Paper Session 19: Presentation 2: Supervisory Multi-Objective Economic Model Predictive Control for Heat Pump Water Heaters for Cost and Carbon Optimization(AT-23-C060)
Supervisory Multi-Objective Economic Model Predictive Control for Heat Pump Water Heaters for Cost and Carbon Optimization(AT-23-C060)
Author: Caton Mande, UC Davis Western Cooling Efficiency Center, Davis, CA, USA
Presenting Author: Loren Dela Rosa, UC Davis Chemical Engineering, Davis, CA, USA
Author: Matthew Ellis, UC Davis Chemical Engineering, Davis, CA, USA
Water heating is the second-largest energy consumer accounting for 20% of total residential end-use energy consumption in the U.S. Heat pump water heaters (HPWHs) offer an efficient way to heat water using electricity which aligns with efforts towards decarbonization and leveraging renewable energy sources. However, they are typically controlled by rule-based strategies that maintain the tank water temperature in a region around the setpoint and have no automated way to account for exogenous inputs like time-varying electric rates or marginal greenhouse gas emissions. Economic model predictive control (EMPC) is ideal choice for HPWHs because the optimization-based predictive control technique determines control actions by predicting system behavior over a horizon and finding the control actions that minimizes an economic cost function. For example, EMPC may be used to automatically shift the electric water heating load from peak to off-peak periods. Or, because residential electricity tariffs can be constant-in-time or varying in pre-determined time windows, the EMPC can also use the thermal storage capacity of the water tank to optimize a secondary objective like greenhouse gas emissions.
Currently, the EMPC has been formulated as a regulatory controller that activates the HPWH’s compressor and electric resistance heating element. The performance of the EMPC with perfect forecasting knowledge was compared to rule-based control in simulation for a single-tank HPWH. The initial results for the EMPC, when optimizing for only electric cost, showed a 42 percent reduction in energy usage over a single day ($0.83 versus $0.51). Additionally, when the cost function was formulated to equally weight cost function to reduce electricity cost and greenhouse gas emissions, the EMPC achieved the same 42 percent reduction in energy usage and reduced the emissions by 31 percent over a single day (1.72x10-3lbCO2 versus 1.18x10-3lbCO2).
In this paper, the EMPC will be reformulated as a supervisory control above the existing rule-based control of a single-tank HPWH. This formulation allows setpoint schedules to be sent to existing equipment with rule-based controls that are accessible through the manufacturer’s API. Simulation and laboratory testing on an HPWH will be used to compare the energy usage, missed gallons, and greenhouse gas emissions of this new formulation to rule-based control.
Learning Objectives:
• define supervisory economic model predictive control and grasp how it could be used as a retrofit
• describe how a cost function is used to automate control decisions that include things like cost and GHG emissions