基于风电场功率特性的日前风电预测误差概率分布研究

Probability Density Function of Day-ahead Wind Power Forecast Errors Based on Power Curves of Wind Farms

风电功率预测误差的分布对电力系统调度决策、备用安排等有着重要的影响。基于风电场日前功率预测"从风速到功率"的实际过程,提出了确定日前风电功率预测误差分布的分析方法。首先,使用最小二乘法拟合风电场的功率特性,分析风电场功率特性拟合误差及其对日前风电功率预测误差的影响;其次,通过模拟日前风速的预测误差,研究风速预测误差对日前风电功率预测误差的影响;最后,使用蒙特卡罗双层抽样技术模拟上述2种误差的共同作用,并使用最大似然估计技术,确定各风速区间对应的日前功率预测误差所服从的分布。基于实际风电场历史数据的算例分析结果表明,所提出的误差分析方法能够准确描述风电场在不同预测风速下的功率预测误差概率分布,并能够确定各分布的适用范围,可为优化调度方案的制定提供参考。

Distribution of wind power forecast error significantly affects the decision of dispatch and reserve etc. in power systems. Based on the ‘wind to power’ day-ahead forecast procedure of wind farms, this paper put forward an approach to determine the probability density function of day-ahead wind power forecast error of wind farms. Firstly, this paper studied the power curve of wind power with least square fitting method and analyzed the influences of power curve-fitting error on day-ahead wind power forecast. Then the fitted power curve was used to study how the wind speed forecast error affects the wind power forecast error in different speed intervals analytically and by simulation. Finally, Monte-Carlo two-stage sampling was used to simulate the common effect of the fitting error of power curve and wind speed forecast error. The distributions of day-ahead forecast errors corresponding to different speed intervals were determined with Maximum Likelihood Estimation. Case study results based on historical data of an actual wind farm show that the proposed method can precisely depict the wind power forecast error under different wind speed, determine the proper wind speed range for each probability distribution, and provide references for optimal dispatch.