Prevention and mitigation of low probability, high impact events is becoming a priority for power system operators, as natural disasters are hitting critical infrastructures with increased frequency all over the world. Protecting power networks against these events means improving their resilience in planning, operation and restoration phases. This paper introduces a framework based on time-varying interval Markov Chains to assess system’s resilience to catastrophic events. After recognizing the difficulties in accurately defining transition probabilities, due to the presence of data uncertainty, this paper proposes a novel approach based on interval mathematics, which allows representing the elements of the transition matrices by intervals, and computing reliable enclosures of the transient state probabilities. The proposed framework is validated on a case study, which is based on the resilience analysis of a power system in the presence of multiple contemporary faults. The results show how the proposed framework can successfully enclose all the possible outcomes obtained through Monte Carlo simulation. The main advantages are the low computational burden and high scalability achieved.
An Interval Mathematic-Based Methodology for Reliable Resilience Analysis of Power Systems in the Presence of Data Uncertainties
