IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v160y2020icp498-512.html
   My bibliography  Save this article

Design and performance analysis of a multi-reflection heliostat field in solar power tower system

Author

Listed:
  • Hu, Yeguang
  • Xu, Zhigang
  • Zhou, Chaoying
  • Du, Jianjun
  • Yao, Yingxue

Abstract

This paper proposes a multi-reflection heliostat to improve solar power tower plant performance. It can eliminate the significant cosine loss by keeping its aperture always facing the sun. The key structural parameters are specially designed for a superior solar image property. On its basis, a case study on the three-reflection heliostat is first conducted. By the field efficiency distribution comparison between the two types of heliostats, the multi-reflection heliostat is found to have an efficiency advantage in central region. Then in reference to the original Gemasolar field, the three-reflection heliostat field is generated by the proposed layout method. Its optical efficiency is 5.56% higher and the land area is 68.31% smaller, compared to the performance of the optimized Gemasolar field. For the purpose of practical application, the heliostat with ganged multi-reflection modules is developed. The occupied land area of its field is 51.28% less than that of the latter one. The prototype of five-reflection heliostat is developed for a further performance validation. In the outdoor test, the solar concentrating performance agrees well with the simulation result.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:160:y:2020:i:c:p:498-512
    DOI: 10.1016/j.renene.2020.06.113
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148120310302
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2020.06.113?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Wang, Kun & He, Ya-Ling & Xue, Xiao-Dai & Du, Bao-Cun, 2017. "Multi-objective optimization of the aiming strategy for the solar power tower with a cavity receiver by using the non-dominated sorting genetic algorithm," Applied Energy, Elsevier, vol. 205(C), pages 399-416.
    3. García, Jesús & Soo Too, Yen Chean & Padilla, Ricardo Vasquez & Beath, Andrew & Kim, Jin-Soo & Sanjuan, Marco E., 2018. "Dynamic performance of an aiming control methodology for solar central receivers due to cloud disturbances," Renewable Energy, Elsevier, vol. 121(C), pages 355-367.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. García, Jesús & Barraza, Rodrigo & Soo Too, Yen Chean & Vásquez-Padilla, Ricardo & Acosta, David & Estay, Danilo & Valdivia, Patricio, 2022. "Transient simulation of a control strategy for solar receivers based on mass flow valves adjustments and heliostats aiming," Renewable Energy, Elsevier, vol. 185(C), pages 1221-1244.
    2. Zeng, Zhichen & Ni, Dong & Xiao, Gang, 2022. "Real-time heliostat field aiming strategy optimization based on reinforcement learning," Applied Energy, Elsevier, vol. 307(C).
    3. García, Jesús & Barraza, Rodrigo & Soo Too, Yen Chean & Vásquez Padilla, Ricardo & Acosta, David & Estay, Danilo & Valdivia, Patricio, 2020. "Aiming clusters of heliostats over solar receivers for distributing heat flux using one variable per group," Renewable Energy, Elsevier, vol. 160(C), pages 584-596.
    4. Conroy, Tim & Collins, Maurice N. & Grimes, Ronan, 2020. "A review of steady-state thermal and mechanical modelling on tubular solar receivers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    5. He, Caitou & Zhao, Hanli & He, Qi & Zhao, Yuhong & Feng, Jieqing, 2021. "Analytical radiative flux model via convolution integral and image plane mapping," Energy, Elsevier, vol. 222(C).
    6. Sánchez-González, Alberto & Rodríguez-Sánchez, María Reyes & Santana, Domingo, 2018. "Aiming factor to flatten the flux distribution on cylindrical receivers," Energy, Elsevier, vol. 153(C), pages 113-125.
    7. Vidal, Alfonso & Gonzalez, Aurelio & Denk, Thorsten, 2020. "A 100 kW cavity-receiver reactor with an integrated two-step thermochemical cycle: Thermal performance under solar transients," Renewable Energy, Elsevier, vol. 153(C), pages 270-279.
    8. Qiu, Yu & Li, Ming-Jia & Wang, Kun & Liu, Zhan-Bin & Xue, Xiao-Dai, 2017. "Aiming strategy optimization for uniform flux distribution in the receiver of a linear Fresnel solar reflector using a multi-objective genetic algorithm," Applied Energy, Elsevier, vol. 205(C), pages 1394-1407.
    9. Dengxin Ai & Ke Xu & Heng Zhang & Tianheng Chen & Guilin Wang, 2022. "Simulation Research on a Cogeneration System of Low-Concentration Photovoltaic/Thermal Coupled with Air-Source Heat Pump," Energies, MDPI, vol. 15(3), pages 1-25, February.
    10. He, Caitou & Duan, Xiaoyue & Zhao, Yuhong & Feng, Jieqing, 2019. "An analytical flux density distribution model with a closed-form expression for a flat heliostat," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    11. Ruidi Zhu & Dong Ni, 2023. "A Model Predictive Control Approach for Heliostat Field Power Regulatory Aiming Strategy under Varying Cloud Shadowing Conditions," Energies, MDPI, vol. 16(7), pages 1-19, March.
    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. Guo, Jia-Qi & Li, Ming-Jia & He, Ya-Ling & Xu, Jin-Liang, 2019. "A study of new method and comprehensive evaluation on the improved performance of solar power tower plant with the CO2-based mixture cycles," Applied Energy, Elsevier, vol. 256(C).
    14. Wang, Kun & Li, Ming-Jia & Guo, Jia-Qi & Li, Peiwen & Liu, Zhan-Bin, 2018. "A systematic comparison of different S-CO2 Brayton cycle layouts based on multi-objective optimization for applications in solar power tower plants," Applied Energy, Elsevier, vol. 212(C), pages 109-121.
    15. Zhu, Han-Hui & Wang, Kun & He, Ya-Ling, 2017. "Thermodynamic analysis and comparison for different direct-heated supercritical CO2 Brayton cycles integrated into a solar thermal power tower system," Energy, Elsevier, vol. 140(P1), pages 144-157.
    16. Wang, Shuang & Asselineau, Charles-Alexis & Fontalvo, Armando & Wang, Ye & Logie, William & Pye, John & Coventry, Joe, 2023. "Co-optimisation of the heliostat field and receiver for concentrated solar power plants," Applied Energy, Elsevier, vol. 348(C).
    17. 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.
    18. 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.
    19. Guo, Jia-Qi & Li, Ming-Jia & Xu, Jin-Liang & Yan, Jun-Jie & Wang, Kun, 2019. "Thermodynamic performance analysis of different supercritical Brayton cycles using CO2-based binary mixtures in the molten salt solar power tower systems," Energy, Elsevier, vol. 173(C), pages 785-798.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:160:y:2020:i:c:p:498-512. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.