IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v307y2024ics0360544224024423.html
   My bibliography  Save this article

Real-time and annual performance evaluation of an ultra-high-temperature concentrating solar collector by developing an MCRT-CFD-ANN coupled model

Author

Listed:
  • Zhang, Yuanting
  • Li, Qing
  • Qiu, Yu

Abstract

Performance evaluation of the solar collector is paramount for the design and optimization of the concentrating solar power system. To evaluate the real-time and annual performance of an ultra-high-temperature collector, a comprehensive model combining an optical-thermal model and an artificial neural network was developed. Initially, the optical performance of the collector was derived by an optical model developed by Monte Carlo ray tracing. Subsequently, by integrating the optical model with a computational fluid dynamics model, the optical-thermal performance of the collector was obtained under various operational conditions. Next, a high-precision artificial neural network model was developed using the data obtained from the optical-thermal model, with a predictive accuracy exceeding an R2 value of 0.9999. Finally, the comprehensive model was employed to analyze the real-time and annual collector performance. The results reveal significant fluctuations in the real-time optical-thermal performance throughout the year, while the monthly field efficiency, receiver efficiency, and collector efficiency demonstrate relatively moderate variations throughout the year, remaining within the ranges of 64.3%–74.9%, 76.4%–82.8%, and 52.8%–61.5%, respectively. Furthermore, the annual field efficiency, receiver efficiency, and collector efficiency can reach 69.7%, 81.6%, and 56.8%, respectively. This study offers reliable models and meaningful insights for the performance predictions and improvements of solar collectors.

