Add building peak load for `eGon2021`

[nesnoj]

Add data for eGon2021 to table demand.egon_building_electricity_peak_loads using DR scaling factors.

The total HH demand for Germany is +9% compared to eGon2035 which will be used in ding0 until higher resoluted data is available.

[nesnoj]

@birgits, who is responsible now? Or did we agree on keeping this in ding0?

[birgits]

I put it on the agenda for tomorrow's project meeting.

[nesnoj]

The proposal above is a very simplified approach. But if we would like to add the SQ scenario to the peak load table (incl. new scaling factor for SQ), what do you think how long that would take in terms of 1) implementation time and 2) pipeline runtime? @nailend

[nailend]

Implementation could get a bit fuzzy but should be possible. I don't think that the pipeline runtime would increase noticeably. This should be the essentiell parts, but will need to check for complications further down the dataset pipeline as its not generic and pretty specified for 2 scenarios only.

• https://github.com/openego/eGon-data/blob/3cbb658d0b809e000776f3662c944dab3fc6a644/src/egon/data/datasets/electricity_demand_timeseries/hh_buildings.py#L694
• https://github.com/openego/eGon-data/blob/ac89575c7e1647f691083492795e9ffde1ba4e70/src/egon/data/datasets/electricity_demand_timeseries/hh_profiles.py#L1355-L1354
• https://github.com/openego/eGon-data/blob/3cbb658d0b809e000776f3662c944dab3fc6a644/src/egon/data/datasets/electricity_demand_timeseries/hh_buildings.py#L647

[nailend]

Since @mltja already has a working solution for ding0 that interpolates from the sectoral demands in the load area this will be a nice-to-have for the completeness of the dataset.