Simulation study finds crew counts and repair times drive power outage restoration speed, suggesting strategic workforce allocation optimizes storm recovery.
Extreme weather can cause severe damage and widespread power outages across utility service areas. The restoration process can be long and costly and emergency managers may have limited computational resources to optimize the restoration process. This study takes an agent based modeling (ABM) approach to optimize the utility storm recovery process in Connecticut. The ABM is able to replicate past storm recoveries and can test future case scenarios. We found that parameters such as the number of outages, repair time range and the number of utility crews working can substantially impact the estimated time to restoration (ETR). Other parameters such as crew starting locations and travel speeds had comparatively minor impacts on the ETR. The ABM can be used to train new emergency managers as well as test strategies for storm restoration optimization.
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Walsh et al. (2018) studied this question.
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