A Severity Risk Index for High Impact Low Probability Events in Transmission Systems due to Extreme Weather

It is evident worldwide that high-impact, lowprobability (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 decisionmaking 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.