Efficient integration of smart appliances for demand response programs

Power utilities rely on Demand Response (DR) programs in order to shave the peak load at critical times, when there is an excessive demand. In the context of automation, DR programs are categorized as manual or automated. With the emergence of home energy management (HEM) systems that monitor and operate the household appliances, the opportunities for automated DR have emerged. For example, smart appliances with deferrable loads can be scheduled to shift their load without consumers' intervention, given that many consumers might not engage enough to perform the manual DR. However, it has been shown that unjustified load compensation from many HEM-enabled consumers in peak times could result in high off-peak demand. Therefore, it is essential for utilities to identify and target the consumers for participation based on certain criteria. To address this issue, in this paper, we proposed a method for the selection of consumers who are using smart appliances with the highest potential for a DR program. The proposed method measures the (1) frequency, (2) consistency, and (3) peak time usage of deferrable loads across several days. We evaluate our approach on a historical real-world electricity consumption dataset from residential households. The findings demonstrate the efficacy of the proposed method to sort consumers (with the smart appliance) based on their potential to participate in DR.