基于预测误差概率密度曲线的风电场集群日前有功优化调度

Day-ahead Optimal Scheduling Based on Prediction Error Probability Density Curve for Wind Farm Cluster

风电场集群日前出力计划是大规模风电基地分层协调有功调度的关键技术。提出了一种基于风电场功率预测误差分布曲线的风电场集群日前有功出力计划的制定方法。首先根据改进后的广义误差分布模型,结合历史实测功率和预测功率拟合出每一个风电场的预测误差概率密度函数,然后按照预测风速大小估计出风电场有功出力的上限,最后以集群内每一个风电场的日前计划指令与实际出力能力偏差的数学期望之和最小为优化目标,采用遗传算法计算得到风电场集群的调度优化指令。结合中国北方某风电集群的实际运行数据进行仿真算例分析,验证了所提方法的有效性。

Day-ahead scheduling for wind farm cluster is the key technology of stratified coordinated active dispatc- hing for large-scale wind power base. This paper presents a method of power generation plan based on forecasting er- ror distribution curve of wind farm cluster. First, according to the improved generalized error distribution model, the probability density function of each wind farm is fitted with historical measured power and predicted power. Then, the upper limit of wind power output is estimated according to the predicted wind speed. Finally, the scheduling optimiza- tion of the wind farm cluster is calculated by using the genetic algorithm to minimize the sum of the deviation expec- tation between planned and actual output power of each wind farm in the cluster. Case study based on the actual op- eration data of wind farm cluster in northern China has shown that the proposed method is effective.