Towards efficient modeling and simulation of district energy systems

Sammanfattning: Dynamic simulation of district energy systems has an increased importance in the transition towards renewable energy sources, lower temperature district heating grids and waste heat recovery from industrial plants and data centers. However, a city-scale, automatically generated and updated simulator that can be used for the whole lifecycle of the plant remains a distant vision. Physics based models are often used for planning and validation, but the complexity is too high to use the models for optimization and automatic control, or for longer time spans.In this thesis, the experiences and challenges from previous district heating simulation projects using a co-simulation approach are summarized, with corresponding research gaps and proposed research directions. Two of the identified shortcomings are investigated in more detail in the thesis: First, a robust and computationally efficient method for prediction of the heat load for buildings is proposed. A deterministic dynamic model is used to predict the space heating load, and a latent variable model using Fourier basis functions predicts the heat load used for e.g. hot tap water and ventilation. The prediction model validity is shown on a multi-dwelling building located in Luleå, Sweden. Second, a probabilistic model based on Gaussian Processes is used to simulate the temperature dynamics of a district heating pipe. The model is trained and validated against a state-of-the-art physics based pipe model. It is shown that the model both replicates the behavior of the reference model, and that it can account for uncertainty of the inputs. By employing a kernel exploiting the underlying physics, many shortcomings of Gaussian Process models can be mitigated. The results suggest that a mix of physics based and probabilistic methods can be one way forward towards a digital twin of a city-scale district energy system. Natural extensions to the published papers would be to research how the methods can be applied to a larger scale district energy system.