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Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids

Author

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  • Li, Zhengmao
  • Wu, Lei
  • Xu, Yan
  • Wang, Luhao
  • Yang, Nan

Abstract

This paper discusses a tri-layer non-cooperative energy trading approach among multiple grid-tied multi-energy microgrids (MEMGs) in the restructured integrated energy market. The heterogeneous uncertainties from renewable energy, market prices, and electric energy loads are also considered via the risk-averse stochastic programming (SP) approach. First, comprehensive operation models of individual MEMGs are presented with the consideration of practical electric energy and thermal network flows as well as battery degradation. Second, to guarantee fair multi-energy trading among MEMGs and deal with adverse effects from all uncertainty sources, a tri-layer Cournot Nash game-based energy bidding method is developed and solved by the SP approach. In the first layer, i.e., day-ahead multi-energy market, optimal energy bids, dispatches of energy storage assets, and thermal flows against uncertainty scenarios are acquired in a risk-averse manner; In the second layer, i.e., intra-day multi-energy market, optimal intra-day energy bids and dispatches of all resources against uncertainty realizations are sequentially calculated; In the third layer, i.e., the real-time multi-energy market, transactions between each MEMG and the wholesale multi-energy market are finalized. Third, for protecting the privacy of individual MEMGs and alleviating the computation burdens, the distributed alternating search procedure is employed to compute the Nash equilibriums in the day-ahead and intra-day markets. In the end, numerical case studies are conducted to verify the effectiveness of our method. From the simulation results, it can be inferred that compared with the traditional cooperative, deterministic and risk-natural methods in the literature, our proposed method is more practical and economical for real-world applications since it comprehensively considers the market competition, uncertainty handling, and energy trading risk.

Suggested Citation

  • Li, Zhengmao & Wu, Lei & Xu, Yan & Wang, Luhao & Yang, Nan, 2023. "Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids," Applied Energy, Elsevier, vol. 331(C).
    • Handle: RePEc:eee:appene:v:331:y:2023:i:c:s0306261922015392
    DOI: 10.1016/j.apenergy.2022.120282
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    References listed on IDEAS

    as
    1. Li, Zhengmao & Xu, Yan, 2018. "Optimal coordinated energy dispatch of a multi-energy microgrid in grid-connected and islanded modes," Applied Energy, Elsevier, vol. 210(C), pages 974-986.
    2. Erol, Özge & Başaran Filik, Ümmühan, 2022. "A Stackelberg game approach for energy sharing management of a microgrid providing flexibility to entities," Applied Energy, Elsevier, vol. 316(C).
    3. Mei, Jie & Chen, Chen & Wang, Jianhui & Kirtley, James L., 2019. "Coalitional game theory based local power exchange algorithm for networked microgrids," Applied Energy, Elsevier, vol. 239(C), pages 133-141.
    4. Wang, Lu & Gu, Wei & Wu, Zhi & Qiu, Haifeng & Pan, Guangsheng, 2020. "Non-cooperative game-based multilateral contract transactions in power-heating integrated systems," Applied Energy, Elsevier, vol. 268(C).
    5. Bhatti, Bilal Ahmad & Broadwater, Robert, 2020. "Distributed Nash Equilibrium Seeking for a Dynamic Micro-grid Energy Trading Game with Non-quadratic Payoffs," Energy, Elsevier, vol. 202(C).
    6. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    7. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    8. Du, Yan & Wang, Zhiwei & Liu, Guangyi & Chen, Xi & Yuan, Haoyu & Wei, Yanli & Li, Fangxing, 2018. "A cooperative game approach for coordinating multi-microgrid operation within distribution systems," Applied Energy, Elsevier, vol. 222(C), pages 383-395.
    9. Oskouei, Morteza Zare & Mohammadi-Ivatloo, Behnam & Abapour, Mehdi & Shafiee, Mahmood & Anvari-Moghaddam, Amjad, 2021. "Privacy-preserving mechanism for collaborative operation of high-renewable power systems and industrial energy hubs," Applied Energy, Elsevier, vol. 283(C).
    10. Wei, Chun & Shen, Zhuzheng & Xiao, Dongliang & Wang, Licheng & Bai, Xiaoqing & Chen, Haoyong, 2021. "An optimal scheduling strategy for peer-to-peer trading in interconnected microgrids based on RO and Nash bargaining," Applied Energy, Elsevier, vol. 295(C).
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