Optimizing the Operation of a Hybrid Ground Source Heat Pump System Under Uncertainty

Hybrid renewable energy systems that combine conventional heating, ventilation, and air conditioning (HVAC) systems and ground source heat pumps (GSHP) have become an attractive alternative for conventional HVAC systems due to their higher cost and energy efficiency.Furthermore, control strategies that exploit predictive information about weather and building occupants' activity can further reduce the system operating costs. This study proposes a Stochastic Model Predictive Control (SMPC) for hybrid GSHP systems considering the stochastic nature of the building space heating demand. In SMPC, near-optimal control decisions are found for the current and future states of the system through the application of Regression Monte-Carlo techniques. We compare the performance of SMPC to that gained by using setpoint Control (SPC) and Model Predictive Control (MPC) which uses a deterministic forecast. It is found that by taking uncertainty into account via SMPC, the operating cost reduction compared to SPC is approximately equal to half of the cost-optimality gap between SPC and an idealized controller that is represented by MPC with perfect future information. Furthermore, we find that MPC using a forecast based on expected values leads to greater operating costs compared to the simpler SPC strategy when variability and uncertainty are present.