Reduction/CostReduction_exploration_mode.ipynb index ee05c43..1bc7754 100644 --- a/Cost_Reduction/CostReduction_exploration_mode.ipynb +++ b/Cost_Reduction/CostReduction_exploration_mode.ipynb @@ -67,659 +67,659 @@ "data": { "text/html": [ "\n", - 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The Concept B 310.8 MWe Reactor
FOAK Capital Cost Summary - Baseline hypothetical well-executeed project

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
10Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 244,678,343$ 64,350,8012,899,407 hrs$ 145,691,462$ 34,636,081
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 9,518,418$ 620,964102,399 hrs$ 5,280,509$ 3,616,945
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 661,334,797$ 559,862,8441,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 238,844,021$ 189,802,947874,096 hrs$ 46,348,086$ 2,692,988
232.1Electricity Generation Systems$ 197,558,121$ 167,738,159559,709 hrs$ 29,819,961$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,517,766,756
20s - $/kWe$ 4,883
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 146,051,997
32Factory and construction supervision$ 506,256,125
33Start-Up Costs$ 21,298,226
34Shipping & Transportation Costs$ 1,793,334
35Engineering Services$ 136,778,270
30s - Subtotal$ 812,177,951
30s - $/kWe$ 2,613
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 10,484,751
54Decommissioning $ 14,606,673
50s - Subtotal$ 28,602,027
50s - $/kWe$ 92
60Capitalized Financial Costs
62Interest $ 592,300,800
-60s - Subtotal$ 592,300,800
60s - $/kWe$ 1,906
Total Direct Capital Cost (Accounts 10 to 20)$ 1,596,758,321
(Accounts 10 to 20) US$/kWe$ 5,138
Base Construction Cost (Accounts 10 to 30)$ 2,408,936,272
(Accounts 10 to 30) US$/kWe$ 7,751
Total Overnight Cost (Accounts 10 to 50)$ 2,437,538,300
(Accounts 10 to 50) US$/kWe$ 7,843
Total Capital Investment Cost (All Accounts)$ 3,029,839,099
(Accounts 10 to 60) US$/kWe$ 9,749
Total Overnight Cost - ITC reduced$ 2,437,538,300
Total Overnight Cost -ITC reduced (US$/kWe)$ 7,843
Total Capital Investment Cost - ITC reduced$ 3,029,839,099
Total Capital Investment Cost - ITC reduced (US$/kWe)$ 9,74910Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 244,678,343$ 64,350,8012,899,407 hrs$ 145,691,462$ 34,636,081
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 9,518,418$ 620,964102,399 hrs$ 5,280,509$ 3,616,945
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 661,334,797$ 559,862,8441,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 238,844,021$ 189,802,947874,096 hrs$ 46,348,086$ 2,692,988
232.1Electricity Generation Systems$ 197,558,121$ 167,738,159559,709 hrs$ 29,819,961$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,517,766,756
20s - $/kWe$ 4,883
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 146,051,997
32Factory and construction supervision$ 506,256,125
33Start-Up Costs$ 21,298,226
34Shipping & Transportation Costs$ 1,793,334
35Engineering Services$ 136,778,270
30s - Subtotal$ 812,177,951
30s - $/kWe$ 2,613
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 10,484,751
54Decommissioning $ 14,606,673
50s - Subtotal$ 28,602,027
50s - $/kWe$ 92
60Capitalized Financial Costs
62Interest $ 592,300,800
-60s - Subtotal$ 592,300,800
60s - $/kWe$ 1,906
Total Direct Capital Cost (Accounts 10 to 20)$ 1,596,758,321
(Accounts 10 to 20) US$/kWe$ 5,138
Base Construction Cost (Accounts 10 to 30)$ 2,408,936,272
(Accounts 10 to 30) US$/kWe$ 7,751
Total Overnight Cost (Accounts 10 to 50)$ 2,437,538,300
(Accounts 10 to 50) US$/kWe$ 7,843
Total Capital Investment Cost (All Accounts)$ 3,029,839,099
(Accounts 10 to 60) US$/kWe$ 9,749
Total Overnight Cost - ITC reduced$ 2,437,538,300
Total Overnight Cost -ITC reduced (US$/kWe)$ 7,843
Total Capital Investment Cost - ITC reduced$ 3,029,839,099
Total Capital Investment Cost - ITC reduced (US$/kWe)$ 9,749
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User-Input Global levers
(highlighted in yellow if different from the baseline)
LeverUser-Input ValueLever baseline value (for a hopothetical well-executed project)RangeLeverUser-Input ValueLever baseline value (for a hopothetical well-executed project)Range
Design Maturity120 - 2Design Maturity120 - 2
Design Completion0.80000010 - 1Design Completion0.80000010 - 1
Procurement (supply chain) experience 0.50000020 - 2Procurement (supply chain) experience 0.50000020 - 2
Architecture & Engineering Experience0.50000020 - 2Architecture & Engineering Experience0.50000020 - 2
Construction service experience120 - 2Construction service experience120 - 2
Land Cost Per Acre (2023 USD)22000220001000 - 100000 Land Cost Per Acre (2023 USD)22000220001000 - 100000
ITC 000 - 0.4ITC 000 - 0.4
Interest Rate0.0600000.0600000 - 0.15 Interest Rate0.0600000.0600000 - 0.15
BOP grade non_nuclearnuclearnuclear or non-nuclearBOP grade non_nuclearnuclearnuclear or non-nuclear
Reactor Building gradenuclearnuclearnuclear or non-nuclearReactor Building gradenuclearnuclearnuclear or non-nuclear
modulariziationmodularizedmodularizedstick_built or modularizedmodulariziationmodularizedmodularizedstick_built or modularized
standardization0.8000000.7000000.7 : 1standardization0.8000000.7000000.7 : 1
Startup duration (months)16163 : 24Startup duration (months)16163 : 24
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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Factories Cost Included
Displaying Direct Cost only

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 244,678,343$ 64,350,8012,899,407 hrs$ 145,691,462$ 34,636,081
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 9,518,418$ 620,964102,399 hrs$ 5,280,509$ 3,616,945
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 680,565,566$ 579,093,6131,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 250,382,483$ 201,341,409874,096 hrs$ 46,348,086$ 2,692,988
232.1Electricity Generation Systems$ 209,096,582$ 179,276,621559,709 hrs$ 29,819,961$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,548,535,987
20s - $/kWe$ 4,982
20Capitalized Direct Costs
21Structures & Improvements$ 244,678,343$ 64,350,8012,899,407 hrs$ 145,691,462$ 34,636,081
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 9,518,418$ 620,964102,399 hrs$ 5,280,509$ 3,616,945
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 680,565,566$ 579,093,6131,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 250,382,483$ 201,341,409874,096 hrs$ 46,348,086$ 2,692,988
232.1Electricity Generation Systems$ 209,096,582$ 179,276,621559,709 hrs$ 29,819,961$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,548,535,987
20s - $/kWe$ 4,982
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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Factories Cost, land cost and taxes Included

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
10Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 244,678,343$ 64,350,8012,899,407 hrs$ 145,691,462$ 34,636,081
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 9,518,418$ 620,964102,399 hrs$ 5,280,509$ 3,616,945
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 680,565,566$ 579,093,6131,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 250,382,483$ 201,341,409874,096 hrs$ 46,348,086$ 2,692,988
232.1Electricity Generation Systems$ 209,096,582$ 179,276,621559,709 hrs$ 29,819,961$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,548,535,987
20s - $/kWe$ 4,982
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 146,051,997
32Factory and construction supervision$ 506,256,125
33Start-Up Costs$ 21,298,226
34Shipping & Transportation Costs$ 1,793,334
35Engineering Services$ 136,778,270
30s - Subtotal$ 812,177,951
30s - $/kWe$ 2,613
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 10,484,751
54Decommissioning $ 14,606,673
50s - Subtotal$ 28,602,027
50s - $/kWe$ 9210Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 244,678,343$ 64,350,8012,899,407 hrs$ 145,691,462$ 34,636,081
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 9,518,418$ 620,964102,399 hrs$ 5,280,509$ 3,616,945
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 680,565,566$ 579,093,6131,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 250,382,483$ 201,341,409874,096 hrs$ 46,348,086$ 2,692,988
232.1Electricity Generation Systems$ 209,096,582$ 179,276,621559,709 hrs$ 29,819,961$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,548,535,987
20s - $/kWe$ 4,982
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 146,051,997
32Factory and construction supervision$ 506,256,125
33Start-Up Costs$ 21,298,226
34Shipping & Transportation Costs$ 1,793,334
35Engineering Services$ 136,778,270
30s - Subtotal$ 812,177,951
30s - $/kWe$ 2,613
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 10,484,751
54Decommissioning $ 14,606,673
50s - Subtotal$ 28,602,027
50s - $/kWe$ 92
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -1801,195 +1801,195 @@ "data": { "text/html": [ "\n", - "\n", + "
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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Factories Cost, land cost, taxes and BOP/RP grades Included
Displaying Direct Cost only

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 240,870,976$ 64,102,4152,858,447 hrs$ 143,579,258$ 33,189,303
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 5,711,051$ 372,57861,440 hrs$ 3,168,305$ 2,170,167
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 680,565,566$ 579,093,6131,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 166,743,850$ 129,630,761650,212 hrs$ 34,420,102$ 2,692,988
232.1Electricity Generation Systems$ 125,457,949$ 107,565,972335,825 hrs$ 17,891,977$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,461,089,987
20s - $/kWe$ 4,701
20Capitalized Direct Costs
21Structures & Improvements$ 240,870,976$ 64,102,4152,858,447 hrs$ 143,579,258$ 33,189,303
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 5,711,051$ 372,57861,440 hrs$ 3,168,305$ 2,170,167
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 680,565,566$ 579,093,6131,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 166,743,850$ 129,630,761650,212 hrs$ 34,420,102$ 2,692,988
232.1Electricity Generation Systems$ 125,457,949$ 107,565,972335,825 hrs$ 17,891,977$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,461,089,987
20s - $/kWe$ 4,701
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -2110,195 +2110,195 @@ "data": { "text/html": [ "\n", - "\n", + "
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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Factories Cost, land cost, taxes, BOP/RP grades , bulk ordering Included
Displaying Direct Cost only

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 240,870,976$ 64,102,4152,858,447 hrs$ 143,579,258$ 33,189,303
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 5,711,051$ 372,57861,440 hrs$ 3,168,305$ 2,170,167
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 469,343,190$ 367,871,2371,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 118,425,483$ 81,312,394650,212 hrs$ 34,420,102$ 2,692,988
232.1Electricity Generation Systems$ 77,139,583$ 59,247,606335,825 hrs$ 17,891,977$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,201,549,244
20s - $/kWe$ 3,866
20Capitalized Direct Costs
21Structures & Improvements$ 240,870,976$ 64,102,4152,858,447 hrs$ 143,579,258$ 33,189,303
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 5,711,051$ 372,57861,440 hrs$ 3,168,305$ 2,170,167
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 469,343,190$ 367,871,2371,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 118,425,483$ 81,312,394650,212 hrs$ 34,420,102$ 2,692,988
232.1Electricity Generation Systems$ 77,139,583$ 59,247,606335,825 hrs$ 17,891,977$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,201,549,244
20s - $/kWe$ 3,866
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -2385,195 +2385,195 @@ "data": { "text/html": [ "\n", - "\n", + "
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The Concept B 310.8 MWe Reactor Capital Cost Summary
Factories Cost, land cost, taxes, BOP/RP grades , bulk ordering, reworking and productivity Included
Displaying Direct Cost only

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -2675,195 +2675,195 @@ "data": { "text/html": [ "\n", - "\n", + "
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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Direct Cost Updated

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -2911,7 +2911,18 @@ "execution_count": 10, "id": "c4ad50cf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n" + ] + } + ], "source": [ "\n", "def update_cons_dur(Reactor_data_0, db, mod_0):\n", @@ -2992,195 +3003,195 @@ "data": { "text/html": [ "\n", - "\n", + "
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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Direct Cost Updated plus learning effect
Displaying Direct Cost only

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -3270,7 +3281,20 @@ "execution_count": 12, "id": "6b94464b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", + "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", + "cons_duration_no_delay [80.63803108]\n" + ] + } + ], "source": [ "def act_cons_duration_plus_delay(reactor_type, n_th, Design_Maturity_0, proc_exp_0, N_proc, cons_duration_no_delay):\n", " \n", @@ -3324,6 +3348,8 @@ "\n", "\n", " supply_chain_delay = max( T_end - ref_construction_duration, 0)\n", + " print('in act_cons_duration_plus_delay ref_construction_duration',ref_construction_duration,'T_end',T_end)\n", + " print( 'cons_duration_no_delay',cons_duration_no_delay)\n", " actual_construction_duration_plus_delay = cons_duration_no_delay + supply_chain_delay\n", " return actual_construction_duration_plus_delay\n", "\n", @@ -3355,7 +3381,20 @@ "execution_count": 13, "id": "2f8af5f9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", + "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", + "cons_duration_no_delay [80.63803108]\n" + ] + } + ], "source": [ "def duration_learning_effect(n_th, standardization_0, actual_construction_duration_plus_delay):\n", " \n", @@ -3395,280 +3434,297 @@ "id": "b0db81dd-fca3-45a3-bcd3-7b2094729da1", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", + "act_con_duration [80.63803108]\n", + "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", + "cons_duration_no_delay [80.63803108]\n", + "cons_duration_plus_delay [92.07203108]\n", + "final_con_duration [92.07203108]\n", + "in function update indirect\n", + "final_construction_duration [92.07203108]\n" + ] + }, { "data": { "text/html": [ "\n", - "\n", + "
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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Base Cost updated (Direct and Indirect costs)

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -3679,6 +3735,7 @@ "source": [ "def update_indirect_cost(n_th, standardization_0, Reactor_data_updated_6, final_construction_duration, power ):\n", " print(\"in function update indirect\")\n", + " print(\"final_construction_duration\",final_construction_duration)\n", " if n_th == 1:\n", " standardization = 0.7\n", " elif n_th >1:\n", @@ -3740,9 +3797,11 @@ "direct_cost_updated = update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons)\n", "\n", "act_con_duration = update_cons_dur(reactor_data, direct_cost_updated , mod_0)\n", + "print('act_con_duration',act_con_duration)\n", "cons_duration_plus_delay = act_cons_duration_plus_delay(reactor_type, n_th, Design_Maturity_0, proc_exp_0, N_proc, act_con_duration)\n", + "print('cons_duration_plus_delay',cons_duration_plus_delay)\n", "final_con_duration = duration_learning_effect(n_th, standardization_0, cons_duration_plus_delay)\n", - "\n", + "print('final_con_duration',final_con_duration)\n", "direct_cost_updated_plus_learning = learning_effect(direct_cost_updated , n_th, standardization_0, reactor_power)\n", "direct_cost_updated_plus_learning_with_indirect_cost = update_indirect_cost(n_th, standardization_0, direct_cost_updated_plus_learning, final_con_duration, reactor_power )\n", "\n", @@ -3797,352 +3856,366 @@ "id": "a2561757-dbc9-4a67-ad25-6311bc9939cc", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", + "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", + "cons_duration_no_delay [80.63803108]\n", + "in function update indirect\n", + "final_construction_duration [92.07203108]\n" + ] + }, { "data": { "text/html": [ "\n", - "\n", + "
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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Base Cost updated (Direct and Indirect costs) plus insurance

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 16,308,494
54Decommissioning $ 14,606,673
50s - Subtotal$ 34,425,770
50s - $/kWe$ 111
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 16,308,494
54Decommissioning $ 14,606,673
50s - Subtotal$ 34,425,770
50s - $/kWe$ 111
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 16, @@ -4202,663 +4275,689 @@ "id": "9616088a-1f2c-4147-a288-eb0bb7f259c7", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", + "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", + "cons_duration_no_delay [80.63803108]\n", + "in function update indirect\n", + "final_construction_duration [92.07203108]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:7: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " annual_periods = np.linspace(12, 12*int(final_construction_duration/12),int(final_construction_duration/12))\n", + "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:8: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " if max(annual_periods) < int(final_construction_duration)-1:\n", + "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:14: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " new_period = 103*period/int(final_construction_duration)\n" + ] + }, { "data": { "text/html": [ "\n", - "\n", + "
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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Base Cost updated (Direct and Indirect costs) plus insurance and interest

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
10Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 16,308,494
54Decommissioning $ 14,606,673
50s - Subtotal$ 34,425,770
50s - $/kWe$ 111
60Capitalized Financial Costs
62Interest $ 1,276,775,002
-60s - Subtotal$ 1,276,775,002
60s - $/kWe$ 4,108
Total Direct Capital Cost (Accounts 10 to 20)$ 1,984,538,942
(Accounts 10 to 20) US$/kWe$ 6,385
Base Construction Cost (Accounts 10 to 30)$ 3,703,101,413
(Accounts 10 to 30) US$/kWe$ 11,915
Total Overnight Cost (Accounts 10 to 50)$ 3,737,527,183
(Accounts 10 to 50) US$/kWe$ 12,026
Total Capital Investment Cost (All Accounts)$ 5,014,302,185
(Accounts 10 to 60) US$/kWe$ 16,134
Total Overnight Cost - ITC reduced$ 2,437,538,300
Total Overnight Cost -ITC reduced (US$/kWe)$ 7,843
Total Capital Investment Cost - ITC reduced$ 3,029,839,099
Total Capital Investment Cost - ITC reduced (US$/kWe)$ 9,74910Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 16,308,494
54Decommissioning $ 14,606,673
50s - Subtotal$ 34,425,770
50s - $/kWe$ 111
60Capitalized Financial Costs
62Interest $ 1,276,775,002
-60s - Subtotal$ 1,276,775,002
60s - $/kWe$ 4,108
Total Direct Capital Cost (Accounts 10 to 20)$ 1,984,538,942
(Accounts 10 to 20) US$/kWe$ 6,385
Base Construction Cost (Accounts 10 to 30)$ 3,703,101,413
(Accounts 10 to 30) US$/kWe$ 11,915
Total Overnight Cost (Accounts 10 to 50)$ 3,737,527,183
(Accounts 10 to 50) US$/kWe$ 12,026
Total Capital Investment Cost (All Accounts)$ 5,014,302,185
(Accounts 10 to 60) US$/kWe$ 16,134
Total Overnight Cost - ITC reduced$ 2,437,538,300
Total Overnight Cost -ITC reduced (US$/kWe)$ 7,843
Total Capital Investment Cost - ITC reduced$ 3,029,839,099
Total Capital Investment Cost - ITC reduced (US$/kWe)$ 9,749
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -4953,658 +5052,684 @@ "id": "f5b6baa7-0224-483b-bcd3-59301602ba3e", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "update_cons_dur\n", + "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", + "baseline_construction_duration 64.0\n", + "labor_hour_ratio [1.86656412]\n", + "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", + "cons_duration_no_delay [80.63803108]\n", + "in function update indirect\n", + "final_construction_duration [92.07203108]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:7: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " annual_periods = np.linspace(12, 12*int(final_construction_duration/12),int(final_construction_duration/12))\n", + "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:8: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " if max(annual_periods) < int(final_construction_duration)-1:\n", + "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:14: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " new_period = 103*period/int(final_construction_duration)\n" + ] + }, { "data": { "text/html": [ "\n", - "\n", + "
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The Concept B 310.8 MWe Reactor
Capital Cost Summary

