Estimating Marginal Effects of Key Factors that Influence Wholesale Electricity Demand and Price Distributions in Texas via Quantile Variable Selection Methods

Abstract

Understanding the key drivers of prices and energy consumption is an important issue, which is complicated because the distributions of prices and consumption are asymmetric and fat-tailed. That is, the sets of relevant covariates can vary depending on the segment of interest in the conditional distributions of price and demand. Using a large data set from the Electric Reliability Council of Texas, this study uses quantile regressions and attendant variable selection methods to choose the most important factors that influence demand and price distributions; subsequently, the marginal effects of these factors are studied. Among the many findings, two critical ones are that the marginal effects of the covariates change throughout the distributions of demand and price, and that the number of relevant variables selected using mean regressions generally exceeds the number selected using quantile regressions. Related consequences for maintaining a reliable electricity market are discussed.