766 lines
24 KiB
Python
766 lines
24 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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# coding: utf-8
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"""
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Creates the network topology from a `ENTSO-E map extract.
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<https://github.com/PyPSA/GridKit/tree/master/entsoe>`_ (March 2022) as a PyPSA
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network.
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Relevant Settings
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-----------------
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.. code:: yaml
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snapshots:
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countries:
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electricity:
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voltages:
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lines:
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types:
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s_max_pu:
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under_construction:
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links:
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p_max_pu:
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under_construction:
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include_tyndp:
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transformers:
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x:
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s_nom:
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type:
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.. seealso::
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Documentation of the configuration file ``config/config.yaml`` at
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:ref:`snapshots_cf`, :ref:`toplevel_cf`, :ref:`electricity_cf`, :ref:`load_cf`,
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:ref:`lines_cf`, :ref:`links_cf`, :ref:`transformers_cf`
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Inputs
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------
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- ``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.
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- ``data/parameter_corrections.yaml``: Corrections for ``data/entsoegridkit``
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- ``data/links_p_nom.csv``: confer :ref:`links`
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- ``data/links_tyndp.csv``: List of projects in the `TYNDP 2018 <https://tyndp.entsoe.eu/tyndp2018/>`_ that are at least *in permitting* with fields for start- and endpoint (names and coordinates), length, capacity, construction status, and project reference ID.
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- ``resources/country_shapes.geojson``: confer :ref:`shapes`
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- ``resources/offshore_shapes.geojson``: confer :ref:`shapes`
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- ``resources/europe_shape.geojson``: confer :ref:`shapes`
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Outputs
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-------
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- ``networks/base.nc``
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.. image:: img/base.png
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:scale: 33 %
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Description
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-----------
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"""
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import logging
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from itertools import product
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import geopandas as gpd
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import networkx as nx
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import numpy as np
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import pandas as pd
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import pypsa
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import shapely
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import shapely.prepared
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import shapely.wkt
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import yaml
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from _helpers import configure_logging
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from scipy import spatial
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from scipy.sparse import csgraph
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from shapely.geometry import LineString, Point
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logger = logging.getLogger(__name__)
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def _get_oid(df):
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if "tags" in df.columns:
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return df.tags.str.extract('"oid"=>"(\d+)"', expand=False)
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else:
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return pd.Series(np.nan, df.index)
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def _get_country(df):
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if "tags" in df.columns:
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return df.tags.str.extract('"country"=>"([A-Z]{2})"', expand=False)
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else:
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return pd.Series(np.nan, df.index)
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def _find_closest_links(links, new_links, distance_upper_bound=1.5):
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treecoords = np.asarray(
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[
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np.asarray(shapely.wkt.loads(s).coords)[[0, -1]].flatten()
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for s in links.geometry
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]
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)
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querycoords = np.vstack(
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[new_links[["x1", "y1", "x2", "y2"]], new_links[["x2", "y2", "x1", "y1"]]]
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)
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tree = spatial.KDTree(treecoords)
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dist, ind = tree.query(querycoords, distance_upper_bound=distance_upper_bound)
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found_b = ind < len(links)
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found_i = np.arange(len(new_links) * 2)[found_b] % len(new_links)
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return (
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pd.DataFrame(
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dict(D=dist[found_b], i=links.index[ind[found_b] % len(links)]),
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index=new_links.index[found_i],
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)
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.sort_values(by="D")[lambda ds: ~ds.index.duplicated(keep="first")]
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.sort_index()["i"]
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)
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def _load_buses_from_eg(eg_buses, europe_shape, config_elec):
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buses = (
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pd.read_csv(
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eg_buses,
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quotechar="'",
