def busmap_for_country(x):
prefix = x.name[0] + x.name[1] + ' '
if len(x) == 1:
return pd.Series(prefix + '0', index=x.index)
weight = weighting_for_country(n, x)
return prefix + busmap_by_kmeans(
n, weight, n_clusters[x.name], buses_i=x.index)
def busmap_for_country(x):
prefix = x.name[0] + x.name[1] + ' '
logger.debug("Determining busmap for country {}".format(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}.")
def busmap_for_country(x):
if isinstance(n_clusters, pd.Series):
n_cluster_c = n_clusters[x.name]
else:
n_cluster_c = n_clusters
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_cluster_c, buses_i=x.index, **algorithm_kwds)
elif algorithm == "spectral":
return prefix + busmap_by_spectral_clustering(
reduce_network(n, x), n_cluster_c, **algorithm_kwds)
else:
raise ValueError(
f"`algorithm` must be one of 'kmeans', 'spectral' or 'louvain'. Is {algorithm}."
)
def kmean_clustering(network, n_clusters=10):
"""
Implement k-mean clustering in existing network
Parameters
----------
network : :class:`pypsa.Network
Overall container of PyPSA
Returns
-------
network : pypsa.Network object
Container for all network components.
"""
def weighting_for_scenario(x):
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 * (100000. / w.max())).astype(int)
def normed(x):
return (x / x.sum()).fillna(0.)
print('start k-mean clustering')
# prepare k-mean
# k-means clustering (first try)
network.generators.control = "PV"
network.buses['v_nom'] = 380.
# problem our lines have no v_nom. this is implicitly defined by the connected buses:
network.lines["v_nom"] = network.lines.bus0.map(network.buses.v_nom)
# adjust the x of the lines which are not 380.
lines_v_nom_b = network.lines.v_nom != 380
network.lines.loc[lines_v_nom_b,
'x'] *= (380. /
network.lines.loc[lines_v_nom_b, 'v_nom'])**2
network.lines.loc[lines_v_nom_b, 'v_nom'] = 380.
trafo_index = network.transformers.index
transformer_voltages = pd.concat([
network.transformers.bus0.map(network.buses.v_nom),
network.transformers.bus1.map(network.buses.v_nom)
],
axis=1)
network.import_components_from_dataframe(
network.transformers.loc[:, ['bus0', 'bus1', 'x', 's_nom']].assign(
x=network.transformers.x *
(380. /
transformer_voltages.max(axis=1))**2).set_index('T' +
trafo_index),
'Line')
network.transformers.drop(trafo_index, inplace=True)
for attr in network.transformers_t:
network.transformers_t[attr] = network.transformers_t[attr].reindex(
columns=[])
#define weighting based on conventional 'old' generator spatial distribution
non_conv_types = {
'biomass', 'wind', 'solar', 'geothermal', 'load shedding',
'extendable_storage'
}
# Attention: network.generators.carrier.unique()
gen = (
network.generators.loc[(network.generators.carrier.isin(non_conv_types)
== False)].groupby('bus').p_nom.sum().reindex(
network.buses.index, fill_value=0.) +
network.storage_units.loc[
(network.storage_units.carrier.isin(non_conv_types)
== False)].groupby('bus').p_nom.sum().reindex(network.buses.index,
fill_value=0.))
load = network.loads_t.p_set.mean().groupby(network.loads.bus).sum()
# k-mean clustering
# busmap = busmap_by_kmeans(network, bus_weightings=pd.Series(np.repeat(1,
# len(network.buses)), index=network.buses.index) , n_clusters= 10)
weight = weighting_for_scenario(network.buses).reindex(network.buses.index,
fill_value=1)
busmap = busmap_by_kmeans(network,
bus_weightings=pd.Series(weight),
n_clusters=n_clusters)
# ToDo change function in order to use bus_strategies or similar
network.generators['weight'] = 1
aggregate_one_ports = components.one_port_components.copy()
aggregate_one_ports.discard('Generator')
clustering = get_clustering_from_busmap(
network,
busmap,
aggregate_generators_weighted=True,
aggregate_one_ports=aggregate_one_ports)
network = clustering.network
#network = cluster_on_extra_high_voltage(network, busmap, with_time=True)
return network
def kmean_clustering(network,
n_clusters=10,
load_cluster=False,
line_length_factor=1.25,
remove_stubs=False,
use_reduced_coordinates=False,
bus_weight_tocsv=None,
bus_weight_fromcsv=None,
n_init=10,
max_iter=300,
tol=1e-4,
n_jobs=1):
""" Main function of the k-mean clustering approach. Maps an original
network to a new one with adjustable number of nodes and new coordinates.
