bjoern/pypsa-eur
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity
b8fee80919
pypsa-eur/scripts/build_electricity_demand.py
Fabian Neumann b8fee80919 Merge branch 'master' into multiyear
2023-10-08 11:55:11 +02:00

343 lines
11 KiB
Python
Executable File
Raw Blame History

# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020 @JanFrederickUnnewehr, The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
This rule downloads the load data from `Open Power System Data Time series.
<https://data.open-power-system-data.org/time_series/>`_. For all countries in
the network, the per country load timeseries with suffix
``_load_actual_entsoe_transparency`` are extracted from the dataset. After
filling small gaps linearly and large gaps by copying time-slice of a given
period, the load data is exported to a ``.csv`` file.
Relevant Settings
-----------------
.. code:: yaml
snapshots:
load:
interpolate_limit:
time_shift_for_large_gaps:
manual_adjustments:
.. seealso::
Documentation of the configuration file ``config/config.yaml`` at
:ref:`load_cf`
Inputs
------
- ``data/load_raw.csv``:
Outputs
-------
- ``resources/load.csv``:
"""
import logging
logger = logging.getLogger(__name__)
import dateutil
import numpy as np
import pandas as pd
from _helpers import configure_logging
from pandas import Timedelta as Delta
def load_timeseries(fn, years, countries, powerstatistics=True):
"""
Read load data from OPSD time-series package version 2020-10-06.
Parameters
----------
years : None or slice()
Years for which to read load data (defaults to
slice("2018","2019"))
fn : str
File name or url location (file format .csv)
countries : listlike
Countries for which to read load data.
powerstatistics: bool
Whether the electricity consumption data of the ENTSOE power
statistics (if true) or of the ENTSOE transparency map (if false)
should be parsed.
Returns
-------
load : pd.DataFrame
Load time-series with UTC timestamps x ISO-2 countries
"""
logger.info(f"Retrieving load data from '{fn}'.")
pattern = "power_statistics" if powerstatistics else "transparency"
pattern = f"_load_actual_entsoe_{pattern}"
def rename(s):
return s[: -len(pattern)]
return (
pd.read_csv(fn, index_col=0, parse_dates=[0])
.tz_localize(None)
.filter(like=pattern)
.rename(columns=rename)
.dropna(how="all", axis=0)
.rename(columns={"GB_UKM": "GB"})
.filter(items=countries)
.loc[years]
)
def consecutive_nans(ds):
return (
ds.isnull()
.astype(int)
.groupby(ds.notnull().astype(int).cumsum()[ds.isnull()])
.transform("sum")
.fillna(0)
)
def fill_large_gaps(ds, shift):
"""
Fill up large gaps with load data from the previous week.
This function fills gaps ragning from 3 to 168 hours (one week).
"""
shift = Delta(shift)
nhours = shift / np.timedelta64(1, "h")
if (consecutive_nans(ds) > nhours).any():
logger.warning(
"There exist gaps larger then the time shift used for "
"copying time slices."
)
time_shift = pd.Series(ds.values, ds.index + shift)
return ds.where(ds.notnull(), time_shift.reindex_like(ds))
def nan_statistics(df):
def max_consecutive_nans(ds):
return (
ds.isnull()
.astype(int)
.groupby(ds.notnull().astype(int).cumsum())
.sum()
.max()
)
consecutive = df.apply(max_consecutive_nans)
total = df.isnull().sum()
max_total_per_month = df.isnull().resample("m").sum().max()
return pd.concat(
[total, consecutive, max_total_per_month],
keys=["total", "consecutive", "max_total_per_month"],
axis=1,
)
def copy_timeslice(load, cntry, start, stop, delta, fn_load=None):
start = pd.Timestamp(start)
stop = pd.Timestamp(stop)
if start in load.index and stop in load.index:
if start - delta in load.index and stop - delta in load.index and cntry in load:
load.loc[start:stop, cntry] = load.loc[
start - delta : stop - delta, cntry
].values
elif fn_load is not None:
duration = pd.date_range(freq="h", start=start - delta, end=stop - delta)
load_raw = load_timeseries(fn_load, duration, [cntry], powerstatistics)
load.loc[start:stop, cntry] = load_raw.loc[
start - delta : stop - delta, cntry
].values
def manual_adjustment(load, fn_load, powerstatistics):
"""
Adjust gaps manual for load data from OPSD time-series package.
1. For the ENTSOE power statistics load data (if powerstatistics is True)
Kosovo (KV) and Albania (AL) do not exist in the data set. Kosovo gets the
same load curve as Serbia and Albania the same as Macdedonia, both scaled
by the corresponding ratio of total energy consumptions reported by
IEA Data browser [0] for the year 2013.
2. For the ENTSOE transparency load data (if powerstatistics is False)
Albania (AL) and Macedonia (MK) do not exist in the data set. Both get the
same load curve as Montenegro, scaled by the corresponding ratio of total energy
consumptions reported by IEA Data browser [0] for the year 2016.
