As one falls, another rises? Residential peak load reduction through electricity rate structures. (September 2020) - Bandyopadhyay, Arkasama; Leibowicz, Benjamin D.; Beagle, Emily A.; Webber, Michael E.
Highlights: Dynamic pricing shifts the residential peak away from the time of overall peak load. Dynamic pricing can have the adverse impact of making the residential peak higher. Time-varying prices do not reduce overall household energy consumption. Dynamic rates incentivize concentration of appliance usage within low-price hours. Abstract: In this study, we analyze the potential for time-varying electricity rate structures to reduce and/or shift peak demand in the residential sector. To do so, we develop a convex optimization model in which a household with four major appliances minimizes electricity costs, with marginally increasing penalties for deviating from temperature set-points or operating appliances at inconvenient times. The four specific appliances we include are: heating, ventilation and air-conditioning (HVAC) systems, electric water heaters (EWHs), electric vehicles (EVs), and pool pumps (PPs). The study incorporates a one-parameter thermal model of the home and the electric water heater, so that the penalties can apply to the room and water temperatures rather than the total appliance loads. Analysis is performed on a community of 100 single-family detached homes in Austin, TX. These homes each host a combination of the four end-use devices while some also have onsite solar panels. We find that dynamic pricing effectively shifts the residential peak away from the time of overall peak load across the electricity system, but can have the adverse impact of making the residential peak higher. The energy consumption does not differ significantly across the different rate structures. Thus, we infer that the time-varying rates encourage customers to concentrate their electricity demand within low-price hours to the extent possible without incurring significant inconvenience. By incorporating the novel approach of including monetary value of customer behavior in price-based demand response models, this study builds a tool to realistically quantify peak load reduction and shifts in the residential sector.