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Information about electricity demands and their spatial and temporal aggregation | ||
The electricity demand considered includes demand from the residential, commercial and industrial sector. | ||
The target values for scenario *eGon2035* are taken from the German grid development plan from 2021 [NEP2021]_, | ||
whereas the distribution on NUTS3-levels corresponds to the data from the research project *DemandRegio* [demandregio]_. | ||
The following table lists the electricity demands per sector: | ||
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.. list-table:: Electricity demand per sector | ||
:widths: 25 50 | ||
:header-rows: 1 | ||
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* - Sector | ||
- Annual electricity demand in TWh | ||
* - residential | ||
- 115.1 | ||
* - commercial | ||
- 123.5 | ||
* - industrial | ||
- 259.5 | ||
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A further spatial and temporal distribution of the electricity demand is needed to fullfil all requirements of the | ||
subsequent grid optimization. Therefore different, sector-specific distributions methods were developed and applied. | ||
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Residential electricity demand | ||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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The annual electricity demands of households on NUTS3-level from *DemandRegio* are scaled to meet the national target | ||
values for the respective scenario in dataset :py:class:`DemandRegio <egon.data.datasets.demandregio.DemandRegio>`. | ||
A further spatial and temporal distribution of residential electricity demands is performed in | ||
:py:class:`HouseholdElectricityDemand <egon.data.datasets.electricity_demand.HouseholdElectricityDemand>` as described | ||
in [Buettner2022]_. | ||
The result is a consistent dataset across aggregation levels with an hourly resolution. | ||
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.. figure:: /images/S27-3.png | ||
:name: spatial_distribution_electricity_demand | ||
:width: 400 | ||
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Electricity demand on NUTS 3-level (upper left); Exemplary MVGD (upper right); Study region in Flensburg (20 Census cells, bottom) from [Buettner2022]_ | ||
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.. figure:: /images/S27-4a.png | ||
:name: aggregation_level_electricity_demand | ||
:width: 400 | ||
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Electricity demand time series on different aggregation levels from [Buettner2022]_ | ||
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Commercial electricity demand | ||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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The distribution of electricity demand from the commercial, trade and service (CTS) sector is also based on data from | ||
*DemandRegio*, which provides annual electricity demands on NUTS3-level for Germany. In dataset | ||
:py:class:`CtsElectricityDemand <egon.data.datasets.electricity_demand.CtsElectricityDemand>` the annual electricity | ||
demands are further distributed to census cells (100x100m cells from [Census]_) based on the distribution of heat demands, | ||
which is taken from the Pan-European Thermal Altlas version 5.0.1 [Peta]_. For further information refer to section | ||
ref:`heat_demand`. | ||
The applied methods for a futher spatial and temporal distribution to buildings is described in [Buettner2022]_ and | ||
performed in dataset :py:class:`CtsDemandBuildings <egon.data.datasets.electricity_demand_timeseries.CtsDemandBuildings>` | ||
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Industrial electricity demand | ||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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To distribute the annual industrial electricity demand OSM landuse data as well as information on industrial sites are | ||
taken into account. | ||
In a first step (:py:class:`CtsElectricityDemand <egon.data.datasets.electricity_demand.CtsElectricityDemand>`) | ||
different sources providing information about specific sites and further information on the industry sector in which | ||
the respective industrial site operates are combined. Here, the three data sources [Hotmaps]_, [sEEnergies]_ and | ||
[Schmidt2018]_ are aligned and joined. | ||
Based on the resulting list of industrial sites in Germany and information on industrial landuse areas from OSM [OSM]_ | ||
which where extracted and processed in :py:class:`OsmLanduse <egon.data.datasets.loadarea.OsmLanduse>` the annual demands | ||
were distributed. | ||
The spatial and temporal distribution is performed in | ||
:py:class:`IndustrialDemandCurves <egon.data.datasets.industry.IndustrialDemandCurves>`. | ||
For the spatial distribution of annual electricity demands from *DemandRegio* [demandregio]_ which are available on | ||
NUTS3-level are in a first step evenly split 50/50 between industrial sites and OSM-polygons tagged as industrial areas. | ||
Per NUTS-3 area the respective shares are then distributed linearily based on the area of the corresponding landuse polygons | ||
and evenly to the identified industrial sites. | ||
In a next step the temporal disaggregation of the annual demands is carried out taking information about the industrial | ||
sectors and sector-specific standard load profiles from [demandregio]_ into account. | ||
Based on the resulting time series and their peak loads the corresponding grid level and grid connections point is | ||
identified. | ||
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Electricity demand in neighbouring countries | ||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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The neighbouring countries considered in the model are represented in a lower spatial resolution of one or two buses per | ||
country. The national demand timeseries in an hourly resolution of the respective countries is taken from the Ten-Year | ||
Network Development Plan, Version 2020 [TYNDP]_. In case no data for the target year is available the data is is | ||
interpolated linearly. | ||
Refer to the corresponding dataset for detailed information: | ||
:py:class:`ElectricalNeighbours <egon.data.datasets.ElectricalNeighbours>` | ||
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