Bayesian-Spatial Optimization of Emergency EV Dispatch Under Multi-Hazard Disruptions: A Behaviorally Informed Framework for Resilient Energy Support in Critical Grid Nodes

Xi Chen, Xiulan Liu, Xijuan Yu, Yongda Li, Shanna Luo () and Xuebin Li
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Xi Chen: Beijing Electric Power Research Institute, State Grid Corporation of China, Beijing 100075, China
Xiulan Liu: Beijing Electric Power Research Institute, State Grid Corporation of China, Beijing 100075, China
Xijuan Yu: Beijing Electric Power Research Institute, State Grid Corporation of China, Beijing 100075, China
Yongda Li: Beijing Electric Power Research Institute, State Grid Corporation of China, Beijing 100075, China
Shanna Luo: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Xuebin Li: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China

Energies, 2025, vol. 18, issue 17, 1-23

Abstract: The growing deployment of electric vehicles (EVs) offers a unique opportunity to utilize them as mobile energy resources during large-scale emergencies. However, existing emergency dispatch strategies often neglect the compounded uncertainties of hazard disruptions, infrastructure fragility, and user behavior. To address this gap, we propose the Emergency-Responsive Aggregation Framework (ERAF)—a behaviorally informed, spatially aware, and probabilistic optimization model for resilient EV energy dispatch. ERAF integrates a Bayesian inference engine to estimate plug-in availability based on hazard exposure, behavioral willingness, and charger operability. This is dynamically coupled with a GIS-based spatial filter that captures road inaccessibility and corridor degradation in real time. The resulting probabilistic availability is fed into a multi-objective dispatch optimizer that jointly considers power support, response time, and delivery reliability. We validate ERAF using a high-resolution case study in Southern California, simulating 122,487 EVs and 937 charging stations across three compound hazard scenarios: earthquake, wildfire, and cyberattack. The results show that conventional deterministic models overestimate dispatchable energy by up to 35%, while ERAF improves deployment reliability by over 28% and reduces average delays by 42%. Behavioral priors reveal significant willingness variation across regions, with up to 47% overestimation in isolated zones. These findings underscore the importance of integrating behavioral uncertainty and spatial fragility into emergency energy planning. ERAF demonstrates that EVs can serve not only as grid assets but also as intelligent mobile agents for adaptive, decentralized resilience.

Keywords: emergency energy dispatch; Bayesian inference; electric vehicle aggregation; multi-hazard resilience; behavioral plug-in modeling; spatiotemporal optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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