A sequentially preventive model enhancing power system resilience against extreme-weather-triggered failures

Abstract

Affected by climate change, extreme weather events are occurring with increasing frequency and severity, resulting in more and more blackouts. Thus, resilience enhancement is drawing wider attention from academia and industry. Nevertheless, most of the existing studies ignore the resilience enhancement operational strategies against the extreme-weather-triggered cascading failures. To solve these problems, this paper proposes a sequentially proactive operational strategy to enhance resilience against extreme-weather-triggered cascading failures. Firstly, due to the difference between the time scales of extreme weather events and cascading failures, the outage process under extreme weather conditions is regarded as a double-time-dimension process, including the weather disaster and cascading failures. Besides, given that extreme weather and cascading failures are random, the random scenarios sampling method is utilized to simulate the double-time-dimension outage process. Further, customized double-time-dimension process constraints are introduced to the proposed model to represent the correlation between extreme weather events and cascading outages. Meanwhile, by regarding the probability of outages scenarios as the weight of each objective, the proposed model can be transformed into mixed-integer linear programming. Numerical studies validate the feasibility and effectiveness of the proposed method. The results demonstrate that the proposed strategies can prevent extreme weather from triggering cascading failures and reduce the size of blackouts, to enhance the resilience performance of the power system. The findings can provide some meaningful insights for system operators to adopt resilience enhancement measures against the extreme-weather-triggered cascading failures.