cluster network: update to new clustering module (pypsa v0.25)
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@ -277,23 +277,6 @@ def progress_retrieve(url, file, disable=False):
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urllib.request.urlretrieve(url, file, reporthook=update_to)
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urllib.request.urlretrieve(url, file, reporthook=update_to)
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def get_aggregation_strategies(aggregation_strategies):
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# default aggregation strategies that cannot be defined in .yaml format must be specified within
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# the function, otherwise (when defaults are passed in the function's definition) they get lost
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# when custom values are specified in the config.
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import numpy as np
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from pypsa.clustering.spatial import _make_consense
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bus_strategies = dict(country=_make_consense("Bus", "country"))
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bus_strategies.update(aggregation_strategies.get("buses", {}))
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generator_strategies = {"build_year": lambda x: 0, "lifetime": lambda x: np.inf}
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generator_strategies.update(aggregation_strategies.get("generators", {}))
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return bus_strategies, generator_strategies
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def mock_snakemake(rulename, configfiles=[], **wildcards):
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def mock_snakemake(rulename, configfiles=[], **wildcards):
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"""
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"""
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This function is expected to be executed from the 'scripts'-directory of '
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This function is expected to be executed from the 'scripts'-directory of '
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@ -133,12 +133,13 @@ import pandas as pd
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import pyomo.environ as po
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import pyomo.environ as po
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import pypsa
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import pypsa
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import seaborn as sns
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import seaborn as sns
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from _helpers import configure_logging, get_aggregation_strategies, update_p_nom_max
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from _helpers import configure_logging, update_p_nom_max
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from pypsa.clustering.spatial import (
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from pypsa.clustering.spatial import (
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busmap_by_greedy_modularity,
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busmap_by_greedy_modularity,
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busmap_by_hac,
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busmap_by_hac,
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busmap_by_kmeans,
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busmap_by_kmeans,
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get_clustering_from_busmap,
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get_clustering_from_busmap,
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make_consense,
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)
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)
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warnings.filterwarnings(action="ignore", category=UserWarning)
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warnings.filterwarnings(action="ignore", category=UserWarning)
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@ -395,10 +396,6 @@ def clustering_for_n_clusters(
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extended_link_costs=0,
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extended_link_costs=0,
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focus_weights=None,
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focus_weights=None,
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):
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):
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bus_strategies, generator_strategies = get_aggregation_strategies(
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aggregation_strategies
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)
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if not isinstance(custom_busmap, pd.Series):
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if not isinstance(custom_busmap, pd.Series):
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busmap = busmap_for_n_clusters(
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busmap = busmap_for_n_clusters(
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n, n_clusters, solver_name, focus_weights, algorithm, feature
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n, n_clusters, solver_name, focus_weights, algorithm, feature
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@ -409,12 +406,12 @@ def clustering_for_n_clusters(
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clustering = get_clustering_from_busmap(
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clustering = get_clustering_from_busmap(
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n,
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n,
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busmap,
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busmap,
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bus_strategies=bus_strategies,
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bus_strategies={"country": make_consense},
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aggregate_generators_weighted=True,
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aggregate_generators_weighted=True,
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aggregate_generators_carriers=aggregate_carriers,
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aggregate_generators_carriers=aggregate_carriers,
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aggregate_one_ports=["Load", "StorageUnit"],
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aggregate_one_ports=["Load", "StorageUnit"],
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line_length_factor=line_length_factor,
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line_length_factor=line_length_factor,
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generator_strategies=generator_strategies,
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generator_strategies=aggregation_strategies["generators"],
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scale_link_capital_costs=False,
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scale_link_capital_costs=False,
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)
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)
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@ -460,7 +457,7 @@ if __name__ == "__main__":
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if "snakemake" not in globals():
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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from _helpers import mock_snakemake
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snakemake = mock_snakemake("cluster_network", simpl="", clusters="5")
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snakemake = mock_snakemake("cluster_network", simpl="", clusters="37c")
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configure_logging(snakemake)
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configure_logging(snakemake)
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params = snakemake.params
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params = snakemake.params
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@ -470,11 +467,13 @@ if __name__ == "__main__":
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exclude_carriers = params.cluster_network["exclude_carriers"]
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exclude_carriers = params.cluster_network["exclude_carriers"]
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aggregate_carriers = set(n.generators.carrier) - set(exclude_carriers)
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aggregate_carriers = set(n.generators.carrier) - set(exclude_carriers)
