Adaptive model predictive control of a heat pump-assisted solar water heating system

Full electrification of building energy systems makes that the electricity-driven heat pump and solar heat become the most promising heating sources for hot water production in the future. The heat pump assisted solar water heater will be a good solution to jointly use these two heating sources. The system has large potential in demand response because of large capacity thermal storage tank which requires reasonable optimal control. In this context, this paper presents an adaptive model predictive control (AMPC) to achieve demand response with adaptive boundary and linear time-varying heat pump efficiency. Using adaptive parameters, the controller improves the adaptability and achieves optimal control under various disturbance conditions. The results demonstrated the AMPC realize 20% cost saving and 12% energy saving compared with PID, which are 7% reduction in costs and a 3% decrease in energy consumption compared to conventional MPC methods. Furthermore, a detailed analysis research was conducted on the controller parameters and disturbances, considering equipment parameters, electricity pricing models, weather conditions, and load types. Notably, the MPC exhibited even greater performance improvements in scenarios involving real-time pricing, concentrated loads, and low heat pump capacity.

Automated summary

This study proposes an adaptive model predictive control method for demand response in building hot water production. The results demonstrate significant cost and energy savings compared to traditional methods, achieving optimal control in various situations.