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An improvement crossover operation method in genetic algorithm and spatial optimization of heliostat field

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  • Wang, Jianxing
  • Duan, Liqiang
  • Yang, Yongping

Abstract

The heliostat field of solar power tower (SPT) system occupies a large proportion of both the total investment and total energy losses of a plant. However, the optimization design of a heliostat field is a challenging work, because there are too many parameters to be optimized. In this paper, a new high-dimensional genetic algorithm toolbox (HDGA) is developed in Visual Studio Community 2015 for the heliostat field design, in which a new crossover strategy is employed for the high-dimensional optimization. The algorithm is verified by both mathematical models and engineering cases, the results show that HDGA is more effective for the high-dimensional problem, and its convergence speed is much faster than that of the genetic algorithm toolbox developed by the University of Sheffield (Sheffield GA). The new algorithm is explained in detail and the optimal field layout is presented. With the new algorithm, a heliostat field referencing to the Gemasolar plant is optimized in this paper. The results show that the optical performance of the heliostat field is improved significantly than that of the un-optimized case, and the optical efficiency of 63.7% is reachable at the design point. At the same time, the annual insolation weighted efficiency is 56.9%.

Suggested Citation

  • Wang, Jianxing & Duan, Liqiang & Yang, Yongping, 2018. "An improvement crossover operation method in genetic algorithm and spatial optimization of heliostat field," Energy, Elsevier, vol. 155(C), pages 15-28.
    • Handle: RePEc:eee:energy:v:155:y:2018:i:c:p:15-28
    DOI: 10.1016/j.energy.2018.05.004
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Arrif, Toufik & Hassani, Samir & Guermoui, Mawloud & Sánchez-González, A. & A.Taylor, Robert & Belaid, Abdelfetah, 2022. "GA-GOA hybrid algorithm and comparative study of different metaheuristic population-based algorithms for solar tower heliostat field design," Renewable Energy, Elsevier, vol. 192(C), pages 745-758.
    2. Wang, Jianxing & Guo, Lili & Zhang, Chengying & Song, Lei & Duan, Jiangyong & Duan, Liqiang, 2020. "Thermal power forecasting of solar power tower system by combining mechanism modeling and deep learning method," Energy, Elsevier, vol. 208(C).
    3. Xie, Qiyue & Guo, Ziqi & Liu, Daifei & Chen, Zhisheng & Shen, Zhongli & Wang, Xiaoli, 2021. "Optimization of heliostat field distribution based on improved Gray Wolf optimization algorithm," Renewable Energy, Elsevier, vol. 176(C), pages 447-458.
    4. Ashikuzzaman, A.K.M. & Adnan, Sakib, 2021. "Optical efficiency comparison of circular heliostat fields: Engender of hybrid layouts," Renewable Energy, Elsevier, vol. 178(C), pages 506-519.
    5. Hu, Yeguang & Xu, Zhigang & Zhou, Chaoying & Du, Jianjun & Yao, Yingxue, 2020. "Design and performance analysis of a multi-reflection heliostat field in solar power tower system," Renewable Energy, Elsevier, vol. 160(C), pages 498-512.
    6. Collado, Francisco J. & Guallar, Jesus, 2019. "Quick design of regular heliostat fields for commercial solar tower power plants," Energy, Elsevier, vol. 178(C), pages 115-125.
    7. Wang, Jianxing & Duan, Liqiang & Yang, Yongping & Yang, Zhiping & Yang, Laishun, 2019. "Study on the general system integration optimization method of the solar aided coal-fired power generation system," Energy, Elsevier, vol. 169(C), pages 660-673.

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