Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

doi:10.1007/s11708-018-0514-x

Front. Energy

2021, Vol. 15

Issue (1) : 240-255 https://doi.org/10.1007/s11708-018-0514-x

RESEARCH ARTICLE

Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

A.S.O. OGUNJUYIGBE, T.R. AYODELE(

), O.D. BAMGBOJE

Power Energy Machines and Drives (PEMD) Research Group, Department of Electrical and Electronic Engineering, University of Ibadan, Nigeria

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Abstract

Most wind turbines within wind farms are set up to face a pre-determined wind direction. However, wind directions are intermittent in nature, leading to less electricity production capacity. This paper proposes an algorithm to solve the wind farm layout optimization problem considering multi-angular (MA) wind direction with the aim of maximizing the total power generated on wind farms and minimizing the cost of installation. A two-stage genetic algorithm (GA) equipped with complementary sampling and uniform crossover is used to evolve a MA layout that will yield optimal output regardless of the wind direction. In the first stage, the optimal wind turbine layouts for 8 different major wind directions were determined while the second stage allows each of the previously determined layouts to compete and inter-breed so as to evolve an optimal MA wind farm layout. The proposed MA wind farm layout is thereafter compared to other layouts whose turbines have focused site specific wind turbine orientation. The results reveal that the proposed wind farm layout improves wind power production capacity with minimum cost of installation compared to the layouts with site specific wind turbine layouts. This paper will find application at the planning stage of wind farm.

Keywords optimal placement wind turbines wind direction genetic algorithm wake effect

Corresponding Author(s): T.R. AYODELE

Online First Date: 19 June 2018 Issue Date: 19 March 2021

Cite this article:

A.S.O. OGUNJUYIGBE,T.R. AYODELE,O.D. BAMGBOJE. Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm[J]. Front. Energy, 2021, 15(1): 240-255.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-018-0514-x
https://academic.hep.com.cn/fie/EN/Y2021/V15/I1/240

Fig.1 100 square cell subdivided wind farm and wind directions

Fig.2 Wake from a single wind turbine

Fig.3 Rate of change of cost with number of turbines (N)

Fig.4 Wind turbine power curve for Vestas V63

Fig.5 Flowchart of stage 1 optimization

x	Chromosomes	P_x	$∑ i = 1 x P i$
1	11010101011010110111011110100001011110110100010110	0.09524	0.09524
2	01111101011110101001001111100100101111101101000110	0.09048	0.18571
3	00100011101011010010111111000110100010101011101000	0.08571	0.27143
4	00001011110100100000101110011001011110011010011000	0.08095	0.35238
5	11010101101011110110011100001110010100001001001101	0.07619	0.42857
6	11011011001110111110001010110101111101110111000010	0.07143	0.50000
7	11000011001101111001100010001110001000100110101010	0.06667	0.56667
8	10100001111001000100110101100101000000010110010011	0.06190	0.62857
9	11101010010010010011010111010111011101101101111001	0.05714	0.68571
10	11000011101000000011100111011000011101001001001100	0.05238	0.73810
11	11101110110010000101000011001100101001001101110001	0.04762	0.78571
12	10010111010110101010101111110110101000001001000011	0.04286	0.82857
13	01111100010000100010010001111010100001110101101011	0.03810	0.86667
14	01011110000111100011101001100011100111111001001010	0.03333	0.90000
15	11100000000001110111111000001011001001110111100110	0.02857	0.92857
16	10111110101011111001000100101101011010101010111000	0.02381	0.95238
17	00111110001010001010010001001010011000100110100001	0.01905	0.97143
18	10111100011000110111111010011110110000111100110011	0.01429	0.98571
19	11001011001011110010011101101111010000001010110111	0.00952	0.99524
20	00011011111001101001000000011010001001111100010100	0.00476	1.00000

Tab.1 Rank weighting

Tab.2 Uniform crossover

Fig.6 Flowchart of stage 2 optimization

Fig.7 Unidirectional layout for eastern wind

Fig.8 Power per turbine for Eastern layout

Tab.3 Optimization result

Fig.9 Optimized MA layout

Fig.10 MA power curve

Tab.4 Comparison of MA layout to a layout in which the wind follows a pre-determined direction (SW) and a layout in which the wind veer off the predetermined direction (South instead of SW)

Fig.11 Comparison between MA layout and unidirectional Southern layout

Fig.12 Comparison between MA layout and unidirectional Eastern layout

Fig.13 Comparison between MA layout and unidirectional Western layout

Fig.14 Comparison between MA layout and unidirectional Northern layout

Fig.15 Power sensitivity

Fig.16 Sensitivity analysis of cost per unit power of some selected wind turbines