Parameters
----------
network : :class:`pypsa.Network
Container for all network components.
n_clusters : int
Desired number of clusters.
load_cluster : boolean
Loads cluster coordinates from a former calculation.
line_length_factor : float
Factor to multiply the crow-flies distance between new buses in order
to get new line lengths.
remove_stubs: boolean
Removes stubs and stubby trees (i.e. sequentially reducing dead-ends).
use_reduced_coordinates: boolean
If True, do not average cluster coordinates, but take from busmap.
bus_weight_tocsv : str
Creates a bus weighting based on conventional generation and load
and save it to a csv file.
bus_weight_fromcsv : str
Loads a bus weighting from a csv file to apply it to the clustering
algorithm.
Returns
-------
network : pypsa.Network object
Container for all network components.
"""
def weighting_for_scenario(x, save=None):
"""
"""
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
weight = ((w * (100000. / w.max())).astype(int)).reindex(
network.buses.index, fill_value=1)
if save:
weight.to_csv(save)
return weight
def normed(x):
return (x / x.sum()).fillna(0.)
print('start k-mean clustering')
# prepare k-mean
# k-means clustering (first try)
network.generators.control = "PV"
network.storage_units.control[network.storage_units.carrier == \
'extendable_storage'] = "PV"
network.buses['v_nom'] = 380.
# problem our lines have no v_nom. this is implicitly defined by the
# connected buses:
network.lines["v_nom"] = network.lines.bus0.map(network.buses.v_nom)
# adjust the x of the lines which are not 380.
lines_v_nom_b = network.lines.v_nom != 380
network.lines.loc[lines_v_nom_b, 'x'] *= \
(380. / network.lines.loc[lines_v_nom_b, 'v_nom'])**2
network.lines.loc[lines_v_nom_b, 'v_nom'] = 380.
trafo_index = network.transformers.index
transformer_voltages = \
pd.concat([network.transformers.bus0.map(network.buses.v_nom),
network.transformers.bus1.map(network.buses.v_nom)], axis=1)
network.import_components_from_dataframe(
network.transformers.
loc[:, ['bus0', 'bus1', 'x', 's_nom', 'capital_cost']].assign(
x=network.transformers.x *
(380. / transformer_voltages.max(axis=1))**2,
length=1).set_index('T' + trafo_index), 'Line')
network.transformers.drop(trafo_index, inplace=True)
for attr in network.transformers_t:
network.transformers_t[attr] = network.transformers_t[attr]\
.reindex(columns=[])
# remove stubs
if remove_stubs:
network.determine_network_topology()
busmap = busmap_by_stubs(network)
network.generators['weight'] = network.generators['p_nom']
aggregate_one_ports = components.one_port_components.copy()
aggregate_one_ports.discard('Generator')
# reset coordinates to the new reduced guys, rather than taking an
# average (copied from pypsa.networkclustering)
if use_reduced_coordinates:
# TODO : FIX THIS HACK THAT HAS UNEXPECTED SIDE-EFFECTS,
# i.e. network is changed in place!!
network.buses.loc[busmap.index, ['x', 'y']] = network.buses.loc[
busmap, ['x', 'y']].values
clustering = get_clustering_from_busmap(
network,
busmap,
aggregate_generators_weighted=True,
aggregate_one_ports=aggregate_one_ports,
line_length_factor=line_length_factor)
network = clustering.network
# define weighting based on conventional 'old' generator spatial
# distribution
non_conv_types = {
'biomass', 'wind_onshore', 'wind_offshore', 'solar', 'geothermal',
'load shedding', 'extendable_storage'
}
# Attention: network.generators.carrier.unique()
gen = (
network.generators.loc[(network.generators.carrier.isin(non_conv_types)
== False)].groupby('bus').p_nom.sum().reindex(
network.buses.index, fill_value=0.) +
network.storage_units.loc[
(network.storage_units.carrier.isin(non_conv_types)
== False)].groupby('bus').p_nom.sum().reindex(network.buses.index,
fill_value=0.))
