To increase the power output or decrease of cost consumption of a wind farm, layout optimization has become a critical method to realize this goal. The past research has already applied intelligent algorithms to optimizing the layout of a wind farm under wind conditions such as single wind speed and wind direction, single wind speed and multiple wind direction and multiple wind speeds and multiple wind directions. However, these wind conditions are all simplified ones and are not suitable for building up a commercial wind farm. In this study, first the author builds up a cost model based on the real wind farms located in Texas and sets up a wind condition model on the basis of local wind condition within a month. After this, the author focuses on the commercial wind farm layout optimization model (CWFLO) based on genetic algorithm. Current commercial wind turbine types and their corresponding hub heights are the objects of optimization with their positions. Four case studies are conducted to investigate: 1. how many wind turbines are actually needed in the given wind farm; 2: how to optimize the layout of an irregular shape wind farm; 3: how to improve the current optimization method; 4: What difference will appear for the layout when changing the cost model. The results achieved clearly demonstrate that this new optimization method is able to improve the fitness value of the objective functions. Key words: wind farm, optimization, genetic algorithm
July 17, 2015
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