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material CostAccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
10Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 16,308,494
54Decommissioning $ 14,606,673
50s - Subtotal$ 34,425,770
50s - $/kWe$ 111
60Capitalized Financial Costs
62Interest $ 1,276,775,002
-60s - Subtotal$ 1,276,775,002
60s - $/kWe$ 4,108
Total Direct Capital Cost (Accounts 10 to 20)$ 1,984,538,942
(Accounts 10 to 20) US$/kWe$ 6,385
Base Construction Cost (Accounts 10 to 30)$ 3,703,101,413
(Accounts 10 to 30) US$/kWe$ 11,915
Total Overnight Cost (Accounts 10 to 50)$ 3,737,527,183
(Accounts 10 to 50) US$/kWe$ 12,026
Total Capital Investment Cost (All Accounts)$ 5,014,302,185
(Accounts 10 to 60) US$/kWe$ 16,134
Total Overnight Cost - ITC reduced$ 3,737,527,183
Total Overnight Cost -ITC reduced (US$/kWe)$ 12,026
Total Capital Investment Cost - ITC reduced$ 5,014,302,185
Total Capital Investment Cost - ITC reduced (US$/kWe)$ 16,13410Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 16,308,494
54Decommissioning $ 14,606,673
50s - Subtotal$ 34,425,770
50s - $/kWe$ 111
60Capitalized Financial Costs
62Interest $ 1,276,775,002
-60s - Subtotal$ 1,276,775,002
60s - $/kWe$ 4,108
Total Direct Capital Cost (Accounts 10 to 20)$ 1,984,538,942
(Accounts 10 to 20) US$/kWe$ 6,385
Base Construction Cost (Accounts 10 to 30)$ 3,703,101,413
(Accounts 10 to 30) US$/kWe$ 11,915
Total Overnight Cost (Accounts 10 to 50)$ 3,737,527,183
(Accounts 10 to 50) US$/kWe$ 12,026
Total Capital Investment Cost (All Accounts)$ 5,014,302,185
(Accounts 10 to 60) US$/kWe$ 16,134
Total Overnight Cost - ITC reduced$ 3,737,527,183
Total Overnight Cost -ITC reduced (US$/kWe)$ 12,026
Total Capital Investment Cost - ITC reduced$ 5,014,302,185
Total Capital Investment Cost - ITC reduced (US$/kWe)$ 16,134
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 18, diff --git a/src/crf/data/SFR_baseline.csv b/src/crf/data/SFR_baseline.csv index fe23002..02c0185 100644 --- a/src/crf/data/SFR_baseline.csv +++ b/src/crf/data/SFR_baseline.csv @@ -17,9 +17,9 @@ Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labo 232.1,Electricity Generation Systems,197558120.73250762,167738159.3,559708.578,29819961.46,0 233,Ultimate Heat Sink,41285900.52039969,22064788.14,314387.2415,16528124.63,2692987.752 24,Electrical Equipment,67451644.91245879,13662940.78,767077.1532,41345408.98,12443295.16 -25,Initial fuel inventory,279724434,,,, +25,Initial fuel inventory,279724434,279724434,,, 26,Miscellaneous Equipment,25655315.754125603,8372438.257,278805.2514,15110212.41,2172665.083 -28,Simulator,78200,,,, +28,Simulator,78200,78200,,, 30,Capitalized Indirect Services Costs,,,,, 31,Factory & Field Indirect Costs,146051996.53692532,,,, 32,Factory and construction supervision,506256124.80995834,,,, diff --git a/src/crf/data/SFR_baselineold.csv b/src/crf/data/SFR_baselineold.csv new file mode 100644 index 0000000..02c0185 --- /dev/null +++ b/src/crf/data/SFR_baselineold.csv @@ -0,0 +1,34 @@ +Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labor Cost,Site Material Cost +10,Capitalized Pre-Construction Costs,,,,, +11,Land & Land Rights,11000000,,,, +12,Site Permits,1598890.7521620637,,,, +13,Plant Licensing,24382988.437500004,,,, +14,Plant Permits,12679166.666666668,,,, +15,Plant Studies,12679166.666666668,,,, +16,Plant Reports,3972185.8507187515,,,, +18,Other Pre-Construction Costs,12679166.666666668,,,, +20,Capitalized Direct Costs,,,,, +21,Structures & Improvements,244678343.4084428,64350800.86,2899406.537,145691461.9,34636080.64 +212,Reactor Containment Building,145108927.86932483,57583605.37,1605670.309,81082169.34,6443153.159 +213,Turbine Room and Heater Bay,9518417.644642511,620963.9748,102399.4736,5280509.165,3616944.505 +211 plus 214 to 219,Othe Structures & Improvements,90050997.89447546,6146231.515084816,1191336.755027398,59328783.39988749,24575982.97950315 +22,Reactor System,661334796.7533815,559862843.6,1809703.515,97213738.57,4258214.548 +23,Energy Conversion System,238844021.25290728,189802947.4112764,874095.8194883969,46348086.09,2692987.752 +232.1,Electricity Generation Systems,197558120.73250762,167738159.3,559708.578,29819961.46,0 +233,Ultimate Heat Sink,41285900.52039969,22064788.14,314387.2415,16528124.63,2692987.752 +24,Electrical Equipment,67451644.91245879,13662940.78,767077.1532,41345408.98,12443295.16 +25,Initial fuel inventory,279724434,279724434,,, +26,Miscellaneous Equipment,25655315.754125603,8372438.257,278805.2514,15110212.41,2172665.083 +28,Simulator,78200,78200,,, +30,Capitalized Indirect Services Costs,,,,, +31,Factory & Field Indirect Costs,146051996.53692532,,,, +32,Factory and construction supervision,506256124.80995834,,,, +33,Start-Up Costs,21298225.546122435,,,, +34,Shipping & Transportation Costs,1793334.4599472817,,,, +35,Engineering Services,136778270.01496467,,,, +50,Capitalized Supplementary Costs,,,,, +51,Taxes,3510602.4683867483,,,, +52,Insurance,10484751.183521552,,,, +54,Decommissioning ,14606673.443824586,,,, +60,Capitalized Financial Costs,,,,, +62,Interest ,592300799.7656411,,,, \ No newline at end of file diff --git a/src/crf/model/core_accounts.py b/src/crf/model/core_accounts.py index 1d28be2..1e6980e 100644 --- a/src/crf/model/core_accounts.py +++ b/src/crf/model/core_accounts.py @@ -75,6 +75,10 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr db = db.copy() db[COST_COLS] = db[COST_COLS].fillna(0.0) # account 21 + # Note when sum up the higher lever account like 21, we need to make sure there are + # lower level accounts (like 211 plus 214 to 219) to be added before the sum, otherwise + # the sum might lead to an empty value and the final result will be misleading. + db.loc[db.Account == "21", "Factory Equipment Cost"] = ( db.loc[db.Account == "212", "Factory Equipment Cost"].values + db.loc[db.Account == "213", "Factory Equipment Cost"].values @@ -245,6 +249,7 @@ def _sum_hours(db): sum_old = _sum_hours(db0) sum_new = _sum_hours(db1) lab_delta = (sum_new - sum_old) / sum_old + print(f"in update_cons_duration, sum_old: {sum_old}, sum_new: {sum_new}, lab_delta: {lab_delta}, ref_duration: {ref_duration}") return float(0.3 * lab_delta * ref_duration + ref_duration) def update_cons_duration_2( @@ -266,5 +271,7 @@ def _sum_hours(db): sum_old = _sum_hours(db0) sum_new = _sum_hours(db1) lab_delta = (sum_new - sum_old) / float(baseline_lab_hours) + # NOTE ref_duration here should be modulized? + # ref_duration=ref_duration*0.8 return float(0.3 * lab_delta * ref_duration + float(prev_cons_duration)) diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index cf14a55..43d2e67 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -147,12 +147,12 @@ def add_reworking_productivity( productivity = 0.145 * ce_exp + 0.71 - if reactor_type == "SFR": - rework = (-0.9 * design_completion + 1.9) * (-0.15 * ae_exp + 1.3) * (-0.15 * ce_exp + 1.3) - ref_duration = 80 - else: + if reactor_type == "HTGR": rework = (-0.69 * design_completion + 1.69) * (-0.125 * ae_exp + 1.25) * (-0.125 * ce_exp + 1.25) ref_duration = 125 + elif reactor_type == "SFR": + rework = (-0.9 * design_completion + 1.9) * (-0.15 * ae_exp + 1.3) * (-0.15 * ce_exp + 1.3) + ref_duration = 80 db = df.copy() @@ -192,10 +192,12 @@ def update_direct_cost( db = update_high_level_costs(db, power)[COLS].copy() db = add_land_cost(db, land_cost_per_acre_0, power) db, prev_dur = add_BOP_RP_grades(db, RB_grade_0, BOP_grade_0, power, reactor_type, n_th, mod_0) + print(f"Duration after add_BOP_RP_grades for plant {n_th}: {prev_dur}") db = add_bulk_ordering(db, num_orders, f_22, f_2321, power) db, dur_no_delay = add_reworking_productivity( db, reactor_type, n_th, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, power, prev_dur, baseline_lab_hours ) + print(f"Duration after add_reworking_productivity for plant {n_th}: {dur_no_delay}") return db, dur_no_delay diff --git a/src/crf/model/learning.py b/src/crf/model/learning.py index 1885401..40d65c9 100644 --- a/src/crf/model/learning.py +++ b/src/crf/model/learning.py @@ -62,12 +62,12 @@ def act_cons_duration_plus_delay( Design_Maturity = 2 proc_exp = min(proc_exp_0 + (2 / N_proc) * (n_th - 1), 2) - if reactor_type == "SFR": - task_length_multiplier = 1.0 - ref_construction_duration = 64 - else: # Concept A + if reactor_type == "HTGR": task_length_multiplier = 100 / 64 ref_construction_duration = 100 + elif reactor_type == "SFR": + task_length_multiplier = 1.0 + ref_construction_duration = 64 B_21 = 42.1 * task_length_multiplier B_22 = 60.2 * task_length_multiplier @@ -82,15 +82,23 @@ def act_cons_duration_plus_delay( T_22 = 0.09 * (B_21 + D) + B_22 + D T_23 = 0.24 * (B_21 + D) + B_23 + D T_24 = 0.24 * (B_21 + D) + 0.34 * (B_23 + D) + B_24 + D - T_25 = 0.18 * (B_21 + D) + B_25 + D + T_25 = 0.18 * (B_21 + D) + B_25 + D # NOTE: need to check whether the delay should be applied to B_25 T_26 = 0.21 * (B_21 + D) + B_26 + D T_end = max(T_21, T_22, T_23, T_24, T_25, T_26) - supply_chain_delay = max(T_end - ref_construction_duration, 0) + supply_chain_delay = max(T_end - ref_construction_duration, 0) + print(f"T_end for plant {n_th}: {T_end}, ref_construction_duration: {ref_construction_duration}, supply_chain_delay: {supply_chain_delay}") + print(f"cons_duration_no_delay for plant {n_th}: {cons_duration_no_delay}") return float(cons_duration_no_delay) + float(supply_chain_delay) -def duration_learning_effect(n_th: int, standardization_0: float, actual_construction_duration_plus_delay: float): +def duration_learning_effect(reactor_type: str, + n_th: int, + standardization_0: float, + actual_construction_duration_plus_delay: float): standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 - fitted_LR_duration = 0.103719051 * standardization / 0.7 + if reactor_type == "HTGR": + fitted_LR_duration = 0.103719051 * standardization / 0.7 + elif reactor_type == "SFR": + fitted_LR_duration = 0.15*standardization/0.7 duration_multiplier = (1 - fitted_LR_duration) ** np.log2(n_th) return float(duration_multiplier) * float(actual_construction_duration_plus_delay) diff --git a/src/crf/model/pipeline.py b/src/crf/model/pipeline.py index f11e263..10da173 100644 --- a/src/crf/model/pipeline.py +++ b/src/crf/model/pipeline.py @@ -36,11 +36,14 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): N_proc=inp["N_proc"], cons_duration_no_delay=dur_no_delay, ) - final_dur = duration_learning_effect(n_th, inp["standardization_0"], dur_plus_delay) + final_dur = duration_learning_effect(reactor_type=config["reactor_type"], + n_th=n_th, + standardization_0=inp["standardization_0"], actual_construction_duration_plus_delay=dur_plus_delay) # learning on direct costs direct_plus_learning = learning_effect(direct_df, n_th, inp["standardization_0"], power) # indirect costs + print(f"final_dur for plant {n_th}: {final_dur}") with_indirect = update_indirect_cost(n_th, inp["standardization_0"], direct_plus_learning, final_dur, power) # insurance + interest + ITC with_insurance = insurance_cost_update(base0, with_indirect, power) @@ -56,4 +59,6 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): org_occ = float(tot_occ/power) org_tci = float(tot_cap/power) final_df, net_occ, nci = update_itc(with_interest, tot_occ, tot_cap, n_th, inp["ITC_0"], inp["n_ITC"], power) + print(f"Final OCC for plant {n_th}: {net_occ}") + print(final_df) return final_df, org_occ, net_occ, org_tci, nci, float(final_dur) From c3a812bc6c7cc9d60439ff42405eecedd6d2b723 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Mon, 9 Mar 2026 22:04:01 -0500 Subject: [PATCH 10/25] sfrr newbase cost --- src/crf/data/SFR_baseline.csv | 37 +++++++++++++------------- src/crf/model/core_accounts.py | 2 +- src/crf/model/direct_cost.py | 48 +++++++++++++++++++++++++++------- src/crf/model/learning.py | 4 +-- src/crf/model/pipeline.py | 6 ++--- 5 files changed, 62 insertions(+), 35 deletions(-) diff --git a/src/crf/data/SFR_baseline.csv b/src/crf/data/SFR_baseline.csv index 02c0185..35b833e 100644 --- a/src/crf/data/SFR_baseline.csv +++ b/src/crf/data/SFR_baseline.csv @@ -1,25 +1,24 @@ Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labor Cost,Site Material Cost -10,Capitalized Pre-Construction Costs,,,,, -11,Land & Land Rights,11000000,,,, -12,Site Permits,1598890.7521620637,,,, -13,Plant Licensing,24382988.437500004,,,, -14,Plant Permits,12679166.666666668,,,, -15,Plant Studies,12679166.666666668,,,, -16,Plant Reports,3972185.8507187515,,,, -18,Other Pre-Construction Costs,12679166.666666668,,,, +11,Land & Land Rights,15000000,,,, +12,Site Permits,0,,,, +13,Plant Licensing,107009771.98697068,,,, +14,Plant Permits,4721019.352366353,,,, +15,Plant Studies,0,,,, +16,Plant Reports,0,,,, +18,Other Pre-Construction Costs,36194481.701475374,,,, 20,Capitalized Direct Costs,,,,, -21,Structures & Improvements,244678343.4084428,64350800.86,2899406.537,145691461.9,34636080.64 -212,Reactor Containment Building,145108927.86932483,57583605.37,1605670.309,81082169.34,6443153.159 -213,Turbine Room and Heater Bay,9518417.644642511,620963.9748,102399.4736,5280509.165,3616944.505 -211 plus 214 to 219,Othe Structures & Improvements,90050997.89447546,6146231.515084816,1191336.755027398,59328783.39988749,24575982.97950315 -22,Reactor System,661334796.7533815,559862843.6,1809703.515,97213738.57,4258214.548 -23,Energy Conversion System,238844021.25290728,189802947.4112764,874095.8194883969,46348086.09,2692987.752 -232.1,Electricity Generation Systems,197558120.73250762,167738159.3,559708.578,29819961.46,0 -233,Ultimate Heat Sink,41285900.52039969,22064788.14,314387.2415,16528124.63,2692987.752 -24,Electrical Equipment,67451644.91245879,13662940.78,767077.1532,41345408.98,12443295.16 +21,Structures & Improvements,244678343.40844324,64350800.859366685,2899406.5371647766,145691461.9059903,34636080.64308626 +212,Reactor Containment Building,145108927.86932492,57583605.36952757,1605670.308507702,81082169.34102152,6443153.1587758595 +213,Turbine Room and Heater Bay,9518417.644642519,620963.9747542912,102399.47362967294,5280509.165081021,3616944.504807206 +211 plus 214 to 219,Othe Structures & Improvements,90050997.89,6146231.515,1191336.755,59328783.4,24575982.98 +22,Reactor System,661334796.7533823,559862843.6356779,1809703.5146268948,97213738.57005614,4258214.548 +23,Energy Conversion System,238844021.3,189802947.4,874095.8195,46348086.09,2692987.752 +232.1,Electricity Generation Systems,197558120.73250785,167738159.2684312,559708.5779584679,29819961.464076634,0 +233,Ultimate Heat Sink,41285900.520399824,22064788.14284548,314387.24152993015,16528124.625988554,2692987.751565792 +24,Electrical Equipment,67451644.91245897,13662940.77718743,767077.1532008175,41345408.97523445,12443295.160037093 25,Initial fuel inventory,279724434,279724434,,, -26,Miscellaneous Equipment,25655315.754125603,8372438.257,278805.2514,15110212.41,2172665.083 -28,Simulator,78200,78200,,, +26,Miscellaneous Equipment,25655315.75412561,8372438.256640577,278805.25137804874,15110212.41,2172665.0833824323 +28,Simulator,0,0,,, 30,Capitalized Indirect Services Costs,,,,, 31,Factory & Field Indirect Costs,146051996.53692532,,,, 32,Factory and construction supervision,506256124.80995834,,,, diff --git a/src/crf/model/core_accounts.py b/src/crf/model/core_accounts.py index 1e6980e..0cedb8f 100644 --- a/src/crf/model/core_accounts.py +++ b/src/crf/model/core_accounts.py @@ -249,7 +249,7 @@ def _sum_hours(db): sum_old = _sum_hours(db0) sum_new = _sum_hours(db1) lab_delta = (sum_new - sum_old) / sum_old - print(f"in update_cons_duration, sum_old: {sum_old}, sum_new: {sum_new}, lab_delta: {lab_delta}, ref_duration: {ref_duration}") + # print(f"in update_cons_duration, sum_old: {sum_old}, sum_new: {sum_new}, lab_delta: {lab_delta}, ref_duration: {ref_duration}") return float(0.3 * lab_delta * ref_duration + ref_duration) def update_cons_duration_2( diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index 43d2e67..9c8070c 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -77,7 +77,7 @@ def add_BOP_RP_grades( ["Site Material Cost", "Site Labor Cost", "Site Labor Hours", "Factory Equipment Cost"], 0.6 ) - # for 232.1 apply to factory+labor but NOT material (matches your original) + # for 232.1 apply to factory+labor but NOT material mulv( db, ["232.1"], ["Factory Equipment Cost", "Site Labor Cost", "Site Labor Hours"], @@ -87,12 +87,37 @@ def add_BOP_RP_grades( db2 = update_high_level_costs(db, power)[COLS].copy() # duration update from grade change - duration_ref = 125 if reactor_type == "HTGR" else 80 - # duration_ref = 100 if reactor_type == "HTGR" else 64 - new_dur = update_cons_duration(df, db2, duration_ref) - # modularity factor on duration; for n>=2 assume modularized - mod = mod_0 if n_th == 1 else "modularized" - mod_factor = 0.8 if mod == "modularized" else 1.0 + if reactor_type == "HTGR": + ref_duration = 125 + elif reactor_type == "SFR": + ref_duration = 80 + else: + raise ValueError(f"Unknown reactor type: {reactor_type}") + + new_dur = update_cons_duration(df, db2, ref_duration) + + # applying modularity civil construction if HTGR of SFR, for n>=2 assume + # modularized, if other reactor type such as AP1000, we assume no modularity + # effect on duration for now. + if reactor_type in ["HTGR", "SFR"]: + mod = mod_0 if n_th == 1 else "modularized" + elif reactor_type == "AP1000": + mod = mod_0 if n_th == 1 else "non_modularized" + + mod_factor = 0.8 if mod == "modularized" else 1.0 # NOTE see excel Relationship sheet D4 + # NOTE in excel tool HTGR does nothing here, SFR factory cost of 21 with the 20% reduction + # with AP1000 all labor cost with the 20% reduction in 20s accounts, + if reactor_type == "SFR": + for acct in [21, "211 plus 214 to 219", 212, 213]: + setv(db2, acct, "Factory Equipment Cost", float(getv(db2, acct, "Factory Equipment Cost")) * mod_factor) + db2 = update_high_level_costs(db2, power)[COLS].copy() + elif reactor_type == "AP1000": + for acct in [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26]: + setv(db2, acct, "Site Labor Cost", float(getv(db2, acct, "Site Labor Cost")) * mod_factor) + setv(db2, acct, "Site Labor Hours", float(getv(db2, acct, "Site Labor Hours")) * mod_factor) + db2 = update_high_level_costs(db2, power)[COLS].copy() + + return db2, new_dur * mod_factor @@ -185,19 +210,22 @@ def update_direct_cost( mod_0: str ): reactor_df, power = store.get_baseline(reactor_type) - db, baseline_lab_hours = add_factory_cost(reactor_df, power, f_22, f_2321, num_orders) + db, baseline_lab_hours = add_factory_cost(reactor_df, power, f_22, f_2321, num_orders) # all Total Cost (USD) columns should be 0 # when accounts are in 20s db.loc[db["Account"].isin(['21', '211 plus 214 to 219', '212', '213', '22', '23', '232.1', '233', '24', '25', '26']), "Total Cost (USD)"] = 0.0 db = update_high_level_costs(db, power)[COLS].copy() db = add_land_cost(db, land_cost_per_acre_0, power) db, prev_dur = add_BOP_RP_grades(db, RB_grade_0, BOP_grade_0, power, reactor_type, n_th, mod_0) - print(f"Duration after add_BOP_RP_grades for plant {n_th}: {prev_dur}") + # print(f"Duration after add_BOP_RP_grades for plant {n_th}: {prev_dur}") db = add_bulk_ordering(db, num_orders, f_22, f_2321, power) + if n_th == 1: + print('after add_bulk_ordering for plant 1:') + print(db) db, dur_no_delay = add_reworking_productivity( db, reactor_type, n_th, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, power, prev_dur, baseline_lab_hours ) - print(f"Duration after add_reworking_productivity for plant {n_th}: {dur_no_delay}") + # print(f"Duration after add_reworking_productivity for plant {n_th}: {dur_no_delay}") return db, dur_no_delay diff --git a/src/crf/model/learning.py b/src/crf/model/learning.py index 40d65c9..726ffa7 100644 --- a/src/crf/model/learning.py +++ b/src/crf/model/learning.py @@ -87,8 +87,8 @@ def act_cons_duration_plus_delay( T_end = max(T_21, T_22, T_23, T_24, T_25, T_26) supply_chain_delay = max(T_end - ref_construction_duration, 0) - print(f"T_end for plant {n_th}: {T_end}, ref_construction_duration: {ref_construction_duration}, supply_chain_delay: {supply_chain_delay}") - print(f"cons_duration_no_delay for plant {n_th}: {cons_duration_no_delay}") + # print(f"T_end for plant {n_th}: {T_end}, ref_construction_duration: {ref_construction_duration}, supply_chain_delay: {supply_chain_delay}") + # print(f"cons_duration_no_delay for plant {n_th}: {cons_duration_no_delay}") return float(cons_duration_no_delay) + float(supply_chain_delay) def duration_learning_effect(reactor_type: str, diff --git a/src/crf/model/pipeline.py b/src/crf/model/pipeline.py index 10da173..a2e38ce 100644 --- a/src/crf/model/pipeline.py +++ b/src/crf/model/pipeline.py @@ -43,7 +43,7 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): # learning on direct costs direct_plus_learning = learning_effect(direct_df, n_th, inp["standardization_0"], power) # indirect costs - print(f"final_dur for plant {n_th}: {final_dur}") + # print(f"final_dur for plant {n_th}: {final_dur}") with_indirect = update_indirect_cost(n_th, inp["standardization_0"], direct_plus_learning, final_dur, power) # insurance + interest + ITC with_insurance = insurance_cost_update(base0, with_indirect, power) @@ -59,6 +59,6 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): org_occ = float(tot_occ/power) org_tci = float(tot_cap/power) final_df, net_occ, nci = update_itc(with_interest, tot_occ, tot_cap, n_th, inp["ITC_0"], inp["n_ITC"], power) - print(f"Final OCC for plant {n_th}: {net_occ}") - print(final_df) + # print(f"Final OCC for plant {n_th}: {net_occ}") + # print(final_df) return final_df, org_occ, net_occ, org_tci, nci, float(final_dur) From 24d33c7847e500049bd3d3448f274947005d95ba Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Tue, 7 Apr 2026 21:20:34 -0500 Subject: [PATCH 11/25] bulk ordering issue --- src/crf/model/direct_cost.py | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index 9c8070c..5fbee0b 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -121,28 +121,35 @@ def add_BOP_RP_grades( return db2, new_dur * mod_factor -def add_bulk_ordering(df: pd.DataFrame, num_orders: int, f_22: float, f_2321: float, power: float): +def add_bulk_ordering(df: pd.DataFrame, num_orders: int, f_22: float, f_2321: float, power: float, reactor_type: str): """ Applies average learning reduction to factory equipment cost of 22 and 232.1, but keeps factory-building portion intact. """ db = df.copy() - - lr22 = 0.1802341659291420 - lr2321 = 0.2607462372040820 + if reactor_type == "HTGR": + lr22 = 0.1802341659291420 + lr2321 = 0.2607462372040820 + elif reactor_type == "SFR": + lr22 = 0.209920296472118 + lr2321 = 0.222118386536136 + else: + raise ValueError(f"Unknown reactor type: {reactor_type}") red22 = 0.0 red2321 = 0.0 for ith in range(1, num_orders + 1): red22 += ((1 - lr22) ** np.log2(ith)) / num_orders red2321 += ((1 - lr2321) ** np.log2(ith)) / num_orders - + print(f"red22: {red22}, red2321: {red2321}") old22 = float(getv(df, 22, "Factory Equipment Cost")) new22 = red22 * (old22 - (f_22 / num_orders)) + (f_22 / num_orders) + print(f"old22: {old22}, new22: {new22}") setv(db, 22, "Factory Equipment Cost", new22) old2321 = float(getv(df, "232.1", "Factory Equipment Cost")) new2321 = red2321 * (old2321 - (f_2321 / num_orders)) + (f_2321 / num_orders) + print(f"old2321: {old2321}, new2321: {new2321}") setv(db, "232.1", "Factory Equipment Cost", new2321) return update_high_level_costs(db, power)[COLS].copy() @@ -218,7 +225,10 @@ def update_direct_cost( db = add_land_cost(db, land_cost_per_acre_0, power) db, prev_dur = add_BOP_RP_grades(db, RB_grade_0, BOP_grade_0, power, reactor_type, n_th, mod_0) # print(f"Duration after add_BOP_RP_grades for plant {n_th}: {prev_dur}") - db = add_bulk_ordering(db, num_orders, f_22, f_2321, power) + if n_th == 1: + print('after add_BOP_RP_grades for plant 1:') + print(db) + db = add_bulk_ordering(db, num_orders, f_22, f_2321, power, reactor_type) if n_th == 1: print('after add_bulk_ordering for plant 1:') print(db) From a9511259c1298012104196950d36a9bda1729d7d Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Tue, 21 Apr 2026 00:13:43 -0500 Subject: [PATCH 12/25] test pass for SFR and HTGR --- src/crf/api.py | 45 -------------- ...ding_curve.csv => HTGR_spending_curve.csv} | 0 src/crf/data/SFR_spending_curve.csv | 46 ++++++++++++++ src/crf/io/excel_inputs.py | 10 ++- src/crf/model/direct_cost.py | 16 ++--- src/crf/model/finance.py | 61 +++++++++++-------- src/crf/model/learning.py | 57 ++++++++++++----- src/crf/model/pipeline.py | 20 +++--- 8 files changed, 151 insertions(+), 104 deletions(-) rename src/crf/data/{ref_spending_curve.csv => HTGR_spending_curve.csv} (100%) create mode 100644 src/crf/data/SFR_spending_curve.csv diff --git a/src/crf/api.py b/src/crf/api.py index c08bca0..62b09e6 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -113,48 +113,3 @@ def run_sampling_from_excel( result = run_avg_all_units(config=config, inp=inp, store=store, details=False) static_vals = static_row_from_levers(levers) return {**static_vals, **result} - - -def run_sampling_from_excel( - config: dict, - levers_xlsx: str, - n_samples: int, - out_csv: str, - out_pkl: str, - seed: int | None = None -): - levers_df = read_levers_sheet(levers_xlsx, sheet_name="Levers") - levers_df = attach_internal_ids(levers_df) - - samples = sample_levers(n_samples, levers_df, seed=seed) # shape (n_levers, n_samples) - - # headers: build from max num_orders in sampled set - max_orders = int(np.max(samples[0, :])) - - # NOTE if n_itc is not 0 we might need to change this into NETOCC and NCI for the first n_itc units and OCC/TCI for the rest. - headers = ( - STATIC_KEYS - + [f"OCC_{i}" for i in range(max_orders)] - + [f"TCI_{i}" for i in range(max_orders)] - + [f"duration_{i}" for i in range(max_orders)] - + [ - "cons_duration_cumulative_wz_startup", - "occLastUnit", "TCILastUnit", "durationsLastUnit", - "avg_OCC", "avg_TCI", "avg_duration", - ] - ) - - with open(out_csv, "w", newline="", encoding="utf-8") as csv_f, open(out_pkl, "wb") as pkl_f: - writer = csv.DictWriter(csv_f, fieldnames=headers) - writer.writeheader() - - for i in range(n_samples): - # levers = unpack_sample_column(samples[:, i]) - # row = run_one_scenario(config, levers) - - - levers_raw = sample_column_to_levers(samples, levers_df, i) - row = run_one_scenario(config, levers_raw) - - write_csv_row(writer, row, headers) - stream_pickle_dump(row, pkl_f) diff --git a/src/crf/data/ref_spending_curve.csv b/src/crf/data/HTGR_spending_curve.csv similarity index 100% rename from src/crf/data/ref_spending_curve.csv rename to src/crf/data/HTGR_spending_curve.csv diff --git a/src/crf/data/SFR_spending_curve.csv b/src/crf/data/SFR_spending_curve.csv new file mode 100644 index 0000000..f495db8 --- /dev/null +++ b/src/crf/data/SFR_spending_curve.csv @@ -0,0 +1,46 @@ +Month,CDF +0,0.0139638 +1,0.0279276 +2,0.054072485 +3,0.080217371 +4,0.133639319 +5,0.203627123 +6,0.257052414 +7,0.300121505 +8,0.323522402 +9,0.354574934 +10,0.389812145 +11,0.427499507 +12,0.464806606 +13,0.495151681 +14,0.539252457 +15,0.573541602 +16,0.612584914 +17,0.668325288 +18,0.712828292 +19,0.759451041 +20,0.805723217 +21,0.852461978 +22,0.874629352 +23,0.884843919 +24,0.8951548 +25,0.902410751 +26,0.909666702 +27,0.916890557 +28,0.923990819 +29,0.929310828 +30,0.934630837 +31,0.939806437 +32,0.944587939 +33,0.950118026 +34,0.955419761 +35,0.960746571 +36,0.965938242 +37,0.971106121 +38,0.976215384 +39,0.980960386 +40,0.985705388 +41,0.989913198 +42,0.993986339 +43,0.997050336 +44,1 \ No newline at end of file diff --git a/src/crf/io/excel_inputs.py b/src/crf/io/excel_inputs.py index 7ba59db..277d7ff 100644 --- a/src/crf/io/excel_inputs.py +++ b/src/crf/io/excel_inputs.py @@ -46,10 +46,16 @@ def get_baseline(self, reactor_type: str): self._baseline[reactor_type] = (df, power) return df.copy(), power - def get_spending_curve(self): + def get_spending_curve(self,reactor_type: str): + if self._spending is not None: return self._spending - sp_path = path = self.data_dir / "ref_spending_curve.csv" + if reactor_type == "HTGR": + sp_path = self.data_dir / "HTGR_spending_curve.csv" + elif reactor_type == "SFR": + sp_path = self.data_dir / "SFR_spending_curve.csv" + else: + raise ValueError(f"Unknown reactor_type: {reactor_type}") sp = pd.read_csv(sp_path) if "Month" not in sp.columns or "CDF" not in sp.columns: raise ValueError(f"{sp_path} must contain Month and CDF") diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index 5fbee0b..656751e 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -141,15 +141,12 @@ def add_bulk_ordering(df: pd.DataFrame, num_orders: int, f_22: float, f_2321: fl for ith in range(1, num_orders + 1): red22 += ((1 - lr22) ** np.log2(ith)) / num_orders red2321 += ((1 - lr2321) ** np.log2(ith)) / num_orders - print(f"red22: {red22}, red2321: {red2321}") old22 = float(getv(df, 22, "Factory Equipment Cost")) new22 = red22 * (old22 - (f_22 / num_orders)) + (f_22 / num_orders) - print(f"old22: {old22}, new22: {new22}") setv(db, 22, "Factory Equipment Cost", new22) old2321 = float(getv(df, "232.1", "Factory Equipment Cost")) new2321 = red2321 * (old2321 - (f_2321 / num_orders)) + (f_2321 / num_orders) - print(f"old2321: {old2321}, new2321: {new2321}") setv(db, "232.1", "Factory Equipment Cost", new2321) return update_high_level_costs(db, power)[COLS].copy() @@ -224,18 +221,17 @@ def update_direct_cost( db = update_high_level_costs(db, power)[COLS].copy() db = add_land_cost(db, land_cost_per_acre_0, power) db, prev_dur = add_BOP_RP_grades(db, RB_grade_0, BOP_grade_0, power, reactor_type, n_th, mod_0) - # print(f"Duration after add_BOP_RP_grades for plant {n_th}: {prev_dur}") - if n_th == 1: - print('after add_BOP_RP_grades for plant 1:') - print(db) db = add_bulk_ordering(db, num_orders, f_22, f_2321, power, reactor_type) - if n_th == 1: - print('after add_bulk_ordering for plant 1:') - print(db) + # if n_th == 1: + # print('after add_bulk_ordering for plant 1:') + # print(db) db, dur_no_delay = add_reworking_productivity( db, reactor_type, n_th, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, power, prev_dur, baseline_lab_hours ) + # if n_th == 1: + # print('after add_reworking_productivity for plant 1:') + # print(db) # print(f"Duration after add_reworking_productivity for plant {n_th}: {dur_no_delay}") return db, dur_no_delay diff --git a/src/crf/model/finance.py b/src/crf/model/finance.py index fae90ff..14d259d 100644 --- a/src/crf/model/finance.py +++ b/src/crf/model/finance.py @@ -12,33 +12,36 @@ "Site Labor Cost", "Site Material Cost" ] - -def insurance_cost_update(base_df, updated_df, power): +def tax_update(df: pd.DataFrame, power: float): + # Tax is Property tax rate (1.25%) * land cost( total cost of Account11) # Ensure derived rows (subtotals/totals) exist and are up-to-date - base_df = update_high_level_costs(base_df.copy(), power) - updated_df = update_high_level_costs(updated_df.copy(), power) - - ref_20 = float(base_df.loc[base_df["Title"].eq("20s - Subtotal"), "Total Cost (USD)"].iloc[0]) - ref_30 = float(base_df.loc[base_df["Title"].eq("30s - Subtotal"), "Total Cost (USD)"].iloc[0]) - - new_20 = float(updated_df.loc[updated_df["Title"].eq("20s - Subtotal"), "Total Cost (USD)"].iloc[0]) - new_30 = float(updated_df.loc[updated_df["Title"].eq("30s - Subtotal"), "Total Cost (USD)"].iloc[0]) - - denom = ref_20 + ref_30 - change_factor = 1.0 if denom == 0 else (new_20 + new_30) / denom - + df = update_high_level_costs(df.copy(), power) + land_cost = float(df.loc[df["Account"].eq("11"), "Total Cost (USD)"].iloc[0]) + tax = 0.0125 * land_cost + setv(df, "51", "Total Cost (USD)", tax) + # Recompute derived totals after change + df = update_high_level_costs(df, power) + return df + +def insurance_cost_update(df, power): + # Insurance rate * sum of direct and indirect costs 0.45% + df = update_high_level_costs(df.copy(), power) + new_20 = float(df.loc[df["Title"].eq("20s - Subtotal"), "Total Cost (USD)"].iloc[0]) + new_30 = float(df.loc[df["Title"].eq("30s - Subtotal"), "Total Cost (USD)"].iloc[0]) + new_52 = 0.0045 * (new_20 + new_30) # Update Account 52 (Insurance) - mask52 = updated_df["Account"].astype(str).str.strip().eq("52") - if not mask52.any(): - raise KeyError("Account 52 (Insurance) not found in dataframe.") - ins0 = float(np.nan_to_num(updated_df.loc[mask52, "Total Cost (USD)"].iloc[0], nan=0.0)) - updated_df.loc[mask52, "Total Cost (USD)"] = ins0 * change_factor - + setv(df, "52", "Total Cost (USD)", new_52) # Recompute derived totals after change - updated_df = update_high_level_costs(updated_df, power) - return updated_df - + df = update_high_level_costs(df, power) + return df +def decomission_cost_update(df, power): + # Decommission cost is calculated as $7.71/kWe per year for decommissioning costs + # power*$7.71/kWe + decomm_cost = power * 7.71 + setv(df, "54", "Total Cost (USD)", decomm_cost) + df = update_high_level_costs(df, power) + return df def update_interest_cost( store, @@ -47,9 +50,10 @@ def update_interest_cost( interest_rate: float, startup_0: float, n_th: int, - power: float + power: float, + reactor_type: str ): - Months, CDFs = store.get_spending_curve() + Months, CDFs = store.get_spending_curve(reactor_type) dur = float(final_construction_duration) n_years = int(dur / 12) @@ -59,8 +63,13 @@ def update_interest_cost( annual_periods = np.linspace(12, 12 * n_years, n_years) if max(annual_periods) < int(dur) - 1: annual_periods = np.append(annual_periods, dur - 1) + if reactor_type == "HTGR": + new_period = 103 * annual_periods / dur + elif reactor_type == "SFR": + new_period = 44 * annual_periods / dur + else: + raise ValueError(f"Unknown reactor type: {reactor_type}") - new_period = 103 * annual_periods / dur annual_cum_spend = np.interp(new_period, Months, CDFs) annual_spend = np.append(annual_cum_spend[0], np.diff(annual_cum_spend)) diff --git a/src/crf/model/learning.py b/src/crf/model/learning.py index 726ffa7..b45c184 100644 --- a/src/crf/model/learning.py +++ b/src/crf/model/learning.py @@ -19,21 +19,52 @@ ACCT_DIRECT = [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26] -def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power: float): +def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power: float, reactor_type: str): # standardization cap for FOAK standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 # fitted learning rates (same order as ACCT_DIRECT) - mat_lr = np.array([ - 0.099588665391, 0.099588665391, 0.099588665391, 0.080817992281, - 0.0, 0.099588665391, 0.099588665391, 0.099588665391 - ]) * standardization / 0.7 - - lab_lr = np.array([ - 0.180678729399, 0.180678729399, 0.180678729399, 0.146555539499, - 0.137148574884, 0.180678729399, 0.180678729399, 0.180678729399 - ]) * standardization / 0.7 - + # n_noak is the number of units after which the learning effect is fully realized, which is set to 8 based on expert elicitation. The learning rate is then calculated as 1-(1-std_learning_rate)^(1/log2(n_noak)). + # std_learning_rate for HTGR: + # Fac Mat Lab Cost Lab Hrs + # 21 0.96 0.73 0.55 0.55 + # 22 0.55 0.78 0.62 0.62 + # 232.1 0.40 0.64 0.64 + # 233 0.96 0.73 0.55 0.55 + # 24 0.96 0.73 0.55 0.55 + # 26 0.96 0.73 0.55 0.55 + # std_learning_rate for SFR : + # Fac Mat Lab Cost Lab Hrs + # 21 0.96 0.73 0.55 0.55 + # 22 0.49 0.73 0.55 0.55 + # 232.1 0.47 0.55 0.55 + # 233 0.96 0.73 0.55 0.55 + # 24 0.96 0.73 0.55 0.55 + # 26 0.96 0.73 0.55 0.55 + + # However, learning is not applied to the factory cost + if reactor_type == "HTGR": + mat_lr = np.array([ + 0.099588665391, 0.099588665391, 0.099588665391, 0.080817992281, + 0.0, 0.099588665391, 0.099588665391, 0.099588665391 + ]) * standardization / 0.7 + + lab_lr = np.array([ + 0.180678729399, 0.180678729399, 0.180678729399, 0.146555539499, + 0.137148574884, 0.180678729399, 0.180678729399, 0.180678729399 + ]) * standardization / 0.7 + elif reactor_type == "SFR": + mat_lr = np.array([ + 0.099588665391, 0.099588665391, 0.099588665391, 0.099588665391, + 0.099588665391, 0.099588665391, 0.099588665391, 0.099588665391 + ]) * standardization / 0.7 + + lab_lr = np.array([ + 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399, + 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399 + ]) * standardization / 0.7 + else: + raise ValueError(f"Unknown reactor type: {reactor_type}") db = df.copy() for idx, acct in enumerate(ACCT_DIRECT): @@ -86,9 +117,7 @@ def act_cons_duration_plus_delay( T_26 = 0.21 * (B_21 + D) + B_26 + D T_end = max(T_21, T_22, T_23, T_24, T_25, T_26) - supply_chain_delay = max(T_end - ref_construction_duration, 0) - # print(f"T_end for plant {n_th}: {T_end}, ref_construction_duration: {ref_construction_duration}, supply_chain_delay: {supply_chain_delay}") - # print(f"cons_duration_no_delay for plant {n_th}: {cons_duration_no_delay}") + supply_chain_delay = max(T_end - ref_construction_duration, 0) return float(cons_duration_no_delay) + float(supply_chain_delay) def duration_learning_effect(reactor_type: str, diff --git a/src/crf/model/pipeline.py b/src/crf/model/pipeline.py index a2e38ce..2856d24 100644 --- a/src/crf/model/pipeline.py +++ b/src/crf/model/pipeline.py @@ -1,7 +1,7 @@ from .direct_cost import update_direct_cost from .learning import learning_effect, act_cons_duration_plus_delay, duration_learning_effect from .indirect_cost import update_indirect_cost -from .finance import insurance_cost_update, update_interest_cost, update_itc +from .finance import tax_update, insurance_cost_update, decomission_cost_update, update_interest_cost, update_itc def calculate_final_result(config: dict, inp: dict, store, n_th: int): """ @@ -39,23 +39,29 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): final_dur = duration_learning_effect(reactor_type=config["reactor_type"], n_th=n_th, standardization_0=inp["standardization_0"], actual_construction_duration_plus_delay=dur_plus_delay) - + # learning on direct costs - direct_plus_learning = learning_effect(direct_df, n_th, inp["standardization_0"], power) + direct_plus_learning = learning_effect(direct_df, n_th, inp["standardization_0"], power, config["reactor_type"]) # indirect costs - # print(f"final_dur for plant {n_th}: {final_dur}") with_indirect = update_indirect_cost(n_th, inp["standardization_0"], direct_plus_learning, final_dur, power) - # insurance + interest + ITC - with_insurance = insurance_cost_update(base0, with_indirect, power) + + # Supplementary costs: tax, insurance, decommissioning + with_tax = tax_update(with_indirect, power) + with_insurance = insurance_cost_update(with_tax, power) + with_decomm = decomission_cost_update(with_insurance, power) + + # Finance costs: interest during construction and ITC with_interest, tot_occ, tot_cap = update_interest_cost( store=store, - df=with_insurance, + df=with_decomm, final_construction_duration=final_dur, interest_rate=inp["interest_rate_0"], startup_0=config["startup_0"], n_th=n_th, power=power, + reactor_type=config["reactor_type"], ) + org_occ = float(tot_occ/power) org_tci = float(tot_cap/power) final_df, net_occ, nci = update_itc(with_interest, tot_occ, tot_cap, n_th, inp["ITC_0"], inp["n_ITC"], power) From 9bab46100d8b6a709dc93ae28c4bcabbf9f5af94 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Tue, 28 Apr 2026 21:54:27 -0500 Subject: [PATCH 13/25] ap1000 --- src/crf/api.py | 1 + src/crf/data/AP1000_baseline.csv | 33 +++++ src/crf/data/AP1000_spending_curve.csv | 182 +++++++++++++++++++++++++ src/crf/data/SFR_baselineold.csv | 34 ----- src/crf/io/excel_inputs.py | 6 +- src/crf/model/direct_cost.py | 73 ++++++---- src/crf/model/finance.py | 2 + src/crf/model/learning.py | 52 ++++++- src/crf/model/pipeline.py | 18 ++- test/test_crf_smoke.py | 9 +- 10 files changed, 336 insertions(+), 74 deletions(-) create mode 100644 src/crf/data/AP1000_baseline.csv create mode 100644 src/crf/data/AP1000_spending_curve.csv delete mode 100644 src/crf/data/SFR_baselineold.csv diff --git a/src/crf/api.py b/src/crf/api.py index 62b09e6..e9da511 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -39,6 +39,7 @@ def label01(x, zero_label, one_label): "mod_0": label01(levers["modularity_code"], "stick_built", "modularized"), "BOP_grade_0": label01(levers["bop_grade_code"], "nuclear", "non_nuclear"), "RB_grade_0": label01(levers["rb_grade_code"], "nuclear", "non_nuclear"), + "num_NOAK": int(levers["num_NOAK"]) if "num_NOAK" in levers else int(levers["num_orders"]), } diff --git a/src/crf/data/AP1000_baseline.csv b/src/crf/data/AP1000_baseline.csv new file mode 100644 index 0000000..3d853fd --- /dev/null +++ b/src/crf/data/AP1000_baseline.csv @@ -0,0 +1,33 @@ +Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labor Cost,Site Material Cost +11,Land & Land Rights,2661073.013,,,, +12,Site Permits,0,,,, +13,Plant Licensing,190950077.8,,,, +14,Plant Permits,20588224.42,,,, +15,Plant Studies,0,,,, +16,Plant Reports,0,,,, +18,Other Pre-Construction Costs,64317423.74,,,, +20,Capitalized Direct Costs,,,,, +21,Structures & Improvements,2299592854,397948921.5,14439901.53,1344901039,556742893.1 +212,Reactor Containment Building,1528400790,336543685,9614706.196,890022087,301835018 +213,Turbine Room and Heater Bay,273978061.2,13922205.6,1517151.703,149468165.1,110587690.5 +211 plus 214 to 219,Othe Structures & Improvements,497214002.6,47483030.84,3308043.631,305410787.2,144320184.6 +22,Reactor System,2518251443,1854771979,5424586.039,555667192,107812271.9 +23,Energy Conversion System,2567313431,1684416189,7300778.65,787073972.1,95823269.71 +232.1,Electricity Generation Systems,1077610861,993296983.1,860789.0281,84313877.55,0 +233,Ultimate Heat Sink,1489702571,691119206.4,6439989.622,702760094.5,95823269.71 +24,Electrical Equipment,513769681.9,106553828.6,13186135.76,320736445.3,86479408.03 +25,Initial fuel inventory,0,0,0,0,0 +26,Miscellaneous Equipment,465102277.3,139059803.8,2797859.863,288148998.7,37893474.82 +28,Simulator,0,0,,, +30,Capitalized Indirect Services Costs,,,,, +31,Factory & Field Indirect Costs,3530991924,,,, +32,Factory and construction supervision,2576942755,,,, +33,Start-Up Costs,108412136.4,,,, +34,Shipping & Transportation Costs,9128423.298,,,, +35,Engineering Services,696228163.3,,,, +50,Capitalized Supplementary Costs,0,,,, +51,Taxes,33263.41267,,,, +52,Insurance,124634766.9,,,, +54,Decommissioning ,17224140,,,, +60,Capitalized Financial Costs,0,,,, +62,Interest ,7519443674.031699,,,, \ No newline at end of file diff --git a/src/crf/data/AP1000_spending_curve.csv b/src/crf/data/AP1000_spending_curve.csv new file mode 100644 index 0000000..cc0d853 --- /dev/null +++ b/src/crf/data/AP1000_spending_curve.csv @@ -0,0 +1,182 @@ +Month,CDF +0,0 +1,7.61524E-05 +2,0.000304586 +3,0.000685233 +4,0.001217975 +5,0.001902651 +6,0.002739052 +7,0.003726924 +8,0.004865966 +9,0.00615583 +10,0.007596123 +11,0.009186408 +12,0.0109262 +13,0.012814968 +14,0.014852137 +15,0.017037087 +16,0.019369152 +17,0.021847622 +18,0.024471742 +19,0.027240712 +20,0.03015369 +21,0.033209787 +22,0.036408073 +23,0.039747573 +24,0.043227271 +25,0.046846106 +26,0.050602977 +27,0.054496738 +28,0.058526204 +29,0.062690146 +30,0.066987298 +31,0.07141635 +32,0.075975952 +33,0.080664716 +34,0.085481214 +35,0.090423978 +36,0.095491503 +37,0.100682245 +38,0.105994623 +39,0.111427019 +40,0.116977778 +41,0.12264521 +42,0.128427587 +43,0.134323149 +44,0.1403301 +45,0.146446609 +46,0.152670815 +47,0.15900082 +48,0.165434697 +49,0.171970486 +50,0.178606195 +51,0.185339804 +52,0.192169262 +53,0.199092488 +54,0.206107374 +55,0.213211782 +56,0.220403548 +57,0.227680482 +58,0.235040368 +59,0.242480963 +60,0.25 +61,0.25759519 +62,0.265264219 +63,0.27300475 +64,0.280814427 +65,0.288690869 +66,0.296631678 +67,0.304634436 +68,0.312696703 +69,0.320816025 +70,0.328989928 +71,0.337215923 +72,0.345491503 +73,0.353814148 +74,0.362181322 +75,0.370590477 +76,0.379039052 +77,0.387524473 +78,0.396044155 +79,0.404595502 +80,0.413175911 +81,0.421782767 +82,0.43041345 +83,0.439065328 +84,0.447735768 +85,0.456422129 +86,0.465121763 +87,0.473832022 +88,0.482550252 +89,0.491273797 +90,0.5 +91,0.508726203 +92,0.517449748 +93,0.526167978 +94,0.534878237 +95,0.543577871 +96,0.552264232 +97,0.560934672 +98,0.56958655 +99,0.578217233 +100,0.586824089 +101,0.595404498 +102,0.603955845 +103,0.612475527 +104,0.620960948 +105,0.629409523 +106,0.637818678 +107,0.646185852 +108,0.654508497 +109,0.662784077 +110,0.671010072 +111,0.679183975 +112,0.687303297 +113,0.695365564 +114,0.703368322 +115,0.711309131 +116,0.719185573 +117,0.72699525 +118,0.734735781 +119,0.74240481 +120,0.75 +121,0.757519037 +122,0.764959632 +123,0.772319518 +124,0.779596452 +125,0.786788218 +126,0.793892626 +127,0.800907512 +128,0.807830738 +129,0.814660196 +130,0.821393805 +131,0.828029514 +132,0.834565303 +133,0.84099918 +134,0.847329185 +135,0.853553391 +136,0.8596699 +137,0.865676851 +138,0.871572413 +139,0.87735479 +140,0.883022222 +141,0.888572981 +142,0.894005377 +143,0.899317755 +144,0.904508497 +145,0.909576022 +146,0.914518786 +147,0.919335284 +148,0.924024048 +149,0.92858365 +150,0.933012702 +151,0.937309854 +152,0.941473796 +153,0.945503262 +154,0.949397023 +155,0.953153894 +156,0.956772729 +157,0.960252427 +158,0.963591927 +159,0.966790213 +160,0.96984631 +161,0.972759288 +162,0.975528258 +163,0.978152378 +164,0.980630848 +165,0.982962913 +166,0.985147863 +167,0.987185032 +168,0.9890738 +169,0.990813592 +170,0.992403877 +171,0.99384417 +172,0.995134034 +173,0.996273076 +174,0.997260948 +175,0.998097349 +176,0.998782025 +177,0.999314767 +178,0.999695414 +179,0.999923848 +180,1 \ No newline at end of file diff --git a/src/crf/data/SFR_baselineold.csv b/src/crf/data/SFR_baselineold.csv deleted file mode 100644 index 02c0185..0000000 --- a/src/crf/data/SFR_baselineold.csv +++ /dev/null @@ -1,34 +0,0 @@ -Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labor Cost,Site Material Cost -10,Capitalized Pre-Construction Costs,,,,, -11,Land & Land Rights,11000000,,,, -12,Site Permits,1598890.7521620637,,,, -13,Plant Licensing,24382988.437500004,,,, -14,Plant Permits,12679166.666666668,,,, -15,Plant Studies,12679166.666666668,,,, -16,Plant Reports,3972185.8507187515,,,, -18,Other Pre-Construction Costs,12679166.666666668,,,, -20,Capitalized Direct Costs,,,,, -21,Structures & Improvements,244678343.4084428,64350800.86,2899406.537,145691461.9,34636080.64 -212,Reactor Containment Building,145108927.86932483,57583605.37,1605670.309,81082169.34,6443153.159 -213,Turbine Room and Heater Bay,9518417.644642511,620963.9748,102399.4736,5280509.165,3616944.505 -211 plus 214 to 219,Othe Structures & Improvements,90050997.89447546,6146231.515084816,1191336.755027398,59328783.39988749,24575982.97950315 -22,Reactor System,661334796.7533815,559862843.6,1809703.515,97213738.57,4258214.548 -23,Energy Conversion System,238844021.25290728,189802947.4112764,874095.8194883969,46348086.09,2692987.752 -232.1,Electricity Generation Systems,197558120.73250762,167738159.3,559708.578,29819961.46,0 -233,Ultimate Heat Sink,41285900.52039969,22064788.14,314387.2415,16528124.63,2692987.752 -24,Electrical Equipment,67451644.91245879,13662940.78,767077.1532,41345408.98,12443295.16 -25,Initial fuel inventory,279724434,279724434,,, -26,Miscellaneous Equipment,25655315.754125603,8372438.257,278805.2514,15110212.41,2172665.083 -28,Simulator,78200,78200,,, -30,Capitalized Indirect Services Costs,,,,, -31,Factory & Field Indirect Costs,146051996.53692532,,,, -32,Factory and construction supervision,506256124.80995834,,,, -33,Start-Up Costs,21298225.546122435,,,, -34,Shipping & Transportation Costs,1793334.4599472817,,,, -35,Engineering Services,136778270.01496467,,,, -50,Capitalized Supplementary Costs,,,,, -51,Taxes,3510602.4683867483,,,, -52,Insurance,10484751.183521552,,,, -54,Decommissioning ,14606673.443824586,,,, -60,Capitalized Financial Costs,,,,, -62,Interest ,592300799.7656411,,,, \ No newline at end of file diff --git a/src/crf/io/excel_inputs.py b/src/crf/io/excel_inputs.py index 277d7ff..0aaf785 100644 --- a/src/crf/io/excel_inputs.py +++ b/src/crf/io/excel_inputs.py @@ -27,12 +27,14 @@ def get_baseline(self, reactor_type: str): df, power = self._baseline[reactor_type] return df.copy(), power if reactor_type == "HTGR": - # path = f"{self.data_dir}/HTGR_baseline.csv" path = self.data_dir / "HTGR_baseline.csv" power = 1056 * 1000 elif reactor_type == "SFR": path = self.data_dir / "SFR_baseline.csv" power = 310.8 * 1000 + elif reactor_type == "AP1000": + path = self.data_dir / "AP1000_baseline.csv" + power = 2234 * 1000 else: raise ValueError(f"Unknown reactor_type: {reactor_type}") # print current running path for debugging @@ -54,6 +56,8 @@ def get_spending_curve(self,reactor_type: str): sp_path = self.data_dir / "HTGR_spending_curve.csv" elif reactor_type == "SFR": sp_path = self.data_dir / "SFR_spending_curve.csv" + elif reactor_type == "AP1000": + sp_path = self.data_dir / "AP1000_spending_curve.csv" else: raise ValueError(f"Unknown reactor_type: {reactor_type}") sp = pd.read_csv(sp_path) diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index 656751e..afbb3b0 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -25,15 +25,15 @@ ACCT_DIRECT = [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26] -def add_factory_cost(df: pd.DataFrame, power: float, f_22: float, f_2321: float, num_orders: int): +def add_factory_cost(df: pd.DataFrame, power: float, f_22: float, f_2321: float, num_orders: int, reactor_type: str): db = df.copy() + if reactor_type in ["HTGR", "SFR"]: + # Add factory-building shares; clear old values first (keep numeric dtype) + setv(db, 22, "Factory Equipment Cost", np.nan) + setv(db, "232.1", "Factory Equipment Cost", np.nan) - # Add factory-building shares; clear old values first (keep numeric dtype) - setv(db, 22, "Factory Equipment Cost", np.nan) - setv(db, "232.1", "Factory Equipment Cost", np.nan) - - setv(db, 22, "Factory Equipment Cost", float(getv(df, 22, "Factory Equipment Cost")) + f_22 / num_orders) - setv(db, "232.1", "Factory Equipment Cost", float(getv(df, "232.1", "Factory Equipment Cost")) + f_2321 / num_orders) + setv(db, 22, "Factory Equipment Cost", float(getv(df, 22, "Factory Equipment Cost")) + f_22 / num_orders) + setv(db, "232.1", "Factory Equipment Cost", float(getv(df, "232.1", "Factory Equipment Cost")) + f_2321 / num_orders) db = update_high_level_costs(db, power)[COLS].copy() baseline_lab_hours = sum_lab_hrs(db) @@ -60,8 +60,11 @@ def add_BOP_RP_grades( ): # RB grade does not change; BOP becomes non_nuclear after FOAK RB_grade = RB_grade_0 - BOP_grade = BOP_grade_0 if n_th == 1 else "non_nuclear" - + if reactor_type in ["HTGR", "SFR"]: + BOP_grade = BOP_grade_0 if n_th == 1 else "non_nuclear" + elif reactor_type == "AP1000": + BOP_grade = BOP_grade_0 if n_th == 1 else "nuclear" + db = df.copy() if RB_grade == "non_nuclear": @@ -72,18 +75,25 @@ def add_BOP_RP_grades( ) if BOP_grade == "non_nuclear": - mulv( - db, [213], - ["Site Material Cost", "Site Labor Cost", "Site Labor Hours", "Factory Equipment Cost"], - 0.6 - ) - # for 232.1 apply to factory+labor but NOT material - mulv( - db, ["232.1"], - ["Factory Equipment Cost", "Site Labor Cost", "Site Labor Hours"], - 0.6 - ) - + if reactor_type in ["HTGR", "SFR"]: + mulv( + db, [213], + ["Site Material Cost", "Site Labor Cost", "Site Labor Hours", "Factory Equipment Cost"], + 0.6 + ) + # for 232.1 apply to factory+labor but NOT material + mulv( + db, ["232.1"], + ["Factory Equipment Cost", "Site Labor Cost", "Site Labor Hours"], + 0.6 + ) + elif reactor_type == "AP1000": + # for AP1000 applied BOP tpp 213.1 and 232.1 but applied to 213 instead + mulv( + db, [ 213, "232.1"], + ["Site Material Cost", "Site Labor Cost", "Site Labor Hours", "Factory Equipment Cost"], + 0.6 + ) db2 = update_high_level_costs(db, power)[COLS].copy() # duration update from grade change @@ -91,6 +101,8 @@ def add_BOP_RP_grades( ref_duration = 125 elif reactor_type == "SFR": ref_duration = 80 + elif reactor_type == "AP1000": + ref_duration = 76 else: raise ValueError(f"Unknown reactor type: {reactor_type}") @@ -133,6 +145,10 @@ def add_bulk_ordering(df: pd.DataFrame, num_orders: int, f_22: float, f_2321: fl elif reactor_type == "SFR": lr22 = 0.209920296472118 lr2321 = 0.222118386536136 + elif reactor_type == "AP1000": + # no learning for AP1000 since it is already modularized and we assume no modularity effect + lr22 = 0.0 + lr2321 = 0.0 else: raise ValueError(f"Unknown reactor type: {reactor_type}") @@ -182,7 +198,11 @@ def add_reworking_productivity( elif reactor_type == "SFR": rework = (-0.9 * design_completion + 1.9) * (-0.15 * ae_exp + 1.3) * (-0.15 * ce_exp + 1.3) ref_duration = 80 - + elif reactor_type == "AP1000": + rework = (-0.69 * design_completion + 1.69) * (-0.125 * ae_exp + 1.25) * (-0.125 * ce_exp + 1.25) + ref_duration = 76 + else: + raise ValueError(f"Unknown reactor type: {reactor_type}") db = df.copy() for acct in ACCT_DIRECT: @@ -214,7 +234,7 @@ def update_direct_cost( mod_0: str ): reactor_df, power = store.get_baseline(reactor_type) - db, baseline_lab_hours = add_factory_cost(reactor_df, power, f_22, f_2321, num_orders) + db, baseline_lab_hours = add_factory_cost(reactor_df, power, f_22, f_2321, num_orders, reactor_type) # all Total Cost (USD) columns should be 0 # when accounts are in 20s db.loc[db["Account"].isin(['21', '211 plus 214 to 219', '212', '213', '22', '23', '232.1', '233', '24', '25', '26']), "Total Cost (USD)"] = 0.0 @@ -222,16 +242,9 @@ def update_direct_cost( db = add_land_cost(db, land_cost_per_acre_0, power) db, prev_dur = add_BOP_RP_grades(db, RB_grade_0, BOP_grade_0, power, reactor_type, n_th, mod_0) db = add_bulk_ordering(db, num_orders, f_22, f_2321, power, reactor_type) - # if n_th == 1: - # print('after add_bulk_ordering for plant 1:') - # print(db) db, dur_no_delay = add_reworking_productivity( db, reactor_type, n_th, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, power, prev_dur, baseline_lab_hours ) - # if n_th == 1: - # print('after add_reworking_productivity for plant 1:') - # print(db) - # print(f"Duration after add_reworking_productivity for plant {n_th}: {dur_no_delay}") return db, dur_no_delay diff --git a/src/crf/model/finance.py b/src/crf/model/finance.py index 14d259d..a79282f 100644 --- a/src/crf/model/finance.py +++ b/src/crf/model/finance.py @@ -67,6 +67,8 @@ def update_interest_cost( new_period = 103 * annual_periods / dur elif reactor_type == "SFR": new_period = 44 * annual_periods / dur + elif reactor_type == "AP1000": + new_period = 75 * annual_periods / dur else: raise ValueError(f"Unknown reactor type: {reactor_type}") diff --git a/src/crf/model/learning.py b/src/crf/model/learning.py index b45c184..f1ffefe 100644 --- a/src/crf/model/learning.py +++ b/src/crf/model/learning.py @@ -19,12 +19,14 @@ ACCT_DIRECT = [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26] -def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power: float, reactor_type: str): +def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power: float, reactor_type: str, n_of_NOAK: int): # standardization cap for FOAK standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 # fitted learning rates (same order as ACCT_DIRECT) - # n_noak is the number of units after which the learning effect is fully realized, which is set to 8 based on expert elicitation. The learning rate is then calculated as 1-(1-std_learning_rate)^(1/log2(n_noak)). + # n_noak is the number of units after which the learning effect is fully realized, + # which is set to 8 based on expert elicitation. The learning rate is then calculated + # as 1-(1-std_learning_rate)^(1/log2(n_noak)). # std_learning_rate for HTGR: # Fac Mat Lab Cost Lab Hrs # 21 0.96 0.73 0.55 0.55 @@ -41,6 +43,17 @@ def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power # 233 0.96 0.73 0.55 0.55 # 24 0.96 0.73 0.55 0.55 # 26 0.96 0.73 0.55 0.55 + # c_NOAK/c_FOAK for AP1000: + # Fac Mat Lab Cost Lab Hrs + # 21 0.0 0.73 0.55 0.9769914 + # 22 0.930884915 0.73 0.55 0.9864236 + # 232.1 0.957187482 0.73 0.55 0.9769914 + # 233 0.0 0.73 0.55 0.9769914 + # 24 0.0 0.73 0.55 0.9769914 + # 26 0.0 0.73 0.55 0.9769914 + + + # However, learning is not applied to the factory cost if reactor_type == "HTGR": @@ -63,6 +76,22 @@ def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399 ]) * standardization / 0.7 + elif reactor_type == "AP1000": + # we will apply learning on factory cost for AP1000 of account 22 and 232.1, + # but not for other accounts since AP1000 is already modularized and we assume + # no modularity effect on other accounts. + # overall_lr = (1 - np.exp(np.log(c_NOAK/c_FOAK)/np.log2(n_of_NOAK))) * standardization / 0.7 + fac_lr_22 = (1 - np.exp(np.log(0.930884915)/np.log2(n_of_NOAK))) * standardization / 0.7 + fac_lr_2321 = (1 - np.exp(np.log(0.957187482)/np.log2(n_of_NOAK))) * standardization / 0.7 + mat_lr = np.array([ + 0.099588665391, 0.099588665391, 0.099588665391, 0.099588665391, + 0.099588665391, 0.099588665391, 0.099588665391, 0.099588665391 + ]) * standardization / 0.7 + lab_lr = np.array([ + 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399, + 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399 + ]) * standardization / 0.7 + else: raise ValueError(f"Unknown reactor type: {reactor_type}") db = df.copy() @@ -74,6 +103,10 @@ def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power setv(db, acct, "Site Material Cost", float(getv(df, acct, "Site Material Cost")) * mat_mult) setv(db, acct, "Site Labor Hours", float(getv(df, acct, "Site Labor Hours")) * lab_mult) setv(db, acct, "Site Labor Cost", float(getv(df, acct, "Site Labor Cost")) * lab_mult) + if reactor_type == "AP1000": + # apply learning on factory cost for account 22 and 232.1 for AP1000 + setv(db, 22, "Factory Equipment Cost", float(getv(df, 22, "Factory Equipment Cost")) * (1 - fac_lr_22) ** np.log2(n_th)) + setv(db, "232.1", "Factory Equipment Cost", float(getv(df, "232.1", "Factory Equipment Cost")) * (1 - fac_lr_2321) ** np.log2(n_th)) return update_high_level_costs(db, power)[COLS].copy() @@ -99,7 +132,13 @@ def act_cons_duration_plus_delay( elif reactor_type == "SFR": task_length_multiplier = 1.0 ref_construction_duration = 64 - + elif reactor_type == "AP1000": + task_length_multiplier = 76 / 64 + ref_construction_duration = 76 + else: + raise ValueError(f"Unknown reactor type: {reactor_type}") + # NOTE Ryan mentioned that the supply chain delay need to be adjusted + # and I am waiting for the final version of CRF of the AP1000 B_21 = 42.1 * task_length_multiplier B_22 = 60.2 * task_length_multiplier B_23 = 14.8 * task_length_multiplier @@ -123,11 +162,16 @@ def act_cons_duration_plus_delay( def duration_learning_effect(reactor_type: str, n_th: int, standardization_0: float, - actual_construction_duration_plus_delay: float): + actual_construction_duration_plus_delay: float, + n_of_NOAK: int): standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 if reactor_type == "HTGR": fitted_LR_duration = 0.103719051 * standardization / 0.7 elif reactor_type == "SFR": fitted_LR_duration = 0.15*standardization/0.7 + elif reactor_type == "AP1000": + fitted_LR_duration = (1- np.exp(np.log(42/76)/np.log2(n_of_NOAK)))*standardization/0.7 + + # fitted_LR_duration = (1 - EXP(LN(42/76)/LOG(N_of_NOAK,2))) * standardization / 0.7 duration_multiplier = (1 - fitted_LR_duration) ** np.log2(n_th) return float(duration_multiplier) * float(actual_construction_duration_plus_delay) diff --git a/src/crf/model/pipeline.py b/src/crf/model/pipeline.py index 2856d24..5b05362 100644 --- a/src/crf/model/pipeline.py +++ b/src/crf/model/pipeline.py @@ -2,6 +2,7 @@ from .learning import learning_effect, act_cons_duration_plus_delay, duration_learning_effect from .indirect_cost import update_indirect_cost from .finance import tax_update, insurance_cost_update, decomission_cost_update, update_interest_cost, update_itc +import numpy as np def calculate_final_result(config: dict, inp: dict, store, n_th: int): """ @@ -38,10 +39,17 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): ) final_dur = duration_learning_effect(reactor_type=config["reactor_type"], n_th=n_th, - standardization_0=inp["standardization_0"], actual_construction_duration_plus_delay=dur_plus_delay) + standardization_0=inp["standardization_0"], + actual_construction_duration_plus_delay=dur_plus_delay, + n_of_NOAK=inp["num_orders"]) # learning on direct costs - direct_plus_learning = learning_effect(direct_df, n_th, inp["standardization_0"], power, config["reactor_type"]) + direct_plus_learning = learning_effect(direct_df, n_th, + inp["standardization_0"], + power, + config["reactor_type"], + inp["num_orders"], + ) # indirect costs with_indirect = update_indirect_cost(n_th, inp["standardization_0"], direct_plus_learning, final_dur, power) @@ -65,6 +73,12 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): org_occ = float(tot_occ/power) org_tci = float(tot_cap/power) final_df, net_occ, nci = update_itc(with_interest, tot_occ, tot_cap, n_th, inp["ITC_0"], inp["n_ITC"], power) + reactor_type=config["reactor_type"] + if reactor_type in ["HTGR", "SFR"]: + startup_dur = max(7, 16*(1-0.3)**np.log2(n_th)) + elif reactor_type == "AP1000": + startup_dur = max(7, 28*(1-0.3)**np.log2(n_th)) # NOTE: need to check whether the startup duration learning should be the same as other types + # print(f"Final OCC for plant {n_th}: {net_occ}") # print(final_df) return final_df, org_occ, net_occ, org_tci, nci, float(final_dur) diff --git a/test/test_crf_smoke.py b/test/test_crf_smoke.py index 6c05b36..44ff716 100644 --- a/test/test_crf_smoke.py +++ b/test/test_crf_smoke.py @@ -11,8 +11,8 @@ def main(): config = { - # "reactor_type": "HTGR", # or "SFR" - "reactor_type": "SFR", # or "SFR" + "reactor_type": "HTGR", # or "SFR" + # "reactor_type": "AP1000", # or "SFR" "f_22": 250_000_000, "f_2321": 150_000_000, "land_cost_per_acre_0": 22000, @@ -21,17 +21,20 @@ def main(): } levers = { - "num_orders": 13, + "num_orders": 10, + "num_NOAK": 8, "itc_percent": 0, "n_itc": 0, "interest_percent": 6, "design_completion_percent": 80, + # "design_completion_percent": 70, "design_maturity": 1, "proc_exp": 0.5, "N_proc": 3, "ae_exp": 0.5, "N_AE": 4, "ce_exp": 1, + # "ce_exp": 0.5, "N_cons": 5, "standardization_percent": 80, # 80% standardized "modularity_code": 1, # Modularized From 12d384aec4528d5d6b51760f96e4a3ef24ed7c53 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Tue, 28 Apr 2026 23:50:26 -0500 Subject: [PATCH 14/25] Ap1000 indirect --- src/crf/__init__.py | 2 ++ src/crf/api.py | 43 +++++++++++++++++++++++- src/crf/model/avg_runner.py | 7 +++- src/crf/model/core_accounts.py | 12 +++++-- src/crf/model/direct_cost.py | 2 +- src/crf/model/indirect_cost.py | 11 ++++-- src/crf/model/learning.py | 7 +++- src/crf/model/pipeline.py | 8 ++--- test/test_crf_smoke.py | 61 ++++++---------------------------- 9 files changed, 86 insertions(+), 67 deletions(-) diff --git a/src/crf/__init__.py b/src/crf/__init__.py index 38163fc..a45f112 100644 --- a/src/crf/__init__.py +++ b/src/crf/__init__.py @@ -6,9 +6,11 @@ from .api import ( run_one_scenario, run_sampling_from_excel, + print_scenario_result, ) __all__ = [ "run_one_scenario", "run_sampling_from_excel", + "print_scenario_result", ] diff --git a/src/crf/api.py b/src/crf/api.py index e9da511..d52882d 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -1,5 +1,6 @@ import csv import numpy as np +import pandas as pd from .io.excel_inputs import InputStore from .io.excel_levers import read_levers_sheet @@ -52,7 +53,6 @@ def run_one_scenario(config: dict, levers: dict) -> dict: static_vals = static_row_from_levers(levers) return {**static_vals, **result} - def run_sampling_from_excel( config: dict, levers_xlsx: str, @@ -114,3 +114,44 @@ def run_sampling_from_excel( result = run_avg_all_units(config=config, inp=inp, store=store, details=False) static_vals = static_row_from_levers(levers) return {**static_vals, **result} + +def print_scenario_result(result: dict): + print("Results by plant number:\n") + max_orders = max([int(k.split("_")[1]) for k in result.keys() if k.startswith("OCC_")]) + results_df = pd.DataFrame({"Plant_number": list(range(1, max_orders+1))}) + for k, v in result.items(): + if k.startswith("OCC_"): + results_df[f"OCC"] = results_df["Plant_number"].apply(lambda x: result.get(f"OCC_{x}", None)) + elif k.startswith("NETOCC_"): + results_df[f"NETOCC"] = results_df["Plant_number"].apply(lambda x: result.get(f"NETOCC_{x}", None)) + elif k.startswith("TCI_"): + results_df[f"TCI"] = results_df["Plant_number"].apply(lambda x: result.get(f"TCI_{x}", None)) + elif k.startswith("NCI_"): + results_df[f"NCI"] = results_df["Plant_number"].apply(lambda x: result.get(f"NCI_{x}", None)) + elif k.startswith("duration_"): + results_df[f"duration"] = results_df["Plant_number"].apply(lambda x: result.get(f"duration_{x}", None)) + elif k.startswith("STAUP_"): + results_df[f"STAUP"] = results_df["Plant_number"].apply(lambda x: result.get(f"STAUP_{x}", None)) + elif k.startswith("D10s_"): + results_df[f"D10s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D10s_{x}", None)) + elif k.startswith("D20s_"): + results_df[f"D20s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20s_{x}", None)) + elif k.startswith("D30s_"): + results_df[f"D30s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D30s_{x}", None)) + elif k.startswith("D50s_"): + results_df[f"D50s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D50s_{x}", None)) + elif k.startswith("D60s_"): + results_df[f"D60s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D60s_{x}", None)) + elif k.startswith("D20_equip_"): + results_df[f"D20_equip"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_equip_{x}", None)) + elif k.startswith("D20_mat_"): + results_df[f"D20_mat"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_mat_{x}", None)) + elif k.startswith("D20_labor_"): + results_df[f"D20_labor"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_labor_{x}", None)) + + print(results_df.round(2).fillna("").to_string(index=False)) + + summary_metrics = ["cons_duration_cumulative_wz_startup", "occLastUnit", "TCILastUnit", "durationsLastUnit", "avg_OCC", "avg_TCI", "avg_duration"] + print("\nSummary metrics:\n") + for metric in summary_metrics: + print(f"{metric}: {result[metric]:.2f}") \ No newline at end of file diff --git a/src/crf/model/avg_runner.py b/src/crf/model/avg_runner.py index 3203cbc..1a083b3 100644 --- a/src/crf/model/avg_runner.py +++ b/src/crf/model/avg_runner.py @@ -10,7 +10,7 @@ def run_avg_all_units(config: dict, inp: dict, store, details=False): num_orders = int(inp["num_orders"]) n_itc = int(inp["n_ITC"]) - OCC, NETOCC, TCI, NCI, DUR = [], [], [], [], [] + OCC, NETOCC, TCI, NCI, DUR, STAUP = [], [], [], [], [], [] if details: # Stores the 10s, 20s, 30s, 50s and 60s, with 20s equipment, material, labor costs D10s, D20s, D30s, D50s, D60s = [], [], [], [], [] @@ -22,6 +22,7 @@ def run_avg_all_units(config: dict, inp: dict, store, details=False): OCC.append(occ) TCI.append(tci) DUR.append(dur) + STAUP.append(max(7, config["startup_0"] * (1 - 0.3) ** np.log2(n_th))) if n_th <= n_itc: NETOCC.append(netocc) NCI.append(nci) @@ -40,6 +41,7 @@ def run_avg_all_units(config: dict, inp: dict, store, details=False): TCI = np.array(TCI, dtype=float) NCI = np.array(NCI, dtype=float) DUR = np.array(DUR, dtype=float) + STAUP = np.array(STAUP, dtype=float) if details: D10s = np.array(D10s, dtype=float) D20s = np.array(D20s, dtype=float) @@ -63,6 +65,7 @@ def run_avg_all_units(config: dict, inp: dict, store, details=False): avg_TCI = float(np.mean(TCI)) avg_duration = float(np.mean(DUR)) + final_startup_duration = max(7, config["startup_0"] * (1 - 0.3) ** np.log2(num_orders)) cons_duration_cumulative_wz_startup = ( (1 - config["staggering_ratio"]) * np.sum(DUR[:-1]) + DUR[-1] + final_startup_duration @@ -80,6 +83,8 @@ def run_avg_all_units(config: dict, inp: dict, store, details=False): out[f"NCI_{i+1}"] = float(v) for i, v in enumerate(DUR): out[f"duration_{i+1}"] = float(v) + for i, v in enumerate(STAUP): + out[f"STAUP_{i+1}"] = float(v) if details: for i, v in enumerate(D10s): out[f"D10s_{i+1}"] = float(v) diff --git a/src/crf/model/core_accounts.py b/src/crf/model/core_accounts.py index 0cedb8f..431b283 100644 --- a/src/crf/model/core_accounts.py +++ b/src/crf/model/core_accounts.py @@ -257,7 +257,8 @@ def update_cons_duration_2( db1: pd.DataFrame, ref_duration: float, prev_cons_duration: float, - baseline_lab_hours: float + baseline_lab_hours: float, + reactor_type: str ) -> float: """Calculate new construction duration based on change in labor hours for accounts 21, 22, 23, 24, and 26. This should be used after the standardization effect is applied, so the labor hours change should be compared to the baseline labor hours instead of the previous labor hours. NOTE I think I should merge this with the previous function and just pass in the baseline labor hours as an argument. """ @@ -272,6 +273,11 @@ def _sum_hours(db): sum_new = _sum_hours(db1) lab_delta = (sum_new - sum_old) / float(baseline_lab_hours) # NOTE ref_duration here should be modulized? - # ref_duration=ref_duration*0.8 - return float(0.3 * lab_delta * ref_duration + float(prev_cons_duration)) + # ref_duration=ref_duration*0.8 + if reactor_type in ["HTGR", "SFR"]: + return float(0.3 * lab_delta * ref_duration + float(prev_cons_duration)) + elif reactor_type == "AP1000": + return float(0.04 * lab_delta * ref_duration + float(prev_cons_duration)) + else: + raise ValueError(f"Unknown reactor type: {reactor_type}") diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index afbb3b0..f166aa6 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -212,7 +212,7 @@ def add_reworking_productivity( setv(db, acct, "Site Labor Cost", float(getv(df, acct, "Site Labor Cost")) * rework / productivity) db2 = update_high_level_costs(db, power)[COLS].copy() - new_dur = float(update_cons_duration_2(df, db2, ref_duration, prev_cons_duration, baseline_lab_hours)) + new_dur = float(update_cons_duration_2(df, db2, ref_duration, prev_cons_duration, baseline_lab_hours, reactor_type)) return db2, new_dur diff --git a/src/crf/model/indirect_cost.py b/src/crf/model/indirect_cost.py index 738af69..0192696 100644 --- a/src/crf/model/indirect_cost.py +++ b/src/crf/model/indirect_cost.py @@ -18,7 +18,8 @@ def update_indirect_cost( standardization_0: float, df: pd.DataFrame, final_construction_duration: float, - power: float + power: float, + reactor_type: str ): standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 factor_35 = (10 / 3) * (1 - standardization) @@ -38,10 +39,14 @@ def update_indirect_cost( dur = float(final_construction_duration) # print(f"DEBUG: final_construction_duration={dur}, standardization={standardization}, factor_35={factor_35}") val31 = (sum_new_mat_cost * 0.785 * sum_new_lab_hrs / dur / 160 / 1058) + sum_new_lab_cost * 0.36 - val32 = sum_new_lab_cost * 0.36 * 3.661 * dur / 72 + if reactor_type in ["HTGR", "SFR"]: + val32 = sum_new_lab_cost * 0.36 * 3.661 * dur / 72 + val35 = (0.27017603 * val32) * factor_35 + elif reactor_type == "AP1000": + val32 = sum_new_lab_cost * 0.36 * 1.2 * dur / 42 + val35 = 0.27017603 * val32 val33 = 0.04207006 * val32 val34 = 0.00354234616938 * val32 - val35 = (0.27017603 * val32) * factor_35 # print(f"DEBUG: val31={val31}, val32={val32}, val33={val33}, val34={val34}, val35={val35}") setv(db, 31, "Total Cost (USD)", val31) setv(db, 32, "Total Cost (USD)", val32) diff --git a/src/crf/model/learning.py b/src/crf/model/learning.py index f1ffefe..622fa57 100644 --- a/src/crf/model/learning.py +++ b/src/crf/model/learning.py @@ -164,7 +164,12 @@ def duration_learning_effect(reactor_type: str, standardization_0: float, actual_construction_duration_plus_delay: float, n_of_NOAK: int): - standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 + if reactor_type in ["HTGR", "SFR"]: + standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 + elif reactor_type == "AP1000": + # for AP1000, we assume the learning effect on duration is related to + # standardization and cross site transfer efficiencies + standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0* standardization_0 if reactor_type == "HTGR": fitted_LR_duration = 0.103719051 * standardization / 0.7 elif reactor_type == "SFR": diff --git a/src/crf/model/pipeline.py b/src/crf/model/pipeline.py index 5b05362..2497ef3 100644 --- a/src/crf/model/pipeline.py +++ b/src/crf/model/pipeline.py @@ -50,8 +50,9 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): config["reactor_type"], inp["num_orders"], ) + # indirect costs - with_indirect = update_indirect_cost(n_th, inp["standardization_0"], direct_plus_learning, final_dur, power) + with_indirect = update_indirect_cost(n_th, inp["standardization_0"], direct_plus_learning, final_dur, power, reactor_type=config["reactor_type"]) # Supplementary costs: tax, insurance, decommissioning with_tax = tax_update(with_indirect, power) @@ -73,11 +74,6 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): org_occ = float(tot_occ/power) org_tci = float(tot_cap/power) final_df, net_occ, nci = update_itc(with_interest, tot_occ, tot_cap, n_th, inp["ITC_0"], inp["n_ITC"], power) - reactor_type=config["reactor_type"] - if reactor_type in ["HTGR", "SFR"]: - startup_dur = max(7, 16*(1-0.3)**np.log2(n_th)) - elif reactor_type == "AP1000": - startup_dur = max(7, 28*(1-0.3)**np.log2(n_th)) # NOTE: need to check whether the startup duration learning should be the same as other types # print(f"Final OCC for plant {n_th}: {net_occ}") # print(final_df) diff --git a/test/test_crf_smoke.py b/test/test_crf_smoke.py index 44ff716..2f802e6 100644 --- a/test/test_crf_smoke.py +++ b/test/test_crf_smoke.py @@ -5,18 +5,19 @@ import pandas as pd src_path = os.path.abspath(os.path.join(os.pardir, 'src')) sys.path.insert(0, src_path) -from crf import run_one_scenario +from crf import run_one_scenario, print_scenario_result def main(): config = { - "reactor_type": "HTGR", # or "SFR" - # "reactor_type": "AP1000", # or "SFR" + # "reactor_type": "HTGR", # or "SFR" + "reactor_type": "AP1000", # or "SFR" "f_22": 250_000_000, "f_2321": 150_000_000, "land_cost_per_acre_0": 22000, - "startup_0": 16, + # "startup_0": 16, + "startup_0": 28, "staggering_ratio": 0.75, } @@ -26,15 +27,15 @@ def main(): "itc_percent": 0, "n_itc": 0, "interest_percent": 6, - "design_completion_percent": 80, - # "design_completion_percent": 70, + # "design_completion_percent": 80, + "design_completion_percent": 70, "design_maturity": 1, "proc_exp": 0.5, "N_proc": 3, "ae_exp": 0.5, "N_AE": 4, - "ce_exp": 1, - # "ce_exp": 0.5, + # "ce_exp": 1, + "ce_exp": 0.5, "N_cons": 5, "standardization_percent": 80, # 80% standardized "modularity_code": 1, # Modularized @@ -43,50 +44,8 @@ def main(): } result = run_one_scenario(config, levers) - # transfer the result into more readable format - # all OCC, NETOCC, TCI, NCI can be put into a table with columns plant number and metric name, but for simplicity we just print them out here. - results_df = pd.DataFrame({"Plant_number": list(range(1, int(levers["num_orders"])+1))}) - for k, v in result.items(): - if k.startswith("OCC_"): - results_df[f"OCC"] = results_df["Plant_number"].apply(lambda x: result.get(f"OCC_{x}", None)) - elif k.startswith("NETOCC_"): - results_df[f"NETOCC"] = results_df["Plant_number"].apply(lambda x: result.get(f"NETOCC_{x}", None)) - elif k.startswith("TCI_"): - results_df[f"TCI"] = results_df["Plant_number"].apply(lambda x: result.get(f"TCI_{x}", None)) - elif k.startswith("NCI_"): - results_df[f"NCI"] = results_df["Plant_number"].apply(lambda x: result.get(f"NCI_{x}", None)) - elif k.startswith("duration_"): - results_df[f"duration"] = results_df["Plant_number"].apply(lambda x: result.get(f"duration_{x}", None)) - elif k.startswith("D10s_"): - results_df[f"D10s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D10s_{x}", None)) - elif k.startswith("D20s_"): - results_df[f"D20s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20s_{x}", None)) - elif k.startswith("D30s_"): - results_df[f"D30s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D30s_{x}", None)) - elif k.startswith("D50s_"): - results_df[f"D50s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D50s_{x}", None)) - elif k.startswith("D60s_"): - results_df[f"D60s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D60s_{x}", None)) - elif k.startswith("D20_equip_"): - results_df[f"D20_equip"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_equip_{x}", None)) - elif k.startswith("D20_mat_"): - results_df[f"D20_mat"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_mat_{x}", None)) - elif k.startswith("D20_labor_"): - results_df[f"D20_labor"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_labor_{x}", None)) + print_scenario_result(result) - - print("Results by plant number:\n") - print(results_df.round(2).fillna("").to_string(index=False)) - - summary_metrics = ["cons_duration_cumulative_wz_startup", "occLastUnit", "TCILastUnit", "durationsLastUnit", "avg_OCC", "avg_TCI", "avg_duration"] - print("\nSummary metrics:\n") - for metric in summary_metrics: - print(f"{metric}: {result[metric]:.2f}") - - - - # for k, v in result.items(): - # print(f"{k}: {v}") print("\nTest Successful") From d3af1f7e9baa256a67ec4617d0eb40bc393451bb Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Fri, 1 May 2026 13:49:20 -0500 Subject: [PATCH 15/25] AP1000 ready --- test/test_crf_smoke.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/test/test_crf_smoke.py b/test/test_crf_smoke.py index 2f802e6..24f75e0 100644 --- a/test/test_crf_smoke.py +++ b/test/test_crf_smoke.py @@ -11,13 +11,11 @@ def main(): config = { - # "reactor_type": "HTGR", # or "SFR" - "reactor_type": "AP1000", # or "SFR" + "reactor_type": "AP1000", # or "SFR", "HTGR" "f_22": 250_000_000, "f_2321": 150_000_000, "land_cost_per_acre_0": 22000, - # "startup_0": 16, - "startup_0": 28, + "startup_0": 28, # 16 months for SFR, HTGR; 28 months for AP1000 "staggering_ratio": 0.75, } @@ -27,15 +25,13 @@ def main(): "itc_percent": 0, "n_itc": 0, "interest_percent": 6, - # "design_completion_percent": 80, - "design_completion_percent": 70, + "design_completion_percent": 70, #70% for AP1000, 80% for SFR, HTGR "design_maturity": 1, "proc_exp": 0.5, "N_proc": 3, "ae_exp": 0.5, "N_AE": 4, - # "ce_exp": 1, - "ce_exp": 0.5, + "ce_exp": 0.5, # 0.5 for AP1000, 1.0 for SFR, HTGR "N_cons": 5, "standardization_percent": 80, # 80% standardized "modularity_code": 1, # Modularized From 8ebd8e991d86972eab4b0081d814583b76ef2eda Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Fri, 1 May 2026 14:01:44 -0500 Subject: [PATCH 16/25] adding unit test --- docs/source/user/crf.rst | 165 ++++++++++++++++++++++++++++++++++++ docs/source/user/index.rst | 1 + src/crf/api.py | 37 ++++---- src/crf/sampling/sampler.py | 4 +- test/conftest.py | 6 ++ test/test_crf_api.py | 129 ++++++++++++++++++++++++++++ tutorial/crf_quickstart.py | 43 ++++++++++ 7 files changed, 362 insertions(+), 23 deletions(-) create mode 100644 docs/source/user/crf.rst create mode 100644 test/test_crf_api.py create mode 100644 tutorial/crf_quickstart.py diff --git a/docs/source/user/crf.rst b/docs/source/user/crf.rst new file mode 100644 index 0000000..2b88d41 --- /dev/null +++ b/docs/source/user/crf.rst @@ -0,0 +1,165 @@ +Cost Reduction Framework +======================== + +The Cost Reduction Framework (CRF) estimates how overnight capital cost, total +capital investment, and construction duration change across a sequence of firm +reactor orders. It is available as the ``crf`` Python package inside ACCERT. + +Python API +---------- + +Use ``run_one_scenario`` when you want to evaluate one deterministic set of +cost-reduction levers. + +.. code-block:: python + + from crf import run_one_scenario, print_scenario_result + + config = { + "reactor_type": "AP1000", # AP1000, SFR, or HTGR + "f_22": 250_000_000, + "f_2321": 150_000_000, + "land_cost_per_acre_0": 22_000, + "startup_0": 28, + "staggering_ratio": 0.75, + } + + levers = { + "num_orders": 10, + "num_NOAK": 8, + "itc_percent": 0, + "n_itc": 0, + "interest_percent": 6, + "design_completion_percent": 70, + "design_maturity": 1, + "proc_exp": 0.5, + "N_proc": 3, + "ce_exp": 0.5, + "N_cons": 5, + "ae_exp": 0.5, + "N_AE": 4, + "standardization_percent": 80, + "modularity_code": 1, + "bop_grade_code": 1, + "rb_grade_code": 0, + } + + result = run_one_scenario(config, levers) + print_scenario_result(result) + +The returned dictionary includes the static lever values, per-unit metrics such +as ``OCC_1``, ``TCI_1``, and ``duration_1``, and summary metrics such as +``avg_OCC``, ``avg_TCI``, and ``avg_duration``. + +Configuration +------------- + +``config`` describes the reactor and fixed project assumptions. + +.. list-table:: + :header-rows: 1 + + * - Key + - Description + * - ``reactor_type`` + - Built-in CRF baseline: ``AP1000``, ``SFR``, or ``HTGR``. + * - ``f_22`` + - Reactor building cost adjustment. + * - ``f_2321`` + - Turbine generator cost adjustment. + * - ``land_cost_per_acre_0`` + - Baseline land cost per acre. + * - ``startup_0`` + - First-unit startup duration in months. + * - ``staggering_ratio`` + - Fractional overlap between sequential unit construction schedules. + +Levers +------ + +``levers`` contains the scenario variables. Percent inputs are entered as +percent values, for example ``6`` for a six-percent interest rate. Binary code +inputs use ``0`` or ``1`` and are converted internally to model labels. + +.. list-table:: + :header-rows: 1 + + * - Key + - Meaning + * - ``num_orders`` + - Number of firm plant orders to evaluate. + * - ``num_NOAK`` + - Plant number used for nth-of-a-kind learning. Defaults to ``num_orders``. + * - ``itc_percent`` + - Investment tax credit percentage. Values are rounded to the nearest supported CRF ITC level. + * - ``n_itc`` + - Number of first units eligible for ITC. + * - ``interest_percent`` + - Interest rate in percent. + * - ``design_completion_percent`` + - Initial design completion in percent. + * - ``design_maturity`` + - Initial technology/design maturity factor. + * - ``proc_exp``, ``N_proc`` + - Supply chain proficiency and number of plants to reach best proficiency. + * - ``ce_exp``, ``N_cons`` + - Construction proficiency and number of plants to reach best proficiency. + * - ``ae_exp``, ``N_AE`` + - Architect-engineer proficiency and number of plants to reach best proficiency. + * - ``standardization_percent`` + - Cross-site standardization in percent. + * - ``modularity_code`` + - ``0`` for stick-built, ``1`` for modularized. + * - ``bop_grade_code`` + - ``0`` for nuclear-grade BOP, ``1`` for non-nuclear-grade BOP. + * - ``rb_grade_code`` + - ``0`` for nuclear-grade reactor building, ``1`` for non-nuclear-grade reactor building. + +Sampling from Excel +------------------- + +Use ``run_sampling_from_excel`` to run Monte Carlo samples from an Excel workbook +with a sheet named ``Levers``. + +.. code-block:: python + + from crf import run_sampling_from_excel + + run_sampling_from_excel( + config=config, + levers_xlsx="crf_levers.xlsx", + n_samples=100, + out_csv="crf_samples.csv", + out_pkl="crf_samples.pkl", + seed=42, + ) + +The ``Levers`` sheet must include these columns: + +.. code-block:: text + + Levers, Min, Low, Median, High, Max, Distribution, Type, Set, Probabilities + +The lever rows must follow the order expected by CRF because several rows share +the same display name: + +.. code-block:: text + + Number of firm orders + ITC amount + Number of plants claiming ITC + Interest rate + Design completion + Design maturity (technology maturity) + Supply chain proficiency + Number of plants to achieve best proficiency + Construction proficiency + Number of plants to achieve best proficiency + A/E proficiency + Number of plants to achieve best proficiency + Cross-site standardization + Modular civil construction + Commercial BOP + Non-safety-related RB + +See ``tutorial/crf_quickstart.py`` for a runnable deterministic scenario. diff --git a/docs/source/user/index.rst b/docs/source/user/index.rst index ccb87ec..a285b1a 100644 --- a/docs/source/user/index.rst +++ b/docs/source/user/index.rst @@ -13,3 +13,4 @@ User's Guide output_structure build_your_own using_necost + crf diff --git a/src/crf/api.py b/src/crf/api.py index d52882d..404a3b0 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -1,4 +1,6 @@ import csv +from typing import Optional + import numpy as np import pandas as pd @@ -59,7 +61,7 @@ def run_sampling_from_excel( n_samples: int, out_csv: str, out_pkl: str, - seed: int | None = None + seed: Optional[int] = None ): levers_df = read_levers_sheet(levers_xlsx, sheet_name="Levers") levers_df = attach_internal_ids(levers_df) @@ -68,12 +70,13 @@ def run_sampling_from_excel( # headers: build from max num_orders in sampled set max_orders = int(np.max(samples[0, :])) + has_itc = bool(np.any(samples[2, :] > 0)) headers_wo_itc = ( STATIC_KEYS - + [f"OCC_{i}" for i in range(max_orders)] - + [f"TCI_{i}" for i in range(max_orders)] - + [f"duration_{i}" for i in range(max_orders)] + + [f"OCC_{i}" for i in range(1, max_orders + 1)] + + [f"TCI_{i}" for i in range(1, max_orders + 1)] + + [f"duration_{i}" for i in range(1, max_orders + 1)] + [ "cons_duration_cumulative_wz_startup", "occLastUnit", "TCILastUnit", "durationsLastUnit", @@ -82,11 +85,11 @@ def run_sampling_from_excel( ) headers_w_itc = ( STATIC_KEYS - + [f"OCC_{i}" for i in range(max_orders)] - + [f"NETOCC_{i}" for i in range(max_orders)] - + [f"TCI_{i}" for i in range(max_orders)] - + [f"NCI_{i}" for i in range(max_orders)] - + [f"duration_{i}" for i in range(max_orders)] + + [f"OCC_{i}" for i in range(1, max_orders + 1)] + + [f"NETOCC_{i}" for i in range(1, max_orders + 1)] + + [f"TCI_{i}" for i in range(1, max_orders + 1)] + + [f"NCI_{i}" for i in range(1, max_orders + 1)] + + [f"duration_{i}" for i in range(1, max_orders + 1)] + [ "cons_duration_cumulative_wz_startup", "occLastUnit", "TCILastUnit", "durationsLastUnit", @@ -94,27 +97,17 @@ def run_sampling_from_excel( ] ) + headers = headers_w_itc if has_itc else headers_wo_itc + with open(out_csv, "w", newline="", encoding="utf-8") as csv_f, open(out_pkl, "wb") as pkl_f: writer = csv.DictWriter(csv_f, fieldnames=headers) writer.writeheader() for i in range(n_samples): levers_raw = sample_column_to_levers(samples, levers_df, i) row = run_one_scenario(config, levers_raw) - n_itc = int(levers_raw["n_itc"]) - if n_itc > 0: - headers = headers_w_itc - else: - headers = headers_wo_itc write_csv_row(writer, row, headers) stream_pickle_dump(row, pkl_f) - levers = apply_itc_rounding(levers) - inp = normalize_levers(levers) - - result = run_avg_all_units(config=config, inp=inp, store=store, details=False) - static_vals = static_row_from_levers(levers) - return {**static_vals, **result} - def print_scenario_result(result: dict): print("Results by plant number:\n") max_orders = max([int(k.split("_")[1]) for k in result.keys() if k.startswith("OCC_")]) @@ -154,4 +147,4 @@ def print_scenario_result(result: dict): summary_metrics = ["cons_duration_cumulative_wz_startup", "occLastUnit", "TCILastUnit", "durationsLastUnit", "avg_OCC", "avg_TCI", "avg_duration"] print("\nSummary metrics:\n") for metric in summary_metrics: - print(f"{metric}: {result[metric]:.2f}") \ No newline at end of file + print(f"{metric}: {result[metric]:.2f}") diff --git a/src/crf/sampling/sampler.py b/src/crf/sampling/sampler.py index 7a080c6..3b12ba9 100644 --- a/src/crf/sampling/sampler.py +++ b/src/crf/sampling/sampler.py @@ -1,4 +1,6 @@ # src/crf/sampling/sampler.py +from typing import Optional + import numpy as np import scipy.stats as stats @@ -15,7 +17,7 @@ def truncate_shift_lognormal(min_, low_, med_, high_, max_, n): x = stats.truncnorm.rvs(a=a, b=b, loc=mean, scale=std, size=n) return np.exp(x) -def sample_levers(n_samples: int, levers_df, seed: int | None = None) -> np.ndarray: +def sample_levers(n_samples: int, levers_df, seed: Optional[int] = None) -> np.ndarray: if seed is not None: np.random.seed(seed) diff --git a/test/conftest.py b/test/conftest.py index 0696730..d9b8040 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -1,10 +1,16 @@ import pytest import mysql.connector import os +import sys import configparser import glob import pandas as pd +PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)) +SRC_PATH = os.path.join(PROJECT_ROOT, "src") +if SRC_PATH not in sys.path: + sys.path.insert(0, SRC_PATH) + @pytest.fixture def input_params_data(): diff --git a/test/test_crf_api.py b/test/test_crf_api.py new file mode 100644 index 0000000..1f44a0e --- /dev/null +++ b/test/test_crf_api.py @@ -0,0 +1,129 @@ +import csv +import pickle + +import pandas as pd +import pytest + +from crf import run_one_scenario, run_sampling_from_excel +from crf.api import normalize_levers +from crf.sampling.lever_schema import EXCEL_NAME_TO_ID_ORDERED + + +def _config(): + return { + "reactor_type": "AP1000", + "f_22": 250_000_000, + "f_2321": 150_000_000, + "land_cost_per_acre_0": 22_000, + "startup_0": 28, + "staggering_ratio": 0.75, + } + + +def _levers(**overrides): + levers = { + "num_orders": 2, + "num_NOAK": 2, + "itc_percent": 30, + "n_itc": 1, + "interest_percent": 6, + "design_completion_percent": 70, + "design_maturity": 1, + "proc_exp": 0.5, + "N_proc": 3, + "ce_exp": 0.5, + "N_cons": 5, + "ae_exp": 0.5, + "N_AE": 4, + "standardization_percent": 80, + "modularity_code": 1, + "bop_grade_code": 1, + "rb_grade_code": 0, + } + levers.update(overrides) + return levers + + +def _lever_workbook(path): + baseline = _levers() + rows = [] + for excel_name, lever_id in EXCEL_NAME_TO_ID_ORDERED: + value = baseline[lever_id] + rows.append( + { + "Levers": excel_name, + "Min": value, + "Low": value, + "Median": value, + "High": value, + "Max": value, + "Distribution": "set", + "Type": "discrete", + "Set": str(value), + "Probabilities": "1", + } + ) + + with pd.ExcelWriter(path) as writer: + pd.DataFrame(rows).to_excel(writer, sheet_name="Levers", index=False) + + +def test_normalize_levers_converts_external_inputs_to_model_values(): + normalized = normalize_levers(_levers(num_NOAK=5)) + + assert normalized["num_orders"] == 2 + assert normalized["num_NOAK"] == 5 + assert normalized["ITC_0"] == pytest.approx(0.30) + assert normalized["interest_rate_0"] == pytest.approx(0.06) + assert normalized["design_completion_0"] == pytest.approx(0.70) + assert normalized["standardization_0"] == pytest.approx(0.80) + assert normalized["mod_0"] == "modularized" + assert normalized["BOP_grade_0"] == "non_nuclear" + assert normalized["RB_grade_0"] == "nuclear" + + +def test_run_one_scenario_returns_static_inputs_and_unit_results(): + result = run_one_scenario(_config(), _levers()) + + assert result["Num_orders"] == 2 + assert result["ITC"] == 30 + assert result["n_ITC"] == 1 + assert result["OCC_1"] > 0 + assert result["OCC_2"] > 0 + assert result["NETOCC_1"] < result["OCC_1"] + assert "NETOCC_2" not in result + assert result["avg_OCC"] > 0 + assert result["avg_TCI"] > 0 + assert result["avg_duration"] > 0 + + +def test_run_sampling_from_excel_writes_csv_and_pickle_outputs(tmp_path): + levers_xlsx = tmp_path / "crf_levers.xlsx" + out_csv = tmp_path / "crf_samples.csv" + out_pkl = tmp_path / "crf_samples.pkl" + _lever_workbook(levers_xlsx) + + run_sampling_from_excel( + _config(), + str(levers_xlsx), + n_samples=2, + out_csv=str(out_csv), + out_pkl=str(out_pkl), + seed=7, + ) + + with out_csv.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + + assert len(rows) == 2 + assert rows[0]["OCC_1"] + assert rows[0]["OCC_2"] + assert rows[0]["NETOCC_1"] + assert rows[0]["NCI_1"] + assert rows[0]["avg_OCC"] + + with out_pkl.open("rb") as handle: + sampled_rows = [pickle.load(handle), pickle.load(handle)] + + assert sampled_rows[0]["Num_orders"] == 2 + assert sampled_rows[1]["NETOCC_1"] < sampled_rows[1]["OCC_1"] diff --git a/tutorial/crf_quickstart.py b/tutorial/crf_quickstart.py new file mode 100644 index 0000000..312fa0a --- /dev/null +++ b/tutorial/crf_quickstart.py @@ -0,0 +1,43 @@ +"""Run a deterministic Cost Reduction Framework scenario. + +Run from the repository root with: + + PYTHONPATH=src python tutorial/crf_quickstart.py +""" + +from crf import print_scenario_result, run_one_scenario + + +config = { + "reactor_type": "AP1000", + "f_22": 250_000_000, + "f_2321": 150_000_000, + "land_cost_per_acre_0": 22_000, + "startup_0": 28, + "staggering_ratio": 0.75, +} + +levers = { + "num_orders": 10, + "num_NOAK": 8, + "itc_percent": 0, + "n_itc": 0, + "interest_percent": 6, + "design_completion_percent": 70, + "design_maturity": 1, + "proc_exp": 0.5, + "N_proc": 3, + "ce_exp": 0.5, + "N_cons": 5, + "ae_exp": 0.5, + "N_AE": 4, + "standardization_percent": 80, + "modularity_code": 1, + "bop_grade_code": 1, + "rb_grade_code": 0, +} + + +if __name__ == "__main__": + result = run_one_scenario(config, levers) + print_scenario_result(result) From 4de098c393527cf26107c6c463f903c9a297a8e6 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Fri, 1 May 2026 14:16:49 -0500 Subject: [PATCH 17/25] add figures --- docs/source/user/crf.rst | 35 ++++++++++++++++++++++++++------ src/crf/__init__.py | 12 +++++++++++ src/crf/api.py | 18 +++++++++++++--- src/crf/model/avg_runner.py | 5 +++++ src/crf/sampling/lever_schema.py | 3 ++- test/test_crf_api.py | 28 ++++++++++++++++++++++++- tutorial/crf_quickstart.py | 8 +++++++- 7 files changed, 97 insertions(+), 12 deletions(-) diff --git a/docs/source/user/crf.rst b/docs/source/user/crf.rst index 2b88d41..a001015 100644 --- a/docs/source/user/crf.rst +++ b/docs/source/user/crf.rst @@ -1,7 +1,7 @@ Cost Reduction Framework ======================== -The Cost Reduction Framework (CRF) estimates how overnight capital cost, total +The Cost Reduction Framework estimates how overnight capital cost, total capital investment, and construction duration change across a sequence of firm reactor orders. It is available as the ``crf`` Python package inside ACCERT. @@ -49,7 +49,30 @@ cost-reduction levers. The returned dictionary includes the static lever values, per-unit metrics such as ``OCC_1``, ``TCI_1``, and ``duration_1``, and summary metrics such as -``avg_OCC``, ``avg_TCI``, and ``avg_duration``. +``avg_OCC``, ``avg_TCI``, ``avg_duration``, and +``occ_reduction_from_FOAK_to_NOAK_percent``. + +Visualization +------------- + +Use ``save_dashboard`` to generate dashboard-style capital-cost figures from a +scenario result. The dashboard includes the same core cost-reduction views as +the Excel dashboard: OCC, TCI, construction duration, cost breakdowns, and the +percent OCC reduction from FOAK to NOAK. + +.. code-block:: python + + from crf import run_one_scenario, save_dashboard + + result = run_one_scenario(config, levers) + save_dashboard( + result, + "cost_reduction_framework_dashboard.png", + title="AP1000 Cost Reduction Framework", + ) + +For downstream analysis, ``results_to_dataframe`` converts the scenario result +to a chart-ready ``pandas.DataFrame`` with one row per plant. Configuration ------------- @@ -62,7 +85,7 @@ Configuration * - Key - Description * - ``reactor_type`` - - Built-in CRF baseline: ``AP1000``, ``SFR``, or ``HTGR``. + - Built-in Cost Reduction Framework baseline: ``AP1000``, ``SFR``, or ``HTGR``. * - ``f_22`` - Reactor building cost adjustment. * - ``f_2321`` @@ -91,7 +114,7 @@ inputs use ``0`` or ``1`` and are converted internally to model labels. * - ``num_NOAK`` - Plant number used for nth-of-a-kind learning. Defaults to ``num_orders``. * - ``itc_percent`` - - Investment tax credit percentage. Values are rounded to the nearest supported CRF ITC level. + - Investment tax credit percentage. Values are rounded to the nearest supported Cost Reduction Framework ITC level. * - ``n_itc`` - Number of first units eligible for ITC. * - ``interest_percent`` @@ -140,8 +163,8 @@ The ``Levers`` sheet must include these columns: Levers, Min, Low, Median, High, Max, Distribution, Type, Set, Probabilities -The lever rows must follow the order expected by CRF because several rows share -the same display name: +The lever rows must follow the order expected by the Cost Reduction Framework +because several rows share the same display name: .. code-block:: text diff --git a/src/crf/__init__.py b/src/crf/__init__.py index a45f112..11b495b 100644 --- a/src/crf/__init__.py +++ b/src/crf/__init__.py @@ -8,9 +8,21 @@ run_sampling_from_excel, print_scenario_result, ) +from .visualization import ( + occ_reduction_from_foak_to_noak, + plot_dashboard, + results_to_dataframe, + save_dashboard, + save_figures, +) __all__ = [ "run_one_scenario", "run_sampling_from_excel", "print_scenario_result", + "occ_reduction_from_foak_to_noak", + "plot_dashboard", + "results_to_dataframe", + "save_dashboard", + "save_figures", ] diff --git a/src/crf/api.py b/src/crf/api.py index 404a3b0..d768584 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -79,7 +79,8 @@ def run_sampling_from_excel( + [f"duration_{i}" for i in range(1, max_orders + 1)] + [ "cons_duration_cumulative_wz_startup", - "occLastUnit", "TCILastUnit", "durationsLastUnit", + "occLastUnit", "occNOAKUnit", "occ_reduction_from_FOAK_to_NOAK_percent", + "TCILastUnit", "durationsLastUnit", "avg_OCC", "avg_TCI", "avg_duration", ] ) @@ -92,7 +93,8 @@ def run_sampling_from_excel( + [f"duration_{i}" for i in range(1, max_orders + 1)] + [ "cons_duration_cumulative_wz_startup", - "occLastUnit", "TCILastUnit", "durationsLastUnit", + "occLastUnit", "occNOAKUnit", "occ_reduction_from_FOAK_to_NOAK_percent", + "TCILastUnit", "durationsLastUnit", "avg_OCC", "avg_TCI", "avg_duration", ] ) @@ -144,7 +146,17 @@ def print_scenario_result(result: dict): print(results_df.round(2).fillna("").to_string(index=False)) - summary_metrics = ["cons_duration_cumulative_wz_startup", "occLastUnit", "TCILastUnit", "durationsLastUnit", "avg_OCC", "avg_TCI", "avg_duration"] + summary_metrics = [ + "cons_duration_cumulative_wz_startup", + "occLastUnit", + "occNOAKUnit", + "occ_reduction_from_FOAK_to_NOAK_percent", + "TCILastUnit", + "durationsLastUnit", + "avg_OCC", + "avg_TCI", + "avg_duration", + ] print("\nSummary metrics:\n") for metric in summary_metrics: print(f"{metric}: {result[metric]:.2f}") diff --git a/src/crf/model/avg_runner.py b/src/crf/model/avg_runner.py index 1a083b3..3f5ec29 100644 --- a/src/crf/model/avg_runner.py +++ b/src/crf/model/avg_runner.py @@ -56,6 +56,9 @@ def run_avg_all_units(config: dict, inp: dict, store, details=False): occLastUnit = float(OCC[-1]) TCILastUnit = float(TCI[-1]) durationsLastUnit = float(DUR[-1]) + noak_unit = min(max(int(inp.get("num_NOAK", num_orders)), 1), num_orders) + occNOAKUnit = float(OCC[noak_unit - 1]) + occ_reduction_from_FOAK_to_NOAK_percent = float((OCC[0] - occNOAKUnit) / OCC[0] * 100.0) if n_itc > 0: # from 1st to n_ITC-th unit (inclusive) have ITC, so use NETOCC/NCI for avg; from (n_ITC+1)-th to last unit have no ITC, so use OCC/TCI for avg; avg_OCC = float(np.mean(NETOCC))*(n_itc/num_orders) + float(np.mean(OCC[n_itc:]))*((num_orders-n_itc)/num_orders) @@ -106,6 +109,8 @@ def run_avg_all_units(config: dict, inp: dict, store, details=False): out.update({ "cons_duration_cumulative_wz_startup": float(cons_duration_cumulative_wz_startup), "occLastUnit": occLastUnit, + "occNOAKUnit": occNOAKUnit, + "occ_reduction_from_FOAK_to_NOAK_percent": occ_reduction_from_FOAK_to_NOAK_percent, "TCILastUnit": TCILastUnit, "durationsLastUnit": durationsLastUnit, "avg_OCC": avg_OCC, diff --git a/src/crf/sampling/lever_schema.py b/src/crf/sampling/lever_schema.py index e82630f..207ba4b 100644 --- a/src/crf/sampling/lever_schema.py +++ b/src/crf/sampling/lever_schema.py @@ -40,7 +40,7 @@ ] STATIC_KEYS = [ - "Num_orders", "ITC", "n_ITC", "interest rate", "Design completion", "Design_Maturity_0", + "Num_orders", "num_NOAK", "ITC", "n_ITC", "interest rate", "Design completion", "Design_Maturity_0", "supply chain exp_0", "N supply chain", "Const Proficiency", "N const prof", "AE", "N AE prof", "standardization", "modularity", "BOP commercial", "RB Safety Related", ] @@ -103,6 +103,7 @@ def static_row_from_levers(levers_raw: dict) -> dict: """ return { "Num_orders": levers_raw["num_orders"], + "num_NOAK": levers_raw.get("num_NOAK", levers_raw["num_orders"]), "ITC": levers_raw["itc_percent"], "n_ITC": levers_raw["n_itc"], "interest rate": levers_raw["interest_percent"], diff --git a/test/test_crf_api.py b/test/test_crf_api.py index 1f44a0e..ac29f0a 100644 --- a/test/test_crf_api.py +++ b/test/test_crf_api.py @@ -4,7 +4,13 @@ import pandas as pd import pytest -from crf import run_one_scenario, run_sampling_from_excel +from crf import ( + occ_reduction_from_foak_to_noak, + results_to_dataframe, + run_one_scenario, + run_sampling_from_excel, + save_dashboard, +) from crf.api import normalize_levers from crf.sampling.lever_schema import EXCEL_NAME_TO_ID_ORDERED @@ -86,6 +92,7 @@ def test_run_one_scenario_returns_static_inputs_and_unit_results(): result = run_one_scenario(_config(), _levers()) assert result["Num_orders"] == 2 + assert result["num_NOAK"] == 2 assert result["ITC"] == 30 assert result["n_ITC"] == 1 assert result["OCC_1"] > 0 @@ -95,6 +102,25 @@ def test_run_one_scenario_returns_static_inputs_and_unit_results(): assert result["avg_OCC"] > 0 assert result["avg_TCI"] > 0 assert result["avg_duration"] > 0 + assert result["occ_reduction_from_FOAK_to_NOAK_percent"] == pytest.approx( + (result["OCC_1"] - result["OCC_2"]) / result["OCC_1"] * 100 + ) + + +def test_visualization_helpers_create_dashboard(tmp_path): + result = run_one_scenario(_config(), _levers()) + frame = results_to_dataframe(result) + out_png = tmp_path / "cost_reduction_framework_dashboard.png" + + assert list(frame["Plant number"]) == [1, 2] + assert frame.loc[1, "OCC reduction from FOAK"] == pytest.approx( + occ_reduction_from_foak_to_noak(result) + ) + + save_dashboard(result, str(out_png), title="Cost Reduction Framework Test") + + assert out_png.exists() + assert out_png.stat().st_size > 0 def test_run_sampling_from_excel_writes_csv_and_pickle_outputs(tmp_path): diff --git a/tutorial/crf_quickstart.py b/tutorial/crf_quickstart.py index 312fa0a..3a372bb 100644 --- a/tutorial/crf_quickstart.py +++ b/tutorial/crf_quickstart.py @@ -5,7 +5,9 @@ PYTHONPATH=src python tutorial/crf_quickstart.py """ -from crf import print_scenario_result, run_one_scenario +from pathlib import Path + +from crf import print_scenario_result, run_one_scenario, save_dashboard config = { @@ -41,3 +43,7 @@ if __name__ == "__main__": result = run_one_scenario(config, levers) print_scenario_result(result) + + output_path = Path("cost_reduction_framework_dashboard.png") + save_dashboard(result, output_path, title="AP1000 Cost Reduction Framework") + print(f"\nSaved dashboard figure to: {output_path.resolve()}") From 885847ea893a120daf7261f1afadeedbb82d0b2b Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Fri, 1 May 2026 14:37:04 -0500 Subject: [PATCH 18/25] change to waterfall plot --- docs/source/user/crf.rst | 6 +- src/crf/__init__.py | 4 + src/crf/api.py | 85 ++++++++++- src/crf/model/direct_cost.py | 56 +++++-- src/crf/model/learning.py | 18 ++- src/crf/model/pipeline.py | 25 +++- src/crf/visualization.py | 276 +++++++++++++++++++++++++++++++++++ test/test_crf_api.py | 19 +++ 8 files changed, 473 insertions(+), 16 deletions(-) create mode 100644 src/crf/visualization.py diff --git a/docs/source/user/crf.rst b/docs/source/user/crf.rst index a001015..5429022 100644 --- a/docs/source/user/crf.rst +++ b/docs/source/user/crf.rst @@ -58,7 +58,7 @@ Visualization Use ``save_dashboard`` to generate dashboard-style capital-cost figures from a scenario result. The dashboard includes the same core cost-reduction views as the Excel dashboard: OCC, TCI, construction duration, cost breakdowns, and the -percent OCC reduction from FOAK to NOAK. +FOAK-to-NOAK OCC reduction waterfall by lever. .. code-block:: python @@ -72,7 +72,9 @@ percent OCC reduction from FOAK to NOAK. ) For downstream analysis, ``results_to_dataframe`` converts the scenario result -to a chart-ready ``pandas.DataFrame`` with one row per plant. +to a chart-ready ``pandas.DataFrame`` with one row per plant, while +``waterfall_to_dataframe`` returns the FOAK-to-NOAK waterfall values using the +lever labels from the Excel dashboard. Configuration ------------- diff --git a/src/crf/__init__.py b/src/crf/__init__.py index 11b495b..a3981b1 100644 --- a/src/crf/__init__.py +++ b/src/crf/__init__.py @@ -4,6 +4,7 @@ """ from .api import ( + calculate_occ_waterfall, run_one_scenario, run_sampling_from_excel, print_scenario_result, @@ -14,10 +15,12 @@ results_to_dataframe, save_dashboard, save_figures, + waterfall_to_dataframe, ) __all__ = [ "run_one_scenario", + "calculate_occ_waterfall", "run_sampling_from_excel", "print_scenario_result", "occ_reduction_from_foak_to_noak", @@ -25,4 +28,5 @@ "results_to_dataframe", "save_dashboard", "save_figures", + "waterfall_to_dataframe", ] diff --git a/src/crf/api.py b/src/crf/api.py index d768584..f89dbd3 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -15,6 +15,7 @@ ) from .sampling.postprocess import apply_itc_rounding from .model.avg_runner import run_avg_all_units +from .model.pipeline import calculate_final_result from .utils.serialize import write_csv_row, stream_pickle_dump @@ -53,7 +54,89 @@ def run_one_scenario(config: dict, levers: dict) -> dict: result = run_avg_all_units(config=config, inp=inp, store=store, details=True) static_vals = static_row_from_levers(levers) - return {**static_vals, **result} + out = {**static_vals, **result} + out["occ_waterfall"] = calculate_occ_waterfall(config, levers) + return out + + +def calculate_occ_waterfall(config: dict, levers: dict) -> list[dict]: + """Calculate FOAK-to-NOAK OCC waterfall contributions by model stage.""" + store = InputStore(config.get("data_dir")) + normalized = normalize_levers(levers) + noak_unit = min(max(int(normalized.get("num_NOAK", normalized["num_orders"])), 1), int(normalized["num_orders"])) + + foak_trace = {} + noak_trace = {} + calculate_final_result(config=config, inp=normalized, store=store, n_th=1, trace=foak_trace) + calculate_final_result(config=config, inp=normalized, store=store, n_th=noak_unit, trace=noak_trace) + + foak_occ = float(foak_trace["final_occ"]) + noak_occ = float(noak_trace["final_occ"]) + + labels = [ + "Bulk-ordering", + "Elimination of rework", + "Supplychain efficiency", + "Labor productivity", + "Experience and cross-site standardization", + "Modular Construction", + "Commercial BOP", + "Non safety-related Reactor Building", + ] + + contributions = { + "Bulk-ordering": _trace_delta(noak_trace, foak_trace, "Bulk-ordering"), + "Elimination of rework": _trace_delta(noak_trace, foak_trace, "Elimination of rework"), + "Supplychain efficiency": ( + _trace_delta(noak_trace, foak_trace, "indirect_cost_delta") + + _trace_delta(noak_trace, foak_trace, "supplementary_cost_delta") + ), + "Labor productivity": _trace_delta(noak_trace, foak_trace, "Labor productivity"), + "Experience and cross-site standardization": _trace_delta( + noak_trace, foak_trace, "Experience and cross-site standardization" + ), + "Modular Construction": _trace_delta(noak_trace, foak_trace, "Modular Construction"), + "Commercial BOP": _trace_delta(noak_trace, foak_trace, "Commercial BOP"), + "Non safety-related Reactor Building": _trace_delta( + noak_trace, foak_trace, "Non safety-related Reactor Building" + ), + } + residual = (noak_occ - foak_occ) - sum(contributions.values()) + contributions["Experience and cross-site standardization"] += residual + + rows = [ + { + "label": "FOAK \n(no firm orders)", + "absolute_change": foak_occ, + "cumulative_occ": foak_occ, + "kind": "total", + } + ] + cumulative = foak_occ + for label in labels: + change = float(contributions[label]) + cumulative += change + rows.append( + { + "label": label, + "absolute_change": change, + "cumulative_occ": cumulative, + "kind": "change", + } + ) + rows.append( + { + "label": "NOAK \n(firm orders)", + "absolute_change": noak_occ, + "cumulative_occ": noak_occ, + "kind": "total", + } + ) + return rows + + +def _trace_delta(noak_trace: dict, foak_trace: dict, key: str) -> float: + return float(noak_trace.get(key, 0.0)) - float(foak_trace.get(key, 0.0)) def run_sampling_from_excel( config: dict, diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index f166aa6..53e38ad 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -25,6 +25,22 @@ ACCT_DIRECT = [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26] +def _total_occ_per_kwe(df: pd.DataFrame, power: float) -> float: + db = update_high_level_costs(df.copy(), power) + return float( + db.loc[ + db["Title"].eq("Total Overnight Cost (Accounts 10 to 50)"), + "Total Cost (USD)", + ].iloc[0] + / power + ) + + +def _record_delta(trace, key: str, before: pd.DataFrame, after: pd.DataFrame, power: float): + if trace is not None: + trace[key] = _total_occ_per_kwe(after, power) - _total_occ_per_kwe(before, power) + + def add_factory_cost(df: pd.DataFrame, power: float, f_22: float, f_2321: float, num_orders: int, reactor_type: str): db = df.copy() if reactor_type in ["HTGR", "SFR"]: @@ -56,7 +72,8 @@ def add_BOP_RP_grades( power: float, reactor_type: str, n_th: int, - mod_0: str + mod_0: str, + trace=None ): # RB grade does not change; BOP becomes non_nuclear after FOAK RB_grade = RB_grade_0 @@ -67,13 +84,16 @@ def add_BOP_RP_grades( db = df.copy() + before_rb = db.copy() if RB_grade == "non_nuclear": mulv( db, [212], ["Site Material Cost", "Site Labor Cost", "Site Labor Hours", "Factory Equipment Cost"], 0.6 ) + _record_delta(trace, "Non safety-related Reactor Building", before_rb, db, power) + before_bop = db.copy() if BOP_grade == "non_nuclear": if reactor_type in ["HTGR", "SFR"]: mulv( @@ -94,6 +114,7 @@ def add_BOP_RP_grades( ["Site Material Cost", "Site Labor Cost", "Site Labor Hours", "Factory Equipment Cost"], 0.6 ) + _record_delta(trace, "Commercial BOP", before_bop, db, power) db2 = update_high_level_costs(db, power)[COLS].copy() # duration update from grade change @@ -119,6 +140,7 @@ def add_BOP_RP_grades( mod_factor = 0.8 if mod == "modularized" else 1.0 # NOTE see excel Relationship sheet D4 # NOTE in excel tool HTGR does nothing here, SFR factory cost of 21 with the 20% reduction # with AP1000 all labor cost with the 20% reduction in 20s accounts, + before_mod = db2.copy() if reactor_type == "SFR": for acct in [21, "211 plus 214 to 219", 212, 213]: setv(db2, acct, "Factory Equipment Cost", float(getv(db2, acct, "Factory Equipment Cost")) * mod_factor) @@ -128,12 +150,13 @@ def add_BOP_RP_grades( setv(db2, acct, "Site Labor Cost", float(getv(db2, acct, "Site Labor Cost")) * mod_factor) setv(db2, acct, "Site Labor Hours", float(getv(db2, acct, "Site Labor Hours")) * mod_factor) db2 = update_high_level_costs(db2, power)[COLS].copy() + _record_delta(trace, "Modular Construction", before_mod, db2, power) return db2, new_dur * mod_factor -def add_bulk_ordering(df: pd.DataFrame, num_orders: int, f_22: float, f_2321: float, power: float, reactor_type: str): +def add_bulk_ordering(df: pd.DataFrame, num_orders: int, f_22: float, f_2321: float, power: float, reactor_type: str, trace=None): """ Applies average learning reduction to factory equipment cost of 22 and 232.1, but keeps factory-building portion intact. @@ -165,7 +188,9 @@ def add_bulk_ordering(df: pd.DataFrame, num_orders: int, f_22: float, f_2321: fl new2321 = red2321 * (old2321 - (f_2321 / num_orders)) + (f_2321 / num_orders) setv(db, "232.1", "Factory Equipment Cost", new2321) - return update_high_level_costs(db, power)[COLS].copy() + db2 = update_high_level_costs(db, power)[COLS].copy() + _record_delta(trace, "Bulk-ordering", df, db2, power) + return db2 def add_reworking_productivity( @@ -179,7 +204,8 @@ def add_reworking_productivity( N_cons: float, power: float, prev_cons_duration: float, - baseline_lab_hours + baseline_lab_hours, + trace=None ): if n_th == 1: design_completion = design_completion_0 @@ -208,10 +234,19 @@ def add_reworking_productivity( for acct in ACCT_DIRECT: setv(db, acct, "Factory Equipment Cost", float(getv(df, acct, "Factory Equipment Cost")) * rework) setv(db, acct, "Site Material Cost", float(getv(df, acct, "Site Material Cost")) * rework) - setv(db, acct, "Site Labor Hours", float(getv(df, acct, "Site Labor Hours")) * rework / productivity) - setv(db, acct, "Site Labor Cost", float(getv(df, acct, "Site Labor Cost")) * rework / productivity) + setv(db, acct, "Site Labor Hours", float(getv(df, acct, "Site Labor Hours")) * rework) + setv(db, acct, "Site Labor Cost", float(getv(df, acct, "Site Labor Cost")) * rework) + + rework_db = update_high_level_costs(db, power)[COLS].copy() + _record_delta(trace, "Elimination of rework", df, rework_db, power) + + db = rework_db.copy() + for acct in ACCT_DIRECT: + setv(db, acct, "Site Labor Hours", float(getv(rework_db, acct, "Site Labor Hours")) / productivity) + setv(db, acct, "Site Labor Cost", float(getv(rework_db, acct, "Site Labor Cost")) / productivity) db2 = update_high_level_costs(db, power)[COLS].copy() + _record_delta(trace, "Labor productivity", rework_db, db2, power) new_dur = float(update_cons_duration_2(df, db2, ref_duration, prev_cons_duration, baseline_lab_hours, reactor_type)) return db2, new_dur @@ -231,7 +266,8 @@ def update_direct_cost( N_AE: float, ce_exp_0: float, N_cons: float, - mod_0: str + mod_0: str, + trace=None ): reactor_df, power = store.get_baseline(reactor_type) db, baseline_lab_hours = add_factory_cost(reactor_df, power, f_22, f_2321, num_orders, reactor_type) @@ -240,11 +276,11 @@ def update_direct_cost( db.loc[db["Account"].isin(['21', '211 plus 214 to 219', '212', '213', '22', '23', '232.1', '233', '24', '25', '26']), "Total Cost (USD)"] = 0.0 db = update_high_level_costs(db, power)[COLS].copy() db = add_land_cost(db, land_cost_per_acre_0, power) - db, prev_dur = add_BOP_RP_grades(db, RB_grade_0, BOP_grade_0, power, reactor_type, n_th, mod_0) - db = add_bulk_ordering(db, num_orders, f_22, f_2321, power, reactor_type) + db, prev_dur = add_BOP_RP_grades(db, RB_grade_0, BOP_grade_0, power, reactor_type, n_th, mod_0, trace=trace) + db = add_bulk_ordering(db, num_orders, f_22, f_2321, power, reactor_type, trace=trace) db, dur_no_delay = add_reworking_productivity( db, reactor_type, n_th, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, - power, prev_dur, baseline_lab_hours + power, prev_dur, baseline_lab_hours, trace=trace ) return db, dur_no_delay diff --git a/src/crf/model/learning.py b/src/crf/model/learning.py index 622fa57..43931cf 100644 --- a/src/crf/model/learning.py +++ b/src/crf/model/learning.py @@ -19,7 +19,18 @@ ACCT_DIRECT = [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26] -def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power: float, reactor_type: str, n_of_NOAK: int): +def _total_occ_per_kwe(df: pd.DataFrame, power: float) -> float: + db = update_high_level_costs(df.copy(), power) + return float( + db.loc[ + db["Title"].eq("Total Overnight Cost (Accounts 10 to 50)"), + "Total Cost (USD)", + ].iloc[0] + / power + ) + + +def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power: float, reactor_type: str, n_of_NOAK: int, trace=None): # standardization cap for FOAK standardization = min(0.7, standardization_0) if n_th == 1 else standardization_0 @@ -108,7 +119,10 @@ def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power setv(db, 22, "Factory Equipment Cost", float(getv(df, 22, "Factory Equipment Cost")) * (1 - fac_lr_22) ** np.log2(n_th)) setv(db, "232.1", "Factory Equipment Cost", float(getv(df, "232.1", "Factory Equipment Cost")) * (1 - fac_lr_2321) ** np.log2(n_th)) - return update_high_level_costs(db, power)[COLS].copy() + db2 = update_high_level_costs(db, power)[COLS].copy() + if trace is not None: + trace["Experience and cross-site standardization"] = _total_occ_per_kwe(db2, power) - _total_occ_per_kwe(df, power) + return db2 def act_cons_duration_plus_delay( diff --git a/src/crf/model/pipeline.py b/src/crf/model/pipeline.py index 2497ef3..fee8aba 100644 --- a/src/crf/model/pipeline.py +++ b/src/crf/model/pipeline.py @@ -4,7 +4,7 @@ from .finance import tax_update, insurance_cost_update, decomission_cost_update, update_interest_cost, update_itc import numpy as np -def calculate_final_result(config: dict, inp: dict, store, n_th: int): +def calculate_final_result(config: dict, inp: dict, store, n_th: int, trace=None): """ One unit (n_th) calculation. Returns: (final_df, levelized_net_OCC, levelized_NCI, final_construction_duration_scalar) @@ -27,6 +27,7 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): ce_exp_0=inp["ce_exp_0"], N_cons=inp["N_cons"], mod_0=inp["mod_0"], + trace=trace, ) # duration: add delay + learning dur_plus_delay = act_cons_duration_plus_delay( @@ -37,11 +38,15 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): N_proc=inp["N_proc"], cons_duration_no_delay=dur_no_delay, ) + if trace is not None: + trace["supply_chain_delay_months"] = float(dur_plus_delay) - float(dur_no_delay) final_dur = duration_learning_effect(reactor_type=config["reactor_type"], n_th=n_th, standardization_0=inp["standardization_0"], actual_construction_duration_plus_delay=dur_plus_delay, n_of_NOAK=inp["num_orders"]) + if trace is not None: + trace["duration_learning_months"] = float(final_dur) - float(dur_plus_delay) # learning on direct costs direct_plus_learning = learning_effect(direct_df, n_th, @@ -49,15 +54,20 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): power, config["reactor_type"], inp["num_orders"], + trace=trace, ) # indirect costs with_indirect = update_indirect_cost(n_th, inp["standardization_0"], direct_plus_learning, final_dur, power, reactor_type=config["reactor_type"]) + if trace is not None: + trace["indirect_cost_delta"] = _occ_per_kwe(with_indirect, power) - _occ_per_kwe(direct_plus_learning, power) # Supplementary costs: tax, insurance, decommissioning with_tax = tax_update(with_indirect, power) with_insurance = insurance_cost_update(with_tax, power) with_decomm = decomission_cost_update(with_insurance, power) + if trace is not None: + trace["supplementary_cost_delta"] = _occ_per_kwe(with_decomm, power) - _occ_per_kwe(with_indirect, power) # Finance costs: interest during construction and ITC with_interest, tot_occ, tot_cap = update_interest_cost( @@ -74,7 +84,20 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int): org_occ = float(tot_occ/power) org_tci = float(tot_cap/power) final_df, net_occ, nci = update_itc(with_interest, tot_occ, tot_cap, n_th, inp["ITC_0"], inp["n_ITC"], power) + if trace is not None: + trace["final_occ"] = org_occ + trace["final_tci"] = org_tci # print(f"Final OCC for plant {n_th}: {net_occ}") # print(final_df) return final_df, org_occ, net_occ, org_tci, nci, float(final_dur) + + +def _occ_per_kwe(df, power): + return float( + df.loc[ + df["Title"].eq("Total Overnight Cost (Accounts 10 to 50)"), + "Total Cost (USD)", + ].iloc[0] + / power + ) diff --git a/src/crf/visualization.py b/src/crf/visualization.py new file mode 100644 index 0000000..6e8e5ba --- /dev/null +++ b/src/crf/visualization.py @@ -0,0 +1,276 @@ +"""Visualization helpers for Cost Reduction Framework results.""" + +from __future__ import annotations + +import os +import tempfile +import textwrap +from pathlib import Path +from typing import Dict, Iterable, Optional + +os.environ.setdefault("MPLCONFIGDIR", tempfile.gettempdir()) + +import matplotlib + +matplotlib.use("Agg") + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + + +CAPITAL_COLORS = { + "occ": "#1f77b4", + "net_occ": "#7fb3d5", + "tci": "#2ca02c", + "nci": "#98df8a", + "duration": "#9467bd", + "startup": "#c5b0d5", + "preconstruction": "#4c78a8", + "equipment": "#72b7b2", + "material": "#f58518", + "labor": "#e45756", + "indirect": "#54a24b", + "supplementary": "#b279a2", + "finance": "#ff9da6", + "reduction": "#d62728", +} + + +def _ordered_suffixes(result: dict, prefix: str) -> list[int]: + suffixes = [] + for key in result: + if key.startswith(prefix): + try: + suffixes.append(int(key.split("_")[-1])) + except ValueError: + pass + return sorted(suffixes) + + +def results_to_dataframe(result: dict) -> pd.DataFrame: + """Convert a ``run_one_scenario`` result dictionary into chart-ready rows.""" + plant_numbers = _ordered_suffixes(result, "OCC_") + if not plant_numbers: + raise ValueError("result does not contain OCC_i values") + + rows = [] + foak_occ = float(result[f"OCC_{plant_numbers[0]}"]) + for plant_number in plant_numbers: + occ = float(result[f"OCC_{plant_number}"]) + rows.append( + { + "Plant number": plant_number, + "OCC": occ, + "Net OCC": result.get(f"NETOCC_{plant_number}", np.nan), + "TCI": result.get(f"TCI_{plant_number}", np.nan), + "NCI": result.get(f"NCI_{plant_number}", np.nan), + "Construction duration": result.get(f"duration_{plant_number}", np.nan), + "Startup duration": result.get(f"STAUP_{plant_number}", np.nan), + "Preconstruction costs": result.get(f"D10s_{plant_number}", np.nan), + "Direct costs": result.get(f"D20s_{plant_number}", np.nan), + "Direct costs: equipment": result.get(f"D20_equip_{plant_number}", np.nan), + "Direct costs: material": result.get(f"D20_mat_{plant_number}", np.nan), + "Direct costs: labor": result.get(f"D20_labor_{plant_number}", np.nan), + "Indirect costs": result.get(f"D30s_{plant_number}", np.nan), + "Supplementary costs": result.get(f"D50s_{plant_number}", np.nan), + "Financing costs": result.get(f"D60s_{plant_number}", np.nan), + "OCC reduction from FOAK": (foak_occ - occ) / foak_occ * 100.0, + } + ) + + return pd.DataFrame(rows) + + +def occ_reduction_from_foak_to_noak(result: dict) -> float: + """Return percent OCC reduction from the FOAK unit to the NOAK unit.""" + if "occ_reduction_from_FOAK_to_NOAK_percent" in result: + return float(result["occ_reduction_from_FOAK_to_NOAK_percent"]) + + noak_unit = int(result.get("Num_orders", max(_ordered_suffixes(result, "OCC_")))) + foak_occ = float(result["OCC_1"]) + noak_occ = float(result[f"OCC_{noak_unit}"]) + return (foak_occ - noak_occ) / foak_occ * 100.0 + + +def waterfall_to_dataframe(result: dict) -> pd.DataFrame: + """Return the FOAK-to-NOAK OCC waterfall rows saved by the API.""" + rows = result.get("occ_waterfall") + if not rows: + raise ValueError("result does not contain occ_waterfall data") + return pd.DataFrame(rows) + + +def plot_dashboard(result: dict, title: Optional[str] = None, figsize=(16, 12)): + """Create a dashboard-style figure from Cost Reduction Framework output. + + The layout mirrors the capital-cost charts in the Excel dashboard: OCC, + TCI, construction duration, cost breakdowns, and the percent OCC reduction + from FOAK to NOAK. + """ + df = results_to_dataframe(result) + noak_unit = int(result.get("num_NOAK", result.get("Num_orders", df["Plant number"].max()))) + noak_unit = min(max(noak_unit, int(df["Plant number"].min())), int(df["Plant number"].max())) + noak_reduction = occ_reduction_from_foak_to_noak(result) + + fig, axes = plt.subplots(3, 2, figsize=figsize, constrained_layout=True) + fig.suptitle(title or "Cost Reduction Framework Dashboard", fontsize=16, fontweight="bold") + + x = df["Plant number"] + + ax = axes[0, 0] + ax.bar(x - 0.18, df["OCC"], width=0.36, label="Overnight Capital Cost (OCC)", color=CAPITAL_COLORS["occ"]) + if df["Net OCC"].notna().any(): + ax.bar(x + 0.18, df["Net OCC"], width=0.36, label="Net OCC (after ITC)", color=CAPITAL_COLORS["net_occ"]) + ax.set_title("Overnight Capital Cost") + ax.set_xlabel("Plant number") + ax.set_ylabel("$/kWe") + ax.legend(fontsize=8) + + ax = axes[0, 1] + ax.bar(x - 0.18, df["TCI"], width=0.36, label="Total Capital Investment (TCI)", color=CAPITAL_COLORS["tci"]) + if df["NCI"].notna().any(): + ax.bar(x + 0.18, df["NCI"], width=0.36, label="Net Capital Investment (NCI)", color=CAPITAL_COLORS["nci"]) + ax.set_title("Capital Investment") + ax.set_xlabel("Plant number") + ax.set_ylabel("$/kWe") + ax.legend(fontsize=8) + + ax = axes[1, 0] + ax.bar(x, df["Construction duration"], label="Construction", color=CAPITAL_COLORS["duration"]) + if df["Startup duration"].notna().any(): + ax.bar( + x, + df["Startup duration"], + bottom=df["Construction duration"], + label="Startup", + color=CAPITAL_COLORS["startup"], + ) + ax.set_title("Total Construction Duration") + ax.set_xlabel("Plant number") + ax.set_ylabel("Months") + ax.legend(fontsize=8) + + ax = axes[1, 1] + breakdown = [ + ("Preconstruction costs", "preconstruction"), + ("Direct costs", "equipment"), + ("Indirect costs", "indirect"), + ("Supplementary costs", "supplementary"), + ("Financing costs", "finance"), + ] + _stacked_bars(ax, x, df, breakdown) + ax.set_title("Capital Cost Breakdown") + ax.set_xlabel("Plant number") + ax.set_ylabel("$/kWe") + ax.legend(fontsize=8) + + ax = axes[2, 0] + direct_breakdown = [ + ("Direct costs: equipment", "equipment"), + ("Direct costs: material", "material"), + ("Direct costs: labor", "labor"), + ("Indirect costs", "indirect"), + ("Supplementary costs", "supplementary"), + ] + _stacked_bars(ax, x, df, direct_breakdown) + ax.set_title("Detailed Cost Components") + ax.set_xlabel("Plant number") + ax.set_ylabel("$/kWe") + ax.legend(fontsize=8) + + ax = axes[2, 1] + _plot_occ_waterfall(ax, result, noak_reduction) + + for ax in axes.flat: + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + ax.tick_params(axis="x", labelrotation=0) + + return fig + + +def save_dashboard(result: dict, out_path: str, title: Optional[str] = None, dpi: int = 180) -> str: + """Save the dashboard figure and return the output path.""" + path = Path(out_path) + path.parent.mkdir(parents=True, exist_ok=True) + fig = plot_dashboard(result, title=title) + fig.savefig(path, dpi=dpi, bbox_inches="tight") + plt.close(fig) + return str(path) + + +def save_figures(result: dict, output_dir: str, prefix: str = "cost_reduction_framework") -> Dict[str, str]: + """Save the dashboard and individual dashboard figures as PNG files.""" + out_dir = Path(output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + saved = { + "dashboard": save_dashboard(result, out_dir / f"{prefix}_dashboard.png"), + } + return saved + + +def _plot_occ_waterfall(ax, result: dict, noak_reduction: float) -> None: + waterfall = waterfall_to_dataframe(result) + labels = [_wrap_label(label) for label in waterfall["label"].tolist()] + x_pos = np.arange(len(waterfall)) + + colors = [] + bottoms = [] + heights = [] + previous = 0.0 + for _, row in waterfall.iterrows(): + if row["kind"] == "total": + bottoms.append(0.0) + heights.append(float(row["cumulative_occ"])) + colors.append("#4c78a8") + previous = float(row["cumulative_occ"]) + else: + change = float(row["absolute_change"]) + if change >= 0: + bottoms.append(previous) + heights.append(change) + colors.append("#f58518") + else: + bottoms.append(previous + change) + heights.append(abs(change)) + colors.append("#54a24b") + previous += change + + ax.bar(x_pos, heights, bottom=bottoms, color=colors, width=0.72) + for idx in range(len(waterfall) - 1): + y = float(waterfall.iloc[idx]["cumulative_occ"]) + ax.plot([idx + 0.36, idx + 1 - 0.36], [y, y], color="#777777", linewidth=0.8) + + ax.set_title("% OCC Reduction from FOAK to NOAK") + ax.set_ylabel("OCC ($/kWe)") + ax.set_xticks(x_pos) + ax.set_xticklabels(labels, rotation=0, ha="center", fontsize=7) + ax.grid(True, axis="y", alpha=0.25) + ax.text( + 0.98, + 0.95, + f"FOAK to NOAK reduction: {noak_reduction:.1f}%", + transform=ax.transAxes, + ha="right", + va="top", + fontsize=9, + bbox={"facecolor": "white", "edgecolor": "#cccccc", "alpha": 0.9}, + ) + + +def _stacked_bars(ax, x: Iterable[int], df: pd.DataFrame, columns: list[tuple[str, str]]) -> None: + bottom = np.zeros(len(df)) + for label, color_key in columns: + values = df[label].astype(float) + if not values.notna().any(): + continue + clean_values = values.fillna(0.0).to_numpy() + ax.bar(x, clean_values, bottom=bottom, label=label, color=CAPITAL_COLORS[color_key]) + bottom += clean_values + + +def _wrap_label(label: str, width: int = 13) -> str: + compact = " ".join(str(label).split()) + return "\n".join(textwrap.wrap(compact, width=width)) diff --git a/test/test_crf_api.py b/test/test_crf_api.py index ac29f0a..4c7d535 100644 --- a/test/test_crf_api.py +++ b/test/test_crf_api.py @@ -10,6 +10,7 @@ run_one_scenario, run_sampling_from_excel, save_dashboard, + waterfall_to_dataframe, ) from crf.api import normalize_levers from crf.sampling.lever_schema import EXCEL_NAME_TO_ID_ORDERED @@ -105,6 +106,24 @@ def test_run_one_scenario_returns_static_inputs_and_unit_results(): assert result["occ_reduction_from_FOAK_to_NOAK_percent"] == pytest.approx( (result["OCC_1"] - result["OCC_2"]) / result["OCC_1"] * 100 ) + waterfall = waterfall_to_dataframe(result) + assert waterfall["label"].tolist() == [ + "FOAK \n(no firm orders)", + "Bulk-ordering", + "Elimination of rework", + "Supplychain efficiency", + "Labor productivity", + "Experience and cross-site standardization", + "Modular Construction", + "Commercial BOP", + "Non safety-related Reactor Building", + "NOAK \n(firm orders)", + ] + assert waterfall.iloc[0]["cumulative_occ"] == pytest.approx(result["OCC_1"]) + assert waterfall.iloc[-1]["cumulative_occ"] == pytest.approx(result["OCC_2"]) + assert waterfall.iloc[1:-1]["absolute_change"].sum() == pytest.approx( + result["OCC_2"] - result["OCC_1"] + ) def test_visualization_helpers_create_dashboard(tmp_path): From 240ee79708d178ef3d5b37bb4a39e4e1e60a01ca Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Fri, 1 May 2026 15:28:00 -0500 Subject: [PATCH 19/25] add htgr and sfr --- docs/source/user/crf.rst | 3 +- src/crf/api.py | 40 +++++++++++++--- src/crf/io/excel_inputs.py | 3 -- src/crf/model/pipeline.py | 20 +++++++- test/test_crf_api.py | 5 ++ ...rf_quickstart.py => crf_ap1000_example.py} | 8 +++- tutorial/crf_htgr_example.py | 48 +++++++++++++++++++ tutorial/crf_sfr_example.py | 48 +++++++++++++++++++ 8 files changed, 162 insertions(+), 13 deletions(-) rename tutorial/{crf_quickstart.py => crf_ap1000_example.py} (84%) create mode 100644 tutorial/crf_htgr_example.py create mode 100644 tutorial/crf_sfr_example.py diff --git a/docs/source/user/crf.rst b/docs/source/user/crf.rst index 5429022..122118f 100644 --- a/docs/source/user/crf.rst +++ b/docs/source/user/crf.rst @@ -187,4 +187,5 @@ because several rows share the same display name: Commercial BOP Non-safety-related RB -See ``tutorial/crf_quickstart.py`` for a runnable deterministic scenario. +Runnable deterministic examples are available in ``tutorial/crf_ap1000_example.py``, +``tutorial/crf_htgr_example.py``, and ``tutorial/crf_sfr_example.py``. diff --git a/src/crf/api.py b/src/crf/api.py index f89dbd3..470640c 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -87,10 +87,7 @@ def calculate_occ_waterfall(config: dict, levers: dict) -> list[dict]: contributions = { "Bulk-ordering": _trace_delta(noak_trace, foak_trace, "Bulk-ordering"), "Elimination of rework": _trace_delta(noak_trace, foak_trace, "Elimination of rework"), - "Supplychain efficiency": ( - _trace_delta(noak_trace, foak_trace, "indirect_cost_delta") - + _trace_delta(noak_trace, foak_trace, "supplementary_cost_delta") - ), + "Supplychain efficiency": _trace_delta(noak_trace, foak_trace, "Supplychain efficiency"), "Labor productivity": _trace_delta(noak_trace, foak_trace, "Labor productivity"), "Experience and cross-site standardization": _trace_delta( noak_trace, foak_trace, "Experience and cross-site standardization" @@ -101,8 +98,7 @@ def calculate_occ_waterfall(config: dict, levers: dict) -> list[dict]: noak_trace, foak_trace, "Non safety-related Reactor Building" ), } - residual = (noak_occ - foak_occ) - sum(contributions.values()) - contributions["Experience and cross-site standardization"] += residual + _allocate_waterfall_residual(contributions, (noak_occ - foak_occ) - sum(contributions.values())) rows = [ { @@ -138,6 +134,38 @@ def calculate_occ_waterfall(config: dict, levers: dict) -> list[dict]: def _trace_delta(noak_trace: dict, foak_trace: dict, key: str) -> float: return float(noak_trace.get(key, 0.0)) - float(foak_trace.get(key, 0.0)) + +def _allocate_waterfall_residual(contributions: dict, residual: float) -> None: + """Allocate downstream model effects across levers that reduce OCC. + + Some model terms, especially indirect and supplementary costs, are + recalculated after direct-cost levers have changed the cost base. The + spreadsheet waterfall distributes those downstream effects across the + levers instead of assigning them to one lever. Do the same here so the + waterfall reconciles without overstating experience/standardization. + """ + if abs(residual) < 1e-9: + return + + if residual < 0: + keys = [ + key for key, value in contributions.items() + if value < 0 and key != "Supplychain efficiency" + ] + else: + keys = [ + key for key, value in contributions.items() + if value > 0 and key != "Supplychain efficiency" + ] + + weight_total = sum(abs(contributions[key]) for key in keys) + if weight_total == 0: + contributions["Experience and cross-site standardization"] += residual + return + + for key in keys: + contributions[key] += residual * abs(contributions[key]) / weight_total + def run_sampling_from_excel( config: dict, levers_xlsx: str, diff --git a/src/crf/io/excel_inputs.py b/src/crf/io/excel_inputs.py index 0aaf785..02ef2bd 100644 --- a/src/crf/io/excel_inputs.py +++ b/src/crf/io/excel_inputs.py @@ -37,8 +37,6 @@ def get_baseline(self, reactor_type: str): power = 2234 * 1000 else: raise ValueError(f"Unknown reactor_type: {reactor_type}") - # print current running path for debugging - print(f"Loading baseline from: {path}") df = pd.read_csv(path) missing = set(COLS) - set(df.columns) if missing: @@ -67,4 +65,3 @@ def get_spending_curve(self,reactor_type: str): cdfs = sp["CDF"].to_numpy(dtype=float) self._spending = (months, cdfs) return self._spending - diff --git a/src/crf/model/pipeline.py b/src/crf/model/pipeline.py index fee8aba..37a4ee0 100644 --- a/src/crf/model/pipeline.py +++ b/src/crf/model/pipeline.py @@ -45,6 +45,11 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int, trace=None standardization_0=inp["standardization_0"], actual_construction_duration_plus_delay=dur_plus_delay, n_of_NOAK=inp["num_orders"]) + no_supply_final_dur = duration_learning_effect(reactor_type=config["reactor_type"], + n_th=n_th, + standardization_0=inp["standardization_0"], + actual_construction_duration_plus_delay=dur_no_delay, + n_of_NOAK=inp["num_orders"]) if trace is not None: trace["duration_learning_months"] = float(final_dur) - float(dur_plus_delay) @@ -59,14 +64,25 @@ def calculate_final_result(config: dict, inp: dict, store, n_th: int, trace=None # indirect costs with_indirect = update_indirect_cost(n_th, inp["standardization_0"], direct_plus_learning, final_dur, power, reactor_type=config["reactor_type"]) - if trace is not None: - trace["indirect_cost_delta"] = _occ_per_kwe(with_indirect, power) - _occ_per_kwe(direct_plus_learning, power) # Supplementary costs: tax, insurance, decommissioning with_tax = tax_update(with_indirect, power) with_insurance = insurance_cost_update(with_tax, power) with_decomm = decomission_cost_update(with_insurance, power) if trace is not None: + no_supply_indirect = update_indirect_cost( + n_th, + inp["standardization_0"], + direct_plus_learning, + no_supply_final_dur, + power, + reactor_type=config["reactor_type"], + ) + no_supply_tax = tax_update(no_supply_indirect, power) + no_supply_insurance = insurance_cost_update(no_supply_tax, power) + no_supply_decomm = decomission_cost_update(no_supply_insurance, power) + trace["Supplychain efficiency"] = _occ_per_kwe(with_decomm, power) - _occ_per_kwe(no_supply_decomm, power) + trace["indirect_cost_delta"] = _occ_per_kwe(with_indirect, power) - _occ_per_kwe(direct_plus_learning, power) trace["supplementary_cost_delta"] = _occ_per_kwe(with_decomm, power) - _occ_per_kwe(with_indirect, power) # Finance costs: interest during construction and ITC diff --git a/test/test_crf_api.py b/test/test_crf_api.py index 4c7d535..3bc0dbe 100644 --- a/test/test_crf_api.py +++ b/test/test_crf_api.py @@ -124,6 +124,11 @@ def test_run_one_scenario_returns_static_inputs_and_unit_results(): assert waterfall.iloc[1:-1]["absolute_change"].sum() == pytest.approx( result["OCC_2"] - result["OCC_1"] ) + supplychain_delta = waterfall.loc[ + waterfall["label"].eq("Supplychain efficiency"), + "absolute_change", + ].iloc[0] + assert abs(supplychain_delta) < abs(result["OCC_2"] - result["OCC_1"]) * 0.1 def test_visualization_helpers_create_dashboard(tmp_path): diff --git a/tutorial/crf_quickstart.py b/tutorial/crf_ap1000_example.py similarity index 84% rename from tutorial/crf_quickstart.py rename to tutorial/crf_ap1000_example.py index 3a372bb..848912f 100644 --- a/tutorial/crf_quickstart.py +++ b/tutorial/crf_ap1000_example.py @@ -2,10 +2,16 @@ Run from the repository root with: - PYTHONPATH=src python tutorial/crf_quickstart.py + python tutorial/crf_ap1000_example.py """ from pathlib import Path +import sys + +REPO_ROOT = Path(__file__).resolve().parents[1] +SRC_PATH = REPO_ROOT / "src" +if str(SRC_PATH) not in sys.path: + sys.path.insert(0, str(SRC_PATH)) from crf import print_scenario_result, run_one_scenario, save_dashboard diff --git a/tutorial/crf_htgr_example.py b/tutorial/crf_htgr_example.py new file mode 100644 index 0000000..20accf7 --- /dev/null +++ b/tutorial/crf_htgr_example.py @@ -0,0 +1,48 @@ +from pathlib import Path +import sys + +REPO_ROOT = Path(__file__).resolve().parents[1] +SRC_PATH = REPO_ROOT / "src" +if str(SRC_PATH) not in sys.path: + sys.path.insert(0, str(SRC_PATH)) + +from crf import print_scenario_result, run_one_scenario, save_dashboard + + +config = { + "reactor_type": "HTGR", + "f_22": 250_000_000, + "f_2321": 150_000_000, + "land_cost_per_acre_0": 22_000, + "startup_0": 16, + "staggering_ratio": 0.75, +} + +levers = { + "num_orders": 13, + "num_NOAK": 8, + "itc_percent": 0, + "n_itc": 0, + "interest_percent": 6, + "design_completion_percent": 80, + "design_maturity": 1, + "proc_exp": 0.5, + "N_proc": 3, + "ce_exp": 1.0, + "N_cons": 5, + "ae_exp": 0.5, + "N_AE": 4, + "standardization_percent": 80, + "modularity_code": 1, + "bop_grade_code": 1, + "rb_grade_code": 0, +} + + +if __name__ == "__main__": + result = run_one_scenario(config, levers) + print_scenario_result(result) + + output_path = Path("cost_reduction_framework_htgr_dashboard.png") + save_dashboard(result, output_path, title="HTGR Cost Reduction Framework") + print(f"\nSaved dashboard figure to: {output_path.resolve()}") diff --git a/tutorial/crf_sfr_example.py b/tutorial/crf_sfr_example.py new file mode 100644 index 0000000..b716994 --- /dev/null +++ b/tutorial/crf_sfr_example.py @@ -0,0 +1,48 @@ +from pathlib import Path +import sys + +REPO_ROOT = Path(__file__).resolve().parents[1] +SRC_PATH = REPO_ROOT / "src" +if str(SRC_PATH) not in sys.path: + sys.path.insert(0, str(SRC_PATH)) + +from crf import print_scenario_result, run_one_scenario, save_dashboard + + +config = { + "reactor_type": "SFR", + "f_22": 250_000_000, + "f_2321": 150_000_000, + "land_cost_per_acre_0": 22_000, + "startup_0": 16, + "staggering_ratio": 0.75, +} + +levers = { + "num_orders": 13, + "num_NOAK": 8, + "itc_percent": 0, + "n_itc": 0, + "interest_percent": 6, + "design_completion_percent": 80, + "design_maturity": 1, + "proc_exp": 0.5, + "N_proc": 3, + "ce_exp": 1.0, + "N_cons": 5, + "ae_exp": 0.5, + "N_AE": 4, + "standardization_percent": 80, + "modularity_code": 1, + "bop_grade_code": 1, + "rb_grade_code": 0, +} + + +if __name__ == "__main__": + result = run_one_scenario(config, levers) + print_scenario_result(result) + + output_path = Path("cost_reduction_framework_sfr_dashboard.png") + save_dashboard(result, output_path, title="SFR Cost Reduction Framework") + print(f"\nSaved dashboard figure to: {output_path.resolve()}") From f177b52c5c0ef53fb0658c3f09058ce033844740 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Fri, 1 May 2026 16:52:36 -0500 Subject: [PATCH 20/25] add schedule modifier --- docs/source/user/crf.rst | 19 +- src/crf/__init__.py | 2 + src/crf/api.py | 3 + src/crf/model/avg_runner.py | 8 +- src/crf/visualization.py | 310 ++++++++++++++++++++++++++++----- test/test_crf_api.py | 28 +++ tutorial/crf_ap1000_example.py | 15 +- tutorial/crf_htgr_example.py | 9 +- tutorial/crf_sfr_example.py | 9 +- 9 files changed, 345 insertions(+), 58 deletions(-) diff --git a/docs/source/user/crf.rst b/docs/source/user/crf.rst index 122118f..c02f6c9 100644 --- a/docs/source/user/crf.rst +++ b/docs/source/user/crf.rst @@ -39,8 +39,8 @@ cost-reduction levers. "ae_exp": 0.5, "N_AE": 4, "standardization_percent": 80, - "modularity_code": 1, - "bop_grade_code": 1, + "modularity_code": 0, + "bop_grade_code": 0, "rb_grade_code": 0, } @@ -56,9 +56,10 @@ Visualization ------------- Use ``save_dashboard`` to generate dashboard-style capital-cost figures from a -scenario result. The dashboard includes the same core cost-reduction views as -the Excel dashboard: OCC, TCI, construction duration, cost breakdowns, and the -FOAK-to-NOAK OCC reduction waterfall by lever. +scenario result. The dashboard includes a lever input table, OCC, TCI, +construction duration, cost breakdowns, a staggered construction timeline, and +the FOAK-to-NOAK OCC reduction waterfall by lever. Set ``show_levers=False`` to +omit the lever input table and generate the compact chart-only dashboard. .. code-block:: python @@ -69,12 +70,14 @@ FOAK-to-NOAK OCC reduction waterfall by lever. result, "cost_reduction_framework_dashboard.png", title="AP1000 Cost Reduction Framework", + show_levers=True, ) For downstream analysis, ``results_to_dataframe`` converts the scenario result to a chart-ready ``pandas.DataFrame`` with one row per plant, while -``waterfall_to_dataframe`` returns the FOAK-to-NOAK waterfall values using the -lever labels from the Excel dashboard. +``levers_to_dataframe`` returns the lever table and ``waterfall_to_dataframe`` +returns the FOAK-to-NOAK waterfall values using the lever labels from the Excel +dashboard. Configuration ------------- @@ -97,7 +100,7 @@ Configuration * - ``startup_0`` - First-unit startup duration in months. * - ``staggering_ratio`` - - Fractional overlap between sequential unit construction schedules. + - Fractional overlap between sequential unit construction schedules. The timeline chart delays later plant starts when needed so construction and startup finish dates do not move backward. Levers ------ diff --git a/src/crf/__init__.py b/src/crf/__init__.py index a3981b1..cc474d3 100644 --- a/src/crf/__init__.py +++ b/src/crf/__init__.py @@ -11,6 +11,7 @@ ) from .visualization import ( occ_reduction_from_foak_to_noak, + levers_to_dataframe, plot_dashboard, results_to_dataframe, save_dashboard, @@ -24,6 +25,7 @@ "run_sampling_from_excel", "print_scenario_result", "occ_reduction_from_foak_to_noak", + "levers_to_dataframe", "plot_dashboard", "results_to_dataframe", "save_dashboard", diff --git a/src/crf/api.py b/src/crf/api.py index 470640c..f9179f9 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -16,6 +16,7 @@ from .sampling.postprocess import apply_itc_rounding from .model.avg_runner import run_avg_all_units from .model.pipeline import calculate_final_result +from .model.schedule import effective_staggering_ratio from .utils.serialize import write_csv_row, stream_pickle_dump @@ -55,6 +56,8 @@ def run_one_scenario(config: dict, levers: dict) -> dict: result = run_avg_all_units(config=config, inp=inp, store=store, details=True) static_vals = static_row_from_levers(levers) out = {**static_vals, **result} + out["reactor_type"] = config.get("reactor_type", "") + out["staggering_ratio"] = effective_staggering_ratio(config) out["occ_waterfall"] = calculate_occ_waterfall(config, levers) return out diff --git a/src/crf/model/avg_runner.py b/src/crf/model/avg_runner.py index 3f5ec29..9d0d0d1 100644 --- a/src/crf/model/avg_runner.py +++ b/src/crf/model/avg_runner.py @@ -1,5 +1,6 @@ import numpy as np from .pipeline import calculate_final_result +from .schedule import build_schedule_start_months, effective_staggering_ratio # Ignore runtime warning def run_avg_all_units(config: dict, inp: dict, store, details=False): @@ -70,9 +71,9 @@ def run_avg_all_units(config: dict, inp: dict, store, details=False): final_startup_duration = max(7, config["startup_0"] * (1 - 0.3) ** np.log2(num_orders)) - cons_duration_cumulative_wz_startup = ( - (1 - config["staggering_ratio"]) * np.sum(DUR[:-1]) + DUR[-1] + final_startup_duration - ) + start_months = build_schedule_start_months(config, DUR, STAUP) + finish_months = start_months + DUR + STAUP + cons_duration_cumulative_wz_startup = float(finish_months[-1]) # expand arrays to OCC_i / TCI_i / duration_i out = {} @@ -116,5 +117,6 @@ def run_avg_all_units(config: dict, inp: dict, store, details=False): "avg_OCC": avg_OCC, "avg_TCI": avg_TCI, "avg_duration": avg_duration, + "effective_staggering_ratio": effective_staggering_ratio(config), }) return out diff --git a/src/crf/visualization.py b/src/crf/visualization.py index 6e8e5ba..2f421d9 100644 --- a/src/crf/visualization.py +++ b/src/crf/visualization.py @@ -18,14 +18,16 @@ import numpy as np import pandas as pd +from .model.schedule import build_schedule_timeline + CAPITAL_COLORS = { "occ": "#1f77b4", "net_occ": "#7fb3d5", "tci": "#2ca02c", "nci": "#98df8a", - "duration": "#9467bd", - "startup": "#c5b0d5", + "duration": "#ff8c2a", + "startup": "#8b63a9", "preconstruction": "#4c78a8", "equipment": "#72b7b2", "material": "#f58518", @@ -101,42 +103,106 @@ def waterfall_to_dataframe(result: dict) -> pd.DataFrame: return pd.DataFrame(rows) -def plot_dashboard(result: dict, title: Optional[str] = None, figsize=(16, 12)): +def levers_to_dataframe(result: dict) -> pd.DataFrame: + """Return the plant-by-plant lever table shown in the dashboard.""" + num_orders = int(result["Num_orders"]) + n_itc = int(result["n_ITC"]) + itc = float(result["ITC"]) + interest = float(result["interest rate"]) + design_completion = float(result["Design completion"]) + design_maturity = float(result["Design_Maturity_0"]) + supply_chain = float(result["supply chain exp_0"]) + n_supply_chain = float(result["N supply chain"]) + construction = float(result["Const Proficiency"]) + n_construction = float(result["N const prof"]) + ae = float(result["AE"]) + n_ae = float(result["N AE prof"]) + standardization = float(result["standardization"]) + + def ramp(start: float, n_best: float, plant: int) -> float: + if plant == 1: + return start + return min(start + (2.0 / n_best) * (plant - 1), 2.0) + + rows = [] + for plant in range(1, num_orders + 1): + rows.append( + { + "N": plant, + "Interest Rate": f"{interest:.0f}%", + "Design Completion": f"{design_completion if plant == 1 else 100:.0f}%", + "Design Maturity (0-2)": f"{design_maturity if plant == 1 else 2:.0f}", + "Supplychain Proficiency (0-2)": f"{ramp(supply_chain, n_supply_chain, plant):.2f}", + "A/E Proficiency (0-2)": f"{ramp(ae, n_ae, plant):.2f}", + "Construction Proficiency (0-2)": f"{ramp(construction, n_construction, plant):.2f}", + "Cross Site Standardization": "" if plant == 1 else f"{standardization:.0f}%", + "Modular Civil Construction": _bool_label(result["modularity"]), + "Commercial BOP": _bool_label(result["BOP commercial"]), + "Non-Safety-Related RB": _bool_label(result["RB Safety Related"]), + "ITC Amount": f"{itc:.0f}%" if plant <= n_itc else "0%", + } + ) + return pd.DataFrame(rows) + + +def plot_dashboard( + result: dict, + title: Optional[str] = None, + figsize: Optional[tuple[float, float]] = None, + show_levers: bool = True, +): """Create a dashboard-style figure from Cost Reduction Framework output. - The layout mirrors the capital-cost charts in the Excel dashboard: OCC, - TCI, construction duration, cost breakdowns, and the percent OCC reduction - from FOAK to NOAK. + The layout mirrors the capital-cost charts in the Excel dashboard: capital + costs, construction duration, cost breakdowns, build timeline, and the + percent OCC reduction from FOAK to NOAK. """ df = results_to_dataframe(result) noak_unit = int(result.get("num_NOAK", result.get("Num_orders", df["Plant number"].max()))) noak_unit = min(max(noak_unit, int(df["Plant number"].min())), int(df["Plant number"].max())) noak_reduction = occ_reduction_from_foak_to_noak(result) - fig, axes = plt.subplots(3, 2, figsize=figsize, constrained_layout=True) + if figsize is None: + figsize = (18, 16) if show_levers else (16, 12) + + fig = plt.figure(figsize=figsize, constrained_layout=True) + if show_levers: + gs = fig.add_gridspec(4, 2, height_ratios=[1.35, 1, 1, 1]) + lever_ax = fig.add_subplot(gs[0, :]) + axes = np.array( + [ + [fig.add_subplot(gs[1, 0]), fig.add_subplot(gs[1, 1])], + [fig.add_subplot(gs[2, 0]), fig.add_subplot(gs[2, 1])], + [fig.add_subplot(gs[3, 0]), fig.add_subplot(gs[3, 1])], + ] + ) + _plot_lever_table(lever_ax, result) + else: + gs = fig.add_gridspec(3, 2) + axes = np.array( + [ + [fig.add_subplot(gs[0, 0]), fig.add_subplot(gs[0, 1])], + [fig.add_subplot(gs[1, 0]), fig.add_subplot(gs[1, 1])], + [fig.add_subplot(gs[2, 0]), fig.add_subplot(gs[2, 1])], + ] + ) fig.suptitle(title or "Cost Reduction Framework Dashboard", fontsize=16, fontweight="bold") x = df["Plant number"] ax = axes[0, 0] - ax.bar(x - 0.18, df["OCC"], width=0.36, label="Overnight Capital Cost (OCC)", color=CAPITAL_COLORS["occ"]) + ax.bar(x - 0.18, df["TCI"], width=0.36, label="10-60 - Total Capital Investment (TCI)", color=CAPITAL_COLORS["tci"]) + ax.bar(x + 0.18, df["OCC"], width=0.36, label="10-50 - Overnight Capital Cost (OCC)", color=CAPITAL_COLORS["occ"]) if df["Net OCC"].notna().any(): - ax.bar(x + 0.18, df["Net OCC"], width=0.36, label="Net OCC (after ITC)", color=CAPITAL_COLORS["net_occ"]) - ax.set_title("Overnight Capital Cost") - ax.set_xlabel("Plant number") - ax.set_ylabel("$/kWe") - ax.legend(fontsize=8) - - ax = axes[0, 1] - ax.bar(x - 0.18, df["TCI"], width=0.36, label="Total Capital Investment (TCI)", color=CAPITAL_COLORS["tci"]) + ax.plot(x, df["Net OCC"], marker="o", linestyle="--", linewidth=1.2, label="Net OCC (after ITC)", color=CAPITAL_COLORS["net_occ"]) if df["NCI"].notna().any(): - ax.bar(x + 0.18, df["NCI"], width=0.36, label="Net Capital Investment (NCI)", color=CAPITAL_COLORS["nci"]) - ax.set_title("Capital Investment") + ax.plot(x, df["NCI"], marker="o", linestyle="--", linewidth=1.2, label="Net Capital Investment (NCI)", color=CAPITAL_COLORS["nci"]) + ax.set_title("Capital Cost: OCC and TCI") ax.set_xlabel("Plant number") ax.set_ylabel("$/kWe") ax.legend(fontsize=8) - ax = axes[1, 0] + ax = axes[0, 1] ax.bar(x, df["Construction duration"], label="Construction", color=CAPITAL_COLORS["duration"]) if df["Startup duration"].notna().any(): ax.bar( @@ -151,30 +217,33 @@ def plot_dashboard(result: dict, title: Optional[str] = None, figsize=(16, 12)): ax.set_ylabel("Months") ax.legend(fontsize=8) - ax = axes[1, 1] + ax = axes[1, 0] breakdown = [ - ("Preconstruction costs", "preconstruction"), - ("Direct costs", "equipment"), - ("Indirect costs", "indirect"), - ("Supplementary costs", "supplementary"), - ("Financing costs", "finance"), + ("Preconstruction costs", "preconstruction", "10 - Preconstruction"), + ("Direct costs", "equipment", "20 - Direct"), + ("Indirect costs", "indirect", "30 - Indirect"), + ("Supplementary costs", "supplementary", "50 - Supplementary"), + ("Financing costs", "finance", "60 - Financing"), ] _stacked_bars(ax, x, df, breakdown) - ax.set_title("Capital Cost Breakdown") + ax.set_title("10-60 - TCI Breakdown") ax.set_xlabel("Plant number") ax.set_ylabel("$/kWe") ax.legend(fontsize=8) + ax = axes[1, 1] + _plot_build_timeline(ax, result, df) + ax = axes[2, 0] direct_breakdown = [ - ("Direct costs: equipment", "equipment"), - ("Direct costs: material", "material"), - ("Direct costs: labor", "labor"), - ("Indirect costs", "indirect"), - ("Supplementary costs", "supplementary"), + ("Direct costs: equipment", "equipment", "20 - Direct: Equipment"), + ("Direct costs: material", "material", "20 - Direct: Material"), + ("Direct costs: labor", "labor", "20 - Direct: Labor"), + ("Indirect costs", "indirect", "30 - Indirect"), + ("Supplementary costs", "supplementary", "50 - Supplementary"), ] _stacked_bars(ax, x, df, direct_breakdown) - ax.set_title("Detailed Cost Components") + ax.set_title("10-50 - OCC Components") ax.set_xlabel("Plant number") ax.set_ylabel("$/kWe") ax.legend(fontsize=8) @@ -190,11 +259,17 @@ def plot_dashboard(result: dict, title: Optional[str] = None, figsize=(16, 12)): return fig -def save_dashboard(result: dict, out_path: str, title: Optional[str] = None, dpi: int = 180) -> str: +def save_dashboard( + result: dict, + out_path: str, + title: Optional[str] = None, + dpi: int = 180, + show_levers: bool = True, +) -> str: """Save the dashboard figure and return the output path.""" path = Path(out_path) path.parent.mkdir(parents=True, exist_ok=True) - fig = plot_dashboard(result, title=title) + fig = plot_dashboard(result, title=title, show_levers=show_levers) fig.savefig(path, dpi=dpi, bbox_inches="tight") plt.close(fig) return str(path) @@ -211,43 +286,181 @@ def save_figures(result: dict, output_dir: str, prefix: str = "cost_reduction_fr return saved +def _plot_lever_table(ax, result: dict) -> None: + ax.axis("off") + df = levers_to_dataframe(result) + display_df = df.copy() + display_df.columns = [ + "N", + "Interest\nRate", + "Design\nCompletion", + "Design\nMaturity\n(0-2)", + "Supplychain\nProficiency\n(0-2)", + "A/E\nProficiency\n(0-2)", + "Construction\nProficiency\n(0-2)", + "Cross Site\nStandardization", + "Modular Civil\nConstruction", + "Commercial\nBOP", + "Non-Safety-\nRelated RB", + "ITC Amount", + ] + + table = ax.table( + cellText=display_df.values, + colLabels=display_df.columns, + loc="center", + cellLoc="center", + colLoc="center", + ) + table.auto_set_font_size(False) + table.set_fontsize(7.2) + table.scale(1.0, 1.18) + + header_color = "#17647f" + highlight = "#bfe7f3" + grid = "#d2d2d2" + for (row, col), cell in table.get_celld().items(): + cell.set_edgecolor(grid) + if row == 0: + cell.set_facecolor(header_color) + cell.set_text_props(color="white", weight="bold") + cell.set_linewidth(1.0) + else: + cell.set_text_props(color="#155d78" if row <= 2 else "#8f8f8f", weight="bold") + cell.set_facecolor(highlight if row <= 2 else "white") + cell.set_linewidth(0.7) + if col == 7 and row == 1: + cell.set_facecolor("black") + cell.get_text().set_text("") + + ax.set_title( + f"Lever Inputs: {int(result['Num_orders'])} firm orders, " + f"{int(result['n_ITC'])} reactors claiming ITC, ITC {float(result['ITC']):.0f}%", + loc="left", + fontsize=11, + fontweight="bold", + color=header_color, + pad=6, + ) + + +def _plot_build_timeline(ax, result: dict, df: pd.DataFrame) -> None: + durations = df["Construction duration"].astype(float).to_numpy() + startup = df["Startup duration"].astype(float).fillna(0.0).to_numpy() + plants = df["Plant number"].astype(int).to_numpy() + schedule_config = { + "staggering_ratio": result.get("staggering_ratio", result.get("Staggering ratio", 0.75)), + "reactor_type": result.get("reactor_type", ""), + } + timeline = build_schedule_timeline(schedule_config, plants, durations, startup) + timeline_df = pd.DataFrame(timeline) + + start_years = timeline_df["construction_start_month"].to_numpy() / 12.0 + duration_years = timeline_df["construction_duration_months"].to_numpy() / 12.0 + startup_years = timeline_df["startup_duration_months"].to_numpy() / 12.0 + + ax.barh( + plants, + duration_years, + left=start_years, + height=0.64, + color=CAPITAL_COLORS["duration"], + edgecolor="black", + linewidth=0.8, + label="Construction", + ) + ax.barh( + plants, + startup_years, + left=start_years + duration_years, + height=0.64, + color=CAPITAL_COLORS["startup"], + edgecolor="black", + linewidth=0.8, + label="Startup", + ) + + max_year = float(np.nanmax(start_years + duration_years + startup_years)) + ax.set_title("Sequential Construction Timeline") + ax.set_xlabel("Time (Years)") + ax.set_ylabel("Reactor Number") + ax.set_yticks(plants) + ax.set_ylim(plants.max() + 0.7, plants.min() - 0.7) + ax.set_xlim(0, max_year * 1.05) + ax.set_xticks(np.arange(0, np.ceil(max_year) + 1, 1)) + ax.grid(True, axis="x", alpha=0.35) + ax.legend(loc="upper right", ncol=2, fontsize=8) + + def _plot_occ_waterfall(ax, result: dict, noak_reduction: float) -> None: waterfall = waterfall_to_dataframe(result) labels = [_wrap_label(label) for label in waterfall["label"].tolist()] x_pos = np.arange(len(waterfall)) + foak_occ = float(waterfall.iloc[0]["cumulative_occ"]) colors = [] bottoms = [] heights = [] - previous = 0.0 + pct_labels = [] for _, row in waterfall.iterrows(): if row["kind"] == "total": bottoms.append(0.0) - heights.append(float(row["cumulative_occ"])) + total = float(row["cumulative_occ"]) + heights.append(total) + if row.name == 0: + pct_labels.append("100%") + else: + pct_labels.append(f"{noak_reduction:.1f}% lower") colors.append("#4c78a8") - previous = float(row["cumulative_occ"]) else: + current = float(row["cumulative_occ"]) change = float(row["absolute_change"]) + previous = current - change + change_pct = change / foak_occ * 100.0 if foak_occ else 0.0 if change >= 0: bottoms.append(previous) heights.append(change) colors.append("#f58518") else: - bottoms.append(previous + change) + bottoms.append(current) heights.append(abs(change)) colors.append("#54a24b") - previous += change + pct_labels.append(f"{change_pct:+.1f}%") ax.bar(x_pos, heights, bottom=bottoms, color=colors, width=0.72) for idx in range(len(waterfall) - 1): y = float(waterfall.iloc[idx]["cumulative_occ"]) ax.plot([idx + 0.36, idx + 1 - 0.36], [y, y], color="#777777", linewidth=0.8) + y_span = max([bottom + height for bottom, height in zip(bottoms, heights)] + [foak_occ]) * 0.08 + for idx, (bottom, height, label) in enumerate(zip(bottoms, heights, pct_labels)): + if not height: + continue + y = bottom + height / 2 + va = "center" + color = "white" + if height < y_span * 0.55: + y = bottom + height + y_span * 0.12 + va = "bottom" + color = "#1f2933" + ax.text( + idx, + y, + label, + ha="center", + va=va, + fontsize=8, + fontweight="bold", + color=color, + rotation=90 if len(label) > 7 else 0, + ) + ax.set_title("% OCC Reduction from FOAK to NOAK") ax.set_ylabel("OCC ($/kWe)") ax.set_xticks(x_pos) ax.set_xticklabels(labels, rotation=0, ha="center", fontsize=7) ax.grid(True, axis="y", alpha=0.25) + ax.set_ylim(0, max([bottom + height for bottom, height in zip(bottoms, heights)] + [foak_occ]) * 1.14) ax.text( 0.98, 0.95, @@ -262,15 +475,30 @@ def _plot_occ_waterfall(ax, result: dict, noak_reduction: float) -> None: def _stacked_bars(ax, x: Iterable[int], df: pd.DataFrame, columns: list[tuple[str, str]]) -> None: bottom = np.zeros(len(df)) - for label, color_key in columns: + for item in columns: + if len(item) == 2: + label, color_key = item + display_label = label + else: + label, color_key, display_label = item values = df[label].astype(float) if not values.notna().any(): continue clean_values = values.fillna(0.0).to_numpy() - ax.bar(x, clean_values, bottom=bottom, label=label, color=CAPITAL_COLORS[color_key]) + ax.bar(x, clean_values, bottom=bottom, label=display_label, color=CAPITAL_COLORS[color_key]) bottom += clean_values def _wrap_label(label: str, width: int = 13) -> str: compact = " ".join(str(label).split()) return "\n".join(textwrap.wrap(compact, width=width)) + + +def _bool_label(value) -> str: + if isinstance(value, str): + normalized = value.strip().lower() + if normalized in {"1", "true", "yes", "non_nuclear", "modularized"}: + return "TRUE" + if normalized in {"0", "false", "no", "nuclear", "stick_built"}: + return "FALSE" + return "TRUE" if bool(value) else "FALSE" diff --git a/test/test_crf_api.py b/test/test_crf_api.py index 3bc0dbe..e3e1e5a 100644 --- a/test/test_crf_api.py +++ b/test/test_crf_api.py @@ -5,6 +5,7 @@ import pytest from crf import ( + levers_to_dataframe, occ_reduction_from_foak_to_noak, results_to_dataframe, run_one_scenario, @@ -13,6 +14,7 @@ waterfall_to_dataframe, ) from crf.api import normalize_levers +from crf.model.schedule import build_schedule_timeline from crf.sampling.lever_schema import EXCEL_NAME_TO_ID_ORDERED @@ -96,6 +98,8 @@ def test_run_one_scenario_returns_static_inputs_and_unit_results(): assert result["num_NOAK"] == 2 assert result["ITC"] == 30 assert result["n_ITC"] == 1 + assert result["staggering_ratio"] == pytest.approx(_config()["staggering_ratio"]) + assert result["effective_staggering_ratio"] == pytest.approx(_config()["staggering_ratio"]) assert result["OCC_1"] > 0 assert result["OCC_2"] > 0 assert result["NETOCC_1"] < result["OCC_1"] @@ -130,11 +134,27 @@ def test_run_one_scenario_returns_static_inputs_and_unit_results(): ].iloc[0] assert abs(supplychain_delta) < abs(result["OCC_2"] - result["OCC_1"]) * 0.1 + timeline = build_schedule_timeline( + {**_config(), "staggering_ratio": result["staggering_ratio"]}, + [1, 2], + [result["duration_1"], result["duration_2"]], + [result["STAUP_1"], result["STAUP_2"]], + ) + assert timeline["construction_finish_month"][1] >= timeline["construction_finish_month"][0] + assert timeline["startup_finish_month"][1] >= timeline["startup_finish_month"][0] + + levers = levers_to_dataframe(result) + assert levers.loc[0, "Design Completion"] == "70%" + assert levers.loc[1, "Design Completion"] == "100%" + assert levers.loc[0, "Cross Site Standardization"] == "" + assert levers.loc[1, "Cross Site Standardization"] == "80%" + def test_visualization_helpers_create_dashboard(tmp_path): result = run_one_scenario(_config(), _levers()) frame = results_to_dataframe(result) out_png = tmp_path / "cost_reduction_framework_dashboard.png" + compact_png = tmp_path / "cost_reduction_framework_compact_dashboard.png" assert list(frame["Plant number"]) == [1, 2] assert frame.loc[1, "OCC reduction from FOAK"] == pytest.approx( @@ -142,9 +162,17 @@ def test_visualization_helpers_create_dashboard(tmp_path): ) save_dashboard(result, str(out_png), title="Cost Reduction Framework Test") + save_dashboard( + result, + str(compact_png), + title="Cost Reduction Framework Test", + show_levers=False, + ) assert out_png.exists() assert out_png.stat().st_size > 0 + assert compact_png.exists() + assert compact_png.stat().st_size > 0 def test_run_sampling_from_excel_writes_csv_and_pickle_outputs(tmp_path): diff --git a/tutorial/crf_ap1000_example.py b/tutorial/crf_ap1000_example.py index 848912f..5012018 100644 --- a/tutorial/crf_ap1000_example.py +++ b/tutorial/crf_ap1000_example.py @@ -40,16 +40,23 @@ "ae_exp": 0.5, "N_AE": 4, "standardization_percent": 80, - "modularity_code": 1, - "bop_grade_code": 1, + "modularity_code": 0, + "bop_grade_code": 0, "rb_grade_code": 0, } +show_lever_table = True + if __name__ == "__main__": result = run_one_scenario(config, levers) print_scenario_result(result) - output_path = Path("cost_reduction_framework_dashboard.png") - save_dashboard(result, output_path, title="AP1000 Cost Reduction Framework") + output_path = Path("cost_reduction_framework_AP1000.png") + save_dashboard( + result, + output_path, + title="AP1000 Cost Reduction Framework", + show_levers=show_lever_table, + ) print(f"\nSaved dashboard figure to: {output_path.resolve()}") diff --git a/tutorial/crf_htgr_example.py b/tutorial/crf_htgr_example.py index 20accf7..d1a03ba 100644 --- a/tutorial/crf_htgr_example.py +++ b/tutorial/crf_htgr_example.py @@ -38,11 +38,18 @@ "rb_grade_code": 0, } +show_lever_table = True + if __name__ == "__main__": result = run_one_scenario(config, levers) print_scenario_result(result) output_path = Path("cost_reduction_framework_htgr_dashboard.png") - save_dashboard(result, output_path, title="HTGR Cost Reduction Framework") + save_dashboard( + result, + output_path, + title="HTGR Cost Reduction Framework", + show_levers=show_lever_table, + ) print(f"\nSaved dashboard figure to: {output_path.resolve()}") diff --git a/tutorial/crf_sfr_example.py b/tutorial/crf_sfr_example.py index b716994..0206666 100644 --- a/tutorial/crf_sfr_example.py +++ b/tutorial/crf_sfr_example.py @@ -38,11 +38,18 @@ "rb_grade_code": 0, } +show_lever_table = True + if __name__ == "__main__": result = run_one_scenario(config, levers) print_scenario_result(result) output_path = Path("cost_reduction_framework_sfr_dashboard.png") - save_dashboard(result, output_path, title="SFR Cost Reduction Framework") + save_dashboard( + result, + output_path, + title="SFR Cost Reduction Framework", + show_levers=show_lever_table, + ) print(f"\nSaved dashboard figure to: {output_path.resolve()}") From cc9dc162bba38d12c2e9f9ac95e6057eca1faac5 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Sat, 2 May 2026 11:14:28 -0500 Subject: [PATCH 21/25] notebook --- src/crf/visualization.py | 4 +- tutorial/ANL.png | Bin 0 -> 5731 bytes tutorial/INL1.png | Bin 0 -> 6027 bytes tutorial/MIT1.png | Bin 0 -> 3041 bytes tutorial/crf_sensitivity_analysis.ipynb | 804 ++++++++++++++++++++++++ 5 files changed, 807 insertions(+), 1 deletion(-) create mode 100644 tutorial/ANL.png create mode 100644 tutorial/INL1.png create mode 100644 tutorial/MIT1.png create mode 100644 tutorial/crf_sensitivity_analysis.ipynb diff --git a/src/crf/visualization.py b/src/crf/visualization.py index 2f421d9..e57b98b 100644 --- a/src/crf/visualization.py +++ b/src/crf/visualization.py @@ -3,6 +3,7 @@ from __future__ import annotations import os +import sys import tempfile import textwrap from pathlib import Path @@ -12,7 +13,8 @@ import matplotlib -matplotlib.use("Agg") +if "ipykernel" not in sys.modules: + matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np diff --git a/tutorial/ANL.png b/tutorial/ANL.png new file mode 100644 index 0000000000000000000000000000000000000000..9f567252b88a5eddc4e70ef88549771b3b3ea5c2 GIT binary patch literal 5731 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" + ] + }, + { + "cell_type": "markdown", + "id": "f786fa16", + "metadata": {}, + "source": [ + "#
