a9f67b313f
* add general implementation to add tyndp and nep. TODO: Remove duplicate? * pre_osm_merge * clean and update base_network.py update default config settings * clean and update base_network.py update default config settings * base_network.py:remove adding of transmission projects add_transmission_project.py: add new script for creating lines and link csv from transmission projects add_electricity.py: add new projects from created csv files * cluster_network.py: do not allow to group lines with different build years together-> requires pypsa update simplify_network.py: fix bug of simplify links * remove legacies of removing transmission projects from base_network * restructure folders:new folder for tranmission project csv add_transmission_projects: improve logging, cleanup * update lines csvs and add default line type for upgraded and new lines * remove duplicate lines which were already in gridkit * allow to connect ac lines only to ac buses * add manual links csv (adjusted links_tyndp.csv) and update default config * add realease note * remove links_tyndp.csv file, references in build_elec.smk and function in base_network.py to add them * add configuration options for transmission projects to documentation and add template folder for transmission projects * update pypsa version in environments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * integrate Fabian's review comments 2 * add electricity:add import pathlib and duplicate printing out of adding line projects * update NEP line csv * address Fabian's comments * build_transmission_projects: address Fabian's final comments simplify_network: use modus to get line type which appears most often * build_transmission_project: change from .geometry to ["geometry"] * build_transmission_projects: remove redundanty line * build_transmission_projects: remove buffer for europe shape because of higher resolution default config: fix wrong key for skip argument in transmission_projects * update configtables and default config * update manual links csv and delete undetected duplicate links in tyndp2020 * final adjustments --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Fabian Neumann <fabian.neumann@outlook.de>
829 lines
26 KiB
Python
829 lines
26 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2017-2024 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 an `ENTSO-E map extract <https://github.com/PyPSA/GridKit/tree/master/entsoe>`_ (March 2022) as a PyPSA network.
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Relevant Settings
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-----------------
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.. code:: yaml
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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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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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- ``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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- ``resources/regions_onshore.geojson``:
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.. image:: img/regions_onshore.png
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:scale: 33 %
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- ``resources/regions_offshore.geojson``:
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.. image:: img/regions_offshore.png
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:scale: 33 %
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Description
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-----------
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Creates the network topology from an ENTSO-E map extract, and create Voronoi shapes for each bus representing both onshore and offshore regions.
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"""
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import logging
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import warnings
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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 REGION_COLS, configure_logging, get_snapshots, set_scenario_config
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from packaging.version import Version, parse
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from scipy.sparse import csgraph
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from scipy.spatial import KDTree
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from shapely.geometry import LineString, Point
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PD_GE_2_2 = parse(pd.__version__) >= Version("2.2")
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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 = 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.where(
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lambda s: s.notnull(), False
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).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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f'Removing buses with voltages {pd.Index(buses.v_nom.unique()).dropna().difference(config_elec["voltages"])}'
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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 _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["carrier"] = "AC"
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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 _reconnect_crimea(lines):
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logger.info("Reconnecting Crimea to the Ukrainian grid.")
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lines_to_crimea = pd.DataFrame(
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{
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"bus0": ["3065", "3181", "3181"],
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"bus1": ["3057", "3055", "3057"],
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"v_nom": [300, 300, 300],
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"num_parallel": [1, 1, 1],
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"length": [140, 120, 140],
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"carrier": ["AC", "AC", "AC"],
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"underground": [False, False, False],
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"under_construction": [False, False, False],
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},
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index=["Melitopol", "Liubymivka left", "Luibymivka right"],
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)
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return pd.concat([lines, lines_to_crimea])
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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, threshold=6):
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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.loc[component_sizes < threshold]
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logger.info(
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f"Removing {len(components_to_remove)} unconnected network components with less than {components_to_remove.max()} buses. In total {components_to_remove.sum()} buses."
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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)
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offshore_shapes = offshore_shapes.reindex(columns=["name", "geometry"]).set_index(
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"name"
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)["geometry"]
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substation_b = buses["symbol"].str.contains(
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"substation|converter station", case=False
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)
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def prefer_voltage(x, which):
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index = x.index
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if len(index) == 1:
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return pd.Series(index, index)
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key = (
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x.index[0]
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if x["v_nom"].isnull().all()
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else getattr(x["v_nom"], "idx" + which)()
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)
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return pd.Series(key, index)
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compat_kws = dict(include_groups=False) if PD_GE_2_2 else {}
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gb = buses.loc[substation_b].groupby(
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["x", "y"], as_index=False, group_keys=False, sort=False
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)
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bus_map_low = gb.apply(prefer_voltage, "min", **compat_kws)
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lv_b = (bus_map_low == bus_map_low.index).reindex(buses.index, fill_value=False)
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bus_map_high = gb.apply(prefer_voltage, "max", **compat_kws)
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hv_b = (bus_map_high == bus_map_high.index).reindex(buses.index, fill_value=False)
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onshore_b = pd.Series(False, buses.index)
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offshore_b = pd.Series(False, buses.index)
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for country in countries:
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onshore_shape = country_shapes[country]
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onshore_country_b = buses_in_shape(onshore_shape)
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onshore_b |= onshore_country_b
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buses.loc[onshore_country_b, "country"] = country
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if country not in offshore_shapes.index:
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continue
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offshore_country_b = buses_in_shape(offshore_shapes[country])
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offshore_b |= offshore_country_b
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buses.loc[offshore_country_b, "country"] = country
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# Only accept buses as low-voltage substations (where load is attached), if
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# they have at least one connection which is not under_construction
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has_connections_b = pd.Series(False, index=buses.index)
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for b, df in product(("bus0", "bus1"), (n.lines, n.links)):
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has_connections_b |= ~df.groupby(b).under_construction.min()
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buses["onshore_bus"] = onshore_b
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buses["substation_lv"] = (
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lv_b & onshore_b & (~buses["under_construction"]) & has_connections_b
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)
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buses["substation_off"] = ((hv_b & offshore_b) | (hv_b & onshore_b)) & (
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~buses["under_construction"]
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)
|
|
|
|
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(
|
|
eg_buses,
|
|
eg_converters,
|
|
eg_transformers,
|
|
eg_lines,
|
|
eg_links,
|
|
links_p_nom,
|
|
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)
|
|
|
|
converters = _load_converters_from_eg(buses, eg_converters)
|
|
|
|
lines = _load_lines_from_eg(buses, eg_lines)
|
|
transformers = _load_transformers_from_eg(buses, eg_transformers)
|
|
|
|
if config["lines"].get("reconnect_crimea", True) and "UA" in config["countries"]:
|
|
lines = _reconnect_crimea(lines)
|
|
|
|
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"
|
|
|
|
time = get_snapshots(snakemake.params.snapshots, snakemake.params.drop_leap_day)
|
|
n.set_snapshots(time)
|
|
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)
|
|
|
|
_set_shapes(n, country_shapes, offshore_shapes)
|
|
|
|
return n
|
|
|
|
|
|
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)
|
|
|
|
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.europe_shape,
|
|
snakemake.input.country_shapes,
|
|
snakemake.input.offshore_shapes,
|
|
snakemake.input.parameter_corrections,
|
|
snakemake.config,
|
|
)
|
|
|
|
onshore_regions, offshore_regions, shapes, offshore_shapes = build_bus_shapes(
|
|
n,
|
|
snakemake.input.country_shapes,
|
|
snakemake.input.offshore_shapes,
|
|
snakemake.params.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)
|