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<p>Changing weather patterns has led to an observable escalation in the frequency and intensity of extreme weather events in recent years. This poses significant challenges to maintaining power system reliability and resilience for weakly connected remote communities or islands. To mitigate the risks associated with frequent extreme weather events, it is necessary to do predictive component failure analysis to plan for mitigation strategies including resource coordination and updating the associated control. This work contributes to: a) developing predictive distribution system component failure analysis amidst uncertainties caused by extreme weather events, b) developing a novel proactive distribution system resource coordination algorithm to enhance distribution power system resilience, c) developing use case with a focus on hurricanes as an example of extreme events. The resource coordination model is formulated as a Mixed-Integer Linear Programming (MILP) model for three-phase unbalanced distribution power systems and validated using a modified IEEE-123 node test feeder. Furthermore, the effectiveness of the proposed model in improving power system resilience is assessed through a set of resilience metrics. The results indicate enhanced power system resilience against extreme weather events.</p>

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Enabling resilient power grid: predictive component failure analysis and proactive energy resource coordination for adverse weather events

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月22日

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  • Linli Jia,
  • Anurag Srivastava

摘要

Changing weather patterns has led to an observable escalation in the frequency and intensity of extreme weather events in recent years. This poses significant challenges to maintaining power system reliability and resilience for weakly connected remote communities or islands. To mitigate the risks associated with frequent extreme weather events, it is necessary to do predictive component failure analysis to plan for mitigation strategies including resource coordination and updating the associated control. This work contributes to: a) developing predictive distribution system component failure analysis amidst uncertainties caused by extreme weather events, b) developing a novel proactive distribution system resource coordination algorithm to enhance distribution power system resilience, c) developing use case with a focus on hurricanes as an example of extreme events. The resource coordination model is formulated as a Mixed-Integer Linear Programming (MILP) model for three-phase unbalanced distribution power systems and validated using a modified IEEE-123 node test feeder. Furthermore, the effectiveness of the proposed model in improving power system resilience is assessed through a set of resilience metrics. The results indicate enhanced power system resilience against extreme weather events.