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Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

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  • Li Sun
  • Bozidar Stojadinovic
  • Giovanni Sansavini

Abstract

In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.

Suggested Citation

  • Li Sun & Bozidar Stojadinovic & Giovanni Sansavini, 2019. "Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities," Risk Analysis, John Wiley & Sons, vol. 39(7), pages 1597-1614, July.
    • Handle: RePEc:wly:riskan:v:39:y:2019:i:7:p:1597-1614
    DOI: 10.1111/risa.13277
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    Cited by:

    1. Amanda Melendez & David Caballero-Russi & Mariantonieta Gutierrez Soto & Luis Felipe Giraldo, 2022. "Computational models of community resilience," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1121-1152, March.
    2. Sun, Li & D'Ayala, Dina & Fayjaloun, Rosemary & Gehl, Pierre, 2021. "Agent-based model on resilience-oriented rapid responses of road networks under seismic hazard," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Magoua, Joseph Jonathan & Li, Nan, 2023. "The human factor in the disaster resilience modeling of critical infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    4. Xuehua Han & Liang Wang & Dandan Xu & He Wei & Xinghua Zhang & Xiaodong Zhang, 2022. "Research Progress and Framework Construction of Urban Resilience Computational Simulation," Sustainability, MDPI, vol. 14(19), pages 1-15, September.

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