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

Kang Hua Cao,Jay Zarnikau,Paul Damien

doi:10.3390/app11157039

Kang Hua Cao, Jay Zarnikau + Show 1 more

Open Access

https://doi.org/10.3390/app11157039

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Abstract

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.

Highlights

Information about energy prices is known in the day-ahead market, but actual realtime prices will deviate from the day-ahead prices for many “hidden” reasons; see [1]
One of the two main aims of this paper is to present a novel methodology that uses unobserved latent factors and exogenous variables to explain energy prices in Texas by modeling these prices as endogenous, multivariate time-series
This paper demonstrated the relevance of latent factors on real-time energy prices using a system-wide approach

Summary

Introduction

Information about energy prices is known in the day-ahead market, but actual realtime prices will deviate from the day-ahead prices for many “hidden” reasons; see [1]. An error in load forecasts, wind forecasts, solar output forecasts, or the outage of a power plant or transmission line, and many other unforeseen events will cause real-time prices to deviate from day-ahead prices These latent factors are difficult to measure and adjust in real-time, and yet their impact on prices can be significant. One of the two main aims of this paper is to present a novel methodology that uses unobserved latent factors and exogenous variables to explain energy prices in Texas by modeling these prices as endogenous, multivariate time-series. This system-wide approach leads to estimating the attendant merit-order effects of baseload generation (nuclear energy) as well as renewable energy generation (wind and solar).

Geographical Scope

Time-series

The Latent Factor Systems Model

Results

Coefficients

Conclusions