Optimization of Wind Farm Micro Sitting Based on Genetic Algorithm

Abstract:

In order to increase wind energy utilization efficiency by the optimization of the wind farm micro sitting, a method which could calculate the wind farm velocity is proposed by consideration of multi turbines wake loss and superposition. Based on the given velocity data of a wind farm, the maximal annual energy production is set as the optimal objective and the ordinates of wind turbines would be the optimal variables, micro sittings of the wind farm turbines are optimized by genetic algorithm. Layout calculation result of the optimal method is quite similar to that of other successful search method, but higher efficiency is reached, and the micro sitting layout is agreement with the regular plum-type layout. Annual energy productions are also calculated under the condition of different wind turbine number. Results show annual energy production increases with the wind turbine number increased, but the increasing trend is lower and lower. The research could provide a reference to wind farm micro-sitting.

• Periodical: Advanced Materials Research (Volumes 347-353)
• Pages: 3545-3550
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.347-353.3545
• Online since: October 2011
• Authors: Chang Xu, Yan Yan, De You Liu, Yuan Zheng, Chen Qi Li
• Keywords: Annual Energy Production, Genetic Algorithm (GA), Micro Sitting, Wake Loss, Wind Farm

References

[1] Jeff A.10 Steps to developing a wind farm [EB/OL].Washington：American Wind Energy Association,2009.http://www.awea.org/documents/factsheets

[2] Song Haihui.Wind Power Technology and Engineering[M].Bei Jing：China Water Power Press,2009：169-172.

[3] Wiser R,Bolinger M.Annual report on U.S. wind power installation,cost,and performance trends:2006[R]. Berkeley, California: U.S. department of Energy, 2007.

DOI: 10.2172/908214

[4] Xu Guobin,Peng Xiufang,Wang Haijun.Micro-siting of Complex Terrain for Wind Power Station [J].Water Resources and Power,2010,28(4)：57-160 (in Chinese).

[5] Grady SA,Hussaini MY,Abdullah MM.Placement of wind turbines using genetic algorithms[J].Renewable Energy,2005,30(2)：259–70.

DOI: 10.1016/j.renene.2004.05.007

[6] 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,1994,51(1)：105–116.

DOI: 10.1016/0167-6105(94)90080-9

[7] Lackner MA,Elkinton CN.An analytical framework for offshore wind farm layout optimization.Wind Engineering,2007,31(1)：17–31.

DOI: 10.1260/030952407780811401

[8] Castro Mora J,Calero Baron JM,Riquelme Santos JM,Burgos Payan M.An evolutive algorithm for wind farm optimal design.Neurocomputing,2007,70(16-18)：2651–8.

DOI: 10.1016/j.neucom.2006.05.017

[9] Andrew Kusiak,Zhe Song.Design of wind farm layout for maximum wind energy capture.Renewable Energy,2010,35(3)：685-694.

DOI: 10.1016/j.renene.2009.08.019

[10] Neustadter HE.Method for evaluating wind turbine wake effects on wind farm performance.Journal of Solar Energy Engineering,1985,107(3)：240–3.

DOI: 10.1115/1.3267685

[11] Jensen NO.A note on wind generator interaction[R].Roskilde, Denmark: Riso National Laboratory, 1983.

[12] Zhang Xihuang.Computer Graphics[M].Bei Jing：Beijing University of Post and Telecommunications Press ,2006：102-105(in Chinese).

[13] Wang Ling.Intelligent optimization algorithm and its application[M].Bei Jing：Tsinghua University Press,2010：102-105.(in Chinese).

[14] Xu Ning, Li Chunguang, Zhang Jian,et al. Studies on Some Modern Optimization Algorithms [J]. Systems Engineering And Electronics,2002,24(12)：100-103 (in Chinese).

[15] Gong Jingyuan. Wind Farm Technical Manual[M]. Bei Jing: China Machine Industry Press, 2003: 32-34 (in Chinese).