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. 

Sustainable energy systems rely on a wide range of energy sources, where an integral part is to use the available energy as efficiently as possible. District energy systems are considered a key factor towards decarbonization as an efficient way of distributing heat and cold within urban areas and facilitating the utilization of renewable energy sources and heat recovery from, e.g., industrial plants and data centers.

A dynamic model of the process can be used to achieve high-performing control and an increased understanding of the district energy system. However, a city-scale, automatically generated, and updated model that can be used for the plant's whole lifecycle remains a distant vision. Large-scale physics-based models are sometimes used for planning and validation, but using the same models for optimization and control, long-term simulation, or running a high number of simulation scenarios can be computationally prohibitive or impossible due to a lack of applicable methods. 

In the thesis, the physics of the district energy grid is presented along with modeling, simulation, and control methods, towards the goal of increasing the computational efficiency and flexibility of the models and methods. The grid is described using graph theoretical concepts and a linear parameter-varying state-space model representation, followed by an introduction to reduced-order models, heat load prediction, Gaussian process models, and feedback control with dead-time compensation for temperature control in district energy systems. 

The main contributions are the six research papers composing the thesis. Experiences, challenges, and possible methods to address the presented problems are summarized in the first paper of the thesis. In the second paper, a method for the prediction of heat load for buildings is presented, followed by a paper on a machine learning-based method for modeling of the thermal dynamics in a district heating pipe. In the following paper, a method for reduced order modeling of district energy grids using graph theoretical methods and spectral clustering is presented. The fifth paper suggests an integrated approach to spatial and energy planning using an optimization-based tool, and the final paper presents a method for decentralized temperature control in district heating networks using dead time compensation. Based on the work in the thesis, conclusions are finally given.