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A Game Theoretical Approach Based Bidding Strategy Optimization for Power Producers in Power Markets with Renewable Electricity

Author

Listed:
  • Yi Tang

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

  • Jing Ling

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

  • Tingting Ma

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

  • Ning Chen

    (China Electric Power Research Institute, Nanjing 210003, China)

  • Xiaofeng Liu

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

  • Bingtuan Gao

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

Abstract

In a competitive electricity market with substantial involvement of renewable electricity, maximizing profits by optimizing bidding strategies is crucial to different power producers including conventional power plants and renewable ones. This paper proposes a game-theoretic bidding optimization method based on bi-level programming, where power producers are at the upper level and utility companies are at the lower level. The competition among the multiple power producers is formulated as a non-cooperative game in which bidding curves are their strategies, while uniform clearing pricing is considered for utility companies represented by an independent system operator. Consequently, based on the formulated game model, the bidding strategies for power producers are optimized for the day-ahead market and the intraday market with considering the properties of renewable energy; and the clearing pricing for the utility companies, with respect to the power quantity from different power producers, is optimized simultaneously. Furthermore, a distributed algorithm is provided to search the solution of the generalized Nash equilibrium. Finally, simulation results were performed and discussed to verify the feasibility and effectiveness of the proposed non-cooperative game-based bi-level optimization approach.

Suggested Citation

  • Yi Tang & Jing Ling & Tingting Ma & Ning Chen & Xiaofeng Liu & Bingtuan Gao, 2017. "A Game Theoretical Approach Based Bidding Strategy Optimization for Power Producers in Power Markets with Renewable Electricity," Energies, MDPI, vol. 10(5), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:627-:d:97538
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    References listed on IDEAS

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    Cited by:

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    2. Chris Johnathon & Ashish Prakash Agalgaonkar & Joel Kennedy & Chayne Planiden, 2021. "Analyzing Electricity Markets with Increasing Penetration of Large-Scale Renewable Power Generation," Energies, MDPI, vol. 14(22), pages 1-15, November.
    3. Debin Fang & Qiyu Ren & Qian Yu, 2018. "How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach," Energies, MDPI, vol. 12(1), pages 1-13, December.
    4. Johnathon, Chris & Agalgaonkar, Ashish Prakash & Planiden, Chayne & Kennedy, Joel, 2023. "A proposed hedge-based energy market model to manage renewable intermittency," Renewable Energy, Elsevier, vol. 207(C), pages 376-384.

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