@incollection{HoffschmidtAlexopoulosGoettscheetal.2022,
  author    = {Hoffschmidt, Bernhard and Alexopoulos, Spiros and G{\"o}ttsche, Joachim and Sauerborn, Markus and Kaufhold, O.},
  title     = {High Concentration Solar Collectors},
  series = {Comprehensive Renewable Energy (Second Edition) / Volume 3: Solar Thermal Systems: Components and Applications},
  booktitle = {Comprehensive Renewable Energy (Second Edition) / Volume 3: Solar Thermal Systems: Components and Applications},
  publisher = {Elsevier},
  address   = {Amsterdam},
  isbn      = {978-0-12-819734-9},
  doi       = {10.1016/B978-0-12-819727-1.00058-3},
  pages     = {198 -- 245},
  year      = {2022},
  abstract  = {Solar thermal concentrated power is an emerging technology that provides clean electricity for the growing energy market. To the solar thermal concentrated power plant systems belong the parabolic trough, the Fresnel collector, the solar dish, and the central receiver system. For high-concentration solar collector systems, optical and thermal analysis is essential. There exist a number of measurement techniques and systems for the optical and thermal characterization of the efficiency of solar thermal concentrated systems. For each system, structure, components, and specific characteristics types are described. The chapter presents additionally an outline for the calculation of system performance and operation and maintenance topics. One main focus is set to the models of components and their construction details as well as different types on the market. In the later part of this article, different criteria for the choice of technology are analyzed in detail.},
  language  = {en}
}
@inproceedings{BlankeSchmidtGoettscheetal.2022,
  author    = {Blanke, Tobias and Schmidt, Katharina S. and G{\"o}ttsche, Joachim and D{\"o}ring, Bernd and Frisch, J{\´e}r{\^o}me and van Treeck, Christoph},
  title     = {Time series aggregation for energy system design: review and extension of modelling seasonal storages},
  series = {Energy Informatics},
  volume    = {5},
  booktitle = {Energy Informatics},
  number    = {1, Article number: 17},
  editor    = {Weidlich, Anke and Neumann, Dirk and Gust, Gunther and Staudt, Philipp and Sch{\"a}fer, Mirko},
  publisher = {Springer Nature},
  issn      = {2520-8942},
  doi       = {10.1186/s42162-022-00208-5},
  pages     = {1 -- 14},
  year      = {2022},
  abstract  = {Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, "Time series aggregation for energy system design: Modeling seasonal storage", has developed a seasonal storage model to address this issue. Simultaneously, the paper "Optimal design of multi-energy systems with seasonal storage" has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results.},
  language  = {en}
}