A multi-stage probabilistic framework to estimate gas-fired generator performance during extreme winter weather

2026-10-03

AI Summary

This study addresses the challenge that plant-level data privacy constraints hinder systematic assessments of gas-fired generator outage risks and performance degradation during extreme winter weather. To overcome this, the work proposes a three-stage Bayesian probabilistic framework that integrates publicly available meteorological data, load profiles, and anonymized NERC records to sequentially infer event occurrence probability, residual capacity, and outage duration. This contribution establishes a transferable reliability assessment paradigm requiring no proprietary data, providing a standardized benchmark for power systems. Empirical analysis in New York State demonstrates that under severe cold and high-load conditions, hourly failure rates reach 24%, available capacity declines to 13% of rated values, and the median duration of complete outages is 12.7 hours.

Abstract

Extreme winter weather has repeatedly disrupted gas-fired power generation in the United States, yet the plant-level data needed to systematically quantify outage risk remain proprietary. Using publicly available weather and electricity demand data together with anonymized generator contingency records from the North American Electric Reliability Corporation (NERC), we develop a three-stage Bayesian probabilistic framework for estimating winter-driven generator performance. Applied to New York State (2013--2022), the framework sequentially estimates: the hourly probability of a generator contingency event, the expected net available capacity conditioned on an event occurring, and the event duration. Colder conditions and higher electricity demand are associated with higher failure probability, lower retained capacity, and longer event duration. Under the most severe observed stress conditions, estimated mean hourly event probability reaches 24\% , while expected mean net available capacity falls to 13\% of nameplate rating. Full outage events have a median duration of 12.7 hours, while partial derating event duration increases from 2.4 to 7.1 hours with capacity loss severity. The proposed framework establishes a transferable baseline that utilities with access to plant-level records can directly extend to obtain more precise reliability estimates for operational planning and resource adequacy assessment.

Problem

• extreme winter weather
• gas-fired generators
• outage risk
• generator performance
• reliability estimation

Innovation

• Bayesian probabilistic framework
• gas-fired generator reliability
• extreme winter weather
• multi-stage estimation
• generator contingency