1. Home
  2. Issues
  3. Volume 29, Issue 1 (2018)
  4. Optimizing the Heliostat Field Layout by ...

Informatica


Optimizing the Heliostat Field Layout by Applying Stochastic Population-Based Algorithms
Volume 29, Issue 1 (2018), pp. 21–39
Pub. online: 1 January 2018      Type: Research Article      Open accessOpen Access
Received
1 May 2017
Accepted
1 December 2017
Published
1 January 2018

Abstract

The heliostat field of Solar Central Receiver Systems takes up to 50% of the initial investment and can cause up to 40% of energetic loss in operation. Hence, it must be carefully optimized. Design procedures usually rely on particular heliostat distribution models. In this work, optimization of the promising biomimetic distribution model is studied. Two stochastic population-based optimizers are applied to maximize the optical efficiency of fields: a genetic algorithm, micraGA, and a memetic one, UEGO. As far as the authors know, they have not been previously applied to this problem. However, they could be a good option according to their structure. Additionally, a Brute-Force Grid is used to estimate the global optimum and a Pure-Random Search is applied as a baseline reference. Our empirical results show that many different configurations of the distribution model lead to very similar solutions. Although micraGA exhibits poor performance, UEGO achieves the best results in a reduced time and seems appropriate for the problem at hand.

References

 
Alexopoulos, S., Hoffschmidt, B. (2017). Advances in solar tower technology. WIREs Energy Environment, 6(1), 1–19.
 
Avila-Marin, A.L., Fernandez-Reche, J., Tellez, F.M. (2013). Evaluation of the potential of central receiver solar power plants: configuration, optimization and trends. Applied Energy, 112, 274–288.
 
Behar, O., Khellaf, A., Mohammedi, K. (2013). A review of studies on central receiver solar thermal power plants. Renewable and Sustainable Energy Reviews, 23(0), 12–39.
 
Besarati, S.M., Goswami, D.Y. (2014). A computationally efficient method for the design of the heliostat field for solar power tower plant. Renewable Energy, 69, 226–232.
 
Brooks, S.H. (1958). A discussion of random methods for seeking maxima. Operations Research, 6(2), 244–251.
 
Camacho, E., Berenguel, M., Rubio, F.R., Martínez, D. (2012). Control of Solar Energy Systems. Springer Science & Business Media.
 
Carrizosa, E., Domínguez-Bravo, C., Fernández-Cara, E., Quero, M. (2014). An optimization approach to the design of multi-size heliostat fields. Technical report, Technical report IMUS.
 
Collado, F.J., Guallar, J. (2012). Campo: Generation of regular heliostat fields. Renewable Energy, 46, 49–59.
 
Collado, F.J., Guallar, J. (2013). A review of optimized design layouts for solar power tower plants with Campo code. Renewable and Sustainable Energy Reviews, 20, 142–154.
 
Collado, F.J., Turégano, J.A. (1989). Calculation of the annual thermal energy supplied by a defined heliostat field. Solar Energy, 42(2), 149–165.
 
Cristóbal, A.G. (2011). Diseño del campo de helióstatos para torres solares de receptor central. Degree Dissertation, Universidad Carlos III de Madrid, Spain.
 
Cruz, N.C., Redondo, J.L., Berenguel, M., Álvarez, J.D., Becerra-Terón, A., Ortigosa, P.M. (2017). High performance computing for the heliostat field layout evaluation. The Journal of Supercomputing, 73, 259–276.
 
Dawkins, R. (1976). The Selfish Gene. Oxford University Press.
 
Gordon, J.M. (2013). Solar Energy: The State of the Art. Taylor & Francis.
 
Greiner, G., Hormann, K. (1998). Efficient clipping of arbitrary polygons. ACM Transactions on Graphics (TOG), 17(2), 71–83.
 
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. The MIT Press.
 
Jelasity, M. (1998). UEGO, an abstract niching technique for global optimization. In: Parallel Problem Solving from Nature-PPSN V. Springer, pp. 378–387.
 
Johnson, A. (2012). Clipper – an open source freeware polygon clipping library. Available from http://www.angusj.com/delphi/clipper.php (Last accessed in March, 2017).
 
Jones, S.A., Lumia, R., Davenport, R., Thomas, R.C., Gorman, D., Kolb, G.J., Donnelly, M.W. (2007). Heliostat cost reduction. In: ASME 2007 Energy Sustainability Conference. American Society of Mechanical Engineers, pp. 1077–1084.
 
Laue, E.G. (1970). The measurement of solar spectral irradiance at different terrestrial elevations. Solar Energy, 13(1), 43–57.
 
Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech Concurrent Computation Program, C3P Report, 826.
 
Müller-Steinhagen, H. (2013). Concentrating solar thermal power. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 371(1996).
 
Mutuberria, A., Pascual, J., Guisado, M.V., Mallor, F. (2015). Comparison of heliostat field layout design methodologies and impact on power plant efficiency. Energy Procedia, 69, 1360–1370.
 
Noone, C.J., Torrilhon, M., Mitsos, A. (2012). Heliostat field optimization: A new computationally efficient model and biomimetic layout. Solar Energy, 86(2), 792–803.
 
