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Clean up EIA-NEMS processing #6

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db3f7e6
remove script to map sc_point_gid to NEMS as this step is now done in…
atpham88 May 13, 2026
a7fd0d4
Remove "#" character from unit ID
atpham88 May 13, 2026
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120 changes: 5 additions & 115 deletions nems_database_processing/c_geospatial_mapping.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,8 +10,6 @@
import sys
import os
import pandas as pd
import numpy as np
import math
import geopandas as gpd
from geopandas import GeoDataFrame
from shapely.geometry import Point
Expand Down Expand Up @@ -131,6 +129,9 @@ def main():
(nems_county_final['TSTATE']=='FL') & (nems_county_final['tech']=='hydro') & (nems_county_final['reeds_ba']=='p91'),
['FIPS','county','reeds_ba']] = ['p12039','Gadsden County','p101']
nems_county_final["Unique ID"] = nems_county_final.index

# =========================================================================
# Save output file:

# Check if all entries in the database have been mapped with appropriate FIPS codes
# In this case, proceed to update tech classes
Expand All @@ -139,130 +140,19 @@ def main():
print('\nAll {} entries in the unit database have been mapped with FIPS codes!'\
.format(len(nems_county_final)))

#%%--------------------------------------------------------------------------------
# Update upv, wind, and geothermal classes
#----------------------------------------------------------------------------------
print('Begin mapping upv, wind, and geothermal units to their appropriate capacity factors/temps')
nems_county_final['reV_mean_resource_temp'] = np.nan
nems_county_final['sc_point_gid'] = np.nan
# Get directory for supply curve data from either nrelnas (only option for now) or zenodo (coming soon)
if sys.platform == 'win32':
remotepath = '/nrelnas01/ReEDS/Supply_Curve_Data'
elif sys.platform == 'darwin':
remotepath = '/Volumes/ReEDS/Supply_Curve_Data' #TODO: Move supply curves to zenodo

# match directory between reV and ReEDS technologies
tech_match = {
"upv": ["upv","pvb_pv","csp-wp","csp-ns"], # upv matches upv, pv, pvb_pv, csp-ns and csp-wp
"wind-ons": ["wind-ons"],
"wind-ofs": ["wind-ofs"],
"geohydro": ["geohydro_allkm", "geothermal"]
}

# Assign resource classes for each technology in tech_list
for tech_reV in list(tech_match.keys()):
for tech_element in tech_match[tech_reV]:
nems_county_tech_class = assign_class_tech(nems_county_final,tech_reV,tech_element,remotepath)
nems_county_tech_class.to_csv((os.path.join(dir, "Outputs","df_assigned_"+tech_element+".csv")))

# =========================================================================
# Save output file:
nems_county_tech_class.drop(columns=['Unique ID'],inplace=True)
print('Unit database updated:')
nems_county_tech_class.to_csv(os.path.join(dir,'Outputs', gdboutname), index=False)
nems_county_final.to_csv(os.path.join(dir,'Outputs', gdboutname), index=False)
# =========================================================================

# If some entries in the database do not have matching FIPS, print out message to fix this issue
else:
print('\nSome {} entries in the unit database still do not have matching FIPS codes.'\
.format(len(nems_no_FIPS)))
print('Please fix this issue by adding these units to user_adjusted_units_missing_lon_lats.csv')
# =========================================================================


#%% ===========================================================================
### --- FUNCTIONS ---
### ===========================================================================

## Calculate the great-circle distance between two points on the Earth (in kilometers) using the Haversine formula.
def haversine(lat1, lon1, lat2, lon2):
R = 6371 # Earth radius in km
phi1, phi2 = math.radians(lat1), math.radians(lat2)
d_phi = math.radians(lat2 - lat1)
d_lambda = math.radians(lon2 - lon1)
a = math.sin(d_phi / 2)**2 + math.cos(phi1) * math.cos(phi2) * math.sin(d_lambda / 2)**2
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
return R * c

