Aggregator decision analysis in residential demand response under uncertain consumer behavior

Abstract

Increasing penetration of renewable energy generation creates new opportunities for aggregators to incentivize consumers to adjust their consumption. This paper evaluates an incentive-based demand response (DR) program for residential consumers from the aggregator’s perspective for load shifting. An experimental study using load data from 2309 Finnish residential consumers for the year 2023 was conducted. The aggregator’s present predictions of future day-ahead (DA) electricity prices and consumer behavior were leveraged to optimally provide incentives, maximizing profits while enhancing consumer welfare. An exponential utility function depicted consumer behavior, and three different future price scenarios were considered for the aggregator, solving a stochastic bi-level optimization problem. The risks faced by the aggregator were incorporated using conditional value at risk (CVaR). Different results were obtained based on the aggregator’s nature, with higher risks observed in the lower tail end. The aggregators could earn €10,656 per year, while the different consumer pools could earn €4833/a, €9375/a, and €5072/a, respectively, if they load shift as expected by the aggregators. To gauge reality, the imbalance caused by consumers not changing their demand as expected was analyzed through a Monte Carlo analysis, showing a reduction in profits for aggregators with an average annual profit of €8500. Overall, this study advances residential DR, enabling aggregators to perform pilot studies to identify actual consumer price elasticity while understanding the needs of both aggregators and consumers.