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Quick design of regular heliostat fields for commercial solar tower power plants

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  • Collado, Francisco J.
  • Guallar, Jesus

Abstract

The design of the collector field for commercial solar power tower plants, including tower height, receiver dimensions and the layout of thousands of heliostats, keeps on being a complex and time consuming task. The details of the full layout optimization for a big surrounding field plant, as Noor III (150 MWe, 7400 heliostats), performed with Campo code, are presented here by the first time in order to contribute suggestions that simplify the optimization without losing accuracy. After the validation of Campo, it is shown that it is able to optimize the whole radial staggered layout of thousands of heliostats with only two parameters. Running more than one hundred and fifty designs of the collector field, a ‘regular’ layout has been found. For big plants, this ‘regular’ layout may be used as a general one for any combination of tower height and receiver dimension tested thus simplifying greatly the process. The differences between the field efficiency for the ‘regular’ layout and that of the actual optimum layout are well less than 1%. Finally, it is found that, for big plants and tall towers, dense layouts work better than expanded layouts, in spite of that blockings are better for the latter.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:178:y:2019:i:c:p:115-125
    DOI: 10.1016/j.energy.2019.04.117
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    Cited by:

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    2. Ghirardi, Elisa & Brumana, Giovanni & Franchini, Giuseppe & Perdichizzi, Antonio, 2021. "Heliostat layout optimization for load-following solar tower plants," Renewable Energy, Elsevier, vol. 168(C), pages 393-405.
    3. 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.
    4. 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.
    5. 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).
    6. Liu, Zengqiang & Lin, Xiaoxia & Zhao, Yuhong & Feng, Jieqing, 2023. "Determination of simulation parameters in Monte Carlo ray tracing for radiative flux density distribution simulation," Energy, Elsevier, vol. 276(C).
    7. Manuel J. Blanco & Victor Grigoriev & Kypros Milidonis & George Tsouloupas & Miguel Larrañeta & Manuel Silva, 2021. "Minimizing the Computational Effort to Optimize Solar Concentrators with the Open-Source Tools SunPATH and Tonatiuh++," Energies, MDPI, vol. 14(15), pages 1-20, July.
    8. 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).
    9. 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.
    10. Zaharaddeen Ali Hussaini & Peter King & Chris Sansom, 2020. "Numerical Simulation and Design of Multi-Tower Concentrated Solar Power Fields," Sustainability, MDPI, vol. 12(6), pages 1-22, March.
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    12. Georgios E. Arnaoutakis & Dimitris Al. Katsaprakakis, 2021. "Concentrating Solar Power Advances in Geometric Optics, Materials and System Integration," Energies, MDPI, vol. 14(19), pages 1-25, September.
    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. Ellingwood, Kevin & Mohammadi, Kasra & Powell, Kody, 2020. "Dynamic optimization and economic evaluation of flexible heat integration in a hybrid concentrated solar power plant," Applied Energy, Elsevier, vol. 276(C).
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