Wind farm optimization considering non-uniformly distributed turbulence intensity

Abstract

Wind-farm optimization is a hot topic in the field of wind energy. Most of the related researches maximize the power output by optimizing turbine locations and hub-heights. Yaw angle can change the wind direction behind turbines and weaken the wake effects, which has seldom been examined for the wind-farm optimization. The relative effectiveness to maximize the power output by varying the turbine layout, hub-height, and yaw angle has not been clarified. In addition, how to combine the optimized strategies to obtain the maximum power output has not been revealed. Furthermore, the existing wake models rarely consider the non-uniformity of the turbulence distributions. Therefore, in the present study, non-yawed and yawed Gaussian wake models considering the additional turbulence from the upstream turbines are adopted, and by using genetic algorithm, for the first time seven optimization strategies, i.e., layout optimization, hub-height optimization, yaw-angle optimization, layout & hub-height optimization, layout & yaw-angle optimization, hub-height & yaw-angle optimization, and layout & hub-height & yaw-angle optimization, are examined systematically. The power output is underestimated after neglecting the non-uniformly distributed turbulence. And it is found that the power output is the most sensitive to the layout variation, followed by the hub-height and yaw-angle variations. Importantly, increasing the complexity of the optimization strategies, the power output from each turbine will be more evenly distributed.