-
Notifications
You must be signed in to change notification settings - Fork 18
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #338 from adrienmellot/feature-uncontrolled-chargi…
…ng-share Feature uncontrolled vs controlled charging Co-authored by: Francesco Sanvito sanvitofrancesco@gmail.com
- Loading branch information
Showing
9 changed files
with
218 additions
and
44 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
106 changes: 106 additions & 0 deletions
106
scripts/transport/road_transport_controlled_charging.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,106 @@ | ||
| import pandas as pd | ||
| import pycountry | ||
|
|
||
|
|
||
| def scale_to_regional_resolution(df, region_country_mapping, populations): | ||
| """ | ||
| Create regional electricity demand for controlled charging. | ||
| ASSUME all road transport is subnationally distributed in proportion to population. | ||
| """ | ||
| df_population_share = ( | ||
| populations.loc[:, "population_sum"] | ||
| .reindex(region_country_mapping.keys()) | ||
| .groupby(by=region_country_mapping) | ||
| .transform(lambda df: df / df.sum()) | ||
| ) | ||
|
|
||
| regional_df = ( | ||
| pd.DataFrame( | ||
| index=df.index, | ||
| data={ | ||
| id: df[country_code] | ||
| for id, country_code in region_country_mapping.items() | ||
| }, | ||
| ) | ||
| .mul(df_population_share) | ||
| .rename(columns=lambda col_name: col_name.replace(".", "-")) | ||
| ) | ||
| pd.testing.assert_series_equal(regional_df.sum(axis=1), df.sum(axis=1)) | ||
| return regional_df | ||
|
|
||
|
|
||
| def scale_to_national_resolution(df): | ||
| df.columns.name = None | ||
| return df | ||
|
|
||
|
|
||
| def scale_to_continental_resolution(df): | ||
| return df.sum(axis=1).to_frame("EUR") | ||
|
|
||
|
|
||
| def convert_annual_distance_to_electricity_demand( | ||
| path_to_controlled_annual_demand: str, | ||
| power_scaling_factor: float, | ||
| first_year: int, | ||
| final_year: int, | ||
| conversion_factors: dict[str, float], | ||
| country_codes: list[str], | ||
| ): | ||
| """ | ||
| Convert annual distance driven demand to electricity demand for | ||
| controlled charging accounting for conversion factors. | ||
| """ | ||
| df_energy_demand = ( | ||
| pd.read_csv(path_to_controlled_annual_demand, index_col=[1, 2]) | ||
| .xs(slice(first_year, final_year), level="year", drop_level=False) | ||
| .assign(value=lambda x: x["value"] * x["vehicle_type"].map(conversion_factors)) | ||
| .groupby(["country_code", "year"]) | ||
| .sum() | ||
| .loc[country_codes] | ||
| .mul(power_scaling_factor) | ||
| .squeeze() | ||
| .unstack("country_code") | ||
| ) | ||
|
|
||
| return -df_energy_demand | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| resolution = snakemake.wildcards.resolution | ||
|
|
||
| path_to_controlled_annual_demand = snakemake.input.annual_controlled_demand | ||
| power_scaling_factor = snakemake.params.power_scaling_factor | ||
| first_year = snakemake.params.first_year | ||
| final_year = snakemake.params.final_year | ||
| conversion_factors = snakemake.params.conversion_factors | ||
| path_to_output = snakemake.output[0] | ||
| country_codes = ( | ||
| [pycountry.countries.lookup(c).alpha_3 for c in snakemake.params.countries], | ||
| ) | ||
| region_country_mapping = ( | ||
| pd.read_csv(snakemake.input.locations, index_col=0) | ||
| .loc[:, "country_code"] | ||
| .to_dict() | ||
| ) | ||
| populations = pd.read_csv(snakemake.input.populations, index_col=0) | ||
|
|
||
| df = convert_annual_distance_to_electricity_demand( | ||
| path_to_controlled_annual_demand, | ||
| power_scaling_factor, | ||
| first_year, | ||
| final_year, | ||
| conversion_factors, | ||
| country_codes, | ||
| ) | ||
|
|
||
| if resolution == "continental": | ||
| df = scale_to_continental_resolution(df) | ||
| elif resolution == "national": | ||
| df = scale_to_national_resolution(df) | ||
| elif resolution == "regional": | ||
| df = scale_to_regional_resolution( | ||
| df, region_country_mapping=region_country_mapping, populations=populations | ||
| ) | ||
| else: | ||
| raise ValueError("Input resolution is not recognised") | ||
| df.T.to_csv(path_to_output, index_label=["id"]) |
Oops, something went wrong.