123 lines
3.5 KiB
Python
Executable File
123 lines
3.5 KiB
Python
Executable File
"""Cluster gas network."""
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import logging
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logger = logging.getLogger(__name__)
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import pandas as pd
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import geopandas as gpd
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from shapely import wkt
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from pypsa.geo import haversine_pts
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from packaging.version import Version, parse
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def concat_gdf(gdf_list, crs='EPSG:4326'):
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"""Concatenate multiple geopandas dataframes with common coordinate reference system (crs)."""
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return gpd.GeoDataFrame(pd.concat(gdf_list), crs=crs)
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def load_bus_regions(onshore_path, offshore_path):
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"""Load pypsa-eur on- and offshore regions and concat."""
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bus_regions_offshore = gpd.read_file(offshore_path)
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bus_regions_onshore = gpd.read_file(onshore_path)
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bus_regions = concat_gdf([bus_regions_offshore, bus_regions_onshore])
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bus_regions = bus_regions.dissolve(by='name', aggfunc='sum')
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return bus_regions
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def build_clustered_gas_network(df, bus_regions, length_factor=1.25):
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for i in [0,1]:
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gdf = gpd.GeoDataFrame(geometry=df[f"point{i}"], crs="EPSG:4326")
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kws = dict(op="within") if parse(gpd.__version__) < Version('0.10') else dict(predicate="within")
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bus_mapping = gpd.sjoin(gdf, bus_regions, how="left", **kws).index_right
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bus_mapping = bus_mapping.groupby(bus_mapping.index).first()
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df[f"bus{i}"] = bus_mapping
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df[f"point{i}"] = df[f"bus{i}"].map(bus_regions.to_crs(3035).centroid.to_crs(4326))
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# drop pipes where not both buses are inside regions
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df = df.loc[~df.bus0.isna() & ~df.bus1.isna()]
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# drop pipes within the same region
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df = df.loc[df.bus1 != df.bus0]
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# recalculate lengths as center to center * length factor
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df["length"] = df.apply(
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lambda p: length_factor * haversine_pts(
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[p.point0.x, p.point0.y],
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[p.point1.x, p.point1.y]
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), axis=1
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)
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# tidy and create new numbered index
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df.drop(["point0", "point1"], axis=1, inplace=True)
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df.reset_index(drop=True, inplace=True)
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return df
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def reindex_pipes(df):
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def make_index(x):
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connector = " <-> " if x.bidirectional else " -> "
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return "gas pipeline " + x.bus0 + connector + x.bus1
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df.index = df.apply(make_index, axis=1)
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df["p_min_pu"] = df.bidirectional.apply(lambda bi: -1 if bi else 0)
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df.drop("bidirectional", axis=1, inplace=True)
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df.sort_index(axis=1, inplace=True)
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def aggregate_parallel_pipes(df):
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strategies = {
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'bus0': 'first',
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'bus1': 'first',
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"p_nom": 'sum',
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"p_nom_diameter": 'sum',
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"max_pressure_bar": "mean",
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"build_year": "mean",
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"diameter_mm": "mean",
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"length": 'mean',
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'name': ' '.join,
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"p_min_pu": 'min',
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}
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return df.groupby(df.index).agg(strategies)
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if __name__ == "__main__":
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if 'snakemake' not in globals():
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from helper import mock_snakemake
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snakemake = mock_snakemake(
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'cluster_gas_network',
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simpl='',
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clusters='37'
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)
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logging.basicConfig(level=snakemake.config['logging_level'])
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fn = snakemake.input.cleaned_gas_network
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df = pd.read_csv(fn, index_col=0)
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for col in ["point0", "point1"]:
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df[col] = df[col].apply(wkt.loads)
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bus_regions = load_bus_regions(
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snakemake.input.regions_onshore,
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snakemake.input.regions_offshore
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)
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gas_network = build_clustered_gas_network(df, bus_regions)
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reindex_pipes(gas_network)
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gas_network = aggregate_parallel_pipes(gas_network)
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gas_network.to_csv(snakemake.output.clustered_gas_network) |