# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Creates networks clustered to ``{cluster}`` number of zones with aggregated
buses, generators and transmission corridors.
Relevant Settings
-----------------
.. code:: yaml
clustering:
cluster_network:
aggregation_strategies:
focus_weights:
solving:
solver:
name:
lines:
length_factor:
.. seealso::
Documentation of the configuration file ``config/config.yaml`` at
:ref:`toplevel_cf`, :ref:`renewable_cf`, :ref:`solving_cf`, :ref:`lines_cf`
Inputs
------
- ``resources/regions_onshore_elec_s{simpl}.geojson``: confer :ref:`simplify`
- ``resources/regions_offshore_elec_s{simpl}.geojson``: confer :ref:`simplify`
- ``resources/busmap_elec_s{simpl}.csv``: confer :ref:`simplify`
- ``networks/elec_s{simpl}.nc``: confer :ref:`simplify`
- ``data/custom_busmap_elec_s{simpl}_{clusters}.csv``: optional input
Outputs
-------
- ``resources/regions_onshore_elec_s{simpl}_{clusters}.geojson``:
.. image:: img/regions_onshore_elec_s_X.png
:scale: 33 %
- ``resources/regions_offshore_elec_s{simpl}_{clusters}.geojson``:
.. image:: img/regions_offshore_elec_s_X.png
:scale: 33 %
- ``resources/busmap_elec_s{simpl}_{clusters}.csv``: Mapping of buses from ``networks/elec_s{simpl}.nc`` to ``networks/elec_s{simpl}_{clusters}.nc``;
- ``resources/linemap_elec_s{simpl}_{clusters}.csv``: Mapping of lines from ``networks/elec_s{simpl}.nc`` to ``networks/elec_s{simpl}_{clusters}.nc``;
- ``networks/elec_s{simpl}_{clusters}.nc``:
.. image:: img/elec_s_X.png
:scale: 40 %
Description
-----------
.. note::
**Why is clustering used both in** ``simplify_network`` **and** ``cluster_network`` **?**
Consider for example a network ``networks/elec_s100_50.nc`` in which
``simplify_network`` clusters the network to 100 buses and in a second
step ``cluster_network``` reduces it down to 50 buses.
In preliminary tests, it turns out, that the principal effect of
changing spatial resolution is actually only partially due to the
transmission network. It is more important to differentiate between
wind generators with higher capacity factors from those with lower
capacity factors, i.e. to have a higher spatial resolution in the
renewable generation than in the number of buses.
The two-step clustering allows to study this effect by looking at
networks like ``networks/elec_s100_50m.nc``. Note the additional
``m`` in the ``{cluster}`` wildcard. So in the example network
there are still up to 100 different wind generators.
In combination these two features allow you to study the spatial
resolution of the transmission network separately from the
spatial resolution of renewable generators.
**Is it possible to run the model without the** ``simplify_network`` **rule?**
No, the network clustering methods in the PyPSA module
`pypsa.clustering.spatial <https://github.com/PyPSA/PyPSA/blob/master/pypsa/clustering/spatial.py>`_
do not work reliably with multiple voltage levels and transformers.
.. tip::
The rule :mod:`cluster_networks` runs
for all ``scenario`` s in the configuration file
the rule :mod:`cluster_network`.
