def combine_upstream_and_gen_df(gen_df, upstream_df):
"""
Combine the generation and upstream dataframes into a single dataframe.
The emissions represented here are the annutal emissions for all power
plants. This dataframe would be suitable for further analysis.
Parameters
----------
gen_df : dataframe
The generator dataframe, generated by get_gen_plus_netl or
get_generation_process_df. Note that get_generation_process_df returns
two dataframes. The intention would be to send the second returned
dataframe (plant-level emissions) to this routine.
upstream_df : dataframe
The upstream dataframe, generated by get_upstream_process_df
"""
import electricitylci.combinator as combine
import electricitylci.import_impacts as import_impacts
print("Combining upstream and generation inventories")
combined_df = combine.concat_clean_upstream_and_plant(gen_df, upstream_df)
canadian_gen = import_impacts.generate_canadian_mixes(combined_df)
combined_df = pd.concat([combined_df, canadian_gen], ignore_index=True)
return combined_df, canadian_gen
def get_generation_process_df(regions=None, **kwargs):
"""
Create a dataframe of emissions from power generation by fuel type in each
region. kwargs would include the upstream emissions dataframe (upstream_df) if
upstream emissions are being included.
Parameters
----------
regions : str, optional
Regions to include in the analysis (the default is None, which uses the value
read from a settings YAML file). Other options include "eGRID", "NERC", "BA",
"US", "FERC", and "EIA"
Returns
-------
DataFrame
Each row represents information about a single emission from a fuel category
in a single region. Columns are:
'Subregion', 'FuelCategory', 'FlowName', 'FlowUUID', 'Compartment',
'Year', 'Source', 'Unit', 'ElementaryFlowPrimeContext',
'TechnologicalCorrelation', 'TemporalCorrelation', 'DataCollection',
'Emission_factor', 'Reliability_Score', 'GeographicalCorrelation',
'GeomMean', 'GeomSD', 'Maximum', 'Minimum'
"""
from electricitylci.generation import create_generation_process_df
from electricitylci.combinator import concat_clean_upstream_and_plant
if config.model_specs.include_renewable_generation is True:
generation_process_df = get_gen_plus_netl()
else:
generation_process_df = create_generation_process_df()
if config.model_specs.include_netl_water is True:
import electricitylci.plant_water_use as water
water_df = water.generate_plant_water_use(
config.model_specs.eia_gen_year)
generation_process_df = concat_clean_upstream_and_plant(
generation_process_df, water_df)
if config.model_specs.include_upstream_processes is True:
try:
upstream_df = kwargs['upstream_df']
upstream_dict = kwargs['upstream_dict']
except KeyError:
print(
"A kwarg named 'upstream_dict' must be included if include_upstream_processes"
"is True")
# upstream_dict = write_upstream_process_database_to_dict(
# upstream_df
# )
# upstream_dict = write_upstream_dicts_to_jsonld(upstream_dict)
combined_df, canadian_gen = combine_upstream_and_gen_df(
generation_process_df, upstream_df)
gen_plus_fuels = add_fuels_to_gen(generation_process_df, upstream_df,
canadian_gen, upstream_dict)
else:
import electricitylci.import_impacts as import_impacts
canadian_gen_df = import_impacts.generate_canadian_mixes(
generation_process_df)
generation_process_df = pd.concat(
[generation_process_df, canadian_gen_df], ignore_index=True)
gen_plus_fuels = generation_process_df
#This change has been made to accomodate the new method of generating
#consumption mixes for FERC regions. They now pull BAs to provide
#a more accurate inventory. The tradeoff here is that it's no longer possible
#to make a FERC region generation mix and also provide the consumption mix.
#Or it could be possible but would requir running through aggregate twice.
# generation_process_df = aggregate_gen(
# gen_plus_fuels, subregion=regions
# )
if regions is None:
regions = config.model_specs.regional_aggregation
if regions in ["BA", "FERC", "US"]:
generation_process_df = aggregate_gen(gen_plus_fuels, subregion="BA")
else:
generation_process_df = aggregate_gen(gen_plus_fuels,
subregion=regions)
return generation_process_df
if __name__ == "__main__":
import electricitylci.coal_upstream as coal
import electricitylci.natural_gas_upstream as ng
import electricitylci.petroleum_upstream as petro
import electricitylci.geothermal as geo
import electricitylci.solar_upstream as solar
import electricitylci.wind_upstream as wind
import electricitylci.nuclear_upstream as nuke
coal_df = coal.generate_upstream_coal(2016)
ng_df = ng.generate_upstream_ng(2016)
petro_df = petro.generate_petroleum_upstream(2016)
geo_df = geo.generate_upstream_geo(2016)
solar_df = solar.generate_upstream_solar(2016)
wind_df = wind.generate_upstream_wind(2016)
nuke_df = nuke.generate_upstream_nuc(2016)
upstream_df = concat_map_upstream_databases(
coal_df, ng_df, petro_df, geo_df, nuke_df, solar_df, wind_df
)
plant_df = altg.create_generation_process_df()
plant_df["stage_code"] = "Power plant"
print(plant_df.columns)
print(upstream_df.columns)
combined_df = concat_clean_upstream_and_plant(plant_df, upstream_df)
canadian_inventory = import_impacts.generate_canadian_mixes(combined_df)
combined_df = pd.concat([combined_df, canadian_inventory])
combined_df.sort_values(
by=["eGRID_ID", "Compartment", "FlowName", "stage_code"], inplace=True
)
combined_df.to_csv(f"{output_dir}/combined_df.csv")