# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Creates the network topology from a `ENTSO-E map extract.
<https://github.com/PyPSA/GridKit/tree/master/entsoe>`_ (March 2022)
or `OpenStreetMap data <https://www.openstreetmap.org/>`_ (Aug 2024)
as a PyPSA
network.
Relevant Settings
-----------------
.. code:: yaml
countries:
electricity:
voltages:
lines:
types:
s_max_pu:
under_construction:
links:
p_max_pu:
under_construction:
transformers:
x:
s_nom:
type:
.. seealso::
Documentation of the configuration file ``config/config.yaml`` at
:ref:`snapshots_cf`, :ref:`toplevel_cf`, :ref:`electricity_cf`, :ref:`load_cf`,
:ref:`lines_cf`, :ref:`links_cf`, :ref:`transformers_cf`
Inputs
------
- ``data/entsoegridkit``: Extract from the geographical vector data of the online `ENTSO-E Interactive Map <https://www.entsoe.eu/data/map/>`_ by the `GridKit <https://github.com/martacki/gridkit>`_ toolkit dating back to March 2022.
- ``data/parameter_corrections.yaml``: Corrections for ``data/entsoegridkit``
- ``data/links_p_nom.csv``: confer :ref:`links`
- ``resources/country_shapes.geojson``: confer :ref:`shapes`
- ``resources/offshore_shapes.geojson``: confer :ref:`shapes`
- ``resources/europe_shape.geojson``: confer :ref:`shapes`
Outputs
-------
- ``networks/base.nc``
.. image:: img/base.png
:scale: 33 %
- ``resources/regions_onshore.geojson``:
.. image:: img/regions_onshore.png
:scale: 33 %
- ``resources/regions_offshore.geojson``:
.. image:: img/regions_offshore.png
:scale: 33 %
Description
-----------
Creates the network topology from an ENTSO-E map extract, and create Voronoi shapes for each bus representing both onshore and offshore regions.
"""
import logging
import warnings
from itertools import product
import geopandas as gpd
import networkx as nx
import numpy as np
import pandas as pd
import pypsa
import shapely
import shapely.prepared
import shapely.wkt
import yaml
from _helpers import REGION_COLS, configure_logging, get_snapshots, set_scenario_config
from packaging.version import Version, parse
from scipy.sparse import csgraph
from scipy.spatial import KDTree
from shapely.geometry import LineString, Point
PD_GE_2_2 = parse(pd.__version__) >= Version("2.2")
logger = logging.getLogger(__name__)
def _get_oid(df):
if "tags" in df.columns:
return df.tags.str.extract(r'"oid"=>"(\d+)"', expand=False)
else:
return pd.Series(np.nan, df.index)
def _get_country(df):
if "tags" in df.columns:
return df.tags.str.extract('"country"=>"([A-Z]{2})"', expand=False)
else:
return pd.Series(np.nan, df.index)
def _find_closest_links(links, new_links, distance_upper_bound=1.5):
treecoords = np.asarray(
[
np.asarray(shapely.wkt.loads(s).coords)[[0, -1]].flatten()
for s in links.geometry
]
)
querycoords = np.vstack(
[new_links[["x1", "y1", "x2", "y2"]], new_links[["x2", "y2", "x1", "y1"]]]
)
tree = KDTree(treecoords)
dist, ind = tree.query(querycoords, distance_upper_bound=distance_upper_bound)
found_b = ind < len(links)
found_i = np.arange(len(new_links) * 2)[found_b] % len(new_links)
return (
pd.DataFrame(
dict(D=dist[found_b], i=links.index[ind[found_b] % len(links)]),
index=new_links.index[found_i],
)
.sort_values(by="D")[lambda ds: ~ds.index.duplicated(keep="first")]
.sort_index()["i"]
)
def _load_buses(buses, europe_shape, config):
buses = (
pd.read_csv(
buses,
quotechar="'",
true_values=["t"],
false_values=["f"],
dtype=dict(bus_id="str"),
)
.set_index("bus_id")
.rename(columns=dict(voltage="v_nom"))
)
if "station_id" in buses.columns:
buses.drop("station_id", axis=1, inplace=True)
buses["carrier"] = buses.pop("dc").map({True: "DC", False: "AC"})
buses["under_construction"] = buses.under_construction.where(
lambda s: s.notnull(), False
).astype(bool)
europe_shape = gpd.read_file(europe_shape).loc[0, "geometry"]
europe_shape_prepped = shapely.prepared.prep(europe_shape)
buses_in_europe_b = buses[["x", "y"]].apply(
lambda p: europe_shape_prepped.contains(Point(p)), axis=1
)
v_nom_min = min(config["electricity"]["voltages"])
v_nom_max = max(config["electricity"]["voltages"])
buses_with_v_nom_to_keep_b = (
(v_nom_min <= buses.v_nom) & (buses.v_nom <= v_nom_max)
| (buses.v_nom.isnull())
| (
buses.carrier == "DC"
) # Keeping all DC buses from the input dataset independent of voltage (e.g. 150 kV connections)
)
logger.info(f"Removing buses outside of range AC {v_nom_min} - {v_nom_max} V")
return pd.DataFrame(buses.loc[buses_in_europe_b & buses_with_v_nom_to_keep_b])
def _load_transformers(buses, transformers):
transformers = pd.read_csv(
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):
converters = pd.read_csv(
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_converters_from_osm(buses, converters):
converters = pd.read_csv(
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"] = ""
return converters
def _load_links_from_eg(buses, links):
