325 lines
12 KiB
Python
325 lines
12 KiB
Python
# coding: utf-8
|
|
|
|
import yaml
|
|
import pandas as pd
|
|
import numpy as np
|
|
import scipy as sp, scipy.spatial
|
|
from scipy.sparse import csgraph
|
|
from operator import attrgetter
|
|
from six import iteritems
|
|
from six.moves import filter
|
|
from itertools import count, chain
|
|
|
|
import shapely, shapely.prepared, shapely.wkt
|
|
from shapely.geometry import Point
|
|
|
|
from vresutils import shapes as vshapes
|
|
from vresutils.graph import BreadthFirstLevels
|
|
|
|
import logging
|
|
logger = logging.getLogger(__name__)
|
|
|
|
import pypsa
|
|
|
|
def _load_buses_from_eg():
|
|
buses = (pd.read_csv(snakemake.input.eg_buses, quotechar="'",
|
|
true_values='t', false_values='f',
|
|
dtype=dict(bus_id="str", under_construction='bool'))
|
|
.set_index("bus_id")
|
|
.drop(['station_id'], axis=1)
|
|
.rename(columns=dict(voltage='v_nom')))
|
|
|
|
buses['carrier'] = buses.pop('dc').map({True: 'DC', False: 'AC'})
|
|
buses['under_construction'] = buses['under_construction'].fillna(False).astype(bool)
|
|
|
|
# remove all buses outside of all countries including exclusive economic zones (offshore)
|
|
europe_shape = vshapes.country_cover(snakemake.config['countries'])
|
|
europe_shape_exterior = shapely.geometry.Polygon(shell=europe_shape.exterior) # no holes
|
|
europe_shape_exterior_prepped = shapely.prepared.prep(europe_shape_exterior)
|
|
buses_in_europe_b = buses[['x', 'y']].apply(lambda p: europe_shape_exterior_prepped.contains(Point(p)), axis=1)
|
|
|
|
buses_with_v_nom_to_keep_b = buses.v_nom.isin(snakemake.config['electricity']['voltages']) | buses.v_nom.isnull()
|
|
logger.info("Removing buses with voltages {}".format(pd.Index(buses.v_nom.unique()).dropna().difference(snakemake.config['electricity']['voltages'])))
|
|
|
|
return pd.DataFrame(buses.loc[buses_in_europe_b & buses_with_v_nom_to_keep_b])
|
|
|
|
def _load_transformers_from_eg(buses):
|
|
transformers = (pd.read_csv(snakemake.input.eg_transformers, quotechar="'",
|
|
true_values='t', false_values='f',
|
|
dtype=dict(transformer_id='str', bus0='str', bus1='str'))
|
|
.set_index('transformer_id'))
|
|
|
|
transformers = _remove_dangling_branches(transformers, buses)
|
|
|
|
return transformers
|
|
|
|
def _load_converters_from_eg(buses):
|
|
converters = (pd.read_csv(snakemake.input.eg_converters, quotechar="'",
|
|
true_values='t', false_values='f',
|
|
dtype=dict(converter_id='str', bus0='str', bus1='str'))
|
|
.set_index('converter_id'))
|
|
|
|
converters = _remove_dangling_branches(converters, buses)
|
|
|
|
converters['carrier'] = 'B2B'
|
|
|
|
return converters
|
|
|
|
|
|
def _load_links_from_eg(buses):
|
|
links = (pd.read_csv(snakemake.input.eg_links, quotechar="'", true_values='t', false_values='f',
|
|
dtype=dict(link_id='str', bus0='str', bus1='str', under_construction="bool"))
|
|
.set_index('link_id'))
|
|
|
|
links['length'] /= 1e3
|
|
|
|
links = _remove_dangling_branches(links, buses)
|
|
|
|
# Add DC line parameters
|
|
links['carrier'] = 'DC'
|
|
|
|
return links
|
|
|
|
def _load_lines_from_eg(buses):
|
|
lines = (pd.read_csv(snakemake.input.eg_lines, quotechar="'", true_values='t', false_values='f',
|
|
dtype=dict(line_id='str', bus0='str', bus1='str',
|
|
underground="bool", under_construction="bool"))
|
|
.set_index('line_id')
|
|
.rename(columns=dict(voltage='v_nom', circuits='num_parallel')))
|
|
|
|
lines['length'] /= 1e3
|
|
|
|
lines = _remove_dangling_branches(lines, buses)
|
|
|
|
return lines
|
|
|
|
def _apply_parameter_corrections(n):
|
|
with open(snakemake.input.parameter_corrections) as f:
|
|
corrections = yaml.load(f)
|
|
|
|
for component, attrs in iteritems(corrections):
|
|
df = n.df(component)
|
|
if attrs is None: continue
|
|
|
|
for attr, repls in iteritems(attrs):
|
|
for i, r in iteritems(repls):
|
|
if i == 'oid':
|
|
df["oid"] = df.tags.str.extract('"oid"=>"(\d+)"', expand=False)
|
|
r = df.oid.map(repls["oid"]).dropna()
|
|
elif i == 'index':
|
|
r = pd.Series(repls["index"])
|
|
else:
|
|
raise NotImplementedError()
|
|
df.loc[r.index, attr] = r.astype(df[attr].dtype)
|
|
|
|
def _set_electrical_parameters_lines(lines):
|
|
v_noms = snakemake.config['electricity']['voltages']
|
|
linetypes = snakemake.config['lines']['types']
|
|
|
|
for v_nom in v_noms:
|
|
lines.loc[lines["v_nom"] == v_nom, 'type'] = linetypes[v_nom]
|
|
|
|
lines['s_max_pu'] = snakemake.config['lines']['s_max_pu']
|
|
if not snakemake.config['lines']['with_under_construction']:
|
|
lines.loc[lines.under_construction.astype(bool), 'num_parallel'] = 0.
