143 lines
5.1 KiB
Python
Executable File
143 lines
5.1 KiB
Python
Executable File
# SPDX-FileCopyrightText: : 2017-2020 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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Retrieves conventional powerplant capacities and locations from `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`_, assigns these to buses and creates a ``.csv`` file. It is possible to amend the powerplant database with custom entries provided in ``data/custom_powerplants.csv``.
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Relevant Settings
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-----------------
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.. code:: yaml
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electricity:
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powerplants_filter:
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custom_powerplants:
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.. seealso::
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Documentation of the configuration file ``config.yaml`` at
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:ref:`electricity`
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Inputs
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------
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- ``networks/base.nc``: confer :ref:`base`.
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- ``data/custom_powerplants.csv``: custom powerplants in the same format as `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`_ provides
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Outputs
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-------
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- ``resource/powerplants.csv``: A list of conventional power plants (i.e. neither wind nor solar) with fields for name, fuel type, technology, country, capacity in MW, duration, commissioning year, retrofit year, latitude, longitude, and dam information as documented in the `powerplantmatching README <https://github.com/FRESNA/powerplantmatching/blob/master/README.md>`_; additionally it includes information on the closest substation/bus in ``networks/base.nc``.
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.. image:: ../img/powerplantmatching.png
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:scale: 30 %
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**Source:** `powerplantmatching on GitHub <https://github.com/FRESNA/powerplantmatching>`_
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Description
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-----------
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The configuration options ``electricity: powerplants_filter`` and ``electricity: custom_powerplants`` can be used to control whether data should be retrieved from the original powerplants database or from custom amendmends. These specify `pandas.query <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html>`_ commands.
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1. Adding all powerplants from custom:
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.. code:: yaml
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powerplants_filter: false
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custom_powerplants: true
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2. Replacing powerplants in e.g. Germany by custom data:
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.. code:: yaml
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powerplants_filter: Country not in ['Germany']
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custom_powerplants: true
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or
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.. code:: yaml
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powerplants_filter: Country not in ['Germany']
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custom_powerplants: Country in ['Germany']
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3. Adding additional built year constraints:
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.. code:: yaml
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powerplants_filter: Country not in ['Germany'] and YearCommissioned <= 2015
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custom_powerplants: YearCommissioned <= 2015
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"""
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import logging
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from _helpers import configure_logging
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import pypsa
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import powerplantmatching as pm
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import pandas as pd
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import numpy as np
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from scipy.spatial import cKDTree as KDTree
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logger = logging.getLogger(__name__)
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def add_custom_powerplants(ppl, custom_powerplants, custom_ppl_query=False):
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if not custom_ppl_query:
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return ppl
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add_ppls = pd.read_csv(custom_powerplants, index_col=0,
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dtype={'bus': 'str'})
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if isinstance(custom_ppl_query, str):
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add_ppls.query(custom_ppl_query, inplace=True)
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return ppl.append(add_ppls, sort=False, ignore_index=True, verify_integrity=True)
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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('build_powerplants')
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configure_logging(snakemake)
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n = pypsa.Network(snakemake.input.base_network)
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countries = n.buses.country.unique()
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ppl = (pm.powerplants(from_url=True)
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.powerplant.fill_missing_decommyears()
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.powerplant.convert_country_to_alpha2()
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.query('Fueltype not in ["Solar", "Wind"] and Country in @countries')
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.replace({'Technology': {'Steam Turbine': 'OCGT'}})
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.assign(Fueltype=lambda df: (
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df.Fueltype
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.where(df.Fueltype != 'Natural Gas',
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df.Technology.replace('Steam Turbine',
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'OCGT').fillna('OCGT')))))
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ppl_query = snakemake.config['electricity']['powerplants_filter']
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if isinstance(ppl_query, str):
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ppl.query(ppl_query, inplace=True)
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# add carriers from own powerplant files:
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ppl = add_custom_powerplants(ppl, custom_powerplants = snakemake.input.custom_powerplants,
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custom_ppl_query = snakemake.config['electricity']['custom_powerplants'])
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cntries_without_ppl = [c for c in countries if c not in ppl.Country.unique()]
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for c in countries:
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substation_i = n.buses.query('substation_lv and country == @c').index
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kdtree = KDTree(n.buses.loc[substation_i, ['x','y']].values)
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ppl_i = ppl.query('Country == @c').index
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tree_i = kdtree.query(ppl.loc[ppl_i, ['lon','lat']].values)[1]
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ppl.loc[ppl_i, 'bus'] = substation_i.append(pd.Index([np.nan]))[tree_i]
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if cntries_without_ppl:
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logging.warning(f"No powerplants known in: {', '.join(cntries_without_ppl)}")
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bus_null_b = ppl["bus"].isnull()
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if bus_null_b.any():
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logging.warning(f"Couldn't find close bus for {bus_null_b.sum()} powerplants")
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ppl.to_csv(snakemake.output[0])
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