links = pd.read_csv(
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
# Skagerrak Link is connected to 132kV bus which is removed in _load_buses.
# Connect to neighboring 380kV bus
links.loc[links.bus1 == "6396", "bus1"] = "6398"
links = _remove_dangling_branches(links, buses)
# Add DC line parameters
links["carrier"] = "DC"
return links
def _load_links_from_osm(buses, links):
links = pd.read_csv(
links,
quotechar="'",
true_values=["t"],
false_values=["f"],
dtype=dict(
link_id="str",
bus0="str",
bus1="str",
voltage="int",
p_nom="float",
),
).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(buses, lines):
lines = (
pd.read_csv(
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["carrier"] = "AC"
lines = _remove_dangling_branches(lines, buses)
return lines
def _apply_parameter_corrections(n, parameter_corrections):
with open(parameter_corrections) as f:
corrections = yaml.safe_load(f)
if corrections is None:
return
for component, attrs in corrections.items():
df = n.df(component)
oid = _get_oid(df)
if attrs is None:
continue
for attr, repls in attrs.items():
for i, r in repls.items():
if i == "oid":
r = oid.map(repls["oid"]).dropna()
elif i == "index":
r = pd.Series(repls["index"])
else:
raise NotImplementedError()
inds = r.index.intersection(df.index)
df.loc[inds, attr] = r[inds].astype(df[attr].dtype)
def _reconnect_crimea(lines):
logger.info("Reconnecting Crimea to the Ukrainian grid.")
lines_to_crimea = pd.DataFrame(
{
"bus0": ["3065", "3181", "3181"],
"bus1": ["3057", "3055", "3057"],
"v_nom": [300, 300, 300],
"num_parallel": [1, 1, 1],
"length": [140, 120, 140],
"carrier": ["AC", "AC", "AC"],
"underground": [False, False, False],
"under_construction": [False, False, False],
},
index=["Melitopol", "Liubymivka left", "Luibymivka right"],
)
return pd.concat([lines, lines_to_crimea])
def _set_electrical_parameters_lines_eg(lines, config):
v_noms = config["electricity"]["voltages"]
linetypes = config["lines"]["types"]
for v_nom in v_noms:
lines.loc[lines["v_nom"] == v_nom, "type"] = linetypes[v_nom]
lines["s_max_pu"] = config["lines"]["s_max_pu"]
return lines
def _set_electrical_parameters_lines_osm(lines, config):
if lines.empty:
lines["type"] = []
return lines
v_noms = config["electricity"]["voltages"]
linetypes = _get_linetypes_config(config["lines"]["types"], v_noms)
lines["carrier"] = "AC"
lines["dc"] = False
lines.loc[:, "type"] = lines.v_nom.apply(
lambda x: _get_linetype_by_voltage(x, linetypes)
)
lines["s_max_pu"] = config["lines"]["s_max_pu"]
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_eg(links, config, links_p_nom):
if links.empty:
return links
p_max_pu = config["links"].get("p_max_pu", 1.0)
links["p_max_pu"] = p_max_pu
links["p_min_pu"] = -p_max_pu
links_p_nom = pd.read_csv(links_p_nom)
# filter links that are not in operation anymore
removed_b = links_p_nom.Remarks.str.contains("Shut down|Replaced", na=False)
links_p_nom = links_p_nom[~removed_b]
# find closest link for all links in links_p_nom
links_p_nom["j"] = _find_closest_links(links, links_p_nom)
links_p_nom = links_p_nom.groupby(["j"], as_index=False).agg({"Power (MW)": "sum"})
p_nom = links_p_nom.dropna(subset=["j"]).set_index("j")["Power (MW)"]
# Don't update p_nom if it's already set
p_nom_unset = (
p_nom.drop(links.index[links.p_nom.notnull()], errors="ignore")
if "p_nom" in links
else p_nom
)
links.loc[p_nom_unset.index, "p_nom"] = p_nom_unset
return links
def _set_electrical_parameters_links_osm(links, config):
if links.empty:
return links
p_max_pu = config["links"].get("p_max_pu", 1.0)
links["p_max_pu"] = p_max_pu
links["p_min_pu"] = -p_max_pu
links["carrier"] = "DC"
links["dc"] = True
return links
