A Latent-Factor System Model for Real-Time Electricity Prices in Texas

• Main Authors: Kang Hua Cao, Paul Damien, Jay Zarnikau
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Applied Sciences
• Subjects: energy prices; renewable energy; system modelling; unobservable factors
• Online Access: https://www.mdpi.com/2076-3417/11/15/7039

A novel methodology to model electricity prices and latent causes as endogenous, multivariate time-series is developed and is applied to the Texas energy market. In addition to exogenous factors like the type of renewable energy and system load, observed prices are also influenced by some combination of latent causes. For instance, prices may be affected by power outages, erroneous short-term weather forecasts, unanticipated transmission bottlenecks, etc. Before disappearing, these hidden, unobserved factors are usually present for a contiguous period of time, thereby affecting prices. Using our system-wide latent factor model, we find that: (a) latent causes have a highly significant impact on prices in Texas; (b) the estimated latent factor series strongly and positively correlates to system-wide prices during peak and off-peak hours; (c) the merit-order effect of wind significantly dampens prices, regardless of region and time of day; and (d) the nuclear baseload generation also significantly lowers prices during a 24-h period in the entire system.