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Fire risk mitigation approach for the battery energy storage systems: Hybridization and bi-level optimization

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  • Wang, Fengjuan
  • Zhao, Chuandang
  • Jiang, Mingkun
  • Shi, Yi

Abstract

Battery energy storage is at the forefront of supporting a more sustainable and resilient electrical system. However, their reactive and hazardous nature can lead to fire hazards or even explosions, raising adverse concerns about their future expansion. Hybridizing different storage technologies is an efficient way to mitigate fire hazards, but no research has explored whether this strategy is efficient and how it can work better. Therefore, this paper emphasizes the integration of lithium-ion batteries with stable storage technologies to mitigate fire risk at a solar power plant to mitigate the generation scheduling deviation. A bilevel optimization model is proposed to characterize the decision conflicts between the authority and the enterprise in determining the capacity and size of different storage technologies when applying the hybridization strategy. In the case study, the influence of the deviation evaluation method, the allowable deviation ratio, the cost-risk parameters, and the authority-required storage duration on the optimization results is discussed. It is found that assessing the deviation by day rather than 15 min not only helps to form a hybrid storage system and leads to lower costs and risks, but also leads to better deviation mitigation efficiency and better policy implementation effects. Although hybridization can reduce battery fire risk, it leads to increased costs or reduced deviation mitigation efficiency. Policy implementation discussion and managerial recommendations are also provided. This paper can serve as an important reference for both the authority and the enterprise to optimize the deployment of storage facilities.

Suggested Citation

  • Wang, Fengjuan & Zhao, Chuandang & Jiang, Mingkun & Shi, Yi, 2025. "Fire risk mitigation approach for the battery energy storage systems: Hybridization and bi-level optimization," Technology in Society, Elsevier, vol. 83(C).
  • Handle: RePEc:eee:teinso:v:83:y:2025:i:c:s0160791x25001356
    DOI: 10.1016/j.techsoc.2025.102945
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