A severity risk index for high impact low probability events in transmission systems due to extreme weather

Abstract

It is evident worldwide that high-impact, low-probability (HILP) events, such as associated to extreme weather, can have disastrous consequences on power systems resilience. In this paper, we propose a Severity Risk Index (SRI) that with the support of smart grid technologies (e.g., real-time monitoring) is capable of providing an indication of the evolving risk of power systems subject to HILP events in a smart and adaptive way, thus potentially contributing to effective decision-making to mitigate such risk. Specific applications considered here refer to windstorm events, for which purpose the proposed SRI is embedded in a Sequential Monte Carlo simulation for capturing the spatiotemporal effects of windstorms passing across transmission networks. Latin Hypercube Sampling and backward scenario reduction method are used to produce a computationally tractable number of representative scenarios for SRI computation. The IEEE 24-bus reliability test system is used to demonstrate the effectiveness of the proposed SRI.