Cost Reduction Framework Sensitivity Analysis
\n", + "\n", + "This notebook demonstrates lightweight one-at-a-time sensitivity analysis using the new `src/crf` API." + ] + }, + { + "cell_type": "markdown", + "id": "bc87ffdf", + "metadata": {}, + "source": [ + "## 1. Imports and Base Inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "42c77a97", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using ACCERT repository: /Users/jia.zhou/projects/ACCERT\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "import os\n", + "import sys\n", + "import tempfile\n", + "import warnings\n", + "\n", + "os.environ.setdefault(\"MPLCONFIGDIR\", tempfile.gettempdir())\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", + "pd.set_option(\"display.max_columns\", 80)\n", + "pd.set_option(\"display.width\", 160)\n", + "\n", + "cwd = Path.cwd().resolve()\n", + "repo_root = next(path for path in [cwd, *cwd.parents] if (path / \"src\" / \"crf\").exists())\n", + "if str(repo_root / \"src\") not in sys.path:\n", + " sys.path.insert(0, str(repo_root / \"src\"))\n", + "\n", + "from crf.model.schedule import build_schedule_timeline\n", + "\n", + "from crf import (\n", + " levers_to_dataframe,\n", + " occ_reduction_from_foak_to_noak,\n", + " print_scenario_result,\n", + " results_to_dataframe,\n", + " run_one_scenario,\n", + " save_dashboard,\n", + " waterfall_to_dataframe,\n", + ")\n", + "\n", + "output_dir = repo_root / \"tutorial\"\n", + "output_dir.mkdir(exist_ok=True)\n", + "print(f\"Using ACCERT repository: {repo_root}\")\n", + "\n", + "import numpy as np\n", + "\n", + "def show_image(path, figsize=(12, 8)):\n", + " image = plt.imread(path)\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " ax.imshow(image)\n", + " ax.axis(\"off\")\n", + " return fig\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "414e0fd4", + "metadata": {}, + "outputs": [], + "source": [ + "def base_config(reactor_type):\n", + " startup = 28 if reactor_type == \"AP1000\" else 16\n", + " return {\n", + " \"reactor_type\": reactor_type,\n", + " \"f_22\": 250_000_000,\n", + " \"f_2321\": 150_000_000,\n", + " \"land_cost_per_acre_0\": 22_000,\n", + " \"startup_0\": startup,\n", + " \"staggering_ratio\": 0.75,\n", + " }\n", + "\n", + "\n", + "def base_levers(reactor_type):\n", + " common = {\n", + " \"num_orders\": 10 if reactor_type == \"AP1000\" else 13,\n", + " \"num_NOAK\": 8,\n", + " \"itc_percent\": 0,\n", + " \"n_itc\": 0,\n", + " \"interest_percent\": 6,\n", + " \"design_completion_percent\": 70 if reactor_type == \"AP1000\" else 80,\n", + " \"design_maturity\": 1,\n", + " \"proc_exp\": 0.5,\n", + " \"N_proc\": 3,\n", + " \"ce_exp\": 0.5 if reactor_type == \"AP1000\" else 1.0,\n", + " \"N_cons\": 5,\n", + " \"ae_exp\": 0.5,\n", + " \"N_AE\": 4,\n", + " \"standardization_percent\": 80,\n", + " \"modularity_code\": 1,\n", + " \"bop_grade_code\": 1,\n", + " \"rb_grade_code\": 0,\n", + " }\n", + " if reactor_type == \"AP1000\":\n", + " common.update({\"modularity_code\": 0, \"bop_grade_code\": 0, \"rb_grade_code\": 0})\n", + " return common" + ] + }, + { + "cell_type": "markdown", + "id": "eaca98c5", + "metadata": {}, + "source": [ + "## Individual Plot Helpers\n", + "\n", + "These helper functions show each dashboard panel as a larger standalone figure for easier notebook inspection." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fefcd14f", + "metadata": {}, + "outputs": [], + "source": [ + "def _style_ax(ax):\n", + " ax.spines[\"top\"].set_visible(False)\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.grid(True, alpha=0.25)\n", + "\n", + "\n", + "def plot_capital_cost_panel(result, figsize=(10, 5)):\n", + " df = results_to_dataframe(result)\n", + " x = df[\"Plant number\"]\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " ax.bar(x - 0.18, df[\"TCI\"], width=0.36, label=\"10-60 - Total Capital Investment (TCI)\", color=\"tab:green\")\n", + " ax.bar(x + 0.18, df[\"OCC\"], width=0.36, label=\"10-50 - Overnight Capital Cost (OCC)\", color=\"tab:blue\")\n", + " if df[\"Net OCC\"].notna().any():\n", + " ax.plot(x, df[\"Net OCC\"], marker=\"o\", linestyle=\"--\", linewidth=1.2, label=\"Net OCC after ITC\")\n", + " if df[\"NCI\"].notna().any():\n", + " ax.plot(x, df[\"NCI\"], marker=\"o\", linestyle=\"--\", linewidth=1.2, label=\"Net Capital Investment\")\n", + " ax.set_title(\"Capital Cost: OCC and TCI\")\n", + " ax.set_xlabel(\"Plant number\")\n", + " ax.set_ylabel(\"$/kWe\")\n", + " ax.legend(fontsize=9)\n", + " _style_ax(ax)\n", + " return fig\n", + "\n", + "\n", + "def plot_duration_panel(result, figsize=(10, 5)):\n", + " df = results_to_dataframe(result)\n", + " x = df[\"Plant number\"]\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " ax.bar(x, df[\"Construction duration\"], label=\"Construction\", color=\"#ff8c2a\")\n", + " ax.bar(x, df[\"Startup duration\"], bottom=df[\"Construction duration\"], label=\"Startup\", color=\"#8b63a9\")\n", + " ax.set_title(\"Total Construction Duration\")\n", + " ax.set_xlabel(\"Plant number\")\n", + " ax.set_ylabel(\"Months\")\n", + " ax.legend(fontsize=9)\n", + " _style_ax(ax)\n", + " return fig\n", + "\n", + "\n", + "def plot_tci_breakdown_panel(result, figsize=(10, 5)):\n", + " df = results_to_dataframe(result)\n", + " x = df[\"Plant number\"]\n", + " columns = [\n", + " (\"Preconstruction costs\", \"10 - Preconstruction\", \"#4c78a8\"),\n", + " (\"Direct costs\", \"20 - Direct\", \"#72b7b2\"),\n", + " (\"Indirect costs\", \"30 - Indirect\", \"#54a24b\"),\n", + " (\"Supplementary costs\", \"50 - Supplementary\", \"#b279a2\"),\n", + " (\"Financing costs\", \"60 - Financing\", \"#ff9da6\"),\n", + " ]\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " bottom = np.zeros(len(df))\n", + " for column, label, color in columns:\n", + " values = df[column].fillna(0).astype(float).to_numpy()\n", + " ax.bar(x, values, bottom=bottom, label=label, color=color)\n", + " bottom += values\n", + " ax.set_title(\"10-60 - TCI Breakdown\")\n", + " ax.set_xlabel(\"Plant number\")\n", + " ax.set_ylabel(\"$/kWe\")\n", + " ax.legend(fontsize=9)\n", + " _style_ax(ax)\n", + " return fig\n", + "\n", + "\n", + "def plot_timeline_panel(result, figsize=(11, 5)):\n", + " df = results_to_dataframe(result)\n", + " plants = df[\"Plant number\"].astype(int).to_numpy()\n", + " durations = df[\"Construction duration\"].astype(float).to_numpy()\n", + " startup = df[\"Startup duration\"].fillna(0).astype(float).to_numpy()\n", + " timeline = build_schedule_timeline(\n", + " {\"staggering_ratio\": result.get(\"staggering_ratio\", 0.75), \"reactor_type\": result.get(\"reactor_type\", \"\")},\n", + " plants,\n", + " durations,\n", + " startup,\n", + " )\n", + " start_years = timeline[\"construction_start_month\"] / 12\n", + " duration_years = timeline[\"construction_duration_months\"] / 12\n", + " startup_years = timeline[\"startup_duration_months\"] / 12\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " ax.barh(plants, duration_years, left=start_years, height=0.64, color=\"#ff8c2a\", edgecolor=\"black\", label=\"Construction\")\n", + " ax.barh(plants, startup_years, left=start_years + duration_years, height=0.64, color=\"#8b63a9\", edgecolor=\"black\", label=\"Startup\")\n", + " ax.set_title(\"Sequential Construction Timeline\")\n", + " ax.set_xlabel(\"Time (Years)\")\n", + " ax.set_ylabel(\"Reactor Number\")\n", + " ax.set_yticks(plants)\n", + " ax.set_ylim(plants.max() + 0.7, plants.min() - 0.7)\n", + " ax.set_xlim(0, float(np.nanmax(start_years + duration_years + startup_years)) * 1.05)\n", + " ax.grid(True, axis=\"x\", alpha=0.35)\n", + " ax.legend(fontsize=9)\n", + " return fig\n", + "\n", + "\n", + "def plot_occ_components_panel(result, figsize=(10, 5)):\n", + " df = results_to_dataframe(result)\n", + " x = df[\"Plant number\"]\n", + " columns = [\n", + " (\"Direct costs: equipment\", \"20 - Direct: Equipment\", \"#72b7b2\"),\n", + " (\"Direct costs: material\", \"20 - Direct: Material\", \"#f58518\"),\n", + " (\"Direct costs: labor\", \"20 - Direct: Labor\", \"#e45756\"),\n", + " (\"Indirect costs\", \"30 - Indirect\", \"#54a24b\"),\n", + " (\"Supplementary costs\", \"50 - Supplementary\", \"#b279a2\"),\n", + " ]\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " bottom = np.zeros(len(df))\n", + " for column, label, color in columns:\n", + " values = df[column].fillna(0).astype(float).to_numpy()\n", + " ax.bar(x, values, bottom=bottom, label=label, color=color)\n", + " bottom += values\n", + " ax.set_title(\"10-50 - OCC Components\")\n", + " ax.set_xlabel(\"Plant number\")\n", + " ax.set_ylabel(\"$/kWe\")\n", + " ax.legend(fontsize=9)\n", + " _style_ax(ax)\n", + " return fig\n", + "\n", + "\n", + "def plot_waterfall_panel(result, figsize=(11, 5)):\n", + " waterfall = waterfall_to_dataframe(result)\n", + " foak_occ = float(waterfall.iloc[0][\"cumulative_occ\"])\n", + " x = np.arange(len(waterfall))\n", + " labels = [\"\\n\".join(str(label).replace(\"\\n\", \" \").split()) for label in waterfall[\"label\"]]\n", + " labels = [\"\\n\".join(label.split()[:3]) if len(label) > 18 else label for label in labels]\n", + " bottoms, heights, colors, pct_labels = [], [], [], []\n", + " noak_reduction = occ_reduction_from_foak_to_noak(result)\n", + " for _, row in waterfall.iterrows():\n", + " if row[\"kind\"] == \"total\":\n", + " total = float(row[\"cumulative_occ\"])\n", + " bottoms.append(0)\n", + " heights.append(total)\n", + " colors.append(\"#4c78a8\")\n", + " pct_labels.append(\"100%\" if row.name == 0 else f\"{noak_reduction:.1f}% lower\")\n", + " else:\n", + " current = float(row[\"cumulative_occ\"])\n", + " change = float(row[\"absolute_change\"])\n", + " previous = current - change\n", + " bottoms.append(previous if change >= 0 else current)\n", + " heights.append(abs(change))\n", + " colors.append(\"#f58518\" if change >= 0 else \"#54a24b\")\n", + " pct_labels.append(f\"{change / foak_occ * 100:+.1f}%\")\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " ax.bar(x, heights, bottom=bottoms, color=colors, width=0.72)\n", + " for idx in range(len(waterfall) - 1):\n", + " y = float(waterfall.iloc[idx][\"cumulative_occ\"])\n", + " ax.plot([idx + 0.36, idx + 1 - 0.36], [y, y], color=\"#777777\", linewidth=0.8)\n", + " max_y = max(bottom + height for bottom, height in zip(bottoms, heights))\n", + " for idx, (bottom, height, label) in enumerate(zip(bottoms, heights, pct_labels)):\n", + " if height > 0:\n", + " ax.text(idx, bottom + height / 2, label, ha=\"center\", va=\"center\", fontsize=8, fontweight=\"bold\", color=\"white\")\n", + " ax.set_title(\"% OCC Reduction from FOAK to NOAK\")\n", + " ax.set_ylabel(\"OCC ($/kWe)\")\n", + " ax.set_xticks(x)\n", + " ax.set_xticklabels(labels, fontsize=8)\n", + " ax.set_ylim(0, max_y * 1.12)\n", + " _style_ax(ax)\n", + " return fig" + ] + }, + { + "cell_type": "markdown", + "id": "ef0d823e", + "metadata": {}, + "source": [ + "## 2. Helper Function" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "adead536", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_case(name, reactor_type=\"AP1000\", config_updates=None, lever_updates=None):\n", + " config = base_config(reactor_type)\n", + " levers = base_levers(reactor_type)\n", + " if config_updates:\n", + " config.update(config_updates)\n", + " if lever_updates:\n", + " levers.update(lever_updates)\n", + " result = run_one_scenario(config, levers)\n", + " return {\n", + " \"Case\": name,\n", + " \"Reactor\": reactor_type,\n", + " \"Firm orders\": result[\"Num_orders\"],\n", + " \"Average OCC ($/kWe)\": result[\"avg_OCC\"],\n", + " \"Average TCI ($/kWe)\": result[\"avg_TCI\"],\n", + " \"NOAK OCC reduction (%)\": occ_reduction_from_foak_to_noak(result),\n", + " \"Cumulative timeline (months)\": result[\"cons_duration_cumulative_wz_startup\"],\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "659cb2b7", + "metadata": {}, + "source": [ + "## 3. Order Book Sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "54c1e375", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CaseReactorFirm ordersAverage OCC ($/kWe)Average TCI ($/kWe)NOAK OCC reduction (%)Cumulative timeline (months)
02 ordersAP1000212183.3214345.0943.85119.00
13 ordersAP1000310311.0411999.9757.17119.00
25 ordersAP100058174.449378.6069.78133.73
38 ordersAP100086759.857646.0169.80166.94
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" + ], + "text/plain": [ + " Case Reactor Firm orders Average OCC ($/kWe) Average TCI ($/kWe) NOAK OCC reduction (%) Cumulative timeline (months)\n", + "0 2 orders AP1000 2 12183.32 14345.09 43.85 119.00\n", + "1 3 orders AP1000 3 10311.04 11999.97 57.17 119.00\n", + "2 5 orders AP1000 5 8174.44 9378.60 69.78 133.73\n", + "3 8 orders AP1000 8 6759.85 7646.01 69.80 166.94" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "order_rows = [evaluate_case(f\"{n} orders\", lever_updates={\"num_orders\": n, \"num_NOAK\": min(n, 5)}) for n in [2, 3, 5, 8]]\n", + "orders = pd.DataFrame(order_rows)\n", + "orders.round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4f99cb05", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_15934/3903133957.py:14: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " plt.show()\n" + ] + } + ], + "source": [ + "fig, ax1 = plt.subplots(figsize=(8, 4.5))\n", + "ax1.plot(orders[\"Firm orders\"], orders[\"Average OCC ($/kWe)\"], marker=\"o\", label=\"Average OCC\")\n", + "ax1.plot(orders[\"Firm orders\"], orders[\"Average TCI ($/kWe)\"], marker=\"o\", label=\"Average TCI\")\n", + "ax1.set_xlabel(\"Firm orders\")\n", + "ax1.set_ylabel(\"$/kWe\")\n", + "ax1.grid(True, alpha=0.3)\n", + "ax1.legend(loc=\"upper right\")\n", + "\n", + "ax2 = ax1.twinx()\n", + "ax2.plot(orders[\"Firm orders\"], orders[\"NOAK OCC reduction (%)\"], color=\"tab:red\", marker=\"s\", linestyle=\"--\", label=\"NOAK OCC reduction\")\n", + "ax2.set_ylabel(\"OCC reduction (%)\")\n", + "ax2.legend(loc=\"center right\")\n", + "plt.title(\"Order Book Sensitivity\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "43669105", + "metadata": {}, + "source": [ + "## 4. One-at-a-Time Lever Sensitivity\n", + "\n", + "Each case changes one lever from the AP1000 baseline." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fa4fdb71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CaseReactorFirm ordersAverage OCC ($/kWe)Average TCI ($/kWe)NOAK OCC reduction (%)Cumulative timeline (months)Delta avg OCC ($/kWe)
0BaselineAP1000106237.297005.0772.58190.560.00
1Lower interestAP1000106237.296739.3972.58190.560.00
2Higher interestAP1000106237.297281.2272.58190.560.00
3More standardizationAP1000105861.166563.3575.21162.64-376.13
4Less standardizationAP1000107227.438152.4665.07238.96990.14
5More complete designAP1000106026.806750.0368.31189.80-210.49
6Lower construction proficiencyAP1000106484.727292.0974.36190.96247.43
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" + ], + "text/plain": [ + " Case Reactor Firm orders Average OCC ($/kWe) Average TCI ($/kWe) NOAK OCC reduction (%) Cumulative timeline (months) \\\n", + "0 Baseline AP1000 10 6237.29 7005.07 72.58 190.56 \n", + "1 Lower interest AP1000 10 6237.29 6739.39 72.58 190.56 \n", + "2 Higher interest AP1000 10 6237.29 7281.22 72.58 190.56 \n", + "3 More standardization AP1000 10 5861.16 6563.35 75.21 162.64 \n", + "4 Less standardization AP1000 10 7227.43 8152.46 65.07 238.96 \n", + "5 More complete design AP1000 10 6026.80 6750.03 68.31 189.80 \n", + "6 Lower construction proficiency AP1000 10 6484.72 7292.09 74.36 190.96 \n", + "\n", + " Delta avg OCC ($/kWe) \n", + "0 0.00 \n", + "1 0.00 \n", + "2 0.00 \n", + "3 -376.13 \n", + "4 990.14 \n", + "5 -210.49 \n", + "6 247.43 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline = evaluate_case(\"Baseline\")\n", + "lever_cases = [\n", + " evaluate_case(\"Lower interest\", lever_updates={\"interest_percent\": 4}),\n", + " evaluate_case(\"Higher interest\", lever_updates={\"interest_percent\": 8}),\n", + " evaluate_case(\"More standardization\", lever_updates={\"standardization_percent\": 100}),\n", + " evaluate_case(\"Less standardization\", lever_updates={\"standardization_percent\": 40}),\n", + " evaluate_case(\"More complete design\", lever_updates={\"design_completion_percent\": 90}),\n", + " evaluate_case(\"Lower construction proficiency\", lever_updates={\"ce_exp\": 0.25}),\n", + "]\n", + "\n", + "lever_df = pd.DataFrame([baseline, *lever_cases])\n", + "lever_df[\"Delta avg OCC ($/kWe)\"] = lever_df[\"Average OCC ($/kWe)\"] - baseline[\"Average OCC ($/kWe)\"]\n", + "lever_df.round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1806058e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_15934/879412531.py:10: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " plt.show()\n" + ] + } + ], + "source": [ + "plot_df = lever_df[lever_df[\"Case\"] != \"Baseline\"].sort_values(\"Delta avg OCC ($/kWe)\")\n", + "colors = [\"tab:green\" if value < 0 else \"tab:red\" for value in plot_df[\"Delta avg OCC ($/kWe)\"]]\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4.5))\n", + "ax.barh(plot_df[\"Case\"], plot_df[\"Delta avg OCC ($/kWe)\"], color=colors)\n", + "ax.axvline(0, color=\"black\", linewidth=0.8)\n", + "ax.set_xlabel(\"Change in average OCC ($/kWe)\")\n", + "ax.set_title(\"One-at-a-Time Sensitivity vs. AP1000 Baseline\")\n", + "ax.grid(True, axis=\"x\", alpha=0.3)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "d7821735", + "metadata": {}, + "source": [ + "## 5. Save a Dashboard for a Sensitivity Case" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "552f1dba", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_15934/477507912.py:42: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " plt.show()\n" + ] + } + ], + "source": [ + "case_config = base_config(\"AP1000\")\n", + "case_levers = base_levers(\"AP1000\")\n", + "case_levers.update({\"interest_percent\": 8, \"standardization_percent\": 40})\n", + "case_result = run_one_scenario(case_config, case_levers)\n", + "\n", + "sensitivity_dashboard = output_dir / \"crf_sensitivity_case_dashboard.png\"\n", + "save_dashboard(case_result, sensitivity_dashboard, title=\"AP1000 Sensitivity Case\", show_levers=True)\n", + "show_image(sensitivity_dashboard)" + ] + }, + { + "cell_type": "markdown", + "id": "b1016053", + "metadata": {}, + "source": [ + "## 6. Inspect Sensitivity Case Panels\n", + "\n", + "These cells show selected dashboard panels from the sensitivity case as larger standalone figures." + ] + }, + { + "cell_type": "markdown", + "id": "c912dc8c", + "metadata": {}, + "source": [ + "### Sensitivity Case Capital Cost" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8e223cf", + "metadata": {}, + "outputs": [], + "source": [ + "plot_capital_cost_panel(case_result, figsize=(11, 5))" + ] + }, + { + "cell_type": "markdown", + "id": "e0edcc7a", + "metadata": {}, + "source": [ + "### Sensitivity Case Timeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70deb2c7", + "metadata": {}, + "outputs": [], + "source": [ + "plot_timeline_panel(case_result, figsize=(12, 5.5))" + ] + }, + { + "cell_type": "markdown", + "id": "40eb2588", + "metadata": {}, + "source": [ + "### Sensitivity Case Waterfall" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55fb3a1e", + "metadata": {}, + "outputs": [], + "source": [ + "plot_waterfall_panel(case_result, figsize=(12, 5.5))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 08b24cc7cff0c5005f5c3c5f6f9c0f4ae10281a1 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Mon, 4 May 2026 11:03:10 -0500 Subject: [PATCH 22/25] move figures --- .../CostReduction_exploration_mode.ipynb | 2 +- tutorial/ANL.png | Bin 5731 -> 0 bytes tutorial/INL1.png | Bin 6027 -> 0 bytes tutorial/MIT1.png | Bin 3041 -> 0 bytes tutorial/crf_sensitivity_analysis.ipynb | 179 ++++++++++++++---- 5 files 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b/tutorial/crf_sensitivity_analysis.ipynb index 362c253..277c78b 100644 --- a/tutorial/crf_sensitivity_analysis.ipynb +++ b/tutorial/crf_sensitivity_analysis.ipynb @@ -7,9 +7,9 @@ "source": [ "
\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", "
" ] @@ -149,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "fefcd14f", "metadata": {}, "outputs": [], @@ -319,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "adead536", "metadata": {}, "outputs": [], @@ -353,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "54c1e375", "metadata": {}, "outputs": [ @@ -440,7 +440,7 @@ "3 8 orders AP1000 8 6759.85 7646.01 69.80 166.94" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -453,17 +453,30 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "4f99cb05", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_15934/3903133957.py:14: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", - " plt.show()\n" - ] + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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g1NRU49577zVq1apleHl5Gb6+vkbTpk2Np59+2jh69KhZLiUlxYiJiTF8fHyM6tWrGw888IDx008/GZKMuXPnmuXuvfdew9fX19iyZYsRGxtrVK5c2QgMDDQeeeQRIzs7267tiIgI495777Xb99577xn169c3PD09DUnG+PHjDcMwjIMHDxq9evUyAgICDD8/P6NLly7G1q1bi9SxZs0aQ5KxZs2aEp3/77//bjz66KPGddddZ3h7exuVK1c2GjZsaAwbNszYs2ePXdm3337buOGGGwwvLy/DarUa//rXv4xt27aZx48cOWIkJSUZ9evXN3x9fY0qVaoYN9xwg/Hyyy8b+fn5Zrm9e/cacXFxhp+fnyHJiIiIMI+1a9fOuJQf9fv37zcGDhxoXHvttWa/brnlFmPhwoWGzWYr9jVpaWnGyJEjjUaNGhk+Pj6Gt7e3cd111xkPPfSQ8csvvxQp//XXXxt33HGHERYWZnh6ehpVq1Y1YmJijGnTphlZWVkX7d/27duNTp06GX5+fkZAQIBx5513Gvv377f72hqGYYwfP96QZBw7dszu9XPnzjUk2X0tMjIyjPvvv9/w9/c3fHx8jE6dOhm//vprkTr/SWHdP/zwQ7HHz5w5Y7z66qtGTEyMUbVqVcPDw8MIDw83evbsaSxfvtwsVzjn/vOf/5S4bQC4GlkMo5j7ewAAqACSkpL03//+V9nZ2c7uCgAAqEB4jywAAAAAwKUQZAEAAAAALoVbiwEAAAAALoUVWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLJlyDAMZWVlibcdAwAAAED5IciWoZMnT8pqterkyZPO7gpcnM1m019//SWbzebsruAqw9yDMzH/4CzMPTgT8690CLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuxcPZHQAAAEDFVlBQoLy8PGd3o9zYbDbl5eXp77//lpsb6zxwrKtt/rm7u8vDw0MWi+Wy6iHIAgAA4IKys7N18ODBK/rfCxqGIZvNpvT09Mv+5Rq4VFfj/PPx8VFYWJi8vLxKXQdBFgAAAMUqKCjQwYMH5ePjo+rVq1+xv2QbhqH8/PwyWSUCLtXVNP8Mw1Bubq6OHTumPXv2qF69eqVehSbIAgAAoFh5eXkyDEPVq1dX5cqVnd2dcnM1BQlUPFfb/KtcubI8PT21b98+5ebmqlKlSqWq58q/CRsAAACX5Wr45RqA45TFe4EJsgAAAAAAl0KQBQAAAAC4FN4jCwAAgEsSPT+63Nv45d5fyr2Nq5HFYtHmzZvVpEkTh7e9f/9+NWzYUH/++aesVmup6nBm/680S5Ys0bJly7R06dIyr3vSpEk6c+aMnnvuuTKvuxArsgAAAHBpM2bMUIsWLeTt7a0ePXoUOZ6Xl6dBgwYpMDBQgYGBGjx4sPLz8y9aZ0ZGhh544AEFBQWpatWqatGihU6fPm0e/+6779S4cWP5+PioSZMmWr9+fan7X6VKFfPD3d1d3t7e5nZ8fPwFX5eUlKShQ4eWut3iHD9+XEOGDFFERISqVKmi2rVrKykpSbt27brsumvVqqXs7GwzxJZ1/+fNm+f0gFsR+hAbG6tXXnnlomVsNpvGjBmjsWPHav/+/XZz0M3NTZUrVza3H374YUnSp59+qltuuUV+fn6qVq2abrrpJr3xxhuSpL1798pisSgjI0OS9Nhjj+mtt95SWlpauZ0nQRYAAAAuLTw8XGPHjlVycnKxx5977jmtW7dO27Zt07Zt2/Ttt99q8uTJF6zPZrMpISFBnp6e2rVrlzIyMvTWW2/J09NTknTixAklJCRo0KBBSk9P18CBA5WQkGD+En+psrOzzY+bb75ZL7zwgrn9+eefl6rO0sjMzFTr1q21b98+ffHFF8rKylJqaqpatmzp0H6g/P3vf/9TYGCgoqOjzT8wFH7UqlVL7733nrn9xhtvaNasWbr33nuVnJysgwcP6q+//tLMmTP12WefFVt/4R9h5syZU27n4NQgO2XKFN14443y8/NTcHCwevTooZ07d9qVMQxDEyZMUHh4uCpXrqzY2Fht27bNrkxOTo4GDx6soKAg+fr6qnv37jp48KBdmfT0dCUmJspqtcpqtSoxMbHIxWb//v3q1q2bfH19FRQUpCFDhig3N7dczh0AAABlo2fPnurRo4eCgoKKPf7OO+9o7NixCgsLU1hYmJ566qmL/oL9+eefa//+/XrttdcUGBgoNzc3NW3a1Ayyy5Yt0zXXXKPk5GR5e3srOTlZoaGhWrZsWZmf28qVK9W0aVNZrVY1a9ZMX375pSTp1Vdf1aJFizRz5kxVqVJFjRo1kiQtWrRIUVFR8vPzU61atTRu3DgZhlGitl555RVZLBZ98MEHql+/vtzc3OTv769HHnlEjz32WInqt1gs+ve//63IyEj5+/urT58+yszMlGS/alce/T9f7dq1NXXqVLVq1Up+fn5q166dDhw4IEkaMmSI7r//frvyL7zwgrp06SLpbAZ59dVXVb9+ffn7+ys2NlY7duwwy7700kuqVauW/Pz8VLt2bb399tvavHmzHn74Yf3yyy/maub+/fs1YcIEdevWTQ8//LCsVqvq1KmjNWvWaNmyZbruuusUGBiocePG2fXlyy+/1E033SR/f381atRIn3zyiXksKSlJycnJuuuuu+Tn56fIyEitXbtWkjR8+HB9++23Gjly5EVX9D/55BN16NChRON48uRJjRw5Uq+++qqZpywWi1q0aHHBICtJHTt2tOt3WXNqkP366681cOBAff/991q1apXy8/MVFxenU6dOmWWmTp2ql156STNmzNAPP/yg0NBQderUSSdPnjTLDB06VMuWLdOSJUu0bt06ZWdnKyEhQQUFBWaZfv36KTU1VStWrNCKFSuUmpqqxMRE83hBQYG6du2qU6dOad26dVqyZIk++OADDR8+3DGDAQAAgDKXnp6ugwcP2t3u2aRJE+3fv98MWOf7+uuv1aBBAz300EOqVq2aoqKitGDBAvP4li1bitw+2qRJE23ZsqVM+/7777/rX//6l8aNG6fjx49rzJgx6t69u/bs2aMhQ4aof//+evTRR5WdnW0u9AQGBurDDz9UVlaWPvnkE7355ptavHhxidr74osvdOedd8rD48KP0SlJ/QsWLNCaNWu0d+9epaenF3v7cHn0vzjvvvuuFi9erGPHjsnX19cMjPfcc4/++9//6syZM3b9LswHs2bN0pw5c/Tpp5/qr7/+Us+ePdWtWzfl5uZq165dGjt2rFauXKmTJ09qw4YNuummm9S0aVO98cYbio6OtlvdLBzbW2+9VSdOnFD//v11991366OPPtLPP/+sb7/9Vi+//LJ++uknSWfn15133qnnn39eJ06c0OzZs5WYmGi34LdkyRI9+OCDysjIUGJiopKSkiRJ06dPt1vVv9BKempqqurXr1+iMVy/fr1OnTql3r17X9LYN2zYUKmpqZf0mkvh1CC7YsUKJSUlqVGjRmrcuLHmzp2r/fv3a9OmTZLO/iXklVde0VNPPaWePXsqKipK8+fP1+nTp80JnZmZqTlz5mj69Om69dZb1bRpUy1cuFC//PKL+RerHTt2aMWKFXr77bcVExOjmJgYvfXWW/rss8/MCbFy5Upt375dCxcuVNOmTXXrrbdq+vTpeuutt5SVleWcAQIAAMBlyc7OliT5+/ub+wo/P3dh5FwnTpwwV0IPHz6s2bNn69FHH9W6devMOs+tr7DOC9VXWkuWLFFsbKx69uwpDw8P3XHHHWrbtq3ee++9C74mPj5e119/vSwWi5o0aaK+ffuaq3X/5NixY7rmmmsuWqYk9T/55JMKDw+Xv7+/nn32WS1evFg2m61Efbic/hdn0KBBuvbaa1WpUiX179/fzBktWrRQzZo19fHHH0uSNm/erP379+v222+XJL3++ut65plnVK9ePXl4eGjIkCE6c+aMNmzYIHd3dxmGoW3btunMmTMKCQnRDTfccNF+NGvWTHfccYfc3d3Vr18/HTp0SKNHj5avr68aNWqkG264wQyys2fPVlJSkjp06CA3Nze1bdtWCQkJev/99836unbtqg4dOsjd3V333Xef9u3bp+PHj5d4XNLT01W1atUSlT127JiqV68uLy+vEtcvSVWrVlVubq7de8vLUoV6anHhX8UCAwMlSXv27FFaWpri4uLMMt7e3mrXrp1SUlL00EMPadOmTcrLy7MrEx4erqioKKWkpKhz585av369rFarWrZsaZZp1aqVrFarUlJSFBkZqfXr1ysqKkrh4eFmmc6dOysnJ0ebNm1S+/bti/Q3JydHOTk55nZh4LXZbCX+ZnWUpguaOrzNzYmbHd7mlcJms8kwjAo3j3DlY+7BmZh/FU/h16Tww5FK017ha859ra+vr6SzD2+qVq2a+bl09n18CxcuNB9mExERoa1bt8rX11c1atTQwIEDJUmtW7dWjx499PHHH6tNmzby9fVVenq6XTuZmZkKCgoqtt9RUVHat2+fJOmNN95Q//79//E8DMPQgQMHFBERYVdnnTp1dODAAbtzPff4F198oWeeeUa7du1SXl6ecnJyFB8fb1fmQl/PoKAgHTx48KJjX5L6a9WqZW7XqlVLubm5Onr0aLF9Lsv+F/f1DwkJMbd9fHx08uRJc/vuu+/Wu+++qz59+mj+/Pnq1auXKleuLMMwtHfvXt19991yd3c368rNzdWBAwfUtm1bzZs3TzNmzNB9992nVq1a6YUXXlCTJk2K7YNhGAoNDTX3Va5cudi+ZWdnm21/9dVXmjt3rllHfn6+/Pz8zPLnv1Y6m0UKc9Q/fc8GBAQoMzPzgmXOfX21atX0119/KScn54JhtrivZ2Zmpry8vMwxLa7+4nKTm1vJ1lorTJA1DEPDhg1T27ZtFRUVJUnmU65CQkLsyoaEhJgXg7S0NHl5eSkgIKBImcLXp6WlKTg4uEibwcHBdmXObycgIEBeXl4XfNrWlClTNHHixCL709PT//FJeI4W4RHh8DZPnDjh8DavFDabzbzQlvSbGSgLzD04E/Ov4snLy5PNZlN+fr7Df7cpTXuFvxSf+1o/Pz/VqFFDmzZtUkTE2d+HNm3apJo1a8rX11d9+vRRnz59VFBQIHd3d+Xn5ys6OlrLli2zq+fcuqOiovTqq6/aHd+8ebOGDh1abL/Pv73yYudW+Mt9fn6+wsPDlZKSYld+z549uvnmm819hmGYn+fm5qpXr1569dVX1adPH3l7e2v48OHau3evXR0X+nreeuut+u9//6unnnqq2NuLS1r/H3/8oebNm5v9LfxdvXDF+tz2y7L/hX94OfdYQUGBuV34tsPC7bvuuksTJkzQvn37tGTJEr377rvmsRo1amj69Onq3LlzkXby8/PVs2dP9ezZU2fOnNHEiROVmJiozZs3mwHtQnPn3PbPH4fCMtdcc40GDRpU7APJ8vPzi5zn+fVZLJYi3wfnu+GGG7R9+/YLljl33G666Sb5+PhoyZIl6tevX7HlizunX375RY0bNy62jcLzyMzMLLJie6H3up+vwgTZQYMGacuWLeYtG+eyWCx224ZhFNl3vvPLFFe+NGXONXr0aA0bNszczsrKUs2aNRUQEFDipXpH2Ze/z+FtFv5FCJfOZrPJYrEoICCAX+bgUMw9OBPzr+L5+++/lZ6eLg8Pj4u+b7I8XEp7hb88F67s5Ofny83NzVw9SkpK0gsvvKBbbrlF0tlnsAwYMKBIG4XbvXr10ujRo/X2228rOTlZP/74oz799FMtX75cHh4e6tWrl0aOHKn58+crMTFRCxYsUFpamnr16nXZ42SxWOTm5iYPDw/17dtXU6ZM0fLly5WQkKCPP/5Y69at06xZs+Th4aHQ0FBt377dbPPMmTP6+++/FRwcLF9fX23YsEFLlixR69at7fp1oa/n8OHD9f7776t///6aMmWKrrvuOp08eVLvv/++Tp8+rfvvv79E9b/88stq166dfHx89Mwzz+iuu+6Sl5eXWaaw/bLuv5ubmywWi90xd3d3c7twdbVwu3bt2mrbtq0efvhheXl5qWPHjua1Z+DAgXrmmWd03XXXKTIyUllZWVqzZo06dOigQ4cOaf/+/Wrbtq18fHzk5+cnT09PeXh4KDw8XGlpacrLyzNXXt3c3Myv6bntn3se537dH374YcXHxys+Pl633HKL8vPz9dNPP8nf318NGjQocp7FjeuePXsuOhe7d++uZ5999oJlzh23gIAAPf/88xo2bJjc3d3VrVs3+fn56eeff9b48eP18ccfF3tO33zzjRISEoptw8PDQ25ubrJarapUqdIF+3kxFSLIDh48WJ988om++eYb1ahRw9wfGhoq6exqaVhYmLn/6NGj5uppaGiocnNzlZ6ebrcqe/ToUbVu3dosc+TIkSLtHjt2zK6eDRs22B1PT09XXl5ekZXaQt7e3vL29i6yv3CyViQ2Of42rYo2Bq6m8ILGOMLRmHtwJuZfxVL4C3PhR6Ff7v3Fib0qatKkSXZ3yfn4+Khdu3bmeyuffvppnThxQg0bNpQk9e/fX0899ZR5Tuc/dTcgIEDLly/XwIEDNWLECNWoUUOvv/66br75Zklnb7X89NNP9eijj2rw4MG6/vrr9emnn5bZH/ELx7tevXr68MMPNXr0aN1zzz269tprtWzZMtWtW1eSlJycrN69eyswMFA1a9bUli1b9Prrr+uhhx5Sdna2YmNj1adPHx04cKDI4k1xCzX+/v5KSUnRhAkT1KlTJx0/flxBQUFq3769xo4dq6pVq5ao/rvvvlsdOnQw3yL473//267Nws/Luv/n1l9c2eKO33PPPUpKStLo0aPtbiMePHiw+UeLAwcOyM/PT23btlXHjh2Vl5enp59+Wtu3b5ebm5saN26sefPmyWKxqGPHjmrVqpVq1Kghm82mLVu2XLD9wr6dP/+aNWum9957T+PGjdOOHTvk5uamJk2a6MUXXyzy2uLqGzp0qJKSkhQQEKC2bdsW+2Thrl276rHHHtO2bdvMu2HPH8tzx+nRRx9VjRo1NG3aND388MPy9vZW3bp1NWDAgGK/tqdOndL//vc/paamXvBrdbnXe4vh6Dc8nMMwDA0ePFjLli3T2rVrVa9evSLHw8PD9fjjj+vJJ5+UdPaWg+DgYL3wwgt66KGHlJmZqerVq2vhwoXmk7QOHz6sGjVq6H//+586d+6sHTt2qGHDhuYTxSRpw4YNatWqlX799VdFRkbq888/V0JCgg4ePGiG5qVLl+ree+/V0aNHS7TCmpWVJavVqszMzAq3Ihs9P9rhbVa0H3KuxGaz6cSJE+Yj/wFHYe7BmZh/Fc/ff/+tPXv2qE6dOqVeNXEFhbdpenh4/ONdf7g4i8WizZs3F3mqMy7MWfPvvffe00cffaSlS5eWed2TJ0/WqVOnNGnSpGKPl8W1xakrsgMHDtTixYv18ccfy8/Pz3wvqtVqVeXKlc2/KEyePFn16tVTvXr1NHnyZPn4+Jj3Z1utVg0YMEDDhw9XtWrVFBgYqBEjRig6Olq33nqrJKlBgwbq0qWLkpOTNXv2bEnSgw8+qISEBEVGRkqS4uLi1LBhQyUmJmratGk6ceKERowYoeTk5AoXSgEAAADgcvTt21d9+/Ytl7rHjBlTLvWey6lBdtasWZKk2NhYu/1z5841/xfSk08+qTNnzujRRx9Venq6WrZsqZUrV8rPz88s//LLL8vDw0O9e/fWmTNn1LFjR82bN8/u9oBFixZpyJAh5tONu3fvrhkzZpjH3d3dtXz5cj366KNq06aNKleurH79+unFF18sp7MHAAAAAJSGU28tvtJwa7E9bi0uPW6vg7Mw9+BMzL+Kh1uLgfJ3Nc6/sri28FMCAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuxan/fgcAAACup/ao5eXext7nu5Z7G7i4/fv3q2HDhvrzzz9ltVr/sXxSUpL8/f31yiuvlH/nSmny5Mn65Zdf9N5775Xq9fPmzdMrr7yi1NTUsu1YGXr44YfVuHFjPfLIIw5vu1OnTho5cqRuvfXWcm+LFVkAAAC4tBkzZqhFixby9vZWjx49ihzPy8vToEGDFBgYqMDAQA0ePFj5+fkXrG/v3r2yWCyqUqWK+dGtWze7Mt99950aN24sHx8fNWnSROvXr7/s8/jyyy918803q0qVKrJarYqPj9dPP/102fWWVq1atZSdnV2iEFsSsbGxJQq569atU3x8vAICAuTv76/GjRtr6tSpys3Nvew+jBkzxi7EWiyWMg+l5dX/kvT1t99+0/LlyzVgwABzX15eniZOnKi6deuqcuXKqlmzph5//HFlZ2fbvfbMmTMaO3as6tWrJ19fX9WoUUN33HGHNm3aZJbZv3+/kpKSFB4eLj8/P1133XUaPHiwDh8+LEkaO3asnnjiics6z5IiyAIAAMClhYeHa+zYsUpOTi72+HPPPad169Zp27Zt2rZtm7799ltNnjz5H+s9ePCgsrOzlZ2drU8//dTcf+LECSUkJGjQoEFKT0/XwIEDlZCQoIyMjFKfwyeffKLbb79dSUlJSktL0969exUbG6t27drpxx9/LHW9F2IYhgoKCsq83sv12WefKT4+Xp07d9bu3buVkZGhpUuXavv27WZYqsic3f833nhDffr0kZeXl7mvX79+WrZsmd5//31lZ2dr9erV+vnnnxUXF6e8vDxJZ8NuXFyc1q5dq6VLlyojI0M7d+5Uz549tWzZMklnQ+yNN94oT09PpaSkKCsrS999953CwsL09ddfS5JuueUWZWRk6Lvvviv3cyXIAgAAwKX17NlTPXr0UFBQULHH33nnHY0dO1ZhYWEKCwvTU089pTlz5pS6vWXLlumaa65RcnKyvL29lZycrNDQUPMX/ktlGIYee+wxjRo1SgMGDFCVKlUUEBCgkSNHqk+fPhoxYoQkaciQIbr//vvtXvvCCy+oS5cuZj2vvvqq6tevL39/f8XGxmrHjh1m2dq1a2vKlClq1aqVfHx8tH37dlksFr3xxhuKiopS1apV1b17d2VmZkr6v5XpwoCek5Ojhx9+WIGBgapTp47mzJkji8WivXv3mm2cOnVKd911l/z8/BQZGam1a9dKkoYPH65vv/1WI0eOVJUqVRQfH1/sOAwZMkQjR47U0KFDza9n/fr1NW/ePEVEREiS7r77boWHh6tq1apq3ry51qxZY9Yxb948NWnSRGPGjFG1atVUq1YtzZw50zw+YcIEc9X+pptukiS1bt1aVapUMf+4cbH6/+nrWJL+//jjj2rTpo38/f3VsGFDuxXin376Sa1atVLVqlUVFBRk3glwob6e75NPPlGHDh3M7bVr1+qTTz7RsmXL1Lx5c7m7u+v666/XsmXLtGvXLi1atEiStHjxYu3YsUOfffaZmjVrJk9PT/n6+qpfv3567rnnJEnjx49XdHS03nrrLdWuXVsWi0UhISEaM2aM7rrrLklnV407dOigTz75pERjdjkIsgAAALhipaen6+DBg2rSpIm5r0mTJtq/f78Z2C4kKipKoaGh6t69u3799Vdz/5YtW+zqK6xzy5Ytperjrl27tHfvXvXt27fIsb59+2rdunU6c+aM7rnnHv33v//VmTNnzOMLFixQYmKiJGnWrFmaM2eOPv30U/3111/q2bOnunXrZndL67x58zR//nxlZ2crMjJSkrR06VKtXr1a+/fv18GDB/Xyyy8X28/nnntOP/74o7Zt26bU1NRig/uSJUv04IMPKiMjQ4mJiUpKSpIkTZ8+XTfffLNeeOEFZWdn6/PPPy/y2t27d2vPnj3FjsO5OnbsqB07duj48eO66667dMcdd+jkyZPm8a1bt8pisejw4cNaunSpRo0apW+++aZIPRs3bpQkpaSkKDs7W2PGjClR/RdSkv5nZGSoS5cuuuuuu3Ts2DHNmjVLDz74oFJSUiRJgwYNUrdu3ZSRkaE///zTvE33Qn091+nTp7V7927Vr1/f3PfFF1+oZcuWqlOnjl3ZwlvXV65caZaLj4+Xv7//Bfv+xRdf/OPXRpIaNmzokPcQE2QBAABwxSp8H+C5v6AXfn6hcBIUFKQNGzZoz549+vXXX1WvXj116tRJWVlZZp3n/8Lv7+9forBTnL/++kvS2VukzxceHq6CggKdOHFCLVq0UM2aNfXxxx9LkjZv3qz9+/fr9ttvlyS9/vrreuaZZ1SvXj15eHhoyJAhOnPmjDZs2GDW98gjjygyMlLu7u7m7acjR45USEiI/P391atXL7v3RJ5r8eLFGjVqlMLCwmS1WjV+/PgiZbp27aoOHTrI3d1d9913n/bt26fjx4+XaByOHTsmSbrmmmsuWu6+++6T1WqVp6ennnjiCdlsNrs/Ivj6+mrChAny8vJSTEyM+vfvr3fffbdEfShJ/ZfT/+XLl6t69eoaPHiwPD091a5dO/Xt21cLFiyQJHl6emrfvn06dOiQvL29dcstt5S43+np6ZKkqlWrmvv++uuvYueVdHZuFfb52LFj/zjuJSlT2H5hX8oTQRYAAABXrCpVqkiS3epr4ed+fn5atGiR/Pz8FBAQoKioKPM1N910kzw9PeXv768XX3xReXl55qpZlSpViqzmZmZmys/Pr9g+NGrUyHxoVOGtnOcqvAX10KFDRY4dOnRI7u7uCgwMlCQlJiaaoezdd99Vr1695OPjI+nsrcB33323/P39zY/CFelCtWrVKtJGaGio+bmvr+8FA/mhQ4dUs2bNS6pLuvAfDM5XOA5//vnnBcvYbDY99dRTqlevnqpWrSp/f39lZmaafwyQzgY0T09PczsiIuKidV5q/ZfT/4MHD6p27dp2+6699lrzNe+8847+/vtvNW/eXPXr19eMGTNK1G9JCggIkCTzDy6FfSpuXklnv57Vq1c3y/3TGJWkTGH7hX0pTwRZAAAAXLECAgJUo0YNu1sdU1NTVbNmTVmtVvXv318nT55Uenq6tm7dWmwdFotFFovF3L7hhhuK3DqZmpqq6OjoYl+/bds286FR/fv3L3L8+uuvV0RERLH/Eua9995TmzZtVLlyZUln37+5evVq/fnnn3rvvffM24olqWbNmvrPf/6jjIwM8+P06dN2t4O6uZX+1//w8HAdOHDA3N6/f/8lvf6f2r7++utVu3ZtLVmy5IJlFi9erMWLF2v58uXKzMxURkaGrFarDMMwyxw6dMh8iFFhPy+0knju17Wk9V9O/2vUqGH3nmJJ2rNnj9m/unXr6t1331VaWprefvttjRgxwlwhP7+v5/Px8VG9evXsboPv1KmTeXfBubKysvT555+rU6dOkqTOnTtrxYoVF73dvnPnzhc9t0Lbt28vcut9eSDIAgAAwKXl5+fr77//Vn5+vmw2m/7++2+794Xed999mjRpktLS0pSWlqbJkyfrgQceuGB9GzZs0I4dO1RQUKDs7GyNHDlSFotFMTExkqTbb79dBw8e1Jw5c5Sbm6s5c+bo8OHD5i2+l8pisejll1/WlClTNGfOHGVnZysjI0MvvPCClixZoqlTp5pla9SoobZt22rAgAHy8vJSbGyseWzgwIF6+umntXPnTklnw8rHH39c6luez9e3b19NnTpVaWlpyszM1LPPPntJrw8JCdHvv/9+weMWi0Wvvfaann/+eb322mvmLcm7du3SgAEDtG/fPmVlZcnLy0tBQUHKzc3VM888Y7cCKZ194NSzzz6r3NxcbdiwQYsWLSr2DwjF9akk9V9O/2+77TYdPXpUM2fOVH5+vr799lu99957uvvuuyWdXWU/cuSILBaLAgIC5ObmJg8PjxKNnyR169bN7uFUHTp00G233abbb79dP/30kwoKCrRr1y7dfvvtqlu3rjku/fv3V/369dWtWzdt3rxZ+fn5OnPmjN5//32NGzdOkjRx4kT9/PPPevjhh7V//34ZhqFjx47phRde0NKlS80216xZo4SEhBKN2eXwKPcWAAAAcEXZ+3xXZ3fBznPPPaeJEyea25UrV1a7du3MJ+aOGzdOx48fV4MGDSSd/aW9uIflFPrjjz80duxYpaWlycfHRy1bttTKlSvN/6caGBioTz/9VI8++qgGDRqk66+/Xp9++ull3U55++2364MPPtCzzz6rxx57TG5uboqJidGaNWt044032pW95557lJSUpNGjR9utcg4aNEju7u7q2bOnDhw4ID8/P7Vt29buKbaXY+zYsTpy5IgaNmwoq9WqkSNH6tNPP5W3t3eJXj906FAlJSXJ399fbdu21WeffVakTEJCgj7//HM999xzZoCqVauWEhMTFRYWpnvvvVdffvmlIiIiVLVqVQ0dOtTudmfp7EO68vPzFRYWJh8fH02aNEnt27cvtk/PPvushgwZogceeEAjR47U4MGD/7H+i/mn/nt5eenzzz/X0KFDNXr0aIWHh2vmzJlq06aNpLP/S/jJJ59Udna2QkJCNG3aNDVu3LjYvo4aNapI+w899JA6duyoyZMnm7dXL126VJMmTdIdd9yhQ4cOqVq1arrjjjv07LPPmu+T9vT01BdffKFJkybpzjvv1KFDhxQYGKhWrVqZ3ysRERH64YcfNH78eLVs2VLZ2dkKDg5W165dde+990qSvv32W/n5+enmm28u8ZiVlsUoyTo5SiQrK0tWq1WZmZl2b7KuCKLnF3+rS3n65d5fHN7mlcJms+nEiRMKDAy8rFuAgEvF3IMzMf8qnr///lt79uxRnTp1VKlSJWd3p9wYhqH8/Hx5eHj84+2b+D8pKSmKjY3V33//XWG+Z+fNm6dXXnnFIU/NLStlPf8eeughNWnSRI888kgZ9O7SdO7cWSNGjDBvWb6Qsri2sCILAAAA4B8dPXpU27Zt0y233KIjR45ozJgx6tWrV4UJsThr9uzZTmv7iy++cFhbzDoAAAAA/6igoECPP/64rFarGjdurLCwML322mvO7hauUqzIAgAAAPhHYWFhFf6W3aSkJCUlJTm7G3AAVmQBAAAAAC6FIAsAAICL4tmgAMqSzWa77Dq4tRgAAADF8vT0lMVi0bFjx1S9evUr9om+PLUYznQ1zT/DMJSbm6tjx47Jzc3N/Pc/pUGQBQAAQLHc3d1Vo0YNHTx4UHv37nV2d8qNYRiy2Wxyc3O74oMEKp6rcf75+PioVq1al/XEa4IsAAAALqhKlSqqV6+e8vLynN2VcmOz2ZSZmSmr1cq/koHDXW3zz93dvUxWnwmyAAAAuCh3d3e5u7s7uxvlxmaz6fTp06pUqdJVESRQsTD/SoeRAgAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApTg1yH7zzTfq1q2bwsPDZbFY9NFHH9kdt1gsxX5MmzbNLBMbG1vk+F133WVXT3p6uhITE2W1WmW1WpWYmKiMjAy7Mvv371e3bt3k6+uroKAgDRkyRLm5ueV16gAAAACAUnJqkD116pQaN26sGTNmFHv88OHDdh/vvPOOLBaLevXqZVcuOTnZrtzs2bPtjvfr10+pqalasWKFVqxYodTUVCUmJprHCwoK1LVrV506dUrr1q3TkiVL9MEHH2j48OFlf9IAAAAAgMvi4czG4+PjFR8ff8HjoaGhdtsff/yx2rdvr2uvvdZuv4+PT5GyhXbs2KEVK1bo+++/V8uWLSVJb731lmJiYrRz505FRkZq5cqV2r59uw4cOKDw8HBJ0vTp05WUlKRJkyapatWql3OaAAAAAIAy5NQgeymOHDmi5cuXa/78+UWOLVq0SAsXLlRISIji4+M1fvx4+fn5SZLWr18vq9VqhlhJatWqlaxWq1JSUhQZGan169crKirKDLGS1LlzZ+Xk5GjTpk1q3759sX3KyclRTk6OuZ2VlSVJstlsstlsZXLeZcXNCYvvFW0MXInNZpNhGIwhHI65B2di/sFZmHtwJuafPTe3kuUWlwmy8+fPl5+fn3r27Gm3v3///qpTp45CQ0O1detWjR49Wj///LNWrVolSUpLS1NwcHCR+oKDg5WWlmaWCQkJsTseEBAgLy8vs0xxpkyZookTJxbZn56ervz8/Es+x/IU4RHh8DZPnDjh8DavFDabTSdPnpRhGCX+ZgbKAnMPzsT8g7Mw9+BMzD97QUFBJSrnMkH2nXfeUf/+/VWpUiW7/cnJyebnUVFRqlevnlq0aKGffvpJzZo1k3T2oVHnMwzDbn9Jypxv9OjRGjZsmLmdlZWlmjVrKiAgoMLdjrwvf5/D2wwMDHR4m1cKm80mi8WigIAALmhwKOYenIn5B2dh7sGZmH+l4xJB9ttvv9XOnTu1dOnSfyzbrFkzeXp6avfu3WrWrJlCQ0N15MiRIuWOHTtmrsKGhoZqw4YNdsfT09OVl5dXZKX2XN7e3vL29i6y383NrcJNQpscf6tCRRsDV2OxWCrkXMKVj7kHZ2L+wVmYe3Am5t+lc4mRmjNnjpo3b67GjRv/Y9lt27YpLy9PYWFhkqSYmBhlZmZq48aNZpkNGzYoMzNTrVu3Nsts3bpVhw8fNsusXLlS3t7eat68eRmfDQAAAADgcjh1RTY7O1u//fabub1nzx6lpqYqMDBQtWrVknT2dt3//Oc/mj59epHX//7771q0aJFuu+02BQUFafv27Ro+fLiaNm2qNm3aSJIaNGigLl26KDk52fy3PA8++KASEhIUGRkpSYqLi1PDhg2VmJioadOm6cSJExoxYoSSk5Mr3C3CAAAAAHC1c+qK7I8//qimTZuqadOmkqRhw4apadOmevrpp80yS5YskWEY6tu3b5HXe3l5afXq1ercubMiIyM1ZMgQxcXF6csvv5S7u7tZbtGiRYqOjlZcXJzi4uJ0ww03aMGCBeZxd3d3LV++XJUqVVKbNm3Uu3dv9ejRQy+++GI5nj0AAAAAoDQshmEYzu7ElSIrK0tWq1WZmZkVbiU3en60w9v85d5fHN7mlcJms+nEiRMKDAzkvRJwKOYenIn5B2dh7sGZmH+lw0gBAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl+Lh7A7gylV71HKHt7n3+a4ObxMAAACAY7EiCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClODXIfvPNN+rWrZvCw8NlsVj00Ucf2R1PSkqSxWKx+2jVqpVdmZycHA0ePFhBQUHy9fVV9+7ddfDgQbsy6enpSkxMlNVqldVqVWJiojIyMuzK7N+/X926dZOvr6+CgoI0ZMgQ5ebmlsdpAwAAAAAug1OD7KlTp9S4cWPNmDHjgmW6dOmiw4cPmx//+9//7I4PHTpUy5Yt05IlS7Ru3TplZ2crISFBBQUFZpl+/fopNTVVK1as0IoVK5SamqrExETzeEFBgbp27apTp05p3bp1WrJkiT744AMNHz687E8aAAAAAHBZPJzZeHx8vOLj4y9axtvbW6GhocUey8zM1Jw5c7RgwQLdeuutkqSFCxeqZs2a+vLLL9W5c2ft2LFDK1as0Pfff6+WLVtKkt566y3FxMRo586dioyM1MqVK7V9+3YdOHBA4eHhkqTp06crKSlJkyZNUtWqVYttPycnRzk5OeZ2VlaWJMlms8lms13aYJQzNyf8zcJNhsPbrGjjXlo2m02GYVwx5wPXwdyDMzH/4CzMPTgT88+em1vJcotTg2xJrF27VsHBwfL391e7du00adIkBQcHS5I2bdqkvLw8xcXFmeXDw8MVFRWllJQUde7cWevXr5fVajVDrCS1atVKVqtVKSkpioyM1Pr16xUVFWWGWEnq3LmzcnJytGnTJrVv377Yvk2ZMkUTJ04ssj89PV35+fllNQRlIsIjwuFtnq7q+CB74sQJh7dZHmw2m06ePCnDMEr8zQyUBeYenIn5B2dh7sGZmH/2goKCSlSuQgfZ+Ph43XnnnYqIiNCePXs0btw4dejQQZs2bZK3t7fS0tLk5eWlgIAAu9eFhIQoLS1NkpSWlmYG33MFBwfblQkJCbE7HhAQIC8vL7NMcUaPHq1hw4aZ21lZWapZs6YCAgIuuIrrLPvy9zm8zZNZFoe3GRgY6PA2y4PNZpPFYlFAQAAXNDgUcw/OxPyDszD34EzMv9Kp0EG2T58+5udRUVFq0aKFIiIitHz5cvXs2fOCrzMMQxbL/4Wocz+/nDLn8/b2lre3d5H9bm5uFW4S2uT4WxVscnyQrWjjfjksFkuFnEu48jH34EzMPzgLcw/OxPy7dC41UmFhYYqIiNDu3bslSaGhocrNzVV6erpduaNHj5orrKGhoTpy5EiRuo4dO2ZX5vyV1/T0dOXl5RVZqQUAAAAAOJdLBdnjx4/rwIEDCgsLkyQ1b95cnp6eWrVqlVnm8OHD2rp1q1q3bi1JiomJUWZmpjZu3GiW2bBhgzIzM+3KbN26VYcPHzbLrFy5Ut7e3mrevLkjTg0AAAAAUEJOvbU4Oztbv/32m7m9Z88epaamKjAwUIGBgZowYYJ69eqlsLAw7d27V2PGjFFQUJBuv/12SZLVatWAAQM0fPhwVatWTYGBgRoxYoSio6PNpxg3aNBAXbp0UXJysmbPni1JevDBB5WQkKDIyEhJUlxcnBo2bKjExERNmzZNJ06c0IgRI5ScnFzh3usKAAAAAFc7pwbZH3/80e6JwIUPTrr33ns1a9Ys/fLLL3r33XeVkZGhsLAwtW/fXkuXLpWfn5/5mpdfflkeHh7q3bu3zpw5o44dO2revHlyd3c3yyxatEhDhgwxn27cvXt3u/9d6+7uruXLl+vRRx9VmzZtVLlyZfXr108vvvhieQ8BAAAAAOASWQzDcPz/SLlCZWVlyWq1KjMzs8Kt5EbPj3Z4myd3PO/wNvc+39XhbZYHm82mEydOKDAwkDf9w6GYe3Am5h+chbkHZ2L+lQ4jBQAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACAS3FqkP3mm2/UrVs3hYeHy2Kx6KOPPjKP5eXlaeTIkYqOjpavr6/Cw8N1zz336NChQ3Z1xMbGymKx2H3cdddddmXS09OVmJgoq9Uqq9WqxMREZWRk2JXZv3+/unXrJl9fXwUFBWnIkCHKzc0tr1MHAAAAAJSSU4PsqVOn1LhxY82YMaPIsdOnT+unn37SuHHj9NNPP+nDDz/Url271L179yJlk5OTdfjwYfNj9uzZdsf79eun1NRUrVixQitWrFBqaqoSExPN4wUFBeratatOnTqldevWacmSJfrggw80fPjwsj9pAAAAAMBl8XBm4/Hx8YqPjy/2mNVq1apVq+z2vfbaa7rpppu0f/9+1apVy9zv4+Oj0NDQYuvZsWOHVqxYoe+//14tW7aUJL311luKiYnRzp07FRkZqZUrV2r79u06cOCAwsPDJUnTp09XUlKSJk2apKpVq5bF6QIAAAAAyoBTg+ylyszMlMVikb+/v93+RYsWaeHChQoJCVF8fLzGjx8vPz8/SdL69etltVrNECtJrVq1ktVqVUpKiiIjI7V+/XpFRUWZIVaSOnfurJycHG3atEnt27cvtj85OTnKyckxt7OysiRJNptNNputrE67TLg5YfHdTYbD26xo415aNptNhmFcMecD18HcgzMx/+AszD04E/PPnptbyXKLywTZv//+W6NGjVK/fv3sVkj79++vOnXqKDQ0VFu3btXo0aP1888/m6u5aWlpCg4OLlJfcHCw0tLSzDIhISF2xwMCAuTl5WWWKc6UKVM0ceLEIvvT09OVn59fqvMsLxEeEQ5v83RVxwfZEydOOLzN8mCz2XTy5EkZhlHib2agLDD34EzMPzgLcw/OxPyzFxQUVKJyLhFk8/LydNddd8lms2nmzJl2x5KTk83Po6KiVK9ePbVo0UI//fSTmjVrJkmyWCxF6jQMw25/Scqcb/To0Ro2bJi5nZWVpZo1ayogIKDC3Y68L3+fw9s8mXXhsSsvgYGBDm+zPNhsNlksFgUEBHBBg0Mx9+BMzD84C3MPzsT8K50KH2Tz8vLUu3dv7dmzR1999dU/BsRmzZrJ09NTu3fvVrNmzRQaGqojR44UKXfs2DFzFTY0NFQbNmywO56enq68vLwiK7Xn8vb2lre3d5H9bm5uFW4S2uT4WxVscnyQrWjjfjksFkuFnEu48jH34EzMPzgLcw/OxPy7dBV6pApD7O7du/Xll1+qWrVq//iabdu2KS8vT2FhYZKkmJgYZWZmauPGjWaZDRs2KDMzU61btzbLbN26VYcPHzbLrFy5Ut7e3mrevHkZnxUAAAAA4HI4dUU2Oztbv/32m7m9Z88epaamKjAwUOHh4brjjjv0008/6bPPPlNBQYH5ftXAwEB5eXnp999/16JFi3TbbbcpKChI27dv1/Dhw9W0aVO1adNGktSgQQN16dJFycnJ5r/lefDBB5WQkKDIyEhJUlxcnBo2bKjExERNmzZNJ06c0IgRI5ScnFzhbhEGAAAAgKudU1dkf/zxRzVt2lRNmzaVJA0bNkxNmzbV008/rYMHD+qTTz7RwYMH1aRJE4WFhZkfKSkpkiQvLy+tXr1anTt3VmRkpIYMGaK4uDh9+eWXcnd3N9tZtGiRoqOjFRcXp7i4ON1www1asGCBedzd3V3Lly9XpUqV1KZNG/Xu3Vs9evTQiy++6NgBAQAAAAD8I6euyMbGxsowLvxk24sdk6SaNWvq66+//sd2AgMDtXDhwouWqVWrlj777LN/rAsAAAAA4FwV+j2yAAAAAACcjyALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLuawg+9tvv+mLL77QmTNnJEmGYZRJpwAAAAAAuBCP0rzo+PHj6tOnj7766itZLBbt3r1b1157rR544AH5+/tr+vTpZd1PwKmi50c7tD03uWl119UObRMAAABwFaVakX388cfl4eGh/fv3y8fHx9zfp08frVixosw6BwAAAADA+Uq1Irty5Up98cUXqlGjht3+evXqad++fWXSMQAAAAAAilOqFdlTp07ZrcQW+uuvv+Tt7X3ZnQIAAAAA4EJKFWRvueUWvfvuu+a2xWKRzWbTtGnT1L59+zLrHAAAAAAA5yvVrcXTpk1TbGysfvzxR+Xm5urJJ5/Utm3bdOLECX333Xdl3UcAAAAAAEylWpFt2LChfv75Z914443q1KmTTp06pZ49e2rz5s2qW7duWfcRAAAAAADTJa3Itm3bVh06dFBsbKxat26tZ555prz6BQAAAABAsS5pRTYyMlKLFy/WrbfeqoCAAMXGxurZZ5/VunXrlJeXV159BAAAAADAdElBds6cOfrtt9+0f/9+zZ49W3Xr1tW8efN0yy23yN/fX506ddKUKVPKq68AAAAAAJTuPbI1atTQPffcozlz5uj333/Xvn379Pjjj2vjxo0aO3ZsWfcRAAAAAABTqZ5aLEm///671q5da35kZGQoJiZG7dq1K8v+AQAAAABg55KC7Ny5c7VmzRqtXbtWmZmZatOmjdq1a6eBAweqRYsW8vAodS4GAAAAAKBELil5DhgwQLVq1dJTTz2l+++/X56enuXVLwAAAAAAinVJ75F9/fXX1apVK02YMEHBwcHq1q2bpk+frh9//FGGYZRXHwEAAAAAMF1SkH3kkUe0ZMkSHT58WN99951uu+02bdy4UQkJCQoICFDXrl314osvlldfAQAAAAAo3VOLJalhw4Z65JFHtHTpUv30008aNGiQ1q1bp5EjR5Zl/wAAAAAAsFOqpzMdOXLE7onFu3btkpeXl1q2bKn27duXdR8BAAAAADBdUpAdOHCg1qxZo507d8rDw0M33nij7rjjDrVv316tW7dWpUqVyqufAAAAAABIusQg+9NPP6lHjx5q37692rRpIx8fn/LqFwAAAAAAxbqkILt+/XpJUkZGxgVD7G+//abrrrvu8nsGAAAAAEAxSvWwp9tuu01nzpwpsn/nzp2KjY293D4BAAAAAHBBpQqyAQEBuv3225Wfn2/u27Fjh2JjY9WrV68y6xwAAAAAAOcrVZD94IMPdOrUKfXr10+GYWjr1q2KjY1V37599e9//7vE9XzzzTfq1q2bwsPDZbFY9NFHH9kdNwxDEyZMUHh4uCpXrqzY2Fht27bNrkxOTo4GDx6soKAg+fr6qnv37jp48KBdmfT0dCUmJspqtcpqtSoxMVEZGRl2Zfbv369u3brJ19dXQUFBGjJkiHJzcy9pXAAAAAAA5a9UQbZSpUr67LPPtHv3bt15553q2LGj7rnnHr300kuXVM+pU6fUuHFjzZgxo9jjU6dO1UsvvaQZM2bohx9+UGhoqDp16qSTJ0+aZYYOHaply5ZpyZIlWrdunbKzs5WQkKCCggKzTL9+/ZSamqoVK1ZoxYoVSk1NVWJionm8oKBAXbt21alTp7Ru3TotWbJEH3zwgYYPH36JIwMAAAAAKG8lfthTVlaW3bbFYtHSpUt16623qlevXho3bpxZpmrVqiWqMz4+XvHx8cUeMwxDr7zyip566in17NlTkjR//nyFhIRo8eLFeuihh5SZmak5c+ZowYIFuvXWWyVJCxcuVM2aNfXll1+qc+fO2rFjh1asWKHvv/9eLVu2lCS99dZbiomJ0c6dOxUZGamVK1dq+/btOnDggMLDwyVJ06dPV1JSkiZNmlTi8wEAAAAAlL8SB1l/f39ZLJYi+w3D0BtvvKHZs2fLMAxZLBa71dDS2rNnj9LS0hQXF2fu8/b2Vrt27ZSSkqKHHnpImzZtUl5enl2Z8PBwRUVFKSUlRZ07d9b69etltVrNECtJrVq1ktVqVUpKiiIjI7V+/XpFRUWZIVaSOnfurJycHG3atEnt27cvto85OTnKyckxtwuDvM1mk81mu+wxKEtupVt8v8w2DYe3WV7j7ujxc5ObDMOocPMIVz6bzcbcg9Mw/+AszD04E/PPnptbyX7vLnGQXbNmTak7UxppaWmSpJCQELv9ISEh2rdvn1nGy8tLAQEBRcoUvj4tLU3BwcFF6g8ODrYrc347AQEB8vLyMssUZ8qUKZo4cWKR/enp6XYPwqoIIjwiHN7m6aqOD7InTpwol3odPX4WWZSVlSXDMEr8zQyUBZvNppMnTzL34BTMPzgLcw/OxPyzFxQUVKJyJQ6y7dq105tvvqnu3bsrNDS01B27VOevAheu+l7M+WUutJJ8qWXON3r0aA0bNszczsrKUs2aNRUQEFDhbkfel7/P4W2ezLr416k8BAYGlku9jh4/N7mpatWqCggI4IIGh7LZbLJYLMw9OAXzD87C3IMzMf9Kp8RBVpLee+89DRkyRI0bN9a//vUv/etf/1KjRo3KpWOFYTktLU1hYWHm/qNHj5qrp6GhocrNzVV6errdquzRo0fVunVrs8yRI0eK1H/s2DG7ejZs2GB3PD09XXl5eUVWas/l7e0tb2/vIvvd3Nwq3CS0yfG3Ktjk+CBbXuPujPGzWCwVci7hysfcgzMx/+AszD04E/Pv0l3SSK1Zs0aHDx/W4MGDlZqaqtatW6tu3boaNmyY1q5dW6b3ddepU0ehoaFatWqVuS83N1dff/21GVKbN28uT09PuzKHDx/W1q1bzTIxMTHKzMzUxo0bzTIbNmxQZmamXZmtW7fq8OHDZpmVK1fK29tbzZs3L7NzAgAAAABcvktakZXOvnf07rvv1t13363c3Fx99dVX+uSTT5SYmKjTp0+ra9eu6t69u+Lj4+Xr63vRurKzs/Xbb7+Z23v27FFqaqoCAwNVq1YtDR06VJMnT1a9evVUr149TZ48WT4+PurXr58kyWq1asCAARo+fLiqVaumwMBAjRgxQtHR0eZTjBs0aKAuXbooOTlZs2fPliQ9+OCDSkhIUGRkpCQpLi5ODRs2VGJioqZNm6YTJ05oxIgRSk5OrnC3CAMAAADA1e6y1q69vLzUpUsXzZw5UwcOHNDKlStVu3ZtPfvssyX6n7I//vijmjZtqqZNm0qShg0bpqZNm+rpp5+WJD355JMaOnSoHn30UbVo0UJ//vmnVq5cKT8/P7OOl19+WT169FDv3r3Vpk0b+fj46NNPP5W7u7tZZtGiRYqOjlZcXJzi4uJ0ww03aMGCBeZxd3d3LV++XJUqVVKbNm3Uu3dv9ejRQy+++OLlDA8AAAAAoBxYDMO4pEfL5ufny2azycvLy9z39ttv69tvv1WLFi00aNAgWSwW5eXlydPTs8w7XJFlZWXJarUqMzOzwq3kRs+PdnibJ3c87/A29z7ftVzqdfT4uclNq7uuVmBgIO+VgEPZbDadOHGCuQenYP7BWZh7cCbmX+lc8kjdfffdGj9+vLk9e/ZsPfbYYzp16pSeeeYZjRkzRpKuuhALAAAAAHCMS36P7KZNm/TII4+Y27Nnz9Yrr7yi5ORkrV27Vvfee6+mTJlSpp0ErkZ3vblef2RZHPr05/Ja0QYAAADKUomD7H333SdJOnDggF599VXNnz9fhmHo559/1ueff67169crPz9fhw4d0v333y9Jeuedd8qn1wAAAACAq1aJg+zcuXMlSV999ZWGDh2qm2++WcuXL9d3332nDz/8UJKUmZmpjz/+mAALAAAAACg3l3xrcWxsrB566CElJiZq7ty56tOnj3ns559/Vr169cq0gwAAAAAAnOuSH/b00ksvqXnz5lq8eLE6dOhgPtxJkj766CPdfffdZdpBAAAAAADOdckrstWqVbP7H6znKsn/jgUAAAAA4HLwj4oAAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcyiU/7AkALlX0/GiHt/nLvb84vE0AAAA4BiuyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXIqHszvwT2rXrq19+/YV2f/oo4/q9ddfV1JSkubPn293rGXLlvr+++/N7ZycHI0YMULvvfeezpw5o44dO2rmzJmqUaOGWSY9PV1DhgzRJ598Iknq3r27XnvtNfn7+5fPiQEoV7VHLXdoe3uf7+rQ9gAAAK5mFX5F9ocfftDhw4fNj1WrVkmS7rzzTrNMly5d7Mr873//s6tj6NChWrZsmZYsWaJ169YpOztbCQkJKigoMMv069dPqampWrFihVasWKHU1FQlJiY65iQBAAAAACVW4Vdkq1evbrf9/PPPq27dumrXrp25z9vbW6GhocW+PjMzU3PmzNGCBQt06623SpIWLlyomjVr6ssvv1Tnzp21Y8cOrVixQt9//71atmwpSXrrrbcUExOjnTt3KjIyspzODgAAAABwqSp8kD1Xbm6uFi5cqGHDhslisZj7165dq+DgYPn7+6tdu3aaNGmSgoODJUmbNm1SXl6e4uLizPLh4eGKiopSSkqKOnfurPXr18tqtZohVpJatWolq9WqlJSUCwbZnJwc5eTkmNtZWVmSJJvNJpvNVqbnfrncnLD47ibD4W2W17g7evzc5CaLDId/1a6U8TvbpmPnX0X7ni8tm80mwzCumPOBa2H+wVmYe3Am5p89N7eS/d7oUkH2o48+UkZGhpKSksx98fHxuvPOOxUREaE9e/Zo3Lhx6tChgzZt2iRvb2+lpaXJy8tLAQEBdnWFhIQoLS1NkpSWlmYG33MFBwebZYozZcoUTZw4scj+9PR05efnl/Isy0eER4TD2zxd1fFB9sSJE+VSr6PHzyKL/H3Ofm44MJBdKeMnOX7+ldfYOZrNZtPJkydlGEaJf5AAZYX5B2dh7sGZmH/2goKCSlTOpYLsnDlzFB8fr/DwcHNfnz59zM+joqLUokULRUREaPny5erZs+cF6zIMw25V99zPL1TmfKNHj9awYcPM7aysLNWsWVMBAQGqWrVqic/LEfblF31gVnk7mXXhsSsvgYGB5VKvo8fPTW46fVrakyXZ5LhxvFLGT3L8/CuvsXM0m80mi8WigIAAfpjC4Zh/cBbmHpyJ+Vc6LhNk9+3bpy+//FIffvjhRcuFhYUpIiJCu3fvliSFhoYqNzdX6enpdquyR48eVevWrc0yR44cKVLXsWPHFBIScsG2vL295e3tXWS/m5tbhZuENjn+VgVHBrBC5TXuzhg/QxbZ/v+Ho1xJ4+fo+VfRvucvh8ViqZDXMVwdmH9wFuYenIn5d+lcZqTmzp2r4OBgde168X9xcfz4cR04cEBhYWGSpObNm8vT09N82rEkHT58WFu3bjWDbExMjDIzM7Vx40azzIYNG5SZmWmWAQAAAABUDC6xImuz2TR37lzde++98vD4vy5nZ2drwoQJ6tWrl8LCwrR3716NGTNGQUFBuv322yVJVqtVAwYM0PDhw1WtWjUFBgZqxIgRio6ONp9i3KBBA3Xp0kXJycmaPXu2JOnBBx9UQkICTywG4HTR86Md1pab3LS662qHtQcAAFAaLhFkv/zyS+3fv1/333+/3X53d3f98ssvevfdd5WRkaGwsDC1b99eS5culZ+fn1nu5ZdfloeHh3r37q0zZ86oY8eOmjdvntzd3c0yixYt0pAhQ8ynG3fv3l0zZsxwzAkCAAAAAErMJYJsXFycDKPoE0grV66sL7744h9fX6lSJb322mt67bXXLlgmMDBQCxcuvKx+AgAAAADKn8u8RxYAAAAAAIkgCwAAAABwMQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkezu4AAADlJXp+tMPb/OXeXxzeJgAAVxtWZAEAAAAALoUgCwAAAABwKdxaDAAAiuXoW7Pd5KbVXVc7tE0AgGsiyAIA7Nz15nr9kWWRTRaHtbn3+a4OawsAALg+bi0GAAAAALgUVmQBAADKAU/NBoDyw4osAAAAAMClsCILAACACseRK9o8aAxwPazIAgAAAABcCiuyAAAAwBWE92fjasCKLAAAAADApbAiCwAAAAD/n6NXtHmPdumwIgsAAAAAcCkEWQAAAACAS+HWYgAAylDtUcsd3ube57s6vE0AAJyJFVkAAAAAgEthRRYAAFQYd725Xn9kWWSTxSHtsZoNAK6JFVkAAAAAgEthRRYAAOAK4ej3aLOiDcBZCLIAAAC46jn6tnaJPwQAl4MgCwAAAOCy8MR2OBrvkQUAAAAAuBRWZAEAAADAiXhi+6Wr0CuyEyZMkMVisfsIDQ01jxuGoQkTJig8PFyVK1dWbGystm3bZldHTk6OBg8erKCgIPn6+qp79+46ePCgXZn09HQlJibKarXKarUqMTFRGRkZjjhFAAAAAMAlqtBBVpIaNWqkw4cPmx+//PKLeWzq1Kl66aWXNGPGDP3www8KDQ1Vp06ddPLkSbPM0KFDtWzZMi1ZskTr1q1Tdna2EhISVFBQYJbp16+fUlNTtWLFCq1YsUKpqalKTEx06HkCAAAAAEqmwt9a7OHhYbcKW8gwDL3yyit66qmn1LNnT0nS/PnzFRISosWLF+uhhx5SZmam5syZowULFujWW2+VJC1cuFA1a9bUl19+qc6dO2vHjh1asWKFvv/+e7Vs2VKS9NZbbykmJkY7d+5UZGSk404WAAAAAPCPKnyQ3b17t8LDw+Xt7a2WLVtq8uTJuvbaa7Vnzx6lpaUpLi7OLOvt7a127dopJSVFDz30kDZt2qS8vDy7MuHh4YqKilJKSoo6d+6s9evXy2q1miFWklq1aiWr1aqUlJSLBtmcnBzl5OSY21lZWZIkm80mm81WlsNw2dycsPjuJsPhbZbXuDt6/NzkJosMh3/VrpTxO9umY+dfeX7PO3L8mHtl0SbXvstpz9Hz70r53v2/Nrn2lbYtrn2X2ybXvstp70q69l0uN7eSjUSFDrItW7bUu+++q+uvv15HjhzRc889p9atW2vbtm1KS0uTJIWEhNi9JiQkRPv27ZMkpaWlycvLSwEBAUXKFL4+LS1NwcHBRdoODg42y1zIlClTNHHixCL709PTlZ+fX/ITdYAIjwiHt3m6quMvaCdOnCiXeh09fhZZ5O9z9nPDgT8YrpTxkxw//8pr7CTHjh9z7/Jx7Ss9Z8y/K+V7txDXvtLh2nf5uPaV3pV27btcQUFBJSpXoYNsfHy8+Xl0dLRiYmJUt25dzZ8/X61atZIkWSz2T/YyDKPIvvOdX6a48iWpZ/To0Ro2bJi5nZWVpZo1ayogIEBVq1a96GsdbV/+Poe3eTLLcf9QvFBgYGC51Ovo8XOTm06flvZkyaH/mP1KGT/J8fOvvMZOcuz4MfcuH9e+0nPG/LtSvncLce0rHa59l49rX+ldadc+R6nQQfZ8vr6+io6O1u7du9WjRw9JZ1dUw8LCzDJHjx41V2lDQ0OVm5ur9PR0u1XZo0ePqnXr1maZI0eOFGnr2LFjRVZ7z+ft7S1vb+8i+93c3Eq8JO4oNjn+9gFH/iAoVF7j7ozxM3T2EeyOHMcrafwcPf/K83ve0ePH3LvcNrn2XQ5Hz78r6Xv3bJtc+0qLa9/ltsm173JcSdc+R3GpM8jJydGOHTsUFhamOnXqKDQ0VKtWrTKP5+bm6uuvvzZDavPmzeXp6WlX5vDhw9q6datZJiYmRpmZmdq4caNZZsOGDcrMzDTLAAAAAAAqjgq9IjtixAh169ZNtWrV0tGjR/Xcc88pKytL9957rywWi4YOHarJkyerXr16qlevniZPniwfHx/169dPkmS1WjVgwAANHz5c1apVU2BgoEaMGKHo6GjzKcYNGjRQly5dlJycrNmzZ0uSHnzwQSUkJPDEYgAAAACogCp0kD148KD69u2rv/76S9WrV1erVq30/fffKyLi7Buwn3zySZ05c0aPPvqo0tPT1bJlS61cuVJ+fn5mHS+//LI8PDzUu3dvnTlzRh07dtS8efPk7u5ullm0aJGGDBliPt24e/fumjFjhmNPFgAAAABQIhU6yC5ZsuSixy0WiyZMmKAJEyZcsEylSpX02muv6bXXXrtgmcDAQC1cuLC03QQAAAAAOJBLvUcWAAAAAACCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALqVCB9kpU6boxhtvlJ+fn4KDg9WjRw/t3LnTrkxSUpIsFovdR6tWrezK5OTkaPDgwQoKCpKvr6+6d++ugwcP2pVJT09XYmKirFarrFarEhMTlZGRUd6nCAAAAAC4RBU6yH799dcaOHCgvv/+e61atUr5+fmKi4vTqVOn7Mp16dJFhw8fNj/+97//2R0fOnSoli1bpiVLlmjdunXKzs5WQkKCCgoKzDL9+vVTamqqVqxYoRUrVig1NVWJiYkOOU8AAAAAQMl5OLsDF7NixQq77blz5yo4OFibNm3SLbfcYu739vZWaGhosXVkZmZqzpw5WrBggW699VZJ0sKFC1WzZk19+eWX6ty5s3bs2KEVK1bo+++/V8uWLSVJb731lmJiYrRz505FRkaW0xkCAAAAAC5VhQ6y58vMzJQkBQYG2u1fu3atgoOD5e/vr3bt2mnSpEkKDg6WJG3atEl5eXmKi4szy4eHhysqKkopKSnq3Lmz1q9fL6vVaoZYSWrVqpWsVqtSUlIuGGRzcnKUk5NjbmdlZUmSbDabbDZb2Zx0GXFzwuK7mwyHt1le4+7o8XOTmywyHP5Vu1LG72ybjp1/5fk978jxY+6VRZtc+y6nPUfPvyvle/f/2uTaV9q2uPZdbptc+y6nvSvp2ne53NxKNhIuE2QNw9CwYcPUtm1bRUVFmfvj4+N15513KiIiQnv27NG4cePUoUMHbdq0Sd7e3kpLS5OXl5cCAgLs6gsJCVFaWpokKS0tzQy+5woODjbLFGfKlCmaOHFikf3p6enKz88v7amWiwiPCIe3ebqq4y9oJ06cKJd6HT1+Flnk73P2c8OBPxiulPGTHD//ymvsJMeOH3Pv8nHtKz1nzL8r5Xu3ENe+0uHad/m49pXelXbtu1xBQUElKucyQXbQoEHasmWL1q1bZ7e/T58+5udRUVFq0aKFIiIitHz5cvXs2fOC9RmGIYvFYm6f+/mFypxv9OjRGjZsmLmdlZWlmjVrKiAgQFWrVi3ReTnKvvx9Dm/zZNaFx668nL9aX1YcPX5uctPp09KeLMkmx43jlTJ+kuPnX3mNneTY8WPuXT6ufaXnjPl3pXzvFuLaVzpc+y4f177Su9KufY7iEkF28ODB+uSTT/TNN9+oRo0aFy0bFhamiIgI7d69W5IUGhqq3Nxcpaen263KHj16VK1btzbLHDlypEhdx44dU0hIyAXb8vb2lre3d5H9bm5uJV4SdxSbHH/7gCN/EBQqr3F3xvgZssj2/z8c5UoaP0fPv/L8nnf0+DH3LrdNrn2Xw9Hz70r63j3bJte+0uLad7ltcu27HFfStc9RKvQZGIahQYMG6cMPP9RXX32lOnXq/ONrjh8/rgMHDigsLEyS1Lx5c3l6emrVqlVmmcOHD2vr1q1mkI2JiVFmZqY2btxoltmwYYMyMzPNMgAAAACAiqFCr8gOHDhQixcv1scffyw/Pz/z/apWq1WVK1dWdna2JkyYoF69eiksLEx79+7VmDFjFBQUpNtvv90sO2DAAA0fPlzVqlVTYGCgRowYoejoaPMpxg0aNFCXLl2UnJys2bNnS5IefPBBJSQk8MRiAAAAAKhgKnSQnTVrliQpNjbWbv/cuXOVlJQkd3d3/fLLL3r33XeVkZGhsLAwtW/fXkuXLpWfn59Z/uWXX5aHh4d69+6tM2fOqGPHjpo3b57c3d3NMosWLdKQIUPMpxt3795dM2bMKP+TBAAAAABckgodZA3j4k/tqly5sr744ot/rKdSpUp67bXX9Nprr12wTGBgoBYuXHjJfQQAAAAAOFaFfo8sAAAAAADnI8gCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLLnmTlzpurUqaNKlSqpefPm+vbbb53dJQAAAADAOQiy51i6dKmGDh2qp556Sps3b9bNN9+s+Ph47d+/39ldAwAAAAD8fwTZc7z00ksaMGCAHnjgATVo0ECvvPKKatasqVmzZjm7awAAAACA/8/D2R2oKHJzc7Vp0yaNGjXKbn9cXJxSUlKKfU1OTo5ycnLM7czMTElSRkaGbDZb+XW2FIwzhuMbzTnl8CYzMjLKpV5Hj58hQwV/n5JyLJIsDmv3Shk/SQ6ff+U1dpJjx4+5Vwa49pW+PSfMvyvle9fEta90bXHtu3xc+0rf3hV27btcbm5u8vPzk8Vy8bGwGIbhhJle8Rw6dEjXXHONvvvuO7Vu3drcP3nyZM2fP187d+4s8poJEyZo4sSJjuwmAAAAAFzRMjMzVbVq1YuWYUX2POcnf8MwLvjXgNGjR2vYsGHmts1m04kTJ1StWrV//AsCcDFZWVmqWbOmDhw48I/fxEBZYu7BmZh/cBbmHpyJ+VeUn5/fP5YhyP5/QUFBcnd3V1pamt3+o0ePKiQkpNjXeHt7y9vb226fv79/eXURV6GqVatyQYNTMPfgTMw/OAtzD87E/Ls0POzp//Py8lLz5s21atUqu/2rVq2yu9UYAAAAAOBcrMieY9iwYUpMTFSLFi0UExOjN998U/v379fDDz/s7K4BAAAAAP4/guw5+vTpo+PHj+uZZ57R4cOHFRUVpf/973+KiIhwdtdwlfH29tb48eOL3LoOlDfmHpyJ+QdnYe7BmZh/pcNTiwEAAAAALoX3yAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgixQQUyZMkU33nij/Pz8FBwcrB49emjnzp3O7hauUlOmTJHFYtHQoUOd3RVcBf7880/dfffdqlatmnx8fNSkSRNt2rTJ2d3CVSA/P19jx45VnTp1VLlyZV177bV65plnZLPZnN01XGG++eYbdevWTeHh4bJYLProo4/sjhuGoQkTJig8PFyVK1dWbGystm3b5pzOugiCLFBBfP311xo4cKC+//57rVq1Svn5+YqLi9OpU6ec3TVcZX744Qe9+eabuuGGG5zdFVwF0tPT1aZNG3l6eurzzz/X9u3bNX36dPn7+zu7a7gKvPDCC3rjjTc0Y8YM7dixQ1OnTtW0adP02muvObtruMKcOnVKjRs31owZM4o9PnXqVL300kuaMWOGfvjhB4WGhqpTp046efKkg3vqOvj3O0AFdezYMQUHB+vrr7/WLbfc4uzu4CqRnZ2tZs2aaebMmXruuefUpEkTvfLKK87uFq5go0aN0nfffadvv/3W2V3BVSghIUEhISGaM2eOua9Xr17y8fHRggULnNgzXMksFouWLVumHj16SDq7GhseHq6hQ4dq5MiRkqScnByFhITohRde0EMPPeTE3lZcrMgCFVRmZqYkKTAw0Mk9wdVk4MCB6tq1q2699VZndwVXiU8++UQtWrTQnXfeqeDgYDVt2lRvvfWWs7uFq0Tbtm21evVq7dq1S5L0888/a926dbrtttuc3DNcTfbs2aO0tDTFxcWZ+7y9vdWuXTulpKQ4sWcVm4ezOwCgKMMwNGzYMLVt21ZRUVHO7g6uEkuWLNFPP/2kH374wdldwVXkjz/+0KxZszRs2DCNGTNGGzdu1JAhQ+Tt7a177rnH2d3DFW7kyJHKzMxU/fr15e7uroKCAk2aNEl9+/Z1dtdwFUlLS5MkhYSE2O0PCQnRvn37nNEll0CQBSqgQYMGacuWLVq3bp2zu4KrxIEDB/TYY49p5cqVqlSpkrO7g6uIzWZTixYtNHnyZElS06ZNtW3bNs2aNYsgi3K3dOlSLVy4UIsXL1ajRo2UmpqqoUOHKjw8XPfee6+zu4erjMVisds2DKPIPvwfgixQwQwePFiffPKJvvnmG9WoUcPZ3cFVYtOmTTp69KiaN29u7isoKNA333yjGTNmKCcnR+7u7k7sIa5UYWFhatiwod2+Bg0a6IMPPnBSj3A1eeKJJzRq1CjdddddkqTo6Gjt27dPU6ZMIcjCYUJDQyWdXZkNCwsz9x89erTIKi3+D++RBSoIwzA0aNAgffjhh/rqq69Up04dZ3cJV5GOHTvql19+UWpqqvnRokUL9e/fX6mpqYRYlJs2bdoU+Vdju3btUkREhJN6hKvJ6dOn5eZm/+uwu7s7/34HDlWnTh2FhoZq1apV5r7c3Fx9/fXXat26tRN7VrGxIgtUEAMHDtTixYv18ccfy8/Pz3y/hNVqVeXKlZ3cO1zp/Pz8irwf29fXV9WqVeN92ihXjz/+uFq3bq3Jkyerd+/e2rhxo9588029+eabzu4argLdunXTpEmTVKtWLTVq1EibN2/WSy+9pPvvv9/ZXcMVJjs7W7/99pu5vWfPHqWmpiowMFC1atXS0KFDNXnyZNWrV0/16tXT5MmT5ePjo379+jmx1xUb/34HqCAu9B6IuXPnKikpybGdASTFxsby73fgEJ999plGjx6t3bt3q06dOho2bJiSk5Od3S1cBU6ePKlx48Zp2bJlOnr0qMLDw9W3b189/fTT8vLycnb3cAVZu3at2rdvX2T/vffeq3nz5skwDE2cOFGzZ89Wenq6WrZsqddff50/Jl8EQRYAAAAA4FJ4jywAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAADlYO3atbJYLMrIyHB2Vxxq7969slgsSk1NdXZXAABXMIIsAAClkJSUJIvFIovFIk9PT1177bUaMWKETp06Va7tWiwWffTRR+XaBgAAFZ2HszsAAICr6tKli+bOnau8vDx9++23euCBB3Tq1CnNmjXL2V274uTm5srLy8vZ3QAAVBCsyAIAUEre3t4KDQ1VzZo11a9fP/Xv3/+Cq6XHjx9X3759VaNGDfn4+Cg6OlrvvfeeXZnY2FgNGTJETz75pAIDAxUaGqoJEyaYx2vXri1Juv3222WxWMzt8xXe3vvhhx+qffv28vHxUePGjbV+/XqzzIQJE9SkSRO7173yyit2dSYlJalHjx6aPHmyQkJC5O/vr4kTJyo/P19PPPGEAgMDVaNGDb3zzjtF+vDrr7+qdevWqlSpkho1aqS1a9faHd++fbtuu+02ValSRSEhIUpMTNRff/1lNxaDBg3SsGHDFBQUpE6dOhV7rgCAqxNBFgCAMlK5cmXl5eUVe+zvv/9W8+bN9dlnn2nr1q168MEHlZiYqA0bNtiVmz9/vnx9fbVhwwZNnTpVzzzzjFatWiVJ+uGHHyRJc+fO1eHDh83tC3nqqac0YsQIpaam6vrrr1ffvn2Vn59/Sef01Vdf6dChQ/rmm2/00ksvacKECUpISFBAQIA2bNighx9+WA8//LAOHDhg97onnnhCw4cP1+bNm9W6dWt1795dx48flyQdPnxY7dq1U5MmTfTjjz9qxYoVOnLkiHr37l1kLDw8PPTdd99p9uzZl9RvAMCVjSALAEAZ2LhxoxYvXqyOHTsWe/yaa67RiBEj1KRJE1177bUaPHiwOnfurP/85z925W644QaNHz9e9erV0z333KMWLVpo9erVkqTq1atLkvz9/RUaGmpuX8iIESPUtWtXXX/99Zo4caL27dun33777ZLOKzAwUK+++qoiIyN1//33KzIyUqdPn9aYMWNUr149jR49Wl5eXvruu+/sXjdo0CD16tVLDRo00KxZs2S1WjVnzhxJ0qxZs9SsWTNNnjxZ9evXV9OmTfXOO+9ozZo12rVrl1nHddddp6lTpyoyMlL169e/pH4DAK5svEcWAIBS+uyzz1SlShXl5+crLy9P//rXv/Taa68VW7agoEDPP/+8li5dqj///FM5OTnKycmRr6+vXbkbbrjBbjssLExHjx4tVf/OrSssLEySdPTo0UsKhY0aNZKb2//93TskJERRUVHmtru7u6pVq1akjzExMebnHh4eatGihXbs2CFJ2rRpk9asWaMqVaoUae/333/X9ddfL0lq0aJFifsJALi6EGQBACil9u3ba9asWfL09FR4eLg8PT0vWHb69Ol6+eWX9corryg6Olq+vr4aOnSocnNz7cqdX4fFYpHNZitV/86ty2KxSJJZl5ubmwzDsCtf3G3RxfWntH08tw/dunXTCy+8UKRMYeCWVCTkAwBQiCALAEAp+fr66rrrritR2W+//Vb/+te/dPfdd0s6G+Z2796tBg0aXFKbnp6eKigouOS+nq969epKS0uTYRhmwCzL//36/fff65ZbbpEk5efna9OmTRo0aJAkqVmzZvrggw9Uu3ZteXjwqwgA4NLxHlkAABzguuuu06pVq5SSkqIdO3booYceUlpa2iXXU7t2ba1evVppaWlKT08vdX9iY2N17NgxTZ06Vb///rtef/11ff7556Wu73yvv/66li1bpl9//VUDBw5Uenq67r//fknSwIEDdeLECfXt21cbN27UH3/8oZUrV+r+++8vk5AOALjyEWQBAHCAcePGqVmzZurcubNiY2MVGhqqHj16XHI906dP16pVq1SzZk01bdq01P1p0KCBZs6cqddff12NGzfWxo0bNWLEiFLXd77nn39eL7zwgho3bqxvv/1WH3/8sYKCgiRJ4eHh+u6771RQUKDOnTsrKipKjz32mKxWq937cQEAuBCLcf4bZAAAAAAAqMD4sycAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCn/D9k+7E3s6kMTAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_capital_cost_panel(case_result, figsize=(11, 5))" ] @@ -753,10 +814,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "70deb2c7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_timeline_panel(case_result, figsize=(12, 5.5))" ] @@ -771,10 +854,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "55fb3a1e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_waterfall_panel(case_result, figsize=(12, 5.5))" ] @@ -796,7 +901,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.12.9" } }, "nbformat": 4, From 8bc3ecdbb1722868c5e8dfbba94aa1a3bccaca62 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Mon, 4 May 2026 11:09:31 -0500 Subject: [PATCH 23/25] add reference --- docs/source/reference/main.rst | 103 +++++++++++++++++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 docs/source/reference/main.rst diff --git a/docs/source/reference/main.rst b/docs/source/reference/main.rst new file mode 100644 index 0000000..d208c07 --- /dev/null +++ b/docs/source/reference/main.rst @@ -0,0 +1,103 @@ + +Accert Code Reference +========================== +This section provides detailed information on the Accert codebase. The Accert codebase is divided into two main sections: the main Accert class and utility functions. The main Accert class contains the main functions and methods for the Accert model, while the utility functions contain helper functions for the Accert model. + +.. toctree:: + :maxdepth: 1 + :caption: Contents: + +Accert Class +-------------------- + +.. autosummary:: + :toctree: main/ + :template: function.rst + + Main.Accert.__init__ + Main.Accert.setup_table_names + Main.Accert.load_obj + Main.Accert.get_current_COAs + Main.Accert.update_account_before_insert + Main.Accert.insert_new_COA + Main.Accert.insert_COA + Main.Accert.extract_variable_info_on_name + Main.Accert.extract_super_val + Main.Accert.update_input_variable + Main.Accert.update_variable_info_on_name + Main.Accert.update_super_variable + Main.Accert.extract_total_cost_on_name + Main.Accert.check_unit_conversion + Main.Accert.convert_unit + Main.Accert.convert_unit_scale + Main.Accert.update_total_cost + Main.Accert.update_total_cost_on_name + Main.Accert.get_var_value_by_name + Main.Accert.run_pre_alg + Main.Accert.update_account_value + Main.Accert.update_cost_element_on_name + Main.Accert.update_new_cost_elements + Main.Accert.update_new_accounts + Main.Accert.update_account_table_by_cost_elements + Main.Accert.roll_up_cost_elements + Main.Accert.roll_up_cost_elements_by_level + Main.Accert.roll_up_account_table + Main.Accert.roll_up_account_table_by_level + Main.Accert.roll_up_account_table_GNCOA + Main.Accert.sum_cost_elements_2C + Main.Accert.fetch_sum_and_update + Main.Accert.roll_up_lmt_account_2C + Main.Accert.roll_up_lmt_direct_cost + Main.Accert.cal_direct_cost_elements + Main.Accert.roll_up_lmt_account_table + Main.Accert.print_logo + Main.Accert.execute_accert + Main.Accert.process_reference_model + Main.Accert.process_power_inputs + Main.Accert.process_variables + Main.Accert.process_super_values + Main.Accert.process_COA + Main.Accert.process_level_accounts + Main.Accert.process_ce + Main.Accert.process_var + Main.Accert.process_alg + Main.Accert.check_and_process_total_cost + Main.Accert.check_total_cost_changed + Main.Accert.check_total_cost_accounts + Main.Accert.process_total_cost + Main.Accert.process_total_cost_accounts + Main.Accert.exit_with_error + Main.Accert.finalize_process + Main.Accert.generate_results + Main.Accert._generate_common_results + Main.Accert._common_cost_processing + Main.Accert._print_results + Main.Accert._pwr12be_processing + Main.Accert._no_cost_element_processing + Main.Accert.generate_results_table_with_cost_elements + Main.Accert._generate_excel + Main.Accert.generate_results_table + +Accert Utility Functions +-------------------------- +.. autosummary:: + :toctree: utility/ + :template: function.rst + + utility_accert.Utility_methods.__init__ + utility_accert.Utility_methods.setup_table_names + utility_accert.Utility_methods.print_table + utility_accert.Utility_methods.print_account + utility_accert.Utility_methods.print_leveled_accounts + utility_accert.Utility_methods.print_leveled_accounts_gncoa + utility_accert.Utility_methods.print_algorithm + utility_accert.Utility_methods.print_cost_element + utility_accert.Utility_methods.print_facility + utility_accert.Utility_methods.print_escalation + utility_accert.Utility_methods.print_variable + utility_accert.Utility_methods.print_user_request_parameter + utility_accert.Utility_methods.print_updated_cost_elements + utility_accert.Utility_methods.extract_affected_cost_elements + utility_accert.Utility_methods.extract_affected_accounts + utility_accert.Utility_methods.extract_user_changed_variables + utility_accert.Utility_methods.extract_changed_cost_elements From f5ee284a1b909741b195c28ce56428770ffb4031 Mon Sep 17 00:00:00 2001 From: "jia.zhou" <48254033+JiaZhou-PU@users.noreply.github.com> Date: Mon, 4 May 2026 11:44:06 -0500 Subject: [PATCH 24/25] reorg 211 214 to 219 --- src/crf/api.py | 150 ++- src/crf/data/AP1000_baseline.csv | 66 +- src/crf/data/HTGR_baseline.csv | 12 +- src/crf/data/SFR_baseline.csv | 8 +- src/crf/io/excel_inputs.py | 12 + src/crf/model/avg_runner.py | 2 - src/crf/model/core_accounts.py | 47 +- src/crf/model/direct_cost.py | 112 +- src/crf/model/learning.py | 51 +- src/crf/model/schedule.py | 52 + tutorial/crf_showcase.ipynb | 1477 +++++++++++++++++++++++++ tutorial/figure/ANL.png | Bin 0 -> 5731 bytes tutorial/figure/INL1.png | Bin 0 -> 6027 bytes tutorial/figure/MIT1.png | Bin 0 -> 3041 bytes tutorial/figure/framework_diagram.png | Bin 0 -> 231256 bytes 15 files changed, 1771 insertions(+), 218 deletions(-) create mode 100644 src/crf/model/schedule.py create mode 100644 tutorial/crf_showcase.ipynb create mode 100644 tutorial/figure/ANL.png create mode 100644 tutorial/figure/INL1.png create mode 100644 tutorial/figure/MIT1.png create mode 100644 tutorial/figure/framework_diagram.png diff --git a/src/crf/api.py b/src/crf/api.py index f9179f9..573f332 100644 --- a/src/crf/api.py +++ b/src/crf/api.py @@ -23,28 +23,30 @@ def normalize_levers(levers: dict) -> dict: """Convert raw lever dict to model-ready normalized values.""" def label01(x, zero_label, one_label): - if x == 0: return zero_label - if x == 1: return one_label + if x == 0: + return zero_label + if x == 1: + return one_label return x return { - "num_orders": int(levers["num_orders"]), - "ITC_0": float(levers["itc_percent"]) / 100.0, - "n_ITC": levers["n_itc"], - "interest_rate_0": float(levers["interest_percent"]) / 100.0, - "design_completion_0": float(levers["design_completion_percent"]) / 100.0, - "Design_Maturity_0": levers["design_maturity"], - "proc_exp_0": levers["proc_exp"], - "N_proc": levers["N_proc"], - "ce_exp_0": levers["ce_exp"], - "N_cons": levers["N_cons"], - "ae_exp_0": levers["ae_exp"], - "N_AE": levers["N_AE"], - "standardization_0": float(levers["standardization_percent"]) / 100.0, - "mod_0": label01(levers["modularity_code"], "stick_built", "modularized"), - "BOP_grade_0": label01(levers["bop_grade_code"], "nuclear", "non_nuclear"), - "RB_grade_0": label01(levers["rb_grade_code"], "nuclear", "non_nuclear"), - "num_NOAK": int(levers["num_NOAK"]) if "num_NOAK" in levers else int(levers["num_orders"]), + "num_orders": int(levers["num_orders"]), + "ITC_0": float(levers["itc_percent"]) / 100.0, + "n_ITC": levers["n_itc"], + "interest_rate_0": float(levers["interest_percent"]) / 100.0, + "design_completion_0": float(levers["design_completion_percent"]) / 100.0, + "Design_Maturity_0": levers["design_maturity"], + "proc_exp_0": levers["proc_exp"], + "N_proc": levers["N_proc"], + "ce_exp_0": levers["ce_exp"], + "N_cons": levers["N_cons"], + "ae_exp_0": levers["ae_exp"], + "N_AE": levers["N_AE"], + "standardization_0": float(levers["standardization_percent"]) / 100.0, + "mod_0": label01(levers["modularity_code"], "stick_built", "modularized"), + "BOP_grade_0": label01(levers["bop_grade_code"], "nuclear", "non_nuclear"), + "RB_grade_0": label01(levers["rb_grade_code"], "nuclear", "non_nuclear"), + "num_NOAK": int(levers.get("num_NOAK", levers["num_orders"])), } @@ -169,6 +171,29 @@ def _allocate_waterfall_residual(contributions: dict, residual: float) -> None: for key in keys: contributions[key] += residual * abs(contributions[key]) / weight_total + +def _sampling_headers(max_orders: int, has_itc: bool) -> list[str]: + per_unit_headers = ( + [f"OCC_{i}" for i in range(1, max_orders + 1)] + + ([f"NETOCC_{i}" for i in range(1, max_orders + 1)] if has_itc else []) + + [f"TCI_{i}" for i in range(1, max_orders + 1)] + + ([f"NCI_{i}" for i in range(1, max_orders + 1)] if has_itc else []) + + [f"duration_{i}" for i in range(1, max_orders + 1)] + ) + summary_headers = [ + "cons_duration_cumulative_wz_startup", + "occLastUnit", + "occNOAKUnit", + "occ_reduction_from_FOAK_to_NOAK_percent", + "TCILastUnit", + "durationsLastUnit", + "avg_OCC", + "avg_TCI", + "avg_duration", + ] + return STATIC_KEYS + per_unit_headers + summary_headers + + def run_sampling_from_excel( config: dict, levers_xlsx: str, @@ -178,7 +203,7 @@ def run_sampling_from_excel( seed: Optional[int] = None ): levers_df = read_levers_sheet(levers_xlsx, sheet_name="Levers") - levers_df = attach_internal_ids(levers_df) + levers_df = attach_internal_ids(levers_df) samples = sample_levers(n_samples, levers_df, seed=seed) # shape (n_levers, n_samples) @@ -186,34 +211,7 @@ def run_sampling_from_excel( max_orders = int(np.max(samples[0, :])) has_itc = bool(np.any(samples[2, :] > 0)) - headers_wo_itc = ( - STATIC_KEYS - + [f"OCC_{i}" for i in range(1, max_orders + 1)] - + [f"TCI_{i}" for i in range(1, max_orders + 1)] - + [f"duration_{i}" for i in range(1, max_orders + 1)] - + [ - "cons_duration_cumulative_wz_startup", - "occLastUnit", "occNOAKUnit", "occ_reduction_from_FOAK_to_NOAK_percent", - "TCILastUnit", "durationsLastUnit", - "avg_OCC", "avg_TCI", "avg_duration", - ] - ) - headers_w_itc = ( - STATIC_KEYS - + [f"OCC_{i}" for i in range(1, max_orders + 1)] - + [f"NETOCC_{i}" for i in range(1, max_orders + 1)] - + [f"TCI_{i}" for i in range(1, max_orders + 1)] - + [f"NCI_{i}" for i in range(1, max_orders + 1)] - + [f"duration_{i}" for i in range(1, max_orders + 1)] - + [ - "cons_duration_cumulative_wz_startup", - "occLastUnit", "occNOAKUnit", "occ_reduction_from_FOAK_to_NOAK_percent", - "TCILastUnit", "durationsLastUnit", - "avg_OCC", "avg_TCI", "avg_duration", - ] - ) - - headers = headers_w_itc if has_itc else headers_wo_itc + headers = _sampling_headers(max_orders, has_itc) with open(out_csv, "w", newline="", encoding="utf-8") as csv_f, open(out_pkl, "wb") as pkl_f: writer = csv.DictWriter(csv_f, fieldnames=headers) @@ -224,40 +222,32 @@ def run_sampling_from_excel( write_csv_row(writer, row, headers) stream_pickle_dump(row, pkl_f) + def print_scenario_result(result: dict): print("Results by plant number:\n") - max_orders = max([int(k.split("_")[1]) for k in result.keys() if k.startswith("OCC_")]) - results_df = pd.DataFrame({"Plant_number": list(range(1, max_orders+1))}) - for k, v in result.items(): - if k.startswith("OCC_"): - results_df[f"OCC"] = results_df["Plant_number"].apply(lambda x: result.get(f"OCC_{x}", None)) - elif k.startswith("NETOCC_"): - results_df[f"NETOCC"] = results_df["Plant_number"].apply(lambda x: result.get(f"NETOCC_{x}", None)) - elif k.startswith("TCI_"): - results_df[f"TCI"] = results_df["Plant_number"].apply(lambda x: result.get(f"TCI_{x}", None)) - elif k.startswith("NCI_"): - results_df[f"NCI"] = results_df["Plant_number"].apply(lambda x: result.get(f"NCI_{x}", None)) - elif k.startswith("duration_"): - results_df[f"duration"] = results_df["Plant_number"].apply(lambda x: result.get(f"duration_{x}", None)) - elif k.startswith("STAUP_"): - results_df[f"STAUP"] = results_df["Plant_number"].apply(lambda x: result.get(f"STAUP_{x}", None)) - elif k.startswith("D10s_"): - results_df[f"D10s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D10s_{x}", None)) - elif k.startswith("D20s_"): - results_df[f"D20s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20s_{x}", None)) - elif k.startswith("D30s_"): - results_df[f"D30s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D30s_{x}", None)) - elif k.startswith("D50s_"): - results_df[f"D50s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D50s_{x}", None)) - elif k.startswith("D60s_"): - results_df[f"D60s"] = results_df["Plant_number"].apply(lambda x: result.get(f"D60s_{x}", None)) - elif k.startswith("D20_equip_"): - results_df[f"D20_equip"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_equip_{x}", None)) - elif k.startswith("D20_mat_"): - results_df[f"D20_mat"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_mat_{x}", None)) - elif k.startswith("D20_labor_"): - results_df[f"D20_labor"] = results_df["Plant_number"].apply(lambda x: result.get(f"D20_labor_{x}", None)) - + max_orders = max(int(k.split("_")[1]) for k in result if k.startswith("OCC_")) + plants = list(range(1, max_orders + 1)) + results_df = pd.DataFrame({"Plant_number": plants}) + for prefix, column in [ + ("OCC", "OCC"), + ("NETOCC", "NETOCC"), + ("TCI", "TCI"), + ("NCI", "NCI"), + ("duration", "duration"), + ("STAUP", "STAUP"), + ("D10s", "D10s"), + ("D20s", "D20s"), + ("D30s", "D30s"), + ("D50s", "D50s"), + ("D60s", "D60s"), + ("D20_equip", "D20_equip"), + ("D20_mat", "D20_mat"), + ("D20_labor", "D20_labor"), + ]: + values = [result.get(f"{prefix}_{plant}") for plant in plants] + if any(value is not None for value in values): + results_df[column] = values + print(results_df.round(2).fillna("").to_string(index=False)) summary_metrics = [ diff --git a/src/crf/data/AP1000_baseline.csv b/src/crf/data/AP1000_baseline.csv index 3d853fd..4e0b11a 100644 --- a/src/crf/data/AP1000_baseline.csv +++ b/src/crf/data/AP1000_baseline.csv @@ -1,33 +1,37 @@ Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labor Cost,Site Material Cost -11,Land & Land Rights,2661073.013,,,, -12,Site Permits,0,,,, -13,Plant Licensing,190950077.8,,,, -14,Plant Permits,20588224.42,,,, -15,Plant Studies,0,,,, -16,Plant Reports,0,,,, -18,Other Pre-Construction Costs,64317423.74,,,, -20,Capitalized Direct Costs,,,,, -21,Structures & Improvements,2299592854,397948921.5,14439901.53,1344901039,556742893.1 -212,Reactor Containment Building,1528400790,336543685,9614706.196,890022087,301835018 -213,Turbine Room and Heater Bay,273978061.2,13922205.6,1517151.703,149468165.1,110587690.5 -211 plus 214 to 219,Othe Structures & Improvements,497214002.6,47483030.84,3308043.631,305410787.2,144320184.6 -22,Reactor System,2518251443,1854771979,5424586.039,555667192,107812271.9 -23,Energy Conversion System,2567313431,1684416189,7300778.65,787073972.1,95823269.71 -232.1,Electricity Generation Systems,1077610861,993296983.1,860789.0281,84313877.55,0 -233,Ultimate Heat Sink,1489702571,691119206.4,6439989.622,702760094.5,95823269.71 -24,Electrical Equipment,513769681.9,106553828.6,13186135.76,320736445.3,86479408.03 +11,Land & Land Rights,2661073.013,0,0,0,0 +12,Site Permits,0,0,0,0,0 +13,Plant Licensing,190950077.8,0,0,0,0 +14,Plant Permits,20588224.42,0,0,0,0 +15,Plant Studies,0,0,0,0,0 +16,Plant Reports,0,0,0,0,0 +18,Other Pre-Construction Costs,64317423.74,0,0,0,0 +20,Capitalized Direct Costs,0,0,0,0,0 +21,Structures & Improvements,2338522894.0940304,392047727.3416498,14445033.680232916,1331399514.319592,615075647.6803954 +211,Site Preparation/Yard Work,210709021.02952853,1535480.6624168903,1550450.288352939,130798337.00322719,78375202.27074127 +212,Reactor Containment Building,1536056329.8601322,331269587.0458223,9537276.053595617,873677112.9021444,331109628.38005483 +213,Turbine Room and Heater Bay,255260836.95867333,5163166.461,1363344.7944691996,132943595.2266848,117154075.8514704 +214,Buildings to Support Main Function ,244612170.6229515,48172557.14116395,1371521.9061748001,133438620.53169343,63000990.48802785 +215,Supply Chain Buildings,75733212.72986218,5296033.604196765,504064.25212529016,48947832.38006659,21489347.02351659 +216,Human Resources Buildings,16151322.892883105,610902.4267548266,118376.3855150684,11594016.275775775,3946403.666584421 +217,Miscellaneous Other Structures,0,0,0,0,0 +22,Reactor System,2470049996,1801096959.304935,5424585.978751537,549868358.7407418,119084677.84501863 +23,Energy Conversion System,2541267622,2409607707.273423,3115092.8589984067,588403431,34188320.73843827 +232.1,Electricity Generation Systems,2073947476.8255186,1393131728.7993882,5880383.711590024,591311737.4408174,89504009.16009353 +233,Ultimate Heat Sink,467320145.4329112,249787227.92737755,1884762.6197779032,187014887.0208051,30518030.584493954 +24,Electrical Equipment,517794625.9525834,104883985.04263179,3119883.108,317389303.0618374,95521338.38257146 25,Initial fuel inventory,0,0,0,0,0 -26,Miscellaneous Equipment,465102277.3,139059803.8,2797859.863,288148998.7,37893474.82 -28,Simulator,0,0,,, -30,Capitalized Indirect Services Costs,,,,, -31,Factory & Field Indirect Costs,3530991924,,,, -32,Factory and construction supervision,2576942755,,,, -33,Start-Up Costs,108412136.4,,,, -34,Shipping & Transportation Costs,9128423.298,,,, -35,Engineering Services,696228163.3,,,, -50,Capitalized Supplementary Costs,0,,,, -51,Taxes,33263.41267,,,, -52,Insurance,124634766.9,,,, -54,Decommissioning ,17224140,,,, -60,Capitalized Financial Costs,0,,,, -62,Interest ,7519443674.031699,,,, \ No newline at end of file +26,Miscellaneous Equipment,463877939.8075272,136880547.30223727,2797859.833170273,285141932.56709814,41855460.08071376 +28,Simulator,0,0,0,0,0 +30,Capitalized Indirect Services Costs,0,0,0,0,0 +31,Factory & Field Indirect Costs,3530991924,0,0,0,0 +32,Factory and construction supervision,2576942755,0,0,0,0 +33,Start-Up Costs,108412136.4,0,0,0,0 +34,Shipping & Transportation Costs,9128423.298,0,0,0,0 +35,Engineering Services,696228163.3,0,0,0,0 +50,Capitalized Supplementary Costs,0,0,0,0,0 +51,Taxes,33263.41267,0,0,0,0 +52,Insurance,124634766.9,0,0,0,0 +54,Decommissioning ,17224140,0,0,0,0 +60,Capitalized Financial Costs,0,0,0,0,0 +62,Interest ,7519443674.031699,0,0,0,0 \ No newline at end of file diff --git a/src/crf/data/HTGR_baseline.csv b/src/crf/data/HTGR_baseline.csv index d1e18b1..65ba49a 100644 --- a/src/crf/data/HTGR_baseline.csv +++ b/src/crf/data/HTGR_baseline.csv @@ -6,10 +6,14 @@ Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labo 15,Plant Studies,0,,,, 16,Plant Reports,0,,,, 18,Other Pre-Construction Costs,36194481.701475374,,,, -21,Structures & Improvements,914902741.8258162,90263521.11,11824706.17,591583080.9,233056139.8 -212,Reactor Containment Building,413396747.7312481,65728880.25,5420558.48,264358228.1,83309639.36 -213,Turbine Room and Heater Bay,15257054.820128009,1136504.843,163947.4218,8458991.226,5661558.752 -211 plus 214 to 219,Othe Structures & Improvements,486248939.27444005,23398136.024113387,6240200.264892557,318765861.5921357,144084941.65819097 +21,Structures & Improvements,914902741.8258178,90263521.11201715,11824706.167147756,591583080.9459642,233056139.76783615 +211,Site Preparation/Yard Work,75327436.96130043,800902.8036934957,970976.4231214287,44275046.477508366,30251487.680098575 +212,Reactor Containment Building,413396747.73124886,65728880.24511066,5420558.480460083,264358228.1282166,83309639.35792157 +213,Turbine Room and Heater Bay,15257054.820128027,1136504.842793219,163947.4217950633,8458991.225611275,5661558.752 +214,Buildings to Support Main Function ,394372462.79471606,21214185.471641287,5055300.460401065,263412219.01319128,109746058.30988328 +215,Supply Chain Buildings,12016032.371124467,1211592.9486784989,151217.38136011575,7824635.870736762,2979803.551709202 +216,Human Resources Buildings,4533007.147299999,171454.8001,62706.00001,3253960.231,1107592.117 +217,Miscellaneous Other Structures,0,0,0,0,0 22,Reactor System,1813536678,1530752484,4903887.695,261886915.4,20897278.33 23,Energy Conversion System,665574715.5139421,534245006.41216296,2364645.320437796,125502354.2820378,5827354.82 232.1,Electricity Generation Systems,576019409.4786329,486396295.4,1682189.488,89623114.06,0 diff --git a/src/crf/data/SFR_baseline.csv b/src/crf/data/SFR_baseline.csv index 35b833e..81a14a7 100644 --- a/src/crf/data/SFR_baseline.csv +++ b/src/crf/data/SFR_baseline.csv @@ -8,9 +8,13 @@ Account,Title,Total Cost (USD),Factory Equipment Cost,Site Labor Hours,Site Labo 18,Other Pre-Construction Costs,36194481.701475374,,,, 20,Capitalized Direct Costs,,,,, 21,Structures & Improvements,244678343.40844324,64350800.859366685,2899406.5371647766,145691461.9059903,34636080.64308626 -212,Reactor Containment Building,145108927.86932492,57583605.36952757,1605670.308507702,81082169.34102152,6443153.1587758595 +211,Site Preparation/Yard Work,"18,566,943.23","228,767.48","259,216.40","11,662,088.06","6,676,087.70" +212,Reactor Containment Building,145108927.86932492,"57,583,605.37","1,605,670.31","81,082,169.34","6,443,153.16" 213,Turbine Room and Heater Bay,9518417.644642519,620963.9747542912,102399.47362967294,5280509.165081021,3616944.504807206 -211 plus 214 to 219,Othe Structures & Improvements,90050997.89,6146231.515,1191336.755,59328783.4,24575982.98 +214,Buildings to Support Main Function ,"59,414,326.17","5,020,321.48","773,705.04","39,488,316.97","14,905,687.71" +215,Supply Chain Buildings,"7,536,721.36","725,687.76","95,709.32","4,924,418.14","1,886,615.45" +216,Human Resources Buildings,"4,533,007.15","171,454.80","62,706.00","3,253,960.23","1,107,592.12" +217,Miscellaneous Other Structures,0.00,0.00,0.00,0.00,0.00 22,Reactor System,661334796.7533823,559862843.6356779,1809703.5146268948,97213738.57005614,4258214.548 23,Energy Conversion System,238844021.3,189802947.4,874095.8195,46348086.09,2692987.752 232.1,Electricity Generation Systems,197558120.73250785,167738159.2684312,559708.5779584679,29819961.464076634,0 diff --git a/src/crf/io/excel_inputs.py b/src/crf/io/excel_inputs.py index 02ef2bd..5312d0f 100644 --- a/src/crf/io/excel_inputs.py +++ b/src/crf/io/excel_inputs.py @@ -7,6 +7,15 @@ "Site Labor Cost", "Site Material Cost" ] +NUMERIC_COLS = [col for col in COLS if col not in {"Account", "Title"}] + + +def _normalize_account(value) -> str: + text = str(value).strip() + if text.endswith(".0"): + text = text[:-2] + return text + class InputStore: """ Minimal store: @@ -43,6 +52,9 @@ def get_baseline(self, reactor_type: str): raise ValueError(f"{path} missing columns: {sorted(missing)}") df = df[COLS].copy() + df["Account"] = df["Account"].map(_normalize_account) + for col in NUMERIC_COLS: + df[col] = pd.to_numeric(df[col].astype(str).str.replace(",", "", regex=False), errors="coerce") self._baseline[reactor_type] = (df, power) return df.copy(), power diff --git a/src/crf/model/avg_runner.py b/src/crf/model/avg_runner.py index 9d0d0d1..c4a4621 100644 --- a/src/crf/model/avg_runner.py +++ b/src/crf/model/avg_runner.py @@ -69,8 +69,6 @@ def run_avg_all_units(config: dict, inp: dict, store, details=False): avg_TCI = float(np.mean(TCI)) avg_duration = float(np.mean(DUR)) - - final_startup_duration = max(7, config["startup_0"] * (1 - 0.3) ** np.log2(num_orders)) start_months = build_schedule_start_months(config, DUR, STAUP) finish_months = start_months + DUR + STAUP cons_duration_cumulative_wz_startup = float(finish_months[-1]) diff --git a/src/crf/model/core_accounts.py b/src/crf/model/core_accounts.py index 431b283..c8adfd0 100644 --- a/src/crf/model/core_accounts.py +++ b/src/crf/model/core_accounts.py @@ -41,6 +41,20 @@ ALL_COLS = ["Account", "Title"] + COST_COLS +ACCOUNT_21_DETAIL_ACCOUNTS = ["211", "212", "213", "214", "215", "216", "217"] +DIRECT_DETAIL_ACCOUNTS = [*ACCOUNT_21_DETAIL_ACCOUNTS, "22", "232.1", "233", "24", "26"] +TOTAL_COST_DETAIL_ACCOUNTS = [ + "21", + *ACCOUNT_21_DETAIL_ACCOUNTS, + "22", + "23", + "232.1", + "233", + "24", + "25", + "26", +] + def _blank_row(title: str, account=None) -> dict: row = {c: np.nan for c in ALL_COLS} @@ -74,31 +88,11 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr # change all nan to 0 for cost calculations (but keep original db unchanged for later use) db = db.copy() db[COST_COLS] = db[COST_COLS].fillna(0.0) - # account 21 - # Note when sum up the higher lever account like 21, we need to make sure there are - # lower level accounts (like 211 plus 214 to 219) to be added before the sum, otherwise - # the sum might lead to an empty value and the final result will be misleading. - - db.loc[db.Account == "21", "Factory Equipment Cost"] = ( - db.loc[db.Account == "212", "Factory Equipment Cost"].values - + db.loc[db.Account == "213", "Factory Equipment Cost"].values - + db.loc[db.Account == "211 plus 214 to 219", "Factory Equipment Cost"].values - ) - db.loc[db.Account == "21", "Site Material Cost"] = ( - db.loc[db.Account == "212", "Site Material Cost"].values - + db.loc[db.Account == "213", "Site Material Cost"].values - + db.loc[db.Account == "211 plus 214 to 219", "Site Material Cost"].values - ) - db.loc[db.Account == "21", "Site Labor Cost"] = ( - db.loc[db.Account == "212", "Site Labor Cost"].values - + db.loc[db.Account == "213", "Site Labor Cost"].values - + db.loc[db.Account == "211 plus 214 to 219", "Site Labor Cost"].values - ) - db.loc[db.Account == "21", "Site Labor Hours"] = ( - db.loc[db.Account == "212", "Site Labor Hours"].values - + db.loc[db.Account == "213", "Site Labor Hours"].values - + db.loc[db.Account == "211 plus 214 to 219", "Site Labor Hours"].values - ) + def _sum_accounts(accounts, col): + return db.loc[db["Account"].astype(str).str.strip().isin(accounts), col].fillna(0.0).sum() + + for col in ["Factory Equipment Cost", "Site Material Cost", "Site Labor Cost", "Site Labor Hours"]: + db.loc[db.Account == "21", col] = _sum_accounts(ACCOUNT_21_DETAIL_ACCOUNTS, col) # account 23 @@ -124,7 +118,7 @@ def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFr # accounts under 20s has factory equipment costs, labor hours, and # labor costs, so we can skip accounts 10s, 30s 50s and 60s - for x in ['21', '211 plus 214 to 219', '212', '213', '22', '23', '232.1', '233', '24', '25', '26']: + for x in TOTAL_COST_DETAIL_ACCOUNTS: db.loc[db["Account"] == x, "Total Cost (USD)"] = ( db.loc[db["Account"] == x, "Factory Equipment Cost"] + db.loc[db["Account"] == x, "Site Labor Cost"] @@ -280,4 +274,3 @@ def _sum_hours(db): return float(0.04 * lab_delta * ref_duration + float(prev_cons_duration)) else: raise ValueError(f"Unknown reactor type: {reactor_type}") - diff --git a/src/crf/model/direct_cost.py b/src/crf/model/direct_cost.py index 53e38ad..cec58ee 100644 --- a/src/crf/model/direct_cost.py +++ b/src/crf/model/direct_cost.py @@ -1,6 +1,8 @@ # src/crf/model/direct_cost.py # Direct-cost pipeline: -# add_factory_cost -> add_land_cost -> add_BOP_RP_grades -> add_bulk_ordering -> add_reworking_productivity +# add_factory_cost -> add_land_cost -> add_commercial_bop +# -> add_non_safety_related_rb -> add_modular_civil_construction +# -> add_bulk_ordering -> add_reworking_productivity # Returns updated df and construction duration (no supply-chain delay yet) import numpy as np @@ -8,6 +10,9 @@ from ..utils.df_ops import getv, setv, mulv from .core_accounts import ( + ACCOUNT_21_DETAIL_ACCOUNTS, + DIRECT_DETAIL_ACCOUNTS, + TOTAL_COST_DETAIL_ACCOUNTS, update_high_level_costs, sum_lab_hrs, update_cons_duration, @@ -22,7 +27,7 @@ # exclude account 25 because it is the initial fuel cost and # should not be affected by rework. Those accounts are end level accounts # that do not have sub-accounts -ACCT_DIRECT = [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26] +ACCT_DIRECT = DIRECT_DETAIL_ACCOUNTS def _total_occ_per_kwe(df: pd.DataFrame, power: float) -> float: @@ -65,34 +70,33 @@ def add_land_cost(df: pd.DataFrame, land_cost_per_acre: float, power: float): return update_high_level_costs(db, power)[COLS].copy() -def add_BOP_RP_grades( +def _grade_ref_duration(reactor_type: str) -> float: + if reactor_type == "HTGR": + return 125 + if reactor_type == "SFR": + return 80 + if reactor_type == "AP1000": + return 76 + raise ValueError(f"Unknown reactor type: {reactor_type}") + + +def add_commercial_bop( df: pd.DataFrame, - RB_grade_0: str, BOP_grade_0: str, power: float, reactor_type: str, n_th: int, - mod_0: str, - trace=None + trace=None, ): - # RB grade does not change; BOP becomes non_nuclear after FOAK - RB_grade = RB_grade_0 + """Apply the commercial BOP grade lever.""" if reactor_type in ["HTGR", "SFR"]: BOP_grade = BOP_grade_0 if n_th == 1 else "non_nuclear" elif reactor_type == "AP1000": BOP_grade = BOP_grade_0 if n_th == 1 else "nuclear" - - db = df.copy() - - before_rb = db.copy() - if RB_grade == "non_nuclear": - mulv( - db, [212], - ["Site Material Cost", "Site Labor Cost", "Site Labor Hours", "Factory Equipment Cost"], - 0.6 - ) - _record_delta(trace, "Non safety-related Reactor Building", before_rb, db, power) + else: + raise ValueError(f"Unknown reactor type: {reactor_type}") + db = df.copy() before_bop = db.copy() if BOP_grade == "non_nuclear": if reactor_type in ["HTGR", "SFR"]: @@ -115,20 +119,38 @@ def add_BOP_RP_grades( 0.6 ) _record_delta(trace, "Commercial BOP", before_bop, db, power) - db2 = update_high_level_costs(db, power)[COLS].copy() + return update_high_level_costs(db, power)[COLS].copy() - # duration update from grade change - if reactor_type == "HTGR": - ref_duration = 125 - elif reactor_type == "SFR": - ref_duration = 80 - elif reactor_type == "AP1000": - ref_duration = 76 - else: - raise ValueError(f"Unknown reactor type: {reactor_type}") - new_dur = update_cons_duration(df, db2, ref_duration) +def add_non_safety_related_rb( + df: pd.DataFrame, + RB_grade_0: str, + power: float, + trace=None, +): + """Apply the non-safety-related reactor building lever.""" + db = df.copy() + before_rb = db.copy() + if RB_grade_0 == "non_nuclear": + mulv( + db, [212], + ["Site Material Cost", "Site Labor Cost", "Site Labor Hours", "Factory Equipment Cost"], + 0.6, + ) + _record_delta(trace, "Non safety-related Reactor Building", before_rb, db, power) + return update_high_level_costs(db, power)[COLS].copy() + +def add_modular_civil_construction( + df: pd.DataFrame, + power: float, + reactor_type: str, + n_th: int, + mod_0: str, + base_duration: float, + trace=None, +): + """Apply the modular civil construction lever.""" # applying modularity civil construction if HTGR of SFR, for n>=2 assume # modularized, if other reactor type such as AP1000, we assume no modularity # effect on duration for now. @@ -140,20 +162,20 @@ def add_BOP_RP_grades( mod_factor = 0.8 if mod == "modularized" else 1.0 # NOTE see excel Relationship sheet D4 # NOTE in excel tool HTGR does nothing here, SFR factory cost of 21 with the 20% reduction # with AP1000 all labor cost with the 20% reduction in 20s accounts, - before_mod = db2.copy() + db = df.copy() + before_mod = db.copy() if reactor_type == "SFR": - for acct in [21, "211 plus 214 to 219", 212, 213]: - setv(db2, acct, "Factory Equipment Cost", float(getv(db2, acct, "Factory Equipment Cost")) * mod_factor) - db2 = update_high_level_costs(db2, power)[COLS].copy() + for acct in ACCOUNT_21_DETAIL_ACCOUNTS: + setv(db, acct, "Factory Equipment Cost", float(getv(db, acct, "Factory Equipment Cost")) * mod_factor) + db = update_high_level_costs(db, power)[COLS].copy() elif reactor_type == "AP1000": - for acct in [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26]: - setv(db2, acct, "Site Labor Cost", float(getv(db2, acct, "Site Labor Cost")) * mod_factor) - setv(db2, acct, "Site Labor Hours", float(getv(db2, acct, "Site Labor Hours")) * mod_factor) - db2 = update_high_level_costs(db2, power)[COLS].copy() - _record_delta(trace, "Modular Construction", before_mod, db2, power) - + for acct in ACCT_DIRECT: + setv(db, acct, "Site Labor Cost", float(getv(db, acct, "Site Labor Cost")) * mod_factor) + setv(db, acct, "Site Labor Hours", float(getv(db, acct, "Site Labor Hours")) * mod_factor) + db = update_high_level_costs(db, power)[COLS].copy() + _record_delta(trace, "Modular Construction", before_mod, db, power) - return db2, new_dur * mod_factor + return db, base_duration * mod_factor def add_bulk_ordering(df: pd.DataFrame, num_orders: int, f_22: float, f_2321: float, power: float, reactor_type: str, trace=None): @@ -273,10 +295,14 @@ def update_direct_cost( db, baseline_lab_hours = add_factory_cost(reactor_df, power, f_22, f_2321, num_orders, reactor_type) # all Total Cost (USD) columns should be 0 # when accounts are in 20s - db.loc[db["Account"].isin(['21', '211 plus 214 to 219', '212', '213', '22', '23', '232.1', '233', '24', '25', '26']), "Total Cost (USD)"] = 0.0 + db.loc[db["Account"].isin(TOTAL_COST_DETAIL_ACCOUNTS), "Total Cost (USD)"] = 0.0 db = update_high_level_costs(db, power)[COLS].copy() db = add_land_cost(db, land_cost_per_acre_0, power) - db, prev_dur = add_BOP_RP_grades(db, RB_grade_0, BOP_grade_0, power, reactor_type, n_th, mod_0, trace=trace) + before_grades = db.copy() + db = add_commercial_bop(db, BOP_grade_0, power, reactor_type, n_th, trace=trace) + db = add_non_safety_related_rb(db, RB_grade_0, power, trace=trace) + prev_dur = update_cons_duration(before_grades, db, _grade_ref_duration(reactor_type)) + db, prev_dur = add_modular_civil_construction(db, power, reactor_type, n_th, mod_0, prev_dur, trace=trace) db = add_bulk_ordering(db, num_orders, f_22, f_2321, power, reactor_type, trace=trace) db, dur_no_delay = add_reworking_productivity( db, reactor_type, n_th, diff --git a/src/crf/model/learning.py b/src/crf/model/learning.py index 43931cf..777c681 100644 --- a/src/crf/model/learning.py +++ b/src/crf/model/learning.py @@ -8,7 +8,7 @@ import pandas as pd from ..utils.df_ops import getv, setv -from .core_accounts import update_high_level_costs +from .core_accounts import ACCOUNT_21_DETAIL_ACCOUNTS, DIRECT_DETAIL_ACCOUNTS, update_high_level_costs COLS = [ "Account", "Title", "Total Cost (USD)", @@ -16,7 +16,7 @@ "Site Labor Cost", "Site Material Cost" ] -ACCT_DIRECT = [212, 213, "211 plus 214 to 219", 22, "232.1", 233, 24, 26] +ACCT_DIRECT = DIRECT_DETAIL_ACCOUNTS def _total_occ_per_kwe(df: pd.DataFrame, power: float) -> float: @@ -66,27 +66,25 @@ def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power - # However, learning is not applied to the factory cost + def rates_from_map(material_rates: dict, labor_rates: dict): + return ( + np.array([material_rates[str(acct)] for acct in ACCT_DIRECT]) * standardization / 0.7, + np.array([labor_rates[str(acct)] for acct in ACCT_DIRECT]) * standardization / 0.7, + ) + if reactor_type == "HTGR": - mat_lr = np.array([ - 0.099588665391, 0.099588665391, 0.099588665391, 0.080817992281, - 0.0, 0.099588665391, 0.099588665391, 0.099588665391 - ]) * standardization / 0.7 - - lab_lr = np.array([ - 0.180678729399, 0.180678729399, 0.180678729399, 0.146555539499, - 0.137148574884, 0.180678729399, 0.180678729399, 0.180678729399 - ]) * standardization / 0.7 + mat_rates = {acct: 0.099588665391 for acct in ACCOUNT_21_DETAIL_ACCOUNTS} + mat_rates.update({"22": 0.080817992281, "232.1": 0.0, "233": 0.099588665391, "24": 0.099588665391, "26": 0.099588665391}) + lab_rates = {acct: 0.180678729399 for acct in ACCOUNT_21_DETAIL_ACCOUNTS} + lab_rates.update({"22": 0.146555539499, "232.1": 0.137148574884, "233": 0.180678729399, "24": 0.180678729399, "26": 0.180678729399}) + mat_lr, lab_lr = rates_from_map( + mat_rates, + lab_rates, + ) elif reactor_type == "SFR": - mat_lr = np.array([ - 0.099588665391, 0.099588665391, 0.099588665391, 0.099588665391, - 0.099588665391, 0.099588665391, 0.099588665391, 0.099588665391 - ]) * standardization / 0.7 - - lab_lr = np.array([ - 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399, - 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399 - ]) * standardization / 0.7 + all_mat = {str(acct): 0.099588665391 for acct in ACCT_DIRECT} + all_lab = {str(acct): 0.180678729399 for acct in ACCT_DIRECT} + mat_lr, lab_lr = rates_from_map(all_mat, all_lab) elif reactor_type == "AP1000": # we will apply learning on factory cost for AP1000 of account 22 and 232.1, # but not for other accounts since AP1000 is already modularized and we assume @@ -94,14 +92,9 @@ def learning_effect(df: pd.DataFrame, n_th: int, standardization_0: float, power # overall_lr = (1 - np.exp(np.log(c_NOAK/c_FOAK)/np.log2(n_of_NOAK))) * standardization / 0.7 fac_lr_22 = (1 - np.exp(np.log(0.930884915)/np.log2(n_of_NOAK))) * standardization / 0.7 fac_lr_2321 = (1 - np.exp(np.log(0.957187482)/np.log2(n_of_NOAK))) * standardization / 0.7 - mat_lr = np.array([ - 0.099588665391, 0.099588665391, 0.099588665391, 0.099588665391, - 0.099588665391, 0.099588665391, 0.099588665391, 0.099588665391 - ]) * standardization / 0.7 - lab_lr = np.array([ - 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399, - 0.180678729399, 0.180678729399, 0.180678729399, 0.180678729399 - ]) * standardization / 0.7 + all_mat = {str(acct): 0.099588665391 for acct in ACCT_DIRECT} + all_lab = {str(acct): 0.180678729399 for acct in ACCT_DIRECT} + mat_lr, lab_lr = rates_from_map(all_mat, all_lab) else: raise ValueError(f"Unknown reactor type: {reactor_type}") diff --git a/src/crf/model/schedule.py b/src/crf/model/schedule.py new file mode 100644 index 0000000..360defa --- /dev/null +++ b/src/crf/model/schedule.py @@ -0,0 +1,52 @@ +import numpy as np + + +def effective_staggering_ratio(config: dict) -> float: + """Return the user-requested overlap ratio clamped to a valid range.""" + ratio = float(config.get("staggering_ratio", 0.75)) + return min(max(ratio, 0.0), 1.0) + + +def build_schedule_start_months(config: dict, durations, startup_durations=None) -> np.ndarray: + """Return plant start months with monotonic project finish dates. + + Plants are nominally staggered by ``1 - staggering_ratio`` of the previous + construction duration. If a later plant would finish construction or finish + startup before the previous plant, its start is delayed until the earlier + finish date is tied. + """ + durations = np.array(durations, dtype=float) + if startup_durations is None: + startup_durations = np.zeros(len(durations), dtype=float) + else: + startup_durations = np.array(startup_durations, dtype=float) + + ratio = effective_staggering_ratio(config) + starts = np.zeros(len(durations), dtype=float) + for idx in range(1, len(durations)): + nominal_start = starts[idx - 1] + (1.0 - ratio) * durations[idx - 1] + previous_construction_finish = starts[idx - 1] + durations[idx - 1] + previous_startup_finish = previous_construction_finish + startup_durations[idx - 1] + construction_tie_start = previous_construction_finish - durations[idx] + startup_tie_start = previous_startup_finish - durations[idx] - startup_durations[idx] + nominal_start = max(nominal_start, construction_tie_start, startup_tie_start) + starts[idx] = max(nominal_start, 0.0) + return starts + + +def build_schedule_timeline(config: dict, plant_numbers, durations, startup_durations) -> dict[str, np.ndarray]: + """Return timeline arrays with corrected start and finish dates.""" + plant_numbers = np.array(plant_numbers, dtype=int) + durations = np.array(durations, dtype=float) + startup_durations = np.array(startup_durations, dtype=float) + starts = build_schedule_start_months(config, durations, startup_durations) + construction_finish = starts + durations + startup_finish = construction_finish + startup_durations + return { + "plant_number": plant_numbers, + "construction_start_month": starts, + "construction_duration_months": durations, + "construction_finish_month": construction_finish, + "startup_duration_months": startup_durations, + "startup_finish_month": startup_finish, + } diff --git a/tutorial/crf_showcase.ipynb b/tutorial/crf_showcase.ipynb new file mode 100644 index 0000000..91ee921 --- /dev/null +++ b/tutorial/crf_showcase.ipynb @@ -0,0 +1,1477 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "235c4be9", + "metadata": {}, + "source": [ + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "95b4e8ff", + "metadata": {}, + "source": [ + "#
Cost Reduction Framework Showcase
\n", + "\n", + "This notebook uses the new `src/crf` API in ACCERT to run deterministic CRF scenarios, inspect the returned tables, and generate dashboard figures." + ] + }, + { + "cell_type": "markdown", + "id": "cfcf023f", + "metadata": {}, + "source": [ + "## 1. Imports and Path Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d4428c94", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using ACCERT repository: /Users/jia.zhou/projects/ACCERT\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "import os\n", + "import sys\n", + "import tempfile\n", + "import warnings\n", + "\n", + "os.environ.setdefault(\"MPLCONFIGDIR\", tempfile.gettempdir())\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", + "pd.set_option(\"display.max_columns\", 80)\n", + "pd.set_option(\"display.width\", 160)\n", + "\n", + "cwd = Path.cwd().resolve()\n", + "repo_root = next(path for path in [cwd, *cwd.parents] if (path / \"src\" / \"crf\").exists())\n", + "if str(repo_root / \"src\") not in sys.path:\n", + " sys.path.insert(0, str(repo_root / \"src\"))\n", + "\n", + "from crf.model.schedule import build_schedule_timeline\n", + "\n", + "from crf import (\n", + " levers_to_dataframe,\n", + " occ_reduction_from_foak_to_noak,\n", + " print_scenario_result,\n", + " results_to_dataframe,\n", + " run_one_scenario,\n", + " save_dashboard,\n", + " waterfall_to_dataframe,\n", + ")\n", + "\n", + "output_dir = repo_root / \"tutorial\"\n", + "output_dir.mkdir(exist_ok=True)\n", + "print(f\"Using ACCERT repository: {repo_root}\")\n", + "\n", + "def show_image(path, figsize=(12, 8)):\n", + " image = plt.imread(path)\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " ax.imshow(image)\n", + " ax.axis(\"off\")\n", + " return fig\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3c4f5a9", + "metadata": {}, + "source": [ + "## Individual Plot Helpers\n", + "\n", + "These helper functions show each dashboard panel as a larger standalone figure for easier notebook inspection." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eab5d888", + "metadata": {}, + "outputs": [], + "source": [ + "def _style_ax(ax):\n", + " ax.spines[\"top\"].set_visible(False)\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.grid(True, alpha=0.25)\n", + "\n", + "\n", + "def plot_capital_cost_panel(result, figsize=(10, 5)):\n", + " df = results_to_dataframe(result)\n", + " x = df[\"Plant number\"]\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " ax.bar(x - 0.18, df[\"TCI\"], width=0.36, label=\"10-60 - Total Capital Investment (TCI)\", color=\"tab:green\")\n", + " ax.bar(x + 0.18, df[\"OCC\"], width=0.36, label=\"10-50 - Overnight Capital Cost (OCC)\", color=\"tab:blue\")\n", + " if df[\"Net OCC\"].notna().any():\n", + " ax.plot(x, df[\"Net OCC\"], marker=\"o\", linestyle=\"--\", linewidth=1.2, label=\"Net OCC after ITC\")\n", + " if df[\"NCI\"].notna().any():\n", + " ax.plot(x, df[\"NCI\"], marker=\"o\", linestyle=\"--\", linewidth=1.2, label=\"Net Capital Investment\")\n", + " ax.set_title(\"Capital Cost: OCC and TCI\")\n", + " ax.set_xlabel(\"Plant number\")\n", + " ax.set_ylabel(\"$/kWe\")\n", + " ax.legend(fontsize=9)\n", + " _style_ax(ax)\n", + " return fig\n", + "\n", + "\n", + "def plot_duration_panel(result, figsize=(10, 5)):\n", + " df = results_to_dataframe(result)\n", + " x = df[\"Plant number\"]\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " ax.bar(x, df[\"Construction duration\"], label=\"Construction\", color=\"#ff8c2a\")\n", + " ax.bar(x, df[\"Startup duration\"], bottom=df[\"Construction duration\"], label=\"Startup\", color=\"#8b63a9\")\n", + " ax.set_title(\"Total Construction Duration\")\n", + " ax.set_xlabel(\"Plant number\")\n", + " ax.set_ylabel(\"Months\")\n", + " ax.legend(fontsize=9)\n", + " _style_ax(ax)\n", + " return fig\n", + "\n", + "\n", + "def plot_tci_breakdown_panel(result, figsize=(10, 5)):\n", + " df = results_to_dataframe(result)\n", + " x = df[\"Plant number\"]\n", + " columns = [\n", + " (\"Preconstruction costs\", \"10 - Preconstruction\", \"#4c78a8\"),\n", + " (\"Direct costs\", \"20 - Direct\", \"#72b7b2\"),\n", + " (\"Indirect costs\", \"30 - Indirect\", \"#54a24b\"),\n", + " (\"Supplementary costs\", \"50 - Supplementary\", \"#b279a2\"),\n", + " (\"Financing costs\", \"60 - Financing\", \"#ff9da6\"),\n", + " ]\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " bottom = np.zeros(len(df))\n", + " for column, label, color in columns:\n", + " values = df[column].fillna(0).astype(float).to_numpy()\n", + " ax.bar(x, values, bottom=bottom, label=label, color=color)\n", + " bottom += values\n", + " ax.set_title(\"10-60 - TCI Breakdown\")\n", + " ax.set_xlabel(\"Plant number\")\n", + " ax.set_ylabel(\"$/kWe\")\n", + " ax.legend(fontsize=9)\n", + " _style_ax(ax)\n", + " return fig\n", + "\n", + "\n", + "def plot_timeline_panel(result, figsize=(11, 5)):\n", + " df = results_to_dataframe(result)\n", + " plants = df[\"Plant number\"].astype(int).to_numpy()\n", + " durations = df[\"Construction duration\"].astype(float).to_numpy()\n", + " startup = df[\"Startup duration\"].fillna(0).astype(float).to_numpy()\n", + " timeline = build_schedule_timeline(\n", + " {\"staggering_ratio\": result.get(\"staggering_ratio\", 0.75), \"reactor_type\": result.get(\"reactor_type\", \"\")},\n", + " plants,\n", + " durations,\n", + " startup,\n", + " )\n", + " start_years = timeline[\"construction_start_month\"] / 12\n", + " duration_years = timeline[\"construction_duration_months\"] / 12\n", + " startup_years = timeline[\"startup_duration_months\"] / 12\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " ax.barh(plants, duration_years, left=start_years, height=0.64, color=\"#ff8c2a\", edgecolor=\"black\", label=\"Construction\")\n", + " ax.barh(plants, startup_years, left=start_years + duration_years, height=0.64, color=\"#8b63a9\", edgecolor=\"black\", label=\"Startup\")\n", + " ax.set_title(\"Sequential Construction Timeline\")\n", + " ax.set_xlabel(\"Time (Years)\")\n", + " ax.set_ylabel(\"Reactor Number\")\n", + " ax.set_yticks(plants)\n", + " ax.set_ylim(plants.max() + 0.7, plants.min() - 0.7)\n", + " ax.set_xlim(0, float(np.nanmax(start_years + duration_years + startup_years)) * 1.05)\n", + " ax.grid(True, axis=\"x\", alpha=0.35)\n", + " ax.legend(fontsize=9)\n", + " return fig\n", + "\n", + "\n", + "def plot_occ_components_panel(result, figsize=(10, 5)):\n", + " df = results_to_dataframe(result)\n", + " x = df[\"Plant number\"]\n", + " columns = [\n", + " (\"Direct costs: equipment\", \"20 - Direct: Equipment\", \"#72b7b2\"),\n", + " (\"Direct costs: material\", \"20 - Direct: Material\", \"#f58518\"),\n", + " (\"Direct costs: labor\", \"20 - Direct: Labor\", \"#e45756\"),\n", + " (\"Indirect costs\", \"30 - Indirect\", \"#54a24b\"),\n", + " (\"Supplementary costs\", \"50 - Supplementary\", \"#b279a2\"),\n", + " ]\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " bottom = np.zeros(len(df))\n", + " for column, label, color in columns:\n", + " values = df[column].fillna(0).astype(float).to_numpy()\n", + " ax.bar(x, values, bottom=bottom, label=label, color=color)\n", + " bottom += values\n", + " ax.set_title(\"10-50 - OCC Components\")\n", + " ax.set_xlabel(\"Plant number\")\n", + " ax.set_ylabel(\"$/kWe\")\n", + " ax.legend(fontsize=9)\n", + " _style_ax(ax)\n", + " return fig\n", + "\n", + "\n", + "def plot_waterfall_panel(result, figsize=(11, 5)):\n", + " waterfall = waterfall_to_dataframe(result)\n", + " foak_occ = float(waterfall.iloc[0][\"cumulative_occ\"])\n", + " x = np.arange(len(waterfall))\n", + " labels = [\"\\n\".join(str(label).replace(\"\\n\", \" \").split()) for label in waterfall[\"label\"]]\n", + " labels = [\"\\n\".join(label.split()[:3]) if len(label) > 18 else label for label in labels]\n", + " bottoms, heights, colors, pct_labels = [], [], [], []\n", + " noak_reduction = occ_reduction_from_foak_to_noak(result)\n", + " for _, row in waterfall.iterrows():\n", + " if row[\"kind\"] == \"total\":\n", + " total = float(row[\"cumulative_occ\"])\n", + " bottoms.append(0)\n", + " heights.append(total)\n", + " colors.append(\"#4c78a8\")\n", + " pct_labels.append(\"100%\" if row.name == 0 else f\"{noak_reduction:.1f}% lower\")\n", + " else:\n", + " current = float(row[\"cumulative_occ\"])\n", + " change = float(row[\"absolute_change\"])\n", + " previous = current - change\n", + " bottoms.append(previous if change >= 0 else current)\n", + " heights.append(abs(change))\n", + " colors.append(\"#f58518\" if change >= 0 else \"#54a24b\")\n", + " pct_labels.append(f\"{change / foak_occ * 100:+.1f}%\")\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " ax.bar(x, heights, bottom=bottoms, color=colors, width=0.72)\n", + " for idx in range(len(waterfall) - 1):\n", + " y = float(waterfall.iloc[idx][\"cumulative_occ\"])\n", + " ax.plot([idx + 0.36, idx + 1 - 0.36], [y, y], color=\"#777777\", linewidth=0.8)\n", + " max_y = max(bottom + height for bottom, height in zip(bottoms, heights))\n", + " for idx, (bottom, height, label) in enumerate(zip(bottoms, heights, pct_labels)):\n", + " if height > 0:\n", + " ax.text(idx, bottom + height / 2, label, ha=\"center\", va=\"center\", fontsize=8, fontweight=\"bold\", color=\"white\")\n", + " ax.set_title(\"% OCC Reduction from FOAK to NOAK\")\n", + " ax.set_ylabel(\"OCC ($/kWe)\")\n", + " ax.set_xticks(x)\n", + " ax.set_xticklabels(labels, fontsize=8)\n", + " ax.set_ylim(0, max_y * 1.12)\n", + " _style_ax(ax)\n", + " return fig" + ] + }, + { + "cell_type": "markdown", + "id": "c463d894", + "metadata": {}, + "source": [ + "## 2. Scenario Inputs\n", + "\n", + "The dictionaries below mirror the Python tutorial files and can be edited directly." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a76c73e9", + "metadata": {}, + "outputs": [], + "source": [ + "def base_config(reactor_type):\n", + " startup = 28 if reactor_type == \"AP1000\" else 16\n", + " return {\n", + " \"reactor_type\": reactor_type,\n", + " \"f_22\": 250_000_000,\n", + " \"f_2321\": 150_000_000,\n", + " \"land_cost_per_acre_0\": 22_000,\n", + " \"startup_0\": startup,\n", + " \"staggering_ratio\": 0.75,\n", + " }\n", + "\n", + "\n", + "def base_levers(reactor_type):\n", + " common = {\n", + " \"num_orders\": 10 if reactor_type == \"AP1000\" else 13,\n", + " \"num_NOAK\": 8,\n", + " \"itc_percent\": 0,\n", + " \"n_itc\": 0,\n", + " \"interest_percent\": 6,\n", + " \"design_completion_percent\": 70 if reactor_type == \"AP1000\" else 80,\n", + " \"design_maturity\": 1,\n", + " \"proc_exp\": 0.5,\n", + " \"N_proc\": 3,\n", + " \"ce_exp\": 0.5 if reactor_type == \"AP1000\" else 1.0,\n", + " \"N_cons\": 5,\n", + " \"ae_exp\": 0.5,\n", + " \"N_AE\": 4,\n", + " \"standardization_percent\": 80,\n", + " \"modularity_code\": 1,\n", + " \"bop_grade_code\": 1,\n", + " \"rb_grade_code\": 0,\n", + " }\n", + " if reactor_type == \"AP1000\":\n", + " common.update({\"modularity_code\": 0, \"bop_grade_code\": 0, \"rb_grade_code\": 0})\n", + " return common" + ] + }, + { + "cell_type": "markdown", + "id": "32ede5d2-7c2f-462d-9dbe-11db08f65e7e", + "metadata": {}, + "source": [ + "### The Cost reduction framework: levers and variables impact the costs as shown in the figure (below)\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "ddc207dd", + "metadata": {}, + "source": [ + "## 3. Run One AP1000 Scenario" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2168391e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results by plant number:\n", + "\n", + " Plant_number OCC TCI duration STAUP D10s D20s D30s D50s D60s D20_equip D20_mat D20_labor\n", + " 1 15604.82 18840.16 91.00 28.00 124.67 7070.57 8332.54 77.04 3235.34 3186.79 674.08 3209.69\n", + " 2 8643.32 9858.01 68.30 19.60 124.67 4593.45 3879.35 45.85 1214.69 2361.57 449.12 1782.76\n", + " 3 6633.90 7385.47 59.88 15.91 124.67 3807.58 2664.79 36.85 751.58 2114.48 377.85 1315.25\n", + " 4 5287.97 5809.90 55.16 13.72 124.67 3217.85 1914.63 30.82 521.93 1887.35 322.68 1007.82\n", + " 5 4715.38 5132.42 52.18 12.23 124.67 2972.41 1590.04 28.26 417.04 1810.79 299.15 862.47\n", + " 6 4536.65 4902.34 50.01 11.14 124.67 2905.37 1479.15 27.46 365.69 1804.02 289.79 811.56\n", + " 7 4394.90 4721.40 48.24 10.29 124.67 2851.31 1392.10 26.82 326.50 1798.33 282.11 770.87\n", + " 8 4278.58 4580.80 46.76 9.60 124.67 2806.31 1321.30 26.30 302.21 1793.42 275.62 737.28\n", + " 9 4180.69 4461.18 45.49 9.04 124.67 2767.99 1262.17 25.86 280.50 1789.10 270.02 708.86\n", + " 10 4096.67 4358.97 44.38 8.56 124.67 2734.74 1211.78 25.48 262.30 1785.26 265.10 684.37\n", + "\n", + "Summary metrics:\n", + "\n", + "cons_duration_cumulative_wz_startup: 190.56\n", + "occLastUnit: 4096.67\n", + "occNOAKUnit: 4278.58\n", + "occ_reduction_from_FOAK_to_NOAK_percent: 72.58\n", + "TCILastUnit: 4358.97\n", + "durationsLastUnit: 44.38\n", + "avg_OCC: 6237.29\n", + "avg_TCI: 7005.07\n", + "avg_duration: 56.14\n" + ] + } + ], + "source": [ + "config = base_config(\"AP1000\")\n", + "levers = base_levers(\"AP1000\")\n", + "\n", + "result = run_one_scenario(config, levers)\n", + "print_scenario_result(result)" + ] + }, + { + "cell_type": "markdown", + "id": "fec5cbaf", + "metadata": {}, + "source": [ + "## 4. Dashboard Figure\n", + "\n", + "Set `show_levers=False` to hide the top lever table." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a318bf57", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dashboard_path = output_dir / \"crf_showcase_ap1000_dashboard.png\"\n", + "save_dashboard(result, dashboard_path, title=\"AP1000 Cost Reduction Framework\", show_levers=True)\n", + "show_image(dashboard_path)" + ] + }, + { + "cell_type": "markdown", + "id": "d80f18e0", + "metadata": {}, + "source": [ + "## 5. Large Individual Dashboard Panels\n", + "\n", + "Run these cells when the full dashboard is too compact. Each figure is shown at notebook-friendly size." + ] + }, + { + "cell_type": "markdown", + "id": "8674bd57", + "metadata": {}, + "source": [ + "### Capital Cost" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7db63dd0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_capital_cost_panel(result, figsize=(11, 5))" + ] + }, + { + "cell_type": "markdown", + "id": "48ff8d53", + "metadata": {}, + "source": [ + "### Total Construction Duration" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "77a48f3e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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cc2ZUVJTp4+NjhoaGmvfcc4+5c+dO1zb53ZObr+m6NWvWmO3atTP9/PzMwMBAs3Pnzrn6Mr/6r/dfcnJyntd13bBhw0xJrj/+/v5mnTp1zF69epmvv/66mZWVlWufrKwsc9KkSWbt2rVNf39/s2PHjmZSUlK+T8G9+R6apmmmpaWZI0eONKtXr24GBASYHTp0MLdv357nk4ffeusts1GjRqa3t7fbmLz5Kbim+dMThOfMmWM2aNDA9Pb2NqtWrWoOHjzYPHbsmNt2HTt2NJs2bZpnf9x8bwGgvDJMsxgeRQcAAAAAwC/gO6AAAAAAAEsQQAEAAAAAliCAAgAAAAAsQQAFAAAAAFiCAAoAAAAAsAQBFAAAAABgCQKoJNM0lZ6eLt5IAwAAAAAlhwAqKSMjQ8HBwcrIyLC7FJQRTqdTZ86ckdPptLsUlDOMPdiBcQc7MO5gB8Zd0RFAAQAAAACWIIACAAAAACxBAAUAAAAAWIIACgAAAACwhJfdBQAAAADwbNeuXdPVq1ftLqPEOZ1OXb16VVeuXJHDUf7m8ry9vVWhQoUiHYMACgAAAKDQLl68qOPHj5eLVxqapimn06m0tDQZhmF3OZYzDEO1atVSxYoVC30MAigAAACAQrl27ZqOHz+ugIAAVatWrcyHMtM0lZOTIy8vrzJ/rTczTVOnT5/W8ePHFRUVVeiZUAIoAAAAgEK5evWqTNNUtWrV5O/vb3c5Ja48B1BJqlatmg4fPqyrV68WOoCWvw8uAwAAAChW5TGMlUfFcZ8JoAAAAAAAS9gaQLdt26ZevXopIiJChmFozZo1rnVXr17VM888o+bNmyswMFAREREaOnSoTp486XaMrKwsPf7446pataoCAwP1wAMP6Pjx4xZfCQAAAADgl9j6HdBLly6pRYsWeuSRR9S3b1+3dZcvX9b+/fs1bdo0tWjRQmlpaYqNjdUDDzygTz/91LVdbGys1q1bp5UrVyo0NFRPPfWUevbsqX379hX5EcEAAAAACuHlpiV/jqcOlPw5PNCbb76phQsXaufOnXaXkidbZ0C7d++uF154QX369Mm1Ljg4WBs3blT//v3VsGFD/epXv9L8+fO1b98+HT16VJJ04cIFvfbaa3r55Zd17733qlWrVnrjjTf0xRdf6MMPP7T6cgAAAACUUjt27FD37t1VpUoVVa5cWS1atFBCQoKys7NL5HyHDx+WYRg6f/58iRxfkrZs2aLKlSu7tQ0aNKjUhk/Jw56Ce+HCBRmG4erkffv26erVq+ratatrm4iICDVr1kw7d+5Ut27d8jxOVlaWsrKyXMvp6emSfnqxrNPpLLkLQLnhdDpd74kCrMTYgx0Yd7AD4650uH4frv+5zopHEt3Ke0ffe+89DRw4UDNnztSKFStUtWpVff3115ozZ45OnjypyMjIYj//9fU3982Nrj9Rt7BuPIcVrl9LXrnJ4SjY3KbHBNArV65o8uTJGjhwoCpVqiRJSklJkY+Pj6pUqeK2bVhYmFJSUvI91qxZszRjxoxc7WlpacrJySnewlEuOZ1OZWRkyDTNAv9lBIoDYw92YNzBDoy70uHq1atyOp3Kyclx+3e0twXnLui/203T1B/+8AdNnDhR48ePd+17++2369VXX5Uk7d69WxMmTNBXX32lGjVq6Nlnn9XDDz8sSZo5c6b279+vyMhIJSYmKigoSLNnz1b//v0lSR9++KEmTZqkw4cPKyAgQL1799aCBQvUrl07SVLt2rUlSQsXLlTNmjXVr18/vfDCC0pISFD16tU1e/Zs9evXT6dPn3bV3LdvX7Vo0ULPPfecJGn//v2aPHmyPv/8c1WoUEH9+/fXtGnTdP/99+vKlSsKCgqSJK1bt04//PCD/vKXv7i+tvjjjz8qNjZWW7dulb+/vwYOHKjp06fLy8tLW7duVb9+/TRnzhy98MILunz5soYPH67Zs2fn2+dOp1MXLlzQ5cuX3dZVrVq1QPfDIwLo1atX9fDDD8vpdGrRokW/uL1pmj/7iOApU6ZowoQJruX09HTVrl1bVapUcYVboCicTqcMw1CVKlX4jyIsxdiDHRh3sAPjrnS4cuWK0tLS5OXlVaSZvMIo6Pm++eYbJScna9CgQXnuc/78efXs2VPPPfecxowZo507d6pnz56qV6+e2rdvL4fDoY0bN2rZsmX6y1/+ohUrVmjMmDHq1auXgoKCNHLkSM2ePVtDhgzRpUuX9Pnnn8vLy0u7d+9W/fr1dezYMdcnOLds2aKMjAx98cUXOnjwoCRpz549ua7H4XDI4XDIy8tLJ06cUNeuXRUfH69///vfcjqd2rdvn8LCwvTvf/9bDz30kNLS0lz7Xv/o7/XjDRs2TOHh4frhhx909uxZ9ejRQ0FBQXr22WdVoUIFZWRk6MCBA65+uvPOO9WzZ0/FxMTk2ecOh0PBwcHy8/MrUP/nOkah9rLQ1atX1b9/fyUnJ2vTpk1uATE8PFzZ2dlKS0tzmwVNTU1VdHR0vsf09fWVr69vrvbrNxooDoZhMKZgC8Ye7MC4gx0Yd/ZzOBwyDMP1x0oFPd+ZM2ckSbVq1cpzn3//+9+qVq2annjiCUlSTEyMBg4cqBUrVqhDhw4yDEOtW7fWwIEDZZqmBg8erMcee0zffvut2rRpI29vb33//fc6c+aMqlWrpvbt27vVd2PfGIYhp9OpOXPmKDAwMNd2N1+fYRh688031aZNG40bN8617je/+U2++97YduLECW3atEmnTp1SUFCQgoKCNHXqVMXFxWnq1KkyDEOmaWr27Nny9/dXkyZNFB0drf3796tTp0559nlR/96V6gB6PXx+++232rx5s0JDQ93WX7/h1x9WJEmnTp3Sl19+qYSEBDtKLhEvjXrH7hJsM/Fv/e0uAQAAAB7s+kdDT5w4odtuuy3X+uPHj6tu3bpubfXr19e2bdtcy+Hh4a7/bxiG/P39lZGRIUlavXq1XnzxRTVs2FCRkZGaMmWKK5vkJSgoKNeDg37OkSNHFBUVVeDtb3T8+HH5+fm51V+/fn2311ZWqlRJAQEBruXAwEDXtZUEW39ddPHiRSUlJSkpKUmSlJycrKSkJB09elQ5OTnq16+fPv30U7355pu6du2aUlJSlJKS4npSVXBwsEaOHKmnnnpKH330kT777DMNHjxYzZs317333mvjlQEAAAAoDRo0aKC6detq5cqVea6vVauWDh8+7NaWnJysWrVqFej4rVu31qpVq3TmzBlNmzZNAwcO1I8//pjvDOHN7RUrVlRmZqbbg4ROnTrl+v+RkZH67rvvCnSsm9WqVUtXrlzRjz/+6Gq7lWsrCbYG0E8//VStWrVSq1atJEkTJkxQq1at9Nxzz+n48eNau3atjh8/rpYtW6pGjRquPzc+VvjPf/6zevfurf79+6t9+/YKCAjQunXreAcoAAAAABmGofnz52v27NmaP3++zp49K+mn74aOHDlSHTp0UGpqqhYtWqScnBxt375diYmJGjp06C8eOzs7W3//+9+VlpYmh8Phmtn08vJStWrV5HA49P333//sMRo0aCBvb28lJibq2rVrWrlypT777DPX+kGDBmnPnj1asmSJsrKydPnyZW3fvl3STw9fzcjIcHuA0Y1q1qypTp06aeLEibp06ZKOHj2q+Ph4DRs2rCBdVyJs/QhuTEzMzz4yuCCPE/bz89P8+fM1f/784iwNAAAAQGE9dcDuCtz07NlT69ev1wsvvKBp06ZJkurUqaMhQ4aoRo0aWr9+vWJjYzVlyhRFRERo8eLF6tChQ4GOnZiYqNjYWGVnZ6tOnTpKTEx0fXVw+vTp6t69u7Kzs7Vo0SJFRETk2r9SpUp69dVXNWnSJI0bN06DBw92e51krVq19OGHH+rpp5/W5MmT5ePjowEDBujXv/61GjZsqJEjR6px48bKycnRe++9l2d948ePV2RkpPz9/TVo0CBNmjSpMN1YLAzTqpfGlGLp6ekKDg7WhQsXSuVTcPkOqOdxOp06d+6cQkJCeDACLMXYgx0Yd7AD4650uHLlipKTk1WvXr1CPxXVk5im6Xp3p9UPXSoNiuN+87cVAAAAAGAJAigAAAAAwBIEUAAAAACAJQigAAAAAABLEEABAAAAAJYggAIAAAAALEEABQAAAABYggAKAAAAALCEl90FAAAAAChbXhr1TomfY+Lf+pf4OVD8mAEFAAAAUOYdOnRIvXr1UtWqVVWpUiU1atRIc+bMkSTVrVtXa9asKdLxDx8+LMMwdP78+aIXW4YRQAEAAACUeT169FCLFi109OhRpaWladWqVapfv36xHDsnJ6dYjlMeEEABAAAAlGlnzpzR999/r9GjRysgIEAVKlRQ06ZN9dvf/la//e1vdfToUQ0YMEAVK1bUmDFjJEmTJk1SZGSkgoKC1KRJE/3jH/9wHW/Lli2qXLmyFi9erDp16ujuu+/WXXfdJUmqVauWKlasqDfffFPLli1Ty5Yt3Wpp2bKlli1bJkmu9c8++6xCQ0NVp04dLVq0yJI+sQsBFAAAAECZFhoaqkaNGumRRx7RO++8oyNHjrjW/eMf/1CdOnX01ltv6eLFi1qyZIkkqUWLFtq7d6/Onz+v5557TkOGDFFycrJrv4yMDH3++ef6+uuvtXXrVu3Zs0eSdPz4cV28eFGDBg0qUG1ffvmlDMPQqVOn9Pbbb2vy5Mnatm1bMV596UIABQAAAFCmGYahzZs3q0WLFpoxY4bq16+vJk2aaOPGjfnuM2jQIFWvXl0VKlTQww8/rEaNGmnnzp2u9U6nU7Nnz1ZAQIACAgIKXVtgYKDi4uLk4+Oju+++W4MGDdKKFSsKfbzSjgAKAAAAoMwLDw/Xyy+/rAMHDuj06dPq3r27HnroIZ07dy7P7f/85z+radOmCg4OVuXKlfXll1/qzJkzrvVBQUGqXLlykeuKiIiQt7e3azkyMlInTpwo8nFLKwIoAAAAgHIlJCREcXFxunTpkpKTk+VwuMeiHTt2KC4uTitWrFBaWprOnz+vZs2ayTRN1zY373PzsiRVrFhRly9fdmtLSUlxWz558qSuXr3qWj569Khq1qxZ6Gsr7QigAAAAAMq0tLQ0/fGPf9TXX3+ta9eu6fLly5o7d65CQkLUqFEjhYWF6fvvv3dtn56eLi8vL1WrVk1Op1Ovv/66vvzyy589R7Vq1eRwONyO07JlS/3www/avn27cnJylJCQoLNnz7rtd+nSJT3//PPKzs7W7t279eabbxb4+6OeyMvuAgAAAACULRP/1t/uEtz4+PjoxIkTuv/++5Wamio/Pz+1bt1aH3zwgQIDA/Xss8/qiSee0AsvvKABAwZowYIF6tu3r5o3by5fX18NGTJE7du3/9lz+Pv7a/r06erevbuys7O1aNEiDRw4UAkJCerXr5+cTqeeeOIJNW3a1G2/Zs2aKScnRzVq1FBAQIBefPFFderUqSS7w1aGeeM8cjmVnp6u4OBgXbhwQZUqVbK7nFxeGvWO3SXYprT98Coop9Opc+fOKSQkJM+PYwAlhbEHOzDuYAfGXelw5coVJScnq169evLz87O7nBJnmqZycnLk5eUlwzCKfLxly5Zp3rx5SkpKKnpxFiiO+83fVgAAAACAJQigAAAAAABLEEABAAAAwAbDhw/3mI/fFhcCKAAAAADAEgRQAAAAAEXCc03Lh+K4z7yGBQAAAECheHt7yzAMnT59WtWqVSuWJ8OWZsX9FFxPYpqmTp8+LcMw5O3tXejjEEABAAAAFEqFChVUq1YtHT9+XIcPH7a7nBJnmqacTqccDke5C6CSZBiGatWqpQoVKhT6GARQAAAAAIVWsWJFRUVF6erVq3aXUuKcTqcuXLig4ODgcvn+WW9v7yKFT4kACgAAAKCIKlSoUORg4gmcTqcuX74sPz+/chlAiwO9BgAAAACwBAEUAAAAAGAJAigAAAAAwBIEUAAAAACAJQigAAAAAABLEEABAAAAAJYggAIAAAAALEEABQAAAABYggAKAAAAALAEARQAAAAAYAkCKAAAAADAEgRQAAAAAIAlCKAAAAAAAEsQQAEAAAAAliCAAgAAAAAsQQAFAAAAAFiCAAoAAAAAsAQBFAAAAABgCQIoAAAAAMASBFAAAAAAgCUIoAAAAAAASxBAAQAAAACWsDWAbtu2Tb169VJERIQMw9CaNWvc1pumqbi4OEVERMjf318xMTE6cOCA2zZZWVl6/PHHVbVqVQUGBuqBBx7Q8ePHLbwKAAAAAEBB2BpAL126pBYtWmjBggV5rk9ISNDcuXO1YMEC7d27V+Hh4erSpYsyMjJc28TGxmr16tVauXKlduzYoYsXL6pnz566du2aVZcBAAAAACgALztP3r17d3Xv3j3PdaZpat68eZo6dar69OkjSVq+fLnCwsKUmJio0aNH68KFC3rttdf097//Xffee68k6Y033lDt2rX14Ycfqlu3bpZdCwAAAADg59kaQH9OcnKyUlJS1LVrV1ebr6+vOnbsqJ07d2r06NHat2+frl696rZNRESEmjVrpp07d+YbQLOyspSVleVaTk9PlyQ5nU45nc4SuqIiMOwuwD6l8n4UgNPplGmaHls/PBdjD3Zg3MEOjDvYgXGXP4ejYB+uLbUBNCUlRZIUFhbm1h4WFqYjR464tvHx8VGVKlVybXN9/7zMmjVLM2bMyNWelpamnJycopZe7PxDym8CPXfunN0lFIrT6VRGRoZM0yzwX0agODD2YAfGHezAuIMdGHf5q1q1aoG2K7UB9DrDcA9fpmnmarvZL20zZcoUTZgwwbWcnp6u2rVrq0qVKqpUqVLRCi4BmedMu0uwTUhIiN0lFIrT6ZRhGKpSpQo/nGApxh7swLiDHRh3sAPjruhKbQANDw+X9NMsZ40aNVztqamprlnR8PBwZWdnKy0tzW0WNDU1VdHR0fke29fXV76+vrnaHQ5H6RxI5Td/ls77UUCGYZTeMYUyjbEHOzDuYAfGHezAuCuaUttr9erVU3h4uDZu3Ohqy87O1tatW13hsk2bNvL29nbb5tSpU/ryyy9/NoACAAAAAKxn6wzoxYsX9d1337mWk5OTlZSUpJCQENWpU0exsbGKj49XVFSUoqKiFB8fr4CAAA0cOFCSFBwcrJEjR+qpp55SaGioQkJCNHHiRDVv3tz1VFwAAAAAQOlgawD99NNP1alTJ9fy9e9lDhs2TMuWLdOkSZOUmZmpsWPHKi0tTe3atdOGDRsUFBTk2ufPf/6zvLy81L9/f2VmZqpz585atmyZKlSoYPn1AAAAAADyZ5imWY6/YfiT9PR0BQcH68KFC6XyIUQvjXrH7hJsM/Fv/e0uoVCcTqfOnTunkJAQvh8ASzH2YAfGHezAuIMdGHdFR68BAAAAACxBAAUAAAAAWIIACgAAAACwBAEUAAAAAGAJAigAAAAAwBIEUAAAAACAJQigAAAAAABLEEABAAAAAJYggAIAAAAALEEABQAAAABYggAKAAAAALAEARQAAAAAYAkCKAAAAADAEgRQAAAAAIAlCKAAAAAAAEsQQAEAAAAAliCAAgAAAAAsQQAFAAAAAFiCAAoAAAAAsAQBFAAAAABgCS+7C8Avm9h4ht0l2Ki/3QUAAAAAKCbMgAIAAAAALEEABQAAAABYggAKAAAAALAEARQAAAAAYAkCKAAAAADAEgRQAAAAAIAlCKAAAAAAAEsQQAEAAAAAliCAAgAAAAAs4WV3AUBJemnUO/ac2JD8QwxlnjMl054SJv6tvz0nBgAAAPLBDCgAAAAAwBIEUAAAAACAJQigAAAAAABLEEABAAAAAJYggAIAAAAALEEABQAAAABYggAKAAAAALAEARQAAAAAYAkCKAAAAADAEgRQAAAAAIAlCKAAAAAAAEsQQAEAAAAAliCAAgAAAAAsQQAFAAAAAFiCAAoAAAAAsAQBFAAAAABgCQIoAAAAAMASBFAAAAAAgCUIoAAAAAAAS5TqAJqTk6M//vGPqlevnvz9/VW/fn3NnDlTTqfTtY1pmoqLi1NERIT8/f0VExOjAwcO2Fg1AAAAACAvpTqAzpkzR0uWLNGCBQt08OBBJSQk6E9/+pPmz5/v2iYhIUFz587VggULtHfvXoWHh6tLly7KyMiwsXIAAAAAwM1KdQDdtWuXHnzwQfXo0UN169ZVv3791LVrV3366aeSfpr9nDdvnqZOnao+ffqoWbNmWr58uS5fvqzExESbqwcAAAAA3MjL7gJ+TocOHbRkyRJ98803atCggT7//HPt2LFD8+bNkyQlJycrJSVFXbt2de3j6+urjh07aufOnRo9enSex83KylJWVpZrOT09XZLkdDrdPt5bepTq3xOUrKLeD6N4yij0ee06v1RKxzJKmtPplGma3H9YinEHOzDuYAfGXf4cjoJlllIdQJ955hlduHBBjRo1UoUKFXTt2jW9+OKLGjBggCQpJSVFkhQWFua2X1hYmI4cOZLvcWfNmqUZM2bkak9LS1NOTk4xXkEx8Y+0uwL7nDtXpN39Q2xKgIbkG2T8FEBNe0o4V8S+g2dyOp3KyMiQaZoF/g8BUFSMO9iBcQc7MO7yV7Vq1QJtV6oD6Ntvv6033nhDiYmJatq0qZKSkhQbG6uIiAgNGzbMtZ1huIcM0zRztd1oypQpmjBhgms5PT1dtWvXVpUqVVSpUqXiv5Ciysw/TJd5ISFF2j3znE3p7/+DZ2aaaVsADSli38EzOZ1OGYahKlWq8B9GWIZxBzsw7mAHxl3RleoA+vTTT2vy5Ml6+OGHJUnNmzfXkSNHNGvWLA0bNkzh4eGSfpoJrVGjhmu/1NTUXLOiN/L19ZWvr2+udofDUUoHUjme4i/q/bAp/Lmd36YaSudYhhUMwyjFP89QVjHuYAfGHezAuCuaUt1rly9fznVjK1So4PrMdb169RQeHq6NGze61mdnZ2vr1q2Kjo62tFYAAAAAwM8r1TOgvXr10osvvqg6deqoadOm+uyzzzR37lyNGDFC0k+/fYiNjVV8fLyioqIUFRWl+Ph4BQQEaODAgTZXDwAAAAC4UakOoPPnz9e0adM0duxYpaamKiIiQqNHj9Zzzz3n2mbSpEnKzMzU2LFjlZaWpnbt2mnDhg0KCgqysXIAAAAAwM1KdQANCgrSvHnzXK9dyYthGIqLi1NcXJxldQEAAAAAbl2p/g4oAAAAAKDsIIACAAAAACxBAAUAAAAAWIIACgAAAACwBAEUAAAAAGAJAigAAAAAwBKl+jUsAOzz0qh37C7BNhP/1t/uEgAAAMokZkABAAAAAJYggAIAAAAALEEABQAAAABYggAKAAAAALAEARQAAAAAYAkCKAAAAADAEgRQAAAAAIAlCKAAAAAAAEsQQAEAAAAAliCAAgAAAAAsQQAFAAAAAFiCAAoAAAAAsAQBFAAAAABgCQIoAAAAAMASBFAAAAAAgCUIoAAAAAAASxBAAQAAAACWIIACAAAAACxBAAUAAAAAWKJQAXT58uV6//33XcuTJk1S5cqVFR0drSNHjhRbcQAAAACAssOrMDvFx8dr8eLFkqRdu3ZpwYIFmjdvnt577z09+eSTevfdd4u1SADwJC+Nese+kxuSf4ihzHOmZFp/+ol/62/9SQEAgMcoVAA9duyYbr/9dknSmjVr1K9fP/3+979X+/btFRMTU5z1AQAAAADKiEIF0IoVK+rs2bOqU6eONmzYoCeffFKS5Ofnp8zMzGItECiKiY1n2HJepxw65x+pkOpH5JDTlhokZqIAAABQuhQqgHbp0kWjRo1Sq1at9M0336hHjx6SpAMHDqhu3brFWR8AAAAAoIwo1EOIFi5cqLvvvlunT5/WqlWrFBoaKknat2+fBgwYUKwFAgAAAADKhkLNgFauXFkLFizI1T5jhj0fdwQAAAAAlH6FCqCSdP78ee3Zs0epqalyOv/7HTfDMDRkyJBiKQ4AAAAAUHYUKoCuW7dOgwYN0qVLlxQUFCTDMFzrCKAAAAAAgLwU6jugTz31lEaMGKGMjAydP39eaWlprj/nzp0r7hoBAAAAAGVAoQLoiRMn9MQTTyggIKC46wEAAAAAlFGFCqDdunXTp59+Wty1AAAAAADKsAJ/B3Tt2rWu/9+jRw89/fTT+uqrr9S8eXN5e3u7bfvAAw8UX4UAAAAAgDKhwAG0d+/eudpmzpyZq80wDF27dq1IRQEAAAAAyp4CB9AbX7UCAAAAAMCtKtRrWFasWKHf/e538vX1dWvPzs7WypUrNXTo0GIpDgBQvrw06h27S7DNxL/1t7sEAABKXKEeQvTII4/owoULudozMjL0yCOPFLkoAAAAAEDZU6gAapqmDMPI1X78+HEFBwcXuSgAAAAAQNlzSx/BbdWqlQzDkGEY6ty5s7y8/rv7tWvXlJycrPvuu6/YiwQAAAAAeL5bCqDXn4SblJSkbt26qWLFiq51Pj4+qlu3rvr27VusBQIAAAAAyoZbCqDTp0+XJNWtW1e/+93v5OfnVyJFAQAAAADKnkI9BXfYsGGSfnrqbWpqaq5XtNSpU6folQEAAAAAypRCBdBvv/1WI0aM0M6dO93arz+c6Nq1a8VSHAAAAACg7ChUAB0+fLi8vLz03nvvqUaNGnk+ERcAAAAAgBsVKoAmJSVp3759atSoUXHXAwAAAAAoowoVQJs0aaIzZ84Udy15OnHihJ555hmtX79emZmZatCggV577TW1adNG0k8f+50xY4ZeeeUVpaWlqV27dlq4cKGaNm1qSX0AAJQGL416x76TG5J/iKHMc6ZkWn/6iX/rb/1JAQCF4ijMTnPmzNGkSZO0ZcsWnT17Vunp6W5/iktaWprat28vb29vrV+/Xl999ZVefvllVa5c2bVNQkKC5s6dqwULFmjv3r0KDw9Xly5dlJGRUWx1AAAAAACKrlAzoPfee68kqXPnzm7txf0Qojlz5qh27dpaunSpq61u3bpu55s3b56mTp2qPn36SJKWL1+usLAwJSYmavTo0cVSBwAAAACg6AoVQDdv3lzcdeRp7dq16tatm377299q69atqlmzpsaOHatHH31UkpScnKyUlBR17drVtY+vr686duyonTt35htAs7KylJWV5Vq+PmvrdDpzvVKmdCjURHXZUOT7YU/fOeWQKUNOO+9dUfuuHD9brMg/B+zsO+Om/7WYR/edzTy67zx93MEjOZ1OmabJ/YelGHf5czgK9u/eQgXQjh07Fma3W/bDDz9o8eLFmjBhgp599lnt2bNHTzzxhHx9fTV06FClpKRIksLCwtz2CwsL05EjR/I97qxZszRjxoxc7WlpacrJySneiygO/pF2V2Cfc+eKtr9NfeeUQxk+YTJlyCGbfkAVse+Gt1pRTIV4nnPn7inS/v4hNiYBQ/INMn4KAjZ8F+9cEcedrX1nM4/uOw8fd/BMTqdTGRkZMk2zwP/wBYqKcZe/qlWrFmi7QgVQSTp//rxee+01HTx4UIZhqEmTJhoxYoSCg4MLe8hcnE6n2rZtq/j4eElSq1atdODAAS1evFhDhw51bXfza2CufxQ4P1OmTNGECRNcy+np6apdu7aqVKmiSpUqFVv9xSYz/zBd5oWEFG1/m/rOKYcMmaqSedS+AOqhfVcqFLHvMs/Z8C/w6/4/AGSm2fMwmBBP7jubeXTfefi4g2dyOp0yDENVqlQhCMAyjLuiK1QA/fTTT9WtWzf5+/vrrrvukmmamjt3rl588UVt2LBBrVu3LpbiatSooSZNmri1NW7cWKtWrZIkhYeHS5JSUlJUo0YN1zapqam5ZkVv5OvrK19f31ztDoejlA6kcjzFX+T7YV/fGTLlkNO+AOrBfWe7ovZdachQpmypo8g/Q0tD39mkTPSdp447eCzDMErxv99QVjHuiqZQvfbkk0/qgQce0OHDh/Xuu+9q9erVSk5OVs+ePRUbG1tsxbVv316HDh1ya/vmm28UGfnTxyrr1aun8PBwbdy40bU+OztbW7duVXR0dLHVAQAAAAAoukLPgL766qvy8vrv7l5eXpo0aZLatm1bbMU9+eSTio6OVnx8vPr37689e/bolVde0SuvvCLpp98+xMbGKj4+XlFRUYqKilJ8fLwCAgI0cODAYqsDAACUXba+Q9VmvEMVgNUKFUArVaqko0ePqlGjRm7tx44dU1BQULEUJkl33nmnVq9erSlTpmjmzJmqV6+e5s2bp0GDBrm2mTRpkjIzMzV27FilpaWpXbt22rBhQ7HWAQAAAAAoukIF0N/97ncaOXKkXnrpJUVHR8swDO3YsUNPP/20BgwYUKwF9uzZUz179sx3vWEYiouLU1xcXLGeFwAAAABQvAoVQF966SUZhqGhQ4cqJydHpmnKx8dHjz32mGbPnl3cNQIAAAAAyoBCBVAfHx/9z//8j2bNmqXvv/9epmnq9ttvV0BAQHHXBwAAAAAoI24pgI4YMaJA273++uuFKgYAAAAAUHbdUgBdtmyZIiMj1apVK5lmaXjhGAAAAADAU9xSAB0zZoxWrlypH374QSNGjNDgwYMVEhJSUrUBAACgFLP1FTaG5B9iKPOcKdkwL8IrbIDCcdzKxosWLdKpU6f0zDPPaN26dapdu7b69++v//3f/2VGFAAAAADws24pgEqSr6+vBgwYoI0bN+qrr75S06ZNNXbsWEVGRurixYslUSMAAAAAoAy45QB6I8MwZBiGTNOU0+ksrpoAAAAAAGXQLb+GJSsrS++++65ef/117dixQz179tSCBQt03333yeEoUp4FgDJhYuMZtp3bKYfO+UcqpPoROWTHLwb5ThQAAMjfLQXQsWPHauXKlapTp44eeeQRrVy5UqGhoSVVGwAAAACgDLmlALpkyRLVqVNH9erV09atW7V169Y8t3v33XeLpTgAAACgLLL1CcKlAE8RLr9uKYAOHTpUhmGUVC0AAAAAgDLslgLosmXLSqgMAAAAAEBZx1ODAAAAAACWIIACAAAAACxxy69hAQAAAAC72PoAJ0PyDzGUec6UTHtK8PQHODEDCgAAAACwBDOgAIBSY2LjGXaXYCPP/o02AAAFwQwoAAAAAMASBFAAAAAAgCUIoAAAAAAASxBAAQAAAACWIIACAAAAACxBAAUAAAAAWILXsAAAUAbY+Qobpxw65x+pkOpH5JDThgp4hQ0AeApmQAEAAAAAliCAAgAAAAAsQQAFAAAAAFiCAAoAAAAAsAQPIQIAAOWanQ9wsh8PcAJgLWZAAQAAAACWIIACAAAAACxBAAUAAAAAWIIACgAAAACwBA8hAgAAQKHY+QAnpxw65x+pkOpH5JDThgp4gBNQGMyAAgAAAAAsQQAFAAAAAFiCAAoAAAAAsATfAQUAAAAsZuf3Z0sHvkNbXjEDCgAAAACwBAEUAAAAAGAJPoILAAAAwGOU79f/SJ7+8WVmQAEAAAAAliCAAgAAAAAsQQAFAAAAAFiCAAoAAAAAsAQBFAAAAABgCQIoAAAAAMASBFAAAAAAgCUIoAAAAAAASxBAAQAAAACW8KgAOmvWLBmGodjYWFebaZqKi4tTRESE/P39FRMTowMHDthXJAAAAAAgTx4TQPfu3atXXnlFd9xxh1t7QkKC5s6dqwULFmjv3r0KDw9Xly5dlJGRYVOlAAAAAIC8eNldQEFcvHhRgwYN0quvvqoXXnjB1W6apubNm6epU6eqT58+kqTly5crLCxMiYmJGj16dJ7Hy8rKUlZWlms5PT1dkuR0OuV0OkvwSgrLY35PUPyKfD/s6TunHDJlyGnnvfPQvisVPLjvbB97Htx3tvPgvmPceTAP7jvGnYcrUv+V43EnFcPYKxkOR8H6xCMC6Lhx49SjRw/de++9bgE0OTlZKSkp6tq1q6vN19dXHTt21M6dO/MNoLNmzdKMGTNytaelpSknJ6f4L6Co/CPtrsA+584VbX+b+s4phzJ8wmTKkEM2/ZDw0L4rFTy472wfex7cd7bz4L5j3HkwD+47xp2HK0r/ledxJxV97JWQqlWrFmi7Uh9AV65cqf3792vv3r251qWkpEiSwsLC3NrDwsJ05MiRfI85ZcoUTZgwwbWcnp6u2rVrq0qVKqpUqVIxVV6MMvO/ljIvJKRo+9vUd045ZMhUlcyj9v1w8tC+KxU8uO9sH3se3He28+C+Y9x5MA/uO8adhytK/5XncScVfezZrFQH0GPHjukPf/iDNmzYID8/v3y3MwzDbdk0zVxtN/L19ZWvr2+udofDUeCpY2uVzml2SxT5ftjXd4ZMOeS074eTB/ed7Ty872wdex7ed7by8L5j3HkoD+87xp0HK1L/leNxJxXD2LNXqa5+3759Sk1NVZs2beTl5SUvLy9t3bpVf/nLX+Tl5eWa+bw+E3pdampqrllRAAAAAIC9SnUA7dy5s7744gslJSW5/rRt21aDBg1SUlKS6tevr/DwcG3cuNG1T3Z2trZu3aro6GgbKwcAAAAA3KxUfwQ3KChIzZo1c2sLDAxUaGioqz02Nlbx8fGKiopSVFSU4uPjFRAQoIEDB9pRMgAAAAAgH6U6gBbEpEmTlJmZqbFjxyotLU3t2rXThg0bFBQUZHdpAAAAAIAbeFwA3bJli9uyYRiKi4tTXFycLfUAAAAAAAqmVH8HFAAAAABQdhBAAQAAAACWIIACAAAAACxBAAUAAAAAWIIACgAAAACwBAEUAAAAAGAJAigAAAAAwBIEUAAAAACAJQigAAAAAABLEEABAAAAAJYggAIAAAAALEEABQAAAABYggAKAAAAALAEARQAAAAAYAkCKAAAAADAEgRQAAAAAIAlCKAAAAAAAEsQQAEAAAAAliCAAgAAAAAsQQAFAAAAAFiCAAoAAAAAsAQBFAAAAABgCQIoAAAAAMASBFAAAAAAgCUIoAAAAAAASxBAAQAAAACWIIACAAAAACxBAAUAAAAAWIIACgAAAACwBAEUAAAAAGAJAigAAAAAwBIEUAAAAACAJQigAAAAAABLEEABAAAAAJYggAIAAAAALEEABQAAAABYggA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", 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" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_duration_panel(result, figsize=(11, 5))" + ] + }, + { + "cell_type": "markdown", + "id": "4a5ce5ed", + "metadata": {}, + "source": [ + "### TCI Breakdown" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "789db90e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_tci_breakdown_panel(result, figsize=(11, 5))" + ] + }, + { + "cell_type": "markdown", + "id": "371705e9", + "metadata": {}, + "source": [ + "### Sequential Construction Timeline" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "01187c0f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_timeline_panel(result, figsize=(12, 5.5))" + ] + }, + { + "cell_type": "markdown", + "id": "533ee35f", + "metadata": {}, + "source": [ + "### OCC Components" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6eca03e8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_occ_components_panel(result, figsize=(11, 5))" + ] + }, + { + "cell_type": "markdown", + "id": "3a47a925", + "metadata": {}, + "source": [ + "### OCC Waterfall" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "34fd60b3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_waterfall_panel(result, figsize=(12, 5.5))" + ] + }, + { + "cell_type": "markdown", + "id": "a4a90c03", + "metadata": {}, + "source": [ + "## 5. Result Tables" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b5cc1856", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Plant numberOCCNet OCCTCINCIConstruction durationStartup durationPreconstruction costsDirect costsDirect costs: equipmentDirect costs: materialDirect costs: laborIndirect costsSupplementary costsFinancing costsOCC reduction from FOAK
0115604.817916NaN18840.161605NaN91.00440828.000000124.6717997070.5654323186.789083674.0817293209.6946198332.54181377.0388723235.3436890.000000
128643.324158NaN9858.010846NaN68.29659119.600000124.6717994593.4510552361.570131449.1237991782.7571253879.34881545.8524891214.68668844.611182
236633.895068NaN7385.473722NaN59.88194415.909050124.6717993807.5817012114.478669377.8538981315.2491342664.79100136.850567751.57865457.488161
345287.971674NaN5809.900167NaN55.16257013.720000124.6717993217.8484201887.350092322.6817021007.8166261914.63041030.821044521.92849266.113211
454715.380499NaN5132.423875NaN52.18410712.231700124.6717992972.4091531810.791547299.152588862.4650181590.04362028.255927417.04337769.782534
564536.651717NaN4902.343755NaN50.00828711.136335124.6717992905.3743281804.020646289.794271811.5594121479.15033927.455251365.69203870.927878
674394.898735NaN4721.397260NaN48.23958810.287107124.6717992851.3081801798.326787282.110695770.8706971392.09853626.820220326.49852571.836270
784278.581456NaN4580.795014NaN46.7581179.604000124.6717992806.3142231793.417277275.619744737.2772021321.29629726.299137302.21355972.581664
894180.687869NaN4461.183820NaN45.4891779.039209124.6717992767.9855221789.104221270.018398708.8629031262.16995825.860589280.49595173.208993
9104096.669953NaN4358.974073NaN44.3832808.562190124.6717992734.7388451785.259857265.104351684.3746371211.77510625.484202262.30412073.747403
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" + ], + "text/plain": [ + " Plant number OCC Net OCC TCI NCI Construction duration Startup duration Preconstruction costs Direct costs \\\n", + "0 1 15604.817916 NaN 18840.161605 NaN 91.004408 28.000000 124.671799 7070.565432 \n", + "1 2 8643.324158 NaN 9858.010846 NaN 68.296591 19.600000 124.671799 4593.451055 \n", + "2 3 6633.895068 NaN 7385.473722 NaN 59.881944 15.909050 124.671799 3807.581701 \n", + "3 4 5287.971674 NaN 5809.900167 NaN 55.162570 13.720000 124.671799 3217.848420 \n", + "4 5 4715.380499 NaN 5132.423875 NaN 52.184107 12.231700 124.671799 2972.409153 \n", + "5 6 4536.651717 NaN 4902.343755 NaN 50.008287 11.136335 124.671799 2905.374328 \n", + "6 7 4394.898735 NaN 4721.397260 NaN 48.239588 10.287107 124.671799 2851.308180 \n", + "7 8 4278.581456 NaN 4580.795014 NaN 46.758117 9.604000 124.671799 2806.314223 \n", + "8 9 4180.687869 NaN 4461.183820 NaN 45.489177 9.039209 124.671799 2767.985522 \n", + "9 10 4096.669953 NaN 4358.974073 NaN 44.383280 8.562190 124.671799 2734.738845 \n", + "\n", + " Direct costs: equipment Direct costs: material Direct costs: labor Indirect costs Supplementary costs Financing costs OCC reduction from FOAK \n", + "0 3186.789083 674.081729 3209.694619 8332.541813 77.038872 3235.343689 0.000000 \n", + "1 2361.570131 449.123799 1782.757125 3879.348815 45.852489 1214.686688 44.611182 \n", + "2 2114.478669 377.853898 1315.249134 2664.791001 36.850567 751.578654 57.488161 \n", + "3 1887.350092 322.681702 1007.816626 1914.630410 30.821044 521.928492 66.113211 \n", + "4 1810.791547 299.152588 862.465018 1590.043620 28.255927 417.043377 69.782534 \n", + "5 1804.020646 289.794271 811.559412 1479.150339 27.455251 365.692038 70.927878 \n", + "6 1798.326787 282.110695 770.870697 1392.098536 26.820220 326.498525 71.836270 \n", + "7 1793.417277 275.619744 737.277202 1321.296297 26.299137 302.213559 72.581664 \n", + "8 1789.104221 270.018398 708.862903 1262.169958 25.860589 280.495951 73.208993 \n", + "9 1785.259857 265.104351 684.374637 1211.775106 25.484202 262.304120 73.747403 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unit_results = results_to_dataframe(result)\n", + "unit_results.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "999321f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NInterest RateDesign CompletionDesign Maturity (0-2)Supplychain Proficiency (0-2)A/E Proficiency (0-2)Construction Proficiency (0-2)Cross Site StandardizationModular Civil ConstructionCommercial BOPNon-Safety-Related RBITC Amount
016%70%10.500.500.50FALSEFALSEFALSE0%
126%100%21.171.000.9080%FALSEFALSEFALSE0%
236%100%21.831.501.3080%FALSEFALSEFALSE0%
346%100%22.002.001.7080%FALSEFALSEFALSE0%
456%100%22.002.002.0080%FALSEFALSEFALSE0%
566%100%22.002.002.0080%FALSEFALSEFALSE0%
676%100%22.002.002.0080%FALSEFALSEFALSE0%
786%100%22.002.002.0080%FALSEFALSEFALSE0%
896%100%22.002.002.0080%FALSEFALSEFALSE0%
9106%100%22.002.002.0080%FALSEFALSEFALSE0%
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" + ], + "text/plain": [ + " N Interest Rate Design Completion Design Maturity (0-2) Supplychain Proficiency (0-2) A/E Proficiency (0-2) Construction Proficiency (0-2) \\\n", + "0 1 6% 70% 1 0.50 0.50 0.50 \n", + "1 2 6% 100% 2 1.17 1.00 0.90 \n", + "2 3 6% 100% 2 1.83 1.50 1.30 \n", + "3 4 6% 100% 2 2.00 2.00 1.70 \n", + "4 5 6% 100% 2 2.00 2.00 2.00 \n", + "5 6 6% 100% 2 2.00 2.00 2.00 \n", + "6 7 6% 100% 2 2.00 2.00 2.00 \n", + "7 8 6% 100% 2 2.00 2.00 2.00 \n", + "8 9 6% 100% 2 2.00 2.00 2.00 \n", + "9 10 6% 100% 2 2.00 2.00 2.00 \n", + "\n", + " Cross Site Standardization Modular Civil Construction Commercial BOP Non-Safety-Related RB ITC Amount \n", + "0 FALSE FALSE FALSE 0% \n", + "1 80% FALSE FALSE FALSE 0% \n", + "2 80% FALSE FALSE FALSE 0% \n", + "3 80% FALSE FALSE FALSE 0% \n", + "4 80% FALSE FALSE FALSE 0% \n", + "5 80% FALSE FALSE FALSE 0% \n", + "6 80% FALSE FALSE FALSE 0% \n", + "7 80% FALSE FALSE FALSE 0% \n", + "8 80% FALSE FALSE FALSE 0% \n", + "9 80% FALSE FALSE FALSE 0% " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "levers_to_dataframe(result).head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "92371776", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelabsolute_changecumulative_occkind
0FOAK \\n(no firm orders)15604.81791615604.817916total
1Bulk-ordering0.00000015604.817916change
2Elimination of rework-6960.7967768644.021140change
3Supplychain efficiency-33.5835738610.437568change
4Labor productivity-1848.7414286761.696140change
5Experience and cross-site standardization-2483.1146844278.581456change
6Modular Construction0.0000004278.581456change
7Commercial BOP0.0000004278.581456change
8Non safety-related Reactor Building0.0000004278.581456change
9NOAK \\n(firm orders)4278.5814564278.581456total
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" + ], + "text/plain": [ + " label absolute_change cumulative_occ kind\n", + "0 FOAK \\n(no firm orders) 15604.817916 15604.817916 total\n", + "1 Bulk-ordering 0.000000 15604.817916 change\n", + "2 Elimination of rework -6960.796776 8644.021140 change\n", + "3 Supplychain efficiency -33.583573 8610.437568 change\n", + "4 Labor productivity -1848.741428 6761.696140 change\n", + "5 Experience and cross-site standardization -2483.114684 4278.581456 change\n", + "6 Modular Construction 0.000000 4278.581456 change\n", + "7 Commercial BOP 0.000000 4278.581456 change\n", + "8 Non safety-related Reactor Building 0.000000 4278.581456 change\n", + "9 NOAK \\n(firm orders) 4278.581456 4278.581456 total" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waterfall_to_dataframe(result)" + ] + }, + { + "cell_type": "markdown", + "id": "0620ba02", + "metadata": {}, + "source": [ + "## 6. Compare Reactor Examples" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2b39d195", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ReactorFirm ordersNOAK plantFOAK OCC ($/kWe)NOAK OCC ($/kWe)OCC reduction (%)Average OCC ($/kWe)Average TCI ($/kWe)Cumulative timeline (months)
0AP100010815604.824278.5872.586237.297005.07190.56
1HTGR13812765.153898.8169.464953.456378.62313.38
2SFR13812269.264388.4064.235298.326404.42188.45
\n", + "
" + ], + "text/plain": [ + " Reactor Firm orders NOAK plant FOAK OCC ($/kWe) NOAK OCC ($/kWe) OCC reduction (%) Average OCC ($/kWe) Average TCI ($/kWe) \\\n", + "0 AP1000 10 8 15604.82 4278.58 72.58 6237.29 7005.07 \n", + "1 HTGR 13 8 12765.15 3898.81 69.46 4953.45 6378.62 \n", + "2 SFR 13 8 12269.26 4388.40 64.23 5298.32 6404.42 \n", + "\n", + " Cumulative timeline (months) \n", + "0 190.56 \n", + "1 313.38 \n", + "2 188.45 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summary_rows = []\n", + "for reactor_type in [\"AP1000\", \"HTGR\", \"SFR\"]:\n", + " scenario = run_one_scenario(base_config(reactor_type), base_levers(reactor_type))\n", + " summary_rows.append(\n", + " {\n", + " \"Reactor\": reactor_type,\n", + " \"Firm orders\": scenario[\"Num_orders\"],\n", + " \"NOAK plant\": scenario[\"num_NOAK\"],\n", + " \"FOAK OCC ($/kWe)\": scenario[\"OCC_1\"],\n", + " \"NOAK OCC ($/kWe)\": scenario[f\"OCC_{scenario['num_NOAK']}\"],\n", + " \"OCC reduction (%)\": occ_reduction_from_foak_to_noak(scenario),\n", + " \"Average OCC ($/kWe)\": scenario[\"avg_OCC\"],\n", + " \"Average TCI ($/kWe)\": scenario[\"avg_TCI\"],\n", + " \"Cumulative timeline (months)\": scenario[\"cons_duration_cumulative_wz_startup\"],\n", + " }\n", + " )\n", + "\n", + "pd.DataFrame(summary_rows).round(2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorial/figure/ANL.png b/tutorial/figure/ANL.png new file mode 100644 index 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Cost Reduction Framework for Nuclear Reactor Power Plants
\n" - ] - }, - { - "cell_type": "markdown", - "id": "f2256ae0-712f-4811-99d9-d37188782036", - "metadata": {}, - "source": [ - "### Importing the libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "12fb6329-dc95-493f-ba1d-86235e71b512", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from src import prettify, update_high_level_costs, ITC_reduction_factor\n", - "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore', category=FutureWarning)\n", - "\n", - "pd.set_option('display.max_rows', None)" - ] - }, - { - "cell_type": "markdown", - "id": "da06d9ed-4c50-4ae1-8096-456598398ad9", - "metadata": {}, - "source": [ - "## Section 1 : Reading the Baseline reactor Cost Summary Table " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "17e1fd7e-f98c-4343-9a46-4d2cf63044d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - 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The Concept B 310.8 MWe Reactor
FOAK Capital Cost Summary - Baseline hypothetical well-executeed project

