Session: 17-01: Poster Presentations

Paper Number: 168085

168085 - Maximizing Hot Water Availability: An Optimization Model for Minimizing Unmet Demand in Small-Volume Heat Pump Water Heaters 

Abstract: 

Heat Pump Water Heaters (HPWH) leverage the heat from ambient air temperature to heat water. HPWHs necessitate manually indicating a heating setpoint temperature. This setpoint serves as a minimum temperature to maintain in the internal tank and generally ranges from 49°C  to 60°C (120°F to 140 °F). This method is up to four times more efficient than commonly used electric resistance water heaters and is a promising technology for reducing energy costs. However, space requirements and electric panel capacity can be substantial barriers to HPWH adoption, particularly in multifamily buildings. Thus, this study is part of a larger project that aims to increase the energy storage capacity of small-volume  120V HPWHs using phase change materials (PCMs) to make HPWHs viable options for space-constrained dwellings. The hot water outlet temperature of HPWHs can fluctuate greatly depending on water usage and setpoint temperature. High-volume draw events, such as showering, deplete hot water reserves and lower the water heater tank temperature. Consequently, hot water outlet temperature falls below comfortable temperatures for showering 40°C (105°F). The tank size of small-volume HPWHs exacerbates these temperature drops. We refer to instances where hot water outlet temperature falls below minimum temperature requirements as unmet demand. This study focuses on the control schemes to minimize unmet demand for small-volume HPWHs.    

We are interested in maximizing hot water availability by predicting how estimated water draw events impact the hot water outlet temperature to minimize unmet demand. Here, we introduce a framework for predicting the outlet temperature on a 1-hour time horizon. By importing minute-level water flow measurements into OCHRE, a simulation framework created by the National Renewable Energy Lab, we simulate HPWHs with integrated PCMs and use the resulting node temperatures to train regression models that anticipate the effects of high-volume water draws on hot water outlet temperature. Our resulting model can forecast hot water outlet temperature from one internal thermocouple. This model allows us to maximize the amount of available hot water by preheating ahead of hot water draws. Current results show that based on 176 days of minute-level draw data from a single HPWH site, we can accurately predict outlet temperatures for additional sites with mean squared errors below 1°C (1.8°F). This level of forecasting allows us to maximize the amount of delivered hot water for 120 V HPWHs and capitalize on variable electricity prices by maximizing preheating during times of the day with the lowest electricity demand.  

Presenting Author: Janelle Domantay Colorado School of Mines

Presenting Author Biography: Janelle Domantay is a PhD student in Operations Research with Engineering at the Colorado School of Mines. Her current research involves optimizing water heater control systems to improve energy efficiency and decrease unmet demand. She holds a master's degree in computer science from the University of Illinois Urbana-Champaign, where she collaborated with the National Renewable Energy Lab on Adaptive Computing and Multi-fidelity simulations. She also earned a bachelor's in computer science from the University of Nevada, Las Vegas.