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The consumption and production of energy are more dynamic as distributed energy resources (DERs) such as solar photovoltaics (PV) are deployed within the electric distribution system. The existing techniques for bulk generation do not take full advantage of DERs and can lead to wasted energy and higher costs for both utility companies and consumers. Commercial and residential building energy management systems are usually on a fixed schedule and are not able to respond to changes in energy price instantaneously. There is a need for a real-time pricing structure that can accommodate the fluctuating cost of energy based on supply and demand, and for an energy management system that is able to respond to the dynamic utility rate. As such, there is a need for a robust energy management control strategy and methodology to validate new approaches. To address this gap, a strategy to control heating, ventilation, and air conditioning (HVAC) systems in a residential house was developed along with a validation methodology. A model of predictive control was implemented to optimize the thermostat setpoints and minimize energy cost for an individual residential house while maintaining thermal comfort of residents. This model was integrated with EnergyPlus simulation via an open source co-simulation platform previously developed at the National Institute of Standards and Technology (NIST). Total energy consumption and cost for consumers were compared between a case with the proposed model and a baseline case that used fixed-temperature setpoint control. The simple dynamic pricing model used in simulations was proportional to the demand of energy at that time of day. This work will contribute to the development of dynamic utility pricing models and residential control strategies for grid-interactive buildings and homes. The outcome of this research can be expanded to different building models or locations in future work.

Residential buildings, accounting for 37% of the total electricity consumption in the United States, are suitable for demand response (DR) programs to support effective and economical operation of the power system. A home energy management system (HEMS) enables residential buildings to participate in such programs, but it is also important for HEMS to account for occupant preferences to ensure occupant satisfaction. For example, people who prefer a higher thermal comfort level are likely to consume more energy. In this study, we used foresee™, a HEMS developed by the National Renewable Energy Lab (NREL), to perform a sensitivity analysis of occupant preferences with the following objectives: minimize utility cost, minimize carbon footprint, and maximize thermal comfort. To incorporate the preferences into the HEMS, the SMARTER method was used to derive a set of weighting factors for each objective. We performed week-long building energy simulations using a model of a home in Fort Collins, Colorado, where there is mandatory time-of-use electricity rate structure. The foresee™ HEMS was used to control the home with six different sets of occupant preferences. The study shows that occupant preferences can have a significant impact on energy consumption and is important to consider when modeling residential buildings. Results show that the HEMS could achieve energy reduction ranging from 3% to 21%, cost savings ranging from 5% to 24%, and carbon emission reduction ranging from 3% to 21%, while also maintaining a low thermal discomfort level ranging from 0.78 K-hour to 6.47 K-hour in a one-week period during winter. These outcomes quantify the impact of varying occupant preferences and will be useful in controlling the electrical grid and developing HEMS solutions.