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
10Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 244,678,343$ 64,350,8012,899,407 hrs$ 145,691,462$ 34,636,081
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 9,518,418$ 620,964102,399 hrs$ 5,280,509$ 3,616,945
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 661,334,797$ 559,862,8441,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 238,844,021$ 189,802,947874,096 hrs$ 46,348,086$ 2,692,988
232.1Electricity Generation Systems$ 197,558,121$ 167,738,159559,709 hrs$ 29,819,961$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,517,766,756
20s - $/kWe$ 4,883
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 146,051,997
32Factory and construction supervision$ 506,256,125
33Start-Up Costs$ 21,298,226
34Shipping & Transportation Costs$ 1,793,334
35Engineering Services$ 136,778,270
30s - Subtotal$ 812,177,951
30s - $/kWe$ 2,613
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 10,484,751
54Decommissioning $ 14,606,673
50s - Subtotal$ 28,602,027
50s - $/kWe$ 92
60Capitalized Financial Costs
62Interest $ 592,300,800
-60s - Subtotal$ 592,300,800
60s - $/kWe$ 1,906
Total Direct Capital Cost (Accounts 10 to 20)$ 1,596,758,321
(Accounts 10 to 20) US$/kWe$ 5,138
Base Construction Cost (Accounts 10 to 30)$ 2,408,936,272
(Accounts 10 to 30) US$/kWe$ 7,751
Total Overnight Cost (Accounts 10 to 50)$ 2,437,538,300
(Accounts 10 to 50) US$/kWe$ 7,843
Total Capital Investment Cost (All Accounts)$ 3,029,839,099
(Accounts 10 to 60) US$/kWe$ 9,749
Total Overnight Cost - ITC reduced$ 2,437,538,300
Total Overnight Cost -ITC reduced (US$/kWe)$ 7,843
Total Capital Investment Cost - ITC reduced$ 3,029,839,099
Total Capital Investment Cost - ITC reduced (US$/kWe)$ 9,749
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# A function to read the inital reactor data from excel\n", - "\n", - "def reactor_data_read(reactor_type):\n", - " \n", - " if reactor_type == \"Concept A\":\n", - " # Reading excel or csv files\n", - " Reactor_data_0 = pd.read_excel('inputs.xlsx', sheet_name = \"Concept_A\", nrows= 69)\n", - " reactor_power = 1056 * 1000 # kw\n", - " \n", - " \n", - " elif reactor_type == \"Concept B\":\n", - " # Reading excel or csv files\n", - " Reactor_data_0 = pd.read_excel('inputs.xlsx', sheet_name = \"Concept_B\", nrows= 69)\n", - " reactor_power = 310.8 * 1000 # kw \n", - " \n", - " db = pd.DataFrame()\n", - " db = Reactor_data_0[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " Reactor_data = db\n", - " return Reactor_data, reactor_power\n", - "\n", - "\n", - "# Example\n", - "reactor_type = \"Concept B\"\n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "Reactor_data_pretty = prettify(reactor_data,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor
FOAK Capital Cost Summary - Baseline hypothetical well-executeed project

