Data-Driven Identification of Consumers With Deferrable Loads for Demand Response Programs

Power utilities leverage demand response (DR) events to effectively reduce the peak load at critical times with excessive power demand. DR programs are generally categorized as manual or automated from the automation perspective. The opportunities for automated DR in the residential sector have emerged with the integration of smart and connected loads. For example, smart appliances with deferrable loads can be scheduled to shift their load without consumers' direct involvement, given that many consumers might not engage sufficiently to participate in the manual DR. However, it has been shown that unjustified load shifting from many consumers in peak times could result in high off-peak demand. Therefore, it is essential for utilities to identify and target consumers for participation according to efficacy criteria. To address this issue, in this letter, we propose a data-driven method for the selection of consumers according to their potential for demand reduction in a DR program. The proposed method characterizes the frequency, consistency, and the peak time usage of deferrable loads across several subsequent days. By measuring the impact on peak-load shaving, we evaluated our approach on a subset of electricity dataset from the Pecan Street Dataport. The findings demonstrate the efficacy of the proposed method in selecting consumers with deferrable loads based on their potential for demand reduction in the future events.