An adaptive robust framework for the optimization of the resilience of interdependent infrastructures under natural hazards

Abstract

This paper proposes a novel adaptive robust optimization (ARO)-based mathematical framework for resilience enhancement of interdependent critical infrastructure (CI) systems against natural hazards (NHs).In this framework, the potential impacts of a specific NH on an infrastructure are firstly evaluated, in terms of failure and recovery probabilities of system components; these are, then, fed into a two-stage ARO model to determine the optimal planning of resilience strategies under limited investment budget, anticipating the most-likely worst realization of the uncertainty of component failures under the NH. For its exact solution, a decomposition method based on simultaneous column-and-row generation (C&RG) is adopted. The approach is applied to a case study concerning the resilience of interdependent power and gas networks (IPGNs) subject to (simulated) wind storms. The numerical results demonstrate the effectiveness of the proposed framework for the optimization of the resilience of interdependent CIs under hazardous events; this provides a valuable tool for making informed pre-hazard preparation decisions. The value of a coordinated pre-hazard planning that takes into account CI interdependencies is also highlighted.

Proactive Resilience of Power Systems Against Natural Disasters: A Literature Review

“…After the degraded phase, the system enters the recovery stage as the system fully recovers to the pre-disturbance level. Finally, in the post-restoration state, the impact of the event on the operation of the system is evaluated and analyzed to prepare adaptive actions to excess the system's resilience to manage similar future events [45].…”

In search of network resilience: An optimization‐based view

“…We refer the reader to the review of Ouyang [162] that discusses modeling approaches for interdependent infrastructures. Fang and Zio [78] offer an alternative framework to that of Ouyang [163] which also involves a tri‐level optimization problem but the outer problem focuses on the probabilistic impacts of a natural hazard and then an adjustable robust optimization problem is examined to capture preparation decisions prior to the actual perturbation.…”

A software platform to support the energy system resilience study

“…Ouyang et al developed a threestage decision-making framework [12] to protect system components against intentional [13] and spatially localized [14] attacks. Fang and Zio formulated resilience enhancement problem as a robust optimization [15,16]. Ouyang et al introduced four mathematical models and their solution algorithms for exactly identifying the optimal protection strategies against worst-case malicious attacks and natural hazards [17].…”