[0] https://www.iea.org/data-and-statistics?country=WORLD&fuel=Electricity%20and%20heat&indicator=TotElecCons
Bosnia and Herzegovina (BA) does not exist in the data set for 2019. It gets the
electricity consumption data from Croatia (HR) for the year 2019, scaled by the
factors derived from https://energy.at-site.be/eurostat-2021/
Parameters
----------
load : pd.DataFrame
Load time-series with UTC timestamps x ISO-2 countries
powerstatistics: bool
Whether argument load comprises the electricity consumption data of
the ENTSOE power statistics or of the ENTSOE transparency map
load_fn: str
File name or url location (file format .csv)
Returns
-------
load : pd.DataFrame
Manual adjusted and interpolated load time-series with UTC
timestamps x ISO-2 countries
"""
if powerstatistics:
if "MK" in load.columns:
if "AL" not in load.columns or load.AL.isnull().values.all():
load["AL"] = load["MK"] * (4.1 / 7.4)
if "RS" in load.columns:
if "KV" not in load.columns or load.KV.isnull().values.all():
load["KV"] = load["RS"] * (4.8 / 27.0)
copy_timeslice(
load, "GR", "2015-08-11 21:00", "2015-08-15 20:00", Delta(weeks=1)
)
copy_timeslice(
load, "AT", "2018-12-31 22:00", "2019-01-01 22:00", Delta(days=2)
)
copy_timeslice(
load, "CH", "2010-01-19 07:00", "2010-01-19 22:00", Delta(days=1)
)
copy_timeslice(
load, "CH", "2010-03-28 00:00", "2010-03-28 21:00", Delta(days=1)
)
# is a WE, so take WE before
copy_timeslice(
load, "CH", "2010-10-08 13:00", "2010-10-10 21:00", Delta(weeks=1)
)
copy_timeslice(
load, "CH", "2010-11-04 04:00", "2010-11-04 22:00", Delta(days=1)
)
copy_timeslice(
load, "NO", "2010-12-09 11:00", "2010-12-09 18:00", Delta(days=1)
)
# whole january missing
copy_timeslice(
load,
"GB",
"2010-01-01 00:00",
"2010-01-31 23:00",
Delta(days=-365),
fn_load,
)
# 1.1. at midnight gets special treatment
copy_timeslice(
load,
"IE",
"2016-01-01 00:00",
"2016-01-01 01:00",
Delta(days=-366),
fn_load,
)
copy_timeslice(
load,
"PT",
"2016-01-01 00:00",
"2016-01-01 01:00",
Delta(days=-366),
fn_load,
)
copy_timeslice(
load,
"GB",
"2016-01-01 00:00",
"2016-01-01 01:00",
Delta(days=-366),
fn_load,
)
else:
if "ME" in load:
if "AL" not in load and "AL" in countries:
load["AL"] = load.ME * (5.7 / 2.9)
if "MK" not in load and "MK" in countries:
load["MK"] = load.ME * (6.7 / 2.9)
if "BA" not in load and "BA" in countries:
load["BA"] = load.HR * (11.0 / 16.2)
copy_timeslice(
load, "BG", "2018-10-27 21:00", "2018-10-28 22:00", Delta(weeks=1)
)
copy_timeslice(
load, "LU", "2019-01-02 11:00", "2019-01-05 05:00", Delta(weeks=-1)
)
copy_timeslice(
load, "LU", "2019-02-05 20:00", "2019-02-06 19:00", Delta(weeks=-1)
)
return load
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("build_electricity_demand", weather_year="")
configure_logging(snakemake)
weather_year = snakemake.wildcards.weather_year
if weather_year:
snapshots = dict(
start=weather_year, end=str(int(weather_year) + 1), inclusive="left"
)
else:
snapshots = snakemake.params.snapshots
snapshots = pd.date_range(freq="h", **snapshots)
fixed_year = snakemake.config["load"].get("fixed_year", False)
years = (
slice(str(fixed_year), str(fixed_year))
if fixed_year
else slice(snapshots[0], snapshots[-1])
)
powerstatistics = snakemake.params.load["power_statistics"]
interpolate_limit = snakemake.params.load["interpolate_limit"]
countries = snakemake.params.countries
snapshots = pd.date_range(freq="h", **snakemake.params.snapshots)
years = slice(snapshots[0], snapshots[-1])
time_shift = snakemake.params.load["time_shift_for_large_gaps"]
load = load_timeseries(snakemake.input[0], years, countries, powerstatistics)
if snakemake.params.load["manual_adjustments"]:
load = manual_adjustment(load, snakemake.input[0], powerstatistics)
if load.empty:
logger.warning("Build electricity demand time series is empty.")
logger.info(f"Linearly interpolate gaps of size {interpolate_limit} and less.")
load = load.interpolate(method="linear", limit=interpolate_limit)
logger.info(
"Filling larger gaps by copying time-slices of period " f"'{time_shift}'."
)
load = load.apply(fill_large_gaps, shift=time_shift)
assert not load.isna().any().any(), (
"Load data contains nans. Adjust the parameters "
"`time_shift_for_large_gaps` or modify the `manual_adjustment` function "
"for implementing the needed load data modifications."
)
# need to reindex load time series to target year
if fixed_year:
load.index = load.index.map(lambda t: t.replace(year=snapshots.year[0]))
load.to_csv(snakemake.output[0])
Reference in New Issue View Git Blame Copy Permalink