发表机构
School of Electrical Engineering and Computer Science（电气工程与计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对极端冬季天气下燃气发电机性能估计问题，提出三阶段贝叶斯概率框架，利用公开数据与NERC记录，在纽约州应用中实现停电概率、容量及持续时间预测，为可靠性评估提供可扩展基线。
AI 中文摘要
极端冬季天气已多次扰乱美国的燃气发电，然而系统量化停电风险所需的电厂级数据仍属专有。利用公开可得的天气和电力需求数据，以及来自北美电力可靠性公司（NERC）的匿名发电机事故记录，我们开发了一个三阶段贝叶斯概率框架，用于估计冬季驱动的发电机性能。将该框架应用于纽约州（2013-2022年），其依次估计：发电机事故事件的每小时概率、事件发生时的预期净可用容量，以及事件持续时间。较冷条件和较高的电力需求与更高的故障概率、更低的保留容量和更长的事件持续时间相关。在最严重的观测压力条件下，估计的平均每小时事件概率达到24%，而预期平均净可用容量降至铭牌额定值的13%。完全停电事件的中位持续时间为12.7小时，而部分降额事件的持续时间随容量损失严重性从2.4小时增加到7.1小时。所提出的框架建立了一个可转移的基线，能够获取电厂级记录的公用事业公司可直接扩展该框架，以获得用于运行规划和资源充足性评估的更精确可靠性估计。
英文摘要
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.
