Modelica-based model predictive control for a CO2 heat pump system: Case study in Oslo

The proliferation of renewable energy technologies challenges the stability of the energy supply, requiring more flexibility from the energy demand. Consequently, methods for controlling heat pumps have received increasing attention. This study presents a Modelica-based Model Predictive Control (MPC) approach designed to maintain the supply water temperature within the 55°C to 75°C range, while minimizing energy use and electricity costs over a one-year period. A detailed and high-fidelity model of a school building in Oslo, Norway, was developed in Modelica and exported as a Functional Mock-up Unit (FMU) to enable seamless integration with MATLAB/Simulink for the real time simulation and control implementation. The results demonstrated that the MPC strategy achieved annual electricity savings of 8.0 MWh (3.2%) and 11,479 NOK 1 (6.7%) compared to a Proportional-Integral (PI) controller, and 85.07 MWh (25.9%) and 46,967 NOK (22.8%) compared to a fixed rule-based baseline controller. These savings were primarily attributed to the ability of MPC to anticipate and respond to future electricity price variations. The findings underscored the effectiveness of MPC as a robust and energy-efficient solution for thermal energy management systems. • Integration of Modelica and MPC via FMU for real-time optimization of a CO 2 heat pump system. • Development of an FMU-based co-simulation framework linking Modelica and MATLAB/Simulink for MPC testing. • The MPC improved demand-side flexibility, achieving 3.2% energy and 6.7% cost savings over PI control and over 20% compared to the baseline. • The MPC and Modelica models were validated against one-year operational data, with horizon tuning balancing cost and comfort objectives. • Demonstrated practical applicability through a school building case study in Oslo using real operational data.