On Efficient Modeling, Simulation and Control of District Energy Systems

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