auto-generate deflator files from inflation_default.csv

cases.csv

@@ -61,7 +61,6 @@ inflation_suffix,File suffix for historical inflation schedule,default,default,
 ivt_suffix,File suffix for ivt csv file,default; small; step,default,
 reg_cap_cost_diff_suffix,File suffix for regional capital cost differences,default,default,
 techs_suffix,File suffix for master list of technologies,default; subsetForTesting,default,
-dollar_year,DO NOT CHANGE FROM 2004 UNTIL ALL FINANCIAL INPUTS HAVE DOLLAR YEAR ADJUSTMENT - Real dollar year for model to calculate and report,2004,2004,
 sys_eval_years,Number of years that the model evaluates investments on,int,30,
 numhintage,"number of hintage bins. Enter 'unit' for unit level bins. Enter 'group' to group by unique technology, region, and heatrate combinations",int,6,
 numbins_windons,Number of interconnection supply curve bins for onshore wind; options are 5 and 1300 (for individual sites lite),int,10,

docs/sources.csv

@@ -225,7 +225,6 @@ RelativeFilePath,RelativeFolderPath,FileName_new,FileExtension,Description_new,I
 /inputs/financials/construction_schedules_default.csv,/inputs/financials,construction_schedules_default,.csv,,,,,,
 /inputs/financials/construction_times_default.csv,/inputs/financials,construction_times_default,.csv,,,,,,
 /inputs/financials/currency_incentives.csv,/inputs/financials,currency_incentives,.csv,,,,,,
-/inputs/financials/deflator.csv,/inputs/financials,deflator,.csv,Dollar year deflator to convert values to 2004$,,,,,
 /inputs/financials/depreciation_schedules_default.csv,/inputs/financials,depreciation_schedules_default,.csv,,,,,,
 /inputs/financials/energy_communities.csv,/inputs/financials,energy_communities,.csv,,,,,,
 /inputs/financials/financials_hydrogen.csv,/inputs/financials,financials_hydrogen,.csv,,,,,,

inputs/scalars.csv

@@ -24,6 +24,7 @@ csp_sm_3,1.3,"--fraction-- solar multiple for csp3"
 csp_sm_4,1.0,"--fraction-- solar multiple for csp4"
 csp_sm_baseline,2.4,"--fraction-- baseline solar multiple to normalize other CSP solar multiples by"
 distloss,0.05,"--fraction-- distribution loss rate from bus to final consumption. A generic estimate of distribution losses taken many years ago from AEO 2006"
+dollar_year,2004,"--year-- real dollar year for model to calculate and report - DO NOT CHANGE FROM 2004 UNTIL ALL FINANCIAL INPUTS HAVE DOLLAR YEAR ADJUSTMENT --"
 euros_dollar,1.124,"--USD/Euro-- dollars per euro exchange rate from treasury department (https://fiscal.treasury.gov/files/reports-statements/treasury-reporting-rates-exchange/ratesofexchangeasofdecember312019.pdf)"
 forced_outage_rate_h2_smr,0.1,"--fraction-- forced outage rate of steam methane reforming"
 gas_elasticity,0.76,"--fraction-- gas supply curve elasticity. long run price elasticity as estimated by: https://www.diw.de/documents/publikationen/73/diw_01.c.441773.de/dp1372.pdf"

postprocessing/bokehpivot/reeds2.py

@@ -16,12 +16,14 @@
 import core
 import copy
 from itertools import product
+from math import prod
 ## Inherit the default present value year to use in plot definitions
 from defaults import DEFAULT_PV_YEAR
 
