Pursuing patterns in the statistics of utility data to analyze grid resilience

Iowa State University, Ames IA

Investigators

Abstract

This NSF project analyzes statistical patterns that have occurred in blackouts or outages of our nation's bulk electrical power transmission grid. The patterns will be extracted from standard utility data that record details of these outages. The project aims to explore and understand these statistical patterns to improve the grid resilience. For example, quantifying the typical patterns in component outages and recovery when severe weather hits the grid can inform making the grid more resilient. Quantifying the most likely patterns of how blackouts start and spread can detect the vulnerabilities to a cascading sequence of outages of grid components. The project complements the knowledge of individual blackouts and their mechanisms in the power engineering field with new approaches based on the statistical patterns occurring across all blackout-related outages in detailed utility data. Processing this real data is foundational to quantifying and preventing blackouts and grounding the field in observed data. New metrics calculated from observed data can transform detecting and help in fixing the most likely grid vulnerabilities. The intellectual merits of the project include deploying an interdisciplinary range of statistical and engineering approaches to analyze, confirm and explain patterns observed in detailed utility outage data. The broader impacts of the project include ways to improve the resilience of the nation’s transmission grid by quantifying and mitigating blackouts. The project will improve classroom education by developing interactive teaching methods. The project will statistically test heavy-tailed probability distributions, design robust resilience metrics, model outage timing with Poisson processes, extract outage and restore processes from data, adapt ideas from motifs from network theory to contingency lists, and identify mechanisms of outages from data. The project will also pursue an entirely new explanation of how the Zipf distribution of event size can arise from the power system engineering that responds to cascades of outages by mitigating those cascades. The expected outcomes include practical resilience event definitions, useful resilience metrics, and expanded risk-based contingency lists for transmission systems that can be calculated from standard utility data and that can be used to help utilities and regulators quantify and mitigate blackout risks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.