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A tri-level framework for distribution-level market clearing considering strategic participation of electrical vehicles and interactions with wholesale market

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  • Wang, Jun
  • Xu, Jian
  • Ke, Deping
  • Liao, Siyang
  • Sun, Yuanzhang
  • Wang, Jingjing
  • Yao, Liangzhong
  • Mao, Beiling
  • Wei, Congying

Abstract

With the increasing penetration of distributed renewable energy sources in distribution networks, a large number of independent market entities such as load serving entities will participate in the distribution-level market, which provides a platform for transparent energy transactions among them. This paper proposes a novel tri-level optimization model for distribution system operator market clearing, fully considering the strategic participation of electrical vehicles and the interactions with the transmission system operator in the market environment. The upper-level model addresses the bidding strategy problem of charging stations and the randomness of renewable energy sources, the middle-level model performs the distribution-level market clearing, and the lower-level model focuses on the joint optimization of power and reserve for transmission network. Furthermore, the Stackelberg game model between distribution system operator and each charging station is converted into a mathematical program with equilibrium constraints formulation, and a decentralized approach based on analytical target cascading algorithm is developed to realize the decoupling of distribution and transmission network. The simulation results show that the proposed distribution-level market clearing method can alleviate the congestion in transmission network and enhance the economic benefits of load serving entities in distribution network.

Suggested Citation

  • Wang, Jun & Xu, Jian & Ke, Deping & Liao, Siyang & Sun, Yuanzhang & Wang, Jingjing & Yao, Liangzhong & Mao, Beiling & Wei, Congying, 2023. "A tri-level framework for distribution-level market clearing considering strategic participation of electrical vehicles and interactions with wholesale market," Applied Energy, Elsevier, vol. 329(C).
    • Handle: RePEc:eee:appene:v:329:y:2023:i:c:s0306261922014878
    DOI: 10.1016/j.apenergy.2022.120230
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    References listed on IDEAS

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    Cited by:

    1. Wang, Jingjing & Yao, Liangzhong & Liang, Jun & Wang, Jun & Cheng, Fan, 2025. "Distributed optimization strategy for networked microgrids based on network partitioning," Applied Energy, Elsevier, vol. 378(PB).
    2. Wang, Jun & Xu, Jian & Wang, Jingjing & Ke, Deping & Yao, Liangzhong & Zhou, Yue & Liao, Siyang, 2024. "Two-stage distributionally robust offering and pricing strategy for a price-maker virtual power plant," Applied Energy, Elsevier, vol. 363(C).
    3. Hosseini Dolatabadi, Sayed Hamid & Bhuiyan, Tanveer Hossain & Chen, Yang & Morales, Jose Luis, 2024. "A stochastic game-theoretic optimization approach for managing local electricity markets with electric vehicles and renewable sources," Applied Energy, Elsevier, vol. 368(C).
    4. Faria, Wandry Rodrigues & Muñoz-Delgado, Gregorio & Contreras, Javier & Pereira Junior, Benvindo Rodrigues, 2024. "A trilevel programming model for the coordination of wholesale and local distribution markets considering GENCOs and proactive customers," Applied Energy, Elsevier, vol. 357(C).
    5. Xia, Yuxin & Savelli, Iacopo & Morstyn, Thomas, 2025. "Integrating local market operations into transmission investment: A tri-level optimization approach," Applied Energy, Elsevier, vol. 378(PA).
    6. G., Varathan & J., Belwin Edward, 2024. "A review of uncertainty management approaches for active distribution system planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 205(C).

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