Suggested Citation

  • Zhang, Yuanting & Li, Qing & Qiu, Yu, 2024. "Real-time and annual performance evaluation of an ultra-high-temperature concentrating solar collector by developing an MCRT-CFD-ANN coupled model," Energy, Elsevier, vol. 307(C).
  • Handle: RePEc:eee:energy:v:307:y:2024:i:c:s0360544224024423
    DOI: 10.1016/j.energy.2024.132668
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224024423
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2024.132668?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. Zhang, Yuanting & Qiu, Yu & Li, Qing & Henry, Asegun, 2022. "Optical-thermal-mechanical characteristics of an ultra-high-temperature graphite receiver designed for concentrating solar power," Applied Energy, Elsevier, vol. 307(C).
    2. Cheilytko, Andrii & Schwarzbözl, Peter & Wieghardt, Kai, 2023. "Modeling of heat conduction processes in porous absorber of open type of solar tower stations," Renewable Energy, Elsevier, vol. 215(C).
    3. 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.
    4. Ye, Kai & Li, Qing & Zhang, Yuanting & Qiu, Yu & Liu, Bin, 2022. "An efficient receiver tube enhanced by a solar transparent aerogel for solar power tower," Energy, Elsevier, vol. 261(PB).
    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. Todd Levin & John Bistline & Ramteen Sioshansi & Wesley J. Cole & Jonghwan Kwon & Scott P. Burger & George W. Crabtree & Jesse D. Jenkins & Rebecca O’Neil & Magnus Korpås & Sonja Wogrin & Benjamin F. , 2023. "Energy storage solutions to decarbonize electricity through enhanced capacity expansion modelling," Nature Energy, Nature, vol. 8(11), pages 1199-1208, November.
    7. Du, Bin & Lund, Peter D. & Wang, Jun, 2021. "Combining CFD and artificial neural network techniques to predict the thermal performance of all-glass straight evacuated tube solar collector," Energy, Elsevier, vol. 220(C).
    8. 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).
    9. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    10. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    11. Qiu, Yu & He, Ya-Ling & Li, Peiwen & Du, Bao-Cun, 2017. "A comprehensive model for analysis of real-time optical performance of a solar power tower with a multi-tube cavity receiver," Applied Energy, Elsevier, vol. 185(P1), pages 589-603.
    12. Zhang, Pengfei & Wang, Yilin & Qiu, Yu & Yan, Hongjie & Wang, Zhaolong & Li, Qing, 2024. "Novel composite phase change materials supported by oriented carbon fibers for solar thermal energy conversion and storage," Applied Energy, Elsevier, vol. 358(C).
    13. Camelo, Henrique do Nascimento & Lucio, Paulo Sérgio & Leal Junior, João Bosco Verçosa & Carvalho, Paulo Cesar Marques de & Santos, Daniel von Glehn dos, 2018. "Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks," Energy, Elsevier, vol. 151(C), pages 347-357.
    14. Barreto, Germilly & Canhoto, Paulo & Collares-Pereira, Manuel, 2019. "Three-dimensional CFD modelling and thermal performance analysis of porous volumetric receivers coupled to solar concentration systems," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    15. Hachicha, Ahmed Amine & Yousef, Bashria A.A. & Said, Zafar & Rodríguez, Ivette, 2019. "A review study on the modeling of high-temperature solar thermal collector systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 280-298.
    16. Wang, Wen-Qi & Li, Ming-Jia & Jiang, Rui & Cheng, Ze-Dong & He, Ya-Ling, 2022. "A comparison between lumped parameter method and computational fluid dynamics method for steady and transient optical-thermal characteristics of the molten salt receiver in solar power tower," Energy, Elsevier, vol. 245(C).
    17. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    18. Li, Meng-Jie & Li, Ming-Jia & Jiang, Rui & Du, Shen & Li, Xiao-Yue, 2024. "Study on the dynamic characteristics of a concentrated solar power plant with the supercritical CO2 Brayton cycle coupled with different thermal energy storage methods," Energy, Elsevier, vol. 288(C).
    19. Chen, Jinli & Xiao, Gang & Xu, Haoran & Zhou, Xin & Yang, Jiamin & Ni, Mingjiang & Cen, Kefa, 2022. "Experiment and dynamic simulation of a solar tower collector system for power generation," Renewable Energy, Elsevier, vol. 196(C), pages 946-958.
    20. Manzolini, Giampaolo & Lucca, Gaia & Binotti, Marco & Lozza, Giovanni, 2021. "A two-step procedure for the selection of innovative high temperature heat transfer fluids in solar tower power plants," Renewable Energy, Elsevier, vol. 177(C), pages 807-822.
    21. Jiang, Rui & Li, Ming-Jia & Wang, Wen-Qi & Li, Meng-Jie & Ma, Teng, 2024. "A novel numerical methodology of solar power tower system for dynamic characteristics analysis and performance prediction," Energy, Elsevier, vol. 292(C).
    22. Rafique, Muhammad M. & Nathan, Graham & Saw, Woei, 2022. "Modelled annual thermal performance of a 50MWth refractory-lined particle-laden solar receiver operating above 1000°C," Renewable Energy, Elsevier, vol. 197(C), pages 1081-1093.
    23. Broeske, Robin Tim & Schwarzbözl, Peter & Birkigt, Lisa & Vasic, Srdan & Dung, Sebastian & Doerbeck, Till & Hoffschmidt, Bernhard, 2023. "Experimentally assessed efficiency improvement of innovative 3D-shaped structures as volumetric absorbers," Renewable Energy, Elsevier, vol. 218(C).
