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Merge pull request #71 from PyPSA/update_link_length

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[WIP] capital cost and underwater fraction update in clustering
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FabianHofmann 2019-10-31 11:27:58 +01:00 committed by GitHub
commit 0c777fc431
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2 changed files with 30 additions and 16 deletions
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3
Snakefile
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@ -178,7 +178,8 @@ rule cluster_network:
network='networks/{network}_s{simpl}.nc', network='networks/{network}_s{simpl}.nc',
regions_onshore="resources/regions_onshore_{network}_s{simpl}.geojson", regions_onshore="resources/regions_onshore_{network}_s{simpl}.geojson",
regions_offshore="resources/regions_offshore_{network}_s{simpl}.geojson", regions_offshore="resources/regions_offshore_{network}_s{simpl}.geojson",
clustermaps=ancient('resources/clustermaps_{network}_s{simpl}.h5') clustermaps=ancient('resources/clustermaps_{network}_s{simpl}.h5'),
tech_costs=COSTS
output: output:
network='networks/{network}_s{simpl}_{clusters}.nc', network='networks/{network}_s{simpl}_{clusters}.nc',
regions_onshore="resources/regions_onshore_{network}_s{simpl}_{clusters}.geojson", regions_onshore="resources/regions_onshore_{network}_s{simpl}_{clusters}.geojson",

43
scripts/cluster_network.py
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@ -97,24 +97,21 @@ logger = logging.getLogger(__name__)
import os import os
import numpy as np import numpy as np
import scipy as sp
from scipy.sparse.csgraph import connected_components
import xarray as xr
import geopandas as gpd import geopandas as gpd
import shapely import shapely
import networkx as nx import matplotlib.pyplot as plt
from shutil import copyfile import seaborn as sns
from six import iteritems
from six.moves import reduce from six.moves import reduce
import pyomo.environ as po import pyomo.environ as po
import pypsa import pypsa
from pypsa.io import import_components_from_dataframe, import_series_from_dataframe from pypsa.networkclustering import (busmap_by_kmeans, busmap_by_spectral_clustering,
from pypsa.networkclustering import (busmap_by_stubs, busmap_by_kmeans, _make_consense, get_clustering_from_busmap)
_make_consense, get_clustering_from_busmap,
aggregategenerators, aggregateoneport) from add_electricity import load_costs
def normed(x): def normed(x):
return (x/x.sum()).fillna(0.) return (x/x.sum()).fillna(0.)
@ -225,7 +222,8 @@ def plot_busmap_for_n_clusters(n, n_clusters=50):
def clustering_for_n_clusters(n, n_clusters, aggregate_carriers=None, def clustering_for_n_clusters(n, n_clusters, aggregate_carriers=None,
line_length_factor=1.25, potential_mode='simple', line_length_factor=1.25, potential_mode='simple',
solver_name="cbc", algorithm="kmeans"): solver_name="cbc", algorithm="kmeans",
extended_link_costs=0):
if potential_mode == 'simple': if potential_mode == 'simple':
p_nom_max_strategy = np.sum p_nom_max_strategy = np.sum
@ -242,9 +240,16 @@ def clustering_for_n_clusters(n, n_clusters, aggregate_carriers=None,
aggregate_generators_carriers=aggregate_carriers, aggregate_generators_carriers=aggregate_carriers,
aggregate_one_ports=["Load", "StorageUnit"], aggregate_one_ports=["Load", "StorageUnit"],
line_length_factor=line_length_factor, line_length_factor=line_length_factor,
generator_strategies={'p_nom_max': p_nom_max_strategy} generator_strategies={'p_nom_max': p_nom_max_strategy},
) scale_link_capital_costs=False)
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 return clustering
def save_to_geojson(s, fn): def save_to_geojson(s, fn):
@ -277,7 +282,9 @@ if __name__ == "__main__":
network='networks/{network}_s{simpl}.nc', network='networks/{network}_s{simpl}.nc',
regions_onshore='resources/regions_onshore_{network}_s{simpl}.geojson', regions_onshore='resources/regions_onshore_{network}_s{simpl}.geojson',
regions_offshore='resources/regions_offshore_{network}_s{simpl}.geojson', regions_offshore='resources/regions_offshore_{network}_s{simpl}.geojson',
clustermaps='resources/clustermaps_{network}_s{simpl}.h5' clustermaps='resources/clustermaps_{network}_s{simpl}.h5',
tech_costs='data/costs.csv',
), ),
output=Dict( output=Dict(
network='networks/{network}_s{simpl}_{clusters}.nc', network='networks/{network}_s{simpl}_{clusters}.nc',
@ -309,6 +316,11 @@ if __name__ == "__main__":
clustering = pypsa.networkclustering.Clustering(n, busmap, linemap, linemap, pd.Series(dtype='O')) clustering = pypsa.networkclustering.Clustering(n, busmap, linemap, linemap, pd.Series(dtype='O'))
else: else:
line_length_factor = snakemake.config['lines']['length_factor'] line_length_factor = snakemake.config['lines']['length_factor']
hvac_overhead_cost = (load_costs(n.snapshot_weightings.sum()/8760,
tech_costs=snakemake.input.tech_costs,
config=snakemake.config['costs'],
elec_config=snakemake.config['electricity'])
.at['HVAC overhead', 'capital_cost'])
def consense(x): def consense(x):
v = x.iat[0] v = x.iat[0]
@ -321,7 +333,8 @@ if __name__ == "__main__":
clustering = clustering_for_n_clusters(n, n_clusters, aggregate_carriers, clustering = clustering_for_n_clusters(n, n_clusters, aggregate_carriers,
line_length_factor=line_length_factor, line_length_factor=line_length_factor,
potential_mode=potential_mode, potential_mode=potential_mode,
solver_name=snakemake.config['solving']['solver']['name']) solver_name=snakemake.config['solving']['solver']['name'],
extended_link_costs=hvac_overhead_cost)
clustering.network.export_to_netcdf(snakemake.output.network) clustering.network.export_to_netcdf(snakemake.output.network)
with pd.HDFStore(snakemake.output.clustermaps, mode='w') as store: with pd.HDFStore(snakemake.output.clustermaps, mode='w') as store: