Study on power system resilience assessment considering cascading failures during wildfire disasters
Baohong Li, Changle Liu, Yue Yin, Qin Jiang, Yingmin Zhang, Tianqi Liu
Abstract
Wildfire disasters, driven by climate change, have increased in frequency and intensity over the past decades, posing unprecedented challenges to the resilience of power systems. These disasters often lead to cascading failures, resulting in severe power outages and significant economic losses. Accurate and efficient resilience assessment is critical for understanding the vulnerabilities of power systems and supporting effective decision-making during wildfire disasters. This paper proposes a comprehensive framework for assessing the resilience of power systems impacted by wildfire disasters. The framework evaluates the probability of transmission line failure, cascading failure-induced load shedding, and resilience indices that capture transient processes. To enhance computational efficiency, the Impact Increment State Enumeration method is incorporated, enabling fast enumeration of various failure elements. Additionally, the sparsity of failure probability sets is leveraged to further optimize the efficiency of the IISE algorithm. The proposed method facilitates rapid and accurate resilience assessments for power systems during wildfire disasters. The framework is validated using both the IEEE 9-bus system and a practical power system in a wildfire-prone area, demonstrating its effectiveness and practical applicability. Results show that the framework can provide actionable insights for resilience planning and decision-making, helping evaluate the impacts of wildfire disasters on power system operations. • Develop a probability model for transmission line failures during wildfire disasters. • Incorporate wildfire spread dynamics and insulation breakdown into failure analysis. • Load shedding and resilience metrics effectively capture the impact of cascading failure process. • Propose an enhanced strategy to improve the computational efficiency of IISE by partitioning failure probability sets. • Provide actionable insights for assessing power system resilience under wildfire disasters.