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A bilevel game-theoretic decision-making framework for strategic retailers in both local and wholesale electricity markets

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  • Hong, Qiuyi
  • Meng, Fanlin
  • Liu, Jian
  • Bo, Rui

Abstract

This paper proposes a bilevel game-theoretic model for multiple strategic retailers participating in both wholesale and local electricity markets while considering customers’ switching behaviors. At the upper level, each retailer maximizes its own profit by making optimal pricing decisions in the retail market and bidding decisions in the day-ahead wholesale (DAW) and local power exchange (LPE) markets. The interaction among multiple strategic retailers is formulated using the Bertrand competition model. For the lower level, there are three optimization problems. First, the welfare maximization problem is formulated for customers to model their switching behaviors among different retailers. Second, a market-clearing problem is formulated for the independent system operator (ISO) in the DAW market. Third, a novel LPE market is developed for retailers to facilitate their power balancing. In addition, the bilevel multi-leader multi-follower Stackelberg game forms an equilibrium problem with equilibrium constraints (EPEC) problem, which is solved by the diagonalization algorithm. Numerical results demonstrate the feasibility and effectiveness of the EPEC model and the importance of modeling customers’ switching behaviors. We corroborate that incentivizing customers’ switching behaviors and increasing the number of retailers facilitates retail competition, which results in reducing strategic retailers’ retail prices and profits. Moreover, the relationship between customers’ switching behaviors and welfare is reflected by a balance between the electricity purchasing cost (i.e., electricity price) and the electricity consumption level.

Suggested Citation

  • Hong, Qiuyi & Meng, Fanlin & Liu, Jian & Bo, Rui, 2023. "A bilevel game-theoretic decision-making framework for strategic retailers in both local and wholesale electricity markets," Applied Energy, Elsevier, vol. 330(PA).
    • Handle: RePEc:eee:appene:v:330:y:2023:i:pa:s0306261922015689
    DOI: 10.1016/j.apenergy.2022.120311
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    References listed on IDEAS

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    2. Hugo Algarvio & António Couto & Fernando Lopes & Ana Estanqueiro, 2024. "Strategic Behavior of Competitive Local Citizen Energy Communities in Liberalized Electricity Markets," Energies, MDPI, vol. 17(8), pages 1-23, April.
    3. Qiuyi Hong & Fanlin Meng & Jian Liu, 2023. "Customised Multi-Energy Pricing: Model and Solutions," Energies, MDPI, vol. 16(4), pages 1-31, February.
    4. Yu, Liying & Wang, Peng & Chen, Zhe & Li, Dewen & Li, Ning & Cherkaoui, Rachid, 2023. "Finding Nash equilibrium based on reinforcement learning for bidding strategy and distributed algorithm for ISO in imperfect electricity market," Applied Energy, Elsevier, vol. 350(C).
    5. Wu, Chenyu & Gu, Wei & Yi, Zhongkai & Chen, Xi & Shi, Zhengkun & Luo, Enbo, 2023. "A multi-rate hybrid model for real-time iterative bidding coupled with power system dynamics," Applied Energy, Elsevier, vol. 337(C).
    6. Cheng, Xiaoyuan & Yao, Ruiqiu & Postnikov, Andrey & Hu, Yukun & Varga, Liz, 2024. "Decentralized intelligent multi-party competitive aggregation framework for electricity prosumers," Applied Energy, Elsevier, vol. 373(C).
    7. 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).
    8. Lai, Chunyang & Kazemtabrizi, Behzad, 2024. "A novel data-driven tighten-constraint method for wind-hydro hybrid power system to improve day-ahead plan performance in real-time operation," Applied Energy, Elsevier, vol. 371(C).

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