Optimization design model of heliostat field based on gravitational search algorithm

Research Article
Open access

Optimization design model of heliostat field based on gravitational search algorithm

Zhihua Lu 1* , Xinchu Zhou 2
  • 1 Shandong University, Jinan, China    
  • 2 Shandong University, Jinan, China    
  • *corresponding author 1186152201@qq.com
ACE Vol.86
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-583-2
ISBN (Online): 978-1-83558-584-9

Abstract

In today’s society, non-renewable resources are becoming increasingly precious, making the utilization and conversion of renewable resources more critical. Heliostat fields play a significant role in the actions taken by various countries to achieve ”carbon peaking” and ”carbon neutrality.” How can the installation and arrangement of heliostats maximize the annual average thermal power output per unit mirror area while achieving the rated power? This paper establishes an efficiency calculation model based on the flat-plate projection-Monte Carlo algorithm and an optimization design model of heliostat fields based on the gravitational search algorithm. The research progresses from shallow to deep, investigating methods to maximize the output thermal power under different constraints. First, it addresses the issue of maximizing the average thermal power output per unit mirror area under fixed heliostat field parameters and rated power conditions. Next, it solves the problem of maximizing the annual average thermal power output per unit mirror area under varying heliostat sizes and installation heights, with fixed rated power. Finally, it points out that the models established in this paper are applicable to complex real-world situations and can effectively improve the thermal efficiency of heliostat fields.

Keywords:

Flat-plate Projection Method, Monte Carlo Method, Heliostat Field, Single-objective Optimization Model, Gravitational Search Algorithm

Lu,Z.;Zhou,X. (2024). Optimization design model of heliostat field based on gravitational search algorithm. Applied and Computational Engineering,86,8-15.
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References

[1]. Zhang, M., Wei, H., Du, X., et al. (2016). Improved algorithm for the shadow and blocking efficiency of heliostat fields in tower solar power plants. Journal of Solar Energy, 37(08), 1998-2003.

[2]. Noone, C. J., Torrilhon, M., & Mitsos, A. (2012). Heliostat field optimization: A new computationally efficient model and biomimetic layout. Solar Energy, 86, 792-803.

[3]. Liu, J. (2023). Modeling and simulation of optical efficiency and optimization layout of heliostat field in tower solar thermal power plants (Master’s thesis). Lanzhou Jiaotong University,(02):2023.

[4]. Collado, F. J., & Guallar, J. (2012). Campo: Generation of regular heliostat fields. Renewable Energy, 46, 49-59.

[5]. Gao, B., Liu, J., Sun, H., et al. (2022). Optimization of heliostat field layout based on adaptive gravitational search algorithm. Journal of Solar Energy, 43(10), 119-125. https://doi.org/10.19912/j.0254-0096.tynxb.2021-0397.

[6]. Xie, F. (2013). Optical simulation and application research of heliostat field in tower solar thermal power systems (Master’s thesis). Zhejiang University.

Cite this article

Lu,Z.;Zhou,X. (2024). Optimization design model of heliostat field based on gravitational search algorithm. Applied and Computational Engineering,86,8-15.

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 6th International Conference on Computing and Data Science

ISBN：978-1-83558-583-2(Print) / 978-1-83558-584-9(Online)
Editor：Alan Wang, Roman Bauer
Conference website: https://www.confcds.org/
Conference date: 12 September 2024
Series: Applied and Computational Engineering
Volume number: Vol.86
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Zhang, M., Wei, H., Du, X., et al. (2016). Improved algorithm for the shadow and blocking efficiency of heliostat fields in tower solar power plants. Journal of Solar Energy, 37(08), 1998-2003.

[2]. Noone, C. J., Torrilhon, M., & Mitsos, A. (2012). Heliostat field optimization: A new computationally efficient model and biomimetic layout. Solar Energy, 86, 792-803.

[3]. Liu, J. (2023). Modeling and simulation of optical efficiency and optimization layout of heliostat field in tower solar thermal power plants (Master’s thesis). Lanzhou Jiaotong University,(02):2023.

[4]. Collado, F. J., & Guallar, J. (2012). Campo: Generation of regular heliostat fields. Renewable Energy, 46, 49-59.

[5]. Gao, B., Liu, J., Sun, H., et al. (2022). Optimization of heliostat field layout based on adaptive gravitational search algorithm. Journal of Solar Energy, 43(10), 119-125. https://doi.org/10.19912/j.0254-0096.tynxb.2021-0397.

[6]. Xie, F. (2013). Optical simulation and application research of heliostat field in tower solar thermal power systems (Master’s thesis). Zhejiang University.

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