A time-dependent model of generator failures and recoveries captures correlated events and quantifies temperature dependence. (1st November 2019)

• Record Type: Journal Article
• Authors: Murphy, Sinnott; Sowell, Fallaw; Apt, Jay
• Is Part Of: Applied energy. Volume 253(2019)
• Publication Date: 2019-11-01
• Subjects: Resource adequacy -- Generating availability data system -- Correlated failures -- Nonhomogeneous Markov model -- Logistic regression; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.113513
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: We quantify the temperature dependence of forced outages for six generator types. Generator transition probabilities are modeled using logistic regression. Nonhomogeneous Markov models capture observed correlated generator failures. Resource adequacy can be improved by accounting for temperature dependence. Abstract: Most current approaches to resource adequacy modeling assume that each generator in a power system fails and recovers independently of other generators with invariant transition probabilities. This assumption has been shown to be wrong. Here we present a new statistical model that allows generator failure models to incorporate correlated failures and recoveries. In the model, transition probabilities are a function of exogenous variables; as an example we use temperature and system load. Model parameters are estimated using 23 years of data for 1845 generators in the USA's largest electricity market. We show that temperature dependencies are statistically significant in all generator types, but are most pronounced for diesel and natural gas generators at low temperatures and nuclear generators at high temperatures. Our approach yields significant improvements in predictive performance compared to current practice, suggesting that explicit models of generator transitions using jointly experienced stressors can help grid planners more precisely manage their systems.