A Hierarchical Resilience Enhancement Framework for Interdependent Critical Infrastructures

Resilience is becoming a key concept for risk assessment and safety management of interdependent critical infrastructures (ICIs). This work proposes a resilience enhancement framework for ICIs. With reference to the accidental event, ex-ante and ex-post solutions for enhancing system resilience are analysed and included into a hierarchical model of resilience enhancement strategies (RES). To provide specific resilience enhancement solutions for ICIs, we integrate the hierarchical model with a model predictive control-based dynamic model of ICI system operation. The relationships between the solutions implemented and their impacts on the system parameters are discussed. A multi-objective optimization (MOO) problem is defined, with the objectives of simultaneously minimizing RES cost and maximizing ICIs resilience. The fast non-dominated sorting genetic algorithm NSGA-II is used to solve the MOO problem. For exemplification, a case study is considered, involving interdependent natural gas network and electric power grid. The results show that the resilience enhancement framework is effective in finding optimal RESs for given ICIs.