District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

This paper presents the development and implementation of a two-degree-of-freedom, model-based controller designed to enhance the efficiency and flexibility of a certain class of circuits used in thermo-hydraulic applications. The controller addresses significant challenges such as time-variable transport delays and actuator coupling, which are common in dynamic testing environments. By utilising a model-based control approach and the Smith predictor configuration, the proposed controller simultaneously tracks supply temperature and mass flow rate with improved performance compared to the proportional–integrative–derivative (PID) controller used in our laboratory. The system’s effectiveness is demonstrated through a virtual hydraulic complex model developed in the OpenModelica environment and experimental tests, following the implementation of the controller in the laboratory real-time control software. Both the simulation and experimental results indicate that the controller can closely follow pre-programmed temperature and flow rate waveforms while effectively rejecting disturbances, with an RMSE reduction of up to about 80% under the specific test protocol used in this work, making it suitable for applications required to deal with the above constrains, such as laboratory dynamic testing and thermo-mechanical and chemical process regulation.