bjoern/pypsa-eur
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

correctly source the existing heating technologies for buildings

Browse Source 
The source URL has changed. It represents the year 2012 and is only
for buildings, not district heating. So the capacities for urban
central are now set to zero from this source.
This commit is contained in:
Tom Brown 2024-01-29 10:02:05 +01:00 committed by Fabian Neumann
parent 560e2854b7
commit f38681f134
3 changed files with 20 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
doc/configtables/licenses-sector.csv
View File

@ -10,7 +10,7 @@ BASt emobility statistics,emobility/,unknown,http://www.bast.de/DE/Verkehrstechn
BDEW heating profile,heat_load_profile_BDEW.csv,unknown,https://github.com/oemof/demandlib BDEW heating profile,heat_load_profile_BDEW.csv,unknown,https://github.com/oemof/demandlib
heating profiles for Aarhus,heat_load_profile_DK_AdamJensen.csv,unknown,Adam Jensen MA thesis at Aarhus University heating profiles for Aarhus,heat_load_profile_DK_AdamJensen.csv,unknown,Adam Jensen MA thesis at Aarhus University
co2 budgets,co2_budget.csv,CC BY 4.0,https://arxiv.org/abs/2004.11009 co2 budgets,co2_budget.csv,CC BY 4.0,https://arxiv.org/abs/2004.11009
existing heating potentials,existing_infrastructure/existing_heating_raw.csv,unknown,https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1 existing heating potentials,existing_infrastructure/existing_heating_raw.csv,unknown,https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en
IRENA existing VRE capacities,existing_infrastructure/{solar|onwind|offwind}_capcity_IRENA.csv,unknown,https://www.irena.org/Statistics/Download-Data IRENA existing VRE capacities,existing_infrastructure/{solar|onwind|offwind}_capcity_IRENA.csv,unknown,https://www.irena.org/Statistics/Download-Data
USGS ammonia production,myb1-2017-nitro.xls,unknown,https://www.usgs.gov/centers/nmic/nitrogen-statistics-and-information USGS ammonia production,myb1-2017-nitro.xls,unknown,https://www.usgs.gov/centers/nmic/nitrogen-statistics-and-information
hydrogen salt cavern potentials,h2_salt_caverns_GWh_per_sqkm.geojson,CC BY 4.0,https://doi.org/10.1016/j.ijhydene.2019.12.161 https://doi.org/10.20944/preprints201910.0187.v1 hydrogen salt cavern potentials,h2_salt_caverns_GWh_per_sqkm.geojson,CC BY 4.0,https://doi.org/10.1016/j.ijhydene.2019.12.161 https://doi.org/10.20944/preprints201910.0187.v1

1 description file/folder licence source
10 BDEW heating profile heat_load_profile_BDEW.csv unknown https://github.com/oemof/demandlib
11 heating profiles for Aarhus heat_load_profile_DK_AdamJensen.csv unknown Adam Jensen MA thesis at Aarhus University
12 co2 budgets co2_budget.csv CC BY 4.0 https://arxiv.org/abs/2004.11009
13 existing heating potentials existing_infrastructure/existing_heating_raw.csv unknown https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1 https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en
14 IRENA existing VRE capacities existing_infrastructure/{solar|onwind|offwind}_capcity_IRENA.csv unknown https://www.irena.org/Statistics/Download-Data
15 USGS ammonia production myb1-2017-nitro.xls unknown https://www.usgs.gov/centers/nmic/nitrogen-statistics-and-information
16 hydrogen salt cavern potentials h2_salt_caverns_GWh_per_sqkm.geojson CC BY 4.0 https://doi.org/10.1016/j.ijhydene.2019.12.161 https://doi.org/10.20944/preprints201910.0187.v1

12
scripts/add_existing_baseyear.py
View File

@ -402,16 +402,10 @@ def add_heating_capacities_installed_before_baseyear(
""" """
logger.debug(f"Adding heating capacities installed before {baseyear}") logger.debug(f"Adding heating capacities installed before {baseyear}")
# Add existing heating capacities, data comes from the study existing_heating = pd.read_csv(snakemake.input.existing_heating_distribution,
# "Mapping and analyses of the current and future (2020 - 2030) header=[0,1],
# heating/cooling fuel deployment (fossil/renewables) " index_col=0)
# https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
# TODO start from original file
existing_heating = pd.read_csv(
snakemake.input.existing_heating_distribution, header=[0, 1], index_col=0
)
techs = existing_heating.columns.get_level_values(1).unique() techs = existing_heating.columns.get_level_values(1).unique()

27
scripts/build_existing_heating_distribution.py
View File

@ -16,9 +16,17 @@ cc = coco.CountryConverter()
def build_existing_heating(): def build_existing_heating():
# retrieve existing heating capacities # retrieve existing heating capacities
existing_heating = pd.read_csv( # Add existing heating capacities, data comes from the study
snakemake.input.existing_heating, index_col=0, header=0 # "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
# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
# data is for buildings only (i.e. NOT district heating) and represents the year 2012
# TODO start from original file
existing_heating = pd.read_csv(snakemake.input.existing_heating,
index_col=0,
header=0)
# data for Albania, Montenegro and Macedonia not included in database # data for Albania, Montenegro and Macedonia not included in database
existing_heating.loc["Albania"] = np.nan existing_heating.loc["Albania"] = np.nan
@ -67,17 +75,14 @@ def build_existing_heating():
nodal_sectoral_totals.sum(axis=1), axis=0 nodal_sectoral_totals.sum(axis=1), axis=0
) )
nodal_heat_name_fraction = pd.DataFrame(dtype=float) nodal_heat_name_fraction = pd.DataFrame(index=district_heat_info.index,
dtype=float)
nodal_heat_name_fraction["urban central"] = dist_fraction nodal_heat_name_fraction["urban central"] = 0.
for sector in sectors: for sector in sectors:
nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[ nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[sector]*(1 - urban_fraction)
sector nodal_heat_name_fraction[f"{sector} urban decentral"] = nodal_sectoral_fraction[sector]*urban_fraction
] * (1 - urban_fraction)
nodal_heat_name_fraction[f"{sector} urban decentral"] = nodal_sectoral_fraction[
sector
] * (urban_fraction - dist_fraction)
nodal_heat_name_tech = pd.concat( nodal_heat_name_tech = pd.concat(
{ {