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Multi-Objective Optimization of Energy Storage Station Configuration in Power Grids Considering the Flexibility of Thermal Load Control

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  • Kaikai Wang

    (State Grid Shanxi Electric Power Company Economic and Technological Research Institute, Taiyuan 030024, China
    Energy Internet Key Laboratory of Shanxi Province, Taiyuan 030024, China)

  • Yao Wang

    (State Grid Shanxi Electric Power Company Economic and Technological Research Institute, Taiyuan 030024, China
    Energy Internet Key Laboratory of Shanxi Province, Taiyuan 030024, China)

  • Jin Gao

    (State Grid Shanxi Electric Power Company Economic and Technological Research Institute, Taiyuan 030024, China
    Energy Internet Key Laboratory of Shanxi Province, Taiyuan 030024, China)

  • Yan Liang

    (State Grid Shanxi Electric Power Company Economic and Technological Research Institute, Taiyuan 030024, China
    Energy Internet Key Laboratory of Shanxi Province, Taiyuan 030024, China)

  • Zhenfei Ma

    (Energy Internet Key Laboratory of Shanxi Province, Taiyuan 030024, China
    Key Laboratory of Cleaner Intelligent Control on Coal & Electricity, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China)

  • Hanyue Liu

    (Energy Internet Key Laboratory of Shanxi Province, Taiyuan 030024, China
    Key Laboratory of Cleaner Intelligent Control on Coal & Electricity, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China)

  • Zening Li

    (Energy Internet Key Laboratory of Shanxi Province, Taiyuan 030024, China
    Key Laboratory of Cleaner Intelligent Control on Coal & Electricity, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China)

Abstract

Given that traditional grid energy storage planning neglects the impact of power supply demand on the effectiveness of storage deployment, the resulting system suffers from limited operational economic performance and restricted renewable energy integration capability. In response to this challenge, this paper presents a multi-objective optimization approach for configuring a distribution network energy storage station (ESS) by incorporating the flexibility of temperature-controlled loads. This approach aims to enhance the efficiency of energy storage utilization, facilitate the local consumption of renewable energy (RE), and achieve mutually beneficial outcomes for both energy providers and consumers. Firstly, a controllable load model for the distribution network was developed, incorporating power balance constraints and the flexibility of temperature-controlled loads. Additionally, an ESS model was formulated, taking into account economic considerations and other influencing factors to ensure optimal deployment and operation. Secondly, this paper introduces a multi-objective optimization strategy for a distribution network ESS, targeting the minimization of both the microgrid operating costs and energy storage allocation costs. The proposed model was solved using the POA-GWO-CSO optimization algorithm to achieve the optimal energy storage deployment and cost efficiency. Finally, the effectiveness of the proposed model was verified through case analysis. The results demonstrate that the proposed grid energy storage optimization configuration model not only satisfies the requirements of both parties, but also enhances the overall system economic performance.

Suggested Citation

  • Kaikai Wang & Yao Wang & Jin Gao & Yan Liang & Zhenfei Ma & Hanyue Liu & Zening Li, 2025. "Multi-Objective Optimization of Energy Storage Station Configuration in Power Grids Considering the Flexibility of Thermal Load Control," Energies, MDPI, vol. 18(10), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2527-:d:1655193
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    References listed on IDEAS

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    1. 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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