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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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    Cited by:

    1. Meng, Fanlin & Ma, Qian & Liu, Zixu & Zeng, Xiao-Jun, 2023. "Multiple dynamic pricing for demand response with adaptive clustering-based customer segmentation in smart grids," Applied Energy, Elsevier, vol. 333(C).
    2. Qiuyi Hong & Fanlin Meng & Jian Liu, 2023. "Customised Multi-Energy Pricing: Model and Solutions," Energies, MDPI, vol. 16(4), pages 1-31, February.
    3. 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).
    4. 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).

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