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Dynamic aiming strategy for central receiver systems

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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    1. 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).
    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. Lin, Xiaoxia & Zheng, Cangping & Huang, Wenjun & Zhao, Yuhong & Feng, Jieqing, 2025. "A novel heliostat aiming optimization framework via differentiable Monte Carlo ray tracing for solar power tower systems," Applied Energy, Elsevier, vol. 388(C).
    6. 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).

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