A Markov Decision Process to Enhance Power System Operation Resilience during Hurricanes

Abstract

This paper proposes a probabilistic proactive generation redispatch strategy to enhance operational resilience of power grids in preparation to and during hurricanes. Existing resilience enhancement strategies usually focus on reducing load curtailments during extreme events without proactively preparing power systems for predicted extreme events. Fragility of system components to extreme events and changing system conditions and constraints exhibit further computational burden and complexities to determine optimal system operation states. In this work, a multi-objective mixed integer linear programming optimization problem is formulated to minimize the amount of load curtailments and generation operation costs when a power system is impacted by a hurricane while adhering to system operational and technical constraints (e.g., ramping rates, minimum up/down times, line constraints, start-up/shut-down generation costs). A Markov decision process is used to model system state transitions and to provide generation redispatch strategies for each possible system state considering component failure probabilities, hurricane spatiotemporal properties, and load variations. The CPLEX solver is integrated with MATLAB to formulate and solve the optimization problem. The proposed strategy is demonstrated on several systems under various hurricane scenarios including the IEEE 30-bus system under various hurricane scenarios. The results show that the proposed solution can improvement power system resilience against hurricanes.