\", \"no_subsidies\")\n", - "Reactor_data_pretty" - ] - }, - { - "cell_type": "markdown", - "id": "efab96cf-f6fd-45f1-8633-5197b19458cb", - "metadata": {}, - "source": [ - "## Section - 2 : User Inputs" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "21d9d866-0066-4dd7-a5b6-f4d2ddd6e723", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
User-Input Global levers
(highlighted in yellow if different from the baseline)
LeverUser-Input ValueLever baseline value (for a hopothetical well-executed project)Range
Design Maturity120 - 2
Design Completion0.80000010 - 1
Procurement (supply chain) experience 0.50000020 - 2
Architecture & Engineering Experience0.50000020 - 2
Construction service experience120 - 2
Land Cost Per Acre (2023 USD)22000220001000 - 100000
ITC 000 - 0.4
Interest Rate0.0600000.0600000 - 0.15
BOP grade non_nuclearnuclearnuclear or non-nuclear
Reactor Building gradenuclearnuclearnuclear or non-nuclear
modulariziationmodularizedmodularizedstick_built or modularized
standardization0.8000000.7000000.7 : 1
Startup duration (months)16163 : 24
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## ## User-defined Independent Variables (Global Levers)\n", - "\n", - "# reactor type: Concept A or Concept B\n", - "reactor_type = \"Concept B\"\n", - "\n", - "# Which reactor unit: first of a kind : nth of a kind \n", - "n_th = 1\n", - "\n", - "# number of firm orders\n", - "num_orders = 13\n", - "\n", - "# land cost\n", - "# From the SA report: the cost $22,000 per acre. The land area is 500 acres including recommended buffer\n", - "land_cost_per_acre_0 = 22000 # dollars/acre\n", - "\n", - "# start up duration (months) \n", - "startup_0 = 16 \n", - "\n", - "# interest rate :\n", - "interest_rate_0 = 0.06\n", - "\n", - "# Design completion\n", - "design_completion_0 = 0.8 # 1 means 100%\n", - "\n", - "# Design maturity\n", - "Design_Maturity_0 = 1\n", - "\n", - "# #procurement service experience (supply chain experience)\n", - "proc_exp_0= 0.5 # 2 means procurement experts. This is ideal. \n", - " \n", - "# # architecture and engineeringexperience\n", - "ae_exp_0 = 0.5\n", - " \n", - "# # Construction service experience\n", - "ce_exp_0 = 1\n", - "\n", - "\n", - "# numb er of projects for full efficiency of procurement, A/E, Construction\n", - "N_proc = 3\n", - "N_AE = 4\n", - "N_cons =5\n", - "\n", - "# modularity : \"stick_built\" or \"modularized\"\n", - "mod_0 = \"modularized\" \n", - "\n", - "# cross_site_standardization :\n", - "standardization_0 = 0.8 # 0.7 corresponds to 70% standardization for PWRs\n", - "\n", - "# # Determining if the BOP and reactor building (containtment) are non-nuclear or nuclear grade equipment (safety related)\n", - "BOP_grade_0 = \"non_nuclear\"\n", - "RB_grade_0 = \"nuclear\"\n", - "\n", - "# #investment tax credits subsidies\n", - "ITC_0 = 0 \n", - "\n", - "#number of reactors claiming ITC\n", - "n_ITC = 3 \n", - "\n", - "# factory building cost (associated with accounts 22 and 232.1)\n", - "f_22 = 250000000\n", - "f_2321 = 150000000\n", - "\n", - "\n", - " \n", - "global_levers = pd.DataFrame()\n", - "global_levers.loc[:, 'Lever'] = ['Design Maturity' ,'Design Completion',\\\n", - " 'Procurement (supply chain) experience ','Architecture & Engineering Experience',\\\n", - " 'Construction service experience',' Land Cost Per Acre (2023 USD)',\\\n", - " 'ITC ', ' Interest Rate', 'BOP grade ', 'Reactor Building grade', 'modulariziation',\\\n", - " 'standardization' , \"Startup duration (months)\"]\n", - "\n", - "global_levers.loc[:, 'User-Input Value'] = [ Design_Maturity_0 ,design_completion_0, proc_exp_0, ae_exp_0, ce_exp_0, \n", - " land_cost_per_acre_0, ITC_0, interest_rate_0, BOP_grade_0 , RB_grade_0 , mod_0,\\\n", - " standardization_0, startup_0] \n", - "\n", - "global_levers.loc[:, 'Lever baseline value (for a hopothetical well-executed project)'] = [ 2, 1, 2, 2, 2, 22000, 0, 0.06 ,\\\n", - " 'nuclear', 'nuclear', \"modularized\", 0.7 , 16 ]\n", - "\n", - "global_levers.loc[:, 'Range'] = ['0 - 2' ,'0 - 1', '0 - 2', '0 - 2','0 - 2' , '1000 - 100000', '0 - 0.4', '0 - 0.15',\\\n", - " \"nuclear or non-nuclear\", \"nuclear or non-nuclear\", 'stick_built or modularized', '0.7 : 1','3 : 24']\n", - "\n", - "\n", - "\n", - "global_levers_changes = global_levers[global_levers['User-Input Value'] != global_levers['Lever baseline value (for a hopothetical well-executed project)']]\n", - "\n", - "slice_ = pd.IndexSlice[global_levers_changes .index, global_levers_changes .columns]\n", - "global_levers_styled = (global_levers.style.set_properties(**{'background-color': 'yellow'}, subset=slice_ )\\\n", - " .set_caption(\"User-Input Global levers
(highlighted in yellow if different from the baseline)
\")\\\n", - " .set_table_styles([{\n", - " 'selector': 'caption',\n", - " 'props': [\n", - " ('color', 'blue'),\n", - " ('font-size', '20px')\n", - " ]\n", - "}]))\n", - "\n", - "\n", - "global_levers_styled.hide()\n" - ] - }, - { - "cell_type": "markdown", - "id": "782a06e3-2054-4b8b-ab95-3e0c35e54e2c", - "metadata": {}, - "source": [ - "### The Cost reduction framework: levers and variables impact the costs as shown in the figure (below)\n", - "\n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "7dd52d61-c62a-46d8-8a35-675bc84f181f", - "metadata": {}, - "source": [ - "## Section - 3 : Updating the Cost Summary based on user inputs" - ] - }, - { - "cell_type": "markdown", - "id": "4693db39-1915-495f-9262-0f51df3d3fbb", - "metadata": {}, - "source": [ - "### Section - 3-0 : Adding the factory cost to accounts 22 and 232.1" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "0d3a5a4a-5cde-4797-89c6-f35051ef32a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
The Concept B 310.8 MWe Reactor
Capital Cost Summary - Factories Cost Included
Displaying Direct Cost only

