Key factors affecting power systems resilience

The frequency and magnitude of severe weather-induced disruptions have intensified globally in recent years. With increasing urbanisation and higher interdependencies between urban systems, weather-induced disruptions have resulted in significant damage to power systems and associated infrastructure systems. To make power systems more resilient against severe weather, it is crucial to understand how resilience is affected by various factors.

Currently, scant attention has been paid to identifying the key factors affecting resilience in a systematic and quantitative manner. In addition, most of the previous studies have focused on assessing the impact of a specific type of severe weather on power systems, such as hurricanes. Studies on resilience under multiple types of severe weather are currently lacking.

In a paper published in Reliability Engineering & System Safety, Dr Lijuan Shen and Prof. Loon Ching Tang of the Cyber-Physical Systems Resilience at the Future Resilient Systems programme, together with collaborator, Yanlin Tang from the East China Normal University, studied factors that could impact power systems resilience under severe weather-induced disruptions.

The authors of the paper external page Understanding key factors affecting power systems resilience studied various factors from three perspectives: the extrinsic disruptions, the intrinsic capacities of a system and the effectiveness of recovery. They looked into two key components associated with each blackout and recovery of power systems, i.e., the number of customers affected and the recovery time. Based on 12 years of historical power outages data from 2007 to 2018 in the United States, they applied feature selection methods to identify and rank key factors affecting power system resilience.

It was found that the types of extrinsic disruption, especially hurricanes of high wind scales, can significantly influence the resilience of power systems. From the perspective of the intrinsic capabilities, the population characteristics have a large impact on the number of customers affected. Power systems tends to be less resilient in more urbanized areas with large populations, and conversely, more resilient in a region with more satellite townships.

Looking at the effectiveness of recovery, the economic status of the affected region is the most significant factor that determines recovery time. In regions with better economic health, power systems tends to be more resilient. In addition, recovery time is greatly influenced by investments in the compliance and enforcement program from the North American Electric Reliability Corporation.

The analysis provides insights for an improved understanding of power system resilience and could be useful in developing strategies to enhance the resilience of infrastructure systems.