  Model predictive control (MPC) strategies allow residential water heaters to
shift load in response to dynamic price signals. Crucially, 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 four different design factors affect control performance and
thermal comfort: (i) control model fidelity, (ii) temperature sensor
configuration, (iii) water draw estimation methodology, and (iv) water draw
forecasting methodology. We propose new methods for estimating water draw
patterns without the use of a flow meter. MPC strategies are compared under two
different time-varying price signals through simulations using a high-fidelity
tank model and real-world draw data. Results show that control model fidelity
and the number of temperature sensors have the largest impact on electricity
costs, while the water draw forecasting methodology has a significant impact on
thermal comfort and the frequency of runout events. Results provide practical
insight into effective MPC design for water heaters in home energy management
systems.
