Model Predictive Control Strategies for Grid-Interactive Water Heaters

Residential electric water heaters have significant load shifting capabilities due to their thermal heat capacity and large energy consumption. Model predictive control (MPC) has been shown to be an effective control strategy that allows water heaters to respond to dynamic price signals. Such control strategies can be deployed in home energy management systems or smart appliances. However, the performance of such strategies is sensitive to various algorithm design choices. In this work, we develop a framework for implementing model predictive controls on residential water heaters for load shifting applications. We use this framework to analyze how different design factors, such as control model fidelity, temperature sensor configuration, water draw estimation methodology, and water draw forecasting methodology, affect control performance and thermal comfort. Algorithm performance is validated through experimental laboratory testing and simulations

Publications

Improving the Load Flexibility of Stratified Electric Water Heaters: Design and Experimental Validation of MPC Strategies
Elizabeth Buechler, Aaron Goldin, and Ram Rajagopal
IEEE Transactions on Smart Grid (2024)

Preprints

Designing model predictive control strategies for grid-interactive water heaters for load shifting applications
Elizabeth Buechler, Aaron Goldin, and Ram Rajagopal
Under review

Presentations

Designing Better Model Predictive Controllers to Maximize the Flexibility of Grid-Interactive Water Heaters
2024 ACEEE Hot Water Forum

Design and Validation of Model Predictive Control Strategies for Water Heater Load Shifting
AWHI Connectivity Working Group Presentation (February 2024)