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Bi-Level Game-Theoretic Bidding Strategy for Large-Scale Renewable Energy Generators Participating in the Energy–Frequency Regulation Market

Author

Listed:
  • Ran Gao

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Shuyan Hui

    (School of Software Engineering, Southeast University, Nanjing 210096, China)

  • Bingtuan Gao

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Xiaofeng Liu

    (School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China)

Abstract

The proportion of grid-connected renewable energy, represented by wind and photovoltaic power, continues to rise. The intermittence and volatility of the power output of renewable energy bring serious challenges to the secure and stable operation of the power system. Adopting a market-based approach to promote the active participation of producers in frequency regulation and other auxiliary service markets besides the energy market is the only way to comprehensively solve the problems of power system security, stability, and economic benefits. Therefore, for the future bidding decision scenario of large-scale renewable energy generators participating in the energy–frequency regulation market, a bi-level game-theoretic bidding model based on mean-field game and non-cooperative game theory is proposed. The inner level is a mean-field game among large-scale renewable energy generators of the same type, and the outer level is a non-cooperative game among different types of generators. A combination of fixed-point iteration and finite-difference method is employed to solve the proposed bi-level bidding decision model. Case analysis indicates that the proposed model can effectively realize the bidding decision optimization for large-scale renewable energy generators in the energy–frequency regulation market. Furthermore, in comparison to traditional proportional bidding model, the proposed model enables renewable energy generators to secure higher profits in the energy–frequency regulation market.

Suggested Citation

  • Ran Gao & Shuyan Hui & Bingtuan Gao & Xiaofeng Liu, 2025. "Bi-Level Game-Theoretic Bidding Strategy for Large-Scale Renewable Energy Generators Participating in the Energy–Frequency Regulation Market," Energies, MDPI, vol. 18(10), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2604-:d:1658292
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    References listed on IDEAS

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    1. Rancilio, G. & Rossi, A. & Falabretti, D. & Galliani, A. & Merlo, M., 2022. "Ancillary services markets in europe: Evolution and regulatory trade-offs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    2. Chen, Jiang & Zhu, Weining & Yu, Qian, 2021. "Estimating half-hourly solar radiation over the Continental United States using GOES-16 data with iterative random forest," Renewable Energy, Elsevier, vol. 178(C), pages 916-929.
    3. Olivier Guéant & Pierre Louis Lions & Jean-Michel Lasry, 2011. "Mean Field Games and Applications," Post-Print hal-01393103, HAL.
    4. Kim, James Hyungkwan & Kahrl, Fredrich & Mills, Andrew & Wiser, Ryan & Montañés, Cristina Crespo & Gorman, Will, 2023. "Economic evaluation of variable renewable energy participation in U.S. ancillary services markets," Utilities Policy, Elsevier, vol. 82(C).
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