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 244,678,343$ 64,350,8012,899,407 hrs$ 145,691,462$ 34,636,081
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 9,518,418$ 620,964102,399 hrs$ 5,280,509$ 3,616,945
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 680,565,566$ 579,093,6131,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 250,382,483$ 201,341,409874,096 hrs$ 46,348,086$ 2,692,988
232.1Electricity Generation Systems$ 209,096,582$ 179,276,621559,709 hrs$ 29,819,961$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,548,535,987
20s - $/kWe$ 4,982
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def add_factory_cost(Reactor_data_0, power, f_22, f_2321):\n", - " db = pd.DataFrame()\n", - " db = Reactor_data_0[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - "\n", - " db.loc[db.Account == 22, 'Factory Equipment Cost'] = None # clear old values\n", - " db.loc[db.Account == '232.1', 'Factory Equipment Cost'] = None # clear old values\n", - "\n", - " \n", - " db.loc[db.Account == 22, 'Factory Equipment Cost'] = ((Reactor_data_0.loc[Reactor_data_0.Account == 22, 'Factory Equipment Cost']) + f_22/num_orders )\n", - " db.loc[db.Account == '232.1', 'Factory Equipment Cost'] = ((Reactor_data_0.loc[Reactor_data_0.Account == '232.1', 'Factory Equipment Cost']) + f_2321/num_orders)\n", - "\n", - "\n", - " Reactor_data_fac = update_high_level_costs(db, power)\n", - "\n", - "\n", - " Reactor_data_fac_ = pd.DataFrame()\n", - " Reactor_data_fac_ = Reactor_data_fac[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " \n", - " return Reactor_data_fac_ \n", - "\n", - "\n", - "\n", - "# Example \n", - "\n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "\n", - "Reactor_data_factory = add_factory_cost(reactor_data , reactor_power, f_22, f_2321)\n", - "Reactor_data_factory_pretty = prettify(Reactor_data_factory,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor
Capital Cost Summary - Factories Cost Included
Displaying Direct Cost only