 rb_globs = {'output_subdir':'/outputs/', 'test_file':['cap.csv','outputs.h5'], 'report_subdir':'/reeds2'}
 this_dir_path = os.path.dirname(os.path.realpath(__file__))
-df_deflator = pd.read_csv(os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, 'inputs/financials/deflator.csv')), index_col=0)
+df_inflation = pd.read_csv(os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, 'inputs/financials/inflation_default.csv')), index_col=0)
+scalars = pd.read_csv(os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, 'inputs/scalars.csv')))
 coststreams = ['eq_gasaccounting_regional','eq_gasaccounting_national','eq_bioused','eq_gasused','eq_objfn_inv','eq_objfn_op']
 vf_valstreams = ['eq_supply_demand_balance','eq_reserve_margin','eq_opres_requirement','eq_rec_requirement','eq_curt_gen_balance','eq_curtailment','eq_storage_in_max','eq_storage_in_min']
 # valuestreams = ['eq_supply_demand_balance','eq_reserve_margin','eq_opres_requirement','eq_rec_requirement','eq_national_gen','eq_annual_cap','eq_curt_gen_balance','eq_curtailment','eq_storage_in_max','eq_storage_in_min','eq_emit_accounting','eq_mingen_lb','eq_mingen_ub','eq_rps_ofswind']
@@ -70,8 +72,12 @@ def apply_inflation(df, **kw):
     return df
 
 def inflate_series(ser_in):
-    return ser_in * 1/df_deflator.loc[int(core.GL['widgets']['var_dollar_year'].value), 'Deflator']
-
+    inflation_dict = dict(zip(df_inflation.index, df_inflation['inflation_rate']))
+    REEDS_DOLLAR_YEAR=scalars.loc[scalars['scalar']=='dollar_year','value'].values[0]
+    deflator_multiplier = prod( inflation_dict[y]
+        for y in range(int(REEDS_DOLLAR_YEAR) + 1, int(core.GL['widgets']['var_dollar_year'].value) + 1 ))
+    return ser_in * deflator_multiplier
+
 def gather_cost_types(df):
     #Gather lists of capital and operation labels
     cost_cats_df = df['cost_cat'].unique().tolist()

postprocessing/compare_cases.py

@@ -138,9 +138,8 @@
 discountrate_scghg = 0.02
 assert discountrate_scghg in [0.015, 0.02, 0.025]
 central_health = {'cr':'ACS', 'model':'EASIUR'}
-reeds_dollaryear = 2004
-output_dollaryear = DEFAULT_DOLLAR_YEAR
 startyear_notes = DEFAULT_PV_YEAR
+dollar_year = int(reeds.io.get_scalars().dollar_year)
 
 colors_social = {
     'CO2': plt.cm.tab20b(4),
@@ -317,7 +316,7 @@ def plot_bars_abs_stacked(
 
 inflatable = reeds.io.get_inflatable(os.path.join(
     reeds_path,'inputs','financials','inflation_default.csv'))
-inflator = inflatable[reeds_dollaryear, output_dollaryear]
+inflator = inflatable[dollar_year, DEFAULT_DOLLAR_YEAR]
 
 scghg = pd.read_csv(
     os.path.join(reeds_path, 'postprocessing', 'plots', 'scghg_annual.csv'),
@@ -331,7 +330,7 @@ def plot_bars_abs_stacked(
 }).set_index(['e','t'])
 scghg_central = (
     scghg[f'2020_{discountrate_scghg*100:.1f}%'].unstack('e')
-    * inflatable[2020, output_dollaryear]
+    * inflatable[2020, DEFAULT_DOLLAR_YEAR]
 )
 
 #%% Colors and mapping
@@ -527,7 +526,7 @@ def plot_bars_abs_stacked(
             .xs(central_health['model'], level='model')
             .groupby('year').sum()
             ['damage_$']
-            ### Inflate from reeds_dollaryear (2004) to bokeh output_dollaryear (2021)
+            ### Inflate from dollar_year (2004) to bokeh DEFAULT_DOLLAR_YEAR (2024)
             * inflator
             ### Convert to $B
             / 1e9