    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. Gentile, Giancarlo & Picotti, Giovanni & Binotti, Marco & Cholette, Michael E. & Manzolini, Giampaolo, 2024. "A comprehensive methodology for the design of solar tower external receivers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    2. 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).
    3. Alamdari, Pedram & Khatamifar, Mehdi & Lin, Wenxian, 2024. "Heat loss analysis review: Parabolic trough and linear Fresnel collectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    4. Su, Zixiang & Yang, Liu & Wang, Hao & Song, Jianzhong & Jiang, Weixue, 2024. "Exergoenvironmental optimization and thermoeconomic assessment of an innovative multistage Brayton cycle with dual expansion and cooling for ultra-high temperature solar power," Energy, Elsevier, vol. 286(C).
    5. Wang, Jikang & Zhang, Yuanting & Zhang, Weichen & Qiu, Yu & Li, Qing, 2022. "Design and evaluation of a lab-scale tungsten receiver for ultra-high-temperature solar energy harvesting," Applied Energy, Elsevier, vol. 327(C).
    6. Chen, Xudong & Li, Chunzhe & Yang, Zhenning & Dong, Yan & Wang, Fuqiang & Cheng, Ziming & Yang, Chun, 2024. "Golf-ball-inspired phase change material capsule: Experimental and numerical simulation analysis of flow characteristics and thermal performance," Energy, Elsevier, vol. 293(C).
    7. Laporte-Azcué, M. & Rodríguez-Sánchez, M.R., 2024. "Thermal efficiency and endurance enhancement of tubular solar receivers using functionally graded materials," Applied Energy, Elsevier, vol. 360(C).
    8. Jiang, Rui & Li, Ming-Jia & Wang, Wen-Qi & Li, Meng-Jie & Ma, Teng, 2024. "A novel numerical methodology of solar power tower system for dynamic characteristics analysis and performance prediction," Energy, Elsevier, vol. 292(C).
    9. Wang, Wen-Qi & He, Ya-Ling & Jiang, Rui, 2022. "A multi-scale solar receiver with peak receiver efficiency over 90% at 720 °C for the next-generation solar power tower," Renewable Energy, Elsevier, vol. 200(C), pages 714-723.
    10. Wang, Zhangyuan & Zhao, Xudong & Han, Zhonghe & Luo, Liang & Xiang, Jinwei & Zheng, Senglin & Liu, Guangming & Yu, Min & Cui, Yu & Shittu, Samson & Hu, Menglong, 2021. "Advanced big-data/machine-learning techniques for optimization and performance enhancement of the heat pipe technology – A review and prospective study," Applied Energy, Elsevier, vol. 294(C).
    11. Laporte-Azcué, M. & Rodríguez-Sánchez, M.R. & González-Gómez, P.A. & Santana, D., 2021. "Assessment of the time resolution used to estimate the central solar receiver lifetime," Applied Energy, Elsevier, vol. 301(C).
    12. Du, Bin & Lund, Peter D. & Wang, Jun, 2021. "Combining CFD and artificial neural network techniques to predict the thermal performance of all-glass straight evacuated tube solar collector," Energy, Elsevier, vol. 220(C).
    13. Harish Kumar Ghritlahre & Purvi Chandrakar & Ashfaque Ahmad, 2021. "A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network," Annals of Data Science, Springer, vol. 8(3), pages 405-449, September.
    14. Zhang, Qiongfang & Yan, Hao & Liu, Yongming, 2024. "Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    15. Huang, Zizhou & Li, Qing & Qiu, Yu, 2024. "Enhancements in thermal properties of binary alkali chloride salt by Al2O3 nanoparticles for thermal energy storage," Energy, Elsevier, vol. 301(C).
    16. Liang, Huaxu & Wang, Fuqiang & Yang, Luwei & Cheng, Ziming & Shuai, Yong & Tan, Heping, 2021. "Progress in full spectrum solar energy utilization by spectral beam splitting hybrid PV/T system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    17. Ye, Kai & Li, Qing & Zhang, Yuanting & Qiu, Yu & Liu, Bin, 2022. "An efficient receiver tube enhanced by a solar transparent aerogel for solar power tower," Energy, Elsevier, vol. 261(PB).
    18. Sedighi, Mohammadreza & Padilla, Ricardo Vasquez & Alamdari, Pedram & Lake, Maree & Rose, Andrew & Izadgoshasb, Iman & Taylor, Robert A., 2020. "A novel high-temperature (>700 °C), volumetric receiver with a packed bed of transparent and absorbing spheres," Applied Energy, Elsevier, vol. 264(C).
    19. Mostafa Esmaeili Shayan & Gholamhassan Najafi & Barat Ghobadian & Shiva Gorjian & Mohamed Mazlan & Mehdi Samami & Alireza Shabanzadeh, 2022. "Flexible Photovoltaic System on Non-Conventional Surfaces: A Techno-Economic Analysis," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    20. Heo, SungKu & Byun, Jaewon & Ifaei, Pouya & Ko, Jaerak & Ha, Byeongmin & Hwangbo, Soonho & Yoo, ChangKyoo, 2024. "Towards mega-scale decarbonized industrial park (Mega-DIP): Generative AI-driven techno-economic and environmental assessment of renewable and sustainable energy utilization in petrochemical industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).

    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:energy:v:307:y:2024:i:c:s0360544224024423. 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.journals.elsevier.com/energy .

    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.