Designing model predictive control strategies for grid-interactive water heaters for load shifting applications

TL;DR

This paper develops a framework for implementing model predictive control on residential water heaters, analyzing how design choices affect load shifting performance, thermal comfort, and cost savings using simulations with real data.

Contribution

It introduces new methods for water draw estimation without flow meters and evaluates the impact of control model fidelity, sensor configuration, and forecasting methods on control performance.

Findings

Control model fidelity and sensor count significantly affect electricity costs.

Water draw forecasting impacts thermal comfort and runout events.

Simulation results guide practical MPC design for home energy management.

Abstract

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.