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1
scripts/build_cop_profiles.py
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@ -40,7 +40,6 @@ Outputs:
References
----------
[1] Staffell et al., Energy & Environmental Science 11 (2012): A review of domestic heat pumps, https://doi.org/10.1039/C2EE22653G.
"""
import xarray as xr

7
scripts/build_daily_heat_demand.py
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@ -3,11 +3,12 @@
#
# SPDX-License-Identifier: MIT
"""
This rule builds heat demand time series using heating degree day (HDD) approximation.
This rule builds heat demand time series using heating degree day (HDD)
approximation.
Snapshots are resampled to daily time resolution and ``Atlite.convert.heat_demand`` is used to convert ambient temperature from the default weather cutout to heat demand time series for the respective cutout.
Snapshots are resampled to daily time resolution and ``Atlite.convert.heat_demand`` is used to convert ambient temperature from the default weather cutout to heat demand time series for the respective cutout.
Heat demand is distributed by population to clustered onshore regions.
Heat demand is distributed by population to clustered onshore regions.
The rule is executed in ``build_sector.smk``.

73
scripts/build_energy_totals.py
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@ -3,8 +3,8 @@
#
# SPDX-License-Identifier: MIT
"""
Build total energy demands and carbon emissions per country using JRC IDEES, eurostat, and EEA data.
Build total energy demands and carbon emissions per country using JRC IDEES,
eurostat, and EEA data.
- Country-specific data is read in :func:`build_eurostat`, :func:`build_idees` and `build_swiss`.
- :func:`build_energy_totals` then combines energy data from Eurostat, Swiss, and IDEES data and :func:`rescale_idees_from_eurostat` rescales IDEES data to match Eurostat data.
@ -16,7 +16,7 @@ Relevant Settings
.. code:: yaml
countries:
energy:
energy:
Inputs
------
@ -41,6 +41,7 @@ Outputs
import logging
import multiprocessing as mp
from functools import partial
from typing import List
import country_converter as coco
import geopandas as gpd
@ -48,7 +49,6 @@ import numpy as np
import pandas as pd
from _helpers import configure_logging, mute_print, set_scenario_config
from tqdm import tqdm
from typing import List
cc = coco.CountryConverter()
logger = logging.getLogger(__name__)
@ -63,7 +63,7 @@ def cartesian(s1: pd.Series, s2: pd.Series) -> pd.DataFrame:
----------
s1: pd.Series
The first pandas Series
s2: pd.Series:
s2: pd.Series:
The second pandas Series.
Returns
@ -73,8 +73,8 @@ def cartesian(s1: pd.Series, s2: pd.Series) -> pd.DataFrame:
Examples
--------
>>> s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
>>> s2 = pd.Series([4, 5, 6], index=['d', 'e', 'f'])
>>> s1 = pd.Series([1, 2, 3], index=["a", "b", "c"])
>>> s2 = pd.Series([4, 5, 6], index=["d", "e", "f"])
>>> cartesian(s1, s2)
d e f
a 4 5 6
@ -83,6 +83,7 @@ def cartesian(s1: pd.Series, s2: pd.Series) -> pd.DataFrame:
"""
return pd.DataFrame(np.outer(s1, s2), index=s1.index, columns=s2.index)
def reverse(dictionary: dict) -> dict:
"""
Reverses the keys and values of a dictionary.
@ -99,7 +100,7 @@ def reverse(dictionary: dict) -> dict:
Examples
--------
>>> d = {'a': 1, 'b': 2, 'c': 3}
>>> d = {"a": 1, "b": 2, "c": 3}
>>> reverse(d)
{1: 'a', 2: 'b', 3: 'c'}
"""
@ -174,7 +175,12 @@ def eurostat_per_country(input_eurostat: str, country: str) -> pd.DataFrame:
return pd.concat(sheet)
def build_eurostat(input_eurostat: str, countries: List[str], nprocesses: int=1, disable_progressbar: bool=False) -> pd.DataFrame:
def build_eurostat(
input_eurostat: str,
countries: List[str],
nprocesses: int = 1,
disable_progressbar: bool = False,
) -> pd.DataFrame:
"""
Return multi-index for all countries' energy data in TWh/a.
@ -270,7 +276,6 @@ def build_swiss() -> pd.DataFrame:
"""
Return a pd.DataFrame of Swiss energy data in TWh/a.
Returns
--------
pd.DataFrame
@ -322,7 +327,7 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
- Calculates energy totals for each sector, stores them in a dictionary and returns them as data frame.
- Assertions ensure indeces of JRC-IDEES data are as expected.
"""
ct_idees = idees_rename.get(ct, ct)
fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
@ -519,9 +524,11 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
return pd.DataFrame(ct_totals)
def build_idees(countries: List[str]) -> pd.DataFrame:
"""
Build energy totals from IDEES database for the given list of countries using :func:`idees_per_country`.
