Modelling and control of large-scale solar thermal systems

Research output: ThesisDoctoral Thesis


In the first main section, models of components of large-scale solar thermal systems are developed and validated. For the most important components (heat exchanger, solar collector and sensible heat storage), two models of different complexity, one simulation-oriented, one control-oriented, are developed. While the simulation-oriented models aim to model the physical behaviour very accurately in order to be used in simulation studies, control-oriented models aim to model the physical behaviour only as accurately as necessary in order to serve as a basis for model-based control strategies. All models are validated with measurement data from a typical solar system, and it is shown that they are sufficiently accurate for their intended purpose. The sum of the models provides a holistic view on all modelling aspects that have to be considered in large-scale solar thermal plants, and serves as a reasonable basis for model-based control strategies and accurate simulation studies of solar systems.
In the second main section, adaptive forecasting methods for the future solar heat production as well as the heat demand are developed and validated with measurement data and using real weather forecasts. These methods are important to most efficiently integrate and operate solar systems by better scheduling heat production, storage and distribution for the near future. In order to be used in real-world applications, the methods are developed with the goal to meet three important practical requirements: simple implementation, automatic adaption to seasonal changes, and wide applicability. The final long-term evaluation for half a year proves that the developed methods can forecast the solar heat production as well as the heat demand very accurately and outperform common forecasting methods, yielding results that are nearly twice as accurate.
In the third main section, model-based control strategies for the high-level as well as for the low-level control of solar thermal systems are developed and validated. For the high-level control an approach is presented which considers future information by using the developed forecasting methods. It achieves higher profits (plus 3 %) and leads to a more stable operation, compared to the existing commercial solution. For the low-level control, model-based control strategies based on the developed models for the heat generation and distribution are presented. The model-based control strategy for the heat generation considers the dynamic behaviour of the collector and especially considers the variable time-delay. This, compared to conventional control strategies, leads to a significantly better control performance in case of fluctuating solar radiation and changing inlet temperatures. The model-based control strategy for the heat distribution follows a modular approach which can be applied for several hydraulic settings, leading to an accurate and independent control of mass flow and temperature, and outperforms state-of-the-art control strategies. For both control levels, care was taken that the applied strategies can be used in real-world applications regarding their mathematical complexity and computational resources required.
In summary, this thesis presents a holistic approach regarding modelling (simulation-oriented models, control-oriented models and adaptive forecasting methods) and control aspects (high-level as well as low-level control) which can help to improve the efficiency of large-scale solar thermal plants on various levels, making them more competitive, and is furthermore essential for a successful integration of these plants in larger energy systems.
Translated title of the contribution Modellierung und Regelung solarthermischer Großanlagen
Original languageEnglish
QualificationDoctor of Technology
Awarding Institution
  • Graz University of Technology (90000)
  • Horn, Martin, Supervisor
  • Gölles, Markus, Supervisor
Publication statusPublished - Sep 2021

Fields of Expertise

  • Sustainable Systems
  • Information, Communication & Computing


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