Laboratory Testing of Model Predictive Control for Cost and Emissions Reduction of Heat Pump Water Heaters

Water heating accounts for 18% of energy consumption and 15% of greenhouse gas (GHG) emissions in U.S. residential buildings. Electric heat pump water heaters (HPWHs) are more energy efficient than electric resistance water heaters, but widespread adoption could strain the grid. Moreover, successful decarbonization requires aligning HPWH operation with electricity generated from clean energy sources like solar and wind. This work demonstrates the effectiveness of a multi-objective economic model predictive control (MPC) in minimizing electricity costs, GHG emissions, and comfort violations for a laboratory HPWH unit over three consecutive days. An automated tuning approach for the MPC cost function is presented, along with a discussion of the practical implementation challenges of the proposed MPC framework. Experimental results show that MPC reduces electricity costs by 31% and GHG emissions by 46% compared to a typical HPWH rule-based control strategy.