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Multi-Time-Scale Energy Storage Stochastic Planning for Power Systems During Typhoon

Author

Listed:
  • Shidong Hong

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Boyu Qin

    (School of Energy and Electrical Engineering, Qinghai University, Xining 810016, China)

  • Peicheng Chen

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Weike Song

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yiwei Su

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Zhe Wu

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Tong Ma

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

The high penetration of renewable energy is becoming an important feature of new power systems. However, the power grid is facing greater threats of failures with the increasing frequency of extreme weather, making it necessary to enhance the resilience of power systems. In this paper, a multi-time-scale energy storage planning system is proposed for power system resilience improvement. Firstly, the characteristics of multi-time-scale energy storage are analyzed, and models of battery energy storage and hydrogen energy storage are established. Secondly, based on an analysis of random extreme weather scenarios, a bi-level stochastic programming model for multi-energy storage aimed at enhancing the resilience of power systems is constructed. Finally, based on the modified IEEE-24 node system, the model solution and example analysis are carried out, and the optimal configuration scheme for multi-energy storage is obtained. The results show that multi-energy storage is able to adjust more flexibly and effectively improve the resilience of the power system. Compared with the configurations of short-term and long-term energy storage systems, adopting multi-timescale energy storage reduces the total cost by 22.77% and 14.08%, respectively, and improves resilience by 4.33% and 0.67%, respectively.

Suggested Citation

  • Shidong Hong & Boyu Qin & Peicheng Chen & Weike Song & Yiwei Su & Zhe Wu & Tong Ma, 2026. "Multi-Time-Scale Energy Storage Stochastic Planning for Power Systems During Typhoon," Sustainability, MDPI, vol. 18(5), pages 1-24, March.
    • Handle: RePEc:gam:jsusta:v:18:y:2026:i:5:p:2416-:d:1876205
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