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Quickly select heliostat candidates and design pattern-free layout using geometric projection method

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  • Cui, Dongyu
  • Bian, Hong
  • Yu, Haizheng

Abstract

Central receiver system is one of the most promising solar energy harvesting technologies, which focuses sunlight onto the receiver through heliostats to generate high temperatures for thermal cycling. The optimal layout of the heliostat plays a crucial role, but the pattern-free heliostat field layout requires multiple uses of computationally intensive shadowing and blocking efficiency, which is highly time-consuming. In this work, a new geometric projection method, Projection radius method (PRM), is proposed. It includes two aspects: (i) Quickly and effectively selecting heliostat candidates with shadowing and blocking potential by the incident and reflected ray projection radius; (ii) Designing the pattern-free heliostat field layout by employing overlap radius instead of shadowing and blocking efficiency. The results indicate that, compared to traditional methods, when designing 2650 heliostats, (i) the calculation time for optical efficiency was reduced by 63%; (ii) optical efficiency increased by 1.23%, and designing a pattern-free layout took only 2.78 h. These advancements facilitate the efficient design of large-scale pattern-free heliostat field layouts.

Suggested Citation

  • Cui, Dongyu & Bian, Hong & Yu, Haizheng, 2024. "Quickly select heliostat candidates and design pattern-free layout using geometric projection method," Renewable Energy, Elsevier, vol. 237(PB).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pb:s0960148124017221
    DOI: 10.1016/j.renene.2024.121654
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    References listed on IDEAS

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    1. 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).
    2. Anderson, Cody B. & Picotti, Giovanni & Cholette, Michael E. & Leslie, Bruce & Steinberg, Theodore A. & Manzolini, Giampaolo, 2023. "Heliostat-field soiling predictions and cleaning resource optimization for solar tower plants," Applied Energy, Elsevier, vol. 352(C).
    3. 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.
    4. Yerudkar, Aditi N. & Kumar, Durgesh & Dalvi, Vishwanath H. & Panse, Sudhir V. & Gaval, Vivek R. & Joshi, Jyeshtharaj B., 2024. "Economically feasible solutions in concentrating solar power technology specifically for heliostats – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    5. 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.
    6. Khaled Obaideen & Abdul Ghani Olabi & Yaser Al Swailmeen & Nabila Shehata & Mohammad Ali Abdelkareem & Abdul Hai Alami & Cristina Rodriguez & Enas Taha Sayed, 2023. "Solar Energy: Applications, Trends Analysis, Bibliometric Analysis and Research Contribution to Sustainable Development Goals (SDGs)," Sustainability, MDPI, vol. 15(2), pages 1-34, January.
    7. 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.
    8. Behar, Omar & Khellaf, Abdallah & Mohammedi, Kamal, 2013. "A review of studies on central receiver solar thermal power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 12-39.
    9. Ortega, Guillermo & Rovira, Antonio, 2020. "A new method for the selection of candidates for shading and blocking in central receiver systems," Renewable Energy, Elsevier, vol. 152(C), pages 961-973.
    10. Collado, Francisco J. & Guallar, Jesús, 2013. "A review of optimized design layouts for solar power tower plants with campo code," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 142-154.
    11. Collado, Francisco J. & Guallar, Jesús, 2012. "Campo: Generation of regular heliostat fields," Renewable Energy, Elsevier, vol. 46(C), pages 49-59.
    12. 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.
    13. 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.
    14. Besarati, Saeb M. & Yogi Goswami, D., 2014. "A computationally efficient method for the design of the heliostat field for solar power tower plant," Renewable Energy, Elsevier, vol. 69(C), pages 226-232.
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