GCAM-USA to CERF: Heat rates by technology, vintage, and state xml query

gcam_to_cerf/README.MD

@@ -170,12 +170,13 @@ Heat rate by technology, vintage, subregion, and year. Heat rate is the amount o
 * regional biomass
 * regional coal
 * wholesale gas
+
 #### CERF Variable Name: 
 * elec_heat_rate_BTUperkWh
 #### CERF required units:
 * BTU/kWh
 #### GCAM-USA Queries: 
-* 'elec coeff'
+* "elec coefs by tech"
 #### Calculations & Transformations Required:
 GCAM-USA reports heat rates in EJ in / EJ out ratios. These need to be converted to BTU in / kWh out.
 
@@ -187,7 +188,7 @@ GCAM-USA reports heat rates in EJ in / EJ out ratios. These need to be converted
         Example:
 
         1.2 EJ/EJ = (1.2 * BRITISH_THERMAL_UNITS_PER_EXAJOULE) / KWH_PER_EXAJOULE
-
+        = 4095.356 BTU/kWh
 <br>
 
 _____________

gcam_to_cerf/get_heat_rate.py

@@ -0,0 +1,108 @@
+import gcamreader
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from standardize_output_format import *
+import os
+
+
+def get_query_by_name(queries, name):
+    return next((x for x in queries if x.title == name), None)
+
+
+def get_heat_rate(
+    path_to_gcam_database:str,
+    gcam_file_name:str,
+    gcam_scenario:str,
+    capacity_crosscheck:pd.DataFrame,
+    save_output=False,
+    gcam_query_name = "elec coefs by tech",
+    path_to_query_file: str = './elec_queries.xml',
+    ):
+
+    # set up unit conversions
+    BRITISH_THERMAL_UNITS_PER_EXAJOULE = 9.48e14
+    KWH_PER_EXAJOULE = 2.77778e11
+
+    # create a Path from str
+    db_path = Path(path_to_gcam_database)
+
+    # create connection to gcam db
+    conn = gcamreader.LocalDBConn(db_path, gcam_file_name)
+
+    # parse the queries file
+    queries = gcamreader.parse_batch_query(path_to_query_file)
+
+    # heat rate is based on technology and input fuel
+    heat_rate = conn.runQuery(get_query_by_name(queries, gcam_query_name))
+
+    # select required fuels
+    fuels = ['nuclearFuelGenII', 'nuclearFuelGenIII', 'refined liquids industrial', 'regional biomass', 'regional coal', 'wholesale gas']
+    heat_rate = heat_rate[heat_rate.input.isin(fuels)]
+
+    # convert EJ in per EJ out to BTU in per kwh out
+    heat_rate['value'] = (heat_rate['value'] * BRITISH_THERMAL_UNITS_PER_EXAJOULE) / KWH_PER_EXAJOULE
+
+    # collect vintage
+    heat_rate['vintage'] = heat_rate['Year']
+
+    # rename columns
+    heat_rate.rename(columns={
+        'Units': 'units',
+        'Year': 'year',
+        'input': 'fuel_type',
+        'value': 'heat_rate_BTUperkWh',
+    }, inplace=True)
+
+    # reduce columns
+    heat_rate = heat_rate[['year', 'vintage', 'region', 'technology', 'fuel_type', 'heat_rate_BTUperkWh']].reset_index(drop=True)
+
+    # create a list of fuels by technology that can be used for collecting fuel prices 
+    tech_to_fuel = heat_rate[['technology', 'fuel_type']].copy().drop_duplicates(ignore_index=True)
+
+    # transform to expected cerf format
+    heat_rate = standardize_format(heat_rate, param='elec_heat_rate_BTUperkWh', scenario=gcam_scenario, 
+                                units='Heat Rate (BTU per kWh)', valueColumn='heat_rate_BTUperkWh')
+
+    # validate against new capacity deployments by vintage
+    heat_rate = pd.merge(capacity_crosscheck, heat_rate, how='left', on=['scenario','region' ,'subRegion','xLabel', 'x', 'vintage', 'class2'])
+
+    #replace renewable heat rates with zero
+    heat_rate['value'] = np.where(heat_rate['class2'].str.contains('wind|PV|CSP|geothermal'),
+                                0,
+                                heat_rate['value'])
+    heat_rate['param'].fillna('elec_heat_rate_BTUperkWh', inplace=True)
+    heat_rate['classLabel2'].fillna('technology', inplace=True)
+    heat_rate['units'].fillna('Heat Rate (BTU per kWh)', inplace=True)
+
+    # print any missing values
+    if heat_rate[heat_rate.value.isna()].empty:
+        print('All required values available')
+    else:
+        for index,row in heat_rate[heat_rate.value.isna()].iterrows():
+            print(f"WARNING: Heat Rate for {row['subRegion']}, {row['class2']}, {row['vintage']} is missing\n") 
+
+    if save_output:
+        os.makedirs(Path('./extracted_data'), exist_ok=True)
+        heat_rate.to_csv(Path(f'./extracted_data/{gcam_scenario}_heat_rates.csv'), index=False)
+    else:
+       pass
+
+    return heat_rate, tech_to_fuel
+
+
+def _get_heat_rate(
+      path_to_gcam_database,
+      gcam_file_name,
+      gcam_scenario
+      ):
+
+  get_heat_rate(
+      path_to_gcam_database,
+      gcam_file_name,
+      gcam_scenario
+      )
+
+
+if __name__ == "__main__":
+  _get_heat_rate()