postprocessing/input_plots.py

@@ -799,6 +799,7 @@ def map_supplycurves(
     """
     ### Get inputs
     sw = reeds.io.get_switches(case)
+    scalars = reeds.io.get_scalars(case)
     dfmap = reeds.io.get_dfmap(case=case)
     for key in dfmap:
         if dfmap[key].crs != crs:
@@ -844,7 +845,7 @@ def map_supplycurves(
         ).to_crs(crs)
     if include_techneutral_adder:
         inflatable = reeds.io.get_inflatable()
-        costadder = float(sw.GSw_TransIntraCost) * inflatable[2004, dollaryear]
+        costadder = float(sw.GSw_TransIntraCost) * inflatable[int(scalars['dollar_year']), dollaryear]
     else:
         costadder = 0
     ## Convert from point to polygons if desired (raster is 11.52 km but include a little extra)

postprocessing/retail_rate_module/ferc_distadmin.py

@@ -10,6 +10,13 @@
 import pandas as pd
 import numpy as np
 import os
+import sys
+
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
+import reeds
+
+dollar_year = int(reeds.io.get_scalars().dollar_year)
 
 
 #################
@@ -172,7 +179,7 @@ def inflatifier(inyear, outyear=2019, inflation=inflation):
     inflatable = pd.Series(inflatable)
     return inflatable
 
-def get_excluded_costs(excludecells=excludecells, inflationpath=None, dollar_year=2004):
+def get_excluded_costs(excludecells=excludecells, inflationpath=None, dollar_year=dollar_year):
     """
     Get subtracted cells so we can add them back in with special treatment.
     Returns monetary values in dollar_year dollars.

postprocessing/retail_rate_module/mean_bias_error.py

@@ -12,9 +12,14 @@
 import pandas as pd
 import os
 import io
+import sys
 import argparse
 import retail_rate_calculations
 
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
+import reeds
+
 pd.options.display.max_columns = 200
 pd.options.display.max_rows = 50
 
@@ -40,16 +45,16 @@
 #%% Inputs ###
 
 validationyears = list(range(2010,2020))
-data_dollar_year = 2004
 
 retailmodulepath = os.path.join(reeds_path,'postprocessing','retail_rate_module','')
 inputs_default_path = retailmodulepath+'inputs_default.csv'
+dollar_year = int(reeds.io.get_scalars().dollar_year)
 
 inflation = pd.read_csv(
     os.path.join(reeds_path,'inputs','financials','inflation_default.csv'),
     index_col=['t'])
 inf_adjust = inflation.loc[
-    data_dollar_year+1:out_dollar_year,
+    dollar_year+1:out_dollar_year,
     'inflation_rate'
 ].cumprod()[out_dollar_year]
 

postprocessing/retail_rate_module/retail_rate_calculations.py

@@ -13,6 +13,7 @@
 ### Local imports
 import ferc_distadmin
 import calculate_historical_capex
+
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))
 import reeds
 from reeds import plots
@@ -371,6 +372,7 @@ def main(run_dir, inputpath='inputs.csv', write=True, verbose=0):
     modeled_years = load_rt['t'].drop_duplicates()
     non_modeled_years = list(set(np.arange(first_year,last_year,1)) - set(modeled_years))
     years_reeds = np.arange(first_year, last_year+1)
+    reeds_dollar_year=reeds.io.get_scalars().dollar_year
 
     # Ingest inflation
     inflation = pd.read_csv(
@@ -1018,7 +1020,7 @@ def main(run_dir, inputpath='inputs.csv', write=True, verbose=0):
             mdir, 'calc_historical_capex',
             'existing_transmission_cost_bystate_USD2024.csv'),
         index_col='state',
-    ).squeeze(1).rename('init_trans_capex') * inflatable[2024, 2004]
+    ).squeeze(1).rename('init_trans_capex') * inflatable[2024, reeds_dollar_year]
     # This approach assumes the existing transmission capacity was built evently 
     # over the trans_timeframe years prior to 2010
     init_trans_capex = pd.concat(

reeds/input_processing/calc_financial_inputs.py

@@ -64,7 +64,7 @@ def calc_financial_inputs(inputs_case):
 
