IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i15p4412-d598891.html
   My bibliography  Save this article

Minimizing the Computational Effort to Optimize Solar Concentrators with the Open-Source Tools SunPATH and Tonatiuh++

Author

Listed:
  • Manuel J. Blanco

    (Energy Division, The Cyprus Institute, 2121 Nicosia, Cyprus
    These authors contributed equally to this work.)

  • Victor Grigoriev

    (Energy Division, The Cyprus Institute, 2121 Nicosia, Cyprus
    These authors contributed equally to this work.)

  • Kypros Milidonis

    (Energy Division, The Cyprus Institute, 2121 Nicosia, Cyprus
    These authors contributed equally to this work.)

  • George Tsouloupas

    (High Performance Computing Facility, The Cyprus Institute, 2121 Nicosia, Cyprus
    These authors contributed equally to this work.)

  • Miguel Larrañeta

    (Department of Energy Engineering, University of Seville, 41092 Sevilla, Spain
    These authors contributed equally to this work.)

  • Manuel Silva

    (Department of Energy Engineering, University of Seville, 41092 Sevilla, Spain
    These authors contributed equally to this work.)

Abstract

Integrals that are of interest in the analysis, design, and optimization of concentrating solar thermal systems (CST), such as the annual optical efficiency of the light collection and concentration (LCC) subsystem, can be accurately computed or estimated in two distinct ways: on the time domain and on the spatial domain. This article explores these two ways, using a case study that is highly representative of the commercial CST systems being deployed worldwide. In the time domain, the computation of these integrals are explored using 1-min, 10-min, and 1-h solar DNI input data and using The Cyprus Institute (CyI)’s High-Performance Computing (HPC) system and an open-source ray tracer, Tonatiuh++, being actively developed at CyI. In the spatial domain, the computation of these integrals is explored using SunPATH, another open-source software tool being actively developed at CyI, in tandem with Tonatiuh++. The comparison between the time and spatial domain approach clearly indicate that the spatial domain approach using SunPATH is dramatically more computationally efficient than the time domain approach. According to the results obtained, at least for the case study analyzed in this article, to compute the annual energy delivered by the LCC subsystem with a relative error less than 0.1%, it is enough to provide SunPATH with 1-h DNI data as input, request from SunPATH the sun position and weights of just 30 points in the celestial sphere, and run Tonatiuh++ to simulate these 30 points using 15 million rays per run. As the test case is highly representative, it is expected that this approach will yield similar results for most CST systems of interest.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4412-:d:598891
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/15/4412/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/15/4412/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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. 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).

    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. 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.
    2. 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.
    3. 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.
    4. Li, Chao & Yang, Zhiping & Zhai, Rongrong & Yang, Yongping & Patchigolla, Kumar & Oakey, John E., 2018. "Off-design thermodynamic performances of a solar tower aided coal-fired power plant for different solar multiples with thermal energy storage," Energy, Elsevier, vol. 163(C), pages 956-968.
    5. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.
    6. Li, Chao & Zhai, Rongrong & Yang, Yongping & Patchigolla, Kumar & Oakey, John E. & Turner, Peter, 2019. "Annual performance analysis and optimization of a solar tower aided coal-fired power plant," Applied Energy, Elsevier, vol. 237(C), pages 440-456.
    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. José de Jesús Jaramillo Serna & Jesús M. López-Lezama, 2019. "Calculation of Distance Protection Settings in Mutually Coupled Transmission Lines: A Comparative Analysis," Energies, MDPI, vol. 12(7), pages 1-32, April.
    9. 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.
    10. 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.
    11. 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).
    12. 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.
    13. 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.
    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).
    15. Enric Prats-Salvado & Nathalie Monnerie & Christian Sattler, 2021. "Synergies between Direct Air Capture Technologies and Solar Thermochemical Cycles in the Production of Methanol," Energies, MDPI, vol. 14(16), pages 1-21, August.
    16. 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.
    17. 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).
    18. Khosravi, Soheil & Neshat, Elaheh & Saray, Rahim Khoshbakhti, 2023. "Thermodynamic analysis of a sorption-enhanced gasification process of municipal solid waste, integrated with concentrated solar power and thermal energy storage systems for co-generation of power and ," Renewable Energy, Elsevier, vol. 214(C), pages 140-153.
    19. 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).

    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:14:y:2021:i:15:p:4412-:d:598891. 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.