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Development of a data driven approach to explore the energy flexibility potential of building clusters

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  • Wang, Andong
  • Li, Rongling
  • You, Shi

Abstract

With the growing use of renewable energy sources, the stability of electrical power systems can be seriously affected by fluctuations in the available power. As one of the potential solutions for this new challenge, the energy flexibility of buildings has become a focus for research and technological development. Most studies have focused on single buildings, with only a few studies on building clusters in which the building models were usually oversimplified in that they did not consider different building types or their thermal characteristics, their occupancy or their occupants’ behaviour. In this paper, we describe a data driven approach to simulating a generic building cluster that could resemble any mix of building archetypes and occupancy. The energy flexibility potential of apartment building clusters was estimated by using data from surveys and available statistics in Denmark for the worst case scenario, i.e. when the end users do not allow any disturbance when they are at home, so that energy flexibility is only available when residents are not at home. In this scenario, no energy flexibility is assumed when buildings are occupied, which yields a conservative estimation. The uncertainty of the energy flexibility potential due to uncertain occupancy and various archetypes was quantified for different scales of building cluster. The resulting hybrid-model is a combination of a building model and an occupancy model and includes the different factors that influence the potential energy flexibility of buildings. The results show that the uncertainty of the energy flexibility decreases when the aggregated number of buildings increases. The uncertainty of energy flexibility was less than 10%, when about 700 households were aggregated. This approach can be used to simulate building energy flexibility for district or even regional level energy planning when the intention is to use the available flexibility to address the challenges caused by fluctuation in the power available from renewable energy sources.

Suggested Citation

  • Wang, Andong & Li, Rongling & You, Shi, 2018. "Development of a data driven approach to explore the energy flexibility potential of building clusters," Applied Energy, Elsevier, vol. 232(C), pages 89-100.
    • Handle: RePEc:eee:appene:v:232:y:2018:i:c:p:89-100
    DOI: 10.1016/j.apenergy.2018.09.187
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    References listed on IDEAS

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    Cited by:

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    3. Francesco Mancini & Sabrina Romano & Gianluigi Lo Basso & Jacopo Cimaglia & Livio de Santoli, 2020. "How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy," Energies, MDPI, vol. 13(13), pages 1-25, July.
    4. Maitanova, Nailya & Schlüters, Sunke & Hanke, Benedikt & von Maydell, Karsten, 2024. "An analytical method for quantifying the flexibility potential of decentralised energy systems," Applied Energy, Elsevier, vol. 364(C).
    5. Monika Hall & Achim Geissler, 2020. "Load Control by Demand Side Management to Support Grid Stability in Building Clusters," Energies, MDPI, vol. 13(19), pages 1-15, October.
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    8. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. 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).
    10. Amadeh, Ali & Lee, Zachary E. & Zhang, K. Max, 2022. "Quantifying demand flexibility of building energy systems under uncertainty," Energy, Elsevier, vol. 246(C).
    11. 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).
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    13. Perera, A.T.D. & Nik, Vahid M. & Wickramasinghe, P.U. & Scartezzini, Jean-Louis, 2019. "Redefining energy system flexibility for distributed energy system design," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Dominković, Dominik Franjo & Junker, Rune Grønborg & Lindberg, Karen Byskov & Madsen, Henrik, 2020. "Implementing flexibility into energy planning models: Soft-linking of a high-level energy planning model and a short-term operational model," Applied Energy, Elsevier, vol. 260(C).
    15. Osaru Agbonaye & Patrick Keatley & Ye Huang & Motasem Bani Mustafa & Neil Hewitt, 2020. "Design, Valuation and Comparison of Demand Response Strategies for Congestion Management," Energies, MDPI, vol. 13(22), pages 1-29, November.
    16. Hu, Maomao & Xiao, Fu, 2020. "Quantifying uncertainty in the aggregate energy flexibility of high-rise residential building clusters considering stochastic occupancy and occupant behavior," Energy, Elsevier, vol. 194(C).
    17. Li, Han & Johra, Hicham & de Andrade Pereira, Flavia & Hong, Tianzhen & Le Dréau, Jérôme & Maturo, Anthony & Wei, Mingjun & Liu, Yapan & Saberi-Derakhtenjani, Ali & Nagy, Zoltan & Marszal-Pomianowska,, 2023. "Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives," Applied Energy, Elsevier, vol. 343(C).
    18. Yu, Xinran & Ergan, Semiha, 2022. "Estimating power demand shaving capacity of buildings on an urban scale using extracted demand response profiles through machine learning models," Applied Energy, Elsevier, vol. 310(C).
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    20. Zhang, Yichi & Johansson, Pär & Kalagasidis, Angela Sasic, 2021. "Techno-economic assessment of thermal energy storage technologies for demand-side management in low-temperature individual heating systems," Energy, Elsevier, vol. 236(C).

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