A Markov reward process-based framework for resilience analysis of multistate energy systems under the threat of extreme events

Zhiguo Zeng, Yi-Ping Fang, Qingqing Zhai and Shijia Du

Reliability Engineering and System Safety, 2021, vol. 209, issue C

Abstract: Energy systems are increasingly exposed to the threats of extreme events like floods, earthquakes and hurricanes. In practice, the behaviors of the systems affected by these extreme events are often modeled by multistate models to facilitate the analysis. In this paper, we develop a generic framework for resilience modeling and analysis of multistate energy systems. A multistate resilience model is developed based on a Markov reward process model, where the degradation and recovery of system performance are characterized by a continuous time discrete state Markov chain and the losses caused by the extreme event is modeled by the reward rates associated with the sojourns in the degradation states and the transitions among the states. Four numerical metrics are defined to describe different aspects of system resilience, i.e. the resistant, absorption, recovery and overall resilience. A simulation-based algorithm is proposed for resilience analysis of multistate energy systems. The developed methods are applied for resilience modeling and analysis of a Nuclear Power Plant (NPP) under the threat of earthquakes. The Markov reward process model is developed following a probabilistic seismic hazard analysis, a fragility analysis and an event tree modeling of accident evolutions. Both a time-static and time-dependent resilience analysis are conducted and the results show that the developed model is able to comprehensively describe the resilience of multistate energy systems under the threats of extreme events.

Keywords: Resilience; Energy system; Extreme events; Multistate system; Markov reward model; Accumulated reward (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000041

DOI: 10.1016/j.ress.2021.107443

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