160 lines
4.6 KiB
Python
160 lines
4.6 KiB
Python
#!/usr/bin/env python
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# -*- 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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"""
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Build hydroelectric inflow time-series for each country.
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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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renewable:
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hydro:
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cutout:
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clip_min_inflow:
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.. seealso::
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Documentation of the configuration file ``config/config.yaml`` at
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:ref:`toplevel_cf`, :ref:`renewable_cf`
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Inputs
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------
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- ``data/bundle/eia_hydro_annual_generation.csv``: Hydroelectricity net generation per country and year (`EIA <https://www.eia.gov/beta/international/data/browser/#/?pa=000000000000000000000000000000g&c=1028i008006gg6168g80a4k000e0ag00gg0004g800ho00g8&ct=0&ug=8&tl_id=2-A&vs=INTL.33-12-ALB-BKWH.A&cy=2014&vo=0&v=H&start=2000&end=2016>`_)
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.. image:: img/hydrogeneration.png
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:scale: 33 %
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- ``resources/country_shapes.geojson``: confer :ref:`shapes`
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- ``"cutouts/" + config["renewable"]['hydro']['cutout']``: confer :ref:`cutout`
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Outputs
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-------
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- ``resources/profile_hydro.nc``:
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=================== ================ =========================================================
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Field Dimensions Description
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=================== ================ =========================================================
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inflow countries, time Inflow to the state of charge (in MW),
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e.g. due to river inflow in hydro reservoir.
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=================== ================ =========================================================
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.. image:: img/inflow-ts.png
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:scale: 33 %
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.. image:: img/inflow-box.png
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:scale: 33 %
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Description
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-----------
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.. seealso::
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:mod:`build_renewable_profiles`
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"""
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import logging
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import atlite
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import country_converter as coco
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import geopandas as gpd
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import pandas as pd
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from _helpers import configure_logging
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cc = coco.CountryConverter()
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def get_eia_annual_hydro_generation(fn, countries):
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# in billion kWh/a = TWh/a
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df = pd.read_csv(
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fn, skiprows=2, index_col=1, na_values=[" ", "--"], decimal=","
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).iloc[1:, 1:]
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df.index = df.index.str.strip()
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former_countries = {
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"Former Czechoslovakia": dict(
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countries=["Czechia", "Slovakia"], start=1980, end=1992
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),
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"Former Serbia and Montenegro": dict(
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countries=["Serbia", "Montenegro"], start=1992, end=2005
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),
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"Former Yugoslavia": dict(
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countries=[
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"Slovenia",
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"Croatia",
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"Bosnia and Herzegovina",
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"Serbia",
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"Montenegro",
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"North Macedonia",
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],
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start=1980,
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end=1991,
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),
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}
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for k, v in former_countries.items():
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period = [str(i) for i in range(v["start"], v["end"] + 1)]
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ratio = df.loc[v["countries"]].T.dropna().sum()
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ratio /= ratio.sum()
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for country in v["countries"]:
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df.loc[country, period] = df.loc[k, period] * ratio[country]
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baltic_states = ["Latvia", "Estonia", "Lithuania"]
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df.loc[baltic_states] = (
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df.loc[baltic_states].T.fillna(df.loc[baltic_states].mean(axis=1)).T
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)
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df.loc["Germany"] = df.filter(like="Germany", axis=0).sum()
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df.loc["Serbia"] += df.loc["Kosovo"].fillna(0.0)
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df = df.loc[~df.index.str.contains("Former")]
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df.drop(["Europe", "Germany, West", "Germany, East", "Kosovo"], inplace=True)
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df.index = cc.convert(df.index, to="iso2")
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df.index.name = "countries"
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df = df.T[countries] * 1e6 # in MWh/a
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return df
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logger = logging.getLogger(__name__)
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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_hydro_profile")
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configure_logging(snakemake)
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params_hydro = snakemake.params.hydro
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cutout = atlite.Cutout(snakemake.input.cutout)
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countries = snakemake.params.countries
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country_shapes = (
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gpd.read_file(snakemake.input.country_shapes)
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.set_index("name")["geometry"]
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.reindex(countries)
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)
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country_shapes.index.name = "countries"
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fn = snakemake.input.eia_hydro_generation
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eia_stats = get_eia_annual_hydro_generation(fn, countries)
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inflow = cutout.runoff(
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shapes=country_shapes,
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smooth=True,
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lower_threshold_quantile=True,
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normalize_using_yearly=eia_stats,
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)
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if "clip_min_inflow" in params_hydro:
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inflow = inflow.where(inflow > params_hydro["clip_min_inflow"], 0)
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inflow.to_netcdf(snakemake.output[0])
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