Auto-generate ng_crf_penalty_st.csv and update financials.py

docs/sources.csv

@@ -562,7 +562,7 @@ RelativeFilePath,RelativeFolderPath,FileName_new,FileExtension,Description_new,I
 /inputs/state_policies/ces_fraction.csv,/inputs/state_policies,ces_fraction,.csv,Annual compliance for states with a CES policy,,,,,
 /inputs/state_policies/forced_retirements.csv,/inputs/state_policies,forced_retirements,.csv,List of regions with mandatory retirement policies for certain technologies,,,,,
 /inputs/state_policies/hydrofrac_policy.csv,/inputs/state_policies,hydrofrac_policy,.csv,,,,,,
-/inputs/state_policies/ng_crf_penalty_st.csv,/inputs/state_policies,ng_crf_penalty_st,.csv,Cost adjustment for NG techs in states where all NG techs must be retired by a certain year,"allt,st",N/A,https://github.nrel.gov/ReEDS/ReEDS-2.0/pull/1220,Inputs,rate (unitless)
+/inputs/state_policies/ng_st_forced.csv,/inputs/state_policies,ng_st_forced,.csv,List of states where natural gas plants must be retired by a certain year,"t,st",N/A,https://github.nrel.gov/ReEDS/ReEDS-2.0/pull/1220,Inputs,year
 /inputs/state_policies/nuclear_subsidies.csv,/inputs/state_policies,nuclear_subsidies,.csv,,,,,,
 /inputs/state_policies/offshore_req_default.csv,/inputs/state_policies,offshore_req_default,.csv,"default state mandates of offshore wind capacity, updated in November 2025","st,allt",,,Inputs,MW
 /inputs/state_policies/oosfrac.csv,/inputs/state_policies,oosfrac,.csv,Defines the fraction of renewable and clean energy credits can be purchased from out of state (oos). Applied for RPS and CES,,,,,

docs/sources_documentation.md

@@ -2849,13 +2849,13 @@
   - [hydrofrac_policy.csv](/inputs/state_policies/hydrofrac_policy.csv)
 ---
 
-  - [ng_crf_penalty_st.csv](/inputs/state_policies/ng_crf_penalty_st.csv)
+  - [ng_st_forced.csv](/inputs/state_policies/ng_st_forced.csv)
     - **File Type:** Inputs
-    - **Description:** Cost adjustment for NG techs in states where all NG techs must be retired by a certain year
-    - **Indices:** allt,st
+    - **Description:** The year for specific states in which natural gas techs must be retired
+    - **Indices:** t,st
     - **Dollar year:** N/A
     - **Citation:** [https://github.nrel.gov/ReEDS/ReEDS-2.0/pull/1220](https://github.nrel.gov/ReEDS/ReEDS-2.0/pull/1220)
-    - **Units:** rate (unitless)
+    - **Units:** year
 
