For grid-interactive building operations, variable refrigerant flow (VRF) systems with variable speed drives are appealing sources. Often combined with a dedicated outdoor air system (DOAS) for ventilation, the VRF-DOAS presents higher complexity for control design. This article proposes a hierarchical Koopman model predictive control (MPC) strategy for the VRF-DOAS system to achieve demand response (DR) operation at the upper layer and frequency regulation (FR) at the lower layer. For the lower layer, an offset-free Koopman MPC is designed to achieve FR tracking with more robustness. To achieve a deeper FR operation, the high-bandwidth RegD FR is targeted with the VRF compressor power. For computationally efficient MPC design, data-driven Koopman models of different time scales are used, which facilitates convex MPC design. The upper layer MPC yields thermal regulation and power References, passed to the lower layer with an interpolation scheme with a more consistent prediction horizon, instead of a shrinking horizon, for MPC design. The proposed control is evaluated with FMI-based co-simulation, where a Modelica-based dynamic simulation model of VRF-DOAS is orchestrated with the MPC design in Python. Simulation results show that the proposed control method can achieve both quality FR performance scores and DR performance, besides satisfactory thermal regulation. The proposed method reduces the net energy cost by 8.2% for a 24-h simulation.
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- Publication year
2026
- Venue
IEEE Transactions on Control Systems Technology
- Publication date
2026-03-01
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