|
|
|
|
return lines
|
|
|
|
def _set_lines_s_nom_from_linetypes(n):
|
|
n.lines['s_nom'] = (
|
|
np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
|
|
n.lines['v_nom'] * n.lines.num_parallel
|
|
)
|
|
|
|
def _set_electrical_parameters_links(links):
|
|
links['p_max_pu'] = snakemake.config['links']['s_max_pu']
|
|
links['p_min_pu'] = -1. * snakemake.config['links']['s_max_pu']
|
|
|
|
links_p_nom = pd.read_csv(snakemake.input.links_p_nom)
|
|
|
|
tree = sp.spatial.KDTree(np.vstack([
|
|
links_p_nom[['x1', 'y1', 'x2', 'y2']],
|
|
links_p_nom[['x2', 'y2', 'x1', 'y1']]
|
|
]))
|
|
|
|
dist, ind = tree.query(
|
|
np.asarray([np.asarray(shapely.wkt.loads(s))[[0, -1]].flatten()
|
|
for s in links.geometry]),
|
|
distance_upper_bound=1.5
|
|
)
|
|
|
|
links_p_nom["j"] =(
|
|
pd.DataFrame(dict(D=dist, i=links_p_nom.index[ind % len(links_p_nom)]), index=links.index)
|
|
.groupby('i').D.idxmin()
|
|
)
|
|
|
|
p_nom = links_p_nom.dropna(subset=["j"]).set_index("j")["Power (MW)"]
|
|
links.loc[p_nom.index, "p_nom"] = p_nom
|
|
|
|
if not snakemake.config['links']['with_under_construction']:
|
|
links.loc[links.under_construction.astype(bool), "p_nom"] = 0.
|
|
|
|
return links
|
|
|
|
def _set_electrical_parameters_converters(converters):
|
|
converters['p_max_pu'] = snakemake.config['links']['s_max_pu']
|
|
converters['p_min_pu'] = -1. * snakemake.config['links']['s_max_pu']
|
|
|
|
converters['p_nom'] = 2000
|
|
|
|
# Converters are combined with links
|
|
converters['under_construction'] = False
|
|
converters['underground'] = False
|
|
|
|
return converters
|
|
|
|
def _set_electrical_parameters_transformers(transformers):
|
|
config = snakemake.config['transformers']
|
|
|
|
## Add transformer parameters
|
|
transformers["x"] = config.get('x', 0.1)
|
|
transformers["s_nom"] = config.get('s_nom', 2000)
|
|
transformers['type'] = config.get('type', '')
|
|
|
|
return transformers
|
|
|
|
def _remove_dangling_branches(branches, buses):
|
|
return pd.DataFrame(branches.loc[branches.bus0.isin(buses.index) & branches.bus1.isin(buses.index)])
|
|
|
|
def _remove_unconnected_components(network):
|
|
_, labels = csgraph.connected_components(network.adjacency_matrix(), directed=False)
|
|
component = pd.Series(labels, index=network.buses.index)
|
|
|
|
component_sizes = component.value_counts()
|
|
components_to_remove = component_sizes.iloc[1:]
|
|
|
|
logger.info("Removing {} unconnected network components with less than {} buses. In total {} buses."