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conventional_carriers = set(params.conventional_carriers)
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if snakemake.wildcards.clusters.endswith("m"):
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if snakemake.wildcards.clusters.endswith("m"):
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n_clusters = int(snakemake.wildcards.clusters[:-1])
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n_clusters = int(snakemake.wildcards.clusters[:-1])
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aggregate_carriers = set(params.conventional_carriers).intersection(
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aggregate_carriers = params.conventional_carriers & aggregate_carriers
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aggregate_carriers
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elif snakemake.wildcards.clusters.endswith("c"):
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)
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n_clusters = int(snakemake.wildcards.clusters[:-1])
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aggregate_carriers = aggregate_carriers - conventional_carriers
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elif snakemake.wildcards.clusters == "all":
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elif snakemake.wildcards.clusters == "all":
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n_clusters = len(n.buses)
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n_clusters = len(n.buses)
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else:
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else:
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@ -497,22 +496,6 @@ if __name__ == "__main__":
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Nyears,
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Nyears,
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).at["HVAC overhead", "capital_cost"]
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).at["HVAC overhead", "capital_cost"]
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def consense(x):
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v = x.iat[0]
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assert (
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x == v
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).all() or x.isnull().all(), "The `potential` configuration option must agree for all renewable carriers, for now!"
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return v
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# translate str entries of aggregation_strategies to pd.Series functions:
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aggregation_strategies = {
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p: {
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k: getattr(pd.Series, v)
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for k, v in params.aggregation_strategies[p].items()
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}
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for p in params.aggregation_strategies.keys()
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}
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custom_busmap = params.custom_busmap
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custom_busmap = params.custom_busmap
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if custom_busmap:
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if custom_busmap:
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custom_busmap = pd.read_csv(
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custom_busmap = pd.read_csv(
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@ -92,14 +92,14 @@ import numpy as np
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import pandas as pd
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import pandas as pd
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import pypsa
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import pypsa
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import scipy as sp
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import scipy as sp
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from _helpers import configure_logging, get_aggregation_strategies, update_p_nom_max
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from _helpers import configure_logging, update_p_nom_max
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from add_electricity import load_costs
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from add_electricity import load_costs
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from cluster_network import cluster_regions, clustering_for_n_clusters
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from cluster_network import cluster_regions, clustering_for_n_clusters
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from pypsa.clustering.spatial import (
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from pypsa.clustering.spatial import (
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aggregategenerators,
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aggregateoneport,
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aggregateoneport,
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busmap_by_stubs,
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busmap_by_stubs,
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get_clustering_from_busmap,
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get_clustering_from_busmap,
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make_consense,
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)
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)
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from pypsa.io import import_components_from_dataframe, import_series_from_dataframe
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from pypsa.io import import_components_from_dataframe, import_series_from_dataframe
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from scipy.sparse.csgraph import connected_components, dijkstra
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from scipy.sparse.csgraph import connected_components, dijkstra
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@ -253,11 +253,15 @@ def _aggregate_and_move_components(
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_adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus, output)
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_adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus, output)
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_, generator_strategies = get_aggregation_strategies(aggregation_strategies)
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generator_strategies = aggregation_strategies["generators"]
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carriers = set(n.generators.carrier) - set(exclude_carriers)
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carriers = set(n.generators.carrier) - set(exclude_carriers)
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generators, generators_pnl = aggregategenerators(
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generators, generators_pnl = aggregateoneport(
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n, busmap, carriers=carriers, custom_strategies=generator_strategies
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n,
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busmap,
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"Generator",
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carriers=carriers,
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custom_strategies=generator_strategies,
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)
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)
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replace_components(n, "Generator", generators, generators_pnl)
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replace_components(n, "Generator", generators, generators_pnl)
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@ -478,9 +482,7 @@ def aggregate_to_substations(n, aggregation_strategies=dict(), buses_i=None):
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busmap = n.buses.index.to_series()
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busmap = n.buses.index.to_series()
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busmap.loc[buses_i] = dist.idxmin(1)
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busmap.loc[buses_i] = dist.idxmin(1)
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bus_strategies, generator_strategies = get_aggregation_strategies(
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bus_strategies = {"country": make_consense}
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aggregation_strategies
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)
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clustering = get_clustering_from_busmap(
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clustering = get_clustering_from_busmap(
