Resilience enhancement for interdependent critical infrastructures

Modern societies depend on the proper and resilient functioning of their critical infrastructures (CIs) to support quality of life to their population. The interdependencies of the CI systems, however, make the CIs increasingly vulnerable to several threats; as a result, natural or human-made disasters can cause significant physical, economic, and social disruptions. Since total beforehand protection cannot be guaranteed, CI protection strategies should focus on resilience enhancement at both the pre- and the post-disaster phase for a better response. This thesis first proposes a resilience assessment framework that provides quantitative means to assess infrastructure resilience for interdependent CI systems. The framework is tested within the Infrastructure Interdependencies Simulator (i2SIM) that models and simulates the CI interdependencies. A coupled Cyber-Physical System (CPS) is modelled within the i2SIM framework to study the process of cascading failures. Resilience enhancement for the pre-disaster preparedness is done with a risk evaluation approach and is proposed by considering the profile of a hurricane to calculate the probability of failure of the network components. Based on the results of the evaluation, a resource allocation optimization is formulated using mixed-integer nonlinear programming (MINLP). For post-disaster resilience enhancement, an optimal reconfiguration algorithm, together with a hybrid load shedding strategy, is developed to find alternative paths to maintain supply to the most critical loads. A modified shortest path search that we call the Optimal Recovery Sequencing (ORS) is used to optimize the repair sequences. The obtained numerical results validate that the recovery ability of the coupled system, and as a result its resilience, is increased with the proposed optimization. Finally, to reduce the computational complexity for very large scenarios and for real-time response, this thesis uses a Soft-Hard Optimal Convergence (SHOC) methodology. Machine learning techniques are used to train an artificial intelligence (AI) agent with thousands of off-line scenarios to provide the initial estimate for the SHOC algorithm. After the disaster occurs, hard optimization methods are used on a small subset of solutions identified by the AI agent. Using this methodology, solution time speedups of 800 times for a 70-bus test system with 30 simultaneous faults are achieved.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat