Modelling of district heating grid

Abstract

The modelling of the district heating grid is of a great significance to the district heating owners and shareholders; simplifying the distribution network through simulation modelling offers a possibility of analyzing its performance, forecasting its behavior and, accordingly, adding an important piece to optimize the generation of heat and explore the potential of utilizing the grid performance for flexibility provision. This work aims to develop a simple, comprehensive, and open-source tool that can capture the behaviour of the district heating networks To achieve the modelling objective, the research strategy identified a set of KPI’s to qualify a suitable modelling approach. A modelling algorithm was then developed to set the modelling steps and identify the required checkpoints. Furthermore, a simplified model was constructed to facilitate the mathematical modelling (the physical relation between parameters), and then a computer code was developed using Python programing language with Anaconda environment. This thesis adopted two modelling procedures: The grid as a storage model and the delay analysis model. The models generated the following parameters: the customer supply temperature and the grid volume flowrate are calculated to illustrate how the generation units are operated to meet the customers’ heating demand. Furthermore, heat loss is estimated as it’s an essential parameter to the feasibility of district heating systems. And finally, the generation return temperature is a crucial indicator of the customers’ efficiency in utilizing the supplied heat and affects the efficiency of the generation unit. The above parameters were compared to the generated data of grid performance, and the two models yielded relevant results that followed the trend of the actual grid performance. The models’ ability to provide insights into the grid performance is investigated. The two models presented the grid performance with the possibility to use them for optimization. They also indicate the potential of using the grid as heat storage to provide flexibility.