## Find the nearest point in points_df to the reference latitude and longitude.
## Returns the point's ID and the distance.
def find_nearest_point(reference_lat, reference_lon, points_df):
points_df['distance'] = points_df.apply(
lambda row: haversine(reference_lat, reference_lon, row['latitude'], row['longitude']),
axis=1
)
nearest_point = points_df.loc[points_df['distance'].idxmin()]
return nearest_point['sc_point_gid'], nearest_point['distance']
## Prepare the supply curve data for a given technology.
## Loads the appropriate supply curve file, assigns nearest supply curve id, and returns a capacity factor AC for all technologies except,
## geohydro, which use a resource temperature with point IDs, class, latitude, and longitude
def prep_supply_curve(tech,tech_element,remotepath):
rev_file = pd.read_csv(os.path.join(reeds_path,'inputs/supply_curve/rev_paths.csv'))
print('Preparing supply curves for ' + tech_element)
# For geohydro and egs, the access case are reference and class definition based on site mean resource temperature.
if tech in ['geohydro']:
rev_file_part=rev_file[(rev_file['tech'] == tech) & (rev_file['access_case'] == 'reference')]
class_def='mean_resource_temp'
# For upv and onshore wind, the access case is open and class definition is based on resource.
else:
rev_file_part=rev_file[(rev_file['tech'] == tech) & (rev_file['access_case'] == 'open')]

# For geohydro and egs, the access case are reference and class definition based on site mean resource temperature.
if tech in ['geohydro']:
class_def='mean_resource_temp'
# For upv and onshore wind, the access case is open and class definition is based on resource.
else:
class_def='capacity_factor_ac'

# Load the supply curve file for the technology
df = pd.read_csv(os.path.join(remotepath,rev_file_part['sc_path'].iloc[0],f"{tech}_{rev_file_part['access_case'].iloc[0]}_ba","results",f"{tech}_supply_curve_raw.csv" ))

# Select relevant columns and convert longitude to negative if needed
df_sc=df[['sc_point_gid','latitude','longitude','state','county',class_def]].copy()
df_sc['longitude'] = df_sc['longitude'] * -1 # Convert longitude to negative if needed
# Assign resource class to each point
df_sc.to_csv(os.path.join(dir, "Outputs","df_sc_"+tech+".csv"))
return(df_sc)

def assign_class_tech(df,tech_reV,tech_element,remotepath):
# Filter generators for the given technology
df_exist = df[df['tech'].str.contains(tech_element, case=False, na=False)]
df_exist = df_exist[['tech','TSTATE', 'county', 'T_LAT', 'T_LONG', 'T_PID', 'Unique ID']].reset_index(drop=True).copy()

df_exist['T_LONG'] = df_exist['T_LONG'].abs() # Ensure longitude is positive for distance calculation
df_sc_point = prep_supply_curve(tech_reV,tech_element,remotepath)
for i in range(len(df_exist)):
print('{} out of {} {} units'.format(i, len(df_exist), tech_reV)) # Progress indicator
# Find unique ID of this point:
unique_id = df_exist['Unique ID'][i]
print(unique_id)
# Find nearest supply curve point
nearest_id, nearest_distance = find_nearest_point(df_exist['T_LAT'][i], abs(df_exist['T_LONG'][i]), df_sc_point)

if tech_reV in ['geohydro', 'egs']:
# Find the mean temp of the nearest_id:
mean_temp = df_sc_point[df_sc_point['sc_point_gid']==nearest_id]['mean_resource_temp'].iloc[0]
# Assign the mean temp to the unit in NEMS database using unique ID:
df.loc[df['Unique ID'] == unique_id, 'reV_mean_resource_temp'] = mean_temp

# Make sure the right tech_rev is matched and the right T_PID is matched:
assert(tech_element in df[df['Unique ID'] == df_exist.loc[i, 'Unique ID']]['tech'].iloc[0])
assert(df_exist[df_exist['Unique ID'] == unique_id]['T_PID'].iloc[0] == df[df['Unique ID']==unique_id]['T_PID'].iloc[0])

# Assign sc_point_gid as the nearest_id:
df.loc[df['Unique ID'] == unique_id, 'sc_point_gid'] = nearest_id
return df

# %% ===========================================================================

main()

3 changes: 2 additions & 1 deletion nems_database_processing/e_additional_inputs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -239,8 +239,9 @@
dfout['in_nems'] = dfout['in_nems'].astype(int)
dfout['in_eia860M'] = dfout['in_eia860M'].astype(int)

# Replace all character '#' in T_PNM as 'no. '
# Replace all character '#' in T_PNM and T_UID as 'no. '
dfout['T_PNM'] = dfout['T_PNM'].str.replace('#', 'no. ')
dfout['T_UID'] = dfout['T_UID'].str.replace('#', 'no. ')

# Remove reeds_ba & resource_region columns:
dfout.drop('reeds_ba', inplace = True, axis = 1)
Expand Down