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Grid support coefficients for electricity-based heating and cooling and field data analysis of present-day installations in Germany

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  • Klein, Konstantin
  • Langner, Robert
  • Kalz, Doreen
  • Herkel, Sebastian
  • Henning, Hans-Martin

Abstract

A new method for assessing the grid interaction of a building’s electricity consumption and generation is presented. It consists of the Grid Support Coefficients GSCabs and GSCrel. GSCabs “weights” the electricity consumption profile with a time-resolved reference quantity which expresses the availability of electricity in the public grid (here: stock electricity price, residual load, cumulative energy consumption or fraction of wind and PV in the electricity mix). GSCrel relates GSCabs to the worst and best achievable values on a scale of −100 to 100 in order to increase the comparability of the results.

Suggested Citation

  • Klein, Konstantin & Langner, Robert & Kalz, Doreen & Herkel, Sebastian & Henning, Hans-Martin, 2016. "Grid support coefficients for electricity-based heating and cooling and field data analysis of present-day installations in Germany," Applied Energy, Elsevier, vol. 162(C), pages 853-867.
    • Handle: RePEc:eee:appene:v:162:y:2016:i:c:p:853-867
    DOI: 10.1016/j.apenergy.2015.10.107
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    References listed on IDEAS

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    1. Salom, Jaume & Marszal, Anna Joanna & Widén, Joakim & Candanedo, José & Lindberg, Karen Byskov, 2014. "Analysis of load match and grid interaction indicators in net zero energy buildings with simulated and monitored data," Applied Energy, Elsevier, vol. 136(C), pages 119-131.
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    Cited by:

    1. Li, Yanxue & Zhang, Xiaoyi & Gao, Weijun & Xu, Wenya & Wang, Zixuan, 2022. "Operational performance and grid-support assessment of distributed flexibility practices among residential prosumers under high PV penetration," Energy, Elsevier, vol. 238(PB).
    2. Ayala-Gilardón, A. & Sidrach-de-Cardona, M. & Mora-López, L., 2018. "Influence of time resolution in the estimation of self-consumption and self-sufficiency of photovoltaic facilities," Applied Energy, Elsevier, vol. 229(C), pages 990-997.
    3. Klein, Konstantin & Herkel, Sebastian & Henning, Hans-Martin & Felsmann, Clemens, 2017. "Load shifting using the heating and cooling system of an office building: Quantitative potential evaluation for different flexibility and storage options," Applied Energy, Elsevier, vol. 203(C), pages 917-937.
    4. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
    5. Heiko Dunkelberg & Maximilian Sondermann & Henning Meschede & Jens Hesselbach, 2019. "Assessment of Flexibilisation Potential by Changing Energy Sources Using Monte Carlo Simulation," Energies, MDPI, vol. 12(4), pages 1-24, February.
    6. Johannes Röder & David Beier & Benedikt Meyer & Joris Nettelstroth & Torben Stührmann & Edwin Zondervan, 2020. "Design of Renewable and System-Beneficial District Heating Systems Using a Dynamic Emission Factor for Grid-Sourced Electricity," Energies, MDPI, vol. 13(3), pages 1-22, February.

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