def _set_electrical_parameters_converters(converters, config):
p_max_pu = config["links"].get("p_max_pu", 1.0)
converters["p_max_pu"] = p_max_pu
converters["p_min_pu"] = -p_max_pu
# if column "p_nom" does not exist, set to 2000
if "p_nom" not in converters:
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):
config = 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, threshold=6):
_, 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.loc[component_sizes < threshold]
logger.info(
f"Removing {len(components_to_remove)} unconnected network components with less than {components_to_remove.max()} buses. In total {components_to_remove.sum()} buses."
)
return network[component == component_sizes.index[0]]
def _set_countries_and_substations(n, config, country_shapes, offshore_shapes):
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 = config["countries"]
country_shapes = gpd.read_file(country_shapes).set_index("name")["geometry"]
# reindexing necessary for supporting empty geo-dataframes
offshore_shapes = gpd.read_file(offshore_shapes)
offshore_shapes = offshore_shapes.reindex(columns=["name", "geometry"]).set_index(
"name"
)["geometry"]
substation_b = buses["symbol"].str.contains(
"substation|converter station", 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)
compat_kws = dict(include_groups=False) if PD_GE_2_2 else {}
gb = buses.loc[substation_b].groupby(
["x", "y"], as_index=False, group_keys=False, sort=False
)
bus_map_low = gb.apply(prefer_voltage, "min", **compat_kws)
lv_b = (bus_map_low == bus_map_low.index).reindex(buses.index, fill_value=False)
bus_map_high = gb.apply(prefer_voltage, "max", **compat_kws)
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.index:
continue
offshore_country_b = buses_in_shape(offshore_shapes[country])
offshore_b |= offshore_country_b
buses.loc[offshore_country_b, "country"] = country
# Only accept buses as low-voltage substations (where load is attached), if
# they have at least one connection which is not under_construction
has_connections_b = pd.Series(False, index=buses.index)
for b, df in product(("bus0", "bus1"), (n.lines, n.links)):
has_connections_b |= ~df.groupby(b).under_construction.min()
buses["onshore_bus"] = onshore_b
buses["substation_lv"] = (
lv_b & onshore_b & (~buses["under_construction"]) & has_connections_b
)
buses["substation_off"] = (offshore_b | (hv_b & onshore_b)) & (
~buses["under_construction"]
)
c_nan_b = buses.country.fillna("na") == "na"
if c_nan_b.sum() > 0:
c_tag = _get_country(buses.loc[c_nan_b])
c_tag.loc[~c_tag.isin(countries)] = np.nan
n.buses.loc[c_nan_b, "country"] = c_tag
c_tag_nan_b = n.buses.country.isnull()
# Nearest country in path length defines country of still homeless buses
# Work-around until commit 705119 lands in pypsa release
n.transformers["length"] = 0.0
graph = n.graph(weight="length")
n.transformers.drop("length", axis=1, inplace=True)
for b in n.buses.index[c_tag_nan_b]:
df = (
pd.DataFrame(
dict(
pathlength=nx.single_source_dijkstra_path_length(
graph, b, cutoff=200
)
)
)
.join(n.buses.country)
.dropna()
)
assert (
not df.empty
), "No buses with defined country within 200km of bus `{}`".format(b)
n.buses.at[b, "country"] = df.loc[df.pathlength.idxmin(), "country"]
logger.warning(
"{} buses are not in any country or offshore shape,"
" {} have been assigned from the tag of the entsoe map,"
" the rest from the next bus in terms of pathlength.".format(
c_nan_b.sum(), c_nan_b.sum() - c_tag_nan_b.sum()
)
)
return buses
def _replace_b2b_converter_at_country_border_by_link(n):
# Affects only the B2B converter in Lithuania at the Polish border at the moment
buscntry = n.buses.country
linkcntry = n.links.bus0.map(buscntry)
