Quantifying Grid Resilience Against Extreme Weather Using Large-Scale Customer Power Outage Data

TL;DR

This paper introduces a quantitative model for power grid resilience based on large-scale outage and weather data, revealing key factors in outage propagation and suggesting targeted infrastructure improvements to enhance resilience.

Contribution

It provides a novel, data-driven definition of grid resilience and demonstrates its application through analysis of real outage data and weather patterns in US service territories.

Findings

Weather effects significantly impact outage duration and propagation.

Targeted infrastructure upgrades can halve outage numbers.

Model accurately predicts outage progression during extreme events.

Abstract

In recent decades, the weather around the world has become more irregular and extreme, often causing large-scale extended power outages. Resilience -- the capability of withstanding, adapting to, and recovering from a large-scale disruption -- has become a top priority for the power sector. However, the understanding of power grid resilience still stays on the conceptual level mostly or focuses on particular components, yielding no actionable results or revealing few insights on the system level. This study provides a quantitatively measurable definition of power grid resilience, using a statistical model inspired by patterns observed from data and domain knowledge. We analyze a large-scale quarter-hourly historical electricity customer outage data and the corresponding weather records, and draw connections between the model and industry resilience practice. We showcase the resilience analysis using three major service territories on the east coast of the United States. Our analysis suggests that cumulative weather effects play a key role in causing immediate, sustained outages, and these outages can propagate and cause secondary outages in neighboring areas. The proposed model also provides some interesting insights into grid resilience enhancement planning. For example, our simulation results indicate that enhancing the power infrastructure in a small number of critical locations can reduce nearly half of the number of customer power outages in Massachusetts. In addition, we have shown that our model achieves promising accuracy in predicting the progress of customer power outages throughout extreme weather events, which can be very valuable for system operators and federal agencies to prepare disaster response.