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Multi-time scale energy management of electric vehicle model-based prosumers by using virtual battery model

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  • Wu, Jiechen
  • Hu, Junjie
  • Ai, Xin
  • Zhang, Zhan
  • Hu, Huanyu

Abstract

The increasing number of small and medium-sized prosumers with distributed energy resources (DERs) has led to the need for innovative operational strategies at the distribution system level. Among them, microgrid (MG) energy management for peer-to-peer power sharing between prosumers is a promising approach. In this study, we develop a multi-time scale optimization method for virtual battery model-based prosumer energy management. First, a day-ahead optimal scheduling model is established based on an integrated virtual battery model that aims to minimize the total dispatching cost of a prosumer-oriented MG without impacting the privacy of individual end-prosumers. Then, close to real-time operation, model predictive control is applied to minimize the deviation between the real-time power and the day-ahead optimal schedule over the control horizon for prosumers with energy storage resources. The simulation results show that the proposed approach employing the integrated virtual battery model to quantitatively characterize the resource flexibility of prosumers yields a good optimization performance and high computing efficiency, compared to those of the approaches modelling traditional DER flexibilities.

Suggested Citation

  • Wu, Jiechen & Hu, Junjie & Ai, Xin & Zhang, Zhan & Hu, Huanyu, 2019. "Multi-time scale energy management of electric vehicle model-based prosumers by using virtual battery model," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:63
    DOI: 10.1016/j.apenergy.2019.113312
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    References listed on IDEAS

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    Cited by:

    1. Li, Jiamei & Ai, Qian & Yin, Shuangrui & Hao, Ran, 2022. "An aggregator-oriented hierarchical market mechanism for multi-type ancillary service provision based on the two-loop Stackelberg game," Applied Energy, Elsevier, vol. 323(C).
    2. Schwidtal, J.M. & Piccini, P. & Troncia, M. & Chitchyan, R. & Montakhabi, M. & Francis, C. & Gorbatcheva, A. & Capper, T. & Mustafa, M.A. & Andoni, M. & Robu, V. & Bahloul, M. & Scott, I.J. & Mbavarir, 2023. "Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    3. Capper, Timothy & Gorbatcheva, Anna & Mustafa, Mustafa A. & Bahloul, Mohamed & Schwidtal, Jan Marc & Chitchyan, Ruzanna & Andoni, Merlinda & Robu, Valentin & Montakhabi, Mehdi & Scott, Ian J. & Franci, 2022. "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    4. Yuxuan Wang & Bingxu Zhang & Chenyang Li & Yongzhang Huang, 2022. "Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility," Energies, MDPI, vol. 15(8), pages 1-22, April.
    5. Gang Xu & Bingxu Zhang & Le Yang & Yi Wang, 2021. "Active and Reactive Power Collaborative Optimization for Active Distribution Networks Considering Bi-Directional V2G Behavior," Sustainability, MDPI, vol. 13(11), pages 1-26, June.

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