     # Set up scen_settings object
     scen_settings = reeds.financials.scen_settings(
-        dollar_year=int(sw['dollar_year']), tech_groups=tech_groups, inputs_case=inputs_case,
+        dollar_year=int(scalars['dollar_year']), tech_groups=tech_groups, inputs_case=inputs_case,
         sw=sw)
 
 

reeds/input_processing/copy_files.py

@@ -148,7 +148,7 @@ def read_runfiles(reeds_path, inputs_case, sw, agglevel_variables):
     return runfiles, non_region_files, region_files
 
 
-def get_source_deflator_map(reeds_path):
+def get_source_deflator_map(reeds_path,inputs_case,write_out=True):
     """
     Get the deflator for each input file
     """
@@ -157,9 +157,29 @@ def get_source_deflator_map(reeds_path):
         os.path.join(reeds_path,'docs','sources.csv'),
         usecols=["RelativeFilePath", "DollarYear"]
     )
-    deflator = pd.read_csv(
-        os.path.join(reeds_path,'inputs','financials','deflator.csv'),
-        header=0, names=['Dollar.Year','Deflator'], index_col='Dollar.Year').squeeze(1)
+
+    ### Deflator Calculation
+    # The deflator values are calculated relative to the reeds dollar year to most recent 
+    # inputs dollar year by using inflatable.
+    # Define the base year and current year for the deflator calculation.
+    scalars = reeds.io.get_scalars(full=True)
+    reeds_dollar_year=int(scalars.loc['dollar_year', 'value'])
+    max_dollar_year = pd.to_numeric(sources_dollaryear['DollarYear'], errors='coerce').max()
+
+    # Define the deflator 
+    inflatable = reeds.io.get_inflatable()  
+    deflator =( 1 / inflatable[reeds_dollar_year].loc[reeds_dollar_year:max_dollar_year]).reset_index() 
+    deflator.columns = ['*Dollar.Year', 'Deflator']
+
+    # if get_source_deflator_map function is being called from mcs_sampler,
+    # we do not want to write out the deflator.csv file as it is only used for 
+    # the copy_files function.
+
+    if write_out:
+        deflator.to_csv(os.path.join(inputs_case, 'deflator.csv'), index=False)
+
+    deflator.rename(columns={'*Dollar.Year': 'Dollar.Year'}, inplace=True)
+
     # Create a mapping between inputs' relative filepaths and their deflation
     # multipliers based on the dollar years their monetary values are in
     sources_dollaryear = (
@@ -172,7 +192,7 @@ def get_source_deflator_map(reeds_path):
         .rename(columns={"DollarYear": "Dollar.Year"})
         .merge(deflator,on="Dollar.Year",how="left")
     )
-
+    
     source_deflator_map = dict(zip(sources_dollaryear["RelativeFilePath"], sources_dollaryear["Deflator"]))
 
     return source_deflator_map
@@ -1623,7 +1643,7 @@ def main(reeds_path, inputs_case):
     # (gswitches.csv is first written at runreeds.py)
     scalar_csv_to_txt(os.path.join(inputs_case,'gswitches.csv'))
 
-    source_deflator_map = get_source_deflator_map(reeds_path)
+    source_deflator_map = get_source_deflator_map(reeds_path,inputs_case)
 
     # Copy non-region files
     write_non_region_files(non_region_files, sw, inputs_case, regions_and_agglevel, source_deflator_map)

reeds/input_processing/mcs_sampler.py

@@ -611,7 +611,7 @@ def get_dist_instructions(reeds_path: str, inputs_case: str) -> Tuple[pd.DataFra
     regions_and_agglevel = copy_files.get_regions_and_agglevel(
         reeds_path, inputs_case, save_regions_and_agglevel=False, overwrite=True)
 
