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Quantifying Power Systems Resilience Using Statistical Analysis and Bayesian Learning

Apsara Adhikari, Charlotte Wertz, Anamika Dubey, Arslan Ahmad, Ian Dobson

TL;DR

This work addresses the challenge of quantifying how weather affects power-grid resilience by integrating outage data with meteorological observations and applying statistical and Bayesian learning. It develops a workflow to extract resiliency metrics from outage records, align them with weather variables, and compare single- vs multi-variable models using Bayes factors. The results show region-specific dominant weather drivers (wind gust for Cook County; wind gust and temperature for Miami-Dade) and robust improvements when incorporating multiple weather parameters, with a multiplicative Bayesian framework capturing interactions. The framework supports predictive resilience analysis and decision-making for risk mitigation, resource allocation, and climate adaptation across urban grids.

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.

Quantifying Power Systems Resilience Using Statistical Analysis and Bayesian Learning

TL;DR

This work addresses the challenge of quantifying how weather affects power-grid resilience by integrating outage data with meteorological observations and applying statistical and Bayesian learning. It develops a workflow to extract resiliency metrics from outage records, align them with weather variables, and compare single- vs multi-variable models using Bayes factors. The results show region-specific dominant weather drivers (wind gust for Cook County; wind gust and temperature for Miami-Dade) and robust improvements when incorporating multiple weather parameters, with a multiplicative Bayesian framework capturing interactions. The framework supports predictive resilience analysis and decision-making for risk mitigation, resource allocation, and climate adaptation across urban grids.

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.

Paper Structure

This paper contains 9 sections, 3 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Data Description
  3. Power Outage Datasets
  4. Weather Datasets
  5. Data Processing and Event Extraction
  6. Resiliency Quantification
  7. Single Regression
  8. Multi-Regression Analysis
  9. Conclusion

Figures (8)

  • Figure 1: Proposed framework for grid resilience modeling as a function of weather parameters.
  • Figure 2: Performance Curve of a sample event
  • Figure 3: Pearson correlation matrix between weather variables and resiliency metrics for Cook County (left) and Miami-Dade County (right).
  • Figure 4: Bayesian Framework for Multi-Regression Analysis
  • Figure 5: Relationship between Wind gust and Resiliency Metrics for Cook County with 95% Credible Interval
  • ...and 3 more figures