@inproceedings{SchwagerAngeleNourietal.2022,
  author    = {Schwager, Christian and Angele, Florian and Nouri, Bijan and Schwarzb{\"o}zl, Peter and Teixeira Boura, Cristiano Jos{\´e} and Herrmann, Ulf},
  title     = {Impact of DNI forecast quality on performance prediction for a commercial scale solar tower: Application of nowcasting DNI maps to dynamic solar tower simulation},
  series = {SolarPACES conference proceedings},
  booktitle = {SolarPACES conference proceedings},
  number    = {Vol. 1},
  publisher = {TIB Open Publishing},
  address   = {Hannover},
  issn      = {2751-9899 (online)},
  doi       = {10.52825/solarpaces.v1i.675},
  pages     = {9 Seiten},
  year      = {2022},
  abstract  = {Concerning current efforts to improve operational efficiency and to lower overall costs of concentrating solar power (CSP) plants with prediction-based algorithms, this study investigates the quality and uncertainty of nowcasting data regarding the implications for process predictions. DNI (direct normal irradiation) maps from an all-sky imager-based nowcasting system are applied to a dynamic prediction model coupled with ray tracing. The results underline the need for high-resolution DNI maps in order to predict net yield and receiver outlet temperature realistically. Furthermore, based on a statistical uncertainty analysis, a correlation is developed, which allows for predicting the uncertainty of the net power prediction based on the corresponding DNI forecast uncertainty. However, the study reveals significant prediction errors and the demand for further improvement in the accuracy at which local shadings are forecasted.},
  language  = {en}
}
@inproceedings{SchwagerAngeleSchwarzboezletal.2023,
  author    = {Schwager, Christian and Angele, Florian and Schwarzb{\"o}zl, Peter and Teixeira Boura, Cristiano Jos{\´e} and Herrmann, Ulf},
  title     = {Model predictive assistance for operational decision making in molten salt receiver systems},
  series = {SolarPACES: Solar Power \& Chemical Energy Systems},
  booktitle = {SolarPACES: Solar Power \& Chemical Energy Systems},
  number    = {2815 / 1},
  publisher = {AIP conference proceedings / American Institute of Physics},
  address   = {Melville, NY},
  isbn      = {978-0-7354-4623-6},
  issn      = {1551-7616 (online)},
  doi       = {10.1063/5.0151514},
  pages     = {8 Seiten},
  year      = {2023},
  abstract  = {Despite the challenges of pioneering molten salt towers (MST), it remains the leading technology in central receiver power plants today, thanks to cost effective storage integration and high cost reduction potential. The limited controllability in volatile solar conditions can cause significant losses, which are difficult to estimate without comprehensive modeling [1]. This paper presents a Methodology to generate predictions of the dynamic behavior of the receiver system as part of an operating assistance system (OAS). Based on this, it delivers proposals if and when to drain and refill the receiver during a cloudy period in order maximize the net yield and quantifies the amount of net electricity gained by this. After prior analysis with a detailed dynamic two-phase model of the entire receiver system, two different reduced modeling approaches where developed and implemented in the OAS. A tailored decision algorithm utilizes both models to deliver the desired predictions efficiently and with appropriate accuracy.},
  language  = {en}
}