Multi-objective Methods For Wind Farm Layout Optimization

The optimized layout of wind farm units as a preliminary project of wind power project construction plays a decisive role in the power generation capacity and economic efficiency of wind farms.The capacity and fatigue damage of wind turbines are important reference indicators for optimal layout.The wake wind speed and additional turbulence intensity caused by unit arrangement have important influences on the productivity and fatigue damage.This paper will focus on wind farm wake models that are suitable for engineering applications while ensuring high computational accuracy.Quantitative analysis of the effects of additional turbulence on wind turbines and fatigue damage.The genetic algorithm algorithm was used to achieve multi-objective optimization of wind farm layout.The main factors considered in the optimization algorithm include the wake loss of wind farms,the power generation of wind farms,effect of additional turbulence intensity in wake area on fatigue life of wind turbine,wind farm construction costs and application maintenance costs.The main research work and conclusions are involved through the following aspects.(1)Aiming at the shortcomings of the axial wake model and the radial wake model,the proposed model is to consider the non-uniform velocity distribution of wind speed and the impact of the radial and axial air flow.Four models,which are Jensen wake model,axial wake model,radial wake model,and the article puts forward the improved two-dimensional wake model,are studied to predict wind farm capacity.Verify the calculation accuracy of different wake model for precise wind farm capacity prediction.Realistic data from a wind farm in Jiangsu China is used to proved the accuracy of the model.The results show that the results calculated by improved two-dimensional wake model to the real data with high accuracy.(2)The main effect mechanism of wake on the annual capacity of a unit is summarized by comparing the calculation of wind farm capacity:First,the size of the space between wind turbines,wake loss decreases with increasing distance between wind turbines.Second,the proportion of wake wind speed in the annual wind speed.The larger the ratio,the greater the wake loss.Third,Influence of wake superposition effect on wind turbine.The effect of superimposed wakes of multiple wind turbines will result in downwind wind turbines reducing annual production due to the presence of wakes.(3)Quantitatively study the effect of additional turbulence on wind turbine capacity and fatigue life.The main conclusions obtained from the study are as follows:First,The presence of additional turbulence will make the wake wind speed recover faster.On the one hand,increasing the production capacity of wind turbines,on the other hand,will lead to an increase in the annual damage of wind turbines and a decrease in fatigue life.Second,From the point of view of the entire life span of the wind turbine,additional turbulence reduces the capacity of the wind turbine during the entire life cycle,and as the unit spacing increases,its impact on the unit's full-life capacity gradually decreases.(4)This paper integrates wind farm wake loss,wind farm power generation,additional turbulence in the wake area,wind farm construction,and operation and maintenance costs.Genetic algorithm is used to achieve multi-objective optimization of wind farms.Compared with the single-objective optimization results,the annual power generation of each wind turbine unit with multi-objective optimization increased by 7.01%,and the cost of wind power for wind farms decreased from 0.321 RMB/kW h to 0.316 RMB/kW h.Wind energy utilization rate of wind farms increased from 83.63%to 89.49%,and additional turbulence decreased from 4.28%to 3.22%.For wind farm layout optimization studies,it is necessary to consider the cost of wind farm electricity and the additional turbulence in the wake area.Using the multi-objective optimization method proposed in this paper can obtain better optimization results.Different optimization layout results reflect the differences in production capacity and investment costs under different wind farm layouts and provide valuable reference for investment in wind farms.