Abstract
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.
Highlights
One of the key aspects of wind farm design is to determine the position of the wind turbines within a given area, and one of the main objectives of this wind turbines layout is to minimize the wake effect between wind turbines
The results indicate that the simulated annealing (SA) algorithm can be successfully applied to the wind farm layout optimization problem
Algorithm can be successfully applied to the wind farm layout optimization problem
Summary
One of the key aspects of wind farm design is to determine the position of the wind turbines within a given area, and one of the main objectives of this wind turbines layout is to minimize the wake effect between wind turbines. The wake generated by upstream wind turbines causes wind speed reduction resulting in power losses in the downstream wind turbines. The unstable turbulent flow caused by the wake increases the fatigue load of those wind turbines affected by the wake. The wind speed deficit by the wake effect can be estimated using a wake model. It is difficult to optimize the wind turbine layout by considering the range of effects of the wake that changes according to the wind direction. To solve the complex wind farm layout optimization (WFLO) problem, various layout optimization methods have been introduced, and many related studies addressed this [1,2]
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