013b705ee4
* Cluster first: build renewable profiles and add all assets after clustering * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * correction: pass landfall_lengths through functions * assign landfall_lenghts correctly * remove parameter add_land_use_constraint * fix network_dict * calculate distance to shoreline, remove underwater_fraction * adjust simplification parameter to exclude Crete from offshore wind connections * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove unused geth2015 hydro capacities * removing remaining traces of {simpl} wildcard * add release notes and update workflow graphics * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: lisazeyen <lisa.zeyen@web.de>
107 lines
3.2 KiB
Python
107 lines
3.2 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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"""
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Add transmission projects and DLR to the network.
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"""
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import logging
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from pathlib import Path
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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 xarray as xr
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from _helpers import configure_logging, set_scenario_config
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logger = logging.getLogger(__name__)
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def attach_transmission_projects(
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n: pypsa.Network, transmission_projects: list[str]
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) -> None:
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logger.info("Adding transmission projects to network.")
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for path in transmission_projects:
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path = Path(path)
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df = pd.read_csv(path, index_col=0, dtype={"bus0": str, "bus1": str})
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if df.empty:
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continue
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if "new_buses" in path.name:
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n.madd("Bus", df.index, **df)
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elif "new_lines" in path.name:
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n.madd("Line", df.index, **df)
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elif "new_links" in path.name:
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n.madd("Link", df.index, **df)
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elif "adjust_lines" in path.name:
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n.lines.update(df)
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elif "adjust_links" in path.name:
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n.links.update(df)
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def attach_line_rating(
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n: pypsa.Network,
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rating: pd.DataFrame,
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s_max_pu: float,
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correction_factor: float,
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max_voltage_difference: float | bool,
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max_line_rating: float | bool,
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) -> None:
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logger.info("Attaching dynamic line rating to network.")
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# TODO: Only considers overhead lines
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n.lines_t.s_max_pu = (rating / n.lines.s_nom[rating.columns]) * correction_factor
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if max_voltage_difference:
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x_pu = (
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n.lines.type.map(n.line_types["x_per_length"])
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* n.lines.length
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/ (n.lines.v_nom**2)
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)
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# need to clip here as cap values might be below 1
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# -> would mean the line cannot be operated at actual given pessimistic ampacity
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s_max_pu_cap = (
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np.deg2rad(max_voltage_difference) / (x_pu * n.lines.s_nom)
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).clip(lower=1)
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n.lines_t.s_max_pu = n.lines_t.s_max_pu.clip(
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lower=1, upper=s_max_pu_cap, axis=1
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)
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if max_line_rating:
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n.lines_t.s_max_pu = n.lines_t.s_max_pu.clip(upper=max_line_rating)
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n.lines_t.s_max_pu *= s_max_pu
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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snakemake = mock_snakemake("add_transmission_projects_and_dlr")
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configure_logging(snakemake)
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set_scenario_config(snakemake)
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params = snakemake.params
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n = pypsa.Network(snakemake.input.network)
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if params["transmission_projects"]["enable"]:
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attach_transmission_projects(n, snakemake.input.transmission_projects)
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if params["dlr"]["activate"]:
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rating = xr.open_dataarray(snakemake.input.dlr).to_pandas().transpose()
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s_max_pu = params["s_max_pu"]
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correction_factor = params["dlr"]["correction_factor"]
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max_voltage_difference = params["dlr"]["max_voltage_difference"]
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max_line_rating = params["dlr"]["max_line_rating"]
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attach_line_rating(
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n,
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rating,
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s_max_pu,
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correction_factor,
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max_voltage_difference,
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max_line_rating,
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)
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n.export_to_netcdf(snakemake.output[0])
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