Add coal cost and emission rate multipliers

coal/NEMS_EIA_coal_units_matching.py

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+"""
+by apham
+Updated Feb 11 2026
+
+This script merges ReEDS's NEMS unit database to EIA923 to assign coal types to each ba.
+"""
+
+import sys
+import os
+import pandas as pd
+
+# Get NEMS database from ReEDS repo
+reeds_path = os.path.expanduser('~/Documents/Github/ReEDS/public_ReEDS/ReEDS')
+sys.path.append(reeds_path)
+
+# Read in EIA 923
+dir = os.getcwd()
+df_EIA923 = pd.read_excel(os.path.join(dir, "inputs", 
+                                       "EIA923_Schedules_2_3_4_5_M_10_2025_17DEC2025.xlsx"),
+                                       sheet_name = "Page 3 Boiler Fuel Data")
+
+df_EIA923.columns = df_EIA923.columns.str.replace('\n', ' ', regex=True)
+df_EIA923 = df_EIA923.rename(columns={'Plant Id':'T_PID','Boiler Id':'T_UID', 
+                                      'Reported Fuel Type Code': 'Fuel Code'})
+
+# Filter out plants from Electric Utility sector
+#df_EIA923 = df_EIA923[(df_EIA923['Sector Name'] == 'Electric Utility')]
+
+# Filter out coal plants from EIA923
+coal_type_EIA923 = ['ANT', 'BIT', 'LIG', 'RC', 'SUB', 'WC']
+df_EIA923_coal = df_EIA923[df_EIA923['Fuel Code'].isin(coal_type_EIA923)]
+#df_EIA923_coal = df_EIA923_coal[df_EIA923_coal['Total Fuel Consumption Quantity']>0]
+
+df_EIA923_coal = df_EIA923_coal[['T_PID','T_UID','Fuel Code','Plant Name',
+                                 'Plant State','Census Region','BA_CODE','YEAR', 
+                                 'Total Fuel Consumption Quantity']]
+df_EIA923_coal['in_EIA'] = 1
+df_EIA923_coal['EIA ID'] = df_EIA923_coal.reset_index().index
+
+# Read in NEMS database
+df_NEMS = pd.read_csv(os.path.join(reeds_path, "inputs", "capacity_exogenous", "ReEDS_generator_database_final_EIA-NEMS.csv"))
+coal_type_NEMS = ['coalolduns', 'coal-igcc', 'coal-new', 'coaloldscr']
+df_NEMS_coal = df_NEMS[df_NEMS['tech'].isin(coal_type_NEMS)]
+df_NEMS_coal = df_NEMS_coal[df_NEMS_coal['RetireYear']>=2025]
+df_NEMS_coal = df_NEMS_coal.drop_duplicates(subset=['T_PID','T_UID'])
+df_NEMS_coal['NEMS ID'] = df_NEMS_coal.reset_index().index
+df_NEMS_coal['in_NEMS'] = 1
+
+# Merge NEMS and EIA923
+df_NEMS_EIA923 = pd.merge(df_NEMS_coal, df_EIA923_coal, on=['T_PID','T_UID'],how='left')
+
+df_NEMS_EIA923 = df_NEMS_EIA923[['NEMS ID','EIA ID', 'in_NEMS', 'in_EIA','tech','Fuel Code', 
+                                 'T_PNM', 'Plant Name', 'T_PID', 'T_UID',
+                                 'StartYear', 'RetireYear', 'Plant State', 
+                                 'summer_power_capacity_MW', 'T_LAT','T_LONG','FIPS',
+                                 'Census Region','T_PCA','Total Fuel Consumption Quantity']]
+
+# Save matched units:
+df_NEMS_EIA923_matched = df_NEMS_EIA923[(df_NEMS_EIA923['in_NEMS']==1) & (df_NEMS_EIA923['in_EIA']==1)]
+df_NEMS_EIA923_matched.to_csv(os.path.join(dir, "inputs", "NEMS_EIA_matched1.csv"),index=False)
+
+# Get unmatched units to rematch second round:
+df_NEMS_unmatched = df_NEMS_EIA923[~((df_NEMS_EIA923['in_NEMS']==1) & (df_NEMS_EIA923['in_EIA']==1))]
+#df_NEMS_unmatched = df_NEMS_unmatched[['T_PID','T_UID', 'EIA ID']]
+df_NEMS_coal_unmatched = df_NEMS_coal.merge(df_NEMS_unmatched,on=['T_PID','T_UID'],how='right')
+df_NEMS_coal_unmatched = df_NEMS_coal_unmatched.rename(columns={'NEMS ID_x':'NEMS ID', 
+                                                                'in_NEMS_x':'in_NEMS',
+                                                                'tech_x':'tech',
+                                                                'T_PNM_x':'T_PNM',
+                                                                'StartYear_x':'StartYear', 
+                                                                'RetireYear_x':'RetireYear', 
+                                                                'summer_power_capacity_MW_x':'summer_power_capacity_MW', 
+                                                                'T_PCA_x':'T_PCA',
+                                                                'Total Fuel Consumption Quantity_x': 'Total Fuel Consumption Quantity',
+                                                                'T_LAT_x':'T_LAT',
+                                                                'T_LONG_x':'T_LONG',
+                                                                'FIPS_x':'FIPS'})
+df_NEMS_coal_unmatched = df_NEMS_coal_unmatched[df_NEMS_EIA923.columns.to_list()]
+df_NEMS_coal_unmatched.to_csv(os.path.join(dir, "inputs", "NEMS_EIA_unmatched1.csv"),index=False)
+print("\n For all the units that are in NEMS but not EIA923 (NEMS_EIA_unmatched1.csv), " \
+        "please manually enter the coal types for those units and save as NEMS_EIA_unmatched1_manually_cleaned.csv " \
+        "before moving to next step")
+