摘要
提出基于风能全年概率分布特征的风电场微观选址优化方法。采用威布尔分布和风向玫瑰图描述风能的全年变化特性,其中威布尔分布的尺度参数和形状参数根据风速统计数据利用最小二乘法获得。以风电场单位功率发电成本最小化为目标函数,运用遗传算法进行微观选址问题的优化求解。仿真结果表明微观选址优化方案反映了风能的全年变化特性,实现了单位功率发电成本的最小化,提高了风能的利用效率。因此,风电场微观选址应系统考虑风能的全年变化特性。
Weibull distribution functions and rose maps were adopted to describe wind annual variations. The least square method was employed to obtain the scale and the shape parameters of the Weibull distribution based on wind speed statistical data. A genetic algorithm was utilized to obtain the optimal solution in terms of minimizing cost per unit power of the wind farm. The simulation results illustrate that the optimal micro-siting solution reflects the characteristics of annual wind variations, minimizes the cost per unit power and improves the efficiency of the wind farm. Therefore, the characteristics of wind annual variations should be systematically considered in the wind farm micro-siting.
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2011年第7期999-1004,共6页
Acta Energiae Solaris Sinica
基金
国家高技术研究发展(863)计划项目(2007AA05Z426)
国家自然科学基金(61075064
60674096)
关键词
遗传算法
微观选址
最小二乘法
威布尔分布
genetic algorithm
optimal micro-siting
least square method
Weibull distribution