load = network.loads_t.p_set.mean().groupby(network.loads.bus).sum()
# k-mean clustering
# busmap = busmap_by_kmeans(network, bus_weightings=pd.Series(np.repeat(1,
# len(network.buses)), index=network.buses.index) , n_clusters= 10)
# State whether to create a bus weighting and save it, create or not save
# it, or use a bus weighting from a csv file
if bus_weight_tocsv is not None:
weight = weighting_for_scenario(x=network.buses, save=bus_weight_tocsv)
elif bus_weight_fromcsv is not None:
weight = pd.Series.from_csv(bus_weight_fromcsv)
weight.index = weight.index.astype(str)
else:
weight = weighting_for_scenario(x=network.buses, save=False)
busmap = busmap_by_kmeans(network,
bus_weightings=pd.Series(weight),
n_clusters=n_clusters,
load_cluster=load_cluster,
n_init=n_init,
max_iter=max_iter,
tol=tol,
n_jobs=n_jobs)
# ToDo change function in order to use bus_strategies or similar
network.generators['weight'] = network.generators['p_nom']
aggregate_one_ports = components.one_port_components.copy()
aggregate_one_ports.discard('Generator')
clustering = get_clustering_from_busmap(
network,
busmap,
aggregate_generators_weighted=True,
aggregate_one_ports=aggregate_one_ports)
return clustering
def kmean_clustering(network):
""" Implement k-mean clustering in existing network
----------
network : :class:`pypsa.Network
Overall container of PyPSA
Returns
-------
"""
def weighting_for_scenario(x):
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())).astype(int)
def normed(x):
return (x / x.sum()).fillna(0.)
print('start k-mean clustering')
# prepare k-mean
# k-means clustering (first try)
network.generators.control = "PV"
network.buses['v_nom'] = 380.
# problem our lines have no v_nom. this is implicitly defined by the connected buses:
network.lines["v_nom"] = network.lines.bus0.map(network.buses.v_nom)
# adjust the x of the lines which are not 380.
lines_v_nom_b = network.lines.v_nom != 380
network.lines.loc[lines_v_nom_b,
'x'] *= (380. /
network.lines.loc[lines_v_nom_b, 'v_nom'])**2
network.lines.loc[lines_v_nom_b, 'v_nom'] = 380.
trafo_index = network.transformers.index
transformer_voltages = pd.concat([
network.transformers.bus0.map(network.buses.v_nom),
network.transformers.bus1.map(network.buses.v_nom)
],
axis=1)
network.import_components_from_dataframe(
network.transformers.loc[:, ['bus0', 'bus1', 'x', 's_nom']].assign(
x=network.transformers.x *
(380. /
transformer_voltages.max(axis=1))**2).set_index('T' +
trafo_index),
'Line')
network.transformers.drop(trafo_index, inplace=True)
for attr in network.transformers_t:
network.transformers_t[attr] = network.transformers_t[attr].reindex(
columns=[])
#ToDo: change conv to types minus wind and solar
conv_types = {
'biomass', 'run_of_river', 'gas', 'oil', 'coal', 'waste', 'uranium'
}
# Attention: network.generators.carrier.unique()
# conv_types only for SH scenario defined!
gen = (network.generators.loc[network.generators.carrier.isin(
conv_types)].groupby('bus').p_nom.sum().reindex(network.buses.index,
fill_value=0.) +
network.storage_units.loc[network.storage_units.carrier.isin(
conv_types)].groupby('bus').p_nom.sum().reindex(
network.buses.index, fill_value=0.))
load = network.loads_t.p_set.mean().groupby(network.loads.bus).sum()
# k-mean clustering
# busmap = busmap_by_kmeans(network, bus_weightings=pd.Series(np.repeat(1,
# len(network.buses)), index=network.buses.index) , n_clusters= 10)
weight = weighting_for_scenario(network.buses).reindex(network.buses.index,
fill_value=1)
busmap = busmap_by_kmeans(network,
bus_weightings=pd.Series(weight),
buses_i=network.buses.index,
n_clusters=10)
# ToDo change function in order to use bus_strategies or similar
clustering = get_clustering_from_busmap(network, busmap)
network = clustering.network
#network = cluster_on_extra_high_voltage(network, busmap, with_time=True)
return network