Wind farm layout using biogeography based optimization

Wind energy is one of the most promising option for the renewable energy. Finding optimum set of locations for wind turbines in a wind farm so that the total energy output of the farm is maximum, is usually referred as the wind farm layout optimization problem (WFLOP). This article presents the solution of WFLOP using a recent unconventional optimization algorithm, Biogeography Based Optimization (BBO). In this article, for a given wind farm not only the optimum locations of the wind turbines are obtained but also the maximum number of turbines is recommended. Experiments have been carried out for wind farms of various sizes. BBO has shown to outperform as compare to earlier methodologies of solving WFLOP.

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

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

[3]  Daniel W. Stroock,et al.  A concise introduction to the theory of integration , 1990 .

[4]  Kalyan Veeramachaneni,et al.  Optimizing the Layout of 1000 Wind Turbines , 2011 .

[5]  Andrew Kusiak,et al.  Design of wind farm layout for maximum wind energy capture , 2010 .

[6]  Serap Ulusam Seçkiner,et al.  Wind farm layout optimization using particle filtering approach , 2013 .

[7]  Tai-Yuan Wang,et al.  A GRASP-VNS algorithm for optimal wind-turbine placement in wind farms , 2012 .

[8]  Enrique Alba,et al.  Simulated Annealing for Optimization of Wind Farm Annual Profit , 2009, 2009 2nd International Symposium on Logistics and Industrial Informatics.

[9]  Serap Ulusam Seçkiner,et al.  Design of wind farm layout using ant colony algorithm , 2012 .

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

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

[12]  D. A. Spera,et al.  Method for Evaluating Wind Turbine Wake Effects on Wind Farm Performance , 1985 .

[13]  Christopher N. Elkinton,et al.  An Analytical Framework for Offshore Wind Farm Layout Optimization , 2007 .

[14]  Kurt Schaldemose Hansen,et al.  Solving the Turbine Positioning Problem for Large Offshore Wind Farms by Simulated Annealing , 2009 .

[15]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

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

[17]  Bryan A. Norman,et al.  Heuristic methods for wind energy conversion system positioning , 2004 .

[18]  Murat Tunc,et al.  Optimal positioning of wind turbines on Gökçeada using multi‐objective genetic algorithm , 2010 .

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

[20]  James F. Manwell,et al.  Algorithms for Offshore Wind Farm Layout Optimization , 2008 .

[21]  Manuel Burgos Payán,et al.  An evolutive algorithm for wind farm optimal design , 2007, Neurocomputing.

[22]  Hou-Sheng Huang,et al.  Distributed Genetic Algorithm for Optimization of Wind Farm Annual Profits , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.