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Bi-Level Optimization-Based Bidding Strategy for Energy Storage Using Two-Stage Stochastic Programming

Author

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  • Kui Hua

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

  • Qingshan Xu

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

  • Lele Fang

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

  • Xin Xu

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

Abstract

Energy storage will play an important role in the new power system with a high penetration of renewable energy due to its flexibility. Large-scale energy storage can participate in electricity market clearing, and knowing how to make more profits through bidding strategies in various types of electricity markets is crucial for encouraging its market participation. This paper considers differentiated bidding parameters for energy storage in a two-stage market with wind power integration, and transforms the market clearing process, which is represented by a two-stage bi-level model, into a single-level model using Karush–Kuhn–Tucker conditions. Nonlinear terms are addressed using binary expansion and the big-M method to facilitate the model solution. Numerical verification is conducted on the modified IEEE RTS-24 and 118-bus systems. The results show that compared to bidding as a price-taker and with marginal cost, the proposed mothod can bring a 16.73% and 13.02% increase in total market revenue, respectively. The case studies of systems with different scales verify the effectiveness and scalability of the proposed method.

Suggested Citation

  • Kui Hua & Qingshan Xu & Lele Fang & Xin Xu, 2025. "Bi-Level Optimization-Based Bidding Strategy for Energy Storage Using Two-Stage Stochastic Programming," Energies, MDPI, vol. 18(16), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:16:p:4447-:d:1729506
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    References listed on IDEAS

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    1. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach," Applied Energy, Elsevier, vol. 222(C), pages 932-950.
    2. Abunima, Hamza & Park, Woan-Ho & Glick, Mark B. & Kim, Yun-Su, 2022. "Two-Stage stochastic optimization for operating a Renewable-Based Microgrid," Applied Energy, Elsevier, vol. 325(C).
    3. Sakti, Apurba & Botterud, Audun & O’Sullivan, Francis, 2018. "Review of wholesale markets and regulations for advanced energy storage services in the United States: Current status and path forward," Energy Policy, Elsevier, vol. 120(C), pages 569-579.
    4. Fan, Wei & Tan, Qingbo & Zhang, Amin & Ju, Liwei & Wang, Yuwei & Yin, Zhe & Li, Xudong, 2023. "A Bi-level optimization model of integrated energy system considering wind power uncertainty," Renewable Energy, Elsevier, vol. 202(C), pages 973-991.
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