On September 16, 2025, I had the opportunity to attend and present at the Smart Energy Systems Conference (SESAAU2025) in Copenhagen. During the conference, I presented my research on advanced control strategies for next-generation heating and cooling networks. To my great honour, this work earned the Best Presentation Award.
This study, co-authored with Prof. Lieve Helsen, focuses on how smarter control can make district heating and cooling systems more efficient, reduce energy use, and support the transition to a low-carbon future. These systems are crucial for cutting greenhouse gas emissions in buildings, and our study shows how advanced predictive control can unlock significant improvements.
District heating and cooling (DHC) networks, particularly Fifth Generation (5GDHC), effectively reduce building energy use and greenhouse gas emissions by integrating low-quality thermal energy sources at neutral temperature levels. However, the operation of these networks is not yet optimized. A key challenge is the integration and control of multiple distributed heat and cold sources, with pumping energy being crucial at neutral temperatures. Rule-Based Control (RBC) sequences are conventionally used to manage these networks, whereas more advanced strategies like Model Predictive Control (MPC) can act as a system integrator, facilitating the transition to an affordable decarbonized heating sector. This paper delves deeper into control strategies for a virtual 5GDHC network, comparing current-practice RBC with a white-box MPC approach through dynamic simulations. The MPC strategy aims to minimize primary energy use while ensuring thermal comfort in the connected buildings. Physics-based models of building envelopes, thermal systems, and hydraulic components are developed in Modelica. The study includes i) RBC development as a baseline, ii) optimal control with a one-year prediction horizon, iii) optimal control with a conventional three-day horizon. A sensitivity analysis evaluates system sizing strategies and their impact on control and performance. Results show that optimal control significantly improves thermal comfort, particularly during transitional seasons, while reducing energy use by over 40%. These benefits are achieved by lowering network temperatures, utilizing anticipatory control, and leveraging the buildings’ thermal inertia and the different building loads. Additionally, MPC enables substantial component size reductions exploiting system flexibility during operation and thus acting as an effective system integrator.
Read the full study on Zenodo!