Data-driven quantification and visualization of resilience metrics of power distribution system

TL;DR

This paper introduces a data-driven framework for quantifying and visualizing the resilience of power distribution systems to extreme weather, using outage and weather data to evaluate and model resilience metrics.

Contribution

It develops a novel three-stage approach combining outage data analysis, weather zone delineation, and fragility modeling to assess distribution grid resilience.

Findings

Effective resilience quantification for wind and precipitation events

Demonstrated framework on two decades of real utility data

Identified key weather factors impacting outages and restoration

Abstract

This paper presents a data-driven approach for quantifying the resilience of distribution power grids to extreme weather events using two key metrics: (a) the number of outages and (b) restoration time. The method leverages historical outage records maintained by power utilities and weather measurements collected by the National Oceanic and Atmospheric Administration (NOAA) to evaluate resilience across a utility's service territory. The proposed framework consists of three stages. First, outage events are systematically extracted from the outage records by temporally and spatially aggregating coincident component outages. In the second stage, weather zones across the service territory are delineated using a Voronoi polygon approach, based on the locations of NOAA weather sensors. Finally, data-driven models for outage fragility and restoration time are developed for each weather zone. These models enable the quantification and visualization of resilience metrics under varying intensities of extreme weather events. The proposed method is demonstrated using real-world data from a US distribution utility, located in Indianapolis, focused on wind- and precipitation-related events. The dataset spans two decades and includes over 160,000 outage records.