短期风电功率动态云模型不确定性预测方法

高比例风电并网场景下,电力系统优化运行势必对风电功率预测精度及其不确定性分析结果的可靠性提出更高要求。现有的不确定性预测研究中大多为整体性的误差分析与建模,难以满足模型在各个时刻和各类天气下的适应性。因此,提出了动态云模型的短期风电功率不确定性预测方法。首先,建立各个预测功率区间段内的单点预测误差云模型,利用云模型数字特征(期望、熵、超熵)生成云滴分布图,以此量化预测不确定性态势。然后,计算给定置信水平下的云滴分位点,以及与之相对应的预测功率可能发生波动的置信范围,即风电功率预测不确定性分析结果。根据实时条件更新云模型,可以提高各个运行时刻点不确定性预测结果的可靠性。以中国北方某风电场运行数据为例进行验证,结果表明与传统的分位数回归方法相比,所提方法可靠性有所提升,能够为电力系统调度决策、备用安排等提供更为可靠的指导信息。

Short-term Uncertainty Forecasting Method for Wind Power Based on Real-time Switching Cloud Model

Power system with high penetration of wind power requires much better performance for the accuracy of wind power forecasting (WPF) and reliability of its uncertainty analysis. Most of studies on the WPF uncertainty analysis mainly focus on the static error analysis and modelling for the overall span, and this will limit the model performance and adaptabilities at various time slots and weather conditions. Therefore, a time-switching cloud model is presented for the short-term WPF uncertainty analysis. First, the cloud models of the deterministic forecasting deviation for each selected predictive power range are established in a real-time updating manner. Then, the distribution of cloud drops can be generated according to three mathematical characteristics of the cloud, i.e. expectation, entropy and ultra-entropy. In this way, the uncertainty conditions of given predictive power ranges can be quantified. Moreover, by calculating the quantile of generated cloud drops, the uncertainty forecasting results can be achieved and expressed as the possible power range at given confidence level. The model performance at each time slot could be improved by updating the cloud model according to the current conditions. To take a Chinese wind farm as an example, the results show that the proposed method achieves more reliable uncertain intervals compared with the traditional quantile regression model and it can provide more reliable information for the dispatch and reserve of the power system.