Estimating the Price Elasticity of Peak Residential Demand using High Frequency Data

This paper studies the price elasticity of the peak electricity demand of the residential sector in Korea using high frequency data collected by AMR (Automatic Meter Reading) system. The main purpose of this paper is to estimate the price elasticity by allowing the nonlinear relationship between price and temperature in the short-run residential electricity demand curve. Specifically, we consider a Logistic Smooth Transition Regression model with functional coefficients to capture the temperature-dependent price elasticity of residential peak demand in Korea. We show conclusive evidence that the non-economic variables influence the price elasticity of peak residential demand in Korea. Our estimation results show that the price elasticity is dependent upon temperature, and peak demand becomes more sensitive when the weather is very hot or cold.