Exemplary unsolved network clustered to 512 nodes:
.. image:: img/elec_s_512.png
:scale: 40 %
:align: center
Exemplary unsolved network clustered to 256 nodes:
.. image:: img/elec_s_256.png
:scale: 40 %
:align: center
Exemplary unsolved network clustered to 128 nodes:
.. image:: img/elec_s_128.png
:scale: 40 %
:align: center
Exemplary unsolved network clustered to 37 nodes:
.. image:: img/elec_s_37.png
:scale: 40 %
:align: center
"""
import logging
import os
import warnings
from functools import reduce
import geopandas as gpd
import linopy
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pypsa
import seaborn as sns
from _helpers import configure_logging, update_p_nom_max
from add_electricity import load_costs
from pypsa.clustering.spatial import (
busmap_by_greedy_modularity,
busmap_by_hac,
busmap_by_kmeans,
get_clustering_from_busmap,
)
warnings.filterwarnings(action="ignore", category=UserWarning)
idx = pd.IndexSlice
logger = logging.getLogger(__name__)
def normed(x):
return (x / x.sum()).fillna(0.0)
def weighting_for_country(n, x):
conv_carriers = {"OCGT", "CCGT", "PHS", "hydro"}
gen = n.generators.loc[n.generators.carrier.isin(conv_carriers)].groupby(
"bus"
).p_nom.sum().reindex(n.buses.index, fill_value=0.0) + n.storage_units.loc[
n.storage_units.carrier.isin(conv_carriers)
].groupby(
"bus"
).p_nom.sum().reindex(
n.buses.index, fill_value=0.0
)
load = n.loads_t.p_set.mean().groupby(n.loads.bus).sum()
b_i = x.index
g = normed(gen.reindex(b_i, fill_value=0))
l = normed(load.reindex(b_i, fill_value=0))
w = g + l
return (w * (100.0 / w.max())).clip(lower=1.0).astype(int)
def get_feature_for_hac(n, buses_i=None, feature=None):
if buses_i is None:
buses_i = n.buses.index
if feature is None:
feature = "solar+onwind-time"
carriers = feature.split("-")[0].split("+")
if "offwind" in carriers:
carriers.remove("offwind")
carriers = np.append(
carriers, n.generators.carrier.filter(like="offwind").unique()
)
if feature.split("-")[1] == "cap":
feature_data = pd.DataFrame(index=buses_i, columns=carriers)
for carrier in carriers:
gen_i = n.generators.query("carrier == @carrier").index
attach = (
n.generators_t.p_max_pu[gen_i]
.mean()
.rename(index=n.generators.loc[gen_i].bus)
)
feature_data[carrier] = attach
if feature.split("-")[1] == "time":
feature_data = pd.DataFrame(columns=buses_i)
for carrier in carriers:
gen_i = n.generators.query("carrier == @carrier").index
attach = n.generators_t.p_max_pu[gen_i].rename(
columns=n.generators.loc[gen_i].bus
)
feature_data = pd.concat([feature_data, attach], axis=0)[buses_i]
feature_data = feature_data.T
# timestamp raises error in sklearn >= v1.2:
feature_data.columns = feature_data.columns.astype(str)
feature_data = feature_data.fillna(0)
return feature_data
def distribute_clusters(n, n_clusters, focus_weights=None, solver_name="scip"):
"""
Determine the number of clusters per country.
"""
L = (
n.loads_t.p_set.mean()
.groupby(n.loads.bus)
.sum()
.groupby([n.buses.country, n.buses.sub_network])
.sum()
.pipe(normed)
)
N = n.buses.groupby(["country", "sub_network"]).size()
assert (
n_clusters >= len(N) and n_clusters <= N.sum()
), f"Number of clusters must be {len(N)} <= n_clusters <= {N.sum()} for this selection of countries."
if isinstance(focus_weights, dict):
total_focus = sum(list(focus_weights.values()))
assert (
total_focus <= 1.0
), "The sum of focus weights must be less than or equal to 1."
for country, weight in focus_weights.items():
L[country] = weight / len(L[country])
remainder = [
c not in focus_weights.keys() for c in L.index.get_level_values("country")
]
L[remainder] = L.loc[remainder].pipe(normed) * (1 - total_focus)
logger.warning("Using custom focus weights for determining number of clusters.")
assert np.isclose(
L.sum(), 1.0, rtol=1e-3
), f"Country weights L must sum up to 1.0 when distributing clusters. Is {L.sum()}."
m = linopy.Model()
clusters = m.add_variables(
lower=1, upper=N, coords=[L.index], name="n", integer=True
)
m.add_constraints(clusters.sum() == n_clusters, name="tot")
# leave out constant in objective (L * n_clusters) ** 2
m.objective = (clusters * clusters - 2 * clusters * L * n_clusters).sum()
if solver_name == "gurobi":
logging.getLogger("gurobipy").propagate = False
elif solver_name != "scip":
logger.info(
f"The configured solver `{solver_name}` does not support quadratic objectives. Falling back to `scip`."
