摘要
提出考虑多个风机尾迹损失与叠加的风场风速计算方法,以通过对风电场微观选址的优化来提高风能利用率。在确定的风场条件下,以风场全年总发电量最大为优化目标,各风机位置坐标为优化变量,采用遗传算法对风电场微观选址进行优化。计算结果表明,所采用的优化算法与搜索法计算结果基本一致,但效率更高,且结果与常规的梅花型风场布置方式相近;比较了风机数量对全年发电量的影响,结果显示,风电场年发电量随风机数量增加逐渐升高,但当风机数量增加至一定程度后,由于风机间尾迹损失的影响,年发电量的增加趋势变缓。研究结果对风电场微观选址具有一定的参考价值。
In order to increase wind energy utilization efficiency by the wind farm micro-sitting optimization, a method for the wind farm velocity calculation was proposed by consideration of multi turbines wake loss and super- position. Based on a certain wind farm velocities data, the maximal annual energy production was the optimal objective and the ordinates of wind turbines was the optimal variables, the micro sitting of wind farm was optimized by genetic algorithm. Layout calculation result of the optimal method are quite similar to that of search method. But higher efficiency is reached, and the micro-sitting layout is agreement with the regular plum-type layout. Annual energy productions are also calculated by different wind turbines. The results showed that annual energy production increases with increasing the number of wind turbine number, however the increasing trend is lower and lower. The research provides a reference to wind farm micro-sitting.
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2013年第3期526-532,共7页
Acta Energiae Solaris Sinica
基金
国家重点基础研究发展(973)计划(2010CB227102)
关键词
风电场
微观选址
尾流损失
年发电量
遗传算法
wind farm
micro-sitting
wake loss
annual energy production
genetic algorithm
