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Enhancing power system reliability through demand flexibility of Grid-Interactive Efficient Buildings: A thermal model-based optimization approach

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  • Pantoš, Miloš
  • Lukas, Lucija

Abstract

The paper evaluates the impact of grid-interactive efficient buildings (GEBs) on power system reliability. To achieve this, a single-zone thermal model of a building is developed, allowing for the simulation of indoor temperature dynamics based on the characteristics of the building envelope and internal thermal mass. The potential of GEBs to adjust their electric consumption, recognized as demand flexibility, is integrated into the direct optimization of reliability indices, specifically Loss of Load Expectation (LOLE) and Expected Energy Not Supplied (EENS). The analysis is conducted using the proposed optimization model in two distinct case studies. In the first scenario, households do not support the power system and only optimize their energy consumption for heating and cooling. In the second scenario, households provide available demand flexibility of GEBs to the power system to enhance reliability. This demand flexibility is constrained by the need to maintain indoor temperature within limits that ensure comfortable use of the space. The analysis highlights the potential of GEBs to improve power system reliability by reducing LOLE and EENS. The primary scientific contribution of this research is the development of an optimization model that incorporates a thermal model of GEBs without assuming a generic demand flexibility for the buildings.

Suggested Citation

  • Pantoš, Miloš & Lukas, Lucija, 2025. "Enhancing power system reliability through demand flexibility of Grid-Interactive Efficient Buildings: A thermal model-based optimization approach," Applied Energy, Elsevier, vol. 381(C).
    • Handle: RePEc:eee:appene:v:381:y:2025:i:c:s0306261924024292
    DOI: 10.1016/j.apenergy.2024.125045
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    References listed on IDEAS

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    1. Khatibi, Mahmood & Rahnama, Samira & Vogler-Finck, Pierre & Dimon Bendtsen, Jan & Afshari, Alireza, 2023. "Towards designing an aggregator to activate the energy flexibility of multi-zone buildings using a hierarchical model-based scheme," Applied Energy, Elsevier, vol. 333(C).
    2. Wang, Yu & Xu, Yan & Tang, Yi, 2019. "Distributed aggregation control of grid-interactive smart buildings for power system frequency support," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Utama, Christian & Troitzsch, Sebastian & Thakur, Jagruti, 2021. "Demand-side flexibility and demand-side bidding for flexible loads in air-conditioned buildings," Applied Energy, Elsevier, vol. 285(C).
    4. Wei, Congying & Wu, Qiuwei & Xu, Jian & Sun, Yuanzhang & Jin, Xiaolong & Liao, Siyang & Yuan, Zhiyong & Yu, Li, 2020. "Distributed scheduling of smart buildings to smooth power fluctuations considering load rebound," Applied Energy, Elsevier, vol. 276(C).
    5. Chu, Wenfeng & Zhang, Yu & He, Wei & Zhang, Sheng & Hu, Zhongting & Ru, Bingqian & Ying, Shangxuan, 2023. "Research on flexible allocation strategy of power grid interactive buildings based on multiple optimization objectives," Energy, Elsevier, vol. 278(PB).
    6. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    7. Shi, Jian & Teh, Jiashen & Alharbi, Bader & Lai, Ching-Ming, 2024. "Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model," Energy, Elsevier, vol. 297(C).
    8. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    9. Shi, Jian & Teh, Jiashen, 2024. "Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion," Applied Energy, Elsevier, vol. 353(PB).
    10. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    11. Božič, Dušan & Pantoš, Miloš, 2015. "Impact of electric-drive vehicles on power system reliability," Energy, Elsevier, vol. 83(C), pages 511-520.
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