Using Smart Meter Data to Estimate Demand Reductions from Residential Direct Load Control Programs

Abstract

Realizing greater demand flexibility is of growing importance for electric utilities in the United States given capacity and resource adequacy concerns from the transition to renewable generation. Demand response (DR) programs, like direct load control, help utilities manage peak loads by temporarily reducing electricity demand. While the effectiveness of direct load control on individual appliances has been substantiated, utility demand-side management strategies often encompass multiple DR programs to maximally reduce demand during peak events. This paper examines a utility DR program designed to reduce peak demand through direct control of residential HVAC units and water heaters. We describe a regression method using smart meter data to quantify the hourly demand reductions from individual direct load control appliances that run simultaneously. This method offers a low-cost way to verify the impact of operational direct load control programs for utilities with smart meters. The results indicate an average reduction of 6.0% to 10.9% for load control of water heaters during events, and control of the HVAC units reduced customer demand by an average of 7.6% to 21.8%. Summer afternoon demand response events saw higher demand reductions than events during winter mornings due to the impacts of HVAC load control. The largest kW demand reductions are for single-family homes but apartments see the largest average percent reductions in demand.