IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i20p8708-d431976.html
   My bibliography  Save this article

A Practical Methodology for the Design and Cost Estimation of Solar Tower Power Plants

Author

Listed:
  • Omar Behar

    (Solar Energy Research Center (SERC-Chile), Av. Tupper 2007 Piso 4, Santiago 8370451, Chile
    Faculty of Engineering, University of Concepcion, Víctor Lamas 1290, Concepción, Chile)

  • Daniel Sbarbaro

    (Solar Energy Research Center (SERC-Chile), Av. Tupper 2007 Piso 4, Santiago 8370451, Chile
    Faculty of Engineering, University of Concepcion, Víctor Lamas 1290, Concepción, Chile)

  • Luis Morán

    (Solar Energy Research Center (SERC-Chile), Av. Tupper 2007 Piso 4, Santiago 8370451, Chile
    Faculty of Engineering, University of Concepcion, Víctor Lamas 1290, Concepción, Chile)

Abstract

Concerns over the environmental influence of greenhouse gas (GHG) emissions have encouraged researchers to develop alternative power technologies. Among the most promising, environmentally friendly power technologies for large-scale applications are solar power tower plants. The implementation of this technology calls for practical modeling and simulation tools to both size the plant and investigate the scale effect on its economic indices. This paper proposes a methodology to design the main components of solar power tower plants and to estimate the specific investment costs and the economic indices. The design approach used in this study was successfully validated through a comparison with the design data of two operational commercial power tower plants; namely, Gemasolar (medium-scale plant of 19.9 MW e ) and Crescent Dunes (large-scale plant of 110 MW e ). The average uncertainty in the design of a fully operational power tower plant is 8.75%. A cost estimation showed the strong influence of the size of the plant on the investment costs, as well as on the economic indices, including payback period, internal rate of return, total life charge costs, and levelized cost of electricity. As an illustrative example, the methodology was applied to design six solar power tower plants in the range of 10–100 MW e for integration into mining processes in Chile. The results show that the levelized cost of electricity decreases from 156 USD/MWh e for the case of a 10-MW e plant to 131 USD/MWh e for the case of a 100-MW e plant. The internal rate of return of plants included in the analyses ranges from 0.77% (for the 10-MW e case) to 2.37% (for 100-MW e case). Consequently, the simple payback ranges from 16 years (for the 100-MW e case) to 19 years (for the 10-MW e case). The sensitivity analysis shows that the size of the solar receiver heavily depends on the allowable heat flux. The degradation rate and the discount rate have a strong influence on economic indices. In addition, both the operation and the deprecation period, as well as the price of electricity, have a crucial impact on the viability of a solar power tower plant. The proposed methodology has great potential to provide key information for prospective analyses for the implementation of power tower technologies to satisfy clean energy needs under a wide range of conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8708-:d:431976
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/20/8708/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/20/8708/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Siala, F.M.F & Elayeb, M.E, 2001. "Mathematical formulation of a graphical method for a no-blocking heliostat field layout," Renewable Energy, Elsevier, vol. 23(1), pages 77-92.
    3. Kincaid, Nicholas & Mungas, Greg & Kramer, Nicholas & Wagner, Michael & Zhu, Guangdong, 2018. "An optical performance comparison of three concentrating solar power collector designs in linear Fresnel, parabolic trough, and central receiver," Applied Energy, Elsevier, vol. 231(C), pages 1109-1121.
    4. 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.
    5. 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.
    6. Yan Luo & Yue Hu & Tao Lu, 2019. "Efficient optimized design of solar power tower plants based on successive response surface methodology," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 14(4), pages 475-486.
    7. 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.
    8. Collado, Francisco J. & Guallar, Jesús, 2012. "Campo: Generation of regular heliostat fields," Renewable Energy, Elsevier, vol. 46(C), pages 49-59.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Valencia-Ortega, G. & Levario-Medina, S. & Angulo-Brown, F. & Barranco-Jiménez, M.A., 2023. "Energetic optimization and local stability of heliothermal plant models under three thermo-economic performance regimes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    2. Gao, Datong & Zhong, Shuai & Ren, Xiao & Kwan, Trevor Hocksun & Pei, Gang, 2022. "The energetic, exergetic, and mechanical comparison of two structurally optimized non-concentrating solar collectors for intermediate temperature applications," Renewable Energy, Elsevier, vol. 184(C), pages 881-898.

    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. 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.
    2. 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.
    3. 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.
    4. Piroozmand, Pasha & Boroushaki, Mehrdad, 2016. "A computational method for optimal design of the multi-tower heliostat field considering heliostats interactions," Energy, Elsevier, vol. 106(C), pages 240-252.
    5. 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).
    6. 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.
    7. 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.
    8. 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).
    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. 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.
    11. 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).
    12. Okoroigwe, Edmund & Madhlopa, Amos, 2016. "An integrated combined cycle system driven by a solar tower: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 337-350.
    13. 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.
    14. 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.
    15. 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.
    16. Saghafifar, Mohammad & Gadalla, Mohamed, 2016. "Thermo-economic analysis of air bottoming cycle hybridization using heliostat field collector: A comparative analysis," Energy, Elsevier, vol. 112(C), pages 698-714.
    17. 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.
    18. Al-Sulaiman, Fahad A. & Atif, Maimoon, 2015. "Performance comparison of different supercritical carbon dioxide Brayton cycles integrated with a solar power tower," Energy, Elsevier, vol. 82(C), pages 61-71.
    19. 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.
    20. Saghafifar, Mohammad & Gadalla, Mohamed, 2017. "Thermo-economic optimization of hybrid solar Maisotsenko bottoming cycles using heliostat field collector: Comparative analysis," Applied Energy, Elsevier, vol. 190(C), pages 686-702.

    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:jsusta:v:12:y:2020:i:20:p:8708-:d:431976. 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.