考虑不同风能特征的风电中长期电量预测方法

Medium-long-term Quantity of Electricity Forecasting Method in Wind Power Considering Different Wind Energy Characteristics

中长期电量预测在编制中长期发电计划、提高新能源消纳以及保障电力系统电量平衡等方面发挥着重要作用。未来气候态预报信息有利于提高中长期电量预测精度,但当前中长期电量预测未能有效挖掘和利用未来气候预报信息,为此,提出了一种考虑不同风能特征的风电中长期电量预测方法,同时为提高预测模型的适应性,以风能资源气候态预报结果数据为输入,通过构建风能特征挖掘模型,实现了不同预报误差特性数据集的筛选,进而结合风电场实际发电数据,基于灰狼优化算法(grey wolf optimizer,GWO)与长短时记忆网络(long short term memory,LSTM)构建了适应性预测模型。将所提法与当前预测方法相比,结果显示:所提出的中长期电量预测方法实现了沿海某风电场及区域总电量预测,且预测模型的性能更优。研究结果验证了所提方法的有效性和先进性。

The medium-long-term electricity forecasting plays an important role in drawing up medium-long-term generation plan, improving new energy consumption and ensuring power balance in power system. The forecast information of future climate state is helpful to improving the forecast precision of medium-long-term quantity of electricity;however, at present the forecast information of medium-long-term quantity of electricity can not be effectively mined and utilized. Consequently, in order to improve the adaptability of the prediction model, a wind energy feature mining model is constructed by taking the wind energy resources regional climate forecast data as input, and the selection of data sets with different forecast error characteristics is realized. Moreover, combined with the actual power generation data of wind farm, the adaptive prediction models are constructed with GWO and LSTM. Compared with the current forecasting methods, the results show that the proposed forecasting method for medium-long-term quantity of electricity can be adopted to realize the total power forecasting of a coastal wind farm and a region, and the performance of the forecasting models is optimal, which proves the validity and advance of the method.