converters_i = n.links.index[
(n.links.carrier == "B2B") & (linkcntry == n.links.bus1.map(buscntry))
]
def findforeignbus(G, i):
cntry = linkcntry.at[i]
for busattr in ("bus0", "bus1"):
b0 = n.links.at[i, busattr]
for b1 in G[b0]:
if buscntry[b1] != cntry:
return busattr, b0, b1
return None, None, None
for i in converters_i:
G = n.graph()
busattr, b0, b1 = findforeignbus(G, i)
if busattr is not None:
comp, line = next(iter(G[b0][b1]))
if comp != "Line":
logger.warning(
"Unable to replace B2B `{}` expected a Line, but found a {}".format(
i, comp
)
)
continue
n.links.at[i, busattr] = b1
n.links.at[i, "p_nom"] = min(
n.links.at[i, "p_nom"], n.lines.at[line, "s_nom"]
)
n.links.at[i, "carrier"] = "DC"
n.links.at[i, "underwater_fraction"] = 0.0
n.links.at[i, "length"] = n.lines.at[line, "length"]
n.remove("Line", line)
n.remove("Bus", b0)
logger.info(
"Replacing B2B converter `{}` together with bus `{}` and line `{}` by an HVDC tie-line {}-{}".format(
i, b0, line, linkcntry.at[i], buscntry.at[b1]
)
)
def _set_links_underwater_fraction(n, offshore_shapes):
if n.links.empty:
return
if not hasattr(n.links, "geometry"):
n.links["underwater_fraction"] = 0.0
else:
offshore_shape = gpd.read_file(offshore_shapes).union_all()
links = gpd.GeoSeries(n.links.geometry.dropna().map(shapely.wkt.loads))
n.links["underwater_fraction"] = (
links.intersection(offshore_shape).length / links.length
)
def _adjust_capacities_of_under_construction_branches(n, config):
lines_mode = config["lines"].get("under_construction", "undef")
if lines_mode == "zero":
n.lines.loc[n.lines.under_construction, "num_parallel"] = 0.0
n.lines.loc[n.lines.under_construction, "s_nom"] = 0.0
elif lines_mode == "remove":
n.mremove("Line", n.lines.index[n.lines.under_construction])
elif lines_mode != "keep":
logger.warning(
"Unrecognized configuration for `lines: under_construction` = `{}`. Keeping under construction lines."
)
links_mode = config["links"].get("under_construction", "undef")
if links_mode == "zero":
n.links.loc[n.links.under_construction, "p_nom"] = 0.0
elif links_mode == "remove":
n.mremove("Link", n.links.index[n.links.under_construction])
elif links_mode != "keep":
logger.warning(
"Unrecognized configuration for `links: under_construction` = `{}`. Keeping under construction links."
)
if lines_mode == "remove" or links_mode == "remove":
# We might need to remove further unconnected components
n = _remove_unconnected_components(n)
return n
def _set_shapes(n, country_shapes, offshore_shapes):
# Write the geodataframes country_shapes and offshore_shapes to the network.shapes component
country_shapes = gpd.read_file(country_shapes).rename(columns={"name": "idx"})
country_shapes["type"] = "country"
offshore_shapes = gpd.read_file(offshore_shapes).rename(columns={"name": "idx"})
offshore_shapes["type"] = "offshore"
all_shapes = pd.concat([country_shapes, offshore_shapes], ignore_index=True)
n.madd(
"Shape",
all_shapes.index,
geometry=all_shapes.geometry,
idx=all_shapes.idx,
type=all_shapes["type"],
)
def base_network(
buses,
converters,
transformers,
lines,
links,
links_p_nom,
europe_shape,
country_shapes,
offshore_shapes,
parameter_corrections,
config,
):
base_network = config["electricity"].get("base_network")
assert base_network in {
"entsoegridkit",
"osm-raw",
"osm-prebuilt",
}, f"base_network must be either 'entsoegridkit', 'osm-raw' or 'osm-prebuilt', but got '{base_network}'"
if base_network == "entsoegridkit":
warnings.warn(
"The 'entsoegridkit' base network is deprecated and will be removed in future versions. Please use 'osm-raw' or 'osm-prebuilt' instead.",
DeprecationWarning,
)
logger.info(f"Creating base network using {base_network}.")
