# SPDX-FileCopyrightText: : 2017-2020 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
focus_weights:
renewable: (keys)
{technology}:
potential:
solving:
solver:
name:
lines:
length_factor:
.. seealso::
Documentation of the configuration file ``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.networkclustering <https://github.com/PyPSA/PyPSA/blob/master/pypsa/networkclustering.py>`_
do not work reliably with multiple voltage levels and transformers.
.. tip::
The rule :mod:`cluster_all_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
from _helpers import configure_logging, update_p_nom_max
import pypsa
import os
import shapely
import pandas as pd
import numpy as np
import geopandas as gpd
import pyomo.environ as po
import matplotlib.pyplot as plt
import seaborn as sns
from functools import reduce
from pypsa.networkclustering import (busmap_by_kmeans, busmap_by_spectral_clustering,
_make_consense, get_clustering_from_busmap)
import warnings
warnings.filterwarnings(action='ignore', category=UserWarning)
from add_electricity import load_costs
idx = pd.IndexSlice
logger = logging.getLogger(__name__)
def normed(x): return (x/x.sum()).fillna(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.) +
n
.storage_units.loc[n.storage_units.carrier.isin(conv_carriers)]
.groupby('bus').p_nom.sum()
.reindex(n.buses.index, fill_value=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. / w.max())).clip(lower=1.).astype(int)
def distribute_clusters(n, n_clusters, focus_weights=None, solver_name="cbc"):
"""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 focus_weights is not None:
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 = po.ConcreteModel()
def n_bounds(model, *n_id):
return (1, N[n_id])
m.n = po.Var(list(L.index), bounds=n_bounds, domain=po.Integers)
m.tot = po.Constraint(expr=(po.summation(m.n) == n_clusters))
m.objective = po.Objective(expr=sum((m.n[i] - L.loc[i]*n_clusters)**2 for i in L.index),
sense=po.minimize)
opt = po.SolverFactory(solver_name)
if not opt.has_capability('quadratic_objective'):
logger.warning(f'The configured solver `{solver_name}` does not support quadratic objectives. Falling back to `ipopt`.')
opt = po.SolverFactory('ipopt')
results = opt.solve(m)
assert results['Solver'][0]['Status'] == 'ok', f"Solver returned non-optimally: {results}"
return pd.Series(m.n.get_values(), index=L.index).round().astype(int)
def busmap_for_n_clusters(n, n_clusters, solver_name, focus_weights=None, algorithm="kmeans", **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)
n.determine_network_topology()
n_clusters = distribute_clusters(n, n_clusters, focus_weights=focus_weights, solver_name=solver_name)
def reduce_network(n, buses):
nr = pypsa.Network()
nr.import_components_from_dataframe(buses, "Bus")
nr.import_components_from_dataframe(n.lines.loc[n.lines.bus0.isin(buses.index) & n.lines.bus1.isin(buses.index)], "Line")
return nr
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 == "spectral":
return prefix + busmap_by_spectral_clustering(reduce_network(n, x), n_clusters[x.name], **algorithm_kwds)
elif algorithm == "louvain":
return prefix + busmap_by_louvain(reduce_network(n, x), n_clusters[x.name], **algorithm_kwds)
else:
raise ValueError(f"`algorithm` must be one of 'kmeans', 'spectral' or 'louvain'. Is {algorithm}.")
return (n.buses.groupby(['country', 'sub_network'], group_keys=False)
.apply(busmap_for_country).squeeze().rename('busmap'))
def clustering_for_n_clusters(n, n_clusters, custom_busmap=False, aggregate_carriers=None,
line_length_factor=1.25, potential_mode='simple', solver_name="cbc",
algorithm="kmeans", extended_link_costs=0, focus_weights=None):
if potential_mode == 'simple':
p_nom_max_strategy = pd.Series.sum
elif potential_mode == 'conservative':
p_nom_max_strategy = pd.Series.min
else:
raise AttributeError(f"potential_mode should be one of 'simple' or 'conservative' but is '{potential_mode}'")
if not isinstance(custom_busmap, pd.Series):
busmap = busmap_for_n_clusters(n, n_clusters, solver_name, focus_weights, algorithm)
else:
busmap = custom_busmap
clustering = get_clustering_from_busmap(
n, busmap,
bus_strategies=dict(country=_make_consense("Bus", "country")),
aggregate_generators_weighted=True,
aggregate_generators_carriers=aggregate_carriers,
aggregate_one_ports=["Load", "StorageUnit"],
line_length_factor=line_length_factor,
generator_strategies={'p_nom_max': p_nom_max_strategy, 'p_nom_min': pd.Series.sum},
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),
fill_value=0))
return clustering
def save_to_geojson(s, fn):
if os.path.exists(fn):
os.unlink(fn)
df = s.reset_index()
schema = {**gpd.io.file.infer_schema(df), 'geometry': 'Unknown'}
df.to_file(fn, driver='GeoJSON', schema=schema)
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)).set_index('name')
geom_c = regions.geometry.groupby(busmap).apply(shapely.ops.unary_union)
regions_c = gpd.GeoDataFrame(dict(geometry=geom_c))
regions_c.index.name = 'name'
save_to_geojson(regions_c, 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', network='elec', simpl='', clusters='5')
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.network)
focus_weights = snakemake.config.get('focus_weights', None)
renewable_carriers = pd.Index([tech
for tech in n.generators.carrier.unique()
if tech in snakemake.config['renewable']])
if snakemake.wildcards.clusters.endswith('m'):
n_clusters = int(snakemake.wildcards.clusters[:-1])
aggregate_carriers = pd.Index(n.generators.carrier.unique()).difference(renewable_carriers)
elif snakemake.wildcards.clusters == 'all':
n_clusters = len(n.buses)
aggregate_carriers = None # All
else:
n_clusters = int(snakemake.wildcards.clusters)
aggregate_carriers = None # All
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.networkclustering.Clustering(n, busmap, linemap, linemap, pd.Series(dtype='O'))
else:
line_length_factor = snakemake.config['lines']['length_factor']
Nyears = n.snapshot_weightings.objective.sum()/8760
hvac_overhead_cost = (load_costs(snakemake.input.tech_costs, snakemake.config['costs'], snakemake.config['electricity'], Nyears)
.at['HVAC overhead', 'capital_cost'])
def consense(x):
v = x.iat[0]
assert ((x == v).all() or x.isnull().all()), (
"The `potential` configuration option must agree for all renewable carriers, for now!"
)
return v
potential_mode = consense(pd.Series([snakemake.config['renewable'][tech]['potential']
for tech in renewable_carriers]))
custom_busmap = snakemake.config["enable"].get("custom_busmap", False)
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,
line_length_factor, potential_mode,
snakemake.config['solving']['solver']['name'],
"kmeans", hvac_overhead_cost, focus_weights)
update_p_nom_max(n)
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)