Build energy totals from IDEES database for the given list of countries
using :func:`idees_per_country`.
Parameters
----------
@ -569,9 +576,15 @@ def build_idees(countries: List[str]) -> pd.DataFrame:
return totals
def build_energy_totals(countries: List[str], eurostat: pd.DataFrame, swiss: pd.DataFrame, idees: pd.DataFrame) -> pd.DataFrame:
def build_energy_totals(
countries: List[str],
eurostat: pd.DataFrame,
swiss: pd.DataFrame,
idees: pd.DataFrame,
) -> pd.DataFrame:
"""
Combine energy totals for the specified countries from Eurostat, Swiss, and IDEES data.
Combine energy totals for the specified countries from Eurostat, Swiss, and
IDEES data.
Parameters
----------
@ -593,10 +606,10 @@ def build_energy_totals(countries: List[str], eurostat: pd.DataFrame, swiss: pd.
-----
- Missing values are filled based on Eurostat energy balances and average values in EU28.
- The function also performs specific calculations for Norway and splits road, rail, and aviation traffic for non-IDEES data.
References
----------
- `Norway heating data <http://www.ssb.no/en/energi-og-industri/statistikker/husenergi/hvert-3-aar/2014-07-14>`_
- `Norway heating data <http://www.ssb.no/en/energi-og-industri/statistikker/husenergi/hvert-3-aar/2014-07-14>`_
"""
eurostat_fuels = {"electricity": "Electricity", "total": "Total all products"}
@ -843,9 +856,11 @@ def build_district_heat_share(countries: List[str], idees: pd.DataFrame) -> pd.S
return district_heat_share
def build_eea_co2(input_co2: str, year: int = 1990, emissions_scope: str = "CO2") -> pd.DataFrame:
def build_eea_co2(
input_co2: str, year: int = 1990, emissions_scope: str = "CO2"
) -> pd.DataFrame:
"""
Calculate CO2 emissions for a given year based on EEA data in Mt
Calculate CO2 emissions for a given year based on EEA data in Mt.
Parameters
----------
@ -923,7 +938,8 @@ def build_eea_co2(input_co2: str, year: int = 1990, emissions_scope: str = "CO2"
def build_eurostat_co2(eurostat: pd.DataFrame, year: int = 1990) -> pd.Series:
"""
Calculate CO2 emissions for a given year based on Eurostat fuel consumption data and fuel-specific emissions.
Calculate CO2 emissions for a given year based on Eurostat fuel consumption
data and fuel-specific emissions.
Parameters
----------
@ -939,7 +955,7 @@ def build_eurostat_co2(eurostat: pd.DataFrame, year: int = 1990) -> pd.Series:
Notes
-----
- The function hard-sets fuel-specific emissions:
- The function hard-sets fuel-specific emissions:
- solid fuels: 0.36 tCO2_equi/MW_th (approximates coal)
- oil: 0.285 tCO2_equi/MW_th (average of distillate and residue)
- natural gas: 0.2 tCO2_equi/MW_th
@ -952,7 +968,7 @@ def build_eurostat_co2(eurostat: pd.DataFrame, year: int = 1990) -> pd.Series:
- Residual oil (No. 6) 0.298
- `EIA Electricity Annual <https://www.eia.gov/electricity/annual/html/epa_a_03.html>`_
"""
eurostat_year = eurostat.xs(year, level="year")
specific_emissions = pd.Series(index=eurostat.columns, dtype=float)
@ -965,7 +981,9 @@ def build_eurostat_co2(eurostat: pd.DataFrame, year: int = 1990) -> pd.Series:
return eurostat_year.multiply(specific_emissions).sum(axis=1)
def build_co2_totals(countries: List[str], eea_co2: pd.DataFrame, eurostat_co2: pd.DataFrame) -> pd.DataFrame:
def build_co2_totals(
countries: List[str], eea_co2: pd.DataFrame, eurostat_co2: pd.DataFrame
) -> pd.DataFrame:
"""
Combine CO2 emissions data from EEA and Eurostat for a list of countries.
@ -1014,7 +1032,9 @@ def build_co2_totals(countries: List[str], eea_co2: pd.DataFrame, eurostat_co2:
return co2
def build_transport_data(countries: List[str], population: pd.DataFrame, idees: pd.DataFrame) -> pd.DataFrame:
def build_transport_data(
countries: List[str], population: pd.DataFrame, idees: pd.DataFrame
) -> pd.DataFrame:
"""
Build transport data for a set of countries based on IDEES data.
@ -1100,9 +1120,7 @@ def build_transport_data(countries: List[str], population: pd.DataFrame, idees:
def rescale_idees_from_eurostat(
idees_countries: List[str],
energy: pd.DataFrame,
eurostat: pd.DataFrame
idees_countries: List[str], energy: pd.DataFrame, eurostat: pd.DataFrame
) -> pd.DataFrame:
"""
Takes JRC IDEES data from 2015 and rescales it by the ratio of the Eurostat
@ -1303,7 +1321,8 @@ def rescale_idees_from_eurostat(
def update_residential_from_eurostat(energy: pd.DataFrame) -> pd.DataFrame:
"""
Updates energy balances for residential from disaggregated data from Eurostat by mutating input data DataFrame.
Updates energy balances for residential from disaggregated data from
Eurostat by mutating input data DataFrame.
Parameters
----------

