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
Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm
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.
optimal placement / wind turbines / wind direction / genetic algorithm / wake effect
[1] |
Ayodele T R, Ogunjuyigbe A S O. 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
CrossRef
Google scholar
|
[2] |
Ayodele T R, Jimoh A A, Munda J L, Agee J T. 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
CrossRef
Google scholar
|
[3] |
Ayodele T R, Ogunjuyigbe A S O. 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
CrossRef
Google scholar
|
[4] |
Bansal R C, Bhatti T S, Kothari D P. On some of the design aspects of wind energy conversion systems. Energy Conversion and Management, 2002, 43(16): 2175–2187
CrossRef
Google scholar
|
[5] |
Patel M. Wind and Power Solar Systems. Boca Raton: CRC Press, 1999
|
[6] |
Ammara I, Leclerc C, Masson C. A viscous three-dimensional differential/actuator-disk method for the aerodynamic analysis of wind farms. Solar Energy Engineering, 2002, 124(4): 345–356
CrossRef
Google scholar
|
[7] |
Ituarte-Villareal C M, Espiritu J F. Optimization of wind turbine placement using a viral based optimization algorithm. Procedia Computer Science, 2011, 6: 469–474
CrossRef
Google scholar
|
[8] |
Wang J, Li X, Zhang X. Genetic optimal micrositing of wind farms by equilateral-triangle mesh. In: Wind Turbines. London: InTech, 2011
|
[9] |
Marmidis G, Lazarou S, Pyrgioti E. Optimal placement of wind turbines in a wind park using monte carlo simulation. Renewable Energy, 2008, 33(7): 1455–1460
CrossRef
Google scholar
|
[10] |
Tabassum M, Mathew K. A genetic algorithm analysis towards optimization solutions. International Journal of Digital Information and Wireless Communications, 2014, 4(1): 124–142
|
[11] |
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
CrossRef
Google scholar
|
[12] |
Grady S A, Hussaini M Y, Abdullah M M. Placement of wind turbines using genetic algorithms. Renewable Energy, 2005, 30(2): 259–270
CrossRef
Google scholar
|
[13] |
Mittal P, Kulkarni K, Mitra K. A novel hybrid optimization methodology to optimize the total number and placement of wind turbines. Renewable Energy, 2016, 86: 133–147
CrossRef
Google scholar
|
[14] |
Serrano González J, Burgos Payán M, Santos J M R, González-Longatt F. A review and recent developments in the optimal wind-turbine micro-siting problem. Renewable & Sustainable Energy Reviews, 2014, 30(2): 133–144
CrossRef
Google scholar
|
[15] |
Mortensen N G. The wind atlas analysis and application program. Mutation Research/environmental Mutagenesis & Related Subjects, 1996, 2(3): 348–349
|
[16] |
Herbert-Acero J F, Probst O, Réthoré P E, Larsen G C, Castillo-Villar K K. A review of methodological approaches for the design and optimization of wind farms. Energies, 2014, 7(11): 6930–7016
CrossRef
Google scholar
|
[17] |
Shakoor R, Hassan M Y, Raheem A, Wu Y K. Wake effect modeling: a review of wind farm layout optimization using Jensen’s model. Renewable & Sustainable Energy Reviews, 2016, 58: 1048–1059
CrossRef
Google scholar
|
[18] |
Katic I, Hojstrup J, Jensen N O. A simple model for cluster efficiency. In: European Wind Energy Conference (EWEC’86), Rome, 1986, 407–410
|
[19] |
González-Longatt F, Wall P P, Terzija V. Wake effect in wind farm performance: steady-state and dynamic behavior. Renewable Energy, 2012, 39(1): 329–338
CrossRef
Google scholar
|
[20] |
Ogunjuyigbe A S O, Ayodele T R, Bamgboje O D, Jimoh A A. Optimal placement of wind turbines within a wind farm using genetic algorithm. In: the International Renewable Energy Congress, Hammamet, Tunisia, 2016, 1–6
|
[21] |
Emami A, Noghreh P. New approach on optimization in placement of wind turbines within wind farm by genetic algorithm. Renewable Energy, 2010, 35(7): 1559–1564
CrossRef
Google scholar
|
[22] |
Marmidis G, Lazarou S, Pyrgioti E. Optimal placement of wind turbines in a wind park using monte carlo simulation. Renewable Energy, 2008, 33(7): 1455–1460
CrossRef
Google scholar
|
[23] |
Haupt R L, Haupt S E. Practical Genetic Algorithm, 2nd ed. New Jersey: John Wiley & Sons, Inc., 2004
|
/
〈 | 〉 |