-    source_deflator_map = copy_files.get_source_deflator_map(reeds_path)
+    source_deflator_map = copy_files.get_source_deflator_map(reeds_path,inputs_case,False)
 
     hierarchy_file = get_hierarchy_file(inputs_case, sw['GSw_RegionResolution'])
 

reeds/input_processing/runfiles.csv

@@ -56,7 +56,7 @@ csapr_ozone_season.csv,inputs/emission_constraints/csapr_ozone_season.csv,int(sw
 currency_incentives.csv,inputs/financials/currency_incentives.csv,1,ignore,ignore,,,,,0,,,,,,
 dac_elec.csv,inputs/consume/dac_elec_{dacscen}.csv,int(sw.GSw_DAC) != 0,ignore,ignore,,,,1,0,,,,,,
 dac_gas.csv,inputs/consume/dac_gas_{GSw_DAC_Gas_Case}.csv,int(sw.GSw_DAC) != 0,ignore,ignore,,,,1,0,,1,,,,
-deflator.csv,inputs/financials/deflator.csv,1,ignore,ignore,,,,,0,,,,,,
+deflator.csv,,1,ignore,ignore,,,,,0,,1,,,,
 degradation_annual.csv,inputs/degradation/degradation_annual_{degrade_suffix}.csv,1,ignore,ignore,,,,,0,,,,,,
 demonstration_plants.csv,inputs/capacity_exogenous/demonstration_plants.csv,int(sw.GSw_NuclearDemo) != 0,sum,ignore,r,"t,i,coolingwatertech,ctt,wst,notes",i,0,0,,,,,,
 depreciation_schedules.csv,inputs/financials/depreciation_schedules_{depreciation_schedules_suffix}.csv,1,ignore,ignore,,,,1,0,,,,,,

reeds/input_processing/transmission.py

@@ -32,11 +32,8 @@
 
 decimals = 5
 drop_canmex = True
-dollar_year = 2004
 weight = 'cost'
 
-costcol = f'USD{dollar_year}perMW'
-
 #%% Set up logger
 log = reeds.log.makelog(
     scriptname=__file__,
@@ -46,6 +43,10 @@
 
 #%% Inputs from switches
 sw = reeds.io.get_switches(inputs_case)
+scalars = reeds.io.get_scalars(inputs_case)
+
+dollar_year = int(scalars['dollar_year'])
+costcol = f'USD{dollar_year}perMW'
 
 ## networksource must end in a 4-digit year indicating the year represented by the network
 trans_init_year = int(sw.GSw_TransNetworkSource[-4:])

reeds/results.py

@@ -213,13 +213,13 @@ def calc_h2prod(case):
 
     return df
 
-def inflate_series(dfin, to_dollar_year=DEFAULT_DOLLAR_YEAR):
+def inflate_series(inputs_case,dfin, to_dollar_year=DEFAULT_DOLLAR_YEAR):
     """
     Inflate a series of financial data to a specified dollar year.
     """
     df_deflator = pd.read_csv(
-        os.path.join(reeds_path, 'inputs', 'financials', 'deflator.csv'),
-        index_col=0,
+        os.path.join(inputs_case, 'deflator.csv'), 
+        index_col=0
     )
     return dfin * 1 / df_deflator.loc[to_dollar_year, 'Deflator']
 
@@ -342,7 +342,7 @@ def calc_systemcost(
     df = systemcost.copy()
 
     # Apply inflation
-    df['Value'] = inflate_series(df['Value'])
+    df['Value'] = inflate_series(inputs_case,df['Value'])
     df.rename(columns={'Value':'Cost (Bil $)'}, inplace=True)
 
     cost_cat_map, cap_type_ls, op_type_ls = gather_cost_types(df)
@@ -388,7 +388,7 @@ def calc_systemcost(
             df_capex_hist=df_capex_hist.loc[:firstmodelyear-1,:]
 
             # Convert to output dollar year
-            df_capex_hist = pd.DataFrame(inflate_series(df_capex_hist))
+            df_capex_hist = pd.DataFrame(inflate_series(inputs_case,df_capex_hist))
 
             # Insert into full cost table
             df = df.join(df_capex_hist, on=['year','r'])