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true_values=["t"],
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false_values=["f"],
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dtype=dict(bus_id="str"),
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)
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.set_index("bus_id")
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.drop(["station_id"], axis=1)
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.rename(columns=dict(voltage="v_nom"))
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)
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buses["carrier"] = buses.pop("dc").map({True: "DC", False: "AC"})
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buses["under_construction"] = buses["under_construction"].fillna(False).astype(bool)
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# remove all buses outside of all countries including exclusive economic zones (offshore)
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europe_shape = gpd.read_file(europe_shape).loc[0, "geometry"]
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europe_shape_prepped = shapely.prepared.prep(europe_shape)
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buses_in_europe_b = buses[["x", "y"]].apply(
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lambda p: europe_shape_prepped.contains(Point(p)), axis=1
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)
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buses_with_v_nom_to_keep_b = (
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buses.v_nom.isin(config_elec["voltages"]) | buses.v_nom.isnull()
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)
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logger.info(
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"Removing buses with voltages {}".format(
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pd.Index(buses.v_nom.unique()).dropna().difference(config_elec["voltages"])
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)
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)
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return pd.DataFrame(buses.loc[buses_in_europe_b & buses_with_v_nom_to_keep_b])
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def _load_transformers_from_eg(buses, eg_transformers):
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transformers = pd.read_csv(
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eg_transformers,
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quotechar="'",
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true_values=["t"],
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false_values=["f"],
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dtype=dict(transformer_id="str", bus0="str", bus1="str"),
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).set_index("transformer_id")
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transformers = _remove_dangling_branches(transformers, buses)
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return transformers
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def _load_converters_from_eg(buses, eg_converters):
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converters = pd.read_csv(
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eg_converters,
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quotechar="'",
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true_values=["t"],
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false_values=["f"],
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dtype=dict(converter_id="str", bus0="str", bus1="str"),
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).set_index("converter_id")
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converters = _remove_dangling_branches(converters, buses)
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converters["carrier"] = "B2B"
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return converters
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def _load_links_from_eg(buses, eg_links):
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links = pd.read_csv(
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eg_links,
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quotechar="'",
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true_values=["t"],
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false_values=["f"],
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dtype=dict(link_id="str", bus0="str", bus1="str", under_construction="bool"),
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).set_index("link_id")
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links["length"] /= 1e3
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# Skagerrak Link is connected to 132kV bus which is removed in _load_buses_from_eg.
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# Connect to neighboring 380kV bus
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links.loc[links.bus1 == "6396", "bus1"] = "6398"
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links = _remove_dangling_branches(links, buses)
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# Add DC line parameters
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links["carrier"] = "DC"
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return links
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def _add_links_from_tyndp(buses, links, links_tyndp, europe_shape):
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links_tyndp = pd.read_csv(links_tyndp)
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# remove all links from list which lie outside all of the desired countries
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europe_shape = gpd.read_file(europe_shape).loc[0, "geometry"]
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europe_shape_prepped = shapely.prepared.prep(europe_shape)
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x1y1_in_europe_b = links_tyndp[["x1", "y1"]].apply(
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lambda p: europe_shape_prepped.contains(Point(p)), axis=1
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)
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x2y2_in_europe_b = links_tyndp[["x2", "y2"]].apply(
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lambda p: europe_shape_prepped.contains(Point(p)), axis=1
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)
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is_within_covered_countries_b = x1y1_in_europe_b & x2y2_in_europe_b
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if not is_within_covered_countries_b.all():
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logger.info(
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"TYNDP links outside of the covered area (skipping): "
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+ ", ".join(links_tyndp.loc[~is_within_covered_countries_b, "Name"])
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)
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links_tyndp = links_tyndp.loc[is_within_covered_countries_b]
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if links_tyndp.empty:
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return buses, links
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has_replaces_b = links_tyndp.replaces.notnull()
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oids = dict(Bus=_get_oid(buses), Link=_get_oid(links))
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keep_b = dict(
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Bus=pd.Series(True, index=buses.index), Link=pd.Series(True, index=links.index)
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)
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for reps in links_tyndp.loc[has_replaces_b, "replaces"]:
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for comps in reps.split(":"):
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oids_to_remove = comps.split(".")
