Optimized Rolling Allocation of Outages for Damage Assesment

TL;DR

This paper introduces a real-time, optimized damage assessment crew allocation system for storm restoration, improving assessment speed and efficiency across multiple states using a novel rolling horizon approach.

Contribution

It presents a novel multi-state deployment of an optimized, rolling horizon damage assessment crew allocation system that accounts for real-world constraints and operator control during live storms.

Findings

Faster damage assessments during storms.

Reduced travel distance and revisits.

Effective multi-state deployment of the system.

Abstract

Natural disasters often inflict severe damage on distribution grids. Rapid, reliable damage assessment (DA) is essential for storm restoration, yet most optimization work targets repair dispatch after faults are identified. This paper presents a production, rolling horizon DA crew allocation system deployed across multiple U.S. states in Eversource Energy's service territory and used during live storms. The method implements a sequential k-job assignment policy per available crew, executed on a fixed cadence and on operators' control. The objective jointly prioritizes critical facilities and customer impact while controlling travel time on the actual road network via the Google Maps API. A key constraint is the absence of live crew GPS; we infer crew locations from the last confirmed DA site and robustify travel estimates for staleness, yielding stable recommendations without continuous tracking. The operator remains in the loop with controls to limit churn and to publish a feasible plan. Using data from the March 7 New Hampshire storm with 90 moderate outages and seven DA crews, we observe shorter time to first assessment, fewer revisits with reduced distance traveled. To our knowledge, this is among the first multi-state enterprise integrated deployments to treat DA crews as a first-class optimized resource in storm restoration.