IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v348y2023ics0306261923008772.html
   My bibliography  Save this article

Co-optimisation of the heliostat field and receiver for concentrated solar power plants

Author

Listed:
  • Wang, Shuang
  • Asselineau, Charles-Alexis
  • Fontalvo, Armando
  • Wang, Ye
  • Logie, William
  • Pye, John
  • Coventry, Joe

Abstract

In a concentrated solar power (CSP) tower plant, it is essential to understand the performance of the subsystem formed by the heliostat field and the receiver, operated with an optimal aiming strategy that guarantees the safety and lifetime of the receiver while maximising performance. State-of-the-art studies optimise the heliostat field, aiming strategy and the receiver independently. However, the field and the receiver are interdependent and co-optimisation of the field-receiver subsystem is necessary to obtain the optimal configuration. Fast and accurate annual performance assessments of the subsystem are needed to calculate the annual energy output and the Levelised Cost of Energy (LCOE) of the full CSP system. In this study, a co-optimisation method is proposed based on coupled instantaneous optical, thermal and mechanical models integrated into a system-level model for annual simulation of full system and economics. The resulting system-model is then used for design optimisation based on a genetic algorithm. Several techniques are implemented to make this complex and computationally expensive problem tractable. The proposed method is used to optimise the design of two systems composed of a surround field and a liquid sodium-cooled cylindrical external receiver for first the annual performance and then the LCOE. The good behaviour of the method is confirmed by a sensitivity study. The LCOE-based optimisation leads to a less efficient system than the efficiency-based optimisation but a higher capacity factor. The methods presented are applicable to other CSP plant configurations, including state-of-art molten salt power tower plants.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:348:y:2023:i:c:s0306261923008772
    DOI: 10.1016/j.apenergy.2023.121513
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923008772
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.121513?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. 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.
    2. 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.
    3. 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).
    4. 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.
    5. Zou, Chongzhe & Zhang, Yanping & Falcoz, Quentin & Neveu, Pierre & Zhang, Cheng & Shu, Weicheng & Huang, Shuhong, 2017. "Design and optimization of a high-temperature cavity receiver for a solar energy cascade utilization system," Renewable Energy, Elsevier, vol. 103(C), pages 478-489.
    6. Zeng, Zhichen & Ni, Dong & Xiao, Gang, 2022. "Real-time heliostat field aiming strategy optimization based on reinforcement learning," Applied Energy, Elsevier, vol. 307(C).
    7. Collado, Francisco J. & Guallar, Jesús, 2012. "Campo: Generation of regular heliostat fields," Renewable Energy, Elsevier, vol. 46(C), pages 49-59.
    8. 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.
    9. Wang, Wen-Qi & Li, Ming-Jia & Cheng, Ze-Dong & Li, Dong & Liu, Zhan-Bin, 2021. "Coupled optical-thermal-stress characteristics of a multi-tube external molten salt receiver for the next generation concentrating solar power," Energy, Elsevier, vol. 233(C).
    10. 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.
    11. 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.
    12. Conroy, Tim & Collins, Maurice N. & Fisher, James & Grimes, Ronan, 2018. "Thermal and mechanical analysis of a sodium-cooled solar receiver operating under a novel heliostat aiming point strategy," Applied Energy, Elsevier, vol. 230(C), pages 590-614.
    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. 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. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    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. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    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:eee:appene:v:348:y:2023:i:c:s0306261923008772. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.