Designing household retail electricity packages based on a quantile regression approach

Abstract

Deregulation of the retail electricity market has led to fierce competition among retail electricity suppliers. This work aims to design retail electricity packages for different groups of residential consumers using the quantile regression approach. Specifically, we segmented user populations by income and conducted a correlation analysis using data from China. A positive relationship between residential electricity consumption per capita (RECp) and residential disposable income (RDI) was found, while no correlation existed between RECp and price. Based on these effort, a pricing strategy for electricity retailers was formulated through the construction of a profit model. As for a real-world application, the retail electricity market in Shanghai was chosen. Quantile regression coefficients indicated that the effects of RDI, consumption expenditure, and price vary across income groups. Subsequently, this paper designed electricity quantities and pricing ratios (αj) for different groups and employed a scenario analysis approach to examine the retailers’ cost and earnings under different scenarios. In addition, through the investigation of the changes in electricity price and the peak-valley ratio over time, time-of-use discount based on peak energy usage was designed. This study helps retailers and policy makers understand the residential consumer characteristics and is informative for retailers in the design of differentiated marketing strategies.