Greenhouse gas emissions are the primary factors causing climate change and global warming, wherein around three quarters of total greenhouse gas emissions are attributed to energy-related activities. This highlights the significance of improving energy efficiency to achieve carbon neutrality and sustainable development. Advanced control strategies can facilitate the energy-efficient/cost-effective operation of energy systems. Model predictive control (MPC), a popular advanced control

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... has received a lot of attention and demonstrated excellent control ability in various applications. ABBREVIATIONS MPC Model predictive control (ler) RBC Rule-based control (ler) HVAC Heating, ventilation, and air conditioning GHG Greenhouse gas EU European union NZE Net zero emissions IEA International energy agency CCUS Carbon capture, utilization and storage PID Proportional-integral-derivative MIMO Multi-input and multi-output PCM Phase change material FMU Functional mock-up unit ANN Artificial neural network RC Resistance and capacitance BOPTEST Building optimization performance test framework TES Thermal energy storage LTES Latent thermal energy storage CHP Combined heat and power plant HP Heat pump GB Gas boiler DH District heating network ATES Aquifer thermal energy storage PV Photovoltaics GCHP Ground-coupled heat pump MILP Mixed-integer linear programming RMSE Root mean square error COP Coefficient of performance CO2 Carbon dioxide AHU Air handling unit [21]. Seeing at least five books and thousands of papers published, the research of MPC in different fields has thrived enormously in the current century [22]. Over time, several valuable review papers on MPC have been published, providing a good insight into MPC ranging from its concept, history, evolution, and industrial applications [21-30]. The initial application dates back to the 1970s, intended for the process industry such as oil refineries, chemical plants, etc [31]. Since then, MPC has been successfully applied in different systems including energy systems. Publications on MPC in various systems are massive, covering wastewater treatment plants [32], fuel cells [33], mechanical systems [34], robots [35], electric vehicles [36], solar thermal power plant [37],smart grid [38], power electronics [39], etc. The discussions above highlight the potential capability of MPC in increasing efficiency and reducing operating costs for energy systems. However, given the inherent diversity of different energy systems, it remains a question whether and how MPC can enable energyefficient or cost-effective operation of different energy systems at different scales? The answer to this question assists with promoting the wide application of MPC for energy systems, which further helps bridge the gap between research studies to practical implementations. Aim and objectives This thesis deals with the analysis and application of MPC in energy systems. More specifically, it aims at thermal energy systems. Thermal energy systems, defined as systems that involve the storage and transfer of heat [40] , are one of the main elements of energy systems. The aim of the thesis is to: Identify and demonstrate how MPC can enable energy-efficient/cost-effective operation of thermal energy systems at different scales The aim of the thesis is further broken down into four main research objectives. • Objective 1 Model and analyze energy production, storage, and consumption of thermal energy systems at different scales. • Objective 2 Demonstrate MPC applications for different thermal energy systems.