Revisiting selection of residential consumers for demand response programs

Abstract

Electrical utilities depend on Demand Response programs to manage peak loads by incentivizing consumers to voluntarily curtail a portion of their load during a specified period. Utilities first categorize consumers based on their energy consumption patterns into different clusters and then request consumers of a particular cluster to participate in the demand response program. At a coarse level, clustering approaches do well, but we may not be able to correctly predict which cluster's profile will fit that day's power availability. We address this issue by examining the consistency of consumer's consumption patterns across several consecutive days. We demonstrate that measuring consistency quantitatively helps to understand predictability of consumer's energy consumption. In the rest of the paper, we provide details of our proposed consistency metric. Further, we propose a methodology to select a few consumers among the consistent ones such that they have a peak at the time specified by the demand response program. We validate our approach using real-world energy consumption data from residential buildings.