Wind Farm Layout Optimization using Real Coded Multi-population Genetic Algorithm

Finding the optimal location of wind turbines is a challenging work by reason of the various effects of the turbine wake. Indeed, on a site gathering several wind turbines, if the turbines are too close the loss of power grows with the wake effect. In this paper, an RC-MPGA (Real Coded Multi-population Genetic Algorithm) method is proposed to search the optimal location of WTs (Wind Turbines) in Square shaped WF (wind farm), Installed on an area of 4000000 m2 ($2000\mathrm{m}\times 2000\mathrm{m}$), with the aim to maximize the electrical power generated by all WTs and grows the annual economic profitability of the WF. By using the same WF environment conditions, we can see that the proposed method is promising and presents an improvement in terms of maximum power generation when compared to other works previously studied in the literature.

[1]  M. Y. Hussaini,et al.  Placement of wind turbines using genetic algorithms , 2005 .

[2]  J. Højstrup,et al.  A Simple Model for Cluster Efficiency , 1987 .

[3]  Anshul Mittal,et al.  Optimization of the Layout of Large Wind Farms using a Genetic Algorithm , 2010 .

[4]  Luciano Castillo,et al.  Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation , 2012 .

[5]  Sten Tronæs Frandsen,et al.  On the wind speed reduction in the center of large clusters of wind turbines , 1992 .

[6]  N. Jensen.  A note on wind generator interaction , 1983 .

[7]  Javier Serrano González,et al.  Optimization of wind farm turbines layout using an evolutive algorithm , 2010 .

[8]  Michele Samorani,et al.  The Wind Farm Layout Optimization Problem , 2011 .

[9]  Imran Amin,et al.  Optimization of wind turbine micrositing: A comparative study , 2012 .

[10]  Hongxing Yang,et al.  Wind turbine layout optimization using multi-population genetic algorithm and a case study in Hong Kong offshore , 2015 .

[11]  Wang Jun,et al.  A fast algorithm based on the submodular property for optimization of wind turbine positioning , 2011 .

[12]  Peng-Yeng Yin,et al.  Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization , 2017 .

[13]  Alireza Emami,et al.  New approach on optimization in placement of wind turbines within wind farm by genetic algorithms , 2010 .

[14]  Weerakorn Ongsakul,et al.  Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients , 2013 .

[15]  Carlo Poloni,et al.  Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm , 1994 .