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c = oids_to_remove.pop(0)
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keep_b[c] &= ~oids[c].isin(oids_to_remove)
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buses = buses.loc[keep_b["Bus"]]
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links = links.loc[keep_b["Link"]]
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links_tyndp["j"] = _find_closest_links(
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links, links_tyndp, distance_upper_bound=0.20
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)
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# Corresponds approximately to 20km tolerances
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if links_tyndp["j"].notnull().any():
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logger.info(
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"TYNDP links already in the dataset (skipping): "
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+ ", ".join(links_tyndp.loc[links_tyndp["j"].notnull(), "Name"])
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)
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links_tyndp = links_tyndp.loc[links_tyndp["j"].isnull()]
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if links_tyndp.empty:
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return buses, links
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tree = spatial.KDTree(buses[["x", "y"]])
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_, ind0 = tree.query(links_tyndp[["x1", "y1"]])
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ind0_b = ind0 < len(buses)
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links_tyndp.loc[ind0_b, "bus0"] = buses.index[ind0[ind0_b]]
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_, ind1 = tree.query(links_tyndp[["x2", "y2"]])
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ind1_b = ind1 < len(buses)
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links_tyndp.loc[ind1_b, "bus1"] = buses.index[ind1[ind1_b]]
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links_tyndp_located_b = (
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links_tyndp["bus0"].notnull() & links_tyndp["bus1"].notnull()
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)
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if not links_tyndp_located_b.all():
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logger.warning(
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"Did not find connected buses for TYNDP links (skipping): "
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+ ", ".join(links_tyndp.loc[~links_tyndp_located_b, "Name"])
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)
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links_tyndp = links_tyndp.loc[links_tyndp_located_b]
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logger.info("Adding the following TYNDP links: " + ", ".join(links_tyndp["Name"]))
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links_tyndp = links_tyndp[["bus0", "bus1"]].assign(
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carrier="DC",
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p_nom=links_tyndp["Power (MW)"],
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length=links_tyndp["Length (given) (km)"].fillna(
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links_tyndp["Length (distance*1.2) (km)"]
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),
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under_construction=True,
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underground=False,
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geometry=(
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links_tyndp[["x1", "y1", "x2", "y2"]].apply(
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lambda s: str(LineString([[s.x1, s.y1], [s.x2, s.y2]])), axis=1
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)
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),
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tags=(
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'"name"=>"'
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+ links_tyndp["Name"]
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+ '", '
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+ '"ref"=>"'
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+ links_tyndp["Ref"]
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+ '", '
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+ '"status"=>"'
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+ links_tyndp["status"]
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+ '"'
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),
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)
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links_tyndp.index = "T" + links_tyndp.index.astype(str)
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links = pd.concat([links, links_tyndp], sort=True)
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return buses, links
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def _load_lines_from_eg(buses, eg_lines):
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lines = (
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pd.read_csv(
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eg_lines,
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quotechar="'",
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true_values=["t"],
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false_values=["f"],
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dtype=dict(
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line_id="str",
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bus0="str",
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bus1="str",
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underground="bool",
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under_construction="bool",
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),
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)
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.set_index("line_id")
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.rename(columns=dict(voltage="v_nom", circuits="num_parallel"))
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)
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lines["length"] /= 1e3
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lines = _remove_dangling_branches(lines, buses)
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return lines
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def _apply_parameter_corrections(n, parameter_corrections):
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with open(parameter_corrections) as f:
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corrections = yaml.safe_load(f)
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if corrections is None:
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return
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for component, attrs in corrections.items():
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df = n.df(component)
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oid = _get_oid(df)
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if attrs is None:
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continue
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for attr, repls in attrs.items():
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for i, r in repls.items():
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if i == "oid":
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r = oid.map(repls["oid"]).dropna()
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elif i == "index":
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r = pd.Series(repls["index"])
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else:
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raise NotImplementedError()
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inds = r.index.intersection(df.index)
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df.loc[inds, attr] = r[inds].astype(df[attr].dtype)
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def _set_electrical_parameters_lines(lines, config):
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v_noms = config["electricity"]["voltages"]
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linetypes = config["lines"]["types"]
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for v_nom in v_noms:
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lines.loc[lines["v_nom"] == v_nom, "type"] = linetypes[v_nom]
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lines["s_max_pu"] = config["lines"]["s_max_pu"]
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return lines
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def _set_lines_s_nom_from_linetypes(n):
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n.lines["s_nom"] = (
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np.sqrt(3)
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* n.lines["type"].map(n.line_types.i_nom)
