Digitalization and operational optimization of district heating networks A multi-site comparative study

This thesis addresses the digitalization and operational optimization of district heating networks through a comparative approach across different replication sites, each characterized by different demand profiles, climate conditions, network topologies and data availability. The main goal is to evaluate how advanced digital tools, such as digital twins, and the integration of phase change (PCM) thermal storage systems can improve operational efficiency, reduce energy consumption and increase network flexibility. All analyses presented were conducted on the Gradyent platform, which enables dynamic simulation and real-time optimization of the thermal and hydraulic behavior of district heating networks. The methodology involved calibrating the digital twins for each network, dynamic modeling of the PCM modules and subsequent optimization of thermal demand and flow temperature at the source. The results for one case study showed a significant reduction in peak demand and a consequent decrease in supply temperature at the source, averaging approximately 22°C compared to the baseline. In another case, demand optimization showed stable and consistent behavior, but the economic analysis demonstrated that the introduction of storage systems is not cost-effective for the replication site, primarily due to limited temperature differentials and implementation costs not justified by the achievable energy benefits. The study confirms that digitalization and predictive modeling can be effective tools for the operational optimization of district heating networks, although they require a case-by-case economic assessment, especially in contexts with low operating temperatures or modest demand variability.