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Enhanced photovoltaic-powered distribution network resilience aided by electric vehicle emergency response with willingness and reward

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  • Wang, Jun
  • Zhao, Yuanlong
  • Li, Hongyan
  • Xu, Xiao

Abstract

With the increasing integration of electric vehicles (EVs) into distribution networks (DNs), leveraging distributed energy resources (DERs) has become essential for mitigating load loss during extreme disaster scenarios. However, the quantification of power support capacity during post-disaster recovery remains inadequate, hindering the optimization of emergency resource deployment. This paper presents a novel two-phase resilience enhancement strategy for DNs, addressing both pre-disaster and post-disaster stages. The strategy includes willingness-reward dual parameter settings to characterize the power support capacity of EV aggregators. DERs are utilized to quickly connect to DNs and provide emergency power after a disaster. In the pre-disaster phase, a two-stage iterative model is developed for mobile energy storage systems (MESS) pre-positioning and DN topology reconfiguration. The post-disaster recovery is formulated as a mixed-integer second-order cone programming model to depict the emergency response of EVs. EV charging stations (EVCS) are aggregated using a fuzzy membership approach to calculate their support power. Sensitivity analysis shows that optimizing EVCS willingness-reward dual parameter settings can significantly enhance power support. Numerical results demonstrate a 48.23 % reduction in load shedding costs through MESS pre-positioning, and a 66.6 % improvement in resilience metrics with optimized willingness-reward dual parameter settings.

Suggested Citation

  • Wang, Jun & Zhao, Yuanlong & Li, Hongyan & Xu, Xiao, 2025. "Enhanced photovoltaic-powered distribution network resilience aided by electric vehicle emergency response with willingness and reward," Applied Energy, Elsevier, vol. 389(C).
    • Handle: RePEc:eee:appene:v:389:y:2025:i:c:s0306261925003496
    DOI: 10.1016/j.apenergy.2025.125619
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