buses = _load_buses(buses, europe_shape, config)
transformers = _load_transformers(buses, transformers)
lines = _load_lines(buses, lines)
if base_network == "entsoegridkit":
links = _load_links_from_eg(buses, links)
converters = _load_converters_from_eg(buses, converters)
# Optionally reconnect Crimea
if (config["lines"].get("reconnect_crimea", True)) & (
"UA" in config["countries"]
):
lines = _reconnect_crimea(lines)
# Set electrical parameters of lines and links
lines = _set_electrical_parameters_lines_eg(lines, config)
links = _set_electrical_parameters_links_eg(links, config, links_p_nom)
elif base_network in {"osm-prebuilt", "osm-raw"}:
links = _load_links_from_osm(buses, links)
converters = _load_converters_from_osm(buses, converters)
# Set electrical parameters of lines and links
lines = _set_electrical_parameters_lines_osm(lines, config)
links = _set_electrical_parameters_links_osm(links, config)
else:
raise ValueError(
"base_network must be either 'entsoegridkit', 'osm-raw', or 'osm-prebuilt'"
)
# Set electrical parameters of transformers and converters
transformers = _set_electrical_parameters_transformers(transformers, config)
converters = _set_electrical_parameters_converters(converters, config)
n = pypsa.Network()
n.name = f"PyPSA-Eur ({base_network})"
time = get_snapshots(snakemake.params.snapshots, snakemake.params.drop_leap_day)
n.set_snapshots(time)
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")
_set_lines_s_nom_from_linetypes(n)
if config["electricity"].get("base_network") == "entsoegridkit":
_apply_parameter_corrections(n, parameter_corrections)
n = _remove_unconnected_components(n)
_set_countries_and_substations(n, config, country_shapes, offshore_shapes)
_set_links_underwater_fraction(n, offshore_shapes)
_replace_b2b_converter_at_country_border_by_link(n)
n = _adjust_capacities_of_under_construction_branches(n, config)
_set_shapes(n, country_shapes, offshore_shapes)
# Add carriers if they are present in buses.carriers
carriers_in_buses = set(n.buses.carrier.dropna().unique())
carriers = carriers_in_buses.intersection({"AC", "DC"})
if carriers:
n.madd("Carrier", carriers)
return n
def _get_linetypes_config(line_types, voltages):
"""
Return the dictionary of linetypes for selected voltages. The dictionary is
a subset of the dictionary line_types, whose keys match the selected
voltages.
Parameters
----------
line_types : dict
Dictionary of linetypes: keys are nominal voltages and values are linetypes.
voltages : list
List of selected voltages.
Returns
-------
Dictionary of linetypes for selected voltages.
"""
# get voltages value that are not available in the line types
vnoms_diff = set(voltages).symmetric_difference(set(line_types.keys()))
if vnoms_diff:
logger.warning(
f"Voltages {vnoms_diff} not in the {line_types} or {voltages} list."
)
return {k: v for k, v in line_types.items() if k in voltages}
def _get_linetype_by_voltage(v_nom, d_linetypes):
"""
Return the linetype of a specific line based on its voltage v_nom.
Parameters
----------
v_nom : float
The voltage of the line.
d_linetypes : dict
Dictionary of linetypes: keys are nominal voltages and values are linetypes.
Returns
-------
The linetype of the line whose nominal voltage is closest to the line voltage.
"""
v_nom_min, line_type_min = min(
d_linetypes.items(),
key=lambda x: abs(x[0] - v_nom),
)
return line_type_min
def voronoi(points, outline, crs=4326):
"""
Create Voronoi polygons from a set of points within an outline.
