IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v180y2021icp55-67.html
   My bibliography  Save this article

Dynamic aiming strategy for central receiver systems

Author

Listed:
  • Speetzen, N.
  • Richter, P.

Abstract

Aiming strategies in central receiver systems search for an optimal assignment between heliostats and aim point on the receiver surface. In this work, we develop an accelerated aiming strategy which can be used for dynamic scenarios such as short-term environmental influences. The strategy bases on the linear formulation of the problem. To achieve a performance close to real-time, we present several accelerations based on carefully chosen methods to reduce the problem size. The performance of different solvers is evaluated and the problem reduction is adjusted according to accuracy, prediction time and computational run-time. The accelerated aiming strategy is applied to central receiver systems with up to 8600 heliostats in a dynamic test scenario with cloud shadows passing over the heliostat field. The accelerated aiming strategy is effective enough to be used as a real-time control strategy.

Suggested Citation

  • Speetzen, N. & Richter, P., 2021. "Dynamic aiming strategy for central receiver systems," Renewable Energy, Elsevier, vol. 180(C), pages 55-67.
    • Handle: RePEc:eee:renene:v:180:y:2021:i:c:p:55-67
    DOI: 10.1016/j.renene.2021.08.060
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148121012209
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2021.08.060?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. López-Ibáñez, Manuel & Dubois-Lacoste, Jérémie & Pérez Cáceres, Leslie & Birattari, Mauro & Stützle, Thomas, 2016. "The irace package: Iterated racing for automatic algorithm configuration," Operations Research Perspectives, Elsevier, vol. 3(C), pages 43-58.
    2. Sánchez-González, Alberto & Santana, Domingo, 2015. "Solar flux distribution on central receivers: A projection method from analytic function," Renewable Energy, Elsevier, vol. 74(C), pages 576-587.
    3. 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.
    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. Carballo, J.A. & Bonilla, J. & Cruz, N.C. & Fernández-Reche, J. & Álvarez, J.D. & Avila-Marin, A. & Berenguel, M., 2025. "Reinforcement learning for heliostat aiming: Improving the performance of Solar Tower plants," Applied Energy, Elsevier, vol. 377(PB).
    2. Zeng, Zhichen & Ni, Dong & Xiao, Gang, 2022. "Real-time heliostat field aiming strategy optimization based on reinforcement learning," Applied Energy, Elsevier, vol. 307(C).
    3. García, Jesús & Barraza, Rodrigo & Soo Too, Yen Chean & Vásquez-Padilla, Ricardo & Acosta, David & Estay, Danilo & Valdivia, Patricio, 2022. "Transient simulation of a control strategy for solar receivers based on mass flow valves adjustments and heliostats aiming," Renewable Energy, Elsevier, vol. 185(C), pages 1221-1244.
    4. 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.
    5. Sánchez-González, Alberto & Kontopyrgos, Marios & Milidonis, Kypros & Georgiou, Marios C., 2024. "Heliostat field aiming strategy based on deterministic optimization: An experimental validation," Renewable Energy, Elsevier, vol. 236(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. Zeng, Zhichen & Ni, Dong & Xiao, Gang, 2022. "Real-time heliostat field aiming strategy optimization based on reinforcement learning," Applied Energy, Elsevier, vol. 307(C).
    2. Sánchez-González, Alberto & Rodríguez-Sánchez, María Reyes & Santana, Domingo, 2018. "Aiming factor to flatten the flux distribution on cylindrical receivers," Energy, Elsevier, vol. 153(C), pages 113-125.
    3. Sánchez-González, Alberto & Kontopyrgos, Marios & Milidonis, Kypros & Georgiou, Marios C., 2024. "Heliostat field aiming strategy based on deterministic optimization: An experimental validation," Renewable Energy, Elsevier, vol. 236(C).
    4. 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.
    5. Asghari, Mohammad & Jaber, Mohamad Y. & Mirzapour Al-e-hashem, S.M.J., 2023. "Coordinating vessel recovery actions: Analysis of disruption management in a liner shipping service," European Journal of Operational Research, Elsevier, vol. 307(2), pages 627-644.
    6. Alex Gliesch & Marcus Ritt, 2022. "A new heuristic for finding verifiable k-vertex-critical subgraphs," Journal of Heuristics, Springer, vol. 28(1), pages 61-91, February.
    7. Carolina G. Marcelino & João V. C. Avancini & Carla A. D. M. Delgado & Elizabeth F. Wanner & Silvia Jiménez-Fernández & Sancho Salcedo-Sanz, 2021. "Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    8. Merad, Faycel & Labar, Hocine & Samira KELAIAIA, Mounia & Necaibia, Salah & Djelailia, Okba, 2019. "A maximum power control based on flexible collector applied to concentrator solar power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 315-331.
    9. Elisama Araújo Silva Oliveira & Elizabeth Wanner & Elisangela Martins Sá & Sérgio Ricardo Souza, 2025. "A local branching-based solution for the multi-period cutting stock problem with tardiness, earliness, and setup costs," Journal of Heuristics, Springer, vol. 31(1), pages 1-57, March.
    10. 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.
    11. Arda, Yasemin & Cattaruzza, Diego & François, Véronique & Ogier, Maxime, 2024. "Home chemotherapy delivery: An integrated production scheduling and multi-trip vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 317(2), pages 468-486.
    12. Véronique François & Yasemin Arda & Yves Crama, 2019. "Adaptive Large Neighborhood Search for Multitrip Vehicle Routing with Time Windows," Transportation Science, INFORMS, vol. 53(6), pages 1706-1730, November.
    13. Ofer M. Shir & Xi. Xing & Herschel. Rabitz, 2021. "Multi-level evolution strategies for high-resolution black-box control," Journal of Heuristics, Springer, vol. 27(6), pages 1021-1055, December.
    14. 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.
    15. Wang, Kun & He, Ya-Ling & Qiu, Yu & Zhang, Yuwen, 2016. "A novel integrated simulation approach couples MCRT and Gebhart methods to simulate solar radiation transfer in a solar power tower system with a cavity receiver," Renewable Energy, Elsevier, vol. 89(C), pages 93-107.
    16. Kallestad, Jakob & Hasibi, Ramin & Hemmati, Ahmad & Sörensen, Kenneth, 2023. "A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 309(1), pages 446-468.
    17. Huang, Weidong & Yu, Liang, 2018. "Development of a new flux density function for a focusing heliostat," Energy, Elsevier, vol. 151(C), pages 358-375.
    18. García, Jesús & Barraza, Rodrigo & Soo Too, Yen Chean & Vásquez Padilla, Ricardo & Acosta, David & Estay, Danilo & Valdivia, Patricio, 2020. "Aiming clusters of heliostats over solar receivers for distributing heat flux using one variable per group," Renewable Energy, Elsevier, vol. 160(C), pages 584-596.
    19. Abdelrahman Hosny & Sherief Reda, 2024. "Automatic MILP solver configuration by learning problem similarities," Annals of Operations Research, Springer, vol. 339(1), pages 909-936, August.
    20. Andrade, Carlos E. & Toso, Rodrigo F. & Gonçalves, José F. & Resende, Mauricio G.C., 2021. "The Multi-Parent Biased Random-Key Genetic Algorithm with Implicit Path-Relinking and its real-world applications," European Journal of Operational Research, Elsevier, vol. 289(1), pages 17-30.

    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:renene:v:180:y:2021:i:c:p:55-67. 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/renewable-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.