 ---
 

inputs/state_policies/ng_st_forced.csv

@@ -0,0 +1,5 @@
+*t,st,tech
+2045,DE,ng
+2045,IL,ng
+2045,VA,ng
+2040,NY,ng

reeds/financials.py

@@ -788,3 +788,74 @@ def param_exporter(df, parameter, file_name, output_dir):
     ### Add '*' to first column name so GAMS reads it as a comment
     df = df.rename(columns={c: (f'*{c}' if not i else c) for i, c in enumerate(df.columns)})
     df.round(5).to_csv(os.path.join(output_dir, f'{file_name}.csv'), index=False, header=True)
+
+
+def calc_ng_crf_penalty_st(inputs_case, years, financials_sys, sw):
+    """
+    Compute the CRF-based cost penalty for natural gas plants in states with
+    forced retirement policies.
+
+    For each (state, forced-retirement year) pair in ng_st_forced.csv, this
+    function calculates how much of a plant's remaining economic life is cut
+    short by the policy. The penalty is the ratio of the CRF over the
+    truncated remaining life to the CRF over the full evaluation period:
+      - value = 100  for years after the forced retirement year 
+      - value > 1    if the plant would be forced to retire before its full economic life 
+
+    States not present in hierarchy.csv (i.e. outside the modeled region) are
+    dropped before any computation. If no forced-retirement states remain,
+    returns an empty DataFrame with columns ['t', 'st', 'value'].
+    """
+    # Load state-level forced retirement years for NG plants
+    ng_st_forced = pd.read_csv(os.path.join(inputs_case, 'ng_st_forced.csv'))
+
+    # Keep only states that exist in this run's modeled region
+    hierarchy = pd.read_csv(os.path.join(inputs_case, 'hierarchy.csv'))
+    valid_states = hierarchy['st'].unique()
+    ng_st_forced = ng_st_forced[ng_st_forced['st'].isin(valid_states)]
+
+    # No forced retirements in this region — return empty result
+    if ng_st_forced.empty:
+        ng_crf_penalty_st=pd.DataFrame(columns=['*t', 'st', 'value'])
+        ng_crf_penalty_st.to_csv(os.path.join(inputs_case, 'ng_crf_penalty_st.csv'),
+                             index=False)
+        return ng_crf_penalty_st
+
+    # Years when retirements are forced, and the model's active year range
+    forced_years = ng_st_forced['*t'].astype(int).unique()
+    years_arr = np.asarray([y for y in years if int(sw['startyear']) <= y <= int(sw['endyear'])])
+
+    # Real discount rate and full-life CRF for each year (the baseline)
+    d_real_arr  = financials_sys.set_index('t')['d_real'].reindex(years_arr).values
+    base_crf_arr = np.array([calc_crf(d, sw['sys_eval_years']) for d in d_real_arr])
+
+    # How many years remain between each build year t and each forced retirement year
+    # shape: (len(years_arr), len(forced_years))
+    diff  = forced_years[None, :] - years_arr[:, None]
+    # Effective remaining life: at least 1 year, at most the full evaluation period
+    n_eff = np.clip(diff, 1, sw['sys_eval_years'])
+
+    # CRF over the truncated life, then normalize by the full-life baseline
+    crf_vals     = np.vectorize(calc_crf)(d_real_arr[:, None], n_eff)
+    value_matrix = np.where(diff <= 0, 100.0, crf_vals / base_crf_arr[:, None])
+
+    # Wrap results in a DataFrame indexed by build year, columns = forced years
+    crf_table = pd.DataFrame(value_matrix, index=years_arr,
+                             columns=forced_years, dtype=float)
+    crf_table.index.name = 't'
+
+    # Reshape to long format: one row per (build year, forced year)
+    crf_long = (
+        crf_table.stack().rename('value')
+        .rename_axis(['t', '*t']).reset_index()
+    )
+
+    # Attach state labels by merging on the forced retirement year
+    ng_crf_penalty_st = (
+        ng_st_forced[['*t', 'st']].merge(crf_long, on='*t', how='left')
+        [['t', 'st', 'value']]
+        .rename(columns={'t': '*t'}))
+
+    ng_crf_penalty_st.to_csv(os.path.join(inputs_case, 'ng_crf_penalty_st.csv'),
+                             index=False)
+    return ng_crf_penalty_st

reeds/input_processing/calc_financial_inputs.py

@@ -96,6 +96,10 @@ def calc_financial_inputs(inputs_case):
         sw['financials_sys_suffix'], inflation_df, modeled_years, 
         years, year_map, sw['sys_eval_years'], scen_settings, scalars['co2_capture_incentive_length'],scalars['h2_ptc_length'])
     financials_sys.to_csv(os.path.join(inputs_case,'financials_sys.csv'),index=False)
+
+    # Calculate the natural gas CRF penalty for states
+    reeds.financials.calc_ng_crf_penalty_st(inputs_case, years, financials_sys, sw)
+
     df_ivt = df_ivt.merge(
         financials_sys[['t', 'pvf_capital', 'crf', 'crf_co2_incentive','crf_h2_incentive','d_real', 'd_nom', 'interest_rate_nom', 
                         'tax_rate', 'debt_fraction', 'rroe_nom']], 

reeds/input_processing/runfiles.csv

@@ -150,7 +150,7 @@ natgas_price_cendiv.csv,inputs/fuelprices/ng_{ngscen}.csv,1,ignore,ignore,wide_c
 national_rps_frac_allScen.csv,inputs/national_generation/national_rps_frac_allScen.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,,,,1,0,,,,,,
 net_gen_existing_hydro.csv,inputs/hydro/net_gen_existing_hydro.csv,1,ignore,ignore,,"t,month",,0,0,,,,,,
 peak_net_imports.csv,inputs/reserves/peak_net_imports.csv,1,ignore,ignore,nercr,t,,0,0,,,,,,
-ng_crf_penalty_st.csv,inputs/state_policies/ng_crf_penalty_st.csv,1,ignore,ignore,st,*t,,0,0,,,,,,
+ng_st_forced.csv,inputs/state_policies/ng_st_forced.csv,1,ignore,ignore,st,*t,,0,0,,,,,,
 ng_demand_elec.csv,inputs/fuelprices/ng_demand_{ngscen}.csv,1,ignore,ignore,wide_cendiv,year,,1,0,,,,,,
 ng_demand_tot.csv,inputs/fuelprices/ng_tot_demand_{ngscen}.csv,1,ignore,ignore,wide_cendiv,year,,1,0,,,,,,
 noretire.csv,inputs/sets/noretire.csv,1,ignore,ignore,,,,,0,,,set,noretire,technologies that will never be retired,