A Scalable Approach to the Assessment of Storm Impact in Distributed Automation Power Grids

We present models and metrics for the survivability assessment of distribution power grid networks accounting for the impact of multiple failures due to large storms. The analytical models used to compute the proposed metrics are built on top of three design principles: state space factorization, state aggregation, and initial state conditioning. Using these principles, we build scalable models that are amenable to analytical treatment and efficient numerical solution. Our models capture the impact of using reclosers and tie switches to enable faster service restoration after large storms.We have evaluated the presented models using data from a real power distribution grid impacted by a large storm: Hurricane Sandy. Our empirical results demonstrate that our models are able to efficiently evaluate the impact of storm hardening investment alternatives on customer affecting metrics such as the expected energy not supplied until complete system recovery.