Transmission System Restoration and Resilience Assessment Using Influence-Graph-Based Cascading Outage Scenarios

This study proposes a resilience assessment framework for power transmission systems under extreme weather conditions by integrating cascading outage modeling and restoration optimization. A Markovian Influence Graph was constructed using DC power flow simulations to generate probabilistic cascading sequences with N-k initial outages under stressed conditions, enabling the identification of critical transmission lines with high propagation influence. Representative scenarios were further analyzed with AC power flow in PSS/E, including redispatch, islanding, and Under Voltage Load Shedding to capture realistic propagation. A mixed-integer programming model was then formulated to optimize restoration sequences, considering generator start-up times, line repair constraints, and crew resources. The simulation results provided stepwise action plans and enabled the calculation of resilience metrics such as Energy Not Served, Expected Energy Not Served, Value at Risk, and resilience curve indices. The findings confirm that the proposed methodology effectively evaluates both outage propagation and recovery processes, offering a practical decision-support tool for operators to enhance resilience against High Impact Low Probability events.