Enhancing resilience of interdependent traffic-electric power system

Abstract Characterizing the interdependencies among highly interconnected critical infrastructure systems with adequate details is critical in devising cost-effective resilience improvement strategies. This study presents a bi-level, stochastic, and simulation-based decision-making framework for prioritizing mitigation and repair resources to maximize the expected resilience improvement of an interdependent traffic-electric power system under budgetary constraints. The upper level seeks to find the optimal resource allocation plan to maximize the expected attainable functionality gain. The lower level characterizes the functionalities of the traffic and electric power systems considering three types of interdependencies based on network flow analysis methods. The dynamic traffic assignment algorithm, rather than the static traffic assignment algorithm, is used in order to capture more realistic traffic dynamics in the congested urban roadway networks. Uncertainties in disruptions, traffic demands, and costs of mitigation and repair actions are also considered in the problem formulation. The problem is solved by the binary particle swarm optimization algorithm initialized with the knapsack-based heuristic, and the priority indices of disrupted components for mitigation and repair are then established based on the solutions. The proposed decision model is demonstrated using a portion of the traffic-electric power system in Galveston, Texas.