Occupant-behavior driven appliance scheduling for residential buildings

Abstract

Building-to-grid integration is one important step in having a smart grid. This integration will support energy efficiency, load balancing, and the incorporation of renewables. This study introduces a residential building energy management solution to control connected devices based on economic signals from the grid and occupant behavior. An air-conditioning unit, a water heater, and an electric vehicle (EV) were modeled and controlled using a traditional on/off controller and model predictive controller (MPC). The MPC is designed to minimize the total building electricity costs while maintaining occupant comfort. The MPC utilizes the building thermal mass, the EV battery storage, and the water heater’s hot water tank to shift electricity use to a period when the price is lower. This controller considers occupant behavior as a constraint in controlling these devices to maintain users’ comfort. The simulation results show a 10%–30% reduction in the electricity bill by applying the MPC in a dynamic electricity price scenario, as compared to the traditional methods of control. The proposed method of control makes buildings responsive to grid signals that provide the potential of peak shaving and ancillary services.