Extracting resilience metrics from distribution utility data using outage and restore process statistics

TL;DR

This paper presents a method to decompose resilience curves from utility data into outage and restore processes, enabling the calculation of key resilience metrics with statistical confidence, thus improving resilience assessment in electric distribution grids.

Contribution

It introduces a novel approach to decompose resilience curves into outage and restore processes using real utility data, providing formulas for resilience metrics and their variability.

Findings

Decomposition of resilience curves into outage and restore processes is always possible.

Formulas for resilience metrics as functions of outage count are derived.

Maximum restore duration with 95% confidence can be predicted.

Abstract

Resilience curves track the accumulation and restoration of outages during an event on an electric distribution grid. We show that a resilience curve generated from utility data can always be decomposed into an outage process and a restore process and that these processes generally overlap in time. We use many events in real utility data to characterize the statistics of these processes, and derive formulas based on these statistics for resilience metrics such as restore duration, customer hours not served, and outage and restore rates. The formulas express the mean value of these metrics as a function of the number of outages in the event. We also give a formula for the variability of restore duration, which allows us to predict a maximum restore duration with 95% confidence. Overall, we give a simple and general way to decompose resilience curves into outage and restore processes and then show how to use these processes to extract resilience metrics from standard distribution system data.