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Demand response in buildings: Unlocking energy flexibility through district-level electro-thermal simulation

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  • Amin, Amin
  • Kem, Oudom
  • Gallegos, Pablo
  • Chervet, Philipp
  • Ksontini, Feirouz
  • Mourshed, Monjur

Abstract

European Union households account for 26% of the final energy consumption, yet their participation in demand response (DR) schemes is virtually non-existent. Relatively small amounts of flexibility primarily from appliances and the lack of DR-enabled energy management solutions in homes have been identified as the main barriers. As part of a Horizon 2020 funded collaborative research and development project, we developed a cloud-based framework for intelligent optimisation of electricity consumption and generation in buildings to upscale and enhance their demand response effectiveness at a district level. Through electro-thermal simulations that unlock building flexibility while considering low-voltage grid constraints, our approach aims to extend technical and economic potential of the whole solution for wider replication at a low computational cost. The framework simultaneously schedules the operation of controllable appliances such as dishwashers, washing machines and dryers in buildings to further reduce peak loads and energy costs of the district. The framework was tested in a real UK district comprising 66 dwellings, which demonstrated its effectiveness in minimising electricity peak loads and costs by prioritising local consumption of the electricity produced from photovoltaics and exploiting the asymmetry on the time-varying electricity tariff. The results show the ability to achieve an average 30% reduction in energy costs for the district while adhering to low-voltage grid constraints.

Suggested Citation

  • Amin, Amin & Kem, Oudom & Gallegos, Pablo & Chervet, Philipp & Ksontini, Feirouz & Mourshed, Monjur, 2022. "Demand response in buildings: Unlocking energy flexibility through district-level electro-thermal simulation," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921011612
    DOI: 10.1016/j.apenergy.2021.117836
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    References listed on IDEAS

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    1. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).
    2. Mansouri, Seyed Amir & Nematbakhsh, Emad & Jordehi, Ahmad Rezaee & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco, 2023. "An interval-based nested optimization framework for deriving flexibility from smart buildings and electric vehicle fleets in the TSO-DSO coordination," Applied Energy, Elsevier, vol. 341(C).
    3. Ajla Mehinovic & Matej Zajc & Nermin Suljanovic, 2023. "Interpretation and Quantification of the Flexibility Sources Location on the Flexibility Service in the Distribution Grid," Energies, MDPI, vol. 16(2), pages 1-18, January.

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