)
solver_name = "scip"
m.solve(solver_name=solver_name)
return m.solution["n"].to_series().astype(int)
def busmap_for_n_clusters(
n,
n_clusters,
solver_name,
focus_weights=None,
algorithm="kmeans",
feature=None,
**algorithm_kwds,
):
if algorithm == "kmeans":
algorithm_kwds.setdefault("n_init", 1000)
algorithm_kwds.setdefault("max_iter", 30000)
algorithm_kwds.setdefault("tol", 1e-6)
algorithm_kwds.setdefault("random_state", 0)
def fix_country_assignment_for_hac(n):
from scipy.sparse import csgraph
# overwrite country of nodes that are disconnected from their country-topology
for country in n.buses.country.unique():
m = n[n.buses.country == country].copy()
_, labels = csgraph.connected_components(
m.adjacency_matrix(), directed=False
)
component = pd.Series(labels, index=m.buses.index)
component_sizes = component.value_counts()
if len(component_sizes) > 1:
disconnected_bus = component[
component == component_sizes.index[-1]
].index[0]
neighbor_bus = n.lines.query(
"bus0 == @disconnected_bus or bus1 == @disconnected_bus"
).iloc[0][["bus0", "bus1"]]
new_country = list(set(n.buses.loc[neighbor_bus].country) - {country})[
0
]
logger.info(
f"overwriting country `{country}` of bus `{disconnected_bus}` "
f"to new country `{new_country}`, because it is disconnected "
"from its initial inter-country transmission grid."
)
n.buses.at[disconnected_bus, "country"] = new_country
return n
if algorithm == "hac":
feature = get_feature_for_hac(n, buses_i=n.buses.index, feature=feature)
n = fix_country_assignment_for_hac(n)
if (algorithm != "hac") and (feature is not None):
logger.warning(
f"Keyword argument feature is only valid for algorithm `hac`. "
f"Given feature `{feature}` will be ignored."
)
n.determine_network_topology()
n_clusters = distribute_clusters(
n, n_clusters, focus_weights=focus_weights, solver_name=solver_name
)
def busmap_for_country(x):
prefix = x.name[0] + x.name[1] + " "
logger.debug(f"Determining busmap for country {prefix[:-1]}")
if len(x) == 1:
return pd.Series(prefix + "0", index=x.index)
weight = weighting_for_country(n, x)
if algorithm == "kmeans":
return prefix + busmap_by_kmeans(
n, weight, n_clusters[x.name], buses_i=x.index, **algorithm_kwds
)
elif algorithm == "hac":
return prefix + busmap_by_hac(
n, n_clusters[x.name], buses_i=x.index, feature=feature.loc[x.index]
)
elif algorithm == "modularity":
return prefix + busmap_by_greedy_modularity(
n, n_clusters[x.name], buses_i=x.index
)
else:
raise ValueError(
f"`algorithm` must be one of 'kmeans' or 'hac'. Is {algorithm}."
)
return (
n.buses.groupby(["country", "sub_network"], group_keys=False)
.apply(busmap_for_country, include_groups=False)
.squeeze()
.rename("busmap")
)
def clustering_for_n_clusters(
n,
n_clusters,
custom_busmap=False,
aggregate_carriers=None,
line_length_factor=1.25,
aggregation_strategies=dict(),
solver_name="scip",
algorithm="hac",
feature=None,
extended_link_costs=0,
focus_weights=None,
):
if not isinstance(custom_busmap, pd.Series):
busmap = busmap_for_n_clusters(
n, n_clusters, solver_name, focus_weights, algorithm, feature
)
else:
busmap = custom_busmap
line_strategies = aggregation_strategies.get("lines", dict())
generator_strategies = aggregation_strategies.get("generators", dict())
one_port_strategies = aggregation_strategies.get("one_ports", dict())
clustering = get_clustering_from_busmap(
n,
busmap,
aggregate_generators_weighted=True,
aggregate_generators_carriers=aggregate_carriers,
aggregate_one_ports=["Load", "StorageUnit"],
line_length_factor=line_length_factor,
line_strategies=line_strategies,
generator_strategies=generator_strategies,
one_port_strategies=one_port_strategies,
scale_link_capital_costs=False,
)
if not n.links.empty:
nc = clustering.network
nc.links["underwater_fraction"] = (
n.links.eval("underwater_fraction * length").div(nc.links.length).dropna()
)
nc.links["capital_cost"] = nc.links["capital_cost"].add(
(nc.links.length - n.links.length)
.clip(lower=0)
.mul(extended_link_costs)
.dropna(),
