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Model predictive control lowers barriers to adoption of heat-pump water heaters: A field study

Levi D. Reyes Premer, Elias N. Pergantis, Leo Semmelmann, Davide Ziviani, Kevin J. Kircher

Abstract

Electric heat-pump water heaters (HPWHs) could reduce the energy costs, emissions, and power grid impacts associated with water heating, the second-largest energy use in United States housing. However, most HPWHs today require 240 V circuits to power the backup resistance heating elements they use to maintain comfort during large water draws. Installing a 240 V circuit can increase the up-front cost of a HPWH by half or more. This paper develops and field-tests the first control system that enables a 120 V HPWH to efficiently maintain comfort without resistance heating elements. The novel model predictive control (MPC) system enables pre-heating in anticipation of large water draws, which it forecasts using an ensemble of machine learning predictors. By shifting electrical load over time, MPC also reduces energy costs on average by 23% and 28% under time-of-use pricing and hourly pricing, respectively, relative to a 240 V HPWH with standard controls. Compared to the increasingly common practice in 120 V HPWHs of storing water at a constant, high temperature (60 °C) to ensure comfort, MPC saves 37% energy on average. In addition to demonstrating MPC's benefits in a real, occupied house, this paper discusses implementation challenges and costs. A simple payback analysis suggests that a 120 V HPWH, operated by the MPC system developed here, would be economically attractive in most installation scenarios.

Model predictive control lowers barriers to adoption of heat-pump water heaters: A field study

Abstract

Electric heat-pump water heaters (HPWHs) could reduce the energy costs, emissions, and power grid impacts associated with water heating, the second-largest energy use in United States housing. However, most HPWHs today require 240 V circuits to power the backup resistance heating elements they use to maintain comfort during large water draws. Installing a 240 V circuit can increase the up-front cost of a HPWH by half or more. This paper develops and field-tests the first control system that enables a 120 V HPWH to efficiently maintain comfort without resistance heating elements. The novel model predictive control (MPC) system enables pre-heating in anticipation of large water draws, which it forecasts using an ensemble of machine learning predictors. By shifting electrical load over time, MPC also reduces energy costs on average by 23% and 28% under time-of-use pricing and hourly pricing, respectively, relative to a 240 V HPWH with standard controls. Compared to the increasingly common practice in 120 V HPWHs of storing water at a constant, high temperature (60 °C) to ensure comfort, MPC saves 37% energy on average. In addition to demonstrating MPC's benefits in a real, occupied house, this paper discusses implementation challenges and costs. A simple payback analysis suggests that a 120 V HPWH, operated by the MPC system developed here, would be economically attractive in most installation scenarios.
Paper Structure (39 sections, 26 equations, 12 figures, 2 tables)

This paper contains 39 sections, 26 equations, 12 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Heat-pump water heaters and their future
  3. Contributions of this paper
  4. Outline of this paper
  5. Related work
  6. Grid-interactive water heating
  7. Industry's demand response controls
  8. Forecasting domestic hot water draws
  9. ERWH studies
  10. Simulation-based HPWH studies
  11. Experimental HPWH studies
  12. Methods
  13. Modeling
  14. Parameter tuning
  15. Water draw forecasting
  16. ...and 24 more sections

Figures (12)

  • Figure 1: Left: ERWH with only resistance heating elements. Center: Hybrid-HPWH with both a heat pump and heating elements. Right: HPOWH with a heat pump only. This paper focuses on HPOWH control.
  • Figure 2: In a two-node water tank model, a thermocline separates the upper and lower water columns. Within each column, temperatures are spatially uniform but time-varying.
  • Figure 3: Weighted mean absolute error over different prediction horizons for each forecast algorithm.
  • Figure 4: IoT infrastructure in the test house. A data acquisition system passes sensor measurements to InfluxDB for cloud storage. A computer in the house runs the MPC and Python scripts that push the set-point adjustment to the water heater via API. The optimizer uses the hot water forecast to compute a new set-point at each time step.
  • Figure 5: Sensors on the HPOWH include thermocouples on the upper and lower tank exterior and the inlet line, as well as a flow meter on the outlet line.
  • ...and 7 more figures