Feature uncontrolled vs controlled charging #338
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,106 @@ | ||
| import pandas as pd | ||
| import pycountry | ||
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| 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()) | ||
| ) | ||
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| 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 | ||
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| def scale_to_national_resolution(df): | ||
| df.columns.name = None | ||
| return df | ||
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| def scale_to_continental_resolution(df): | ||
| return df.sum(axis=1).to_frame("EUR") | ||
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| 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") | ||
| ) | ||
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| return -df_energy_demand | ||
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| if __name__ == "__main__": | ||
| resolution = snakemake.wildcards.resolution | ||
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| 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) | ||
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| df = convert_annual_distance_to_electricity_demand( | ||
| path_to_controlled_annual_demand, | ||
| power_scaling_factor, | ||
| first_year, | ||
| final_year, | ||
| conversion_factors, | ||
| country_codes, | ||
| ) | ||
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| 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"]) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,29 +1,50 @@ | ||
| techs: | ||
| demand_road_transport_electrified_uncontrolled: | ||
| essentials: | ||
| name: 'Uncontrolled electrified road transport demand -- follows a timeseries' | ||
| parent: demand | ||
| carrier: electricity | ||
| constraints: | ||
| resource: file=demand/uncontrolled-electrified-road-transport.csv | ||
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| demand_road_transport_historic_electrified_uncontrolled: | ||
| essentials: | ||
| name: 'Removes historic electrified road transport demand -- assumed uncontrolled' | ||
| parent: supply | ||
| carrier: electricity | ||
| constraints: | ||
| resource: file=demand/uncontrolled-road-transport-historic-electrification.csv | ||
| resource_min_use: 1 | ||
|
There was a problem hiding this comment. should this be "force_resource"? There was a problem hiding this comment. I've updated it, indeed I think it makes more sense |
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| demand_road_transport_electrified_controlled: | ||
| essentials: | ||
| name: 'Controlled electrified road transport demand' | ||
| parent: demand | ||
| carrier: electricity | ||
| constraints: | ||
| force_resource: false | ||
| resource: -.inf | ||
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| overrides: | ||
| keep-historic-electricity-demand-from-road-transport: | ||
| {% for id, location in locations.iterrows() %} | ||
| {{ id }}.techs.demand_road_transport_historic_electrified_uncontrolled.exists: False | ||
| {% endfor %} | ||
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| {% for year in locations.columns %} | ||
| {{ year }}_transport_controlled_electrified_demand: | ||
| group_constraints: | ||
| {% for location in locations.index %} | ||
| {{ location }}_annual_controlled_electricity_demand: | ||
| locs: [{{ location }}] | ||
| techs: [demand_road_transport_electrified_controlled] | ||
| carrier_con_equals: | ||
| electricity: {{ locations.loc[location, year] }} # {{ (1 / scaling_factors.power) | unit("MWh") }} | ||
| {% endfor %} | ||
| {% endfor %} | ||
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| locations: | ||
| {% for id, location in locations.iterrows() %} | ||
| {{ id }}.techs.demand_road_transport_electrified_uncontrolled: | ||
|
There was a problem hiding this comment. Needs controlled tech here too. I'd maybe update to: {{ id }}.techs:
demand_road_transport_electrified_uncontrolled:
demand_road_transport_historic_electrified_uncontrolled:
demand_road_transport_electrified_controlled:
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| {{ id }}.techs.demand_road_transport_historic_electrified_uncontrolled: | ||
| {% endfor %} | ||
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I haven't looked, but how close is this to a copy-paste of the
uncontrolledpython file? If it is very similar, we should consider using the two files with a controlled/uncontrolled flag that decides on the functions to use etc.There was a problem hiding this comment.
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It's fairly different, because the
uncontrolledscripts generate timeseries per vehicle type which are then aggregated and rescaled per resolution. This last step is similar but still different because contains different data structures. Thecontrolledscript is actually closer to therescale.pyin the heat sector.