Heat demands comprise space heating and drinking hot water demands from residential and commercial trade and service (CTS) buildings. Process heat demands from the industry are, depending on the required temperature level, modelled as electricity, hydrogen or methane demand.

The spatial distribution of annual heat demands is taken from the Pan-European Thermal Atlas version 5.0.1 [Peta]. This source provides data on annual European residential and CTS heat demands per census cell for the year 2015. In order to model future demands, the demand distribution extracted by Peta is then scaled to meet a national annual demand from external sources. The following national demands are taken for the selected scenarios:

Heat demands per sector and scenario

	Residential sector	CTS sector	Sources
eGon2035	379 TWh	122 TWh	[Energiereferenzprognose]
eGon100RE	284 TWh	89 TWh	[Energiereferenzprognose]

The resulting annual heat demand data per census cell is stored in the database table demand.egon_peta_heat. The implementation of these data processing steps can be found in HeatDemandImport.

Figure Spatial distribution of residential heat demand per census cell in scenario eGon2035 shows the census cell distribution of residential heat demands for scenario eGon2035, categorized for different levels of annual demands.

Spatial distribution of residential heat demand per census cell in scenario eGon2035

In a next step, the annual demand per census cell is further disaggregated to buildings. In case of residential buildings the demand is equally distributed to all residential buildings within the census cell. The annual demand per residential building is not saved in any database table but needs to be calculated from the annual demand in the census cell in table demand.egon_peta_heat and the number of residential buildings in table demand.egon_heat_timeseries_selected_profiles (see also query below). The disaggregation of the annual CTS heat demand per census cell to buildings is done analogous to the disaggregation of the electricity demand, which is in detail described in section Spatial disaggregation of CTS demand to buildings. The share of each CTS building of the corresponding HV-MV substation’s heat profile is for both the eGon2035 and eGon100RE scenario written to the database table EgonCtsHeatDemandBuildingShare. The peak heat demand per building, including residential and CTS demand, in the two scenarios eGon2035 and eGon100RE is calculated in the datasets HeatPumps2035 and HeatPumpsPypsaEurSec, respectively, and written to table demand.egon_building_heat_peak_loads.

The hourly heat demand profiles are for both sectors created in the Dataset HeatTimeSeries. For residential heat demand profiles a pool of synthetically created bottom-up demand profiles is used. Depending on the mean temperature per day, these profiles are randomly assigned to each residential building. The methodology is described in detail in [Buettner2022]. Data on residential heat demand profiles is stored in the database within the tables demand.egon_heat_timeseries_selected_profiles, demand.egon_daily_heat_demand_per_climate_zone, boundaries.egon_map_zensus_climate_zones. To create the profiles for a selected building, these tables have to be combined, e.g. like this:

SELECT (b.demand/f.count * UNNEST(e.idp) * d.daily_demand_share)*1000 AS demand_profile
FROM (SELECT * FROM demand.egon_heat_timeseries_selected_profiles,
UNNEST(selected_idp_profiles) WITH ORDINALITY as selected_idp) a
JOIN demand.egon_peta_heat b
ON b.zensus_population_id = a.zensus_population_id
JOIN boundaries.egon_map_zensus_climate_zones c
ON c.zensus_population_id = a.zensus_population_id
JOIN demand.egon_daily_heat_demand_per_climate_zone d
ON (c.climate_zone = d.climate_zone AND d.day_of_year = ordinality)
JOIN demand.egon_heat_idp_pool e
ON selected_idp = e.index
JOIN (SELECT zensus_population_id, COUNT(building_id)
FROM demand.egon_heat_timeseries_selected_profiles
GROUP BY zensus_population_id
) f
ON f.zensus_population_id = a.zensus_population_id
WHERE a.building_id = SELECTED_BUILDING_ID
AND b.scenario = 'eGon2035'
AND b.sector = 'residential';

Exemplary resulting residential heat demand time series for a selected day in winter and summer considering different aggregation levels are visualized in figures Temporal distribution of residential heat demand for a selected day in winter and Temporal distribution of residential heat demand for a selected day in summer.

Temporal distribution of residential heat demand for a selected day in winter

Temporal distribution of residential heat demand for a selected day in summer

The temporal disaggregation of CTS heat demand is done using Standard Load Profiles Gas from demandregio [demandregio] considering different profiles per CTS branch.