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A Trading Model for the Electricity Spot Market That Takes into Account the Preference for Energy Storage Trading

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  • Qikai Ma

    (College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Bo Liu

    (College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Jiang Li

    (College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

Abstract

With the continuous expansion of new energy installed capacity, the flexible regulation role of energy storage in the electricity spot market is becoming more and more prominent. However, traditional trading models often ignore the multiple trading preferences of energy storage. In this paper, we propose an electricity spot market trading model that considers the trading preferences of energy storage to incentivize energy storage to participate more actively in the market. First, the trading preferences of energy storage are modeled with a utility function in which the time preference coefficient and price elasticity are introduced. Then, the utility function is embedded into the spot market clearing model to establish a two-tier model of the spot market, which maximizes social welfare in the upper tier and maximizes energy storage benefits in the lower tier. Finally, the model is solved using KKT and large M methods, and its effectiveness is evaluated on the IEEE39 node system and on a real grid in a specific region.

Suggested Citation

  • Qikai Ma & Bo Liu & Jiang Li, 2025. "A Trading Model for the Electricity Spot Market That Takes into Account the Preference for Energy Storage Trading," Energies, MDPI, vol. 18(9), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2322-:d:1647929
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    References listed on IDEAS

    as
    1. Cristian Junge & Dharik Mallapragada & Richard Schmalensee, 2022. "Energy Storage Investment and Operation in Efficient Electric Power Systems," The Energy Journal, , vol. 43(6), pages 1-24, November.
    2. Wang, Zhuo & Hou, Hui & Zhao, Bo & Zhang, Leiqi & Shi, Ying & Xie, Changjun, 2024. "Risk-averse stochastic capacity planning and P2P trading collaborative optimization for multi-energy microgrids considering carbon emission limitations: An asymmetric Nash bargaining approach," Applied Energy, Elsevier, vol. 357(C).
    3. Cristian Junge, Dharik Mallapragada, and Richard Schmalensee, 2022. "Energy Storage Investment and Operation in Efficient Electric Power Systems," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    4. He, YongXiu & Liu, PeiLiang & Zhou, Li & Zhang, Yan & Liu, Yang, 2021. "Competitive model of pumped storage power plants participating in electricity spot Market——in case of China," Renewable Energy, Elsevier, vol. 173(C), pages 164-176.
    5. Zhai, Xiangyu & Li, Zening & Li, Zhengmao & Xue, Yixun & Chang, Xinyue & Su, Jia & Jin, Xiaolong & Wang, Peng & Sun, Hongbin, 2025. "Risk-averse energy management for integrated electricity and heat systems considering building heating vertical imbalance: An asynchronous decentralized approach," Applied Energy, Elsevier, vol. 383(C).
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