Abstract
The optimal positioning of wind turbines, even in one dimension, is a problem with no analytical solution. This article describes the application of computational intelligence techniques to solve this problem. A systematic analysis of the optimal positioning of wind turbines on a straight line, on flat terrain, and considering wake effects has been conducted using both simulated annealing and genetic algorithms. Free parameters were the number of wind turbines, the distances between wind turbines and wind turbine hub heights. Climate and terrain characteristics were varied, like incoming wind speed, wind direction, air density, and surface roughness length, producing different patterns of positioning. Analytical functions were used to model wake effects quantifying the reduction in speed after the wind passes through a wind turbine. Conclusions relevant to the placement of wind turbines for several cases are presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Burton, T., Sharpe, D., Jenkins, N., Bossanyi, E.: Historical Development. In: Wind Energy Handbook, pp. 1–9. John Wiley, New York (2001)
Saheb-Koussa, D., Haddadi, M., Belhamel, M.: Economic and technical study of a hybrid system (wind-photovoltaic-diesel) for rural electrification in Algeria. Applied Energy 86(7–8), 1024–1030 (2009)
Graedel, T.E., Crutzen, P.J.: Atmospheric Change: An Earth System Perspective. Freedman, New York (1993)
Burton, T., Sharpe, D., Jenkins, N., Bossanyi, E.: Turbulence in Wakes and Wind Farms. In: Wind Energy Handbook, pp. 35–39. John Wiley, New York (2001)
Vermeera, L., Sørensen, J., Crespo, A.: Wind turbine wake aerodynamics. Progress in Aerospace Sciences 39, 467–510 (2003)
Katic, I., Hojstrup, J., Jensen, N.O.: A simple model for cluster efficiency. In: European Wind Energy Association Conference and Exhibition, Rome, Italy, pp. 407–410 (1986)
Larsen, C.G.: A simple wake calculation procedure. Technical Report Risø-M-2760, Risø National Laboratory, Rosklide, Denmark (December 1988)
Ishihara, T., Yamaguchi, A., Fujino, Y.: Development of a new wake model based on a wind tunnel experiment. In: Global Wind Power (2004)
Crespo, A., Hernandez, J.: Turbulence characteristics in wind-turbine wakes. Journal of Wind Engineering and Industrial Aerodynamics 61, 71–85 (1995)
Castro, J., Calero, J.M., Riquelme, J.M., Burgos, M.: An evolutive algorithm for wind farm optimal design. Neurocomputing 70, 2651–2658 (2007)
Grady, S.A., Hussaini, M.Y., Abdullah, M.M.: Placement of wind turbines using genetic algorithms. Renewable Energy 30, 259–270 (2005)
Marmidis, G., Lazarou, S., Pyrgioti, E.: Optimal placement of wind turbines in a wind park using Monte Carlo simulation. Renewable Energy 33, 1455–1460 (2008)
Mosetti, G., Poloni, C., Diviacco, B.: Optimization of wind turbine positioning in large wind farms by means of a genetic algorithm. Journal of Wind Engineering and Industrial Aerodynamics 51(1), 105–116 (1994)
Aarts, E., Korst, J.: Simulated Annealing and Boltzman Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing. John Wiley, New York (1989)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Valenzuela-Rendón, M.: The virtual gene genetic algorithm. In: Genetic and Evolutionary Computation Conference (GECCO 2003), pp. 1457–1468 (2003)
Burton, T., Sharpe, D., Jenkins, N., Bossanyi, E.: The Boundary Layer. In: Wind Energy Handbook, pp. 18–21. John Wiley, New York (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Herbert-Acero, JF., Franco-Acevedo, JR., Valenzuela-Rendón, M., Probst-Oleszewski, O. (2009). Linear Wind Farm Layout Optimization through Computational Intelligence. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds) MICAI 2009: Advances in Artificial Intelligence. MICAI 2009. Lecture Notes in Computer Science(), vol 5845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05258-3_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-05258-3_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05257-6
Online ISBN: 978-3-642-05258-3
eBook Packages: Computer ScienceComputer Science (R0)