Quantifying Power Systems Resilience Using Statistical Analysis and Bayesian Learning

TL;DR

This paper introduces a statistical and Bayesian framework to quantify power system resilience by analyzing the impacts of weather variables like wind speed, temperature, and precipitation on outage data, aiding risk assessment and mitigation strategies.

Contribution

It develops a novel combined statistical and Bayesian approach to model weather effects on power system resilience using real outage and weather data.

Findings

Weather variables significantly influence resilience metrics.

Joint effects of weather parameters are more impactful than individual effects.

The framework aids in risk assessment and decision-making for power grid resilience.

Abstract

The increasing frequency and intensity of extreme weather events is significantly affecting the power grid, causing large-scale outages and impacting power system resilience. Yet limited work has been done on systematically modeling the impacts of weather parameters to quantify resilience. This study presents a framework using statistical and Bayesian learning approaches to quantitatively model the relationship between weather parameters and power system resilience metrics. By leveraging real-world publicly available outage and weather data, we identify key weather variables of wind speed, temperature, and precipitation influencing a particular region's resilience metrics. A case study of Cook County, Illinois, and Miami-Dade County, Florida, reveals that these weather parameters are critical factors in resiliency analysis and risk assessment. Additionally, we find that these weather variables have combined effects when studied jointly compared to their effects in isolation. This framework provides valuable insights for understanding how weather events affect power distribution system performance, supporting decision-makers in developing more effective strategies for risk mitigation, resource allocation, and adaptation to changing climatic conditions.