Optimal Real-Time Residential Thermal Energy Management for Peak-Load Shifting With Experimental Verification

Controlling residential thermal loads and thermal energy storage is a viable tactic to engage end-users in demand response programs (DRPs). This paper focuses on the development of an optimal real-time thermal energy management system (TEMS) for smart homes to respond to DRP for peak-load shifting. The proposed TEMS combines two model predictive controllers to manage two thermal energy storage systems, a water storage tank (WST) and the building thermal mass, to schedule residential heat pump loads to off-peak periods. The intention is to manage the operation of a ground source heat pump (GSHP) to produce the desired amount of thermal energy by controlling the volume and temperature of the stored water in the WST while optimizing the operation of the heat distributors to control indoor temperature. The primary contributions are the development of a new control strategy for GSHPs coupled with WST based on building identification to minimize total energy consumption and cost. This paper also proposes a real-time indoor dynamic temperature set-point strategy based on real-time pricing tariffs for enhancing peak-load shifting of heat pump loads with an acceptable variation in thermal comfort. Simulation and experimental results demonstrate that the proposed TEMS has significant potential for real-time peak-load shifting.