fill_value=0,
)
return clustering
def cluster_regions(busmaps, input=None, output=None):
busmap = reduce(lambda x, y: x.map(y), busmaps[1:], busmaps[0])
for which in ("regions_onshore", "regions_offshore"):
regions = gpd.read_file(getattr(input, which))
regions = regions.reindex(columns=["name", "geometry"]).set_index("name")
regions_c = regions.dissolve(busmap)
regions_c.index.name = "name"
regions_c = regions_c.reset_index()
regions_c.to_file(getattr(output, which))
def plot_busmap_for_n_clusters(n, n_clusters, fn=None):
busmap = busmap_for_n_clusters(n, n_clusters)
cs = busmap.unique()
cr = sns.color_palette("hls", len(cs))
n.plot(bus_colors=busmap.map(dict(zip(cs, cr))))
if fn is not None:
plt.savefig(fn, bbox_inches="tight")
del cs, cr
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("cluster_network", simpl="", clusters="37")
configure_logging(snakemake)
params = snakemake.params
solver_name = snakemake.config["solving"]["solver"]["name"]
n = pypsa.Network(snakemake.input.network)
# remove integer outputs for compatibility with PyPSA v0.26.0
n.generators.drop("n_mod", axis=1, inplace=True, errors="ignore")
exclude_carriers = params.cluster_network["exclude_carriers"]
aggregate_carriers = set(n.generators.carrier) - set(exclude_carriers)
conventional_carriers = set(params.conventional_carriers)
if snakemake.wildcards.clusters.endswith("m"):
n_clusters = int(snakemake.wildcards.clusters[:-1])
aggregate_carriers = params.conventional_carriers & aggregate_carriers
elif snakemake.wildcards.clusters.endswith("c"):
n_clusters = int(snakemake.wildcards.clusters[:-1])
aggregate_carriers = aggregate_carriers - conventional_carriers
elif snakemake.wildcards.clusters == "all":
n_clusters = len(n.buses)
else:
n_clusters = int(snakemake.wildcards.clusters)
if params.cluster_network.get("consider_efficiency_classes", False):
carriers = []
for c in aggregate_carriers:
gens = n.generators.query("carrier == @c")
low = gens.efficiency.quantile(0.10)
high = gens.efficiency.quantile(0.90)
if low >= high:
carriers += [c]
else:
labels = ["low", "medium", "high"]
suffix = pd.cut(
gens.efficiency, bins=[0, low, high, 1], labels=labels
).astype(str)
carriers += [f"{c} {label} efficiency" for label in labels]
n.generators.update(
{"carrier": gens.carrier + " " + suffix + " efficiency"}
)
aggregate_carriers = carriers
if n_clusters == len(n.buses):
# Fast-path if no clustering is necessary
busmap = n.buses.index.to_series()
linemap = n.lines.index.to_series()
clustering = pypsa.clustering.spatial.Clustering(
n, busmap, linemap, linemap, pd.Series(dtype="O")
)
else:
Nyears = n.snapshot_weightings.objective.sum() / 8760
hvac_overhead_cost = load_costs(
snakemake.input.tech_costs,
params.costs,
params.max_hours,
Nyears,
).at["HVAC overhead", "capital_cost"]
custom_busmap = params.custom_busmap
if custom_busmap:
custom_busmap = pd.read_csv(
snakemake.input.custom_busmap, index_col=0, squeeze=True
)
custom_busmap.index = custom_busmap.index.astype(str)
logger.info(f"Imported custom busmap from {snakemake.input.custom_busmap}")
clustering = clustering_for_n_clusters(
n,
n_clusters,
custom_busmap,
aggregate_carriers,
params.length_factor,
params.aggregation_strategies,
solver_name,
params.cluster_network["algorithm"],
params.cluster_network["feature"],
hvac_overhead_cost,
params.focus_weights,
)
update_p_nom_max(clustering.network)
if params.cluster_network.get("consider_efficiency_classes"):
labels = [f" {label} efficiency" for label in ["low", "medium", "high"]]
nc = clustering.network
nc.generators["carrier"] = nc.generators.carrier.replace(labels, "", regex=True)
clustering.network.meta = dict(
snakemake.config, **dict(wildcards=dict(snakemake.wildcards))
)
clustering.network.export_to_netcdf(snakemake.output.network)
for attr in (
"busmap",
"linemap",
): # also available: linemap_positive, linemap_negative
getattr(clustering, attr).to_csv(snakemake.output[attr])
cluster_regions((clustering.busmap,), snakemake.input, snakemake.output)