Ortigosa, P.M., García, I., Jelasity, M. (2001a). Reliability and performance of UEGO, a clustering-based global optimizer. Journal of Global Optimization, 19(3), 265–289.
 
Ortigosa, P.M., García, I., Jelasity, M. (2001b). Two approaches for parallelizing the UEGO algorithm. In: Optimization Theory. Springer, pp. 159–177.
 
Ortigosa, P.M., Redondo, J.L., García, I., Fernández, J.J. (2007). A population global optimization algorithm to solve the image alignment problem in electron crystallography. Journal of Global Optimization, 37(4), 527–539.
 
Pitz-Paal, R., Botero, N.B., Steinfeld, A. (2011). Heliostat field layout optimization for high-temperature solar thermochemical processing. Solar Energy, 85(2), 334–343.
 
Ramos, A., Ramos, F. (2012). Strategies in tower solar power plant optimization. Solar Energy, 86(9), 2536–2548.
 
Ramos, A., Ramos, F. (2014). Heliostat blocking and shadowing efficiency in the video-game era. arXiv:1402.1690.
 
Reddy, V.S., Kaushik, S.C., Ranjan, K.R., Tyagi, S.K. (2013). State-of-the-art of solar thermal power plants – a review. Renewable and Sustainable Energy Reviews, 27, 258–273.
 
Redondo, J.L. (2009). Solving Competitive Location Problems via Memetic Algorithms. High Performance Computing Approaches, Vol. 258. Universidad, Almería.
 
Redondo, J.L., Fernández, J., García, I., Ortigosa, P.M. (2009a). A robust and efficient global optimization algorithm for planar competitive location problems. Annals of Operations Research, 167(1), 87–106.
 
Redondo, J.L., Fernández, J., García, I., Ortigosa, P.M. (2009b). Solving the multiple competitive facilities location and design problem on the plane. Evolutionary Computation, 17(1), 21–53.
 
Sánchez, M., Romero, M. (2006). Methodology for generation of heliostat field layout in central receiver systems based on yearly normalized energy surfaces. Solar Energy, 80(7), 861–874.
 
Solis, F.J., Wets, R.J.B. (1981). Minimization by random search techniques. Mathematics of Operations Research, 6(1), 19–30.
 
Stine, W.B., Geyer, M. (2001). Power from the Sun. Power from the sun.net. http://powerfromthesun.net/book.html (last accessed in March, 2017).
 
Vatti, B.R. (1992). A generic solution to polygon clipping. Communications of the ACM, 35(7), 56–63.
 
Wei, X., Lu, Z., Lin, Z., Zhang, H., Ni, Z. (2007). Optimization procedure for design of heliostat field layout of a 1mwe solar tower thermal power plant. In: Photonics Asia 2007. International Society for Optics and Photonics, pp. 684119–684119.
 
Zhang, H. (2007). Multi-objective thermoeconomic optimisation of the design of heliostat field of solar tower power plants. Technical report.

Biographies

Cruz Nicolas C.
ncalvocruz@ual.es

N.C. Cruz is a predoctoral researcher at the Informatics Department at University of Almería, Spain. He studied the degree and master in computer engineering at the University of Almería. He is currently doing his PhD thanks to the Spanish FPU program. His publications and more information about him can be found in www.hpca.ual.es/~ncalvo. His research interests are solar energy, global optimization and high performance computing.

Redondo Juana L.
jlredondo@ual.es

J.L. Redondo is a researcher at the Informatics Department at University of Almerría, Spain. She is a fellow of the Spanish ‘Ramón y Cajal’ contract program, co-financed by the European Social Fund. Some of her publications are available at www.scopus.com/authid/detail.uri?authorId=35206862500. Her research interests include high performance computing, global optimization and applications.

Álvarez Jose D.
jhervas@ual.es

J.D. Álvarez is a postdoctoral researcher of Informatics Department at the University of Almería, Spain. He obtained, in 2008, from the same university, the PhD degree in Automatic Control in Solar Plants. Currently, he is a fellow of the Spanish ‘Ramón y Cajal’ contract program, co-financed by the European Social Fund. Some of his publications can be found in www.scopus.com/authid/detail.uri?authorId=16303147700. His research interests cover automatic control, solar energy and energy efficient in buildings.

Berenguel Manuel
beren@ual.es

M. Berenguel is a professor of automatic control and systems engineering and head of the research group ‘Automatic Control, Robotics and Mechatronics’ at the University of Almería, Spain. Some of his publications can be found in www.scopus.com/authid/detail.uri?authorId=6701834872. His research interests are in control education and in predictive and hierarchical control, with applications to solar energy systems, agriculture and biotechnology.

Ortigosa Pilar M.
ortigosa@ual.es

P.M. Ortigosa is a professor at the Informatics Department at University of Almería, Spain. She obtained her PhD from the University of Málaga. Her publications can be found in www.scopus.com/authid/detail.uri?authorId=6602759441. Her research interests include high performance computing, global optimization and applications.


Full article Related articles Cited by PDF XML
Full article Related articles Cited by PDF XML

Copyright
© 2018 Vilnius University
Open access article under the CC BY license.

Keywords
heliostat field optimization population-based algorithms

Metrics
since January 2020
1412

Article info
views

820

Full article
views

614

PDF
downloads

216

XML
downloads


Share


RSS