013b705ee4
* Cluster first: build renewable profiles and add all assets after clustering * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * correction: pass landfall_lengths through functions * assign landfall_lenghts correctly * remove parameter add_land_use_constraint * fix network_dict * calculate distance to shoreline, remove underwater_fraction * adjust simplification parameter to exclude Crete from offshore wind connections * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove unused geth2015 hydro capacities * removing remaining traces of {simpl} wildcard * add release notes and update workflow graphics * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: lisazeyen <lisa.zeyen@web.de>
128 lines
3.9 KiB
Python
128 lines
3.9 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Builds the electricity demand for base regions based on population and GDP.
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"""
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import logging
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from itertools import product
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import geopandas as gpd
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import numpy as np
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import pandas as pd
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import pypsa
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import scipy.sparse as sparse
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import xarray as xr
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from _helpers import configure_logging, set_scenario_config
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from shapely.prepared import prep
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logger = logging.getLogger(__name__)
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def normed(s: pd.Series) -> pd.Series:
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return s / s.sum()
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def shapes_to_shapes(orig: gpd.GeoSeries, dest: gpd.GeoSeries) -> sparse.lil_matrix:
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"""
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Adopted from vresutils.transfer.Shapes2Shapes()
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"""
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orig_prepped = list(map(prep, orig))
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transfer = sparse.lil_matrix((len(dest), len(orig)), dtype=float)
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for i, j in product(range(len(dest)), range(len(orig))):
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if orig_prepped[j].intersects(dest.iloc[i]):
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area = orig.iloc[j].intersection(dest.iloc[i]).area
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transfer[i, j] = area / dest.iloc[i].area
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return transfer
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def upsample_load(
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n: pypsa.Network,
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regions_fn: str,
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load_fn: str,
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nuts3_fn: str,
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gdp_pop_non_nuts3_fn: str,
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distribution_key: dict[str, float],
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) -> pd.DataFrame:
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substation_lv_i = n.buses.index[n.buses["substation_lv"]]
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gdf_regions = gpd.read_file(regions_fn).set_index("name").reindex(substation_lv_i)
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load = pd.read_csv(load_fn, index_col=0, parse_dates=True)
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nuts3 = gpd.read_file(nuts3_fn).set_index("index")
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gdp_weight = distribution_key.get("gdp", 0.6)
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pop_weight = distribution_key.get("pop", 0.4)
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data_arrays = []
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for cntry, group in gdf_regions.geometry.groupby(gdf_regions.country):
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load_ct = load[cntry]
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if cntry in ["UA", "MD"]:
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# separate handling because nuts3 provides no data for UA+MD
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gdp_pop_non_nuts3 = gpd.read_file(gdp_pop_non_nuts3_fn).set_index("Bus")
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gdp_pop_non_nuts3 = gdp_pop_non_nuts3.loc[
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(gdp_pop_non_nuts3.country == cntry)
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& (gdp_pop_non_nuts3.index.isin(substation_lv_i))
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]
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factors = normed(
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gdp_weight * normed(gdp_pop_non_nuts3["gdp"])
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+ pop_weight * normed(gdp_pop_non_nuts3["pop"])
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)
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elif len(group) == 1:
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factors = pd.Series(1.0, index=group.index)
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else:
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nuts3_cntry = nuts3.loc[nuts3.country == cntry]
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transfer = shapes_to_shapes(group, nuts3_cntry.geometry).T.tocsr()
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gdp_n = pd.Series(
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transfer.dot(nuts3_cntry["gdp"].fillna(1.0).values), index=group.index
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)
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pop_n = pd.Series(
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transfer.dot(nuts3_cntry["pop"].fillna(1.0).values), index=group.index
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)
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factors = normed(gdp_weight * normed(gdp_n) + pop_weight * normed(pop_n))
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data_arrays.append(
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xr.DataArray(
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factors.values * load_ct.values[:, np.newaxis],
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dims=["time", "bus"],
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coords={"time": load_ct.index.values, "bus": factors.index.values},
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)
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)
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return xr.concat(data_arrays, dim="bus")
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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snakemake = mock_snakemake("build_electricity_demand_base")
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configure_logging(snakemake)
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set_scenario_config(snakemake)
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params = snakemake.params
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n = pypsa.Network(snakemake.input.base_network)
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load = upsample_load(
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n,
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regions_fn=snakemake.input.regions,
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load_fn=snakemake.input.load,
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nuts3_fn=snakemake.input.nuts3,
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gdp_pop_non_nuts3_fn=snakemake.input.get("gdp_pop_non_nuts3"),
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distribution_key=params.distribution_key,
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)
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load.name = "electricity demand (MW)"
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comp = dict(zlib=True, complevel=9, least_significant_digit=5)
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load.to_netcdf(snakemake.output[0], encoding={load.name: comp})
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