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.