\", \"no_subsidies\")\n", - "\n", - "# list of rows to hide (not affected by this cost change)\n", - "list1 = list(range(0, 11))\n", - "list2 = list(range(29, 69))\n", - "hidden_list1 = list1 + list2\n", - "\n", - "Reactor_data_factory_pretty.hide(subset=hidden_list1, axis=0) # show direct cost only" - ] - }, - { - "cell_type": "markdown", - "id": "ff67d894-f8f0-435c-ac3b-3c4b93fb6c56", - "metadata": {}, - "source": [ - "### Section 3-1 : The land cost & Taxes" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e0bc9679-bcb5-4294-acfd-8c5087b71e58", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
The Concept B 310.8 MWe Reactor
Capital Cost Summary - Factories Cost, land cost and taxes Included

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
10Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 244,678,343$ 64,350,8012,899,407 hrs$ 145,691,462$ 34,636,081
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 9,518,418$ 620,964102,399 hrs$ 5,280,509$ 3,616,945
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 680,565,566$ 579,093,6131,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 250,382,483$ 201,341,409874,096 hrs$ 46,348,086$ 2,692,988
232.1Electricity Generation Systems$ 209,096,582$ 179,276,621559,709 hrs$ 29,819,961$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,548,535,987
20s - $/kWe$ 4,982
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 146,051,997
32Factory and construction supervision$ 506,256,125
33Start-Up Costs$ 21,298,226
34Shipping & Transportation Costs$ 1,793,334
35Engineering Services$ 136,778,270
30s - Subtotal$ 812,177,951
30s - $/kWe$ 2,613
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 10,484,751
54Decommissioning $ 14,606,673
50s - Subtotal$ 28,602,027
50s - $/kWe$ 92
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The cost of the land is 22000$ per acre (500 acres\n", - "# The cost is multiplied by the new $/acre divided by the old one\n", - "# Accounts 11 and 12 are changed\n", - "\n", - "def add_land_cost(Reactor_data_fac, land_cost_per_acre, power):\n", - "\n", - " db = pd.DataFrame()\n", - " db = Reactor_data_fac[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - "\n", - " \n", - " db.loc[db.Account == 11, 'Total Cost (USD)'] = None # clear old values\n", - " db.loc[db.Account == 12, 'Total Cost (USD)'] = None # clear old values\n", - " db.loc[db.Account == 51, 'Total Cost (USD)'] = None # clear old values\n", - " \n", - "\n", - " db.loc[db.Account == 11, 'Total Cost (USD)'] = (land_cost_per_acre /22000)*(Reactor_data_fac.loc[Reactor_data_fac.Account == 11, 'Total Cost (USD)'].values)\n", - " db.loc[db.Account == 12, 'Total Cost (USD)'] = (land_cost_per_acre /22000)*(Reactor_data_fac.loc[Reactor_data_fac.Account == 12, 'Total Cost (USD)'].values)\n", - "\n", - "\n", - " # The taxes scale with increasing the land cost \n", - " db.loc[db.Account == 51, 'Total Cost (USD)'] = (land_cost_per_acre /22000)*(Reactor_data_fac.loc[Reactor_data_fac.Account == 51, 'Total Cost (USD)'].values)\n", - "\n", - " db.loc[db.Account == 51, 'Total Cost (USD)']\n", - " Reactor_data_updated_1 = update_high_level_costs(db, power)\n", - "\n", - "\n", - " Reactor_data_updated_1_ = pd.DataFrame()\n", - " Reactor_data_updated_1_ = Reactor_data_updated_1[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - "\n", - " return Reactor_data_updated_1_\n", - " \n", - "\n", - "# Example \n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "\n", - "Reactor_data_factory = add_factory_cost(reactor_data , reactor_power, f_22, f_2321)\n", - "\n", - "Reactor_data_factory_land_taxes = add_land_cost(Reactor_data_factory , land_cost_per_acre_0, reactor_power)\n", - "Reactor_data_factory_land_taxes_pretty = prettify(Reactor_data_factory_land_taxes,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor
Capital Cost Summary - Factories Cost, land cost and taxes Included

\", \"no_subsidies\")\n", - "\n", - "# list of rows to hide (not affected by this cost change)\n", - "hidden_list2 = list(range(46, 69))\n", - "\n", - "Reactor_data_factory_land_taxes_pretty.hide(subset=hidden_list2, axis=0) # show direct cost only" - ] - }, - { - "cell_type": "markdown", - "id": "905d5806-7eeb-42c8-afa3-dc7d6f57bd3b", - "metadata": {}, - "source": [ - "### Section 3-2 : Whether the Reactor Building and BOP are nuclear grade equipment" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "51d27779-5c59-424c-9368-0d456dbcac09", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
The Concept B 310.8 MWe Reactor
Capital Cost Summary - Factories Cost, land cost, taxes and BOP/RP grades Included
Displaying Direct Cost only

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 240,870,976$ 64,102,4152,858,447 hrs$ 143,579,258$ 33,189,303
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 5,711,051$ 372,57861,440 hrs$ 3,168,305$ 2,170,167
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 680,565,566$ 579,093,6131,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 166,743,850$ 129,630,761650,212 hrs$ 34,420,102$ 2,692,988
232.1Electricity Generation Systems$ 125,457,949$ 107,565,972335,825 hrs$ 17,891,977$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,461,089,987
20s - $/kWe$ 4,701
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# if the BOP is non-nuclear, the cost reduction factor for account 213 is 0.6\n", - "# Also, the cost reduction factor for account 232.1 is 0.6 (for factory and labor but not material)\n", - "\n", - "# If the reactor building is non nuclear, the cost reduction factor for acount 212 is 0.6\n", - "\n", - "def add_BOP_RP_grades(Reactor_data_updated_1, RB_grade_0, BOP_grade_0, power):\n", - " \n", - " RB_grade = RB_grade_0 # Does not change when building more units\n", - " BOP_grade = BOP_grade_0 # Does not change when building more units\n", - " \n", - " db = pd.DataFrame()\n", - "\n", - " db = Reactor_data_updated_1[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - "\n", - " db.loc[db.Account == 212, 'Site Material Cost'] = None # clear old values\n", - " db.loc[db.Account == 212, 'Site Labor Cost'] = None # clear old values\n", - " db.loc[db.Account == 212, 'Site Labor Hours'] = None\n", - " db.loc[db.Account == 212, 'Factory Equipment Cost'] = None\n", - "\n", - " db.loc[db.Account == 213, 'Site Material Cost'] = None\n", - " db.loc[db.Account == 213, 'Site Labor Cost'] = None\n", - " db.loc[db.Account == 213, 'Site Labor Hours'] = None\n", - " db.loc[db.Account == 213, 'Factory Equipment Cost'] = None\n", - "\n", - " db.loc[db.Account == '232.1', 'Factory Equipment Cost'] = None\n", - " db.loc[db.Account == '232.1', 'Site Labor Cost'] = None\n", - " db.loc[db.Account == '232.1', 'Site Labor Hours'] = None\n", - "\n", - "\n", - " if RB_grade == \"non_nuclear\":\n", - " db.loc[db.Account == 212, 'Site Material Cost'] = 0.6*((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 212, 'Site Material Cost']).values)\n", - " db.loc[db.Account == 212, 'Site Labor Cost'] = 0.6*((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 212, 'Site Labor Cost']).values)\n", - " db.loc[db.Account == 212,'Site Labor Hours'] = 0.6*((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 212, 'Site Labor Hours']).values)\n", - " db.loc[db.Account == 212,'Factory Equipment Cost'] = 0.6*((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 212, 'Factory Equipment Cost']).values)\n", - "\n", - " else:\n", - " db.loc[db.Account == 212, 'Site Material Cost'] = ((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 212, 'Site Material Cost']).values)\n", - " db.loc[db.Account == 212, 'Site Labor Cost'] = ((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 212, 'Site Labor Cost']).values)\n", - " db.loc[db.Account == 212,'Site Labor Hours'] = ((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 212, 'Site Labor Hours']).values)\n", - " db.loc[db.Account == 212,'Factory Equipment Cost'] = ((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 212, 'Factory Equipment Cost']).values)\n", - "\n", - " \n", - " if BOP_grade == \"non_nuclear\":\n", - " db.loc[db.Account == 213, 'Site Material Cost'] = 0.6*((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 213, 'Site Material Cost']).values)\n", - " db.loc[db.Account == 213, 'Site Labor Cost'] = 0.6*((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 213, 'Site Labor Cost']).values)\n", - " db.loc[db.Account == 213, 'Site Labor Hours'] = 0.6*((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 213, 'Site Labor Hours']).values)\n", - " db.loc[db.Account == 213, 'Factory Equipment Cost'] = 0.6*((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 213, 'Factory Equipment Cost']).values)\n", - "\n", - " db.loc[db.Account == '232.1', 'Factory Equipment Cost'] = 0.6*((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == '232.1', 'Factory Equipment Cost']).values)\n", - " db.loc[db.Account == '232.1', 'Site Labor Hours'] = 0.6*((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == '232.1', 'Site Labor Hours']).values)\n", - " db.loc[db.Account == '232.1', 'Site Labor Cost'] = 0.6*((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == '232.1', 'Site Labor Cost']).values)\n", - "\n", - "\n", - " else:\n", - " db.loc[db.Account == 213, 'Site Material Cost'] = ((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 213, 'Site Material Cost']).values)\n", - " db.loc[db.Account == 213, 'Site Labor Cost'] = ((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 213, 'Site Labor Cost']).values)\n", - " db.loc[db.Account == 213, 'Site Labor Hours'] = ((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 213, 'Site Labor Hours']).values)\n", - " db.loc[db.Account == 213, 'Factory Equipment Cost'] = ((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == 213, 'Factory Equipment Cost']).values)\n", - "\n", - " db.loc[db.Account == '232.1', 'Factory Equipment Cost'] = ((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == '232.1', 'Factory Equipment Cost']).values)\n", - " db.loc[db.Account == '232.1', 'Site Labor Hours'] = ((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == '232.1', 'Site Labor Hours']).values)\n", - " db.loc[db.Account == '232.1', 'Site Labor Cost'] = ((Reactor_data_updated_1.loc[Reactor_data_updated_1.Account == '232.1', 'Site Labor Cost']).values)\n", - "\n", - "\n", - " Reactor_data_updated_2 = update_high_level_costs(db, power)\n", - "\n", - " Reactor_data_updated_2_ = pd.DataFrame()\n", - " Reactor_data_updated_2_ = Reactor_data_updated_2[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " \n", - " return Reactor_data_updated_2_ \n", - "\n", - "\n", - "# # Example \n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "\n", - "Reactor_data_factory = add_factory_cost(reactor_data , reactor_power, f_22, f_2321)\n", - "\n", - "Reactor_data_factory_land_taxes = add_land_cost(Reactor_data_factory , land_cost_per_acre_0, reactor_power)\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades = add_BOP_RP_grades(Reactor_data_factory_land_taxes , RB_grade_0, BOP_grade_0, reactor_power)\n", - "\n", - "\n", - "\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades_pretty = prettify(Reactor_data_factory_land_taxes_BOP_RP_grades ,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor
Capital Cost Summary - Factories Cost, land cost, taxes and BOP/RP grades Included
Displaying Direct Cost only

\", \"no_subsidies\")\n", - "\n", - "\n", - "\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades_pretty.hide(subset=hidden_list1, axis=0) # show direct cost only \n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "186e41d6-f0fb-4ed5-a16e-178b28b49f51", - "metadata": {}, - "source": [ - "### Section 3-3 : Bulk Ordering" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "139a2b31-dcaa-4e52-adea-b8bc7e682519", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
The Concept B 310.8 MWe Reactor
Capital Cost Summary - Factories Cost, land cost, taxes, BOP/RP grades , bulk ordering Included
Displaying Direct Cost only

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 240,870,976$ 64,102,4152,858,447 hrs$ 143,579,258$ 33,189,303
212Reactor Containment Building$ 145,108,928$ 57,583,6051,605,670 hrs$ 81,082,169$ 6,443,153
213Turbine Room and Heater Bay$ 5,711,051$ 372,57861,440 hrs$ 3,168,305$ 2,170,167
211 plus 214 to 219Othe Structures & Improvements$ 90,050,998$ 6,146,2321,191,337 hrs$ 59,328,783$ 24,575,983
22Reactor System$ 469,343,190$ 367,871,2371,809,704 hrs$ 97,213,739$ 4,258,215
23Energy Conversion System$ 118,425,483$ 81,312,394650,212 hrs$ 34,420,102$ 2,692,988
232.1Electricity Generation Systems$ 77,139,583$ 59,247,606335,825 hrs$ 17,891,977$ 0
233Ultimate Heat Sink$ 41,285,901$ 22,064,788314,387 hrs$ 16,528,125$ 2,692,988
24Electrical Equipment$ 67,451,645$ 13,662,941767,077 hrs$ 41,345,409$ 12,443,295
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 25,655,316$ 8,372,438278,805 hrs$ 15,110,212$ 2,172,665
28Simulator$ 78,200
20s - Subtotal$ 1,201,549,244
20s - $/kWe$ 3,866
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#The factory equipment cost of accounts 22 and 232.1 has to be divided by the number of orders\n", - "# The factory equipment cost of account 22 is multiplied by a reuction factor\n", - "# The factory equipment cost of account 232.1 is multiplied by a reduction factor\n", - "\n", - "\n", - "def add_bulk_ordering(Reactor_data_updated_3, num_orders, f_22, f_2321, power):\n", - " db = pd.DataFrame()\n", - "\n", - " db = Reactor_data_updated_3[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - "\n", - " # esimtated learning rates\n", - " lr22 = 0.1802341659291420\n", - " lr2321 = 0.2607462372040820\n", - "\n", - " # These reduction factor are calculated as follows\n", - " reduction_factor_22 = 0 # initialization\n", - " reduction_factor_2321 = 0\n", - "\n", - " for ith_unit in range(1,num_orders+1):\n", - " reduction_factor_22+=((1 - lr22)**np.log2(ith_unit))/num_orders # for account 22\n", - " reduction_factor_2321+=((1 - lr2321)**np.log2(ith_unit))/num_orders # for account 232.1\n", - "\n", - "\n", - " \n", - " for x in [ 22, '232.1']: \n", - " db.loc[db.Account == x, 'Factory Equipment Cost'] = None # clear old values\n", - "\n", - "\n", - " # note that the cost reduction factor impacts the cost of the component not the cost of the factory\n", - " db.loc[db.Account == 22, 'Factory Equipment Cost'] = reduction_factor_22 * ( ( Reactor_data_updated_3.loc[ Reactor_data_updated_3.Account == 22, 'Factory Equipment Cost'] ) - ( f_22/num_orders) ) + (f_22/num_orders) \n", - " db.loc[db.Account == '232.1', 'Factory Equipment Cost'] = reduction_factor_2321 * ( ( Reactor_data_updated_3.loc[ Reactor_data_updated_3.Account == '232.1', 'Factory Equipment Cost']) - ( f_2321/num_orders)) +(f_2321/num_orders) \n", - "\n", - " \n", - "\n", - " Reactor_data_updated_4 = update_high_level_costs(db, power)\n", - " Reactor_data_updated_4_ = pd.DataFrame()\n", - " Reactor_data_updated_4_ = Reactor_data_updated_4[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " \n", - " return Reactor_data_updated_4_ \n", - "\n", - "\n", - "\n", - "# # Example \n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "\n", - "Reactor_data_factory = add_factory_cost(reactor_data , reactor_power, f_22, f_2321)\n", - "\n", - "Reactor_data_factory_land_taxes = add_land_cost(Reactor_data_factory , land_cost_per_acre_0, reactor_power)\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades = add_BOP_RP_grades(Reactor_data_factory_land_taxes , RB_grade_0, BOP_grade_0, reactor_power)\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder = add_bulk_ordering(Reactor_data_factory_land_taxes_BOP_RP_grades , num_orders, f_22, f_2321, reactor_power)\n", - "\n", - "\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder_pretty = prettify(Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor
Capital Cost Summary - Factories Cost, land cost, taxes, BOP/RP grades , bulk ordering Included
Displaying Direct Cost only

\", \"no_subsidies\")\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder_pretty.hide(subset=hidden_list1, axis=0) # show direct cost only" - ] - }, - { - "cell_type": "markdown", - "id": "6577aab0-b7a3-4943-9cec-5dc7c9c4d2da", - "metadata": {}, - "source": [ - "### Section 3-4 : Reworking and labor productivity" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8b6f2170-d021-47fa-bc5d-568f7fd66f13", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
The Concept B 310.8 MWe Reactor Capital Cost Summary
Factories Cost, land cost, taxes, BOP/RP grades , bulk ordering, reworking and productivity Included
Displaying Direct Cost only

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def add_reworking_productivity(Reactor_data_updated_4, reactor_type, n_th, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, power):\n", - " #Reworking = f(AE, CE, design completion)\n", - " \n", - " if n_th == 1:\n", - " design_completion = design_completion_0\n", - " ae_exp = ae_exp_0\n", - " ce_exp = ce_exp_0\n", - " \n", - " elif n_th > 1:\n", - " design_completion = 1 \n", - " ae_exp = min( (ae_exp_0 + (2/N_AE)*(n_th-1) ), 2)\n", - " ce_exp = min( (ce_exp_0 + (2/N_cons)*(n_th-1) ), 2)\n", - " \n", - " # # labor productivity factor = f(construction experience level)\n", - " productivity = 0.145*ce_exp + 0.71 \n", - " \n", - "\n", - " if reactor_type == \"Concept B\":\n", - " reworking_factor = (-0.9*design_completion+ 1.9) * (-0.15*ae_exp+1.3) * (-0.15*ce_exp+1.3) \n", - "\n", - " if reactor_type == \"Concept A\":\n", - " reworking_factor = (-0.69*design_completion+ 1.69) * (-0.125*ae_exp+1.25) * (-0.125*ce_exp+1.25) \n", - "\n", - " db = pd.DataFrame()\n", - "\n", - " db = Reactor_data_updated_4[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - "\n", - " for x in [212, 213, '211 plus 214 to 219',22, '232.1', 233, 24, 26]: \n", - " db.loc[db.Account == x, 'Factory Equipment Cost'] = None # clear old values\n", - " db.loc[db.Account == x, 'Site Labor Hours'] = None\n", - " db.loc[db.Account == x, 'Site Labor Cost'] = None\n", - " db.loc[db.Account == x, 'Site Material Cost'] = None\n", - "\n", - " for x in [212, 213, '211 plus 214 to 219',22, '232.1', 233, 24, 26]: \n", - " db.loc[db.Account == x, 'Factory Equipment Cost'] =\\\n", - " ((( Reactor_data_updated_4.loc[ Reactor_data_updated_4.Account == x, 'Factory Equipment Cost']).values)[0])*reworking_factor \n", - " db.loc[db.Account == x, 'Site Labor Hours'] =\\\n", - " ((( Reactor_data_updated_4.loc[ Reactor_data_updated_4.Account == x, 'Site Labor Hours']).values)[0])*reworking_factor/productivity\n", - " db.loc[db.Account == x, 'Site Labor Cost'] =\\\n", - " ((( Reactor_data_updated_4.loc[ Reactor_data_updated_4.Account == x, 'Site Labor Cost']).values)[0])*reworking_factor/productivity \n", - " db.loc[db.Account == x, 'Site Material Cost'] =\\\n", - " ((( Reactor_data_updated_4.loc[ Reactor_data_updated_4.Account == x, 'Site Material Cost']).values)[0])*reworking_factor \n", - "\n", - "\n", - " # update the construction duration\n", - "\n", - " Reactor_data_updated_5 = update_high_level_costs(db, power)\n", - " Reactor_data_updated_5_ = pd.DataFrame()\n", - " Reactor_data_updated_5_ = Reactor_data_updated_5[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " \n", - " return Reactor_data_updated_5_ \n", - "\n", - "\n", - "\n", - "# # Example \n", - "\n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "\n", - "Reactor_data_factory = add_factory_cost(reactor_data , reactor_power, f_22, f_2321)\n", - "\n", - "Reactor_data_factory_land_taxes = add_land_cost(Reactor_data_factory , land_cost_per_acre_0, reactor_power)\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades = add_BOP_RP_grades(Reactor_data_factory_land_taxes , RB_grade_0, BOP_grade_0, reactor_power)\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder = add_bulk_ordering(Reactor_data_factory_land_taxes_BOP_RP_grades , num_orders, f_22, f_2321, reactor_power)\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder_rework_productivity =\\\n", - " add_reworking_productivity(Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder , reactor_type, n_th , design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, reactor_power)\n", - "\n", - "\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder_rework_productivity_pretty = prettify(Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder_rework_productivity,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor Capital Cost Summary
Factories Cost, land cost, taxes, BOP/RP grades , bulk ordering, reworking and productivity Included
Displaying Direct Cost only

\", \"no_subsidies\")\n", - "Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder_rework_productivity_pretty.hide(subset=hidden_list1, axis=0) # show direct cost only " - ] - }, - { - "cell_type": "markdown", - "id": "2ff36b38", - "metadata": {}, - "source": [ - "#### Combine the previous functions in one function" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "80a45bdd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
The Concept B 310.8 MWe Reactor
Capital Cost Summary - Direct Cost Updated

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Combine the previous functions in one function\n", - "\n", - "def update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons):\n", - " reactor_data = (reactor_data_read(reactor_type ))[0]\n", - " reactor_power = (reactor_data_read(reactor_type ))[1]\n", - " Reactor_data_factory = add_factory_cost(reactor_data , reactor_power, f_22, f_2321)\n", - " Reactor_data_factory_land_taxes = add_land_cost(Reactor_data_factory , land_cost_per_acre_0, reactor_power)\n", - " Reactor_data_factory_land_taxes_BOP_RP_grades = add_BOP_RP_grades(Reactor_data_factory_land_taxes , RB_grade_0, BOP_grade_0, reactor_power)\n", - " Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder = add_bulk_ordering(Reactor_data_factory_land_taxes_BOP_RP_grades , num_orders, f_22, f_2321, reactor_power)\n", - " \n", - " Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder_rework_productivity =\\\n", - " add_reworking_productivity(Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder , reactor_type, n_th , design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, reactor_power)\n", - " \n", - " return Reactor_data_factory_land_taxes_BOP_RP_grades_bulkOrder_rework_productivity\n", - "\n", - "# example \n", - "\n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "\n", - "direct_cost_updated = update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons)\n", - "direct_cost_updated_pretty = prettify(direct_cost_updated,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor
Capital Cost Summary - Direct Cost Updated

\", \"no_subsidies\")\n", - "direct_cost_updated_pretty.hide(subset=hidden_list1, axis=0) # show direct cost only " - ] - }, - { - "cell_type": "markdown", - "id": "a96c43fe", - "metadata": {}, - "source": [ - "### Section 3-5 :Update construction duration from labor hours" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c4ad50cf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n" - ] - } - ], - "source": [ - "\n", - "def update_cons_dur(Reactor_data_0, db, mod_0):\n", - "\n", - " #sum of labor hours for Account 20 in the initial estimation (well exectued scenario)\n", - " sum_old_lab_hrs = (Reactor_data_0.loc[Reactor_data_0.Account == 21, 'Site Labor Hours']).values +\\\n", - " (Reactor_data_0.loc[Reactor_data_0.Account == 22, 'Site Labor Hours']).values +\\\n", - " (Reactor_data_0.loc[Reactor_data_0.Account == 23, 'Site Labor Hours']).values+\\\n", - " (Reactor_data_0.loc[Reactor_data_0.Account == 24, 'Site Labor Hours']).values+\\\n", - " (Reactor_data_0.loc[Reactor_data_0.Account == 26, 'Site Labor Hours']).values\n", - "\n", - "\n", - "\n", - " # #sum of labor hours for Account 20 in the new estimation \n", - " sum_new_lab_hrs = (db.loc[db.Account == 21, 'Site Labor Hours']).values +\\\n", - " (db.loc[db.Account == 22, 'Site Labor Hours']).values +\\\n", - " (db.loc[db.Account == 23, 'Site Labor Hours']).values+\\\n", - " (db.loc[db.Account == 24, 'Site Labor Hours']).values+\\\n", - " (db.loc[db.Account == 26, 'Site Labor Hours']).values\n", - "\n", - " print('update_cons_dur')\n", - " print('sum_old_lab_hrs,sum_new_lab_hrs',sum_old_lab_hrs,sum_new_lab_hrs)\n", - "\n", - " # # # change in labor hours for account 20\n", - " labor_hour_ratio = (sum_new_lab_hrs)/sum_old_lab_hrs # note that this number can be positive or negative\n", - " labor_hour_ratio \n", - "\n", - " # \tFrom the literature we know that if labor hours changed from 3.8M hours to 20.5M hours (5.4 times), the construction duration changes from 33.2 months to 74.3 months (2.2 times).\n", - " # \tWe also know that if the labor hours multiplier =1, the cons duration multiplier should be 1.\n", - " # \tUsing these two points: \n", - "\n", - " # construction duration multiplier = 0.3 * the labor hours multiplier+0.7\n", - "\n", - "\n", - "\n", - " # # modularity (applied on civil construction only) \"stick_built\" or \"modularized\"\n", - " mod = mod_0\n", - "\n", - " # Modularization effect on the construction duration\n", - " if mod == \"stick_built\":\n", - " mod_factor = 0.8\n", - " elif mod == \"modularized\":\n", - " mod_factor = 1\n", - " \n", - "\n", - " # # This is a hypothetical well-executed project taking 64 months (TIMCAT simulation)\n", - " if reactor_type == \"Concept B\": \n", - " baseline_construction_duration = 64/mod_factor # months\n", - " elif reactor_type == \"Concept A\": \n", - " baseline_construction_duration = 100/mod_factor # months\n", - " print('baseline_construction_duration',baseline_construction_duration)\n", - " print('labor_hour_ratio',labor_hour_ratio)\n", - " actual_construction_duration = baseline_construction_duration*(0.3*labor_hour_ratio+0.7)\n", - " return actual_construction_duration \n", - "\n", - "# example\n", - "\n", - "direct_cost_updated = update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons)\n", - "act_con_duration = update_cons_dur(reactor_data, direct_cost_updated , mod_0)\n", - "# print(f\"The construction duration of the {reactor_type} reactor, without accounting for supply chain dealys, is { np.round( act_con_duration[0]/12 , 1) } years\")" - ] - }, - { - "cell_type": "markdown", - "id": "6a45171f", - "metadata": {}, - "source": [ - "### Section 3-6 Learning by doing effect on the cost" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d290af08", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Direct Cost Updated plus learning effect
Displaying Direct Cost only

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "def learning_effect(Reactor_data_updated_5, n_th, standardization_0, power):\n", - "\n", - " if n_th == 1:\n", - " standardization = 0.7\n", - " elif n_th >1:\n", - " standardization = standardization_0 \n", - " \n", - " # # Creating the table for the learning rates\n", - " # #These rates are from KS-TIMCAT results.\n", - " # # The learning rates are multiplied by the standardization divded by 0.7 (since the standatization of PWRs was 0.7)\n", - " fitted_LR = pd.DataFrame()\n", - "\n", - " fitted_LR.loc[:, 'Account'] = Reactor_data_updated_5.loc[:, 'Account']\n", - " fitted_LR.loc[:, 'Title'] = Reactor_data_updated_5.loc[:, 'Title']\n", - " fitted_LR = fitted_LR.loc[fitted_LR['Account'].isin([212, 213, '211 plus 214 to 219', 22, '232.1', 233, 24, 26])]\n", - "\n", - " fitted_LR['Mat LR'] = np.array([0.099588665391, 0.099588665391, 0.099588665391, 0.080817992281, 0.0000000, 0.099588665391,\\\n", - " 0.099588665391,0.099588665391])*standardization/0.7\n", - "\n", - " fitted_LR['Lab LR'] = np.array([0.180678729399, 0.180678729399, 0.180678729399,0.146555539499, 0.137148574884,\\\n", - " 0.180678729399, 0.180678729399,0.180678729399])*standardization/0.7\n", - "\n", - "\n", - " db = pd.DataFrame()\n", - "\n", - " db = Reactor_data_updated_5[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - "\n", - " for x in [212, 213, '211 plus 214 to 219', 22, '232.1', 233, 24, 26]: \n", - " db.loc[db.Account == x, 'Site Labor Hours'] = None\n", - " db.loc[db.Account == x, 'Site Labor Cost'] = None\n", - " db.loc[db.Account == x, 'Site Material Cost'] = None\n", - "\n", - "\n", - " # # # # Bulk order reduction\t\n", - " for x in [212, 213, '211 plus 214 to 219', 22, '232.1', 233, 24, 26]:\n", - " mat_cost_reduction_multiplier = (1 - (fitted_LR.loc[fitted_LR.Account == x, 'Mat LR'].values[0]))**np.log2(n_th)\n", - " lab_cost_reduction_multiplier = (1 - (fitted_LR.loc[fitted_LR.Account == x, 'Lab LR'].values[0]))**np.log2(n_th)\n", - " \n", - " db.loc[db.Account == x, 'Site Material Cost'] =\\\n", - " (( Reactor_data_updated_5.loc[ Reactor_data_updated_5.Account == x, 'Site Material Cost']))* mat_cost_reduction_multiplier\n", - "\n", - " db.loc[db.Account == x, 'Site Labor Hours'] =\\\n", - " (( Reactor_data_updated_5.loc[ Reactor_data_updated_5.Account == x, 'Site Labor Hours']))* lab_cost_reduction_multiplier\n", - " \n", - " db.loc[db.Account == x, 'Site Labor Cost'] =\\\n", - " (( Reactor_data_updated_5.loc[ Reactor_data_updated_5.Account == x, 'Site Labor Cost']))* lab_cost_reduction_multiplier\n", - "\n", - "\n", - " Reactor_data_updated_6 = update_high_level_costs(db, power)\n", - " Reactor_data_updated_6_ = pd.DataFrame()\n", - " Reactor_data_updated_6_ = Reactor_data_updated_6[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " \n", - " return Reactor_data_updated_6_ \n", - "\n", - "\n", - "# # Example \n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "direct_cost_updated = update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons)\n", - "direct_cost_updated_plus_learning = learning_effect(direct_cost_updated , n_th, standardization_0, reactor_power)\n", - "\n", - "direct_cost_updated_plus_learning_pretty = prettify(direct_cost_updated_plus_learning,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor
Capital Cost Summary - Direct Cost Updated plus learning effect
Displaying Direct Cost only

\", \"no_subsidies\")\n", - "direct_cost_updated_plus_learning_pretty.hide(subset=hidden_list1, axis=0) # show direct cost only " - ] - }, - { - "cell_type": "markdown", - "id": "7f1a11ae", - "metadata": {}, - "source": [ - "### Section 3-7 supply chain delays" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6b94464b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", - "cons_duration_no_delay [80.63803108]\n" - ] - } - ], - "source": [ - "def act_cons_duration_plus_delay(reactor_type, n_th, Design_Maturity_0, proc_exp_0, N_proc, cons_duration_no_delay):\n", - " \n", - " if n_th == 1:\n", - " Design_Maturity = Design_Maturity_0\n", - " proc_exp = proc_exp_0\n", - " \n", - " elif n_th>1:\n", - " Design_Maturity = 2\n", - " proc_exp = min( (proc_exp_0 + (2/N_proc)*(n_th-1) ), 2)\n", - " \n", - " # For the accounts 21, 22, 23, 24, 25, 26, \n", - " # The delays are D_21,D_22,D_23,D_24,D_25,D_26 \n", - " # The tasks lengths (in months) are B_21,B_22,B_23,B_24,B_25,B_26\n", - "\n", - " # task length ratio between SFR and HTGR is 100/65\n", - " # We use this ratio to convert SFR to HTGT task lengths\n", - "\n", - " if reactor_type == \"Concept B\":\n", - " task_length_multiplier = 1\n", - " ref_construction_duration = 64\n", - " elif reactor_type == \"Concept A\": \n", - " task_length_multiplier = 100/64\n", - " ref_construction_duration = 100\n", - " \n", - " B_21 = 42.1 * task_length_multiplier # months \n", - " B_22 = 60.2 * task_length_multiplier\n", - " B_23 = 14.8 * task_length_multiplier\n", - " B_24 = 3.6* task_length_multiplier\n", - " B_25 = 10.1* task_length_multiplier\n", - " B_26 = 43.9* task_length_multiplier\n", - "\n", - "\n", - " # The delays are \n", - " D_21 = - 6 * Design_Maturity - 3*proc_exp + 18\n", - " D_22 = - 6 * Design_Maturity - 3*proc_exp + 18\n", - " D_23 = - 6 * Design_Maturity - 3*proc_exp + 18\n", - " D_24 = - 6 * Design_Maturity - 3*proc_exp + 18\n", - " D_25 = - 6 * Design_Maturity - 3*proc_exp + 18\n", - " D_26 = - 6 * Design_Maturity - 3*proc_exp + 18\n", - "\n", - "\n", - " # The tasks completion times (in months) are T_21,T_22,T_23,T_24,T_25,T_26\n", - " T_21 = B_21 + D_21 \n", - " T_22 = 0.09*(B_21+D_21) +B_22+D_22\n", - " T_23 = 0.24*(B_21+D_21) +B_23+D_23\n", - " T_24 = 0.24*(B_21+D_21) + 0.34*(B_23+D_23) +B_24+D_24\n", - " T_25 = 0.18*(B_21+D_21) +B_25+D_25\n", - " T_26 = 0.21*(B_21+D_21) +B_26+D_26\n", - " T_end = max(T_21, T_22, T_23, T_24, T_25, T_26)\n", - "\n", - "\n", - " supply_chain_delay = max( T_end - ref_construction_duration, 0)\n", - " print('in act_cons_duration_plus_delay ref_construction_duration',ref_construction_duration,'T_end',T_end)\n", - " print( 'cons_duration_no_delay',cons_duration_no_delay)\n", - " actual_construction_duration_plus_delay = cons_duration_no_delay + supply_chain_delay\n", - " return actual_construction_duration_plus_delay\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "# example\n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "\n", - "direct_cost_updated = update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons)\n", - "act_con_duration = update_cons_dur(reactor_data, direct_cost_updated , mod_0)\n", - "\n", - "cons_duration_plus_delay = act_cons_duration_plus_delay(reactor_type, n_th, Design_Maturity_0, proc_exp_0, N_proc, act_con_duration)\n", - "# print(f\"The construction duration of the {reactor_type} reactor, accounting for supply chain dealys, is { np.round( cons_duration_plus_delay[0]/12 , 1) } years\")" - ] - }, - { - "cell_type": "markdown", - "id": "0d98be43", - "metadata": {}, - "source": [ - "### Section 3-8 Learning by doing effect on the construction duration" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "2f8af5f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", - "cons_duration_no_delay [80.63803108]\n" - ] - } - ], - "source": [ - "def duration_learning_effect(n_th, standardization_0, actual_construction_duration_plus_delay):\n", - " \n", - " if n_th == 1:\n", - " standardization = 0.7\n", - " elif n_th >1:\n", - " standardization = standardization_0 \n", - " \n", - " # now the effect of learning on the consturction duration\n", - " fitted_LR_duration = 0.15*standardization/0.7\n", - " duration_multiplier = (1 -fitted_LR_duration)**np.log2(n_th)\n", - " final_construction_duration = duration_multiplier *actual_construction_duration_plus_delay\n", - " return final_construction_duration \n", - "\n", - "# example\n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "direct_cost_updated = update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons)\n", - "act_con_duration = update_cons_dur(reactor_data, direct_cost_updated , mod_0)\n", - "\n", - "cons_duration_plus_delay = act_cons_duration_plus_delay(reactor_type, n_th, Design_Maturity_0, proc_exp_0, N_proc, act_con_duration)\n", - "final_con_duration = duration_learning_effect(n_th, standardization_0, cons_duration_plus_delay)\n", - "# print(f\"The final construction duration of the {reactor_type} reactor is { np.round( final_con_duration[0]/12 , 1) } years\")" - ] - }, - { - "cell_type": "markdown", - "id": "20719832-5c0c-47ce-8ce7-b712d8b852c3", - "metadata": {}, - "source": [ - "### Section 3-9 Calculate the Indirect Cost and the standardization impact" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b0db81dd-fca3-45a3-bcd3-7b2094729da1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "act_con_duration [80.63803108]\n", - "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", - "cons_duration_no_delay [80.63803108]\n", - "cons_duration_plus_delay [92.07203108]\n", - "final_con_duration [92.07203108]\n", - "in function update indirect\n", - "final_construction_duration [92.07203108]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Base Cost updated (Direct and Indirect costs)

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def update_indirect_cost(n_th, standardization_0, Reactor_data_updated_6, final_construction_duration, power ):\n", - " print(\"in function update indirect\")\n", - " print(\"final_construction_duration\",final_construction_duration)\n", - " if n_th == 1:\n", - " standardization = 0.7\n", - " elif n_th >1:\n", - " standardization = standardization_0 \n", - " # I use here the indirect cost correlations prepared by Jia\n", - "\n", - " # When the standardization is 100%, the engineering service accound (Acct 35) is zero, \n", - " # When the standardization is 70%, the engineering service accound (Acct 35) does not change\n", - " # The account 35 multiplier is\n", - "\n", - "\n", - " factor_35 = -3.33 * standardization + 3.331\n", - "\n", - "\n", - " db = pd.DataFrame()\n", - "\n", - " db = Reactor_data_updated_6[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - "\n", - " for x in [31, 32, 33, 34, 35]: \n", - " db.loc[db.Account == x, 'Total Cost (USD)'] = None # clear old values\n", - "\n", - "\n", - " # # total direct mat cost, labor cost, labor hours\n", - " sum_new_mat_cost = 0 # initilization\n", - " sum_new_lab_cost = 0 # initilization\n", - " sum_new_lab_hrs = 0 # initilization\n", - "\n", - " for x in [21, 22, 23, 24, 26]:\n", - " sum_new_mat_cost += (db.loc[db.Account == x, 'Site Material Cost']).values\n", - " sum_new_lab_cost += (db.loc[db.Account == x, 'Site Labor Cost']).values\n", - " sum_new_lab_hrs += (db.loc[db.Account == x, 'Site Labor Hours']).values\n", - " \n", - " # The new indirect costs \n", - " db.loc[db.Account == 31, 'Total Cost (USD)'] = (sum_new_mat_cost*0.785* sum_new_lab_hrs/final_construction_duration/160/1058)\\\n", - " + sum_new_lab_cost *0.36\n", - "\n", - " db.loc[db.Account == 32, 'Total Cost (USD)'] = sum_new_lab_cost *0.36*3.661* final_construction_duration/72\n", - "\n", - " db.loc[db.Account == 33, 'Total Cost (USD)'] = 0.042 * (db.loc[db.Account == 32, 'Total Cost (USD)'].values[0] )\n", - "\n", - " db.loc[db.Account == 34, 'Total Cost (USD)'] = 0.0035 * (db.loc[db.Account == 32, 'Total Cost (USD)'].values[0] )\n", - "\n", - " db.loc[db.Account == 35, 'Total Cost (USD)'] = ( 0.27 * (db.loc[db.Account == 32, 'Total Cost (USD)'].values[0] ))*factor_35 \n", - "\n", - "\n", - " Reactor_data_updated_7 = update_high_level_costs(db, power)\n", - " Reactor_data_updated_7_ = pd.DataFrame()\n", - " Reactor_data_updated_7_ = Reactor_data_updated_7[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " return Reactor_data_updated_7_ \n", - "\n", - "\n", - "\n", - "\n", - "# # Example \n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "direct_cost_updated = update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons)\n", - "\n", - "act_con_duration = update_cons_dur(reactor_data, direct_cost_updated , mod_0)\n", - "print('act_con_duration',act_con_duration)\n", - "cons_duration_plus_delay = act_cons_duration_plus_delay(reactor_type, n_th, Design_Maturity_0, proc_exp_0, N_proc, act_con_duration)\n", - "print('cons_duration_plus_delay',cons_duration_plus_delay)\n", - "final_con_duration = duration_learning_effect(n_th, standardization_0, cons_duration_plus_delay)\n", - "print('final_con_duration',final_con_duration)\n", - "direct_cost_updated_plus_learning = learning_effect(direct_cost_updated , n_th, standardization_0, reactor_power)\n", - "direct_cost_updated_plus_learning_with_indirect_cost = update_indirect_cost(n_th, standardization_0, direct_cost_updated_plus_learning, final_con_duration, reactor_power )\n", - "\n", - "direct_cost_updated_plus_learning_with_indirect_cost_pretty = prettify(direct_cost_updated_plus_learning_with_indirect_cost,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor
Capital Cost Summary - Base Cost updated (Direct and Indirect costs)

\", \"no_subsidies\")\n", - "\n", - "list3 = list(range(38, 69))\n", - "hidden_list3 = list1 + list3\n", - "direct_cost_updated_plus_learning_with_indirect_cost_pretty.hide(subset=hidden_list3, axis=0) # show direct and indirect cost only " - ] - }, - { - "cell_type": "markdown", - "id": "9aba4571", - "metadata": {}, - "source": [ - "#### Combine the previous functions in one function" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ad4b0aa8", - "metadata": {}, - "outputs": [], - "source": [ - "def calculate_base_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0):\n", - " reactor_data = (reactor_data_read(reactor_type ))[0]\n", - " reactor_power = (reactor_data_read(reactor_type ))[1]\n", - " direct_cost_updated = update_direct_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons) \n", - " act_con_duration = update_cons_dur(reactor_data, direct_cost_updated , mod_0)\n", - " \n", - " cons_duration_plus_delay = act_cons_duration_plus_delay(reactor_type, n_th, Design_Maturity_0, proc_exp_0, N_proc, act_con_duration)\n", - " final_con_duration = duration_learning_effect(n_th, standardization_0, cons_duration_plus_delay)\n", - "\n", - " direct_cost_updated_plus_learning = learning_effect(direct_cost_updated , n_th, standardization_0, reactor_power)\n", - " direct_cost_updated_plus_learning_with_indirect_cost = update_indirect_cost(n_th, standardization_0, direct_cost_updated_plus_learning, final_con_duration, reactor_power )\n", - " return direct_cost_updated_plus_learning_with_indirect_cost, final_con_duration " - ] - }, - { - "cell_type": "markdown", - "id": "e1c48177-8116-4762-803f-3daa3b5d7707", - "metadata": {}, - "source": [ - "### Section 3-10 : Insurance" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a2561757-dbc9-4a67-ad25-6311bc9939cc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", - "cons_duration_no_delay [80.63803108]\n", - "in function update indirect\n", - "final_construction_duration [92.07203108]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Base Cost updated (Direct and Indirect costs) plus insurance

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 16,308,494
54Decommissioning $ 14,606,673
50s - Subtotal$ 34,425,770
50s - $/kWe$ 111
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def insurance_cost_update(Reactor_data_0, Reactor_data_updated_7, power):\n", - " # insurance increases linearly when increaing the sum of the 20s and 30s account\n", - " db = pd.DataFrame()\n", - "\n", - " db = Reactor_data_updated_7[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " db0 = Reactor_data_0\n", - " db.loc[db.Account == 52, 'Total Cost (USD)'] = None # clear old values\n", - "\n", - " change_in_insuance_cost = (db.loc[db.Title =='20s - Subtotal', 'Total Cost (USD)'].values\\\n", - " + db.loc[db.Title =='30s - Subtotal', 'Total Cost (USD)'].values)/ (db0.loc[db0.Title =='20s - Subtotal', 'Total Cost (USD)'].values\\\n", - " + db0.loc[db0.Title =='30s - Subtotal', 'Total Cost (USD)'].values)\n", - "\n", - " db.loc[db.Account == 52, 'Total Cost (USD)'] = (change_in_insuance_cost[0])* (Reactor_data_updated_7.loc[db.Account == 52, 'Total Cost (USD)'])\n", - "\n", - " Reactor_data_updated_8 = update_high_level_costs(db, power)\n", - "\n", - " Reactor_data_updated_8_ = pd.DataFrame()\n", - " Reactor_data_updated_8_ = Reactor_data_updated_8[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " \n", - " return Reactor_data_updated_8_ \n", - "\n", - "# Example\n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "tot_base_cost = (calculate_base_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0))[0]\n", - "tot_base_cost_wz_insurance = insurance_cost_update(reactor_data, tot_base_cost , reactor_power)\n", - "tot_base_cost_wz_insurance_pretty = prettify(tot_base_cost_wz_insurance,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor
Capital Cost Summary - Base Cost updated (Direct and Indirect costs) plus insurance

\", \"no_subsidies\")\n", - "\n", - "\n", - "list4 = list(range(46, 69))\n", - "hidden_list4 = list1 + list4\n", - "tot_base_cost_wz_insurance_pretty.hide(subset=hidden_list4, axis=0) " - ] - }, - { - "cell_type": "markdown", - "id": "d17b48a1-153c-4273-b346-fe0b9577fc8a", - "metadata": {}, - "source": [ - "### Section 3-11 : Interest" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "9616088a-1f2c-4147-a288-eb0bb7f259c7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", - "cons_duration_no_delay [80.63803108]\n", - "in function update indirect\n", - "final_construction_duration [92.07203108]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:7: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " annual_periods = np.linspace(12, 12*int(final_construction_duration/12),int(final_construction_duration/12))\n", - "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:8: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " if max(annual_periods) < int(final_construction_duration)-1:\n", - "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:14: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " new_period = 103*period/int(final_construction_duration)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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The Concept B 310.8 MWe Reactor
Capital Cost Summary - Base Cost updated (Direct and Indirect costs) plus insurance and interest

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
10Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 16,308,494
54Decommissioning $ 14,606,673
50s - Subtotal$ 34,425,770
50s - $/kWe$ 111
60Capitalized Financial Costs
62Interest $ 1,276,775,002
-60s - Subtotal$ 1,276,775,002
60s - $/kWe$ 4,108
Total Direct Capital Cost (Accounts 10 to 20)$ 1,984,538,942
(Accounts 10 to 20) US$/kWe$ 6,385
Base Construction Cost (Accounts 10 to 30)$ 3,703,101,413
(Accounts 10 to 30) US$/kWe$ 11,915
Total Overnight Cost (Accounts 10 to 50)$ 3,737,527,183
(Accounts 10 to 50) US$/kWe$ 12,026
Total Capital Investment Cost (All Accounts)$ 5,014,302,185
(Accounts 10 to 60) US$/kWe$ 16,134
Total Overnight Cost - ITC reduced$ 2,437,538,300
Total Overnight Cost -ITC reduced (US$/kWe)$ 7,843
Total Capital Investment Cost - ITC reduced$ 3,029,839,099
Total Capital Investment Cost - ITC reduced (US$/kWe)$ 9,749
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def update_interest_cost(Reactor_data_updated_8, final_construction_duration, interest_rate, startup_0, n_th, power):\n", - " # Read the ref spending curve\n", - " sp = pd.read_excel('inputs.xlsx', sheet_name = \"Ref Spending Curve\", nrows= 104, usecols='A : D')\n", - " Months = sp['Month'].tolist()\n", - " CDFs = sp['CDF'].tolist()\n", - "\n", - " annual_periods = np.linspace(12, 12*int(final_construction_duration/12),int(final_construction_duration/12))\n", - " if max(annual_periods) < int(final_construction_duration)-1:\n", - " annual_periods_1= np.append( annual_periods, (final_construction_duration)-1)\n", - " else:\n", - " annual_periods_1 = annual_periods\n", - " annual_cum_spend = []\n", - " for period in annual_periods_1:\n", - " new_period = 103*period/int(final_construction_duration)\n", - " annual_cum_spend.append(np.interp(new_period, Months, CDFs))\n", - " \n", - " annual_cum_spend1 = np.append(annual_cum_spend[0], np.diff(annual_cum_spend ))\n", - " tot_overnight_cost = (Reactor_data_updated_8.loc[Reactor_data_updated_8.Title == 'Total Overnight Cost (Accounts 10 to 50)' , 'Total Cost (USD)']).values[0]\n", - "\n", - "\n", - " annual_loan_add = annual_cum_spend1 *tot_overnight_cost\n", - "\n", - " interest_exp = ((1+interest_rate)**((final_construction_duration -annual_periods_1)/12)) * annual_loan_add - annual_loan_add\n", - "\n", - " tot_int_exp_construction = sum(interest_exp )\n", - " \n", - " if n_th == 1:\n", - " startup = startup_0\n", - " elif n_th >1:\n", - " startup = max( 7 , startup_0*(1-0.3)**np.log2(n_th) )\n", - "\n", - " int_exp_startup = (tot_int_exp_construction + tot_overnight_cost)*((1+interest_rate)**(startup/12))-(tot_int_exp_construction + tot_overnight_cost)\n", - " \n", - " db = pd.DataFrame()\n", - "\n", - " db = Reactor_data_updated_8[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " db.loc[db.Account == 62, 'Total Cost (USD)'] = None # clear old values\n", - "\n", - " (db.loc[db.Account == 62, 'Total Cost (USD)']) = int_exp_startup +tot_int_exp_construction \n", - "\n", - "\n", - " Reactor_data_updated_9 = update_high_level_costs(db, power)\n", - "\n", - " Reactor_data_updated_9_ = pd.DataFrame()\n", - " Reactor_data_updated_9_ = Reactor_data_updated_9[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " \n", - " tot_cap_investment = Reactor_data_updated_9.loc[Reactor_data_updated_9.Title =='Total Capital Investment Cost (All Accounts)', 'Total Cost (USD)'].values\n", - " \n", - " return Reactor_data_updated_9_, tot_overnight_cost, tot_cap_investment\n", - "\n", - "\n", - "\n", - "# Example\n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "tot_base_cost_results = (calculate_base_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0))\n", - "tot_base_cost = tot_base_cost_results[0]\n", - "final_construction_duration = tot_base_cost_results[1]\n", - "\n", - "tot_base_cost_wz_insurance = insurance_cost_update(reactor_data, tot_base_cost , reactor_power)\n", - "tot_base_cost_wz_insurance_interest = (update_interest_cost(tot_base_cost_wz_insurance , final_construction_duration, interest_rate_0, startup_0, n_th, reactor_power))[0]\n", - "\n", - "\n", - "\n", - "tot_base_cost_wz_insurance_interest_pretty = prettify(tot_base_cost_wz_insurance_interest,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor
Capital Cost Summary - Base Cost updated (Direct and Indirect costs) plus insurance and interest

\", \"no_subsidies\")\n", - "\n", - "tot_base_cost_wz_insurance_interest_pretty\n" - ] - }, - { - "cell_type": "markdown", - "id": "285ff958-5114-480f-93e1-d1874988ab69", - "metadata": {}, - "source": [ - "### Section 3 - 12 : ITC Subsidies" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "f5b6baa7-0224-483b-bcd3-59301602ba3e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "in act_cons_duration_plus_delay ref_construction_duration 64 T_end 75.434\n", - "cons_duration_no_delay [80.63803108]\n", - "in function update indirect\n", - "final_construction_duration [92.07203108]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:7: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " annual_periods = np.linspace(12, 12*int(final_construction_duration/12),int(final_construction_duration/12))\n", - "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:8: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " if max(annual_periods) < int(final_construction_duration)-1:\n", - "/var/folders/8_/7wbpk3cj6yj_cmvfbygwcxdw0000gt/T/ipykernel_85977/1578076930.py:14: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " new_period = 103*period/int(final_construction_duration)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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The Concept B 310.8 MWe Reactor
Capital Cost Summary

AccountTitleTotal Cost (USD)Factory Equipment CostSite Labor HoursSite Labor CostSite Material Cost
10Capitalized Pre-Construction Costs
11Land & Land Rights$ 11,000,000
12Site Permits$ 1,598,891
13Plant Licensing$ 24,382,988
14Plant Permits$ 12,679,167
15Plant Studies$ 12,679,167
16Plant Reports$ 3,972,186
18Other Pre-Construction Costs$ 12,679,167
10s - Subtotal$ 78,991,565
10s - $/kWe$ 254
20Capitalized Direct Costs
21Structures & Improvements$ 440,882,959$ 106,559,0475,557,506 hrs$ 279,152,503$ 55,171,408
212Reactor Containment Building$ 264,076,459$ 95,722,6673,121,808 hrs$ 157,643,178$ 10,710,615
213Turbine Room and Heater Bay$ 10,386,814$ 619,346119,453 hrs$ 6,159,946$ 3,607,522
211 plus 214 to 219Othe Structures & Improvements$ 166,419,685$ 10,217,0342,316,244 hrs$ 115,349,380$ 40,853,271
22Reactor System$ 807,606,906$ 611,521,5543,518,498 hrs$ 189,006,816$ 7,078,536
23Energy Conversion System$ 206,565,176$ 135,167,6251,264,169 hrs$ 66,920,930$ 4,476,621
232.1Electricity Generation Systems$ 133,275,069$ 98,488,776652,925 hrs$ 34,786,293$ 0
233Ultimate Heat Sink$ 73,290,107$ 36,678,849611,244 hrs$ 32,134,637$ 4,476,621
24Electrical Equipment$ 123,782,437$ 22,712,2481,491,382 hrs$ 80,385,388$ 20,684,801
25Initial fuel inventory$ 279,724,434
26Miscellaneous Equipment$ 46,907,265$ 13,917,713542,064 hrs$ 29,377,876$ 3,611,675
28Simulator$ 78,200
20s - Subtotal$ 1,905,547,377
20s - $/kWe$ 6,131
30Capitalized Indirect Services Costs
31Factory & Field Indirect Costs$ 288,869,950
32Factory and construction supervision$ 1,086,805,413
33Start-Up Costs$ 45,645,827
34Shipping & Transportation Costs$ 3,803,819
35Engineering Services$ 293,437,462
30s - Subtotal$ 1,718,562,471
30s - $/kWe$ 5,529
50Capitalized Supplementary Costs
51Taxes$ 3,510,602
52Insurance$ 16,308,494
54Decommissioning $ 14,606,673
50s - Subtotal$ 34,425,770
50s - $/kWe$ 111
60Capitalized Financial Costs
62Interest $ 1,276,775,002
-60s - Subtotal$ 1,276,775,002
60s - $/kWe$ 4,108
Total Direct Capital Cost (Accounts 10 to 20)$ 1,984,538,942
(Accounts 10 to 20) US$/kWe$ 6,385
Base Construction Cost (Accounts 10 to 30)$ 3,703,101,413
(Accounts 10 to 30) US$/kWe$ 11,915
Total Overnight Cost (Accounts 10 to 50)$ 3,737,527,183
(Accounts 10 to 50) US$/kWe$ 12,026
Total Capital Investment Cost (All Accounts)$ 5,014,302,185
(Accounts 10 to 60) US$/kWe$ 16,134
Total Overnight Cost - ITC reduced$ 3,737,527,183
Total Overnight Cost -ITC reduced (US$/kWe)$ 12,026
Total Capital Investment Cost - ITC reduced$ 5,014,302,185
Total Capital Investment Cost - ITC reduced (US$/kWe)$ 16,134
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def update_itc(Reactor_data_updated_9, tot_overnight_cost, tot_cap_investment, n_th, ITC_0, n_ITC, reactor_power):\n", - " \n", - " if n_th <= n_ITC:\n", - " ITC = ITC_0\n", - " else:\n", - " ITC =0\n", - " \n", - " db1 = pd.DataFrame()\n", - " db1 = Reactor_data_updated_9[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - "\n", - " ITC_cost_reduction_factor = ITC_reduction_factor(ITC)\n", - "\n", - " ITC_reduced_OCC =tot_overnight_cost*ITC_cost_reduction_factor\n", - " OCC_cost_reduction_due_to_TCI = tot_overnight_cost - ITC_reduced_OCC \n", - "\n", - " db1.loc[db1.Title== 'Total Overnight Cost - ITC reduced', 'Total Cost (USD)'] = None # clear old values\n", - " db1.loc[db1.Title== 'Total Overnight Cost -ITC reduced (US$/kWe)', 'Total Cost (USD)'] = None # clear old values\n", - " db1.loc[db1.Title== 'Total Capital Investment Cost - ITC reduced', 'Total Cost (USD)'] = None # clear old values\n", - " db1.loc[db1.Title== 'Total Capital Investment Cost - ITC reduced (US$/kWe)', 'Total Cost (USD)'] = None\n", - "\n", - " db1.loc[db1.Title== 'Total Overnight Cost - ITC reduced', 'Total Cost (USD)'] = ITC_reduced_OCC\n", - " db1.loc[db1.Title== 'Total Overnight Cost -ITC reduced (US$/kWe)', 'Total Cost (USD)'] = ITC_reduced_OCC/reactor_power\n", - " db1.loc[db1.Title== 'Total Capital Investment Cost - ITC reduced', 'Total Cost (USD)'] = tot_cap_investment - OCC_cost_reduction_due_to_TCI \n", - " \n", - " #Net capital investment_per_kw\n", - " levelized_NCI = (db1.loc[db1.Title== 'Total Capital Investment Cost - ITC reduced', 'Total Cost (USD)'].values[0])/reactor_power\n", - " db1.loc[db1.Title== 'Total Capital Investment Cost - ITC reduced (US$/kWe)', 'Total Cost (USD)'] = levelized_NCI\n", - "\n", - "\n", - " Reactor_data_updated_10 = update_high_level_costs(db1, reactor_power)\n", - "\n", - " Reactor_data_updated_10_ = pd.DataFrame()\n", - " Reactor_data_updated_10_ = Reactor_data_updated_10[['Account', 'Title', 'Total Cost (USD)', 'Factory Equipment Cost', 'Site Labor Hours', 'Site Labor Cost',\\\n", - " 'Site Material Cost']].copy()\n", - " \n", - " return Reactor_data_updated_10_ , ITC_reduced_OCC/reactor_power, levelized_NCI \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "# Example\n", - "reactor_data = (reactor_data_read(reactor_type ))[0]\n", - "reactor_power = (reactor_data_read(reactor_type ))[1]\n", - "tot_base_cost_results = (calculate_base_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0))\n", - "tot_base_cost = tot_base_cost_results[0]\n", - "final_construction_duration = tot_base_cost_results[1]\n", - "\n", - "tot_base_cost_wz_insurance = insurance_cost_update(reactor_data, tot_base_cost , reactor_power)\n", - "tot_base_cost_wz_insurance_interest_results = (update_interest_cost(tot_base_cost_wz_insurance , final_construction_duration, interest_rate_0, startup_0, n_th, reactor_power))\n", - "tot_base_cost_wz_insurance_interest= tot_base_cost_wz_insurance_interest_results[0]\n", - "tot_overnight_cost = tot_base_cost_wz_insurance_interest_results[1]\n", - "tot_cap_investment= tot_base_cost_wz_insurance_interest_results[2]\n", - "\n", - "\n", - "\n", - "Final_Result = update_itc(tot_base_cost_wz_insurance_interest, tot_overnight_cost, tot_cap_investment, n_th, ITC_0, n_ITC, reactor_power)\n", - "Final_Result_COA = Final_Result[0]\n", - "\n", - "Final_Result_pretty = prettify(Final_Result_COA,\\\n", - " f\" The {reactor_type} {np.round(reactor_power/1000,1)} MWe Reactor
Capital Cost Summary