"""
pts = gpd.GeoSeries(
gpd.points_from_xy(points.x, points.y),
index=points.index,
crs=crs,
)
voronoi = pts.voronoi_polygons(extend_to=outline).clip(outline)
# can be removed with shapely 2.1 where order is preserved
# https://github.com/shapely/shapely/issues/2020
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
pts = gpd.GeoDataFrame(geometry=pts)
voronoi = gpd.GeoDataFrame(geometry=voronoi)
joined = gpd.sjoin_nearest(pts, voronoi, how="right")
return joined.dissolve(by="Bus").reindex(points.index).squeeze()
def build_bus_shapes(n, country_shapes, offshore_shapes, countries):
country_shapes = gpd.read_file(country_shapes).set_index("name")["geometry"]
offshore_shapes = gpd.read_file(offshore_shapes)
offshore_shapes = offshore_shapes.reindex(columns=REGION_COLS).set_index("name")[
"geometry"
]
onshore_regions = []
offshore_regions = []
for country in countries:
c_b = n.buses.country == country
onshore_shape = country_shapes[country]
onshore_locs = (
n.buses.loc[c_b & n.buses.onshore_bus]
.sort_values(
by="substation_lv", ascending=False
) # preference for substations
.drop_duplicates(subset=["x", "y"], keep="first")[["x", "y"]]
)
onshore_regions.append(
gpd.GeoDataFrame(
{
"name": onshore_locs.index,
"x": onshore_locs["x"],
"y": onshore_locs["y"],
"geometry": voronoi(onshore_locs, onshore_shape),
"country": country,
},
crs=n.crs,
)
)
if country not in offshore_shapes.index:
continue
offshore_shape = offshore_shapes[country]
offshore_locs = n.buses.loc[c_b & n.buses.substation_off, ["x", "y"]]
offshore_regions_c = gpd.GeoDataFrame(
{
"name": offshore_locs.index,
"x": offshore_locs["x"],
"y": offshore_locs["y"],
"geometry": voronoi(offshore_locs, offshore_shape),
"country": country,
},
crs=n.crs,
)
sel = offshore_regions_c.to_crs(3035).area > 10 # m2
offshore_regions_c = offshore_regions_c.loc[sel]
offshore_regions.append(offshore_regions_c)
shapes = pd.concat(onshore_regions, ignore_index=True).set_crs(n.crs)
return onshore_regions, offshore_regions, shapes, offshore_shapes
def append_bus_shapes(n, shapes, type):
"""
Append shapes to the network. If shapes with the same component and type
already exist, they will be removed.
Parameters:
n (pypsa.Network): The network to which the shapes will be appended.
shapes (geopandas.GeoDataFrame): The shapes to be appended.
**kwargs: Additional keyword arguments used in `n.madd`.
Returns:
None
"""
remove = n.shapes.query("component == 'Bus' and type == @type").index
n.mremove("Shape", remove)
offset = n.shapes.index.astype(int).max() + 1 if not n.shapes.empty else 0
shapes = shapes.rename(lambda x: int(x) + offset)
n.madd(
"Shape",
shapes.index,
geometry=shapes.geometry,
idx=shapes.name,
component="Bus",
type=type,
)
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("base_network")
configure_logging(snakemake)
set_scenario_config(snakemake)
countries = snakemake.params.countries
buses = snakemake.input.buses
converters = snakemake.input.converters
transformers = snakemake.input.transformers
lines = snakemake.input.lines
links = snakemake.input.links
europe_shape = snakemake.input.europe_shape
country_shapes = snakemake.input.country_shapes
offshore_shapes = snakemake.input.offshore_shapes
config = snakemake.config
if "links_p_nom" in snakemake.input.keys():
links_p_nom = snakemake.input.links_p_nom
else:
links_p_nom = None
if "parameter_corrections" in snakemake.input.keys():
parameter_corrections = snakemake.input.parameter_corrections
else:
parameter_corrections = None
n = base_network(
buses,
converters,
transformers,
lines,
links,
links_p_nom,
europe_shape,
country_shapes,
offshore_shapes,
parameter_corrections,
config,
)
onshore_regions, offshore_regions, shapes, offshore_shapes = build_bus_shapes(
n,
country_shapes,
offshore_shapes,
countries,
)
shapes.to_file(snakemake.output.regions_onshore)
# append_bus_shapes(n, shapes, "onshore")
if offshore_regions:
shapes = pd.concat(offshore_regions, ignore_index=True)
shapes.to_file(snakemake.output.regions_offshore)
# append_bus_shapes(n, shapes, "offshore")
else:
offshore_shapes.to_frame().to_file(snakemake.output.regions_offshore)
n.meta = snakemake.config
n.export_to_netcdf(snakemake.output.base_network)