Wind farm layout optimization based on support vector regression guided genetic algorithm with consideration of participation among landowners

2019 ◽  
Vol 196 ◽  
pp. 1267-1281 ◽  
Author(s):  
Xinglong Ju ◽  
Feng Liu ◽  
Li Wang ◽  
Wei-Jen Lee
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Wind Farm ◽  
Layout Optimization ◽  
Support Vector ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization

Wind farm layout optimization using genetic algorithm and its application to Daegwallyeong wind farm

JMST Advances ◽  
2019 ◽  
Vol 1 (4) ◽  
pp. 249-257 ◽  
Author(s):  
Jeong Woo Park ◽  
Bo Sung An ◽  
Yoon Seung Lee ◽  
Hyunsuk Jung ◽  
Ikjin Lee
Keyword(s):  
Genetic Algorithm ◽  
Wind Farm ◽  
Layout Optimization ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization

scholarly journals Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4403 ◽  
Author(s):  
Yang ◽  
Cho
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Wind Turbines ◽  
Wind Farm ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Layout Optimization ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Annealing Algorithm

The optimal layout of wind turbines is an important factor in the wind farm design process, and various attempts have been made to derive optimal deployment results. For this purpose, many approaches to optimize the layout of turbines using various optimization algorithms have been developed and applied across various studies. Among these methods, the most widely used optimization approach is the genetic algorithm, but the genetic algorithm handles many independent variables and requires a large amount of computation time. A simulated annealing algorithm is also a representative optimization algorithm, and the simulation process is similar to the wind turbine layout process. However, despite its usefulness, it has not been widely applied to the wind farm layout optimization problem. In this study, a wind farm layout optimization method was developed based on simulated annealing, and the performance of the algorithm was evaluated by comparing it to those of previous studies under three wind scenarios; likewise, the applicability was examined. A regular layout and optimal number of wind turbines, never before observed in previous studies, were obtained and they demonstrated the best fitness values for all the three considered scenarios. The results indicate that the simulated annealing (SA) algorithm can be successfully applied to the wind farm layout optimization problem.

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