3
scripts/build_existing_heating_distribution.py
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@ -6,7 +6,7 @@
Builds table of existing heat generation capacities for initial planning
horizon.
Existing heat generation capacities are distributed to nodes based on population.
Existing heat generation capacities are distributed to nodes based on population.
Within the nodes, the capacities are distributed to sectors (residential and services) based on sectoral consumption and urban/rural based population distribution.
Inputs:
@ -37,7 +37,6 @@ Notes:
References:
-----------
- "Mapping and analyses of the current and future (2020 - 2030) heating/cooling fuel deployment (fossil/renewables)" (https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en)
"""
import country_converter as coco
import numpy as np

8
scripts/build_heat_totals.py
View File

@ -27,8 +27,10 @@ idx = pd.IndexSlice
def approximate_heat_demand(energy_totals: pd.DataFrame, hdd: pd.DataFrame):
"""
Approximate heat demand for a set of countries based on energy totals and heating degree days (HDD).
A polynomial regression of heat demand on HDDs is performed on the data from 2007 to 2021. Then, for 2022 and 2023, the heat demand is estimated from known HDDs based on the regression.
Approximate heat demand for a set of countries based on energy totals and
heating degree days (HDD). A polynomial regression of heat demand on HDDs
is performed on the data from 2007 to 2021. Then, for 2022 and 2023, the
heat demand is estimated from known HDDs based on the regression.
Parameters
----------
@ -46,7 +48,7 @@ def approximate_heat_demand(energy_totals: pd.DataFrame, hdd: pd.DataFrame):
-----
- Missing data is filled forward for GB in 2020 and backward for CH from 2007 to 2009.
- If only one year of heating data is available for a country, a point (0, 0) is added to make the polynomial fit work.
"""
"""
countries = hdd.columns.intersection(energy_totals.index.levels[0])

2
scripts/build_hourly_heat_demand.py
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@ -5,7 +5,7 @@
"""
Build hourly heat demand time series from daily heat demand.
Water and space heating demand profiles are generated using intraday profiles from BDEW. Different profiles are used for the residential and services sectors as well as weekdays and weekend.
Water and space heating demand profiles are generated using intraday profiles from BDEW. Different profiles are used for the residential and services sectors as well as weekdays and weekend.
The daily heat demand is multiplied by the intraday profile to obtain the hourly heat demand time series. The rule is executed in ``build_sector.smk``.

2
scripts/build_solar_thermal_profiles.py
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@ -5,7 +5,7 @@
"""
Build solar thermal collector profile time series.
Uses ``atlite.Cutout.solar_thermal` to compute heat generation for clustered onshore regions from population layout and weather data cutout.
Uses ``atlite.Cutout.solar_thermal` to compute heat generation for clustered onshore regions from population layout and weather data cutout.
The rule is executed in ``build_sector.smk``.
.. seealso::

1
scripts/build_temperature_profiles.py
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@ -34,7 +34,6 @@ Outputs
- ``resources/temp_soil_<scope>_elec_s<simpl>_<clusters>.nc``:
- ``resources/temp_air_<scope>_elec_s<simpl>_<clusters>.nc`
"""
import atlite