(116h) Demonstration of a Cloud-Based Supervisory Economic Model Predictive Control for Residential Heat Pump Water Heaters

Water heating is responsible for 18% of energy consumption and 15% of greenhouse gas emissions (GHG) in U.S. residential buildings [1,2]. As the share of renewable energy in U.S. electricity generation continues to increase, the market share of heat pump water heaters (HPWHs) grows as government policies and initiatives encourage the adoption of these energy-efficient electric technologies. However, the rise in HPWH adoption raises concerns about increased grid strain, potentially leading to power outages. The emergence of cloud-connected HPWHs [3] presents an opportunity to implement demand management strategies as a cost-effective alternative to expanding the power grid, ensuring reliable and affordable electricity [4]. With the right control strategy, cloud-connected HPWH devices can proactively respond to utility rate signals, prioritizing pre-heating during periods of low grid demand, and utilizing low-cost, low-GHG electricity while ensuring comfort. However, most HPWHs are controlled by rule-based control (RBC) that lacks adaptability to dynamic signals [5].

Economic model predictive control (MPC) is an advanced control strategy that uses a dynamic model of the system to predict future system behavior and determines the control actions that minimize an economic cost function [6]. MPC is ideal for automating load shifting in HPWHs as it can explicitly account for physical constraints, tank thermal dynamics, and forecasts of exogenous variables such as electricity prices and hot water demand events to optimize HPWH operation. While simulation-based studies have showcased the effectiveness of MPC for load shifting of residential HPWHs (e.g., [7]-[10]), long-term field demonstrations remain relatively limited. Starke et al. [11] developed a supervisory MPC that issues temperature setpoints to HVAC and HPWH systems in a microgrid-equipped community comprising 62 detached, single-family homes, with the study lasting for at least two years. Pergantis et al. [4] referenced five field demonstration studies utilizing supervisory MPC for residential HPWHs, with only two of these experiments lasting beyond a month. Long-term field demonstrations of MPC for residential HPWHs are important as they provide a framework for deploying MPC at scale, identify practical implementation challenges, and validate the potential of MPC to optimize load shifting.

To this end, our study presents the development and long-term field demonstration of a cloud-based supervisory MPC for HPWHs in 20 multi-family housing units located in Woodland and San Jose, California. Our proposed MPC framework seeks to contribute to the limited literature on long-term MPC field demonstrations for residential HPWHs. Specifically, we discuss the details of the cloud-based supervisory MPC control architecture design that addresses several practical design considerations, such as handling potential connectivity issues, asynchronously sampled telemetry, and communication and processing delays. To evaluate the effectiveness of our MPC approach, we conduct a comparative analysis with RBC across several housing units, focusing on performance metrics such as electricity cost, GHG emissions, and the ability to consistently meet hot water demand.

References

[1] “2020 residential energy consumption survey,” U.S. Energy Information Administration, 2023. [Online]. Available: https://www.eia.gov/consumption/residential/data/2020/c&e/pdf/ce3.1.pdf

[2] J. Leung, “Decarbonizing U.S. buildings,” Center for Climate and Energy Solutions, Tech. Rep., 2018.

[3] V. Wilk, “Digitalization and IoT for Heat Pumps Task 1: State of the Art”, AIT Australian Institute of Technology, Tech. Rep., 2023.

[4] E. N. Pergantis, Priyadarshan, N. Al Theeb, P. Dhillon, J. P. Ore, D. Ziviani, E. A. Groll and K. J. Kircher, “Field demonstration of predictive heating control for an all-electric house in a cold climate”, Applied Energy, vol. 360, pp. 122820, 2024.

[5] T. Q. Pean, J. Salom, R. Costa-Castello, “Review of control strategies for improving the energy flexibility provided by heat pump systems in buildings,'' Journal of Process Control, vol. 74, pp. 35–49, 2019.

[6] M. Ellis, H. Durand, and D. Christofides, “A tutorial review of economic model predictive control methods”, Journal of Process Control, vol. 24, pp. 1156–1178, 2014.

[7] X. Jin, J. Maguire, and D. Christensen, “Model predictive control of heat pump water heaters for energy efficiency,” in Proceedings of the 18th ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, USA, Aug. 2014, pp. 133–145.

[8] E. Wanjiru, S. Sichilalu, and X. Xia, “Model predictive control of heat pump water heater-instantaneous shower powered by integrated renewable-grid energy systems,” Applied Energy, vol. 204, pp. 1333–1346, 2017.

[9] L. dela Rosa, C. Mande, and M. J. Ellis, “Supervisory multi-objective economic model predictive control for heat pump water heaters for cost and carbon optimization,” in ASHRAE Transactions, Atlanta, GA, USA, Feb. 2023, pp. 517–525.

[10] L. dela Rosa, C. Mande, H. Richardson, and M. J. Ellis, “Integrating greenhouse gas emissions into model predictive control of heat pump water heaters,” in Proceedings of the American Control Conference, San Diego, CA, USA, Jun. 2023, pp. 4044–4050.

[11] M. Starke, J. Munk, H. Zandi, T. Kuruganti, H. Buckberry and J. Hall and J. Leverette, “Agent-Based System for Transactive Control of Smart Residential Neighborhoods,” in Proceedings of the IEEE Power & Energy Society General Meeting, Atlanta, GA, USA, Aug. 2019, pp. 1-5.