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A novel robust optimization method for mobile energy storage pre-positioning

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  • Yuan, Hening
  • Shen, Yueqing
  • Xie, Xuehua

Abstract

The traditional power distribution network is transitioning to an active electrical distribution network due to the integration of distributed energy resources. Simultaneously, the increasing occurrence of extreme weather requires power networks to be more resilient. Distributed energy resources, especially mobile energy storage systems (MESS), play a crucial role in enhancing the resilience of electrical distribution networks. However, research is lacking on pre-positioning of MESS to enhance resilience, efficiency and electrical resource utilization in post-disaster operations. To address these issues, this paper introduces a proactive MESS pre-positioning method in active electrical distribution networks considering the uncertainties of distributed generation output. Firstly, the flexible resources in active distribution networks are modeled, including distributed generation, MESS and Electric Vehicles. Then, a robust optimization model is established for the pre-positioning of MESS considering the PV output uncertainty, where the big-M method and the column constraint generation algorithm are used to calculate the optimal capacity and location of the MESS. Finally, the effectiveness of the MESS pre-positioning model is verified using the IEEE 33-node system and the IEEE 141-node system, respectively. The simulation results show that the load loss at most of the nodes is significantly reduced and the total system cost is reduced by 17.65% compared with the case of fixed MESS access location. The results also show that when the number of MESS is low, each additional MESS unit reduces the total load shedding cost by about 20%. Moreover, the proposed robust optimization model for MESS pre-positioning is also effective in large-scale systems.

Suggested Citation

  • Yuan, Hening & Shen, Yueqing & Xie, Xuehua, 2025. "A novel robust optimization method for mobile energy storage pre-positioning," Applied Energy, Elsevier, vol. 379(C).
    • Handle: RePEc:eee:appene:v:379:y:2025:i:c:s0306261924021937
    DOI: 10.1016/j.apenergy.2024.124810
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