This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi-level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability-based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi-level max-min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one-level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more-informed prehazard preparation decisions.

An Optimization-Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

Sansavini, Giovanni;Zio, Enrico
2019

Abstract

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi-level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability-based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi-level max-min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one-level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more-informed prehazard preparation decisions.
Attacker–defender interdiction model; critical infrastructures; mathematical optimization; natural hazards; network interdiction; vulnerability analysis; Safety, Risk, Reliability and Quality; Physiology (medical)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1077931
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