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integrate artificial load data supplement into build_electricity_demand

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This commit is contained in:
Fabian Neumann 2024-03-04 19:37:52 +01:00
parent 96f71d7e9c
commit 8904943a87
5 changed files with 35 additions and 94 deletions
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3
config/config.default.yaml
View File

@ -73,8 +73,6 @@ enable:
build_cutout: false
retrieve_irena: false
retrieve_cutout: true
retrieve_opsd_load_data: true
retrieve_artificial_load_data: false
build_natura_raster: false
retrieve_natura_raster: true
custom_busmap: false
@ -295,6 +293,7 @@ load:
manual_adjustments: true # false
scaling_factor: 1.0
fixed_year: false # false or year (e.g. 2013)
supplement_missing_data_artificially: true
# docs
# TODO: PyPSA-Eur merge issue in prepare_sector_network.py

59
rules/build_electricity.smk
View File

@ -18,45 +18,26 @@ if config["enable"].get("prepare_links_p_nom", False):
"../scripts/prepare_links_p_nom.py"
if config["enable"].get("retrieve_opsd_load_data", True):
rule build_electricity_demand:
params:
snapshots=config_provider("snapshots"),
countries=config_provider("countries"),
load=config_provider("load"),
input:
ancient("data/electricity_demand_raw.csv"),
output:
resources("electricity_demand.csv"),
log:
logs("build_electricity_demand.log"),
resources:
mem_mb=5000,
conda:
"../envs/environment.yaml"
script:
"../scripts/build_electricity_demand.py"
if config["enable"].get("retrieve_artificial_load_data", False):
rule build_artificial_load_data:
input:
ancient("data/load_artificial_raw.csv"),
output:
resources("electricity_demand.csv"),
log:
logs("build_artificial_load_data.log"),
resources:
mem_mb=5000,
conda:
"../envs/environment.yaml"
script:
"../scripts/build_artificial_load_data.py"
ruleorder: build_artificial_load_data > build_electricity_demand
rule build_electricity_demand:
params:
snapshots=config_provider("snapshots"),
countries=config_provider("countries"),
load=config_provider("load"),
input:
reported=ancient("data/electricity_demand_raw.csv"),
artificial=lambda w: ancient("data/load_artificial_raw.csv")
if config_provider("load", "supplement_missing_data_artificially")(w)
else [],
output:
resources("electricity_demand.csv"),
log:
logs("build_electricity_demand.log"),
resources:
mem_mb=5000,
conda:
"../envs/environment.yaml"
script:
"../scripts/build_electricity_demand.py"
rule build_powerplants:

9
rules/retrieve.smk
View File

@ -180,9 +180,7 @@ if config["enable"]["retrieve"]:
"../scripts/retrieve_gas_infrastructure_data.py"
if config["enable"]["retrieve"] and config["enable"].get(
"retrieve_opsd_load_data", True
):
if config["enable"]["retrieve"]:
rule retrieve_electricity_demand:
params:
@ -200,9 +198,7 @@ if config["enable"]["retrieve"] and config["enable"].get(
"../scripts/retrieve_electricity_demand.py"
if config["enable"]["retrieve"] and config["enable"].get(
"retrieve_artificial_load_data", False
):
if config["enable"]["retrieve"]:
rule retrieve_artificial_load_data:
input:
@ -220,6 +216,7 @@ if config["enable"]["retrieve"] and config["enable"].get(
retries: 2
run:
move(input[0], output[0])
validate_checksum(output[0], input[0])
if config["enable"]["retrieve"]:

42
scripts/build_artificial_load_data.py
View File

@ -1,42 +0,0 @@
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
This rule downloads the load data.
"""
import logging
logger = logging.getLogger(__name__)
import pandas as pd
from _helpers import configure_logging
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("build_artificial_load_data")
configure_logging(snakemake)
snapshots = pd.date_range(freq="h", **snakemake.params.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])
)
countries = snakemake.config["countries"]
load = pd.read_csv(snakemake.input[0], index_col=0, parse_dates=True).loc[
snapshots, countries
]
assert not load.isna().any().any(), "Load data contains nans."
if fixed_year:
load.index = load.index.map(lambda t: t.replace(year=snapshots.year[0]))
load.to_csv(snakemake.output[0])

16
scripts/build_electricity_demand.py
View File

@ -277,16 +277,15 @@ if __name__ == "__main__":
time_shift = snakemake.params.load["time_shift_for_large_gaps"]
load = load_timeseries(snakemake.input[0], years, countries)
load = load_timeseries(snakemake.input.reported, years, countries)
if "UA" in countries:
# attach load of UA (best data only for entsoe transparency)
load_ua = load_timeseries(snakemake.input[0], "2018", ["UA"], False)
load_ua = load_timeseries(snakemake.input.reported, "2018", ["UA"])
snapshot_year = str(snapshots.year.unique().item())
time_diff = pd.Timestamp("2018") - pd.Timestamp(snapshot_year)
load_ua.index -= (
time_diff # hack indices (currently, UA is manually set to 2018)
)
# hack indices (currently, UA is manually set to 2018)
load_ua.index -= time_diff
load["UA"] = load_ua
# attach load of MD (no time-series available, use 2020-totals and distribute according to UA):
# https://www.iea.org/data-and-statistics/data-browser/?country=MOLDOVA&fuel=Energy%20consumption&indicator=TotElecCons
@ -307,6 +306,13 @@ if __name__ == "__main__":
)
load = load.apply(fill_large_gaps, shift=time_shift)
if snakemake.params.load["supplement_missing_data_artificially"]:
logger.info("Supplement missing data with artificial data.")
fn = snakemake.input.artificial
artificial_load = pd.read_csv(fn, index_col=0, parse_dates=True)
artificial_load = artificial_load.loc[snapshots, countries]
load = load.combine_first(artificial_load)
assert not load.isna().any().any(), (
"Load data contains nans. Adjust the parameters "
"`time_shift_for_large_gaps` or modify the `manual_adjustment` function "