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>
130 lines
3.6 KiB
Python
Executable File
130 lines
3.6 KiB
Python
Executable File
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Cluster gas transmission network to clustered model regions.
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"""
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import logging
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import geopandas as gpd
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import pandas as pd
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from _helpers import configure_logging, set_scenario_config
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from pypsa.geo import haversine_pts
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from shapely import wkt
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logger = logging.getLogger(__name__)
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def concat_gdf(gdf_list, crs="EPSG:4326"):
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"""
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Concatenate multiple geopandas dataframes with common coordinate reference
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system (crs).
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"""
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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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"""
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Load pypsa-eur on- and offshore regions and concat.
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"""
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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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bus_mapping = gpd.sjoin(gdf, bus_regions, how="left", predicate="within")[
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"name"
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]
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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(
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bus_regions.to_crs(3035).centroid.to_crs(4326)
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)
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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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if df.empty:
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return df
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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
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* haversine_pts([p.point0.x, p.point0.y], [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, prefix="gas pipeline"):
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def make_index(x):
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connector = " <-> " if x.bidirectional else " -> "
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return prefix + " " + 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 _helpers import mock_snakemake
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snakemake = mock_snakemake("cluster_gas_network", clusters="37")
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configure_logging(snakemake)
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set_scenario_config(snakemake)
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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, 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)
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