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* n.lines["v_nom"]
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* n.lines.num_parallel
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)
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def _set_electrical_parameters_links(links, config, links_p_nom):
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if links.empty:
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return links
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p_max_pu = config["links"].get("p_max_pu", 1.0)
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links["p_max_pu"] = p_max_pu
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links["p_min_pu"] = -p_max_pu
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links_p_nom = pd.read_csv(links_p_nom)
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# filter links that are not in operation anymore
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removed_b = links_p_nom.Remarks.str.contains("Shut down|Replaced", na=False)
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links_p_nom = links_p_nom[~removed_b]
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# find closest link for all links in links_p_nom
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links_p_nom["j"] = _find_closest_links(links, links_p_nom)
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links_p_nom = links_p_nom.groupby(["j"], as_index=False).agg({"Power (MW)": "sum"})
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p_nom = links_p_nom.dropna(subset=["j"]).set_index("j")["Power (MW)"]
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# Don't update p_nom if it's already set
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p_nom_unset = (
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p_nom.drop(links.index[links.p_nom.notnull()], errors="ignore")
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if "p_nom" in links
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else p_nom
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)
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links.loc[p_nom_unset.index, "p_nom"] = p_nom_unset
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return links
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def _set_electrical_parameters_converters(converters, config):
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p_max_pu = config["links"].get("p_max_pu", 1.0)
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converters["p_max_pu"] = p_max_pu
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converters["p_min_pu"] = -p_max_pu
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converters["p_nom"] = 2000
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# Converters are combined with links
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converters["under_construction"] = False
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converters["underground"] = False
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return converters
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def _set_electrical_parameters_transformers(transformers, config):
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config = config["transformers"]
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## Add transformer parameters
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transformers["x"] = config.get("x", 0.1)
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transformers["s_nom"] = config.get("s_nom", 2000)
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transformers["type"] = config.get("type", "")
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return transformers
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def _remove_dangling_branches(branches, buses):
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return pd.DataFrame(
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branches.loc[branches.bus0.isin(buses.index) & branches.bus1.isin(buses.index)]
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)
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def _remove_unconnected_components(network):
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_, labels = csgraph.connected_components(network.adjacency_matrix(), directed=False)
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component = pd.Series(labels, index=network.buses.index)
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component_sizes = component.value_counts()
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components_to_remove = component_sizes.iloc[1:]
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logger.info(
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"Removing {} unconnected network components with less than {} buses. In total {} buses.".format(
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len(components_to_remove),
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components_to_remove.max(),
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components_to_remove.sum(),
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)
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)
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return network[component == component_sizes.index[0]]
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def _set_countries_and_substations(n, config, country_shapes, offshore_shapes):
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buses = n.buses
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def buses_in_shape(shape):
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shape = shapely.prepared.prep(shape)
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return pd.Series(
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np.fromiter(
|
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(
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shape.contains(Point(x, y))
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for x, y in buses.loc[:, ["x", "y"]].values
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),
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dtype=bool,
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count=len(buses),
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),
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index=buses.index,
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)
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countries = config["countries"]
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country_shapes = gpd.read_file(country_shapes).set_index("name")["geometry"]
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# reindexing necessary for supporting empty geo-dataframes
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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)
|
|
|
|
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.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["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.isnull()
|
|
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).unary_union
|
|
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 base_network(
|
|
eg_buses,
|
|
eg_converters,
|
|
eg_transformers,
|
|
eg_lines,
|
|
eg_links,
|
|
links_p_nom,
|
|
links_tyndp,
|
|
europe_shape,
|
|
country_shapes,
|
|
offshore_shapes,
|
|
parameter_corrections,
|
|
config,
|
|
):
|
|
buses = _load_buses_from_eg(eg_buses, europe_shape, config["electricity"])
|
|
|
|
links = _load_links_from_eg(buses, eg_links)
|
|
if config["links"].get("include_tyndp"):
|
|
buses, links = _add_links_from_tyndp(buses, links, links_tyndp, europe_shape)
|
|
|
|
converters = _load_converters_from_eg(buses, eg_converters)
|
|
|
|
lines = _load_lines_from_eg(buses, eg_lines)
|
|
transformers = _load_transformers_from_eg(buses, eg_transformers)
|
|
|
|
lines = _set_electrical_parameters_lines(lines, config)
|
|
transformers = _set_electrical_parameters_transformers(transformers, config)
|
|
links = _set_electrical_parameters_links(links, config, links_p_nom)
|
|
converters = _set_electrical_parameters_converters(converters, config)
|
|
|
|
n = pypsa.Network()
|
|
n.name = "PyPSA-Eur"
|
|
|
|
n.set_snapshots(pd.date_range(freq="h", **config["snapshots"]))
|
|
n.madd("Carrier", ["AC", "DC"])
|
|
|
|
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)
|
|
|
|
_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)
|
|
|
|
return n
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if "snakemake" not in globals():
|
|
from _helpers import mock_snakemake
|
|
|
|
snakemake = mock_snakemake("base_network")
|
|
configure_logging(snakemake)
|
|
|
|
n = base_network(
|
|
snakemake.input.eg_buses,
|
|
snakemake.input.eg_converters,
|
|
snakemake.input.eg_transformers,
|
|
snakemake.input.eg_lines,
|
|
snakemake.input.eg_links,
|
|
snakemake.input.links_p_nom,
|
|
snakemake.input.links_tyndp,
|
|
snakemake.input.europe_shape,
|
|
snakemake.input.country_shapes,
|
|
snakemake.input.offshore_shapes,
|
|
snakemake.input.parameter_corrections,
|
|
snakemake.config,
|
|
)
|
|
|
|
n.meta = snakemake.config
|
|
n.export_to_netcdf(snakemake.output[0])
|