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The current operation of district heating networks often relies on static analyses and control parameters. In the future, possible integration of renewable energy sources like solar or geothermal energy are getting more and more important. To investigate the impact of these new energy source in combination with new control strategies, dynamic simulation models for district heating and cooling systems are getting more important. However, these models are often large and therefore have large computation times and require manual effort to create and optimize them. Thus, this paper investigates in the simulation of a large and meshed district heating network. We present a workflow for automated generation and model simplification of simulation models based on GIS data. The validity of the model simplification is proven and the usability of the model is demonstrated by a Use Case with two different control strategies.

District heating and cooling (DHC) networks, and in particular, the fifth generation of DHC networks, offer great potential in increasing the overall system efficiency and reducing CO2 emissions in the heating and cooling of urban districts. Due to the growing complexity of these energy systems, the use of new planning methods, such as the use of dynamic simulation models based on Modelica, becomes more important. However, especially with large, complex thermal networks, there is a high effort for manual model construction and parameterization. For this reason, we present a framework for automated model generation of DHC networks based on simulation models in Modelica written in Python. The core function of the Python framework is to transform a graph representation of a district heating network into a dynamic simulation model. The authors briefly describe the workflow and demonstrate its applicability with three different use cases. We investigate the impact of different design decisions, e.g., comparing the difference between central and decentral pumps as well as a combination of both in one network. In addition, we present the results of evaluating the impact of different network temperature levels or pipe insulation compared to the overall energy supplied to the network, leading to the conclusion that the presented framework is capable of reducing the manual effort for performing DHC network simulations with Modelicaand allows to easily perform parameter studies in an early planning phases in the future.

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