|
|
.format(len(components_to_remove), components_to_remove.max(), components_to_remove.sum()))
|
|
|
|
return network[component == component_sizes.index[0]]
|
|
|
|
def _set_countries_and_substations(n):
|
|
|
|
buses = n.buses
|
|
|
|
def buses_in_shape(shape):
|
|
shape = shapely.prepared.prep(shape)
|
|
return pd.Series(
|
|
np.fromiter((shape.contains(Point(x, y))
|
|
for x, y in buses.loc[:,["x", "y"]].values),
|
|
dtype=bool, count=len(buses)),
|
|
index=buses.index
|
|
)
|
|
|
|
countries = snakemake.config['countries']
|
|
country_shapes = vshapes.countries(subset=countries, add_KV_to_RS=True,
|
|
tolerance=0.01, minarea=0.1)
|
|
offshore_shapes = vshapes.eez(subset=countries, tolerance=0.01)
|
|
|
|
substation_b = buses['symbol'].str.contains('substation', case=False)
|
|
|
|
def prefer_voltage(x, which):
|
|
index = x.index
|
|
if len(index) == 1:
|
|
return pd.Series(index, index)
|
|
key = (x.index[0]
|
|
if x['v_nom'].isnull().all()
|
|
else getattr(x['v_nom'], 'idx' + which)())
|
|
return pd.Series(key, index)
|
|
|
|
gb = buses.loc[substation_b].groupby(['x', 'y'], as_index=False,
|
|
group_keys=False, sort=False)
|
|
bus_map_low = gb.apply(prefer_voltage, 'min')
|
|
lv_b = (bus_map_low == bus_map_low.index).reindex(buses.index, fill_value=False)
|
|
bus_map_high = gb.apply(prefer_voltage, 'max')
|
|
hv_b = (bus_map_high == bus_map_high.index).reindex(buses.index, fill_value=False)
|
|
|
|
onshore_b = pd.Series(False, buses.index)
|
|
offshore_b = pd.Series(False, buses.index)
|
|
|
|
for country in countries:
|
|
onshore_shape = country_shapes[country]
|
|
onshore_country_b = buses_in_shape(onshore_shape)
|
|
onshore_b |= onshore_country_b
|
|
|
|
buses.loc[onshore_country_b, 'country'] = country
|
|
|
|
if country not in offshore_shapes: continue
|
|
offshore_country_b = buses_in_shape(offshore_shapes[country])
|
|
offshore_b |= offshore_country_b
|
|
|
|
buses.loc[offshore_country_b, 'country'] = country
|
|
|
|
buses['substation_lv'] = lv_b & onshore_b & (~ buses['under_construction'])
|
|
buses['substation_off'] = (offshore_b | (hv_b & onshore_b)) & (~ buses['under_construction'])
|
|
|
|
# Nearest country in numbers of hops defines country of homeless buses
|
|
c_nan_b = buses.country.isnull()
|
|
c = n.buses['country']
|
|
graph = n.graph()
|
|
n.buses.loc[c_nan_b, 'country'] = \
|
|
[(next(filter(len, map(lambda x: c.loc[x].dropna(), BreadthFirstLevels(graph, [b]))))
|
|
.value_counts().index[0])
|
|
for b in buses.index[c_nan_b]]
|
|
|
|
return buses
|
|
|
|
def base_network():
|
|
buses = _load_buses_from_eg()
|
|
|
|
links = _load_links_from_eg(buses)
|
|
converters = _load_converters_from_eg(buses)
|
|
|
|
lines = _load_lines_from_eg(buses)
|
|
transformers = _load_transformers_from_eg(buses)
|
|
|
|
lines = _set_electrical_parameters_lines(lines)
|
|
transformers = _set_electrical_parameters_transformers(transformers)
|
|
links = _set_electrical_parameters_links(links)
|
|
converters = _set_electrical_parameters_converters(converters)
|
|
|
|
n = pypsa.Network()
|
|
n.name = 'PyPSA-Eur'
|
|
|
|
n.set_snapshots(pd.date_range(freq='h', **snakemake.config['snapshots']))
|
|
|
|
n.import_components_from_dataframe(buses, "Bus")
|
|
n.import_components_from_dataframe(lines, "Line")
|
|
n.import_components_from_dataframe(transformers, "Transformer")
|
|
n.import_components_from_dataframe(links, "Link")
|
|
n.import_components_from_dataframe(converters, "Link")
|
|
|
|
n = _remove_unconnected_components(n)
|
|
|
|
_set_lines_s_nom_from_linetypes(n)
|
|
|
|
_apply_parameter_corrections(n)
|
|
|
|
_set_countries_and_substations(n)
|
|
|
|
return n
|
|
|
|
if __name__ == "__main__":
|
|
# Detect running outside of snakemake and mock snakemake for testing
|
|
if 'snakemake' not in globals():
|
|
from vresutils.snakemake import MockSnakemake, Dict
|
|
snakemake = MockSnakemake(
|
|
path='..',
|
|
wildcards={},
|
|
input=Dict(
|
|
eg_buses='data/entsoegridkit/buses.csv',
|
|
eg_lines='data/entsoegridkit/lines.csv',
|
|
eg_links='data/entsoegridkit/links.csv',
|
|
eg_converters='data/entsoegridkit/converters.csv',
|
|
eg_transformers='data/entsoegridkit/transformers.csv',
|
|
parameter_corrections='data/parameter_corrections.yaml',
|
|
links_p_nom='data/links_p_nom.csv'
|
|
),
|
|
output = ['networks/base_LC.nc']
|
|
)
|
|
|
|
logging.basicConfig(level=snakemake.config['logging_level'])
|
|
|
|
n = base_network()
|
|
n.export_to_netcdf(snakemake.output[0])
|