Abstract
This article examines electric power restoration following catastrophic damage in modern cities and regions due to extreme events and disasters. Recovery time and non-restoration probability are derived using new data from a comprehensive range of recent massive hurricanes, extensive wildfires, severe snowstorms, and damaging cyclones. Despite their totally disparate origins, over three orders of magnitude severe wildfires and hurricanes have the same non-restoration probability trends, which are of simple exponential form. The results fall into categories that are dependent on and grouped by the degree of damage and social disruption. The implications are discussed for emergency response planning. These new results demonstrate that the scientific laws of probability and human learning, which dominate risk in modern technologies and societies are also applicable to a wide range of disasters and extreme events.
Highlights
This research provides a new technical and realistic basis for determining the likelihood of when electric power will be restored after damage due to extreme events and disasters
The recovery time and non-restoration probability are derived by using new power outage data from a wide range of recent hurricanes, storms, snowstorms, cyclones
The result is a new method for making outage time predictions in repairable power systems following disasters that is independent of the specific electrical system and its protocols
Summary
This research provides a new technical and realistic basis for determining the likelihood of when electric power will be restored after damage due to extreme events and disasters. The recovery time and non-restoration probability are derived by using new power outage data from a wide range of recent hurricanes, storms, snowstorms, cyclones,. Using limited extreme event data, Duffey (2014) showed that the probability of restoration was significantly lower, or restoration took much longer, for an unexpected storm (Superstorm Sandy) and a major earthquake-induced tsunami in Japan. This delayed recovery time and/or lower probability of restoration were attributable to the extensive damage, widespread social disruption, and overloaded emergency response capability. The previous work and models are outlined and an expanded validation presented, which results in new predictive correlations for the dynamic restoration probability for extreme events
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