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An efficient code to optimize the heliostat field and comparisons between the biomimetic spiral and staggered layout

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  • Zhang, Maolong
  • Yang, Lijun
  • Xu, Chao
  • Du, Xiaoze

Abstract

The optical efficiency of the heliostat field is one of the most crucial factors in solar power tower (SPT) technology. The design of heliostat field always costs much computing time to get one accurate result of the blocking and shading factor, which influences the optical efficiency of the heliostat field significantly. A predicting model was developed, which took advantage of flat mapping method to avoid unnecessary calculations. An improved code combining the flat mapping and ray tracing methods was presented, which was superior to the known codes no matter in computing accuracy or computing speed. With the computing code developed, an optimizing code making use of both the Rosen projection method and simulated annealing smart algorithm was created to optimize each parameter of the heliostat field. Comparisons between the radial staggered and the biomimetic spiral layouts of heliostat field were executed for the built commercial plants by the new developed code. The analysis found that the spiral layout was more competitive in the north field, while it behaved worse in the circular and south field because of cosine effect. A hybrid field combining two types of layouts was proposed, which behaved better than both spiral and staggered layouts.

Suggested Citation

  • Zhang, Maolong & Yang, Lijun & Xu, Chao & Du, Xiaoze, 2016. "An efficient code to optimize the heliostat field and comparisons between the biomimetic spiral and staggered layout," Renewable Energy, Elsevier, vol. 87(P1), pages 720-730.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:720-730
    DOI: 10.1016/j.renene.2015.11.015
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    Cited by:

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    2. Zhang, Maolong & Du, Xiaoze & Pang, Liping & Xu, Chao & Yang, Lijun, 2016. "Performance of double source boiler with coal-fired and solar power tower heat for supercritical power generating unit," Energy, Elsevier, vol. 104(C), pages 64-75.
    3. Farges, O. & Bézian, J.J. & El Hafi, M., 2018. "Global optimization of solar power tower systems using a Monte Carlo algorithm: Application to a redesign of the PS10 solar thermal power plant," Renewable Energy, Elsevier, vol. 119(C), pages 345-353.
    4. Nicolás C. Cruz & José D. Álvarez & Juana L. Redondo & Jesús Fernández-Reche & Manuel Berenguel & Rafael Monterreal & Pilar M. Ortigosa, 2017. "A New Methodology for Building-Up a Robust Model for Heliostat Field Flux Characterization," Energies, MDPI, vol. 10(5), pages 1-17, May.
    5. 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.
    6. Merchán, R.P. & Santos, M.J. & Medina, A. & Calvo Hernández, A., 2022. "High temperature central tower plants for concentrated solar power: 2021 overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    7. 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.
    8. Rizvi, Arslan A. & Yang, Dong, 2022. "A detailed account of calculation of shading and blocking factor of a heliostat field," Renewable Energy, Elsevier, vol. 181(C), pages 292-303.
    9. Saghafifar, Mohammad & Gadalla, Mohamed & Mohammadi, Kasra, 2019. "Thermo-economic analysis and optimization of heliostat fields using AINEH code: Analysis of implementation of non-equal heliostats (AINEH)," Renewable Energy, Elsevier, vol. 135(C), pages 920-935.
    10. 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.
    11. Cruz, N.C. & Salhi, S. & Redondo, J.L. & Álvarez, J.D. & Berenguel, M. & Ortigosa, P.M., 2018. "Hector, a new methodology for continuous and pattern-free heliostat field optimization," Applied Energy, Elsevier, vol. 225(C), pages 1123-1131.
    12. 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.
    13. Zhang, Maolong & Xu, Chao & Du, Xiaoze & Amjad, Muhammad & Wen, Dongsheng, 2017. "Off-design performance of concentrated solar heat and coal double-source boiler power generation with thermocline energy storage," Applied Energy, Elsevier, vol. 189(C), pages 697-710.
    14. Merchán, R.P. & Santos, M.J. & Heras, I. & Gonzalez-Ayala, J. & Medina, A. & Hernández, A. Calvo, 2020. "On-design pre-optimization and off-design analysis of hybrid Brayton thermosolar tower power plants for different fluids and plant configurations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    15. Chao Li & Rongrong Zhai & Yongping Yang, 2017. "Optimization of a Heliostat Field Layout on Annual Basis Using a Hybrid Algorithm Combining Particle Swarm Optimization Algorithm and Genetic Algorithm," Energies, MDPI, vol. 10(11), pages 1-15, November.
    16. Yamani, Noureddine & Khellaf, Abdallah & Mohammedi, Kamal & Behar, Omar, 2017. "Assessment of solar thermal tower technology under Algerian climate," Energy, Elsevier, vol. 126(C), pages 444-460.

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