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n,
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n,
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@ -534,15 +536,6 @@ if __name__ == "__main__":
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n = pypsa.Network(snakemake.input.network)
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n = pypsa.Network(snakemake.input.network)
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Nyears = n.snapshot_weightings.objective.sum() / 8760
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Nyears = n.snapshot_weightings.objective.sum() / 8760
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# translate str entries of aggregation_strategies to pd.Series functions:
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aggregation_strategies = {
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p: {
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k: getattr(pd.Series, v)
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for k, v in params.aggregation_strategies[p].items()
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}
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for p in params.aggregation_strategies.keys()
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}
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n, trafo_map = simplify_network_to_380(n)
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n, trafo_map = simplify_network_to_380(n)
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technology_costs = load_costs(
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technology_costs = load_costs(
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@ -560,7 +553,7 @@ if __name__ == "__main__":
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params.p_max_pu,
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params.p_max_pu,
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params.simplify_network["exclude_carriers"],
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params.simplify_network["exclude_carriers"],
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snakemake.output,
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snakemake.output,
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aggregation_strategies,
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params.aggregation_strategies,
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)
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)
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busmaps = [trafo_map, simplify_links_map]
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busmaps = [trafo_map, simplify_links_map]
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@ -573,12 +566,12 @@ if __name__ == "__main__":
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params.length_factor,
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params.length_factor,
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params.simplify_network,
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params.simplify_network,
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snakemake.output,
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snakemake.output,
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aggregation_strategies=aggregation_strategies,
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aggregation_strategies=params.aggregation_strategies,
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)
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)
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busmaps.append(stub_map)
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busmaps.append(stub_map)
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if params.simplify_network["to_substations"]:
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if params.simplify_network["to_substations"]:
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n, substation_map = aggregate_to_substations(n, aggregation_strategies)
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n, substation_map = aggregate_to_substations(n, params.aggregation_strategies)
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busmaps.append(substation_map)
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busmaps.append(substation_map)
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# treatment of outliers (nodes without a profile for considered carrier):
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# treatment of outliers (nodes without a profile for considered carrier):
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@ -592,7 +585,9 @@ if __name__ == "__main__":
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logger.info(
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logger.info(
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f"clustering preparation (hac): aggregating {len(buses_i)} buses of type {carrier}."
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f"clustering preparation (hac): aggregating {len(buses_i)} buses of type {carrier}."
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)
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)
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n, busmap_hac = aggregate_to_substations(n, aggregation_strategies, buses_i)
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n, busmap_hac = aggregate_to_substations(
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n, params.aggregation_strategies, buses_i
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)
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busmaps.append(busmap_hac)
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busmaps.append(busmap_hac)
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if snakemake.wildcards.simpl:
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if snakemake.wildcards.simpl:
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@ -603,20 +598,22 @@ if __name__ == "__main__":
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solver_name,
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solver_name,
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params.simplify_network["algorithm"],
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params.simplify_network["algorithm"],
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params.simplify_network["feature"],
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params.simplify_network["feature"],
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aggregation_strategies,
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params.aggregation_strategies,
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)
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)
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busmaps.append(cluster_map)
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busmaps.append(cluster_map)
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# some entries in n.buses are not updated in previous functions, therefore can be wrong. as they are not needed
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# some entries in n.buses are not updated in previous functions, therefore can be wrong. as they are not needed
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# and are lost when clustering (for example with the simpl wildcard), we remove them for consistency:
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# and are lost when clustering (for example with the simpl wildcard), we remove them for consistency:
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buses_c = {
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remove = [
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"symbol",
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"symbol",
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"tags",
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"tags",
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"under_construction",
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"under_construction",
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"substation_lv",
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"substation_lv",
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"substation_off",
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"substation_off",
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}.intersection(n.buses.columns)
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"geometry",
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n.buses = n.buses.drop(buses_c, axis=1)
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]
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n.buses.drop(remove, axis=1, inplace=True, errors="ignore")
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n.lines.drop(remove, axis=1, errors="ignore", inplace=True)
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update_p_nom_max(n)
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update_p_nom_max(n)
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