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Predicting Outage Restoration in Advance of Storms Impact
  • Tara Walsh ,
  • Aaron Spaulding ,
  • Diego Cerrai
Tara Walsh
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Aaron Spaulding
University of Connecticut

Corresponding Author:[email protected]

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Diego Cerrai
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The increasing frequency and intensity of high impact storms, especially in Northeast United States, requires utilities and emergency managers to be increasingly prepared for lengthy power outage restorations. Historically, restoration has relied on emergency managers decennial experience with limited access to predictive models. This study highlights the development of a combined system composed of the UConn Outage Prediction Model (OPM) for predicting weather-related damage in the distribution system and an Agent-Based Model (ABM) for estimating the time to electric power restoration. The combined system is validated using Outage Management System (OMS) and crew deployment information for four historical extreme weather events that occurred in the State of Connecticut in the past decade. Through the ABM’s ability to test different restoration strategies, we study the impact that human knowledge and decisions have on the outage restoration curve. Furthermore, we use the model to test how the restoration could have been different if crews were allocated to area work centers based on the location of damage predictions from the UConn OPM and on increased crew counts, reflecting a more aggressive storm preparedness. This test highlights how an OPM-ABM system can benefit emergency preparedness and response managers in advance of storms impact.