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Optimal offer-bid strategy of an energy storage portfolio: A linear quasi-relaxation approach

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  • Tómasson, Egill
  • Hesamzadeh, Mohammad Reza
  • Wolak, Frank A.

Abstract

This paper proposes a model of the behavior of an expected profit-maximizing merchant storage owner with the ability to exercise unilateral market power. The resulting non-linear bilevel optimization problem is transformed into a single-level stochastic bilinear program using the Karush-Kuhn-Tucker conditions of the lower-level Independent System Operator dispatch problem. By discretizing the offers and bids of the merchant storage owner, the problem is formulated as a stochastic disjunctive program. Using the disjunctive nature of the derived program, a specialized branch-and-bound algorithm that applies a linear quasi-relaxation of the merchant storage problem is proposed. Our solution algorithm is able to solve the problem in an efficient manner; returning the charge and discharge strategies for the merchant storage owner that yield the highest expected profits. Simulations of test systems reveal the various abilities of the merchant storage owner to exercise unilateral market power. Those include demand withholding, generation withholding and under-use which result in an increased congestion in both space and time when compared to the welfare-maximizing use of storage. Factors such as uncertain bids by other players, final state-of-charge requirements and arbitrage by other storage players are investigated. Moreover, numerical results demonstrate the superior computational performance of the proposed solution algorithm when benchmarked against current practices in the literature.

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  • Tómasson, Egill & Hesamzadeh, Mohammad Reza & Wolak, Frank A., 2020. "Optimal offer-bid strategy of an energy storage portfolio: A linear quasi-relaxation approach," Applied Energy, Elsevier, vol. 260(C).
  • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s0306261919319385
    DOI: 10.1016/j.apenergy.2019.114251
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    1. Huang, Qisheng & Xu, Yunjian & Courcoubetis, Costas, 2020. "Stackelberg competition between merchant and regulated storage investment in wholesale electricity markets," Applied Energy, Elsevier, vol. 264(C).
    2. Thakur, Jagruti & Hesamzadeh, Mohammad Reza & Date, Paresh & Bunn, Derek, 2023. "Pricing and hedging wind power prediction risk with binary option contracts," Energy Economics, Elsevier, vol. 126(C).
    3. Fang, Xichen & Guo, Hongye & Zhang, Xian & Wang, Xuanyuan & Chen, Qixin, 2022. "An efficient and incentive-compatible market design for energy storage participation," Applied Energy, Elsevier, vol. 311(C).
    4. Xia, Yuanxing & Xu, Qingshan & Chen, Lu & Du, Pengwei, 2022. "The flexible roles of distributed energy storages in peer-to-peer transactive energy market: A state-of-the-art review," Applied Energy, Elsevier, vol. 327(C).
    5. Liu, Shiyu & Ren, Yanzhe & Zhang, Zhenyu & Xiao, Yao & Bie, Zhaohong & Wang, Xifan, 2021. "Optimal bid-offer strategy for a virtual energy storage merchant: A stochastic bi-level model with all-scenario feasibility," Applied Energy, Elsevier, vol. 299(C).
    6. Jerry Anunrojwong & Santiago R. Balseiro & Omar Besbes & Bolun Xu, 2024. "Battery Operations in Electricity Markets: Strategic Behavior and Distortions," Papers 2406.18685, arXiv.org.

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