Improving the computational performance of district heating network simulation

District heating and cooling (DHC) systems are gaining importance due to their ability to integrate renewable and waste heat sources and connect with other infrastructures. However, the complexity of DHC systems rises as technical components and potential interactions grow, and energy demands increase. Heat transportation via water flow involves time- and temperature-dependent changes based on mass flow rates and heat losses, influenced by the dynamic properties of consumers and distribution pipes. To optimise DHC operations and simulate their complexities, dynamic modelling tools are necessary. However, dynamically simulating large-scale DHC networks are computationally demanding. One method to improve district heating networks (DHNs) is to replace dynamic pipe models with static models, simplifying the network's topological complexities. This paper proposes a framework for simulating DHNs using the NetworkX Python package to create and manipulate complex networks and the Modelica environment to simulate physical systems. The study highlights the constraints of the pipe replacement workflow due to its dependence on seasonal variation. The findings indicate that replacing pipe models can reduce CPU time by 30% to 65% in simulations, depending on the required accuracy during the summer season.