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Published on 7 March 2025
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Guo,L. (2025). A novel computational approach to accelerate shading and blocking efficiency in solar tower power plants. Theoretical and Natural Science,95,70-79.
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A novel computational approach to accelerate shading and blocking efficiency in solar tower power plants

Linrui Guo *,1,
  • 1 College of Physics and Optoelectronic Engineering,

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/2024.21339

Abstract

This study presents a novel computational approach to enhance the efficiency of calculating shading and blocking in heliostat fields for solar tower power plants. Traditional methods for determining shading and blocking efficiency often involve substantial computational overhead due to the need to evaluate numerous heliostats within a field. To address this, our proposed method focuses on predicting potential heliostats that may cause shading or blocking, thereby significantly reducing the computation time and resource consumption. Through experimental validation, we demonstrate that our method maintains high computational accuracy and reliability while improving computational efficiency. This approach is critical for optimizing heliostat field layouts, ultimately leading to improved efficiency and reduced costs in solar tower power stations.

Keywords

Heliostat field, Solar tower power plants, Shading and blocking efficiency, Computational efficiency

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Cite this article

Guo,L. (2025). A novel computational approach to accelerate shading and blocking efficiency in solar tower power plants. Theoretical and Natural Science,95,70-79.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Applied Physics and Mathematical Modeling

Conference website: https://2024.confapmm.org/
ISBN:978-1-83558-983-0(Print) / 978-1-83558-984-7(Online)
Conference date: 20 September 2024
Editor:Marwan Omar
Series: Theoretical and Natural Science
Volume number: Vol.95
ISSN:2753-8818(Print) / 2753-8826(Online)

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