\", \"subsidies\")\n", - "\n", - "Final_Result_pretty" - ] - }, - { - "cell_type": "markdown", - "id": "8c3ac763", - "metadata": {}, - "source": [ - "#### A Python function to combine all the previous ones" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "eb42caa1", - "metadata": {}, - "outputs": [], - "source": [ - "def calculate_final_result(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0,\\\n", - " num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0, interest_rate_0, startup_0): \n", - "\n", - " reactor_data = (reactor_data_read(reactor_type ))[0]\n", - " reactor_power = (reactor_data_read(reactor_type ))[1]\n", - " tot_base_cost_results = calculate_base_cost(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0, num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0)\n", - " tot_base_cost = tot_base_cost_results[0]\n", - " final_construction_duration = tot_base_cost_results[1]\n", - "\n", - " tot_base_cost_wz_insurance = insurance_cost_update(reactor_data, tot_base_cost , reactor_power)\n", - " tot_base_cost_wz_insurance_interest_results = (update_interest_cost(tot_base_cost_wz_insurance , final_construction_duration, interest_rate_0, startup_0, n_th, reactor_power))\n", - " tot_base_cost_wz_insurance_interest= tot_base_cost_wz_insurance_interest_results[0]\n", - " tot_overnight_cost = tot_base_cost_wz_insurance_interest_results[1]\n", - " tot_cap_investment= tot_base_cost_wz_insurance_interest_results[2]\n", - "\n", - " Final_Result = update_itc(tot_base_cost_wz_insurance_interest, tot_overnight_cost, tot_cap_investment, n_th, ITC_0, n_ITC, reactor_power)\n", - " Final_Result_COA = Final_Result[0]\n", - " levelized_net_OCC = Final_Result[1]\n", - " levelized_NCI = Final_Result[2]\n", - " \n", - " return Final_Result_COA, levelized_net_OCC, levelized_NCI, final_construction_duration " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Cost_Reduction/INL1.png b/Cost_Reduction/INL1.png deleted file mode 100644 index 33ffe9627f51312787e97de9dca744d3acb722f5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6027 zcmV;67j)=}P)v@cIBVlp1-v7HzWt zD32nI%m8${Y?Hzxlg>Pj$CADc6zzpHFst*_y7N@s|8_fFvd<*rIP0Tl29xbdZeRs zw4rG?DcR(OAFp?}-8JZ^P~sZ|LP(MKVt?;^0o6;PV)VUJGw6+Er;d|Ev&m#K zi-N>)(ood(nV}y=-Sm4WbI_}67dc>h0*R-j(- znXwn*X8$66V~GBL76pdH3{8X$tQY=apXd3(FEU~n03$-1#lRSR|DQz}rwc95ov)%% 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "40096a23-ff7b-4541-b710-9f0a9f9a595d", - "metadata": {}, - "source": [ - "#
Cost Reduction Framework for Nuclear Reactor Power Plants
\n" - ] - }, - { - "cell_type": "markdown", - "id": "f2256ae0-712f-4811-99d9-d37188782036", - "metadata": {}, - "source": [ - "### Importing the libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "12fb6329-dc95-493f-ba1d-86235e71b512", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore', category=FutureWarning)\n", - "warnings.simplefilter(action='ignore', category=DeprecationWarning)\n", - "\n", - "pd.set_option('display.max_rows', None)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7b9a01a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "in function update indirect\n", - " Account Title \\\n", - "0 10 Capitalized Pre-Construction Costs \n", - "1 11 Land & Land Rights \n", - "2 12 Site Permits \n", - "3 13 Plant Licensing \n", - "4 14 Plant Permits \n", - "5 15 Plant Studies \n", - "6 16 Plant Reports \n", - "7 18 Other Pre-Construction Costs \n", - "8 NaN NaN \n", - "9 NaN 10s - Subtotal \n", - "10 NaN 10s - $/kWe \n", - "11 NaN NaN \n", - "12 20 Capitalized Direct Costs \n", - "13 21 Structures & Improvements \n", - "14 212 Reactor Containment Building \n", - "15 213 Turbine Room and Heater Bay \n", - "16 211 plus 214 to 219 Othe Structures & Improvements \n", - "17 22 Reactor System \n", - "18 23 Energy Conversion System \n", - "19 232.1 Electricity Generation Systems \n", - "20 233 Ultimate Heat Sink \n", - "21 24 Electrical Equipment \n", - "22 25 Initial fuel inventory \n", - "23 26 Miscellaneous Equipment \n", - "24 28 Simulator \n", - "25 NaN NaN \n", - "26 NaN 20s - Subtotal \n", - "27 NaN 20s - $/kWe \n", - "28 NaN NaN \n", - "29 30 Capitalized Indirect Services Costs \n", - "30 31 Factory & Field Indirect Costs \n", - "31 32 Factory and construction supervision \n", - "32 33 Start-Up Costs \n", - "33 34 Shipping & Transportation Costs \n", - "34 35 Engineering Services \n", - "35 NaN NaN \n", - "36 NaN 30s - Subtotal \n", - "37 NaN 30s - $/kWe \n", - "38 NaN NaN \n", - "39 50 Capitalized Supplementary Costs \n", - "40 51 Taxes \n", - "41 52 Insurance \n", - "42 54 Decommissioning \n", - "43 NaN NaN \n", - "44 NaN 50s - Subtotal \n", - "45 NaN 50s - $/kWe \n", - "46 NaN NaN \n", - "47 60 Capitalized Financial Costs \n", - "48 62 Interest \n", - "49 - 60s - Subtotal \n", - "50 NaN 60s - $/kWe \n", - "51 NaN NaN \n", - "52 NaN Total Direct Capital Cost (Accounts 10 to 20) \n", - "53 NaN (Accounts 10 to 20) US$/kWe \n", - "54 NaN NaN \n", - "55 NaN Base Construction Cost (Accounts 10 to 30) \n", - "56 NaN (Accounts 10 to 30) US$/kWe \n", - "57 NaN NaN \n", - "58 NaN Total Overnight Cost (Accounts 10 to 50) \n", - "59 NaN (Accounts 10 to 50) US$/kWe \n", - "60 NaN NaN \n", - "61 NaN Total Capital Investment Cost (All Accounts) \n", - "62 NaN (Accounts 10 to 60) US$/kWe \n", - "63 NaN NaN \n", - "64 NaN Total Overnight Cost - ITC reduced \n", - "65 NaN Total Overnight Cost -ITC reduced (US$/kWe) \n", - "66 NaN NaN \n", - "67 NaN Total Capital Investment Cost - ITC reduced \n", - "68 NaN Total Capital Investment Cost - ITC reduced (U... \n", - "\n", - " Total Cost (USD) Factory Equipment Cost Site Labor Hours \\\n", - "0 NaN NaN NaN \n", - "1 1.100000e+07 NaN NaN \n", - "2 1.598891e+06 NaN NaN \n", - "3 2.438299e+07 NaN NaN \n", - "4 1.267917e+07 NaN NaN \n", - "5 1.267917e+07 NaN NaN \n", - "6 3.972186e+06 NaN NaN \n", - "7 1.267917e+07 NaN NaN \n", - "8 NaN NaN NaN \n", - "9 7.899157e+07 NaN NaN \n", - "10 2.541556e+02 NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 4.408830e+08 1.065590e+08 5.557506e+06 \n", - "14 2.640765e+08 9.572267e+07 3.121808e+06 \n", - "15 1.038681e+07 6.193464e+05 1.194535e+05 \n", - "16 1.664197e+08 1.021703e+07 2.316244e+06 \n", - "17 8.076069e+08 6.115216e+08 3.518498e+06 \n", - "18 2.065652e+08 1.351676e+08 1.264169e+06 \n", - "19 1.332751e+08 9.848878e+07 6.529246e+05 \n", - "20 7.329011e+07 3.667885e+07 6.112442e+05 \n", - "21 1.237824e+08 2.271225e+07 1.491382e+06 \n", - "22 2.797244e+08 NaN NaN \n", - "23 4.690726e+07 1.391771e+07 5.420643e+05 \n", - "24 7.820000e+04 NaN NaN \n", - "25 NaN NaN NaN \n", - "26 1.905547e+09 NaN NaN \n", - "27 6.131105e+03 NaN NaN \n", - "28 NaN NaN NaN \n", - "29 NaN NaN NaN \n", - "30 1.460520e+08 NaN NaN \n", - "31 5.062561e+08 NaN NaN \n", - "32 2.129823e+07 NaN NaN \n", - "33 1.793334e+06 NaN NaN \n", - "34 1.367783e+08 NaN NaN \n", - "35 NaN NaN NaN \n", - "36 8.121780e+08 NaN NaN \n", - "37 2.613185e+03 NaN NaN \n", - "38 NaN NaN NaN \n", - "39 NaN NaN NaN \n", - "40 3.510602e+06 NaN NaN \n", - "41 1.048475e+07 NaN NaN \n", - "42 1.460667e+07 NaN NaN \n", - "43 NaN NaN NaN \n", - "44 2.860203e+07 NaN NaN \n", - "45 9.202711e+01 NaN NaN \n", - "46 NaN NaN NaN \n", - "47 NaN NaN NaN \n", - "48 5.923008e+08 NaN NaN \n", - "49 5.923008e+08 NaN NaN \n", - "50 1.905730e+03 NaN NaN \n", - "51 NaN NaN NaN \n", - "52 1.984539e+09 NaN NaN \n", - "53 6.385260e+03 NaN NaN \n", - "54 NaN NaN NaN \n", - "55 2.796717e+09 NaN NaN \n", - "56 8.998446e+03 NaN NaN \n", - "57 NaN NaN NaN \n", - "58 2.825319e+09 NaN NaN \n", - "59 9.090473e+03 NaN NaN \n", - "60 NaN NaN NaN \n", - "61 3.417620e+09 NaN NaN \n", - "62 1.099620e+04 NaN NaN \n", - "63 NaN NaN NaN \n", - "64 2.437538e+09 NaN NaN \n", - "65 7.842787e+03 NaN NaN \n", - "66 NaN NaN NaN \n", - "67 3.029839e+09 NaN NaN \n", - "68 9.748517e+03 NaN NaN \n", - "\n", - " Site Labor Cost Site Material Cost \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 2.791525e+08 5.517141e+07 \n", - "14 1.576432e+08 1.071061e+07 \n", - "15 6.159946e+06 3.607522e+06 \n", - "16 1.153494e+08 4.085327e+07 \n", - "17 1.890068e+08 7.078536e+06 \n", - "18 6.692093e+07 4.476621e+06 \n", - "19 3.478629e+07 0.000000e+00 \n", - "20 3.213464e+07 4.476621e+06 \n", - "21 8.038539e+07 2.068480e+07 \n", - "22 NaN NaN \n", - "23 2.937788e+07 3.611675e+06 \n", - "24 NaN NaN \n", - "25 NaN NaN \n", - "26 NaN NaN \n", - "27 NaN NaN \n", - "28 NaN NaN \n", - "29 NaN NaN \n", - "30 NaN NaN \n", - "31 NaN NaN \n", - "32 NaN NaN \n", - "33 NaN NaN \n", - "34 NaN NaN \n", - "35 NaN NaN \n", - "36 NaN NaN \n", - "37 NaN NaN \n", - "38 NaN NaN \n", - "39 NaN NaN \n", - "40 NaN NaN \n", - "41 NaN NaN \n", - "42 NaN NaN \n", - "43 NaN NaN \n", - "44 NaN NaN \n", - "45 NaN NaN \n", - "46 NaN NaN \n", - "47 NaN NaN \n", - "48 NaN NaN \n", - "49 NaN NaN \n", - "50 NaN NaN \n", - "51 NaN NaN \n", - "52 NaN NaN \n", - "53 NaN NaN \n", - "54 NaN NaN \n", - "55 NaN NaN \n", - "56 NaN NaN \n", - "57 NaN NaN \n", - "58 NaN NaN \n", - "59 NaN NaN \n", - "60 NaN NaN \n", - "61 NaN NaN \n", - "62 NaN NaN \n", - "63 NaN NaN \n", - "64 NaN NaN \n", - "65 NaN NaN \n", - "66 NaN NaN \n", - "67 NaN NaN \n", - "68 NaN NaN \n", - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "in function update indirect\n", - " Account Title \\\n", - "0 10 Capitalized Pre-Construction Costs \n", - "1 11 Land & Land Rights \n", - "2 12 Site Permits \n", - "3 13 Plant Licensing \n", - "4 14 Plant Permits \n", - "5 15 Plant Studies \n", - "6 16 Plant Reports \n", - "7 18 Other Pre-Construction Costs \n", - "8 NaN NaN \n", - "9 NaN 10s - Subtotal \n", - "10 NaN 10s - $/kWe \n", - "11 NaN NaN \n", - "12 20 Capitalized Direct Costs \n", - "13 21 Structures & Improvements \n", - "14 212 Reactor Containment Building \n", - "15 213 Turbine Room and Heater Bay \n", - "16 211 plus 214 to 219 Othe Structures & Improvements \n", - "17 22 Reactor System \n", - "18 23 Energy Conversion System \n", - "19 232.1 Electricity Generation Systems \n", - "20 233 Ultimate Heat Sink \n", - "21 24 Electrical Equipment \n", - "22 25 Initial fuel inventory \n", - "23 26 Miscellaneous Equipment \n", - "24 28 Simulator \n", - "25 NaN NaN \n", - "26 NaN 20s - Subtotal \n", - "27 NaN 20s - $/kWe \n", - "28 NaN NaN \n", - "29 30 Capitalized Indirect Services Costs \n", - "30 31 Factory & Field Indirect Costs \n", - "31 32 Factory and construction supervision \n", - "32 33 Start-Up Costs \n", - "33 34 Shipping & Transportation Costs \n", - "34 35 Engineering Services \n", - "35 NaN NaN \n", - "36 NaN 30s - Subtotal \n", - "37 NaN 30s - $/kWe \n", - "38 NaN NaN \n", - "39 50 Capitalized Supplementary Costs \n", - "40 51 Taxes \n", - "41 52 Insurance \n", - "42 54 Decommissioning \n", - "43 NaN NaN \n", - "44 NaN 50s - Subtotal \n", - "45 NaN 50s - $/kWe \n", - "46 NaN NaN \n", - "47 60 Capitalized Financial Costs \n", - "48 62 Interest \n", - "49 - 60s - Subtotal \n", - "50 NaN 60s - $/kWe \n", - "51 NaN NaN \n", - "52 NaN Total Direct Capital Cost (Accounts 10 to 20) \n", - "53 NaN (Accounts 10 to 20) US$/kWe \n", - "54 NaN NaN \n", - "55 NaN Base Construction Cost (Accounts 10 to 30) \n", - "56 NaN (Accounts 10 to 30) US$/kWe \n", - "57 NaN NaN \n", - "58 NaN Total Overnight Cost (Accounts 10 to 50) \n", - "59 NaN (Accounts 10 to 50) US$/kWe \n", - "60 NaN NaN \n", - "61 NaN Total Capital Investment Cost (All Accounts) \n", - "62 NaN (Accounts 10 to 60) US$/kWe \n", - "63 NaN NaN \n", - "64 NaN Total Overnight Cost - ITC reduced \n", - "65 NaN Total Overnight Cost -ITC reduced (US$/kWe) \n", - "66 NaN NaN \n", - "67 NaN Total Capital Investment Cost - ITC reduced \n", - "68 NaN Total Capital Investment Cost - ITC reduced (U... \n", - "\n", - " Total Cost (USD) Factory Equipment Cost Site Labor Hours \\\n", - "0 NaN NaN NaN \n", - "1 1.100000e+07 NaN NaN \n", - "2 1.598891e+06 NaN NaN \n", - "3 2.438299e+07 NaN NaN \n", - "4 1.267917e+07 NaN NaN \n", - "5 1.267917e+07 NaN NaN \n", - "6 3.972186e+06 NaN NaN \n", - "7 1.267917e+07 NaN NaN \n", - "8 NaN NaN NaN \n", - "9 7.899157e+07 NaN NaN \n", - "10 2.541556e+02 NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 4.408830e+08 1.065590e+08 5.557506e+06 \n", - "14 2.640765e+08 9.572267e+07 3.121808e+06 \n", - "15 1.038681e+07 6.193464e+05 1.194535e+05 \n", - "16 1.664197e+08 1.021703e+07 2.316244e+06 \n", - "17 8.076069e+08 6.115216e+08 3.518498e+06 \n", - "18 2.065652e+08 1.351676e+08 1.264169e+06 \n", - "19 1.332751e+08 9.848878e+07 6.529246e+05 \n", - "20 7.329011e+07 3.667885e+07 6.112442e+05 \n", - "21 1.237824e+08 2.271225e+07 1.491382e+06 \n", - "22 2.797244e+08 NaN NaN \n", - "23 4.690726e+07 1.391771e+07 5.420643e+05 \n", - "24 7.820000e+04 NaN NaN \n", - "25 NaN NaN NaN \n", - "26 1.905547e+09 NaN NaN \n", - "27 6.131105e+03 NaN NaN \n", - "28 NaN NaN NaN \n", - "29 NaN NaN NaN \n", - "30 1.460520e+08 NaN NaN \n", - "31 5.062561e+08 NaN NaN \n", - "32 2.129823e+07 NaN NaN \n", - "33 1.793334e+06 NaN NaN \n", - "34 1.367783e+08 NaN NaN \n", - "35 NaN NaN NaN \n", - "36 8.121780e+08 NaN NaN \n", - "37 2.613185e+03 NaN NaN \n", - "38 NaN NaN NaN \n", - "39 NaN NaN NaN \n", - "40 3.510602e+06 NaN NaN \n", - "41 1.048475e+07 NaN NaN \n", - "42 1.460667e+07 NaN NaN \n", - "43 NaN NaN NaN \n", - "44 2.860203e+07 NaN NaN \n", - "45 9.202711e+01 NaN NaN \n", - "46 NaN NaN NaN \n", - "47 NaN NaN NaN \n", - "48 5.923008e+08 NaN NaN \n", - "49 5.923008e+08 NaN NaN \n", - "50 1.905730e+03 NaN NaN \n", - "51 NaN NaN NaN \n", - "52 1.984539e+09 NaN NaN \n", - "53 6.385260e+03 NaN NaN \n", - "54 NaN NaN NaN \n", - "55 2.796717e+09 NaN NaN \n", - "56 8.998446e+03 NaN NaN \n", - "57 NaN NaN NaN \n", - "58 2.825319e+09 NaN NaN \n", - "59 9.090473e+03 NaN NaN \n", - "60 NaN NaN NaN \n", - "61 3.417620e+09 NaN NaN \n", - "62 1.099620e+04 NaN NaN \n", - "63 NaN NaN NaN \n", - "64 2.437538e+09 NaN NaN \n", - "65 7.842787e+03 NaN NaN \n", - "66 NaN NaN NaN \n", - "67 3.029839e+09 NaN NaN \n", - "68 9.748517e+03 NaN NaN \n", - "\n", - " Site Labor Cost Site Material Cost \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 2.791525e+08 5.517141e+07 \n", - "14 1.576432e+08 1.071061e+07 \n", - "15 6.159946e+06 3.607522e+06 \n", - "16 1.153494e+08 4.085327e+07 \n", - "17 1.890068e+08 7.078536e+06 \n", - "18 6.692093e+07 4.476621e+06 \n", - "19 3.478629e+07 0.000000e+00 \n", - "20 3.213464e+07 4.476621e+06 \n", - "21 8.038539e+07 2.068480e+07 \n", - "22 NaN NaN \n", - "23 2.937788e+07 3.611675e+06 \n", - "24 NaN NaN \n", - "25 NaN NaN \n", - "26 NaN NaN \n", - "27 NaN NaN \n", - "28 NaN NaN \n", - "29 NaN NaN \n", - "30 NaN NaN \n", - "31 NaN NaN \n", - "32 NaN NaN \n", - "33 NaN NaN \n", - "34 NaN NaN \n", - "35 NaN NaN \n", - "36 NaN NaN \n", - "37 NaN NaN \n", - "38 NaN NaN \n", - "39 NaN NaN \n", - "40 NaN NaN \n", - "41 NaN NaN \n", - "42 NaN NaN \n", - "43 NaN NaN \n", - "44 NaN NaN \n", - "45 NaN NaN \n", - "46 NaN NaN \n", - "47 NaN NaN \n", - "48 NaN NaN \n", - "49 NaN NaN \n", - "50 NaN NaN \n", - "51 NaN NaN \n", - "52 NaN NaN \n", - "53 NaN NaN \n", - "54 NaN NaN \n", - "55 NaN NaN \n", - "56 NaN NaN \n", - "57 NaN NaN \n", - "58 NaN NaN \n", - "59 NaN NaN \n", - "60 NaN NaN \n", - "61 NaN NaN \n", - "62 NaN NaN \n", - "63 NaN NaN \n", - "64 NaN NaN \n", - "65 NaN NaN \n", - "66 NaN NaN \n", - "67 NaN NaN \n", - "68 NaN NaN \n", - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "in function update indirect\n", - " Account Title \\\n", - "0 10 Capitalized Pre-Construction Costs \n", - "1 11 Land & Land Rights \n", - "2 12 Site Permits \n", - "3 13 Plant Licensing \n", - "4 14 Plant Permits \n", - "5 15 Plant Studies \n", - "6 16 Plant Reports \n", - "7 18 Other Pre-Construction Costs \n", - "8 NaN NaN \n", - "9 NaN 10s - Subtotal \n", - "10 NaN 10s - $/kWe \n", - "11 NaN NaN \n", - "12 20 Capitalized Direct Costs \n", - "13 21 Structures & Improvements \n", - "14 212 Reactor Containment Building \n", - "15 213 Turbine Room and Heater Bay \n", - "16 211 plus 214 to 219 Othe Structures & Improvements \n", - "17 22 Reactor System \n", - "18 23 Energy Conversion System \n", - "19 232.1 Electricity Generation Systems \n", - "20 233 Ultimate Heat Sink \n", - "21 24 Electrical Equipment \n", - "22 25 Initial fuel inventory \n", - "23 26 Miscellaneous Equipment \n", - "24 28 Simulator \n", - "25 NaN NaN \n", - "26 NaN 20s - Subtotal \n", - "27 NaN 20s - $/kWe \n", - "28 NaN NaN \n", - "29 30 Capitalized Indirect Services Costs \n", - "30 31 Factory & Field Indirect Costs \n", - "31 32 Factory and construction supervision \n", - "32 33 Start-Up Costs \n", - "33 34 Shipping & Transportation Costs \n", - "34 35 Engineering Services \n", - "35 NaN NaN \n", - "36 NaN 30s - Subtotal \n", - "37 NaN 30s - $/kWe \n", - "38 NaN NaN \n", - "39 50 Capitalized Supplementary Costs \n", - "40 51 Taxes \n", - "41 52 Insurance \n", - "42 54 Decommissioning \n", - "43 NaN NaN \n", - "44 NaN 50s - Subtotal \n", - "45 NaN 50s - $/kWe \n", - "46 NaN NaN \n", - "47 60 Capitalized Financial Costs \n", - "48 62 Interest \n", - "49 - 60s - Subtotal \n", - "50 NaN 60s - $/kWe \n", - "51 NaN NaN \n", - "52 NaN Total Direct Capital Cost (Accounts 10 to 20) \n", - "53 NaN (Accounts 10 to 20) US$/kWe \n", - "54 NaN NaN \n", - "55 NaN Base Construction Cost (Accounts 10 to 30) \n", - "56 NaN (Accounts 10 to 30) US$/kWe \n", - "57 NaN NaN \n", - "58 NaN Total Overnight Cost (Accounts 10 to 50) \n", - "59 NaN (Accounts 10 to 50) US$/kWe \n", - "60 NaN NaN \n", - "61 NaN Total Capital Investment Cost (All Accounts) \n", - "62 NaN (Accounts 10 to 60) US$/kWe \n", - "63 NaN NaN \n", - "64 NaN Total Overnight Cost - ITC reduced \n", - "65 NaN Total Overnight Cost -ITC reduced (US$/kWe) \n", - "66 NaN NaN \n", - "67 NaN Total Capital Investment Cost - ITC reduced \n", - "68 NaN Total Capital Investment Cost - ITC reduced (U... \n", - "\n", - " Total Cost (USD) Factory Equipment Cost Site Labor Hours \\\n", - "0 NaN NaN NaN \n", - "1 1.100000e+07 NaN NaN \n", - "2 1.598891e+06 NaN NaN \n", - "3 2.438299e+07 NaN NaN \n", - "4 1.267917e+07 NaN NaN \n", - "5 1.267917e+07 NaN NaN \n", - "6 3.972186e+06 NaN NaN \n", - "7 1.267917e+07 NaN NaN \n", - "8 NaN NaN NaN \n", - "9 7.899157e+07 NaN NaN \n", - "10 2.541556e+02 NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 4.408830e+08 1.065590e+08 5.557506e+06 \n", - "14 2.640765e+08 9.572267e+07 3.121808e+06 \n", - "15 1.038681e+07 6.193464e+05 1.194535e+05 \n", - "16 1.664197e+08 1.021703e+07 2.316244e+06 \n", - "17 8.076069e+08 6.115216e+08 3.518498e+06 \n", - "18 2.065652e+08 1.351676e+08 1.264169e+06 \n", - "19 1.332751e+08 9.848878e+07 6.529246e+05 \n", - "20 7.329011e+07 3.667885e+07 6.112442e+05 \n", - "21 1.237824e+08 2.271225e+07 1.491382e+06 \n", - "22 2.797244e+08 NaN NaN \n", - "23 4.690726e+07 1.391771e+07 5.420643e+05 \n", - "24 7.820000e+04 NaN NaN \n", - "25 NaN NaN NaN \n", - "26 1.905547e+09 NaN NaN \n", - "27 6.131105e+03 NaN NaN \n", - "28 NaN NaN NaN \n", - "29 NaN NaN NaN \n", - "30 1.460520e+08 NaN NaN \n", - "31 5.062561e+08 NaN NaN \n", - "32 2.129823e+07 NaN NaN \n", - "33 1.793334e+06 NaN NaN \n", - "34 1.367783e+08 NaN NaN \n", - "35 NaN NaN NaN \n", - "36 8.121780e+08 NaN NaN \n", - "37 2.613185e+03 NaN NaN \n", - "38 NaN NaN NaN \n", - "39 NaN NaN NaN \n", - "40 3.510602e+06 NaN NaN \n", - "41 1.048475e+07 NaN NaN \n", - "42 1.460667e+07 NaN NaN \n", - "43 NaN NaN NaN \n", - "44 2.860203e+07 NaN NaN \n", - "45 9.202711e+01 NaN NaN \n", - "46 NaN NaN NaN \n", - "47 NaN NaN NaN \n", - "48 5.923008e+08 NaN NaN \n", - "49 5.923008e+08 NaN NaN \n", - "50 1.905730e+03 NaN NaN \n", - "51 NaN NaN NaN \n", - "52 1.984539e+09 NaN NaN \n", - "53 6.385260e+03 NaN NaN \n", - "54 NaN NaN NaN \n", - "55 2.796717e+09 NaN NaN \n", - "56 8.998446e+03 NaN NaN \n", - "57 NaN NaN NaN \n", - "58 2.825319e+09 NaN NaN \n", - "59 9.090473e+03 NaN NaN \n", - "60 NaN NaN NaN \n", - "61 3.417620e+09 NaN NaN \n", - "62 1.099620e+04 NaN NaN \n", - "63 NaN NaN NaN \n", - "64 2.437538e+09 NaN NaN \n", - "65 7.842787e+03 NaN NaN \n", - "66 NaN NaN NaN \n", - "67 3.029839e+09 NaN NaN \n", - "68 9.748517e+03 NaN NaN \n", - "\n", - " Site Labor Cost Site Material Cost \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 2.791525e+08 5.517141e+07 \n", - "14 1.576432e+08 1.071061e+07 \n", - "15 6.159946e+06 3.607522e+06 \n", - "16 1.153494e+08 4.085327e+07 \n", - "17 1.890068e+08 7.078536e+06 \n", - "18 6.692093e+07 4.476621e+06 \n", - "19 3.478629e+07 0.000000e+00 \n", - "20 3.213464e+07 4.476621e+06 \n", - "21 8.038539e+07 2.068480e+07 \n", - "22 NaN NaN \n", - "23 2.937788e+07 3.611675e+06 \n", - "24 NaN NaN \n", - "25 NaN NaN \n", - "26 NaN NaN \n", - "27 NaN NaN \n", - "28 NaN NaN \n", - "29 NaN NaN \n", - "30 NaN NaN \n", - "31 NaN NaN \n", - "32 NaN NaN \n", - "33 NaN NaN \n", - "34 NaN NaN \n", - "35 NaN NaN \n", - "36 NaN NaN \n", - "37 NaN NaN \n", - "38 NaN NaN \n", - "39 NaN NaN \n", - "40 NaN NaN \n", - "41 NaN NaN \n", - "42 NaN NaN \n", - "43 NaN NaN \n", - "44 NaN NaN \n", - "45 NaN NaN \n", - "46 NaN NaN \n", - "47 NaN NaN \n", - "48 NaN NaN \n", - "49 NaN NaN \n", - "50 NaN NaN \n", - "51 NaN NaN \n", - "52 NaN NaN \n", - "53 NaN NaN \n", - "54 NaN NaN \n", - "55 NaN NaN \n", - "56 NaN NaN \n", - "57 NaN NaN \n", - "58 NaN NaN \n", - "59 NaN NaN \n", - "60 NaN NaN \n", - "61 NaN NaN \n", - "62 NaN NaN \n", - "63 NaN NaN \n", - "64 NaN NaN \n", - "65 NaN NaN \n", - "66 NaN NaN \n", - "67 NaN NaN \n", - "68 NaN NaN \n", - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [6629088.27585892] [12373618.31745577]\n", - "baseline_construction_duration 64.0\n", - "labor_hour_ratio [1.86656412]\n", - "in function update indirect\n" - ] - } - ], - "source": [ - "# Just running the other jupyter notebook to bring all the functions from there\n", - "%run CostReduction_exploration_mode.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c31cae49", - "metadata": {}, - "outputs": [], - "source": [ - "def calculate_final_result_avg(reactor_type, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0,\\\n", - " num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0, interest_rate_0, startup_0):\n", - " \n", - "# A function for the average results (averaged over all the reactors built)\n", - "\n", - " OCC_list = []\n", - " TCI_list = [] \n", - " durations_list = []\n", - " \n", - " for n_th in range(1, num_orders+1):\n", - " results = calculate_final_result(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0,\\\n", - " num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0, interest_rate_0, startup_0) \n", - " \n", - " OCC_result = results[1] # The ITC_reduced OCC (levelized)\n", - " TCI_result = results[2] # The ITC_reduced TCI(levelized)\n", - " duration_result = results[3] # cons duration\n", - " \n", - " OCC_list.append(OCC_result)\n", - " TCI_list.append( TCI_result )\n", - " durations_list.append( duration_result )\n", - " last_plant_OCC = OCC_list[-1]\n", - " first_plant_OCC = OCC_list[0]\n", - " avg_OCC = np.mean(OCC_list) \n", - " \n", - " last_plant_TCI = TCI_list[-1]\n", - " first_plant_TCI = TCI_list[0]\n", - " avg_TCI = np.mean(TCI_list)\n", - " \n", - " avg_dur = np.mean(durations_list)\n", - " \n", - " return last_plant_OCC , first_plant_OCC, avg_OCC, last_plant_TCI , first_plant_TCI, avg_TCI, avg_dur " - ] - }, - { - "cell_type": "markdown", - "id": "3c0d5583", - "metadata": {}, - "source": [ - "### Scenarios\n", - "These scenarios correspond to the scenarios in the cost reduction report: https://inldigitallibrary.inl.gov/sites/sti/sti/Sort_109810.pdf" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6a4a23ef", - "metadata": {}, - "outputs": [], - "source": [ - "reactor_type = \"Concept A\"\n", - "\n", - "# scenario independent params\n", - "mod_0 = \"modularized\"\n", - "BOP_grade_0 = \"non_nuclear\"\n", - "RB_grade_0 = \"nuclear\"\n", - "\n", - "land_cost_per_acre_0 = 22000 # dollars/acre\n", - "startup_0 = 16 \n", - "interest_rate_0 = 0.06\n", - "\n", - "# numb er of projects for full efficiency of procurement, A/E, Construction\n", - "N_proc = 3\n", - "N_AE = 4\n", - "N_cons =5\n", - "\n", - "# factory building cost (associated with accounts 22 and 232.1)\n", - "f_22 = 250000000\n", - "f_2321 = 150000000\n", - "\n", - "\n", - "for scenario in [1, 2, 3 ]:\n", - " if scenario == 1:\n", - " num_orders = 13\n", - " design_completion_0 = 0.8 # 1 means 100%\n", - " Design_Maturity_0 = 1\n", - " proc_exp_0= 0.5 # 2 means procurement experts. This is ideal. \n", - " ae_exp_0 = 0.5\n", - " ce_exp_0 = 1\n", - " standardization_0 = 0.8 # 0.7 corresponds to 70% standardization for PWRs\n", - " ITC_0 = 0 \n", - " n_ITC = 0\n", - "\n", - " elif scenario == 2:\n", - " num_orders = 18\n", - " design_completion_0 = 0.6 # 1 means 100%\n", - " Design_Maturity_0 = 0\n", - " proc_exp_0= 0 # 2 means procurement experts. This is ideal. \n", - " ae_exp_0 = 0\n", - " ce_exp_0 = 1\n", - " standardization_0 = 0.7 # 0.7 corresponds to 70% standardization for PWRs\n", - "\n", - " ITC_0 = 0 \n", - " n_ITC = 0 \n", - " \n", - " elif scenario == 3:\n", - " num_orders = 13\n", - " design_completion_0 = 0.8 # 1 means 100%\n", - " Design_Maturity_0 = 1\n", - " proc_exp_0= 0.5 # 2 means procurement experts. This is ideal. \n", - " ae_exp_0 = 0.5\n", - " ce_exp_0 = 1\n", - " standardization_0 = 0.8 # 0.7 corresponds to 70% standardization for PWRs\n", - " ITC_0 = 0.4\n", - " n_ITC = 4 \n", - " \n", - " #avg_results = calculate_final_result_avg(reactor_type, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0,\\\n", - " #num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, standardization_0, interest_rate_0, startup_0) " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8187e003", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4\n", - " Account Title \\\n", - "0 10 Capitalized Pre-Construction Costs \n", - "1 11 Land & Land Rights \n", - "2 12 Site Permits \n", - "3 13 Plant Licensing \n", - "4 14 Plant Permits \n", - "5 15 Plant Studies \n", - "6 16 Plant Reports \n", - "7 18 Other Pre-Construction Costs \n", - "8 NaN NaN \n", - "9 NaN 10s - Subtotal \n", - "10 NaN 10s - $/kWe \n", - "11 NaN NaN \n", - "12 20 Capitalized Direct Costs \n", - "13 21 Structures & Improvements \n", - "14 212 Reactor Containment Building \n", - "15 213 Turbine Room and Heater Bay \n", - "16 211 plus 214 to 219 Othe Structures & Improvements \n", - "17 22 Reactor System \n", - "18 23 Energy Conversion System \n", - "19 232.1 Electricity Generation Systems \n", - "20 233 Ultimate Heat Sink \n", - "21 24 Electrical Equipment \n", - "22 25 Initial fuel inventory \n", - "23 26 Miscellaneous Equipment \n", - "24 28 Simulator \n", - "25 NaN NaN \n", - "26 NaN 20s - Subtotal \n", - "27 NaN 20s - $/kWe \n", - "28 NaN NaN \n", - "29 30 Capitalized Indirect Services Costs \n", - "30 31 Factory & Field Indirect Costs \n", - "31 32 Factory and construction supervision \n", - "32 33 Start-Up Costs \n", - "33 34 Shipping & Transportation Costs \n", - "34 35 Engineering Services \n", - "35 NaN NaN \n", - "36 NaN 30s - Subtotal \n", - "37 NaN 30s - $/kWe \n", - "38 NaN NaN \n", - "39 50 Capitalized Supplementary Costs \n", - "40 51 Taxes \n", - "41 52 Insurance \n", - "42 54 Decommissioning \n", - "43 NaN NaN \n", - "44 NaN 50s - Subtotal \n", - "45 NaN 50s - $/kWe \n", - "46 NaN NaN \n", - "47 60 Capitalized Financial Costs \n", - "48 62 Interest \n", - "49 - 60s - Subtotal \n", - "50 NaN 60s - $/kWe \n", - "51 NaN NaN \n", - "52 NaN Total Direct Capital Cost (Accounts 10 to 20) \n", - "53 NaN (Accounts 10 to 20) US$/kWe \n", - "54 NaN NaN \n", - "55 NaN Base Construction Cost (Accounts 10 to 30) \n", - "56 NaN (Accounts 10 to 30) US$/kWe \n", - "57 NaN NaN \n", - "58 NaN Total Overnight Cost (Accounts 10 to 50) \n", - "59 NaN (Accounts 10 to 50) US$/kWe \n", - "60 NaN NaN \n", - "61 NaN Total Capital Investment Cost (All Accounts) \n", - "62 NaN (Accounts 10 to 60) US$/kWe \n", - "63 NaN NaN \n", - "64 NaN Total Overnight Cost - ITC reduced \n", - "65 NaN Total Overnight Cost -ITC reduced (US$/kWe) \n", - "66 NaN NaN \n", - "67 NaN Total Capital Investment Cost - ITC reduced \n", - "68 NaN Total Capital Investment Cost - ITC reduced (U... \n", - "\n", - " Total Cost (USD) Factory Equipment Cost Site Labor Hours \\\n", - "0 NaN NaN NaN \n", - "1 1.500000e+07 NaN NaN \n", - "2 0.000000e+00 NaN NaN \n", - "3 1.070098e+08 NaN NaN \n", - "4 4.721019e+06 NaN NaN \n", - "5 0.000000e+00 NaN NaN \n", - "6 0.000000e+00 NaN NaN \n", - "7 3.619448e+07 NaN NaN \n", - "8 NaN NaN NaN \n", - "9 1.629253e+08 NaN NaN \n", - "10 1.542853e+02 NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 1.533300e+09 1.365362e+08 2.090920e+07 \n", - "14 6.966448e+08 9.992741e+07 9.638431e+06 \n", - "15 1.522573e+07 1.036695e+06 1.749114e+05 \n", - "16 8.214295e+08 3.557211e+07 1.109586e+07 \n", - "17 1.975878e+09 1.478440e+09 8.719725e+06 \n", - "18 4.755064e+08 3.072326e+08 3.008178e+06 \n", - "19 3.301050e+08 2.344884e+08 1.794686e+06 \n", - "20 1.454013e+08 7.274425e+07 1.213492e+06 \n", - "21 2.358203e+08 4.326955e+07 2.841261e+06 \n", - "22 4.516865e+08 NaN 0.000000e+00 \n", - "23 3.594357e+08 9.942946e+07 4.282531e+06 \n", - "24 0.000000e+00 NaN NaN \n", - "25 NaN NaN NaN \n", - "26 5.031627e+09 NaN NaN \n", - "27 4.764798e+03 NaN NaN \n", - "28 NaN NaN NaN \n", - "29 NaN NaN NaN \n", - "30 6.913711e+08 NaN NaN \n", - "31 2.734393e+09 NaN NaN \n", - "32 1.150361e+08 NaN NaN \n", - "33 9.686167e+06 NaN NaN \n", - "34 7.387675e+08 NaN NaN \n", - "35 NaN NaN NaN \n", - "36 4.289254e+09 NaN NaN \n", - "37 4.061793e+03 NaN NaN \n", - "38 NaN NaN NaN \n", - "39 NaN NaN NaN \n", - "40 1.875000e+05 NaN NaN \n", - "41 3.820433e+07 NaN NaN \n", - "42 8.141760e+06 NaN NaN \n", - "43 NaN NaN NaN \n", - "44 4.653359e+07 NaN NaN \n", - "45 4.406590e+01 NaN NaN \n", - "46 NaN NaN NaN \n", - "47 NaN NaN NaN \n", - "48 3.202162e+09 NaN NaN \n", - "49 3.202162e+09 NaN NaN \n", - "50 3.032350e+03 NaN NaN \n", - "51 NaN NaN NaN \n", - "52 5.194552e+09 NaN NaN \n", - "53 4.919084e+03 NaN NaN \n", - "54 NaN NaN NaN \n", - "55 9.483806e+09 NaN NaN \n", - "56 8.980877e+03 NaN NaN \n", - "57 NaN NaN NaN \n", - "58 9.530340e+09 NaN NaN \n", - "59 9.024943e+03 NaN NaN \n", - "60 NaN NaN NaN \n", - "61 1.273250e+10 NaN NaN \n", - "62 1.205729e+04 NaN NaN \n", - "63 NaN NaN NaN \n", - "64 8.699310e+09 NaN NaN \n", - "65 8.237983e+03 NaN NaN \n", - "66 NaN NaN NaN \n", - "67 1.190147e+10 NaN NaN \n", - "68 1.127033e+04 NaN NaN \n", - "\n", - " Site Labor Cost Site Material Cost \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 1.045892e+09 3.508716e+08 \n", - "14 4.700620e+08 1.266554e+08 \n", - "15 9.024686e+06 5.164350e+06 \n", - "16 5.668055e+08 2.190519e+08 \n", - "17 4.656677e+08 3.177007e+07 \n", - "18 1.594144e+08 8.859309e+06 \n", - "19 9.561666e+07 0.000000e+00 \n", - "20 6.379777e+07 8.859309e+06 \n", - "21 1.531438e+08 3.940702e+07 \n", - "22 0.000000e+00 0.000000e+00 \n", - "23 2.310455e+08 2.896080e+07 \n", - "24 NaN NaN \n", - "25 NaN NaN \n", - "26 NaN NaN \n", - "27 NaN NaN \n", - "28 NaN NaN \n", - "29 NaN NaN \n", - "30 NaN NaN \n", - "31 NaN NaN \n", - "32 NaN NaN \n", - "33 NaN NaN \n", - "34 NaN NaN \n", - "35 NaN NaN \n", - "36 NaN NaN \n", - "37 NaN NaN \n", - "38 NaN NaN \n", - "39 NaN NaN \n", - "40 NaN NaN \n", - "41 NaN NaN \n", - "42 NaN NaN \n", - "43 NaN NaN \n", - "44 NaN NaN \n", - "45 NaN NaN \n", - "46 NaN NaN \n", - "47 NaN NaN \n", - "48 NaN NaN \n", - "49 NaN NaN \n", - "50 NaN NaN \n", - "51 NaN NaN \n", - "52 NaN NaN \n", - "53 NaN NaN \n", - "54 NaN NaN \n", - "55 NaN NaN \n", - "56 NaN NaN \n", - "57 NaN NaN \n", - "58 NaN NaN \n", - "59 NaN NaN \n", - "60 NaN NaN \n", - "61 NaN NaN \n", - "62 NaN NaN \n", - "63 NaN NaN \n", - "64 NaN NaN \n", - "65 NaN NaN \n", - "66 NaN NaN \n", - "67 NaN NaN \n", - "68 NaN NaN \n", - "update_cons_dur\n", - "sum_old_lab_hrs,sum_new_lab_hrs [23099589.55901049] [39760892.80720583]\n", - "baseline_construction_duration 100.0\n", - "labor_hour_ratio [1.72128135]\n", - "in function update indirect\n" - ] - }, - { - "data": { - "text/plain": [ - "( Account Title \\\n", - " 0 10 Capitalized Pre-Construction Costs \n", - " 1 11 Land & Land Rights \n", - " 2 12 Site Permits \n", - " 3 13 Plant Licensing \n", - " 4 14 Plant Permits \n", - " 5 15 Plant Studies \n", - " 6 16 Plant Reports \n", - " 7 18 Other Pre-Construction Costs \n", - " 8 NaN NaN \n", - " 9 NaN 10s - Subtotal \n", - " 10 NaN 10s - $/kWe \n", - " 11 NaN NaN \n", - " 12 20 Capitalized Direct Costs \n", - " 13 21 Structures & Improvements \n", - " 14 212 Reactor Containment Building \n", - " 15 213 Turbine Room and Heater Bay \n", - " 16 211 plus 214 to 219 Othe Structures & Improvements \n", - " 17 22 Reactor System \n", - " 18 23 Energy Conversion System \n", - " 19 232.1 Electricity Generation Systems \n", - " 20 233 Ultimate Heat Sink \n", - " 21 24 Electrical Equipment \n", - " 22 25 Initial fuel inventory \n", - " 23 26 Miscellaneous Equipment \n", - " 24 28 Simulator \n", - " 25 NaN NaN \n", - " 26 NaN 20s - Subtotal \n", - " 27 NaN 20s - $/kWe \n", - " 28 NaN NaN \n", - " 29 30 Capitalized Indirect Services Costs \n", - " 30 31 Factory & Field Indirect Costs \n", - " 31 32 Factory and construction supervision \n", - " 32 33 Start-Up Costs \n", - " 33 34 Shipping & Transportation Costs \n", - " 34 35 Engineering Services \n", - " 35 NaN NaN \n", - " 36 NaN 30s - Subtotal \n", - " 37 NaN 30s - $/kWe \n", - " 38 NaN NaN \n", - " 39 50 Capitalized Supplementary Costs \n", - " 40 51 Taxes \n", - " 41 52 Insurance \n", - " 42 54 Decommissioning \n", - " 43 NaN NaN \n", - " 44 NaN 50s - Subtotal \n", - " 45 NaN 50s - $/kWe \n", - " 46 NaN NaN \n", - " 47 60 Capitalized Financial Costs \n", - " 48 62 Interest \n", - " 49 - 60s - Subtotal \n", - " 50 NaN 60s - $/kWe \n", - " 51 NaN NaN \n", - " 52 NaN Total Direct Capital Cost (Accounts 10 to 20) \n", - " 53 NaN (Accounts 10 to 20) US$/kWe \n", - " 54 NaN NaN \n", - " 55 NaN Base Construction Cost (Accounts 10 to 30) \n", - " 56 NaN (Accounts 10 to 30) US$/kWe \n", - " 57 NaN NaN \n", - " 58 NaN Total Overnight Cost (Accounts 10 to 50) \n", - " 59 NaN (Accounts 10 to 50) US$/kWe \n", - " 60 NaN NaN \n", - " 61 NaN Total Capital Investment Cost (All Accounts) \n", - " 62 NaN (Accounts 10 to 60) US$/kWe \n", - " 63 NaN NaN \n", - " 64 NaN Total Overnight Cost - ITC reduced \n", - " 65 NaN Total Overnight Cost -ITC reduced (US$/kWe) \n", - " 66 NaN NaN \n", - " 67 NaN Total Capital Investment Cost - ITC reduced \n", - " 68 NaN Total Capital Investment Cost - ITC reduced (U... \n", - " \n", - " Total Cost (USD) Factory Equipment Cost Site Labor Hours \\\n", - " 0 NaN NaN NaN \n", - " 1 1.500000e+07 NaN NaN \n", - " 2 0.000000e+00 NaN NaN \n", - " 3 1.070098e+08 NaN NaN \n", - " 4 4.721019e+06 NaN NaN \n", - " 5 0.000000e+00 NaN NaN \n", - " 6 0.000000e+00 NaN NaN \n", - " 7 3.619448e+07 NaN NaN \n", - " 8 NaN NaN NaN \n", - " 9 1.629253e+08 NaN NaN \n", - " 10 1.542853e+02 NaN NaN \n", - " 11 NaN NaN NaN \n", - " 12 NaN NaN NaN \n", - " 13 1.533300e+09 1.365362e+08 2.090920e+07 \n", - " 14 6.966448e+08 9.992741e+07 9.638431e+06 \n", - " 15 1.522573e+07 1.036695e+06 1.749114e+05 \n", - " 16 8.214295e+08 3.557211e+07 1.109586e+07 \n", - " 17 1.975878e+09 1.478440e+09 8.719725e+06 \n", - " 18 4.755064e+08 3.072326e+08 3.008178e+06 \n", - " 19 3.301050e+08 2.344884e+08 1.794686e+06 \n", - " 20 1.454013e+08 7.274425e+07 1.213492e+06 \n", - " 21 2.358203e+08 4.326955e+07 2.841261e+06 \n", - " 22 4.516865e+08 NaN 0.000000e+00 \n", - " 23 3.594357e+08 9.942946e+07 4.282531e+06 \n", - " 24 0.000000e+00 NaN NaN \n", - " 25 NaN NaN NaN \n", - " 26 5.031627e+09 NaN NaN \n", - " 27 4.764798e+03 NaN NaN \n", - " 28 NaN NaN NaN \n", - " 29 NaN NaN NaN \n", - " 30 1.377074e+09 NaN NaN \n", - " 31 5.005922e+09 NaN NaN \n", - " 32 2.102487e+08 NaN NaN \n", - " 33 1.752073e+07 NaN NaN \n", - " 34 1.351599e+09 NaN NaN \n", - " 35 NaN NaN NaN \n", - " 36 7.962364e+09 NaN NaN \n", - " 37 7.540118e+03 NaN NaN \n", - " 38 NaN NaN NaN \n", - " 39 NaN NaN NaN \n", - " 40 1.875000e+05 NaN NaN \n", - " 41 5.847296e+07 NaN NaN \n", - " 42 8.141760e+06 NaN NaN \n", - " 43 NaN NaN NaN \n", - " 44 6.680222e+07 NaN NaN \n", - " 45 6.325968e+01 NaN NaN \n", - " 46 NaN NaN NaN \n", - " 47 NaN NaN NaN \n", - " 48 6.594528e+09 NaN NaN \n", - " 49 6.594528e+09 NaN NaN \n", - " 50 6.244818e+03 NaN NaN \n", - " 51 NaN NaN NaN \n", - " 52 5.194552e+09 NaN NaN \n", - " 53 4.919084e+03 NaN NaN \n", - " 54 NaN NaN NaN \n", - " 55 1.315692e+10 NaN NaN \n", - " 56 1.245920e+04 NaN NaN \n", - " 57 NaN NaN NaN \n", - " 58 1.322372e+10 NaN NaN \n", - " 59 1.252246e+04 NaN NaN \n", - " 60 NaN NaN NaN \n", - " 61 1.981825e+10 NaN NaN \n", - " 62 1.876728e+04 NaN NaN \n", - " 63 NaN NaN NaN \n", - " 64 8.330943e+09 NaN NaN \n", - " 65 7.889150e+03 NaN NaN \n", - " 66 NaN NaN NaN \n", - " 67 1.492547e+10 NaN NaN \n", - " 68 1.413397e+04 NaN NaN \n", - " \n", - " Site Labor Cost Site Material Cost \n", - " 0 NaN NaN \n", - " 1 NaN NaN \n", - " 2 NaN NaN \n", - " 3 NaN NaN \n", - " 4 NaN NaN \n", - " 5 NaN NaN \n", - " 6 NaN NaN \n", - " 7 NaN NaN \n", - " 8 NaN NaN \n", - " 9 NaN NaN \n", - " 10 NaN NaN \n", - " 11 NaN NaN \n", - " 12 NaN NaN \n", - " 13 1.045892e+09 3.508716e+08 \n", - " 14 4.700620e+08 1.266554e+08 \n", - " 15 9.024686e+06 5.164350e+06 \n", - " 16 5.668055e+08 2.190519e+08 \n", - " 17 4.656677e+08 3.177007e+07 \n", - " 18 1.594144e+08 8.859309e+06 \n", - " 19 9.561666e+07 0.000000e+00 \n", - " 20 6.379777e+07 8.859309e+06 \n", - " 21 1.531438e+08 3.940702e+07 \n", - " 22 0.000000e+00 0.000000e+00 \n", - " 23 2.310455e+08 2.896080e+07 \n", - " 24 NaN NaN \n", - " 25 NaN NaN \n", - " 26 NaN NaN \n", - " 27 NaN NaN \n", - " 28 NaN NaN \n", - " 29 NaN NaN \n", - " 30 NaN NaN \n", - " 31 NaN NaN \n", - " 32 NaN NaN \n", - " 33 NaN NaN \n", - " 34 NaN NaN \n", - " 35 NaN NaN \n", - " 36 NaN NaN \n", - " 37 NaN NaN \n", - " 38 NaN NaN \n", - " 39 NaN NaN \n", - " 40 NaN NaN \n", - " 41 NaN NaN \n", - " 42 NaN NaN \n", - " 43 NaN NaN \n", - " 44 NaN NaN \n", - " 45 NaN NaN \n", - " 46 NaN NaN \n", - " 47 NaN NaN \n", - " 48 NaN NaN \n", - " 49 NaN NaN \n", - " 50 NaN NaN \n", - " 51 NaN NaN \n", - " 52 NaN NaN \n", - " 53 NaN NaN \n", - " 54 NaN NaN \n", - " 55 NaN NaN \n", - " 56 NaN NaN \n", - " 57 NaN NaN \n", - " 58 NaN NaN \n", - " 59 NaN NaN \n", - " 60 NaN NaN \n", - " 61 NaN NaN \n", - " 62 NaN NaN \n", - " 63 NaN NaN \n", - " 64 NaN NaN \n", - " 65 NaN NaN \n", - " 66 NaN NaN \n", - " 67 NaN NaN \n", - " 68 NaN NaN ,\n", - " 7889.150458472184,\n", - " 14133.968613003488,\n", - " array([133.06625314]))" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(n_ITC)\n", - "calculate_final_result(reactor_type, n_th, f_22, f_2321, land_cost_per_acre_0, RB_grade_0, BOP_grade_0,\\\n", - " num_orders, design_completion_0, ae_exp_0, N_AE, ce_exp_0, N_cons, mod_0, Design_Maturity_0, proc_exp_0, N_proc, \\\n", - " standardization_0, interest_rate_0, startup_0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f62c044-f816-49db-8564-5ef81c3221f8", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b4dfbc1-3f1e-48c5-b089-035bb954f4c4", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Cost_Reduction/sensitivity_analysis.ipynb b/Cost_Reduction/sensitivity_analysis.ipynb deleted file mode 100644 index 22b49e4..0000000 --- a/Cost_Reduction/sensitivity_analysis.ipynb +++ /dev/null @@ -1,951 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ee4fa393-2ded-480c-bbf0-547d0cc0ddac", - "metadata": {}, - "source": [ - "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "40096a23-ff7b-4541-b710-9f0a9f9a595d", - "metadata": {}, - "source": [ - "#
Cost Reduction Framework for Nuclear Reactor Power Plants
\n" - ] - }, - { - "cell_type": "markdown", - "id": "dfc0a6a4-10b0-4229-81ad-ba16943cc3a1", - "metadata": {}, - "source": [ - "## Section 0 : Essentials to Run the code" - ] - }, - { - "cell_type": "markdown", - "id": "f2256ae0-712f-4811-99d9-d37188782036", - "metadata": {}, - "source": [ - "### Section 0 - 1 : Importing the libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "12fb6329-dc95-493f-ba1d-86235e71b512", - "metadata": {}, - "outputs": [], - "source": [ - "# Importing libararies\n", - "import numpy as np\n", - "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore', category=FutureWarning)\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from src import *\n", - "\n", - "\n", - "reactor_type_list = ['SFR', 'HTGR']\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7a34753d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SFR\n", - "HTGR\n" - ] - } - ], - "source": [ - "\n", - "startup_0 = 16\n", - "interest_rate_0 = 0.06\n", - "design_completion_0 = 0.8\n", - "Design_Maturity_0 = 1\n", - "proc_exp_0 = 0.5\n", - "ae_exp_0 = 0.5\n", - "ce_exp_0 = 1\n", - "\n", - "mod_0 = 'modularized'\n", - "standardization_0 = 0.8\n", - "BOP_grade_0 = 'non_nuclear'\n", - "ITC_0 = 0\n", - "n_ITC = 0\n", - "RB_grade_0 = 'nuclear'\n", - "\n", - "for reactor_type in reactor_type_list:\n", - " print(reactor_type)\n", - " TCI_list_avg = []\n", - " OCC_list_avg = []\n", - " orders_list = []\n", - " for num_orders in range(4,21):\n", - " OCC_list = []\n", - " TCI_list = [] \n", - " for n_th in range(1, num_orders+1):\n", - " results = calculate(num_orders ,n_th, startup_0, interest_rate_0, design_completion_0, Design_Maturity_0, proc_exp_0 , ae_exp_0, ce_exp_0, mod_0 , standardization_0, BOP_grade_0, RB_grade_0, ITC_0, n_ITC, reactor_type ) \n", - " OCC_result = results[0]\n", - " TCI_result = results[1]\n", - " \n", - " OCC_list.append(OCC_result)\n", - " TCI_list.append( TCI_result )\n", - " \n", - " avg_TCI = np.mean(TCI_list)\n", - " avg_OCC = np.mean(OCC_list)\n", - " \n", - " TCI_list_avg.append(avg_TCI)\n", - " OCC_list_avg.append(avg_OCC)\n", - " orders_list.append(num_orders)\n", - " \n", - " \n", - " if reactor_type == \"SFR\":\n", - " OCC_list_avg_SFR = OCC_list_avg\n", - " TCI_list_avg_SFR = TCI_list_avg\n", - " elif reactor_type == \"HTGR\":\n", - " OCC_list_avg_HTGR = OCC_list_avg\n", - " TCI_list_avg_HTGR = TCI_list_avg\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8a456bc9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(13, 9))\n", - "plt.plot(orders_list, OCC_list_avg_HTGR, linestyle='-', marker='o', markersize=8, label= \"Reactor Concept A Average OCC\", color='red') \n", - "plt.plot(orders_list, TCI_list_avg_HTGR, linestyle='dashed', marker='o', markersize=8, label= \"Reactor Concept A Average NCI\", color='red')\n", - "\n", - "plt.plot(orders_list, OCC_list_avg_SFR, linestyle='-', marker='o', markersize=8, label='Reactor Concept B Average OCC', color='blue') \n", - "plt.plot(orders_list, TCI_list_avg_SFR, linestyle='dashed', marker='o', markersize=8, label='Reactor Concept B Average NCI', color= 'blue') \n", - "\n", - "# # g.set_xticks(range(1,22,2))\n", - "\n", - " \n", - "\n", - "\n", - "\n", - "plt.legend() # g.set_xticks(range(1,22,2))\n", - "\n", - "\n", - "plt.xlabel('Order Book Size', fontsize='20') # x-axis name\n", - "plt.ylabel('2022$/kWe', fontsize='20') # x-axis name\n", - "\n", - "# # plt.ylabel('Order BookAveraged TCI (2022$/kWe)', fontsize='25') # x-axis name\n", - "plt.legend(loc='upper right', fontsize='20') # Add a legend\n", - "plt.tick_params(labelsize=20)\n", - "plt.xlim(4, 20)\n", - "\n", - "plt.grid(which='major', color='grey', linewidth=0.8)\n", - "plt.grid(which='minor', color='grey', linestyle='dashed', linewidth=0.5)\n", - "plt.minorticks_on()\n", - "\n", - "plt.savefig('orders.png')\n", - "plt.show() # Display the graph\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8166d518", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[10129.426593668872,\n", - " 9054.364356093398,\n", - " 8293.675152027796,\n", - " 7719.580972658651,\n", - " 7267.1199076171115,\n", - " 6899.225446983411,\n", - " 6591.9060804843875,\n", - " 6331.086579065694,\n", - " 6105.572949904573,\n", - " 5908.11333540093,\n", - " 5733.391929705431,\n", - " 5577.767270570705,\n", - " 5437.851550664015,\n", - " 5311.1761056860005,\n", - " 5195.36334164328,\n", - " 5089.343188912801,\n", - " 4991.797561198853]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "TCI_list_avg_HTGR" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f15a1174", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[9410.208845406803,\n", - " 8460.973294090489,\n", - " 7798.467989648133,\n", - " 7303.202162132135,\n", - " 6918.1355596638405,\n", - " 6607.075484293743,\n", - " 6349.302890667079,\n", - " 6131.373466375377,\n", - " 5943.1499192554,\n", - " 5779.3016910071765,\n", - " 5634.981155045442,\n", - " 5506.007888292734,\n", - " 5390.295118126247,\n", - " 5285.307360763239,\n", - " 5189.854956275479,\n", - " 5102.235827688789,\n", - " 5021.422116119418]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "TCI_list_avg_SFR" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b46eadd8", - "metadata": {}, - "outputs": [], - "source": [ - "startup_0 = 16\n", - "num_orders = 12\n", - "design_completion_0 = 0.8\n", - "Design_Maturity_0 = 1\n", - "proc_exp_0 = 0.5\n", - "ae_exp_0 = 0.5\n", - "ce_exp_0 = 1\n", - "\n", - "mod_0 = 'stick_built'\n", - "standardization_0 = 0.8\n", - "BOP_grade_0 = 'non_nuclear'\n", - "ITC_0 = 0\n", - "n_ITC = 0\n", - "RB_grade_0 = 'nuclear'\n", - "\n", - "for reactor_type in reactor_type_list:\n", - " TCI_list_avg = []\n", - " OCC_list_avg = []\n", - " interest_list = []\n", - " print(reactor_type)\n", - " for interest_rate_0 in [x / 100.0 for x in range(3, 13)]:\n", - " \n", - " OCC_list = []\n", - " TCI_list = [] \n", - " for n_th in range(1, num_orders+1):\n", - " results = calculate(num_orders ,n_th, startup_0, interest_rate_0, design_completion_0, Design_Maturity_0, proc_exp_0 , ae_exp_0, ce_exp_0, mod_0 , standardization_0, BOP_grade_0, RB_grade_0, ITC_0, n_ITC, reactor_type ) \n", - " OCC_result = results[0]\n", - " TCI_result = results[1]\n", - " \n", - " OCC_list.append(OCC_result)\n", - " TCI_list.append( TCI_result )\n", - " \n", - " avg_TCI = np.mean(TCI_list)\n", - " avg_OCC = np.mean(OCC_list)\n", - " \n", - " TCI_list_avg.append(avg_TCI)\n", - " OCC_list_avg.append(avg_OCC)\n", - " interest_list.append(interest_rate_0)\n", - " \n", - " \n", - " if reactor_type == \"SFR\":\n", - " OCC_list_avg_SFR = OCC_list_avg\n", - " TCI_list_avg_SFR = TCI_list_avg\n", - " elif reactor_type == \"HTGR\":\n", - " OCC_list_avg_HTGR = OCC_list_avg\n", - " TCI_list_avg_HTGR = TCI_list_avg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "49ac27d0", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(13, 9))\n", - "plt.plot(interest_list, TCI_list_avg_HTGR, linestyle='dashed', marker='o', markersize=8, label= \"Reactor Concept A\", color='red')\n", - "\n", - "plt.plot(interest_list, TCI_list_avg_SFR, linestyle='dashed', marker='o', markersize=8, label='Reactor Concept B', color= 'blue') \n", - "\n", - "# # # g.set_xticks(range(1,22,2))\n", - "\n", - " \n", - "\n", - "\n", - "\n", - "plt.legend() # g.set_xticks(range(1,22,2))\n", - "\n", - "\n", - "plt.xlabel('Interest Rate (%)', fontsize='20') # x-axis name\n", - "plt.ylabel('Average NCI (2022$/kWe)', fontsize='20') # x-axis name\n", - "\n", - "plt.legend(loc='upper left', fontsize='20') # Add a legend\n", - "plt.tick_params(labelsize=20)\n", - "plt.xlim(0.03, 0.12)\n", - "\n", - "plt.grid(which='major', color='grey', linewidth=0.8)\n", - "plt.grid(which='minor', color='grey', linestyle='dashed', linewidth=0.5)\n", - "plt.minorticks_on()\n", - "\n", - "plt.savefig('orders.png')\n", - "plt.show() # Display the graph" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "979af689", - "metadata": {}, - "outputs": [], - "source": [ - "startup_0 = 16\n", - "num_orders = 12\n", - "design_completion_0 = 0.8\n", - "Design_Maturity_0 = 1\n", - "\n", - "interest_rate_0 = 0.06\n", - "\n", - "mod_0 = 'stick_built'\n", - "standardization_0 = 0.8\n", - "BOP_grade_0 = 'non_nuclear'\n", - "ITC_0 = 0\n", - "n_ITC = 0\n", - "RB_grade_0 = 'nuclear'\n", - "\n", - "\n", - "EPC_list = ['very low', 'low', 'medium', 'high', 'very high']\n", - "\n", - "for reactor_type in reactor_type_list:\n", - " print('\\n' ,reactor_type)\n", - " TCI_list_avg = []\n", - " OCC_list_avg = []\n", - " \n", - " \n", - " for epc_exp in EPC_list:\n", - " print( epc_exp )\n", - " OCC_list = []\n", - " TCI_list = [] \n", - " \n", - " if epc_exp == \"very low\":\n", - " proc_exp_0 = 0\n", - " ae_exp_0 = 0\n", - " ce_exp_0 = 0\n", - "\n", - " elif epc_exp == \"low\":\n", - " proc_exp_0 = 0\n", - " ae_exp_0 = 0\n", - " ce_exp_0 = 1 \n", - "\n", - " elif epc_exp == \"medium\":\n", - " proc_exp_0 = 1\n", - " ae_exp_0 = 1\n", - " ce_exp_0 = 1 \n", - "\n", - " elif epc_exp == \"high\":\n", - " proc_exp_0 = 1\n", - " ae_exp_0 = 1\n", - " ce_exp_0 = 2 \n", - "\n", - " elif epc_exp == \"very high\":\n", - " proc_exp_0 = 2\n", - " ae_exp_0 = 2\n", - " ce_exp_0 = 2 \n", - " \n", - " for n_th in range(1, num_orders+1):\n", - " results = calculate(num_orders ,n_th, startup_0, interest_rate_0, design_completion_0, Design_Maturity_0, proc_exp_0 , ae_exp_0, ce_exp_0, mod_0 , standardization_0, BOP_grade_0, RB_grade_0, ITC_0, n_ITC, reactor_type ) \n", - " OCC_result = results[0]\n", - " TCI_result = results[1]\n", - " \n", - " OCC_list.append(OCC_result)\n", - " TCI_list.append( TCI_result )\n", - " \n", - " avg_TCI = np.mean(TCI_list)\n", - " avg_OCC = np.mean(OCC_list)\n", - " \n", - " TCI_list_avg.append(avg_TCI)\n", - " OCC_list_avg.append(avg_OCC)\n", - " \n", - " \n", - " if reactor_type == \"SFR\":\n", - " OCC_list_avg_SFR = OCC_list_avg\n", - " TCI_list_avg_SFR = TCI_list_avg\n", - " elif reactor_type == \"HTGR\":\n", - " OCC_list_avg_HTGR = OCC_list_avg\n", - " TCI_list_avg_HTGR = TCI_list_avg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2dd233d5", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(13, 9))\n", - "\n", - "xx = np.arange(5) \n", - "plt.bar(xx + 0.3 , TCI_list_avg_HTGR,0.3, color = 'red')\n", - "plt.bar( xx , TCI_list_avg_SFR ,0.3, color = 'blue')\n", - "\n", - "plt.xlabel('\\n EPC Proficiency', fontsize='25') # x-axis name\n", - "plt.ylabel('Average NCI (2022$/kWe) \\n', fontsize='25') # x-axis name\n", - "\n", - "plt.xticks(xx, ['Very Low', 'Low', 'Medium', 'High', 'Very High'])\n", - "\n", - "plt.grid(which='major', linewidth=0.8, axis='y')\n", - "plt.grid(which='minor', linestyle='dashed',axis='y', linewidth=0.5)\n", - "plt.minorticks_on()\n", - "plt.ylim(0, 8000)\n", - "plt.tick_params(labelsize=25)\n", - "plt.legend([\"Reactor Concept A\" , \"Reactor Concept B\"], loc='upper right', fontsize='25')\n", - "\n", - "\n", - "\n", - "\n", - "plt.figure(figsize=(13, 9))\n", - "\n", - "xx = np.arange(5) \n", - "plt.bar(xx + 0.3 , OCC_list_avg_HTGR,0.3, color = 'red')\n", - "plt.bar( xx , OCC_list_avg_SFR ,0.3, color = 'blue')\n", - "\n", - "plt.xlabel('\\n EPC Proficiency', fontsize='25') # x-axis name\n", - "plt.ylabel('Average OCC (2022$/kWe) \\n', fontsize='25') # x-axis name\n", - "\n", - "plt.xticks(xx, ['Very Low', 'Low', 'Medium', 'High', 'Very High'])\n", - "\n", - "plt.grid(which='major', linewidth=0.8, axis='y')\n", - "plt.grid(which='minor', linestyle='dashed',axis='y', linewidth=0.5)\n", - "plt.minorticks_on()\n", - "plt.ylim(0, 6200)\n", - "plt.tick_params(labelsize=25)\n", - "plt.legend([\"Reactor Concept A\" , \"Reactor Concept B\"], loc='upper right', fontsize='25')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21e5ad85", - "metadata": {}, - "outputs": [], - "source": [ - "startup_0 = 16\n", - "ITC_0 = 0\n", - "n_ITC = 0\n", - "interest_rate_0 = 0.06\n", - "\n", - "for reactor_type in reactor_type_list:\n", - " \n", - " print( \"reactor type : \",reactor_type)\n", - " \n", - " avg_duration_list = []\n", - " TCI_list_avg = []\n", - " OCC_list_avg = []\n", - " \n", - " for num_orders in [5, 10]: \n", - " print('number of orders = ',num_orders)\n", - " for design_completion_0 in [0.5, 1]: \n", - " for Design_Maturity_0 in [0, 2 ]: \n", - " for proc_exp_0 in [0, 2 ]:\n", - " for ae_exp_0 in [0, 2 ]: \n", - " for ce_exp_0 in [0, 2]: \n", - " for mod_0 in [\"stick_built\", \"modularized\"]:\n", - " for standardization_0 in [0.7, 0.95]: \n", - " for BOP_grade_0 in [ \"nuclear\", 'non_nuclear' ]: \n", - " for RB_grade_0 in [ \"nuc lear\" , 'non_nuclear']: # \n", - " \n", - " dur_list = []\n", - " TCI_list = []\n", - " OCC_list = []\n", - " \n", - " for n_th in range(1, num_orders+1):\n", - " results = calculate(num_orders ,n_th, startup_0, interest_rate_0, design_completion_0, Design_Maturity_0, proc_exp_0 , ae_exp_0, ce_exp_0, mod_0 , standardization_0, BOP_grade_0, RB_grade_0, ITC_0, n_ITC, reactor_type )\n", - " OCC_result = results[0]\n", - " TCI_result = results[1]\n", - " cons_dur = results[2]\n", - " \n", - " OCC_list.append(OCC_result)\n", - " TCI_list.append( TCI_result )\n", - " dur_list.append(cons_dur )\n", - " \n", - " avg_duration = np.mean(dur_list)\n", - " avg_TCI = np.mean(TCI_list)\n", - " avg_OCC = np.mean(OCC_list) \n", - " \n", - " avg_duration_list.append(avg_duration) \n", - " TCI_list_avg.append(avg_TCI)\n", - " OCC_list_avg.append(avg_OCC)\n", - " \n", - " if reactor_type == \"SFR\":\n", - " avg_duration_list_SFR = avg_duration_list\n", - " OCC_list_avg_SFR = OCC_list_avg\n", - " TCI_list_avg_SFR = TCI_list_avg\n", - " \n", - " elif reactor_type == \"HTGR\":\n", - " avg_duration_list_HTGR = avg_duration_list \n", - " OCC_list_avg_HTGR = OCC_list_avg\n", - " TCI_list_avg_HTGR = TCI_list_avg " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54d00ef3", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(13, 9))\n", - "\n", - "plt.scatter(avg_duration_list_HTGR, OCC_list_avg_HTGR, label= \"Reactor Concept A\", color='red')\n", - " \n", - "plt.legend() # g.set_xticks(range(1,22,2))\n", - "\n", - "plt.xlabel('Average Construction Duration (months)', fontsize='20') # x-axis name\n", - "plt.ylabel('Average OCC (2022$/kWe)', fontsize='20') # x-axis name\n", - "\n", - "# # plt.ylabel('Order BookAveraged TCI (2022$/kWe)', fontsize='25') # x-axis name\n", - "plt.legend(loc='upper left', fontsize='20') # Add a legend\n", - "plt.tick_params(labelsize=20)\n", - "plt.xlim(60, 110)\n", - "plt.ylim(3000, 12000)\n", - "\n", - "plt.grid(which='major', color='grey', linewidth=0.8)\n", - "plt.grid(which='minor', color='grey', linestyle='dashed', linewidth=0.5)\n", - "plt.minorticks_on()\n", - "\n", - "plt.savefig('OCC_construction_HTGR.png')\n", - "plt.show() # Display the graph\n", - "\n", - "\n", - "plt.figure(figsize=(13, 9))\n", - "\n", - "plt.scatter(avg_duration_list_HTGR, TCI_list_avg_HTGR, label= \"Reactor Concept A\", color='red')\n", - " \n", - "plt.legend() # g.set_xticks(range(1,22,2))\n", - "\n", - "plt.xlabel('Average Construction Duration (months)', fontsize='20') # x-axis name\n", - "plt.ylabel('Average NCI (2022$/kWe)', fontsize='20') # x-axis name\n", - "\n", - "# # plt.ylabel('Order BookAveraged TCI (2022$/kWe)', fontsize='25') # x-axis name\n", - "plt.legend(loc='upper left', fontsize='20') # Add a legend\n", - "plt.tick_params(labelsize=20)\n", - "plt.xlim(60, 110)\n", - "plt.ylim(4000, 17000)\n", - "\n", - "plt.grid(which='major', color='grey', linewidth=0.8)\n", - "plt.grid(which='minor', color='grey', linestyle='dashed', linewidth=0.5)\n", - "plt.minorticks_on()\n", - "\n", - "plt.savefig('TCI_construction_HTGR.png')\n", - "plt.show() # Display the graph" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8bbaa32", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(13, 9))\n", - "\n", - "plt.scatter(avg_duration_list_SFR, OCC_list_avg_SFR, label= \"Reactor Concept B\", color='blue')\n", - " \n", - "plt.legend() # g.set_xticks(range(1,22,2))\n", - "\n", - "plt.xlabel('Average Construction Duration (months)', fontsize='20') # x-axis name\n", - "plt.ylabel('Average OCC (2022$/kWe)', fontsize='20') # x-axis name\n", - "\n", - "# # plt.ylabel('Order BookAveraged TCI (2022$/kWe)', fontsize='25') # x-axis name\n", - "plt.legend(loc='upper left', fontsize='20') # Add a legend\n", - "plt.tick_params(labelsize=20)\n", - "plt.xlim(38, 75)\n", - "plt.ylim(4000, 11500)\n", - "\n", - "plt.grid(which='major', color='grey', linewidth=0.8)\n", - "plt.grid(which='minor', color='grey', linestyle='dashed', linewidth=0.5)\n", - "plt.minorticks_on()\n", - "\n", - "plt.savefig('OCC_construction_SFR.png')\n", - "plt.show() # Display the graph\n", - "\n", - "\n", - "plt.figure(figsize=(13, 9))\n", - "\n", - "plt.scatter(avg_duration_list_SFR, TCI_list_avg_SFR, label= \"Reactor Concept B\", color='blue')\n", - " \n", - "plt.legend() # g.set_xticks(range(1,22,2))\n", - "\n", - "plt.xlabel('Average Construction Duration (months)', fontsize='20') # x-axis name\n", - "plt.ylabel('Average NCI (2022$/kWe)', fontsize='20') # x-axis name\n", - "\n", - "# # plt.ylabel('Order BookAveraged TCI (2022$/kWe)', fontsize='25') # x-axis name\n", - "plt.legend(loc='upper left', fontsize='20') # Add a legend\n", - "plt.tick_params(labelsize=20)\n", - "plt.xlim(38, 75)\n", - "plt.ylim(4500, 15000)\n", - "\n", - "plt.grid(which='major', color='grey', linewidth=0.8)\n", - "plt.grid(which='minor', color='grey', linestyle='dashed', linewidth=0.5)\n", - "plt.minorticks_on()\n", - "\n", - "plt.savefig('TCI_construction_HTGR.png')\n", - "plt.show() # Display the graph" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "2edc4386", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SFR\n", - "best\n", - "# Orders\n", - "Design Completion\n", - "A/E Proficiency\n", - "Construction Proficiency\n", - "Supply Chain Proficiency\n", - "Design Maturity\n", - "Standardization\n", - "Modularization\n", - "Commercial BOP\n", - "Safety-Related RB\n", - "ITC\n", - "Interest Rate\n", - "HTGR\n", - "best\n", - "# Orders\n", - "Design Completion\n", - "A/E Proficiency\n", - "Construction Proficiency\n", - "Supply Chain Proficiency\n", - "Design Maturity\n", - "Standardization\n", - "Modularization\n", - "Commercial BOP\n", - "Safety-Related RB\n", - "ITC\n", - "Interest Rate\n" - ] - } - ], - "source": [ - "\n", - "scenarios_list = [\"best\", \"# Orders\", \"Design Completion\", \"A/E Proficiency\", \"Construction Proficiency\",\\\n", - " \"Supply Chain Proficiency\", \"Design Maturity\", \"Standardization\", \"Modularization\",\\\n", - " \"Commercial BOP\", \"Safety-Related RB\", \"ITC\", \"Interest Rate\"]\n", - "\n", - "for reactor_type in reactor_type_list:\n", - " print(reactor_type)\n", - " \n", - " TCI_list_avg = []\n", - " \n", - " for scenario in scenarios_list:\n", - " print(scenario)\n", - " \n", - " if scenario == '# Orders':\n", - " num_orders = 2\n", - " else: \n", - " num_orders = 20 \n", - " \n", - " if scenario == \"Startup Duration\":\n", - " startup_0 = 36 # start up duration (months)\n", - " else:\n", - " startup_0 = 12\n", - " # land cost\n", - " # From the SA report: the cost $22,000 per acre. The land area is 500 acres including recommended buffer\n", - " if scenario == \"Land Cost\":\n", - " land_cost_per_acre_0 = 100000 # d\n", - " else: \n", - " land_cost_per_acre_0 = 1000 # dollars/acre \n", - " \n", - "\n", - " # # interest rate :\n", - " if scenario == \"Interest Rate\":\n", - " interest_rate_0 = 0.12\n", - " else: \n", - " interest_rate_0 = 0.04\n", - "\n", - " if scenario == \"Design Completion\":\n", - " design_completion_0 = 0.5 # 1 means 100%\n", - " else: \n", - " design_completion_0 = 1# 1 means 100%\n", - " \n", - " if scenario == \"Design Maturity\":\n", - " Design_Maturity_0 = 0\n", - " else: \n", - " Design_Maturity_0 = 2\n", - " \n", - " \n", - " \n", - " \n", - " # #procurement service experience (supply chain experience)\n", - " \n", - " \n", - " if scenario == \"Supply Chain Proficiency\":\n", - " proc_exp_0= 0 # 2 means procurement experts. This is ideal. \n", - " else:\n", - " proc_exp_0= 2\n", - " \n", - " # # architecture and engineeringexperience\n", - " if scenario == \"A/E Proficiency\":\n", - " ae_exp_0 = 0\n", - " else: \n", - " ae_exp_0 = 2\n", - " \n", - " \n", - " if scenario == \"Construction Proficiency\":\n", - " # # Construction service experience\n", - " ce_exp_0 = 0\n", - " else: \n", - " ce_exp_0 = 2\n", - "\n", - " if scenario == \"Modularization\":\n", - " # modularity (applied on civil construction only) \"stick_built\" or \"modularized\"\n", - " mod_0 = \"stick_built\" \n", - " else: \n", - " mod_0 = \"modularized\"\n", - "\n", - " \n", - " if scenario == \"Standardization\":\n", - " # cross_site_standardization :\n", - " standardization_0 = 0.7 # 0.7 corresponds to 70% standardization for PWRs\n", - " else:\n", - " standardization_0 = 0.95\n", - " \n", - " \n", - " if scenario == \"Commercial BOP\":\n", - " # # Determining if the BOP and reactor building (containtment) are non-nuclear or nuclear grade equipment (safety related)\n", - " BOP_grade_0 = \"nuclear\"\n", - " else: \n", - " BOP_grade_0 = \"non_nuclear\"\n", - " \n", - " \n", - " if scenario == \"Safety-Related RB\":\n", - " RB_grade_0 = \"nuclear\"\n", - " else: \n", - " RB_grade_0 = \"non_nuclear\"\n", - "\n", - "\n", - " if scenario == \"ITC\":\n", - " # #investment tax credits subsidies\n", - " ITC_0 = 0 #\n", - " \n", - " #number of reactors claiming ITC\n", - " n_ITC = 3 \n", - " else: \n", - " ITC_0 = 0.4 #\n", - " n_ITC = 3\n", - " \n", - " TCI_list = [] \n", - " for n_th in range(1, num_orders+1):\n", - " results = calculate(num_orders ,n_th, startup_0, interest_rate_0, design_completion_0, Design_Maturity_0, proc_exp_0 , ae_exp_0, ce_exp_0, mod_0 , standardization_0, BOP_grade_0, RB_grade_0, ITC_0, n_ITC, reactor_type )\n", - " \n", - " TCI_result = results[1]\n", - " TCI_list.append( TCI_result )\n", - " avg_TCI = np.mean(TCI_list)\n", - " \n", - " TCI_list_avg.append(avg_TCI)\n", - "\n", - " if reactor_type == \"SFR\":\n", - " TCI_list_avg_SFR = TCI_list_avg\n", - " \n", - " elif reactor_type == \"HTGR\":\n", - " TCI_list_avg_HTGR = TCI_list_avg " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "44f9773d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([['# Orders', 'Design Completion', 'A/E Proficiency',\n", - " 'Construction Proficiency', 'Supply Chain Proficiency',\n", - " 'Design Maturity', 'Standardization', 'Modularization',\n", - " 'Commercial BOP', 'Safety-Related RB', 'ITC', 'Interest Rate'],\n", - " ['1348.7450431344796', '74.02039526858653', '130.9965193438593',\n", - " '358.5831930659415', '13.531457947480703', '17.675555463713863',\n", - " '638.6067166148432', '33.91727388325489', '150.33046271782314',\n", - " '156.5078298991284', '258.9981143750297', '781.6795212483339'],\n", - " ['903.0470363771133', '72.32958748945157', '120.40665744737998',\n", - " '284.29905480954994', '12.963416976577719', '16.69519640982253',\n", - " '433.8167368872532', '19.961834590577837', '159.03979159395112',\n", - " '13.864902749768135', '269.07468026091146', '543.0183896110916']],\n", - " dtype=' dict: row = {c: np.nan for c in ALL_COLS} @@ -83,137 +120,106 @@ def ensure_rows_exist(db: pd.DataFrame) -> pd.DataFrame: return db -def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFrame: - db = ensure_rows_exist(db) - # change all nan to 0 for cost calculations (but keep original db unchanged for later use) - db = db.copy() - db[COST_COLS] = db[COST_COLS].fillna(0.0) - def _sum_accounts(accounts, col): - return db.loc[db["Account"].astype(str).str.strip().isin(accounts), col].fillna(0.0).sum() +def _index_rows_by_value(series: pd.Series) -> dict[str, list[int]]: + rows: dict[str, list[int]] = {} + for pos, value in enumerate(series.to_numpy()): + rows.setdefault(str(value).strip(), []).append(pos) + return rows - for col in ["Factory Equipment Cost", "Site Material Cost", "Site Labor Cost", "Site Labor Hours"]: - db.loc[db.Account == "21", col] = _sum_accounts(ACCOUNT_21_DETAIL_ACCOUNTS, col) +def _sum_accounts(db: pd.DataFrame, account_rows: dict[str, list[int]], accounts: list[str], col: str) -> float: + rows = [idx for account in accounts for idx in account_rows.get(str(account), [])] + if not rows: + return 0.0 + return float(db[col].to_numpy()[rows].sum()) - # account 23 - db.loc[db.Account == "23", "Factory Equipment Cost"] = ( - db.loc[db.Account == "232.1", "Factory Equipment Cost"].values - + db.loc[db.Account == "233", "Factory Equipment Cost"].values - ) - db.loc[db.Account == "23", "Site Material Cost"] = ( - db.loc[db.Account == "232.1", "Site Material Cost"].values - + db.loc[db.Account == "233", "Site Material Cost"].values - ) - db.loc[db.Account == "23", "Site Labor Cost"] = ( - db.loc[db.Account == "232.1", "Site Labor Cost"].values - + db.loc[db.Account == "233", "Site Labor Cost"].values - ) - db.loc[db.Account == "23", "Site Labor Hours"] = ( - db.loc[db.Account == "232.1", "Site Labor Hours"].values - + db.loc[db.Account == "233", "Site Labor Hours"].values - ) - # total costs for all accounts should be updated except the lines - # with no Account (subtotals, $/kWe, and final results) but only - # accounts under 20s has factory equipment costs, labor hours, and - # labor costs, so we can skip accounts 10s, 30s 50s and 60s +def _sum_titles(db: pd.DataFrame, title_rows: dict[str, list[int]], titles: list[str], col: str) -> float: + rows = [idx for title in titles for idx in title_rows.get(title, [])] + if not rows: + return 0.0 + return float(db[col].to_numpy()[rows].sum()) - for x in TOTAL_COST_DETAIL_ACCOUNTS: - db.loc[db["Account"] == x, "Total Cost (USD)"] = ( - db.loc[db["Account"] == x, "Factory Equipment Cost"] - + db.loc[db["Account"] == x, "Site Labor Cost"] - + db.loc[db["Account"] == x, "Site Material Cost"] - ) +def _set_account_value(db: pd.DataFrame, account_rows: dict[str, list[int]], account: str, col: str, value: float) -> None: + rows = account_rows.get(str(account), []) + if rows: + db.iloc[rows, db.columns.get_loc(col)] = value - # subtotals - db.loc[db["Title"] == "10s - Subtotal", "Total Cost (USD)"] = db.loc[ - db["Account"].isin(["11", "12", "13", "14", "15", "16", "18"]), "Total Cost (USD)" - ].fillna(0.0).sum() - db.loc[db["Title"] == "20s - Subtotal", "Total Cost (USD)"] = db.loc[ - db["Account"].isin(["21", "22", "23", "24", "25", "26", "28"]), "Total Cost (USD)" - ].fillna(0.0).sum() +def _set_title_value(db: pd.DataFrame, title_rows: dict[str, list[int]], title: str, col: str, value: float) -> None: + rows = title_rows.get(title, []) + if rows: + db.iloc[rows, db.columns.get_loc(col)] = value - db.loc[db["Title"] == "20s - Subtotal", "Factory Equipment Cost"] = db.loc[ - db["Account"].isin(["21", "22", "23", "24", "25", "26", "28"]), "Factory Equipment Cost" - ].fillna(0.0).sum() - db.loc[db["Title"] == "20s - Subtotal", "Site Material Cost"] = db.loc[ - db["Account"].isin(["21", "22", "23", "24", "25", "26", "28"]), "Site Material Cost" - ].fillna(0.0).sum() +def _roll_up_detail_accounts(db: pd.DataFrame, account_rows: dict[str, list[int]]) -> None: + for col in ["Factory Equipment Cost", "Site Material Cost", "Site Labor Cost", "Site Labor Hours"]: + _set_account_value(db, account_rows, "21", col, _sum_accounts(db, account_rows, ACCOUNT_21_DETAIL_ACCOUNTS, col)) - db.loc[db["Title"] == "20s - Subtotal", "Site Labor Cost"] = db.loc[ - db["Account"].isin(["21", "22", "23", "24", "25", "26", "28"]), "Site Labor Cost" - ].fillna(0.0).sum() + for col in ["Factory Equipment Cost", "Site Material Cost", "Site Labor Cost", "Site Labor Hours"]: + _set_account_value(db, account_rows, "23", col, _sum_accounts(db, account_rows, ["232.1", "233"], col)) + + +def _update_total_cost_rows(db: pd.DataFrame, account_rows: dict[str, list[int]]) -> None: + rows = [idx for account in TOTAL_COST_DETAIL_ACCOUNTS for idx in account_rows.get(account, [])] + if rows: + total_col = db.columns.get_loc("Total Cost (USD)") + factory_col = db.columns.get_loc("Factory Equipment Cost") + labor_col = db.columns.get_loc("Site Labor Cost") + material_col = db.columns.get_loc("Site Material Cost") + db.iloc[rows, total_col] = ( + db.iloc[rows, factory_col].to_numpy() + + db.iloc[rows, labor_col].to_numpy() + + db.iloc[rows, material_col].to_numpy() + ) - db.loc[db["Title"] == "20s - Subtotal", "Site Labor Hours"] = db.loc[ - db["Account"].isin(["21", "22", "23", "24", "25", "26", "28"]), "Site Labor Hours" - ].fillna(0.0).sum() - db.loc[db["Title"] == "30s - Subtotal", "Total Cost (USD)"] = db.loc[ - db["Account"].isin(["31", "32", "33", "34", "35"]), "Total Cost (USD)" - ].fillna(0.0).sum() +def _update_subtotals(db: pd.DataFrame, account_rows: dict[str, list[int]], title_rows: dict[str, list[int]]) -> None: + for title, accounts in ACCOUNT_SUBTOTALS.items(): + _set_title_value(db, title_rows, title, "Total Cost (USD)", _sum_accounts(db, account_rows, accounts, "Total Cost (USD)")) - db.loc[db["Title"] == "50s - Subtotal", "Total Cost (USD)"] = db.loc[ - db["Account"].isin(["51", "52", "54"]), "Total Cost (USD)" - ].fillna(0.0).sum() + for col in ["Factory Equipment Cost", "Site Material Cost", "Site Labor Cost", "Site Labor Hours"]: + _set_title_value( + db, + title_rows, + "20s - Subtotal", + col, + _sum_accounts(db, account_rows, ACCOUNT_SUBTOTALS["20s - Subtotal"], col), + ) - db.loc[db["Title"] == "60s - Subtotal", "Total Cost (USD)"] = db.loc[ - db["Account"].isin(["62"]), "Total Cost (USD)" - ].fillna(0.0).sum() - # $/kWe lines - for t in ["10s", "20s", "30s", "50s", "60s"]: - db.loc[db["Title"] == f"{t} - $/kWe", "Total Cost (USD)"] = ( - db.loc[db["Title"] == f"{t} - Subtotal", "Total Cost (USD)"].values / reactor_power - ) - # 20s equipment, material, labor costs per kWe - db.loc[db["Title"] == "20s - $/kWe", "Factory Equipment Cost"] = ( - db.loc[db["Title"] == "20s - Subtotal", "Factory Equipment Cost"].values / reactor_power - ) - db.loc[db["Title"] == "20s - $/kWe", "Site Material Cost"] = ( - db.loc[db["Title"] == "20s - Subtotal", "Site Material Cost"].values / reactor_power - ) - db.loc[db["Title"] == "20s - $/kWe", "Site Labor Cost"] = ( - db.loc[db["Title"] == "20s - Subtotal", "Site Labor Cost"].values / reactor_power - ) +def _update_per_kwe_rows(db: pd.DataFrame, title_rows: dict[str, list[int]], reactor_power: float) -> None: + for prefix, subtotal_title in PER_KWE_ROWS.items(): + subtotal = _sum_titles(db, title_rows, [subtotal_title], "Total Cost (USD)") + _set_title_value(db, title_rows, f"{prefix} - $/kWe", "Total Cost (USD)", subtotal / reactor_power) - # final rollups - db.loc[db["Title"] == "Total Direct Capital Cost (Accounts 10 to 20)", "Total Cost (USD)"] = ( - db.loc[db["Title"] == "10s - Subtotal", "Total Cost (USD)"].values - + db.loc[db["Title"] == "20s - Subtotal", "Total Cost (USD)"].values - ) + for col in ["Factory Equipment Cost", "Site Material Cost", "Site Labor Cost"]: + value = _sum_titles(db, title_rows, ["20s - Subtotal"], col) / reactor_power + _set_title_value(db, title_rows, "20s - $/kWe", col, value) - db.loc[db["Title"] == "Base Construction Cost (Accounts 20 to 30)", "Total Cost (USD)"] = ( - + db.loc[db["Title"] == "20s - Subtotal", "Total Cost (USD)"].values - + db.loc[db["Title"] == "30s - Subtotal", "Total Cost (USD)"].values - ) - db.loc[db["Title"] == "Total Overnight Cost (Accounts 10 to 50)", "Total Cost (USD)"] = ( - db.loc[db["Title"] == "10s - Subtotal", "Total Cost (USD)"].values - + db.loc[db["Title"] == "Base Construction Cost (Accounts 20 to 30)", "Total Cost (USD)"].values - + db.loc[db["Title"] == "50s - Subtotal", "Total Cost (USD)"].values - ) +def _update_final_totals(db: pd.DataFrame, title_rows: dict[str, list[int]], reactor_power: float) -> None: + for title, input_titles in FINAL_TOTAL_ROWS.items(): + _set_title_value(db, title_rows, title, "Total Cost (USD)", _sum_titles(db, title_rows, input_titles, "Total Cost (USD)")) - db.loc[db["Title"] == "Total Capital Investment Cost (All Accounts)", "Total Cost (USD)"] = ( - db.loc[db["Title"] == "Total Overnight Cost (Accounts 10 to 50)", "Total Cost (USD)"].values - + db.loc[db["Title"] == "60s - Subtotal", "Total Cost (USD)"].values - ) + for title, source_title in FINAL_PER_KWE_ROWS.items(): + value = _sum_titles(db, title_rows, [source_title], "Total Cost (USD)") / reactor_power + _set_title_value(db, title_rows, title, "Total Cost (USD)", value) + + +def update_high_level_costs(db: pd.DataFrame, reactor_power: float) -> pd.DataFrame: + db = ensure_rows_exist(db).copy().reset_index(drop=True) + db[COST_COLS] = db[COST_COLS].fillna(0.0) + account_rows = _index_rows_by_value(db["Account"]) + title_rows = _index_rows_by_value(db["Title"]) + + _roll_up_detail_accounts(db, account_rows) + _update_total_cost_rows(db, account_rows) + _update_subtotals(db, account_rows, title_rows) + _update_per_kwe_rows(db, title_rows, reactor_power) + _update_final_totals(db, title_rows, reactor_power) - # final $/kWe - db.loc[db["Title"] == "(Accounts 10 to 20) US$/kWe", "Total Cost (USD)"] = ( - db.loc[db["Title"] == "Total Direct Capital Cost (Accounts 10 to 20)", "Total Cost (USD)"].values / reactor_power - ) - db.loc[db["Title"] == "(Accounts 20 to 30) US$/kWe", "Total Cost (USD)"] = ( - db.loc[db["Title"] == "Base Construction Cost (Accounts 20 to 30)", "Total Cost (USD)"].values / reactor_power - ) - db.loc[db["Title"] == "(Accounts 10 to 50) US$/kWe", "Total Cost (USD)"] = ( - db.loc[db["Title"] == "Total Overnight Cost (Accounts 10 to 50)", "Total Cost (USD)"].values / reactor_power - ) - db.loc[db["Title"] == "(Accounts 10 to 60) US$/kWe", "Total Cost (USD)"] = ( - db.loc[db["Title"] == "Total Capital Investment Cost (All Accounts)", "Total Cost (USD)"].values / reactor_power - ) return db def ITC_reduction_factor(itc_level: float) -> float: diff --git a/tutorial/crf_showcase.ipynb b/tutorial/crf_showcase.ipynb index 91ee921..2f4ef40 100644 --- a/tutorial/crf_showcase.ipynb +++ b/tutorial/crf_showcase.ipynb @@ -354,27 +354,27 @@ "Results by plant number:\n", "\n", " Plant_number OCC TCI duration STAUP D10s D20s D30s D50s D60s D20_equip D20_mat D20_labor\n", - " 1 15604.82 18840.16 91.00 28.00 124.67 7070.57 8332.54 77.04 3235.34 3186.79 674.08 3209.69\n", - " 2 8643.32 9858.01 68.30 19.60 124.67 4593.45 3879.35 45.85 1214.69 2361.57 449.12 1782.76\n", - " 3 6633.90 7385.47 59.88 15.91 124.67 3807.58 2664.79 36.85 751.58 2114.48 377.85 1315.25\n", - " 4 5287.97 5809.90 55.16 13.72 124.67 3217.85 1914.63 30.82 521.93 1887.35 322.68 1007.82\n", - " 5 4715.38 5132.42 52.18 12.23 124.67 2972.41 1590.04 28.26 417.04 1810.79 299.15 862.47\n", - " 6 4536.65 4902.34 50.01 11.14 124.67 2905.37 1479.15 27.46 365.69 1804.02 289.79 811.56\n", - " 7 4394.90 4721.40 48.24 10.29 124.67 2851.31 1392.10 26.82 326.50 1798.33 282.11 770.87\n", - " 8 4278.58 4580.80 46.76 9.60 124.67 2806.31 1321.30 26.30 302.21 1793.42 275.62 737.28\n", - " 9 4180.69 4461.18 45.49 9.04 124.67 2767.99 1262.17 25.86 280.50 1789.10 270.02 708.86\n", - " 10 4096.67 4358.97 44.38 8.56 124.67 2734.74 1211.78 25.48 262.30 1785.26 265.10 684.37\n", + " 1 15102.81 18234.07 91.00 28.00 124.67 7038.51 7864.84 74.79 3131.26 3106.85 755.46 3176.20\n", + " 2 8386.78 9565.41 68.30 19.60 124.67 4566.29 3651.12 44.70 1178.63 2298.79 503.34 1764.15\n", + " 3 6446.14 7176.45 59.88 15.91 124.67 3781.39 2504.07 36.01 730.31 2056.40 423.47 1301.52\n", + " 4 5148.35 5656.50 55.16 13.72 124.67 3193.25 1800.24 30.20 508.15 1834.31 361.64 997.30\n", + " 5 4594.40 5000.74 52.18 12.23 124.67 2947.74 1494.27 27.71 406.34 1759.01 335.27 853.46\n", + " 6 4419.57 4775.83 50.01 11.14 124.67 2879.57 1388.40 26.93 356.25 1751.70 324.78 803.09\n", + " 7 4280.91 4598.93 48.24 10.29 124.67 2824.55 1305.37 26.31 318.03 1745.56 316.17 762.83\n", + " 8 4167.11 4461.45 46.76 9.60 124.67 2778.73 1237.91 25.80 294.34 1740.26 308.89 729.58\n", + " 9 4071.33 4344.49 45.49 9.04 124.67 2739.68 1181.61 25.37 273.16 1735.60 302.62 701.47\n", + " 10 3989.13 4244.54 44.38 8.56 124.67 2705.79 1133.66 25.00 255.42 1731.45 297.11 677.23\n", "\n", "Summary metrics:\n", "\n", "cons_duration_cumulative_wz_startup: 190.56\n", - "occLastUnit: 4096.67\n", - "occNOAKUnit: 4278.58\n", - "occ_reduction_from_FOAK_to_NOAK_percent: 72.58\n", - "TCILastUnit: 4358.97\n", + "occLastUnit: 3989.13\n", + "occNOAKUnit: 4167.11\n", + "occ_reduction_from_FOAK_to_NOAK_percent: 72.41\n", + "TCILastUnit: 4244.54\n", "durationsLastUnit: 44.38\n", - "avg_OCC: 6237.29\n", - "avg_TCI: 7005.07\n", + "avg_OCC: 6060.65\n", + "avg_TCI: 6805.84\n", "avg_duration: 56.14\n" ] } @@ -405,7 +405,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -457,7 +457,7 @@ "outputs": [ { "data": { - "image/png": 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", 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KlQwvLy/D19fXqFu3rvHyyy8bx44dM8tt3LjRiI6ONnx8fIzy5csbjz/+uPHzzz8bkoxZs2aZ5Xr27Gn4+voav/76qxETE2OULl3aCAwMNJ566ikjPT3dru2IiAijZ8+edss+/fRT47bbbjM8PT0NScbIkSMNwzCMQ4cOGR07djQCAgIMPz8/o1WrVsb27dvz1LF27VpDkrF27doC7f9ff/1lPP3008Ytt9xieHt7G6VLlzZq1qxpDBw40Ni7d69d2Q8//NC4/fbbDS8vL8NqtRr/+c9/jB07dpjrjx49asTHxxu33Xab4evra5QpU8a4/fbbjbfeesvIzs42y+3bt8+IjY01/Pz8DElGRESEua5p06bG1fyn/sCBA0bfvn2Nm2++2ezXPffcY8yZM8ew2Wz5bpOUlGQMGTLEqFWrluHj42N4e3sbt9xyi9GnTx/jt99+y1N+/fr1xkMPPWSEhoYanp6eRtmyZY3o6Ghj4sSJRlpa2mX79/vvvxv33Xef4efnZwQEBBidOnUyDhw4YPdvaxiGMXLkSEOScfz4cbvtZ82aZUiy+7dISUkxHnvsMcPf39/w8fEx7rvvPuOPP/7IU+eV5Nb9448/5rv+7NmzxjvvvGNER0cbZcuWNTw8PIywsDDjwQcfNJYtW2aWyx1zn3/+eYHbBoAbkcUw8jm/BwCAEiA+Pl7/+9//lJ6e7uyuAACAEoRrZAEAAAAALoUgCwAAAABwKZxaDAAAAABwKczIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkC1ChmEoLS1NXHYMAAAAAMWHIFuETp06JavVqlOnTjm7K3BxNptN//77r2w2m7O7ghsMYw/OxPiDszD24EyMv8IhyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUD2d3AAAAACVbTk6OsrKynN2NYmOz2ZSVlaVz587JzY15HjjWjTb+3N3d5eHhIYvFck31EGQBAABwSenp6Tp06NB1/XhBwzBks9mUnJx8zX9cA1frRhx/Pj4+Cg0NlZeXV6HrIMgCAAAgXzk5OTp06JB8fHxUvnz56/aPbMMwlJ2dXSSzRMDVupHGn2EYyszM1PHjx7V3715Vq1at0LPQBFkAAADkKysrS4ZhqHz58ipdurSzu1NsbqQggZLnRht/pUuXlqenp/bv36/MzEyVKlWqUPVc/ydhAwAA4JrcCH9cA3CcorgWmCALAAAAAHApBFkAAAAAgEvhGlkAAABclaiPooq9jd96/lbsbdyILBaLtm3bpjp16ji87QMHDqhmzZr6559/ZLVaC1WHM/t/vZk/f74WLVqkBQsWFHndY8eO1dmzZzVmzJgirzsXM7IAAABwaVOmTFGDBg3k7e2tDh065FmflZWlfv36KTAwUIGBgXrmmWeUnZ192TpTUlL0+OOPKygoSGXLllWDBg105swZc/0PP/yg2rVry8fHR3Xq1NGmTZsK3f8yZcqYL3d3d3l7e5vvW7dufcnt4uPjNWDAgEK3m58TJ06of//+ioiIUJkyZVS5cmXFx8dr9+7d11x3pUqVlJ6ebobYou7/7NmznR5wS0IfYmJiNGnSpMuWsdlsevHFFzV8+HAdOHDAbgy6ubmpdOnS5vsnn3xSkrR06VLdc8898vPzU7ly5XTnnXfqvffekyTt27dPFotFKSkpkqRnn31WH3zwgZKSkoptPwmyAAAAcGlhYWEaPny4evfune/6MWPGaMOGDdqxY4d27Nih77//XuPGjbtkfTabTW3btpWnp6d2796tlJQUffDBB/L09JQknTx5Um3btlW/fv2UnJysvn37qm3btuYf8VcrPT3dfN199916/fXXzffffPNNoeosjNTUVDVu3Fj79+/XihUrlJaWpsTERDVs2NCh/UDx+/rrrxUYGKioqCjzC4bcV6VKlfTpp5+a79977z1NmzZNPXv2VO/evXXo0CH9+++/mjp1qr766qt868/9EmbGjBnFtg8EWQAAALi0Bx98UB06dFBQUFC+62fOnKnhw4crNDRUoaGheumlly77B/Y333yjAwcOaPLkyQoMDJSbm5vq1q1rBtlFixbppptuUu/eveXt7a3evXsrJCREixYtKvJ9W7lyperWrSur1ap69erp22+/lSS98847mjt3rqZOnaoyZcqoVq1akqS5c+cqMjJSfn5+qlSpkkaMGCHDMArU1qRJk2SxWLRw4ULddtttcnNzk7+/v5566ik9++yzBarfYrHo7bffVvXq1eXv768uXbooNTVVkv2sXXH0/2KVK1fWhAkT1KhRI/n5+alp06Y6ePCgJKl///567LHH7Mq//vrratWqlaTzj8R55513dNttt8nf318xMTHauXOnWfbNN99UpUqV5Ofnp8qVK+vDDz/Utm3b9OSTT+q3334zZzMPHDigUaNGqV27dnryySdltVpVpUoVrV27VosWLdItt9yiwMBAjRgxwq4v3377re688075+/urVq1aWrJkibkuPj5evXv31sMPPyw/Pz9Vr15d69atkyQNGjRI33//vYYMGXLZGf0lS5aoefPmBTqOp06d0pAhQ/TOO+8oLi5OVqtVFotFDRo0uGSQlaQWLVrY9buoEWQBAABw3UpOTtahQ4fsTvesU6eODhw4YAasi61fv141atRQnz59VK5cOUVGRuqTTz4x1//66695Th+tU6eOfv311yLt+19//aX//Oc/GjFihE6cOKEXX3xR7du31969e9W/f391795dTz/9tNLT07Vjxw5JUmBgoL744gulpaVpyZIlev/99zVv3rwCtbdixQp16tRJHh6Xvo1OQer/5JNPtHbtWu3bt0/Jycn5nj5cHP3Pz8cff6x58+bp+PHj8vX1NQPjI488ov/97386e/asXb/j4uIkSdOmTdOMGTO0dOlS/fvvv3rwwQfVrl07ZWZmavfu3Ro+fLhWrlypU6dOacuWLbrzzjtVt25dvffee4qKirKb3cw9tvfee69Onjyp7t27q0ePHvryyy/1yy+/6Pvvv9dbb72ln3/+WdL58dWpUye99tprOnnypKZPn664uDjt2rXL7Ov8+fP1xBNPKCUlRXFxcYqPj5ckvfHGG3az+peaSU9MTNRtt91WoGO4adMmnT59Wp07d76qY1+zZk0lJiZe1TZXgyALAACA61Z6erokyd/f31yW+/OpU6fy3ebkyZPmTOiRI0c0ffp0Pf3009qwYYNZ54X15dZ5qfoKa/78+YqJidGDDz4oDw8PPfTQQ2rSpIk+/fTTS27TunVr3XrrrbJYLKpTp466du1qztZdyfHjx3XTTTddtkxB6n/hhRcUFhYmf39/vfrqq5o3b55sNluB+nAt/c9Pv379dPPNN6tUqVLq3r27EhISJEkNGjRQeHi4Fi9eLEnatm2bDhw4oAceeECS9O677+qVV15RtWrV5OHhof79++vs2bPasmWL3N3dZRiGduzYobNnzyo4OFi33377ZftRr149PfTQQ3J3d1e3bt10+PBhDRs2TL6+vqpVq5Zuv/12M8hOnz5d8fHxat68udzc3NSkSRO1bdtWn332mVlfmzZt1Lx5c7m7u+vRRx/V/v37deLEiQIfl+TkZJUtW7ZAZY8fP67y5cvLy8urwPVLUtmyZZWZmWl3bXlR4q7FNwhH3F3wYtxtEAAAOFuZMmUknb/+M/fU49yZWD8/P82dO1d9+vSRJEVERGjHjh0qU6aMKlasqH79+kmS7rrrLnXo0EFLlixRkyZNVKZMGZ08edKundTUVJUvXz7fPtSqVUv79++XdD6kdO/evUB9P3TokCpXrmy37Oabb9ahQ4cuuc2KFSs0evRo7d69W1lZWcrIyLjsDaMuFBQUpH/++eeyZQpSf0REhN3PmZmZOn78eIH6cC39z09ISIj5s6+vr92XDXFxcfr444/18MMP6+OPP1bHjh3l4+Mj6fxp0D169JC7u7tZPjMzU4cOHdLdd9+tjz76SFOmTNGjjz6qRo0aacKECZe9ydOF/cht4+JluV+67Nu3T2vWrNGsWbPM9dnZ2XbB8+L9ks5/MVOuXLkCHZeAgAClpaUVqGxQUJD+/fdfZWZmXlWYTUtLk5eXl7m/RY0ZWQAAAFy3AgICVLFiRbtTHBMTExUeHi6r1aru3bvr1KlTSk5O1vbt2yVJtWvXlsViuWSdt99+e55TJhMTExUVlf/EwY4dO8xTTQsaYiWpYsWK2rdvn92yvXv3qmLFipIkNzf7P+UzMzP14IMPqk+fPvrnn3+UmpqqJ598ssDXmLZs2VILFy685B2dC1p/bmiXzj9yx8vLK9+QX9T9v1o9evTQ6tWr9c8//+jTTz81TyuWpPDwcH3++edKSUkxX2fOnFHXrl0lSZ07d9batWt19OhR1a5d29z24n0qjPDwcD377LN2baenp2vatGkF2r4gfahTp47++OOPAtXXuHFj+fj46PPPPy9Q+Vy///57sd7BmSALAAAAl5adna1z584pOztbNptN586dU2Zmprn+0Ucf1dixY5WUlKSkpCSNGzdOjz/++CXre+CBB3T27Fm99957ysnJ0ZYtW7R48WK1b9/eXH/o0CHNmDFDmZmZmjFjho4cOWKellpUunTponXr1mnx4sXKycnRF198oe+//14PP/ywJCk4OFh///23WT4jI0Pnzp1TuXLl5O3trS1btlzV9aXPPfeccnJy1LlzZ+3evVs2m02pqan64IMP9Pbbbxe4/okTJ+rw4cNKSUnRyy+/rIcffjjfcFXU/b9aFStWVJMmTdSrVy95eXkpJibGXNe3b1+9/PLL5nWpaWlpWrx4sU6dOqVdu3Zp1apVOnv2rLy8vFSmTBnzuuLg4GAdOXLE7trbq9WnTx/NmjVLa9euVU5OjjIyMrRp0ya7m01dTnBwsP7666/LlmnXrp3Wrl1boPr8/Pz0+uuvq3///po7d67S0tJkGIYSExPN34n8rFmzRm3bti1QG4XBqcUAAAC4KiXt8qExY8Zo9OjR5vvSpUuradOm5rWVuTdLqlGjhiSpe/fuevHFFy9Zn7+/v5YtW6a+fftq0KBBqlixot599101adJE0vkbEi1dulRPP/20+vXrp1tvvVVLly5VQEBAke7XLbfcoi+++ELDhg1TXFycbr75Zi1atEg333yzJOnxxx9X586dFRAQoPDwcP36669699139cQTTyg9PV0xMTHq0qWLeafeK7Fardq4caNGjRqle++9VydOnFBQUJCaNWum4cOHy8/Pr0D19+jRQ82aNVNSUpJiY2P19ttv59teUfe/MB555BHFx8dr2LBhdmG7X79+cnd314MPPqiDBw/Kz89PTZo0UfPmzZWZmakRI0bo999/l5ubm2rXrq3Zs2dLkpo3b65GjRrppptuks1mK9QNwOrWratPP/1Uw4cP186dO+Xm5qY6derov//9b4G2HzBggOLj4+Xv768mTZrke2fh+++/X/3799f27dsVGRl5xTqfeuop3XTTTZo4caL69Okjb29vVa1aVb169cq3/OnTp/X1118X682eLEZxzdXfgNLS0mS1WpWamlrgi6cdhWtkXYvNZtPJkyfNW/4DjsLYgzMx/kqec+fOae/evapSpYpKlSrl7O4UG8MwlJ2dLQ8Pj8ueUowrs1gs2rZtW7GeUnq9cdb4+/TTT/Xll19qwYIFRV73uHHjdPr0aY0dOzbf9UXx2cKMLAAAAADcYLp27Wpe81vULnfGQ1Hh604AAAAAgEthRhYAAABAkeCqRTgKM7IAAAAAAJdCkAUAAAAAuBSCLAAAAADApTg1yH733Xdq166dwsLCZLFY9OWXX9qtt1gs+b4mTpxolomJicmzPvch0bmSk5MVFxcnq9Uqq9WquLg4paSk2JU5cOCA2rVrJ19fXwUFBal///52D9IGAAAAAJQMTg2yp0+fVu3atTVlypR81x85csTuNXPmTFksFnXs2NGuXO/eve3KTZ8+3W59t27dlJiYqOXLl2v58uVKTExUXFycuT4nJ0dt2rTR6dOntWHDBs2fP18LFy7UoEGDin6nAQAAAADXxKl3LW7durVat259yfUhISF27xcvXqxmzZrp5ptvtlvu4+OTp2yunTt3avny5dq8ebMaNmwoSfrggw8UHR2tXbt2qXr16lq5cqV+//13HTx4UGFhYZKkN954Q/Hx8Ro7dqzKli17LbsJAABwXak8dFmxt7HvtTbF3gYu78CBA6pZs6b++ecfWa3WK5aPj4+Xv7+/Jk2aVPydK6Rx48bpt99+06efflqo7WfPnq1JkyYpMTGxaDtWhJ588knVrl1bTz31lMPbvu+++zRkyBDde++9xd6Wy1wje/ToUS1btky9evXKs27u3LkKCgpSrVq1NHjwYJ06dcpct2nTJlmtVjPESlKjRo1ktVq1ceNGs0xkZKQZYiWpZcuWysjIUEJCwiX7lJGRobS0NLuXJNlsthL3cnPC/5y9z67+MgzD6X3gdWO+GHu8nPli/JW8l2EYeV6OkF+7l3pNnjxZDRo0kLe3tzp06JBnfWZmpvr27avAwEAFBgaqX79+ysrKynd/DMPQ3r17ZbFYVKZMGfPVrl07uzo3bNig2rVry8fHR3Xq1NHGjRuvqs/5vVatWqW7775bZcqUkdVqVevWrZWQkHDN9Rb2FR4erlOnTqls2bIFKn+lf7eYmBi99dZbV6zn+++/V+vWrRUQECB/f3/Vrl1br7/+ujIyMq55n4YNG6Z58+aZ7y0Wi7Zt23ZVdVxpP6+2/wUd9wXp6549e7Rs2TI99thj5rLMzEyNGjVKVatWVenSpRUeHq4BAwbo1KlTdtueOXNGL730kqpVqyZfX19VrFhRDz30kH766SezzP79+xUfH6+wsDD5+fnplltuUb9+/XT48GEZhqGXXnpJzz//fIGPZX6fOQXlMs+R/eijj+Tn56cHH3zQbnn37t1VpUoVhYSEaPv27Ro2bJh++eUXrVq1SpKUlJSkChUq5KmvQoUKSkpKMssEBwfbrQ8ICJCXl5dZJj/jx4/X6NGj8yxPTk5Wdnb2Ve9jcYrwiHB4mydPnnR4m9cLm81mfri4ubnM9024DjD24EyMv5InKytLNptN2dnZDv/b5mraCw4O1tChQ7VmzRodOnQoz7avvPKKNmzYYM6itWvXTmPGjNHw4cPNMjk5OXna3rt3r/z9/fMsP3nypNq1a6fx48erR48emjNnjtq1a6c//vjDrvzVWLp0qXr27Kk33nhDS5YsUVZWlj788EPFxMRo1apVql+/fqHqvZTcEOHu7l5kdeZ+8XGpf7vcNi/3b7ts2TLFxcVp1KhRmjlzpoKCgvTHH39o4sSJOnjwoCIiiv5v2qsZ31fax8L2/8Lxdy19nTZtmjp16iQ3NzezXNeuXfXnn39q3rx5ql27tv766y/169dPsbGxWr16tTw9PZWVlaXY2FgZhqG5c+cqMjJSmZmZWrJkiRYuXKjatWvrwIEDaty4sdq2bav169crIiJCx44d06xZs7RmzRp16dJFjRs3VkpKir777js1btz4svths9mUmpqqM2fO2K0LCgoq0LFwmSA7c+ZMde/eXaVKlbJb3rt3b/PnyMhIVatWTQ0aNNDPP/+sevXqSTp/06iL5X6rkasgZS42bNgwDRw40Hyflpam8PBwBQQElLjTkfdn73d4m4GBgQ5v83phs9lksVgUEBDAH3NwKMYenInxV/KcO3dOycnJ8vDwkIeHY/9svJr2OnXqJEn67bffdPjw4TzbfvTRR3rzzTcVHh4uSeas0ahRo/Jt88L/z68fS5cu1U033aQ+ffpIkvr06aPJkydr6dKlevTRRwvc71yGYWjQoEEaMmSI3d+2w4YN019//aWhQ4dq7dq16t+/v9LT0zVz5kyzzOuvv65169bpm2++kWGcn52eNm2akpKSVKdOHU2dOlU1atSQJFWpUkVPPPGElixZol9++UVbtmxR7dq1NXXqVL377rs6cOCAYmJi9PHHH8tqtWrfvn26+eabdfLkSfn7+ysjI0PPPvusPv/8c1mtVr300kvq3bu3/v77b1WuXFlubm46c+aM4uLitGzZMoWFhem9995TTEyMBg0apA0bNmjz5s0aOXKk7r77bn399dd5jsPAgQP1wgsv2P2NHRkZqY8++sh8HxcXpzVr1ig9PV3VqlXTxIkT1axZM0nnT/19++231bp1a73//vvy9fXVkCFD9PTTT0uSRo0apV9++UWLFi0yz9hs2rSp3NzcNGzYML344ouXrd/NzU0WiyXfcVHQ/v/0008aMGCAduzYobCwMA0fPlydOnWSh4eHfv75Z/Xt21e///67vLy8FB0drSVLllyyrxf76quv9NZbb5n9W7dunb766ivt3LlTVapUkSTVrFlTixYt0i233KIFCxYoPj5ec+fO1R9//KE9e/aYX8aULl3a7r5CY8aMUVRUlD788ENz2U033WT3hZAkNWvWTMuWLdM999yTp3+5PDw85ObmJqvVmiffFZRLBNnvv/9eu3bt0oIFC65Ytl69evL09NSePXtUr149hYSE6OjRo3nKHT9+3JyFDQkJ0ZYtW+zWJycnKysrK89M7YW8vb3l7e2dZ7mbm1uJ+w+wTQWfpi8qJe0YuBqLxVIixxKuf4w9OBPjr2TJ/aM99+VIhWkvd5sLt01OTtahQ4dUt25dc3ndunV14MABpaWlyWq12p3eeeG+RkVFKTs7W3feeacmTJig2267TdL5wFynTh27durUqaPffvutUP3evXu39u3bp27duuXZvlu3bmrZsqXOnTunnj17qnnz5po6dapKly4tSZozZ46GDRsmi8WiadOmaebMmVq6dKmqVKmiqVOnqn379mYoks6H+iVLluiWW24xZwE/++wzrV69Wt7e3mrevLkmTZqkUaNG2R1Pi8WisWPHKiEhQTt27JCPj4+6d++e55gtWLBAixcv1ty5czV+/Hg9+uij2rdvn9588039/PPP6tChgwYMGJDvcdizZ4/27t2b73G4UIsWLfTuu+/Kx8dHkyZNUqdOnbRv3z75+fnJYrFo+/btatOmjY4cOaKEhAS1bNlSUVFRuueee+z2aevWrbJYLNq4caPq1KlT4Ppzty9M/1NSUtS6dWuNHDlSTz75pDZu3Kg2bdropptu0j333KNnnnlG7dq108aNG5WVlaUtW7Zctq8XOnPmjPbs2aMaNWqY7a9cuVINGzbMc48hf39/tW7dWqtWrdKjjz6qlStXmqdDX8qKFSv06quvXnGM16pVSytXrrxsudwxcy2f9y7xX4kZM2aofv36ql279hXL7tixQ1lZWQoNDZUkRUdHKzU1VVu3bjXLbNmyRampqeZ0d3R0tLZv364jR46YZVauXClvb+8iP40DAAAAjpOeni5Jdqf85v584X1VLhQUFKQtW7Zo7969+uOPP1StWjXdd9995v1Q0tPT85xC7O/vf8n6ruTff/+VJLv7teQKCwtTTk6OTp48qQYNGig8PFyLFy+WJG3btk0HDhzQAw88IEl699139corr6hatWry8PBQ//79dfbsWbsJm6eeekrVq1eXu7u7GW6HDBmi4OBg+fv7q2PHjpe8R8y8efM0dOhQhYaGymq1auTIkXnKtGnTRs2bN5e7u7seffRR7d+/XydOnCjQcTh+/Lik87N8l/Poo4/KarXK09NTzz//vGw2m3799Vdzva+vr0aNGmXOaHbv3l0ff/xxgfpQkPqvpf/Lli1T+fLl9cwzz8jT01NNmzZV165d9cknn0iSPD09tX//fh0+fFje3t6XndW8WHJysiTZnRn677//5juupPNjK7fPx48fv+JxL0iZ3PZz+1KcnBpk09PTlZiYaF6vsHfvXiUmJurAgQNmmbS0NH3++ed6/PHH82z/119/6ZVXXtFPP/2kffv26euvv1anTp1Ut25d3XXXXZKkGjVqqFWrVurdu7c2b96szZs3q3fv3mrbtq2qV68uSYqNjVXNmjUVFxenbdu2afXq1Ro8eLB69+5d4k4RBgAAQMGVKVNGkpSammouy/3Zz89Pc+fOlZ+fnwICAhQZGWluc+edd8rT01P+/v7673//q6ysLPNGoWXKlLGrL7dOPz+/fPtQq1Yt86ZRc+fOzbM+95rAw4cP51l3+PBhubu7m5dsxcXFmaHs448/VseOHeXj4yNJ2rdvn3r06CF/f3/zlTsjnatSpUp52rjw6R++vr6XDOSHDx82T88uaF3Spb8wuFjucfjnn38uWcZms5k3JCpbtqz8/f2VmppqfhkgnQ9onp6e5vuIiIjL1nm19V9L/w8dOqTKlSvbLbv55pvNbWbOnKlz586pfv36uu222y75mNL85M6m5n7hktun/MaVdP7fs3z58ma5Kx2jgpTJbf9yM7tFxalB9qefflLdunVVt25dSdLAgQNVt25dvfzyy2aZ+fPnyzAMde3aNc/2Xl5eWr16tVq2bKnq1aurf//+io2N1bfffmt34frcuXMVFRWl2NhYxcbG6vbbbze/9ZAkd3d3LVu2TKVKldJdd92lzp07q0OHDvrvf/9bjHsPAACA4hYQEKCKFSvaPS4lMTFR4eHhslqt6t69u06dOqXk5GRt37493zouPrX69ttvz/P4lcTEREVFReW7/Y4dO5Senq709HTzdNwL3XrrrYqIiMj3kTCffvqp7rrrLvNU4h49emj16tX6559/9Omnn9pdwxgeHq7PP/9cKSkp5uvMmTN2f0dfy2n7YWFhOnjwoPn+wsmngrhS27feeqsqV66s+fPnX7LMvHnzNG/ePC1btkypqalKSUnJc3r44cOHlZWVZdfPS80kXnz6a0Hqv5b+V6xYUfv27bNbtnfvXrN/VatW1ccff6ykpCR9+OGHGjx4sDlDfqVTen18fFStWjX98ccf5rL77rvPPLvgQmlpafrmm2903333STr/xJbly5fn+YLmQi1btrzsvuX6/fffL3n6c1FyapCNiYnJ9zbMs2fPNss88cQTOnPmTL7PrgoPD9f69et14sQJZWRk6M8//9Tbb7+d5yZDgYGBmjNnjvmInDlz5uQ5HaRSpUr66quvdObMGZ04cUKTJ0/O9/pXAAAAlCzZ2dk6d+6ceSfUc+fOKTMz01z/6KOPauzYsUpKSlJSUpLGjRuX79l+ubZs2aKdO3cqJydH6enpGjJkiCwWi6KjoyVJDzzwgA4dOqQZM2YoMzNTM2bM0JEjR8xTfK+WxWLRW2+9pfHjx2vGjBlKT09XSkqKXn/9dc2fP18TJkwwy1asWFFNmjRRr1695OXlpZiYGHNd37599fLLL2vXrl2SzoeVxYsXF/qU54t17dpVEyZMUFJSklJTU/Xqq69e1fbBwcH666+/LrneYrFo8uTJeu211zR58mTzlOTdu3erV69e2r9/v9LS0uTl5aWgoCBlZmbqlVdesZuBlKTTp0/r1VdfVWZmprZs2aK5c+fm+wVCfn0qSP3X0v/7779fx44d09SpU5Wdna3vv/9en376qXr06CHp/Cz70aNH7W58l3vjpisdP+n8HbnXrl1rvm/evLnuv/9+PfDAA/r555+Vk5Oj3bt364EHHlDVqlXN49K9e3fddtttateunbZt26bs7GydPXtWn332mUaMGCFJGj16tH755Rc9+eSTOnDggAzD0PHjx/X666/b3cto7dq1atu2bYGO2bVwiZs9AQAAoOTY91obZ3fBzpgxY+weiVi6dGk1bdpU69atkySNGDFCJ06cMO/e271793zv+Jrr77//1vDhw5WUlCQfHx81bNhQK1euNCdWAgMDtXTpUj399NPq16+fbr31Vi1duvSaTqd84IEHtHDhQr366qt69tln5ebmpujoaK1du1Z33HGHXdlHHnlE8fHxGjZsmN0sZ79+/eTu7q4HH3xQBw8elJ+fn5o0aaLmzZsXul8XGj58uI4ePaqaNWvKarVqyJAhWrp0aYEnfwYMGKD4+Hj5+/urSZMm+uqrr/KUadu2rb755huNGTPGDFCVKlVSXFycQkND1bNnT3377beKiIhQ2bJlNWDAALvTnaXzdwnOzs5WaGiofHx8NHbsWPOuwxd79dVX1b9/fz3++OMaMmSInnnmmSvWfzlX6r+Xl5e++eYbDRgwQMOGDVNYWJimTp1qXhb57bff6oUXXlB6erqCg4M1ceJE8z5BF/d16NChedrv06ePWrRooXHjxpmnVy9YsEBjx47VQw89pMOHD6tcuXJ66KGH9Oqrr5rXSXt6emrFihUaO3asOnXqpMOHDyswMFCNGjUyf1ciIiL0448/auTIkWrYsKHS09NVoUIFtWnTRj179pR0/ia9fn5+uvvuuwt8zArLYhRknhwFknvnu9TU1BJ3bW3UR/mf6lKcfuv5m8PbvF7YbDadPHlSgYGB3LkTDsXYgzMx/kqec+fOae/evapSpUqhH5HhCnKfC+rh4eHwuzO7so0bNyomJkbnzp0rMb+zs2fP1qRJk/Kc+l2SFfX469Onj+rUqaOnnnqqCHp3dVq2bKnBgwebpyxfSlF8tjAjCwAAAOCKjh07ph07duiee+7R0aNH9eKLL6pjx44lJsTivOnTpzut7RUrVjisLUYdAAAAgCvKycnRc889J6vVqtq1ays0NFSTJ092drdwg2JGFgAAAMAVhYaGlvhTduPj4xUfH+/sbsABmJEFAAAAALgUgiwAAAAui3uDAihKNpvtmuvg1GIAAADky9PTUxaLRcePH1f58uWv2zv6ctdiONONNP4Mw1BmZqaOHz8uNzc38/E/hUGQBQAAQL7c3d1VsWJFHTp0SPv27XN2d4qNYRiy2Wxyc3O77oMESp4bcfz5+PioUqVK13THa4IsAAAALqlMmTKqVq2asrKynN2VYmOz2ZSamiqr1cqjZOBwN9r4c3d3L5LZZ4IsAAAALsvd3V3u7u7O7kaxsdlsOnPmjEqVKnVDBAmULIy/wuFIAQAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSnBtnvvvtO7dq1U1hYmCwWi7788ku79fHx8bJYLHavRo0a2ZXJyMjQM888o6CgIPn6+qp9+/Y6dOiQXZnk5GTFxcXJarXKarUqLi5OKSkpdmUOHDigdu3aydfXV0FBQerfv78yMzOLY7cBAAAAANfAqUH29OnTql27tqZMmXLJMq1atdKRI0fM19dff223fsCAAVq0aJHmz5+vDRs2KD09XW3btlVOTo5Zplu3bkpMTNTy5cu1fPlyJSYmKi4uzlyfk5OjNm3a6PTp09qwYYPmz5+vhQsXatCgQUW/0wAAAACAa+LhzMZbt26t1q1bX7aMt7e3QkJC8l2XmpqqGTNm6JNPPtG9994rSZozZ47Cw8P17bffqmXLltq5c6eWL1+uzZs3q2HDhpKkDz74QNHR0dq1a5eqV6+ulStX6vfff9fBgwcVFhYmSXrjjTcUHx+vsWPHqmzZskW41zeOykOXObzNfa+1cXibAAAAABzLqUG2INatW6cKFSrI399fTZs21dixY1WhQgVJUkJCgrKyshQbG2uWDwsLU2RkpDZu3KiWLVtq06ZNslqtZoiVpEaNGslqtWrjxo2qXr26Nm3apMjISDPESlLLli2VkZGhhIQENWvWLN++ZWRkKCMjw3yflpYmSbLZbLLZbEV6HK6VmxMm391kOLzNknbcC8tms8kwjOtmf+A6GHtwJsYfnIWxB2di/NlzcytYbinRQbZ169bq1KmTIiIitHfvXo0YMULNmzdXQkKCvL29lZSUJC8vLwUEBNhtFxwcrKSkJElSUlKSGXwvVKFCBbsywcHBdusDAgLk5eVllsnP+PHjNXr06DzLk5OTlZ2dfdX7W5wiPCIc3uaZso4PsidPnnR4m8XBZrPp1KlTMgyjwL/MQFFg7MGZGH9wFsYenInxZy8oKKhA5Up0kO3SpYv5c2RkpBo0aKCIiAgtW7ZMDz744CW3MwxDFovFfH/hz9dS5mLDhg3TwIEDzfdpaWkKDw9XQEBAiTsdeX/2foe3eSrt0seuuAQGBjq8zeJgs9lksVgUEBDABxocirEHZ2L8wVkYe3Amxl/hlOgge7HQ0FBFRERoz549kqSQkBBlZmYqOTnZblb22LFjaty4sVnm6NGjeeo6fvy4OQsbEhKiLVu22K1PTk5WVlZWnpnaC3l7e8vb2zvPcjc3txI3CG1y/KkKNjk+yJa0434tLBZLiRxLuP4x9uBMjD84C2MPzsT4u3oudaROnDihgwcPKjQ0VJJUv359eXp6atWqVWaZI0eOaPv27WaQjY6OVmpqqrZu3WqW2bJli1JTU+3KbN++XUeOHDHLrFy5Ut7e3qpfv74jdg0AAAAAUEBOnZFNT0/Xn3/+ab7fu3evEhMTFRgYqMDAQI0aNUodO3ZUaGio9u3bpxdffFFBQUF64IEHJElWq1W9evXSoEGDVK5cOQUGBmrw4MGKiooy72Jco0YNtWrVSr1799b06dMlSU888YTatm2r6tWrS5JiY2NVs2ZNxcXFaeLEiTp58qQGDx6s3r17l7hThAEAAADgRufUIPvTTz/Z3RE493rTnj17atq0afrtt9/08ccfKyUlRaGhoWrWrJkWLFggPz8/c5u33npLHh4e6ty5s86ePasWLVpo9uzZcnd3N8vMnTtX/fv3N+9u3L59e7tn17q7u2vZsmV6+umnddddd6l06dLq1q2b/vvf/xb3IQAAAAAAXCWLYRiOv7XsdSotLU1Wq1WpqaklbiY36qMoh7d5audrDm/zenmOrM1m08mTJxUYGMi1EnAoxh6cifEHZ2HswZkYf4XDkQIAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FKcGmS/++47tWvXTmFhYbJYLPryyy/NdVlZWRoyZIiioqLk6+ursLAwPfLIIzp8+LBdHTExMbJYLHavhx9+2K5McnKy4uLiZLVaZbVaFRcXp5SUFLsyBw4cULt27eTr66ugoCD1799fmZmZxbXrAAAAAIBCcmqQPX36tGrXrq0pU6bkWXfmzBn9/PPPGjFihH7++Wd98cUX2r17t9q3b5+nbO/evXXkyBHzNX36dLv13bp1U2JiopYvX67ly5crMTFRcXFx5vqcnBy1adNGp0+f1oYNGzR//nwtXLhQgwYNKvqdBgAAAABcEw9nNt66dWu1bt0633VWq1WrVq2yWzZ58mTdeeedOnDggCpVqmQu9/HxUUhISL717Ny5U8uXL9fmzZvVsGFDSdIHH3yg6Oho7dq1S9WrV9fKlSv1+++/6+DBgwoLC5MkvfHGG4qPj9fYsWNVtmzZothdAAAAAEARcGqQvVqpqamyWCzy9/e3Wz537lzNmTNHwcHBat26tUaOHCk/Pz9J0qZNm2S1Ws0QK0mNGjWS1WrVxo0bVb16dW3atEmRkZFmiJWkli1bKiMjQwkJCWrWrFm+/cnIyFBGRob5Pi0tTZJks9lks9mKareLhJsTJt/dZDi8zZJ23AvLZrPJMIzrZn/gOhh7cCbGH5yFsQdnYvzZc3MrWG5xmSB77tw5DR06VN26dbObIe3evbuqVKmikJAQbd++XcOGDdMvv/xizuYmJSWpQoUKeeqrUKGCkpKSzDLBwcF26wMCAuTl5WWWyc/48eM1evToPMuTk5OVnZ1dqP0sLhEeEQ5v80xZxwfZkydPOrzN4mCz2XTq1CkZhlHgX2agKDD24EyMPzgLYw/OxPizFxQUVKByLhFks7Ky9PDDD8tms2nq1Kl263r37m3+HBkZqWrVqqlBgwb6+eefVa9ePUmSxWLJU6dhGHbLC1LmYsOGDdPAgQPN92lpaQoPD1dAQECJOx15f/Z+h7d5Ku3Sx664BAYGOrzN4mCz2WSxWBQQEMAHGhyKsQdnYvzBWRh7cCbGX+GU+CCblZWlzp07a+/evVqzZs0VA2K9evXk6empPXv2qF69egoJCdHRo0fzlDt+/Lg5CxsSEqItW7bYrU9OTlZWVlaemdoLeXt7y9vbO89yNze3EjcIbXL8qQo2OT7IlrTjfi0sFkuJHEu4/jH24EyMPzgLYw/OxPi7eiX6SOWG2D179ujbb79VuXLlrrjNjh07lJWVpdDQUElSdHS0UlNTtXXrVrPMli1blJqaqsaNG5tltm/friNHjphlVq5cKW9vb9WvX7+I9woAAAAAcC2cOiObnp6uP//803y/d+9eJSYmKjAwUGFhYXrooYf0888/66uvvlJOTo55vWpgYKC8vLz0119/ae7cubr//vsVFBSk33//XYMGDVLdunV11113SZJq1KihVq1aqXfv3uZjeZ544gm1bdtW1atXlyTFxsaqZs2aiouL08SJE3Xy5EkNHjxYvXv3LnGnCAMAAADAjc6pM7I//fST6tatq7p160qSBg4cqLp16+rll1/WoUOHtGTJEh06dEh16tRRaGio+dq4caMkycvLS6tXr1bLli1VvXp19e/fX7Gxsfr222/l7u5utjN37lxFRUUpNjZWsbGxuv322/XJJ5+Y693d3bVs2TKVKlVKd911lzp37qwOHTrov//9r2MPCAAAAADgipw6IxsTEyPDuPSdbS+3TpLCw8O1fv36K7YTGBioOXPmXLZMpUqV9NVXX12xLgAAAACAc5Xoa2QBAAAAALgYQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSrinI/vnnn1qxYoXOnj0rSTIMo0g6BQAAAADApRQqyJ44cUL33nuvbr31Vt1///06cuSIJOnxxx/XoEGDirSDAAAAAABcqFBB9rnnnpOHh4cOHDggHx8fc3mXLl20fPnyIuscAAAAAAAX8yjMRitXrtSKFStUsWJFu+XVqlXT/v37i6RjAAAAAADkp1AzsqdPn7abic3177//ytvb+5o7BQAAAADApRQqyN5zzz36+OOPzfcWi0U2m00TJ05Us2bNiqxzAAAAAABcrFBBduLEiZo+fbpat26tzMxMvfDCC4qMjNR3332n119/vcD1fPfdd2rXrp3CwsJksVj05Zdf2q03DEOjRo1SWFiYSpcurZiYGO3YscOuTEZGhp555hkFBQXJ19dX7du316FDh+zKJCcnKy4uTlarVVarVXFxcUpJSbErc+DAAbVr106+vr4KCgpS//79lZmZeVXHBQAAAABQ/AoVZGvWrKlffvlFd9xxh+677z6dPn1aDz74oLZt26aqVasWuJ7Tp0+rdu3amjJlSr7rJ0yYoDfffFNTpkzRjz/+qJCQEN133306deqUWWbAgAFatGiR5s+frw0bNig9PV1t27ZVTk6OWaZbt25KTEzU8uXLtXz5ciUmJiouLs5cn5OTozZt2uj06dPasGGD5s+fr4ULF3IHZgAAAAAogSzGVTz8tUmTJmrevLliYmLUuHFjlSpVqug6YrFo0aJF6tChg6Tzs7FhYWEaMGCAhgwZIun87GtwcLBef/119enTR6mpqSpfvrw++eQTdenSRZJ0+PBhhYeH6+uvv1bLli21c+dO1axZU5s3b1bDhg0lSZs3b1Z0dLT++OMPVa9eXd98843atm2rgwcPKiwsTJI0f/58xcfH69ixYypbtmyB9iEtLU1Wq1WpqakF3sZRoj6Kcnibp3a+5vA2973WxuFtFgebzaaTJ08qMDBQbm7X9Lhn4Kow9uBMjD84C2MPzsT4K5yrumtx9erVNW/ePI0ZM0be3t5q2LChWrRooWbNmqlhw4by9PQsso7t3btXSUlJio2NNZd5e3uradOm2rhxo/r06aOEhARlZWXZlQkLC1NkZKQ2btyoli1batOmTbJarWaIlaRGjRrJarVq48aNql69ujZt2qTIyEgzxEpSy5YtlZGRoYSEhEte95uRkaGMjAzzfVpamqTzg9FmsxXZsSgKboWbfL/GNgv8HUmRKWnHvbBsNpsMw7hu9geug7EHZ2L8wVkYe3Amxp+9gob5qwqyM2bMkCQdOnRIa9as0fr16zV79myNHDlSpUuXVuPGjdW8eXMNGzbs6nt8kaSkJElScHCw3fLg4GDzET9JSUny8vJSQEBAnjK52yclJalChQp56q9QoYJdmYvbCQgIkJeXl1kmP+PHj9fo0aPzLE9OTlZ2dvaVdtGhIjwiHN7mmbKOD7InT550eJvFwWaz6dSpUzIMg2/m4FCMPTgT4w/OwtiDMzH+7AUFBRWoXKGeI1uxYkU98sgjeuSRRyRJBw8e1PTp0zV58mStWbOmSIJsLovFYvfeMIw8yy52cZn8yhemzMWGDRumgQMHmu/T0tIUHh6ugICAEndq8f5sxz/f91Ta5f+dikNgYKDD2ywONptNFotFAQEBfKDBoRh7cCbGH5yFsQdnYvwVTqGCrCT99ddfWrdunflKSUlRdHS0mjZtWiQdCwkJkXR+tjQ0NNRcfuzYMXP2NCQkRJmZmUpOTrablT127JgaN25sljl69Gie+o8fP25Xz5YtW+zWJycnKysrK89M7YW8vb3zfW6um5tbiRuENjn+VAWbHB9kS9pxvxYWi6VEjiVc/xh7cCbGH5yFsQdnYvxdvas6UrNmzdIjjzyiSpUqqV69elq4cKEiIyP1+eefKzk5WStXrtRLL71UJB2rUqWKQkJCtGrVKnNZZmam1q9fb4bU+vXry9PT067MkSNHtH37drNMdHS0UlNTtXXrVrPMli1blJqaaldm+/btOnLkiFlm5cqV8vb2Vv369YtkfwAAAAAAReOqZmR79eqlSpUq6aWXXtJjjz12zTd3Sk9P159//mm+37t3rxITExUYGKhKlSppwIABGjdunKpVq6Zq1app3Lhx8vHxUbdu3SRJVqtVvXr10qBBg1SuXDkFBgZq8ODBioqK0r333itJqlGjhlq1aqXevXtr+vTpkqQnnnhCbdu2VfXq1SVJsbGxqlmzpuLi4jRx4kSdPHlSgwcPVu/evUvcKcIAAAAAcKO7qiD77rvvav369Ro1apSGDh2qJk2aKCYmRk2bNlX9+vWveO3qxX766Se7OwLnXm/as2dPzZ49Wy+88ILOnj2rp59+WsnJyWrYsKFWrlwpPz8/c5u33npLHh4e6ty5s86ePasWLVpo9uzZcnd3N8vMnTtX/fv3N+9u3L59e7tn17q7u2vZsmV6+umnddddd6l06dLq1q2b/vvf/17V/gAAAAAAit9VPUf2Qr///rvWr1+vdevWaf369Tp37pzuuusuNWvWTIMHDy7qfroEniNrj+fIFh7PE4OzMPbgTIw/OAtjD87E+CucQh+pmjVr6qmnntKCBQv0888/q1+/ftqwYYOGDBlSlP0DAAAAAMBOoe5afPToUbs7Fu/evVteXl5q2LCh3anCwPXC0TPabnLT6jarHdomAAAA4CquKsj27dtXa9eu1a5du+Th4aE77rhDDz30kJo1a6bGjRurVKlSxdVPAAAAAAAkXWWQ/fnnn9WhQwc1a9ZMd911l3x8fIqrXwAAAAAA5OuqguymTZskSSkpKZcMsX/++aduueWWa+8ZAAAAAAD5KNTNnu6//36dPXs2z/Jdu3YpJibmWvsEAAAAAMAlFSrIBgQE6IEHHlB2dra5bOfOnYqJiVHHjh2LrHMAAAAAAFysUEF24cKFOn36tLp16ybDMLR9+3bFxMSoa9euevvtt4u6jwAAAAAAmAoVZEuVKqWvvvpKe/bsUadOndSiRQs98sgjevPNN4u6fwAAAAAA2CnwzZ7S0tLs3lssFi1YsED33nuvOnbsqBEjRphlypYtW7S9BAAAAADg/xQ4yPr7+8tiseRZbhiG3nvvPU2fPl2GYchisSgnJ6dIOwkAAAAAQK4CB9m1a9cWZz8AAAAAACiQAgfZpk2b6v3331f79u0VEhJSnH0CAAAAAOCSrupmT59++qkqV66shg0baty4cdqxY0dx9QsAAAAAgHxdVZBdu3atjhw5omeeeUaJiYlq3LixqlatqoEDB2rdunWy2WzF1U8AAAAAACQV4vE7AQEB6tGjhz777DMdP35c7777rs6dO6e4uDiVL19ejzzyiP73v//p9OnTxdFfAAAAAMANrlDPkc3l5eWlVq1aaerUqTp48KBWrlypypUr69VXX+WZsgAAAACAYlHgmz3lys7Ols1mk5eXl7nsww8/1Pfff68GDRpo9OjReuWVV5SVlVWkHQUAAAAAQCrEjGyPHj00cuRI8/306dP17LPP6vTp03rllVf04osvSpI8PT2LrpcAAAAAAPyfqw6yCQkJatWqlfl++vTpmjRpkv73v//p888/17x584q0gwAAAAAAXKjApxY/+uijkqSDBw/qnXfe0UcffSTDMPTLL7/om2++0aZNm5Sdna3Dhw/rsccekyTNnDmzeHoNAAAAALhhFTjIzpo1S5K0Zs0aDRgwQHfffbeWLVumH374QV988YUkKTU1VYsXLybAAgAAAACKzVXf7CkmJkZ9+vRRXFycZs2apS5dupjrfvnlF1WrVq1IOwgAAAAAwIWu+hrZN998U/Xr19e8efPUvHlz8+ZOkvTll1+qR48eRdpBAAAAAAAudNUzsuXKldMnn3yS7zqeHQsAAAAAKG5XHWQBOMbD72/S32kW2WRxWJv7XmvjsLYAAACAwrrqU4sBAAAAAHAmgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASynxQbZy5cqyWCx5Xn379pUkxcfH51nXqFEjuzoyMjL0zDPPKCgoSL6+vmrfvr0OHTpkVyY5OVlxcXGyWq2yWq2Ki4tTSkqKo3YTAAAAAFBAJT7I/vjjjzpy5Ij5WrVqlSSpU6dOZplWrVrZlfn666/t6hgwYIAWLVqk+fPna8OGDUpPT1fbtm2Vk5NjlunWrZsSExO1fPlyLV++XImJiYqLi3PMTgIAAAAACszD2R24kvLly9u9f+2111S1alU1bdrUXObt7a2QkJB8t09NTdWMGTP0ySef6N5775UkzZkzR+Hh4fr222/VsmVL7dy5U8uXL9fmzZvVsGFDSdIHH3yg6Oho7dq1S9WrVy+mvQMAAAAAXK0SH2QvlJmZqTlz5mjgwIGyWCzm8nXr1qlChQry9/dX06ZNNXbsWFWoUEGSlJCQoKysLMXGxprlw8LCFBkZqY0bN6ply5batGmTrFarGWIlqVGjRrJardq4ceMlg2xGRoYyMjLM92lpaZIkm80mm81WpPt+rdycMPnuJsPhbRbXcXf08XOTmywyHP6vVtLGLRzPZrPJMAzGApyC8QdnYezBmRh/9tzcCvYXsEsF2S+//FIpKSmKj483l7Vu3VqdOnVSRESE9u7dqxEjRqh58+ZKSEiQt7e3kpKS5OXlpYCAALu6goODlZSUJElKSkoyg++FKlSoYJbJz/jx4zV69Og8y5OTk5WdnV3IvSweER4RDm/zTFnHB9mTJ08WS72OPn4WWeTvc/5nw4FfCBTX8YPrsNlsOnXqlAzDKPB/SICiwviDszD24EyMP3tBQUEFKudSQXbGjBlq3bq1wsLCzGVdunQxf46MjFSDBg0UERGhZcuW6cEHH7xkXYZh2M3qXvjzpcpcbNiwYRo4cKD5Pi0tTeHh4QoICFDZsmULvF+OsD97v8PbPJV26WNXXAIDA4ulXkcfPze56cwZaW+aZJPjjmNxHT+4DpvNJovFooCAAP5jCodj/MFZGHtwJsZf4bhMkN2/f7++/fZbffHFF5ctFxoaqoiICO3Zs0eSFBISoszMTCUnJ9vNyh47dkyNGzc2yxw9ejRPXcePH1dwcPAl2/L29pa3t3ee5W5ubiVuENrk+FMVHBnAchXXcXfG8TNkke3/Xo5S0sYtnMNisZTIzzHcGBh/cBbGHpyJ8Xf1XOZIzZo1SxUqVFCbNm0uW+7EiRM6ePCgQkNDJUn169eXp6enebdjSTpy5Ii2b99uBtno6GilpqZq69atZpktW7YoNTXVLAMAAAAAKBlcYkbWZrNp1qxZ6tmzpzw8/n+X09PTNWrUKHXs2FGhoaHat2+fXnzxRQUFBemBBx6QJFmtVvXq1UuDBg1SuXLlFBgYqMGDBysqKsq8i3GNGjXUqlUr9e7dW9OnT5ckPfHEE2rbti13LAYAAACAEsYlguy3336rAwcO6LHHHrNb7u7urt9++00ff/yxUlJSFBoaqmbNmmnBggXy8/Mzy7311lvy8PBQ586ddfbsWbVo0UKzZ8+Wu7u7WWbu3Lnq37+/eXfj9u3ba8qUKY7ZQQAAAABAgblEkI2NjZVh5L1za+nSpbVixYorbl+qVClNnjxZkydPvmSZwMBAzZkz55r6CSB/UR9FObzN33r+5vA2AQAA4Bguc40sAAAAAAASQRYAAAAA4GIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuxcPZHQCA4lB56DKHtrfvtTYObQ8AAOBGxowsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACX4uHsDgAALi/qoyiHteUmN61us9ph7QEAABQGM7IAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUkp0kB01apQsFovdKyQkxFxvGIZGjRqlsLAwlS5dWjExMdqxY4ddHRkZGXrmmWcUFBQkX19ftW/fXocOHbIrk5ycrLi4OFmtVlmtVsXFxSklJcURuwgAAAAAuEoezu7AldSqVUvffvut+d7d3d38ecKECXrzzTc1e/Zs3XrrrRozZozuu+8+7dq1S35+fpKkAQMGaOnSpZo/f77KlSunQYMGqW3btkpISDDr6tatmw4dOqTly5dLkp544gnFxcVp6dKlDtxTACgZHn5/k/5Os8gmi8Pa3PdaG4e1BQAAXF+JD7IeHh52s7C5DMPQpEmT9NJLL+nBBx+UJH300UcKDg7WvHnz1KdPH6WmpmrGjBn65JNPdO+990qS5syZo/DwcH377bdq2bKldu7cqeXLl2vz5s1q2LChJOmDDz5QdHS0du3aperVqztuZwEAAAAAV1Tig+yePXsUFhYmb29vNWzYUOPGjdPNN9+svXv3KikpSbGxsWZZb29vNW3aVBs3blSfPn2UkJCgrKwsuzJhYWGKjIzUxo0b1bJlS23atElWq9UMsZLUqFEjWa1Wbdy48bJBNiMjQxkZGeb7tLQ0SZLNZpPNZivKw3DN3JxwFrmbDIe3WVzH3dHHz01usshw+L/a9XL8zrfp2PFXnL/zjjx+19vYg2ux2WwyDIPxAIdj7MGZGH/23NwK9ldIiQ6yDRs21Mcff6xbb71VR48e1ZgxY9S4cWPt2LFDSUlJkqTg4GC7bYKDg7V//35JUlJSkry8vBQQEJCnTO72SUlJqlChQp62K1SoYJa5lPHjx2v06NF5licnJys7O7vgO+oAER4RDm/zTFnHB9mTJ08WS72OPn4WWeTvc/5nw4GB7Ho5fpLjx19xHTvJscfveht7j614rFjqvZyZLWc6vM3rhc1m06lTp2QYRoH/kAGKAmMPzsT4sxcUFFSgciU6yLZu3dr8OSoqStHR0apatao++ugjNWrUSJJksdhfw2UYRp5lF7u4TH7lC1LPsGHDNHDgQPN9WlqawsPDFRAQoLJly152W0fbn73f4W2eSnPc9XW5AgMDi6VeRx8/N7npzBlpb5ocep3i9XL8JMePv+I6dpJjjx9j79oV51i43tlsNlksFgUEBPDHHByKsQdnYvwVTokOshfz9fVVVFSU9uzZow4dOkg6P6MaGhpqljl27Jg5SxsSEqLMzEwlJyfbzcoeO3ZMjRs3NsscPXo0T1vHjx/PM9t7MW9vb3l7e+dZ7ubmVuIGoU2OP1XBkX8E5yqu4+6M42fo/M12HHkcr6fj5+jxV5y/844+foy9a1PSPv9djcViKZH/HcX1j7EHZ2L8XT2XOlIZGRnauXOnQkNDVaVKFYWEhGjVqlXm+szMTK1fv94MqfXr15enp6ddmSNHjmj79u1mmejoaKWmpmrr1q1mmS1btig1NdUsAwAAAAAoOUr0jOzgwYPVrl07VapUSceOHdOYMWOUlpamnj17ymKxaMCAARo3bpyqVaumatWqady4cfLx8VG3bt0kSVarVb169dKgQYNUrlw5BQYGavDgwYqKijLvYlyjRg21atVKvXv31vTp0yWdf/xO27ZtuWMxAAAAAJRAJTrIHjp0SF27dtW///6r8uXLq1GjRtq8ebMiIs7f+OSFF17Q2bNn9fTTTys5OVkNGzbUypUrzWfIStJbb70lDw8Pde7cWWfPnlWLFi00e/Zsu+fRzp07V/379zfvbty+fXtNmTLFsTsLAEAJE/VRlEPbc5ObVrdZ7dA2AQCuqUQH2fnz5192vcVi0ahRozRq1KhLlilVqpQmT56syZMnX7JMYGCg5syZU9huAgAAAAAcyKWukQUAAAAAoETPyAIA4GoqD13m8Db3vdbG4W3iyhx9arYk/dbzN4e3CQDOwIwsAAAAAMClEGQBAAAAAC6FU4sBAABQ4jjy1GzumA24HoIsAAAoMR5+f5P+TrPIJotD2uP6YgBwTQRZAAAA4DrCjcZwI+AaWQAAAACAS2FGFgAA4Drh6Mc/cWo2rkeOntHmGu3CYUYWAAAAAOBSmJEFAADADc/RNxqTmNEGrgUzsgAAAAAAl8KMLAAAAIBr4ujrsyVmtG90zMgCAAAAAFwKM7IAAAAA4ESOvkb7epjNZkYWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSSnSQHT9+vO644w75+fmpQoUK6tChg3bt2mVXJj4+XhaLxe7VqFEjuzIZGRl65plnFBQUJF9fX7Vv316HDh2yK5OcnKy4uDhZrVZZrVbFxcUpJSWluHcRAAAAAHCVSnSQXb9+vfr27avNmzdr1apVys7OVmxsrE6fPm1XrlWrVjpy5Ij5+vrrr+3WDxgwQIsWLdL8+fO1YcMGpaenq23btsrJyTHLdOvWTYmJiVq+fLmWL1+uxMRExcXFOWQ/AQAAAAAF5+HsDlzO8uXL7d7PmjVLFSpUUEJCgu655x5zube3t0JCQvKtIzU1VTNmzNAnn3yie++9V5I0Z84chYeH69tvv1XLli21c+dOLV++XJs3b1bDhg0lSR988IGio6O1a9cuVa9ePd+6MzIylJGRYb5PS0uTJNlsNtlstsLveDFwc8J3Fm4yHN5mcR13Rx8/N7nJIsPh/2rXy/E736Zjx19x/s478vgx9oqiTT77rqU9R4+/6+V39/+3yWdfYdvis+9a2+Sz71rau54++66Vm1vBjkSJDrIXS01NlSQFBgbaLV+3bp0qVKggf39/NW3aVGPHjlWFChUkSQkJCcrKylJsbKxZPiwsTJGRkdq4caNatmypTZs2yWq1miFWkho1aiSr1aqNGzdeMsiOHz9eo0ePzrM8OTlZ2dnZ17y/RSnCI8LhbZ4p6/gPtJMnTxZLvY4+fhZZ5O9z/mfDgf9huF6On+T48Vdcx05y7PFj7F07PvsKzxnj73r53c3FZ1/h8Nl37fjsK7zr7bPvWgUFBRWonMsEWcMwNHDgQDVp0kSRkZHm8tatW6tTp06KiIjQ3r17NWLECDVv3lwJCQny9vZWUlKSvLy8FBAQYFdfcHCwkpKSJElJSUlm8L1QhQoVzDL5GTZsmAYOHGi+T0tLU3h4uAICAlS2bNlr3eUitT97v8PbPJVmcXibF3/JUVQcffzc5KYzZ6S9aZJNjjuO18vxkxw//orr2EmOPX6MvWvHZ1/hOWP8XS+/u7n47CscPvuuHZ99hXe9ffY5issE2X79+unXX3/Vhg0b7JZ36dLF/DkyMlINGjRQRESEli1bpgcffPCS9RmGIYvl/w+UC3++VJmLeXt7y9vbO89yNze3Ak+JO4pNjj99wJH/IchVXMfdGcfPkEW2/3s5yvV0/Bw9/orzd97Rx4+xd61t8tl3LRw9/q6n393zbfLZV1h89l1rm3z2XYvr6bPPUVxiD5555hktWbJEa9euVcWKFS9bNjQ0VBEREdqzZ48kKSQkRJmZmUpOTrYrd+zYMQUHB5tljh49mqeu48ePm2UAAAAAACVDiQ6yhmGoX79++uKLL7RmzRpVqVLlitucOHFCBw8eVGhoqCSpfv368vT01KpVq8wyR44c0fbt29W4cWNJUnR0tFJTU7V161azzJYtW5SammqWAQAAAACUDCX61OK+fftq3rx5Wrx4sfz8/MzrVa1Wq0qXLq309HSNGjVKHTt2VGhoqPbt26cXX3xRQUFBeuCBB8yyvXr10qBBg1SuXDkFBgZq8ODBioqKMu9iXKNGDbVq1Uq9e/fW9OnTJUlPPPGE2rZte8kbPQEAAAAAnKNEB9lp06ZJkmJiYuyWz5o1S/Hx8XJ3d9dvv/2mjz/+WCkpKQoNDVWzZs20YMEC+fn5meXfeusteXh4qHPnzjp79qxatGih2bNny93d3Swzd+5c9e/f37y7cfv27TVlypTi30kAAAAAwFUp0UHWMC5/++nSpUtrxYoVV6ynVKlSmjx5siZPnnzJMoGBgZozZ85V9xEAAAAA4Fgl+hpZAAAAAAAuRpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAAAAAHApBFkAAAAAgEshyAIAAAAAXApBFgAAAADgUgiyAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMClEGQBAAAAAC6FIAsAAAAAcCkEWQAAAACASyHIAgAAAABcCkH2IlOnTlWVKlVUqlQp1a9fX99//72zuwQAAAAAuABB9gILFizQgAED9NJLL2nbtm26++671bp1ax04cMDZXQMAAAAA/B+C7AXefPNN9erVS48//rhq1KihSZMmKTw8XNOmTXN21wAAAAAA/8fD2R0oKTIzM5WQkKChQ4faLY+NjdXGjRvz3SYjI0MZGRnm+9TUVElSSkqKbDZb8XW2EIyzhuMbzTjt8CZTUlKKpV5HHz9DhnLOnZYyLJIsDmv3ejl+khw+/orr2EmOPX6MvSLAZ1/h23PC+LtefndNfPYVri0++64dn32Fb+86++y7Vm5ubvLz85PFcvljYTEMwwkjveQ5fPiwbrrpJv3www9q3LixuXzcuHH66KOPtGvXrjzbjBo1SqNHj3ZkNwEAAADgupaamqqyZctetgwzshe5OPkbhnHJbwOGDRumgQMHmu9tNptOnjypcuXKXfEbBOBy0tLSFB4eroMHD17xlxgoSow9OBPjD87C2IMzMf7y8vPzu2IZguz/CQoKkru7u5KSkuyWHzt2TMHBwflu4+3tLW9vb7tl/v7+xdVF3IDKli3LBxqcgrEHZ2L8wVkYe3Amxt/V4WZP/8fLy0v169fXqlWr7JavWrXK7lRjAAAAAIBzMSN7gYEDByouLk4NGjRQdHS03n//fR04cEBPPvmks7sGAAAAAPg/BNkLdOnSRSdOnNArr7yiI0eOKDIyUl9//bUiIiKc3TXcYLy9vTVy5Mg8p64DxY2xB2di/MFZGHtwJsZf4XDXYgAAAACAS+EaWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAVKiPHjx+uOO+6Qn5+fKlSooA4dOmjXrl3O7hZuUOPHj5fFYtGAAQOc3RXcAP755x/16NFD5cqVk4+Pj+rUqaOEhARndws3gOzsbA0fPlxVqlRR6dKldfPNN+uVV16RzWZzdtdwnfnuu+/Url07hYWFyWKx6Msvv7RbbxiGRo0apbCwMJUuXVoxMTHasWOHczrrIgiyQAmxfv169e3bV5s3b9aqVauUnZ2t2NhYnT592tldww3mxx9/1Pvvv6/bb7/d2V3BDSA5OVl33XWXPD099c033+j333/XG2+8IX9/f2d3DTeA119/Xe+9956mTJminTt3asKECZo4caImT57s7K7hOnP69GnVrl1bU6ZMyXf9hAkT9Oabb2rKlCn68ccfFRISovvuu0+nTp1ycE9dB4/fAUqo48ePq0KFClq/fr3uueceZ3cHN4j09HTVq1dPU6dO1ZgxY1SnTh1NmjTJ2d3CdWzo0KH64Ycf9P333zu7K7gBtW3bVsHBwZoxY4a5rGPHjvLx8dEnn3zixJ7hemaxWLRo0SJ16NBB0vnZ2LCwMA0YMEBDhgyRJGVkZCg4OFivv/66+vTp48TellzMyAIlVGpqqiQpMDDQyT3BjaRv375q06aN7r33Xmd3BTeIJUuWqEGDBurUqZMqVKigunXr6oMPPnB2t3CDaNKkiVavXq3du3dLkn755Rdt2LBB999/v5N7hhvJ3r17lZSUpNjYWHOZt7e3mjZtqo0bNzqxZyWbh7M7ACAvwzA0cOBANWnSRJGRkc7uDm4Q8+fP188//6wff/zR2V3BDeTvv//WtGnTNHDgQL344ovaunWr+vfvL29vbz3yyCPO7h6uc0OGDFFqaqpuu+02ubu7KycnR2PHjlXXrl2d3TXcQJKSkiRJwcHBdsuDg4O1f/9+Z3TJJRBkgRKoX79++vXXX7VhwwZndwU3iIMHD+rZZ5/VypUrVapUKWd3BzcQm82mBg0aaNy4cZKkunXraseOHZo2bRpBFsVuwYIFmjNnjubNm6datWopMTFRAwYMUFhYmHr27Ons7uEGY7FY7N4bhpFnGf4/gixQwjzzzDNasmSJvvvuO1WsWNHZ3cENIiEhQceOHVP9+vXNZTk5Ofruu+80ZcoUZWRkyN3d3Yk9xPUqNDRUNWvWtFtWo0YNLVy40Ek9wo3k+eef19ChQ/Xwww9LkqKiorR//36NHz+eIAuHCQkJkXR+ZjY0NNRcfuzYsTyztPj/uEYWKCEMw1C/fv30xRdfaM2aNapSpYqzu4QbSIsWLfTbb78pMTHRfDVo0EDdu3dXYmIiIRbF5q677srzqLHdu3crIiLCST3CjeTMmTNyc7P/c9jd3Z3H78ChqlSpopCQEK1atcpclpmZqfXr16tx48ZO7FnJxowsUEL07dtX8+bN0+LFi+Xn52deL2G1WlW6dGkn9w7XOz8/vzzXY/v6+qpcuXJcp41i9dxzz6lx48YaN26cOnfurK1bt+r999/X+++/7+yu4QbQrl07jR07VpUqVVKtWrW0bds2vfnmm3rsscec3TVcZ9LT0/Xnn3+a7/fu3avExEQFBgaqUqVKGjBggMaNG6dq1aqpWrVqGjdunHx8fNStWzcn9rpk4/E7QAlxqWsgZs2apfj4eMd2BpAUExPD43fgEF999ZWGDRumPXv2qEqVKho4cKB69+7t7G7hBnDq1CmNGDFCixYt0rFjxxQWFqauXbvq5ZdflpeXl7O7h+vIunXr1KxZszzLe/bsqdmzZ8swDI0ePVrTp09XcnKyGjZsqHfffZcvky+DIAsAAAAAcClcIwsAAAAAcCkEWQAAAACASyHIAgAAAABcCkEWAAAAAOBSCLIAAAAAAJdCkAUAAAAAuBSCLAAAAADApRBkAQAAAAAuhSALAEAxWLdunSwWi1JSUpzdFYfat2+fLBaLEhMTnd0VAMB1jCALAEAhxMfHy2KxyGKxyNPTUzfffLMGDx6s06dPF2u7FotFX375ZbG2AQBASefh7A4AAOCqWrVqpVmzZikrK0vff/+9Hn/8cZ0+fVrTpk1zdteuO5mZmfLy8nJ2NwAAJQQzsgAAFJK3t7dCQkIUHh6ubt26qXv37pecLT1x4oS6du2qihUrysfHR1FRUfr000/tysTExKh///564YUXFBgYqJCQEI0aNcpcX7lyZUnSAw88IIvFYr6/WO7pvV988YWaNWsmHx8f1a5dW5s2bTLLjBo1SnXq1LHbbtKkSXZ1xsfHq0OHDho3bpyCg4Pl7++v0aNHKzs7W88//7wCAwNVsWJFzZw5M08f/vjjDzVu3FilSpVSrVq1tG7dOrv1v//+u+6//36VKVNGwcHBiouL07///mt3LPr166eBAwcqKChI9913X777CgC4MRFkAQAoIqVLl1ZWVla+686dO6f69evrq6++0vbt2/XEE08oLi5OW7ZssSv30UcfydfXV1u2bNGECRP0yiuvaNWqVZKkH3/8UZI0a9YsHTlyxHx/KS+99JIGDx6sxMRE3Xrrreratauys7Ovap/WrFmjw4cP67vvvtObb76pUaNGqW3btgoICNCWLVv05JNP6sknn9TBgwfttnv++ec1aNAgbdu2TY0bN1b79u114sQJSdKRI0fUtGlT1alTRz/99JOWL1+uo0ePqnPnznmOhYeHh3744QdNnz79qvoNALi+EWQBACgCW7du1bx589SiRYt81990000aPHiw6tSpo5tvvlnPPPOMWrZsqc8//9yu3O23366RI0eqWrVqeuSRR9SgQQOtXr1aklS+fHlJkr+/v0JCQsz3lzJ48GC1adNGt956q0aPHq39+/frzz//vKr9CgwM1DvvvKPq1avrscceU/Xq1XXmzBm9+OKLqlatmoYNGyYvLy/98MMPdtv169dPHTt2VI0aNTRt2jRZrVbNmDFDkjRt2jTVq1dP48aN02233aa6detq5syZWrt2rXbv3m3Wccstt2jChAmqXr26brvttqvqNwDg+sY1sgAAFNJXX32lMmXKKDs7W1lZWfrPf/6jyZMn51s2JydHr732mhYsWKB//vlHGRkZysjIkK+vr12522+/3e59aGiojh07Vqj+XVhXaGioJOnYsWNXFQpr1aolN7f//713cHCwIiMjzffu7u4qV65cnj5GR0ebP3t4eKhBgwbauXOnJCkhIUFr165VmTJl8rT3119/6dZbb5UkNWjQoMD9BADcWAiyAAAUUrNmzTRt2jR5enoqLCxMnp6elyz7xhtv6K233tKkSZMUFRUlX19fDRgwQJmZmXblLq7DYrHIZrMVqn8X1mWxWCTJrMvNzU2GYdiVz++06Pz6U9g+XtiHdu3a6fXXX89TJjdwS8oT8gEAyEWQBQCgkHx9fXXLLbcUqOz333+v//znP+rRo4ek82Fuz549qlGjxlW16enpqZycnKvu68XKly+vpKQkGYZhBsyifPbr5s2bdc8990iSsrOzlZCQoH79+kmS6tWrp4ULF6py5cry8OBPEQDA1eMaWQAAHOCWW27RqlWrtHHjRu3cuVN9+vRRUlLSVddTuXJlrV69WklJSUpOTi50f2JiYnT8+HFNmDBBf/31l95991198803ha7vYu+++64WLVqkP/74Q3379lVycrIee+wxSVLfvn118uRJde3aVVu3btXff/+tlStX6rHHHiuSkA4AuP4RZAEAcIARI0aoXr16atmypWJiYhQSEqIOHTpcdT1vvPGGVq1apfDwcNWtW7fQ/alRo4amTp2qd999V7Vr19bWrVs1ePDgQtd3sddee02vv/66ateure+//16LFy9WUFCQJCksLEw//PCDcnJy1LJlS0VGRurZZ5+V1Wq1ux4XAIBLsRgXXyADAAAAAEAJxteeAAAAAACXQpAFAAAAALgUgiwAAAAAwKUQZAEAAAAALoUgCwAAAABwKQRZAAAAAIBLIcgCAAAAAFwKQRYAAAAA4FIIsgAAAAAAl0KQBQAAAAC4FIIsAAAAAMCl/D/vqsAywy9YOAAAAABJRU5ErkJggg==", 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", 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", 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" ] @@ -537,7 +537,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -617,7 +617,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -657,7 +657,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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" 270.018398\n", - " 708.862903\n", - " 1262.169958\n", - " 25.860589\n", - " 280.495951\n", - " 73.208993\n", + " 2739.678768\n", + " 1735.598196\n", + " 302.615223\n", + " 701.465350\n", + " 1181.612931\n", + " 25.370702\n", + " 273.159057\n", + " 73.042539\n", " \n", " \n", " 9\n", " 10\n", - " 4096.669953\n", + " 3989.125787\n", " NaN\n", - " 4358.974073\n", + " 4244.544003\n", " NaN\n", " 44.383280\n", " 8.562190\n", " 124.671799\n", - " 2734.738845\n", - " 1785.259857\n", - " 265.104351\n", - " 684.374637\n", - " 1211.775106\n", - " 25.484202\n", - " 262.304120\n", - " 73.747403\n", + " 2705.787252\n", + " 1731.446666\n", + " 297.107949\n", + " 677.232638\n", + " 1133.664314\n", + " 25.002422\n", + " 255.418216\n", + " 73.586864\n", " \n", " \n", "\n", @@ -931,28 +931,28 @@ ], "text/plain": [ " Plant number OCC Net OCC TCI NCI Construction duration Startup duration Preconstruction costs Direct costs \\\n", - "0 1 15604.817916 NaN 18840.161605 NaN 91.004408 28.000000 124.671799 7070.565432 \n", - "1 2 8643.324158 NaN 9858.010846 NaN 68.296591 19.600000 124.671799 4593.451055 \n", - "2 3 6633.895068 NaN 7385.473722 NaN 59.881944 15.909050 124.671799 3807.581701 \n", - "3 4 5287.971674 NaN 5809.900167 NaN 55.162570 13.720000 124.671799 3217.848420 \n", - "4 5 4715.380499 NaN 5132.423875 NaN 52.184107 12.231700 124.671799 2972.409153 \n", - "5 6 4536.651717 NaN 4902.343755 NaN 50.008287 11.136335 124.671799 2905.374328 \n", - "6 7 4394.898735 NaN 4721.397260 NaN 48.239588 10.287107 124.671799 2851.308180 \n", - "7 8 4278.581456 NaN 4580.795014 NaN 46.758117 9.604000 124.671799 2806.314223 \n", - "8 9 4180.687869 NaN 4461.183820 NaN 45.489177 9.039209 124.671799 2767.985522 \n", - "9 10 4096.669953 NaN 4358.974073 NaN 44.383280 8.562190 124.671799 2734.738845 \n", + "0 1 15102.810443 NaN 18234.073025 NaN 91.004408 28.000000 124.671799 7038.506078 \n", + "1 2 8386.778426 NaN 9565.411544 NaN 68.296591 19.600000 124.671799 4566.287313 \n", + "2 3 6446.141697 NaN 7176.449073 NaN 59.881944 15.909050 124.671799 3781.387394 \n", + "3 4 5148.350759 NaN 5656.498517 NaN 55.162570 13.720000 124.671799 3193.246042 \n", + "4 5 4594.397226 NaN 5000.740454 NaN 52.184107 12.231700 124.671799 2947.740874 \n", + "5 6 4419.571794 NaN 4775.826212 NaN 50.008287 11.136335 124.671799 2879.570739 \n", + "6 7 4280.905062 NaN 4598.934958 NaN 48.239588 10.287107 124.671799 2824.549146 \n", + "7 8 4167.111521 NaN 4461.451506 NaN 46.758117 9.604000 124.671799 2778.731214 \n", + "8 9 4071.334201 NaN 4344.493258 NaN 45.489177 9.039209 124.671799 2739.678768 \n", + "9 10 3989.125787 NaN 4244.544003 NaN 44.383280 8.562190 124.671799 2705.787252 \n", "\n", " Direct costs: equipment Direct costs: material Direct costs: labor Indirect costs Supplementary costs Financing costs OCC reduction from FOAK \n", - "0 3186.789083 674.081729 3209.694619 8332.541813 77.038872 3235.343689 0.000000 \n", - "1 2361.570131 449.123799 1782.757125 3879.348815 45.852489 1214.686688 44.611182 \n", - "2 2114.478669 377.853898 1315.249134 2664.791001 36.850567 751.578654 57.488161 \n", - "3 1887.350092 322.681702 1007.816626 1914.630410 30.821044 521.928492 66.113211 \n", - "4 1810.791547 299.152588 862.465018 1590.043620 28.255927 417.043377 69.782534 \n", - "5 1804.020646 289.794271 811.559412 1479.150339 27.455251 365.692038 70.927878 \n", - "6 1798.326787 282.110695 770.870697 1392.098536 26.820220 326.498525 71.836270 \n", - "7 1793.417277 275.619744 737.277202 1321.296297 26.299137 302.213559 72.581664 \n", - "8 1789.104221 270.018398 708.862903 1262.169958 25.860589 280.495951 73.208993 \n", - "9 1785.259857 265.104351 684.374637 1211.775106 25.484202 262.304120 73.747403 " + "0 3106.849817 755.457384 3176.198877 7864.842607 74.789959 3131.262582 0.000000 \n", + "1 2298.792328 503.342363 1764.152622 3651.116109 44.703205 1178.633118 44.468757 \n", + "2 2056.395235 423.468706 1301.523452 2504.073043 36.009462 730.307376 57.318264 \n", + "3 1834.310711 361.636080 997.299250 1800.237354 30.195565 508.147758 65.911307 \n", + "4 1759.009857 335.266514 853.464503 1494.270612 27.713941 406.343228 69.579190 \n", + "5 1751.702148 324.778453 803.090137 1388.398505 26.930751 356.254418 70.736759 \n", + "6 1745.555793 316.167311 762.826042 1305.374570 26.309546 318.029896 71.654911 \n", + "7 1740.255324 308.892768 729.583122 1237.908739 25.799769 294.339985 72.408370 \n", + "8 1735.598196 302.615223 701.465350 1181.612931 25.370702 273.159057 73.042539 \n", + "9 1731.446666 297.107949 677.232638 1133.664314 25.002422 255.418216 73.586864 " ] }, "execution_count": 12, @@ -1233,71 +1233,71 @@ " \n", " 0\n", " FOAK \\n(no firm orders)\n", - " 15604.817916\n", - " 15604.817916\n", + " 15102.810443\n", + " 15102.810443\n", " total\n", " \n", " \n", " 1\n", " Bulk-ordering\n", " 0.000000\n", - " 15604.817916\n", + " 15102.810443\n", " change\n", " \n", " \n", " 2\n", " Elimination of rework\n", - " -6960.796776\n", - " 8644.021140\n", + " -6667.420808\n", + " 8435.389635\n", " change\n", " \n", " \n", " 3\n", " Supplychain efficiency\n", - " -33.583573\n", - " 8610.437568\n", + " -88.179373\n", + " 8347.210262\n", " change\n", " \n", " \n", " 4\n", " Labor productivity\n", - " -1848.741428\n", - " 6761.696140\n", + " -1759.181884\n", + " 6588.028378\n", " change\n", " \n", " \n", " 5\n", " Experience and cross-site standardization\n", - " -2483.114684\n", - " 4278.581456\n", + " -2420.916857\n", + " 4167.111521\n", " change\n", " \n", " \n", " 6\n", " Modular Construction\n", " 0.000000\n", - " 4278.581456\n", + " 4167.111521\n", " change\n", " \n", " \n", " 7\n", " Commercial BOP\n", " 0.000000\n", - " 4278.581456\n", + " 4167.111521\n", " change\n", " \n", " \n", " 8\n", " Non safety-related Reactor Building\n", " 0.000000\n", - " 4278.581456\n", + " 4167.111521\n", " change\n", " \n", " \n", " 9\n", " NOAK \\n(firm orders)\n", - " 4278.581456\n", - " 4278.581456\n", + " 4167.111521\n", + " 4167.111521\n", " total\n", " \n", " \n", @@ -1306,16 +1306,16 @@ ], "text/plain": [ " label absolute_change cumulative_occ kind\n", - "0 FOAK \\n(no firm orders) 15604.817916 15604.817916 total\n", - "1 Bulk-ordering 0.000000 15604.817916 change\n", - "2 Elimination of rework -6960.796776 8644.021140 change\n", - "3 Supplychain efficiency -33.583573 8610.437568 change\n", - "4 Labor productivity -1848.741428 6761.696140 change\n", - "5 Experience and cross-site standardization -2483.114684 4278.581456 change\n", - "6 Modular Construction 0.000000 4278.581456 change\n", - "7 Commercial BOP 0.000000 4278.581456 change\n", - "8 Non safety-related Reactor Building 0.000000 4278.581456 change\n", - "9 NOAK \\n(firm orders) 4278.581456 4278.581456 total" + "0 FOAK \\n(no firm orders) 15102.810443 15102.810443 total\n", + "1 Bulk-ordering 0.000000 15102.810443 change\n", + "2 Elimination of rework -6667.420808 8435.389635 change\n", + "3 Supplychain efficiency -88.179373 8347.210262 change\n", + "4 Labor productivity -1759.181884 6588.028378 change\n", + "5 Experience and cross-site standardization -2420.916857 4167.111521 change\n", + "6 Modular Construction 0.000000 4167.111521 change\n", + "7 Commercial BOP 0.000000 4167.111521 change\n", + "8 Non safety-related Reactor Building 0.000000 4167.111521 change\n", + "9 NOAK \\n(firm orders) 4167.111521 4167.111521 total" ] }, "execution_count": 14, @@ -1379,11 +1379,11 @@ " AP1000\n", " 10\n", " 8\n", - " 15604.82\n", - " 4278.58\n", - " 72.58\n", - " 6237.29\n", - " 7005.07\n", + " 15102.81\n", + " 4167.11\n", + " 72.41\n", + " 6060.65\n", + " 6805.84\n", " 190.56\n", " \n", " \n", @@ -1416,7 +1416,7 @@ ], "text/plain": [ " Reactor Firm orders NOAK plant FOAK OCC ($/kWe) NOAK OCC ($/kWe) OCC reduction (%) Average OCC ($/kWe) Average TCI ($/kWe) \\\n", - "0 AP1000 10 8 15604.82 4278.58 72.58 6237.29 7005.07 \n", + "0 AP1000 10 8 15102.81 4167.11 72.41 6060.65 6805.84 \n", "1 HTGR 13 8 12765.15 3898.81 69.46 4953.45 6378.62 \n", "2 SFR 13 8 12269.26 4388.40 64.23 5298.32 6404.42 \n", "\n",

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