A novel hybrid optimization methodology to optimize the total number and placement of wind turbines

Due to increasing penetration of wind energy in the recent times, wind farmers tend to generate increasing amount of energy out of wind farms. In order to achieve the target, many wind farms are operated with a layout design of numerous turbines placed close to each other in a limited land area leading to greater energy losses due to ‘wake effects’. Moreover, these turbines need to satisfy many other constraints such as topological constraints, minimum allowable capacity factors, inter-turbine distances, noise constraints etc. Thus, the problem of placing wind turbines in a farm to maximize the overall produced energy while satisfying all constraints is highly constrained and complex. Existing methods to solve the turbine placement problem typically assume knowledge about the total number of turbines to be placed in the farm. However, in reality, wind farm developers often have little or no information about the best number of turbines to be placed in a farm. This study proposes a novel hybrid optimization methodology to simultaneously determine the optimum total number of turbines to be placed in a wind farm along with their optimal locations. The proposed hybrid methodology is a combination of probabilistic genetic algorithms and deterministic gradient based optimization methods. Application of the proposed method on representative case studies yields higher Annual Energy Production (AEP) than the results found by using two of the existing methods.

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

[2]  Javier Serrano González,et al.  A review and recent developments in the optimal wind-turbine micro-siting problem , 2014 .

[3]  Bin Duan,et al.  Modified genetic algorithm for layout optimization of multi-type wind turbines , 2014, 2014 American Control Conference.

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

[5]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

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

[7]  Siamak Kazemzadeh Hannani,et al.  Wind farm layout optimization using imperialist competitive algorithm , 2014 .

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

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

[10]  Cristina H. Amon,et al.  Multi-Objective Wind Farm Layout Optimization Considering Energy Generation and Noise Propagation With NSGA-II , 2014 .

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

[12]  Xuan Zhang,et al.  Binary-real coding genetic algorithm for wind turbine positioning in wind farm , 2014 .

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

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

[15]  Shafiqur Rehman,et al.  Iterative non-deterministic algorithms in on-shore wind farm design: A brief survey , 2013 .

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

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

[18]  Wen Zhong Shen,et al.  Wind farm layout optimization in complex terrain: A preliminary study on a Gaussian hill , 2014 .

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

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

[21]  J. Christopher Beck,et al.  Solving wind farm layout optimization with mixed integer programs and constraint programs , 2014, EURO J. Comput. Optim..

[22]  Prateek Mittal,et al.  A fast and effective algorithm to optimize the total number and placement of wind turbines , 2014, 2014 IEEE Global Humanitarian Technology Conference - South Asia Satellite (GHTC-SAS).