IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i13p4943-d1179203.html
   My bibliography  Save this article

Parametric Study and Optimization of No-Blocking Heliostat Field Layout

Author

Listed:
  • Dhikra Derbal

    (Research Laboratory of Industrial Risks, Non-Destructive Control and Operating Safety, Mechanical Engineering Department, Badji Mokhtar Annaba University, Annaba 23000, Algeria)

  • Abdallah Abderrezak

    (Research Laboratory of Industrial Risks, Non-Destructive Control and Operating Safety, Mechanical Engineering Department, Badji Mokhtar Annaba University, Annaba 23000, Algeria)

  • Seif Eddine Chehaidia

    (Département de Génie Mécanique, Ecole Nationale Polytechnique de Constantine (ENPC), Constantine 25000, Algeria)

  • Majdi T. Amin

    (Mechanical Engineering Technology, Yanbu Industrial College (YIC), Royal Commission Yanbu Colleges & Institutes, Alnahdah, Yanbu Al Sinaiyah, Yanbu 46452, Saudi Arabia)

  • Mohamed I. Mosaad

    (Electrical Engineering Department, Faculty of Engineering, Damietta University, Damietta 34511, Egypt
    Electrical & Electronics Engineering Technology Department, Yanbu Industrial College (YIC), Royal Commission Yanbu Colleges & Institutes, Alnahdah, Yanbu Al Sinaiyah, Yanbu 46452, Saudi Arabia)

  • Tarek A. Abdul-Fattah

    (Department of Engineering Physics and Mathematics, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt)

Abstract

Generating electric power using solar thermal systems is effective, particularly for countries with high solar potential. In order to decide on a relevant location to implement the solar tower plant and develop the mathematical model of a no-blocking heliostat field, a meteorological assessment was discussed in this paper. In addition, a parametric study was examined to evaluate the effect of the designed parameters (heliostat size, heliostat height from the ground, tower height, receiver aperture, and the minimum radius) on the solar field’s performance. The preliminary solar field was then compared to the final design using the optimal design parameters. The obtained results showed that “Tamanrasset City” satisfied the necessary conditions for implementing a solar tower plant. According to preliminary solar field generation, no heliostat blocked its neighbor with a blocking efficiency of 100%. An analysis of its performance revealed that the optimized solar field would be capable of producing 15, 6571 MW, operating at an optical efficiency of 76.95%, and the enhancement rate of both efficiency and power output was 8.1%.

