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Regionalised heat demand and power-to-heat capacities in Germany – An open dataset for assessing renewable energy integration

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  • Heitkoetter, Wilko
  • Medjroubi, Wided
  • Vogt, Thomas
  • Agert, Carsten

Abstract

Higher shares of fluctuating generation from renewable energy sources in the power system lead to an increase in grid balancing demand. One approach for avoiding curtailment of renewable energies is to use excess electricity feed-in for heating applications. To assess in which regions power-to-heat technologies can contribute to renewable energy integration, detailed data on the spatial distribution of the heat demand are needed. We determine the overall heat load in the residential building sector and the share covered by electric heating technologies for each administrative district in Germany, with a temporal resolution of 15 min. Using a special evaluation of German census data, we defined 729 building categories and assigned individual heat demand values. Furthermore, heating types and different classes of installed heating capacity were defined. Our analysis showed that the share of small-scale single-storey heating and large-scale central heating is higher in cities, whereas there is more medium-scale central heating in rural areas. This results from the different shares of single and multi-family houses in the respective regions. To determine the electrically-covered heat demand, we took into account heat pumps and resistive heating technologies. All results, as well as the developed code, are published under open source licenses and can thus also be used by other researchers for the assessment of power-to-heat for renewable energy integration.

Suggested Citation

  • Heitkoetter, Wilko & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2020. "Regionalised heat demand and power-to-heat capacities in Germany – An open dataset for assessing renewable energy integration," Applied Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919318483
    DOI: 10.1016/j.apenergy.2019.114161
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    Cited by:

    1. Wilko Heitkoetter & Wided Medjroubi & Thomas Vogt & Carsten Agert, 2022. "Economic Assessment of Demand Response Using Coupled National and Regional Optimisation Models," Energies, MDPI, vol. 15(22), pages 1-25, November.
    2. Wang, Shubin & Sun, Shaolong & Zhao, Erlong & Wang, Shouyang, 2021. "Urban and rural differences with regional assessment of household energy consumption in China," Energy, Elsevier, vol. 232(C).
    3. Millar, Michael-Allan & Yu, Zhibin & Burnside, Neil & Jones, Greg & Elrick, Bruce, 2021. "Identification of key performance indicators and complimentary load profiles for 5th generation district energy networks," Applied Energy, Elsevier, vol. 291(C).
    4. Abdulraheem Salaymeh & Irene Peters & Stefan Holler, 2024. "Factoring Building Refurbishment and Climatic Effect into Heat Demand Assessments and Forecasts: Case Study and Open Datasets for Germany," Energies, MDPI, vol. 17(3), pages 1-21, January.
    5. Allouhi, Amine, 2022. "Techno-economic and environmental accounting analyses of an innovative power-to-heat concept based on solar PV systems and a geothermal heat pump," Renewable Energy, Elsevier, vol. 191(C), pages 649-661.
    6. Seul-Ye Lim & Jeoung-Sik Min & Seung-Hoon Yoo, 2021. "Price and Income Elasticities of Residential Heat Demand from District Heating System: A Price Sensitivity Measurement Experiment in South Korea," Sustainability, MDPI, vol. 13(13), pages 1-10, June.
    7. Triebs, Merlin Sebastian & Tsatsaronis, George, 2022. "From heat demand to heat supply: How to obtain more accurate feed-in time series for district heating systems," Applied Energy, Elsevier, vol. 311(C).
    8. Ikäheimo, Jussi & Lindroos, Tomi J. & Kiviluoma, Juha, 2023. "Impact of climate and geological storage potential on feasibility of hydrogen fuels," Applied Energy, Elsevier, vol. 342(C).

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