Heliostat Clustering for Aiming Point Strategies Optimization
DOI:
https://doi.org/10.52825/solarpaces.v2i.814Keywords:
Concentrated Solar Energy, Tower Technology, Heliostat, Aiming Point StrategyAbstract
The performance of solar tower systems is closely linked to the aiming point strategy of the heliostats. The optimization process of obtaining the best aiming point strategy for a field is complex and has a high computational cost. The use of the clustering technique relieves the requirements by decreasing the space of possible solutions to the problem. Results show that the application of the technique for aiming point strategy optimization reduces the time of optimization significantly.
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S. Zoller, E. Koepf, D. Nizamina, M. Stephan, A. Patané, P. Haueter, M. Romero, J. González-Aguilar, D. Lieftink, E. de Wit, S. Brendelberger, A. Sizmann, A. Steinfied. “A solar tower fuel plant for the thermochemical production of kerosene from H2O and CO2“ in Joule, Volume 6, Issue 7, 20 July 2022, Pages 1606-1616 doi: https://doi.org/10.1016/j.joule.2022.06.012
B. Grange, G. Flamant, “Aiming Strategy on a Prototype-Scale Solar Receiver: Coupling of Tabu Search, Ray-Tracing and Thermal Models”, Sustainability 2021, 13, 3920, doi: https://doi.org/10.3390/su13073920
S. M. Besarati, D. Y. Goswami, E. K. Stefanakos, “Optimal heliostat aiming strategy for uniform distribution of heat flux on the receiver of a solar power tower plant”, Energy Conversion and Management, vol. 84, 2014, doi: https://doi.org/10.1016/j.enconman.2014.04.030.
D. Maldonado, R. Flesch, A. Reinholz., P. Scharzbözl, “Evaluation of Aim Point Optimization Methods”, SolarPACES 2017 AIP Conf. Proc. 2033, doi https://doi.org/10.1063/1.5067061.
B. Belhomme, R. Pitz-Paal, Robert, P.Schwarzbözl, “Optimization of Heliostat Aim Point Selection for Central Receiver Systems Based on the Ant Colony Optimization Metaheuristic”, Journal of Solar Energy Engineering. 136, 2014, doi: https://doi.org/10.1115/1.4024738.
A. Lukasov, “Hierarchical agglomerative clustering procedure”, Pattern Recognition, vol. 11, issues 5-6, pp. 365-381, 1979, doi: https://doi.org/10.1016/0031-3203(79)90049-9.
R. Osuna, R. Olavarrıa, R. Morillo, M. Sanchez, F. Cantero, V. Fernandez-Quer J. A. Hartigan, M. A. Wong, “Algorithm AS 136: a k-means clustering algorithm”, Journal of the Royal Statistical Society, Series C, 1979, vol. 28, no 1, pp. 100-108, doi: https://doi.org/10.2307/2346830.
T. Kohonen, „ Self-organizing maps“, 3rd Edition, Springer-Verlag, Berlin, Heidelberg, New York, 2001, doi: http://dx.doi.org/10.1007/978-3-642-56927-2.
I. Les, A. Mutuberria, P. Schöttl., P. Nitz., E. Leonardi, L. Pisani, “Optical performance comparison between heliostat field generation algorithms”. In: 2018 Proceedings SolarPaces, doi: https://doi.org/10.1063/1.5067056
A. Mutuberria, “Tonatiuh, A Monte Carlo ray tracer for the optical simulation of solar concentrating systems”, https://github.com/iat-cener/tonatiuh
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Copyright (c) 2024 Olaia Itoiz, Amaia Mutuberria, Marcelino Sánchez
This work is licensed under a Creative Commons Attribution 4.0 International License.
Accepted 2024-07-10
Published 2024-08-28
Funding data
-
European Commission
Grant numbers TED2021-132190B-C21;PID2021-125786OB-C21