Suggested Citation

  • Dhikra Derbal & Abdallah Abderrezak & Seif Eddine Chehaidia & Majdi T. Amin & Mohamed I. Mosaad & Tarek A. Abdul-Fattah, 2023. "Parametric Study and Optimization of No-Blocking Heliostat Field Layout," Energies, MDPI, vol. 16(13), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4943-:d:1179203
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/13/4943/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/13/4943/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Garba, Ifeoluwa & Bellingham, Richard, 2021. "Energy poverty: Estimating the impact of solid cooking fuels on GDP per capita in developing countries - Case of sub-Saharan Africa," Energy, Elsevier, vol. 221(C).
    3. Carrizosa, E. & Domínguez-Bravo, C. & Fernández-Cara, E. & Quero, M., 2015. "Optimization of multiple receivers solar power tower systems," Energy, Elsevier, vol. 90(P2), pages 2085-2093.
    4. Alhussein Albarbar & Abdullah Arar, 2019. "Performance Assessment and Improvement of Central Receivers Used for Solar Thermal Plants," Energies, MDPI, vol. 12(16), pages 1-27, August.
    5. 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.
    6. 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.
    7. Leonardi, Erminia & D’Aguanno, Bruno, 2011. "CRS4-2: A numerical code for the calculation of the solar power collected in a central receiver system," Energy, Elsevier, vol. 36(8), pages 4828-4837.
    8. Mutezo, G. & Mulopo, J., 2021. "A review of Africa's transition from fossil fuels to renewable energy using circular economy principles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    9. Leijiao Ge & Tianshuo Du & Changlu Li & Yuanliang Li & Jun Yan & Muhammad Umer Rafiq, 2022. "Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications," Energies, MDPI, vol. 15(23), pages 1-24, November.
    10. 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.
    11. M. Osama abed elraouf & Mansour Aljohani & Mohamed I. Mosaad & Tarek A. AbdulFattah, 2022. "Mitigating Misfire and Fire-through Faults in Hybrid Renewable Energy Systems Utilizing Dynamic Voltage Restorer," Energies, MDPI, vol. 15(16), pages 1-16, August.
    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. 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.
    2. 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.
    3. 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.
    4. Amani, Madjid & Ghenaiet, Adel, 2020. "Novel hybridization of solar central receiver system with combined cycle power plant," Energy, Elsevier, vol. 201(C).
    5. Cruz, N.C. & Redondo, J.L. & Berenguel, M. & Álvarez, J.D. & Ortigosa, P.M., 2017. "Review of software for optical analyzing and optimizing heliostat fields," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1001-1018.
    6. Ashley, Thomas & Carrizosa, Emilio & Fernández-Cara, Enrique, 2017. "Optimisation of aiming strategies in Solar Power Tower plants," Energy, Elsevier, vol. 137(C), pages 285-291.
    7. Atif, Maimoon. & Al-Sulaiman, Fahad A., 2017. "Energy and exergy analyses of solar tower power plant driven supercritical carbon dioxide recompression cycles for six different locations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 153-167.
    8. 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.
    9. Ashley, Thomas & Carrizosa, Emilio & Fernández-Cara, Enrique, 2019. "Heliostat field cleaning scheduling for Solar Power Tower plants: A heuristic approach," Applied Energy, Elsevier, vol. 235(C), pages 653-660.
    10. Ortega, Guillermo & Rovira, Antonio, 2020. "A fast and accurate methodology for the calculation of the shading and blocking efficiency in central receiver systems," Renewable Energy, Elsevier, vol. 154(C), pages 58-70.
    11. 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.
    12. Omar Behar & Daniel Sbarbaro & Luis Morán, 2020. "A Practical Methodology for the Design and Cost Estimation of Solar Tower Power Plants," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
    13. S M Mezbahul Amin & Abul Hasnat & Nazia Hossain, 2023. "Designing and Analysing a PV/Battery System via New Resilience Indicators," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
    14. Anna Zsofia Bajomi & Nóra Feldmár & Sergio Tirado-Herrero, 2021. "Will Plans to Ease Energy Poverty Go Up in Smoke? Assessing the Hungarian NECP through the Lens of Solid Fuel Users’ Vulnerabilities," Sustainability, MDPI, vol. 13(23), pages 1-20, November.
    15. Zuo, Yuhang & Li, Yawei & Zhou, Hao, 2022. "Numerical study on preheating process of molten salt tower receiver in windy conditions," Energy, Elsevier, vol. 251(C).
    16. Icaro Figueiredo Vilasboas & Julio Augusto Mendes da Silva & Osvaldo José Venturini, 2023. "On the Summarization of Meteorological Data for Solar Thermal Power Generation Forecast," Energies, MDPI, vol. 16(7), pages 1-10, April.
    17. Hussain, C.M. Iftekhar & Duffy, Aidan & Norton, Brian, 2020. "Thermophotovoltaic systems for achieving high-solar-fraction hybrid solar-biomass power generation," Applied Energy, Elsevier, vol. 259(C).
    18. 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.
    19. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    20. Guo, Zhiling & Zhuang, Zhan & Tan, Hongjun & Liu, Zhengguang & Li, Peiran & Lin, Zhengyuan & Shang, Wen-Long & Zhang, Haoran & Yan, Jinyue, 2023. "Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets," Renewable Energy, Elsevier, vol. 219(P1).

    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:gam:jeners:v:16:y:2023:i:13:p:4943-:d:1179203. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.