The system incorporates a ground-source heat pump (GSHP), solar thermal collectors, and a low-temperature district heating (DH) network. Additionally, booster heat pumps (BHPs) provide localized heating. The existing rule-based controller (RBC) governs the system but faces challenges in optimizing energy use and thermal comfort.
The article proposes a Model Predictive Controller (MPC) as a system integrator for enhanced performance. The MPC utilizes a mathematical model, predictions, constraints, and a cost function to optimize control actions. The predictive horizon considers future events, enabling anticipation. The study compares MPC and RBC through a simulation of De Schipjes, evaluating electrical energy use, thermal comfort, and COP (Coefficient of Performance).
Results indicate MPC’s superiority, providing better thermal comfort in winter with similar energy use and 31% less energy use in spring while maintaining comfort. The MPC’s ability to anticipate and adjust network temperatures optimizes the GSHP’s COP, reducing overall energy consumption. Heating buildings at night and using the thermal inertia of the envelopes contribute to improved performance.
In conclusion, the MPC, acting as a system integrator, significantly enhances overall system performance, including energy efficiency, cost-effectiveness, and a higher share of renewable and residual energy sources. The study underscores the potential of advanced control strategies in achieving sustainable residential heating in line with ambitious climate goals.
Jelger Jansen, Lieve Helsen, Belgium
This text has been shortened by the HPC team