1	T R Ayodele, A S O Ogunjuyigbe. Increasing household solar energy penetration through load partitioning based on quality of life: the case study of Nigeria. Sustainable Cities and Society, 2015, 18: 21–31 https://doi.org/10.1016/j.scs.2015.05.005
2	T R Ayodele, A A Jimoh, J L Munda, J T Agee. Viability and economic analysis of wind energy resource for power generation in Johannesburg, South Africa. International Journal of Sustainable Energy, 2014, 33(2): 284–303 https://doi.org/10.1080/14786451.2012.762777
3	T R Ayodele, A S O Ogunjuyigbe. Wind energy potential of vesleskarvet and the feasibility of meeting the South African’s SANAE IV energy demand. Renewable & Sustainable Energy Reviews, 2016, 56: 226–234 https://doi.org/10.1016/j.rser.2015.11.053
4	R C Bansal, T S Bhatti, D P Kothari. On some of the design aspects of wind energy conversion systems. Energy Conversion and Management, 2002, 43(16): 2175–2187 https://doi.org/10.1016/S0196-8904(01)00166-2
5	M Patel. Wind and Power Solar Systems. Boca Raton: CRC Press, 1999
6	I Ammara, C Leclerc, C Masson. A viscous three-dimensional differential/actuator-disk method for the aerodynamic analysis of wind farms. Solar Energy Engineering, 2002, 124(4): 345–356 https://doi.org/10.1115/1.1510870
7	C M Ituarte-Villareal, J F Espiritu. Optimization of wind turbine placement using a viral based optimization algorithm. Procedia Computer Science, 2011, 6: 469–474 https://doi.org/10.1016/j.procs.2011.08.087
8	J Wang, X Li, X Zhang. Genetic optimal micrositing of wind farms by equilateral-triangle mesh. In: Wind Turbines. London: InTech, 2011
9	G Marmidis, S Lazarou, E Pyrgioti. Optimal placement of wind turbines in a wind park using monte carlo simulation. Renewable Energy, 2008, 33(7): 1455–1460 https://doi.org/10.1016/j.renene.2007.09.004
10	M Tabassum, K Mathew. A genetic algorithm analysis towards optimization solutions. International Journal of Digital Information and Wireless Communications, 2014, 4(1): 124–142
11	G Mosetti, C Poloni, B Diviacco. 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 https://doi.org/10.1016/0167-6105(94)90080-9
12	S A Grady, M Y Hussaini, M M Abdullah. Placement of wind turbines using genetic algorithms. Renewable Energy, 2005, 30(2): 259–270 https://doi.org/10.1016/j.renene.2004.05.007
13	P Mittal, K Kulkarni, K Mitra. A novel hybrid optimization methodology to optimize the total number and placement of wind turbines. Renewable Energy, 2016, 86: 133–147 https://doi.org/10.1016/j.renene.2015.07.100
14	J Serrano González, M Burgos Payán, J M R Santos, F González-Longatt. A review and recent developments in the optimal wind-turbine micro-siting problem. Renewable & Sustainable Energy Reviews, 2014, 30(2): 133–144 https://doi.org/10.1016/j.rser.2013.09.027
15	N G Mortensen. The wind atlas analysis and application program. Mutation Research/environmental Mutagenesis & Related Subjects, 1996, 2(3): 348–349
16	J F Herbert-Acero, O Probst, P E Réthoré, G C Larsen, K K Castillo-Villar. A review of methodological approaches for the design and optimization of wind farms. Energies, 2014, 7(11): 6930–7016 https://doi.org/10.3390/en7116930
17	R Shakoor, M Y Hassan, A Raheem, Y K Wu. Wake effect modeling: a review of wind farm layout optimization using Jensen’s model. Renewable & Sustainable Energy Reviews, 2016, 58: 1048–1059 https://doi.org/10.1016/j.rser.2015.12.229
18	I Katic, J Hojstrup, N O Jensen. A simple model for cluster efficiency. In: European Wind Energy Conference (EWEC’86), Rome, 1986, 407–410
19	F González-Longatt, P P Wall, V Terzija. Wake effect in wind farm performance: steady-state and dynamic behavior. Renewable Energy, 2012, 39(1): 329–338 https://doi.org/10.1016/j.renene.2011.08.053
20	A S O Ogunjuyigbe, T R Ayodele, O D Bamgboje, A A Jimoh. Optimal placement of wind turbines within a wind farm using genetic algorithm. In: the International Renewable Energy Congress, Hammamet, Tunisia, 2016, 1–6
21	A Emami, P Noghreh. New approach on optimization in placement of wind turbines within wind farm by genetic algorithm. Renewable Energy, 2010, 35(7): 1559–1564 https://doi.org/10.1016/j.renene.2009.11.026
22	G Marmidis, S Lazarou, E Pyrgioti. Optimal placement of wind turbines in a wind park using monte carlo simulation. Renewable Energy, 2008, 33(7): 1455–1460 https://doi.org/10.1016/j.renene.2007.09.004
23	R L Haupt, S E Haupt. Practical Genetic Algorithm, 2nd ed. New Jersey: John Wiley & Sons, Inc., 2004

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