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

• Published in: Risk analysis Vol. 39; no. 9; pp. 1949 - 1969
• Main Authors: Fang, Yi‐Ping, Sansavini, Giovanni, Zio, Enrico
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.09.2019; Wiley
• Subjects: Attacker–defender interdiction model; Bridges; Computer simulation; Conservatism; Critical components; Critical infrastructure; critical infrastructures; Decision makers; Electric bridges; Electric power; Electric power grids; Engineering Sciences; Hazard identification; Infrastructure; Integer programming; Linear programming; Mathematical models; mathematical optimization; Mixed integer; Natural disasters; natural hazards; network interdiction; Optimization; Power; Risk assessment; Solvers; Vulnerability; vulnerability analysis; vulnerability analysis; network interdiction; natural hazards; critical infrastructures; Attacker-defender interdiction model; mathematical optimization; attacker-defender interdiction model
• ISSN: 0272-4332; 1539-6924; 1539-6924
• DOI: 10.1111/risa.13287

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.