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Research on Day-Ahead Optimal Scheduling of Wind–PV–Thermal–Pumped Storage Based on the Improved Multi-Objective Jellyfish Search Algorithm

Author

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  • Yunfei Hu

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
    Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China)

  • Kefei Zhang

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
    Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China)

  • Sheng Liu

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
    Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China)

  • Zhong Wang

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
    Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China)

Abstract

As the share of renewable energy in modern power systems continues to grow, its inherent uncertainty and variability pose severe challenges to grid stability and the accuracy of traditional thermal power dispatch. To address this issue, this study fully exploits the fast response and flexible operation of variable-speed pumped storage (VS-PS) by developing a day-ahead scheduling model for a wind–photovoltaic–thermal–VS-PS system. The optimization model aims to minimize system operating costs, carbon emissions, and thermal power output fluctuations, while maximizing the regulation flexibility of the VS-PS plant. It is assessed using the improved multi-objective jellyfish search (IMOJS) algorithm, and its effectiveness is demonstrated through comparison with a fixed-speed pumped storage (FS-PS) system. Simulation results show that the proposed model significantly outperforms the traditional FS-PS system: it increases renewable energy accommodation capacity by an average of 68.51%, reduces total operating costs by 14.13%, and lowers carbon emissions by 3.63%.

Suggested Citation

  • Yunfei Hu & Kefei Zhang & Sheng Liu & Zhong Wang, 2025. "Research on Day-Ahead Optimal Scheduling of Wind–PV–Thermal–Pumped Storage Based on the Improved Multi-Objective Jellyfish Search Algorithm," Energies, MDPI, vol. 18(9), pages 1-38, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2308-:d:1647065
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    References listed on IDEAS

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