考虑尾流效应的风电场短期功率空间预测模型

Spatial model for short term wind power prediction considering wake effects

由于风电的波动性和随机性,并网运行的风电场会对电力系统稳定性产生一定的影响。针对风电场输出功率的不确定性,构建了一种基于物理方法的空间相关模型来对短期风电功率进行预测。首先,该模型根据风电场风力机之间的空间位置及排布关系,计算风力机之间的尾流效应,导出了各风力机处的连续微分方程,然后使用有限体积法将网格点处的微分方程进行离散化,推导出风速空间相关矩阵,通过给定的边界条件求解上述微分方程,得出各个风力机的输入风速。最后将风速计算结果输入功率转化曲线得到各风力机的输出功率,从而预测风电场的输出功率。研究结果表明,空间相关模型可以较好地量化每台风力机的输出功率,将风电场输出功率的不确定性转化为相对量化的确定性关系,具有一定的实用价值。

System integration of wind farms into power grids has influence on power systems operation due to its fluctuation and stochasticity. A short term prediction model based on spatial correlation approach is introduced to deal with the uncertainty of wind power. Firstly, continuous partial differential equation of each wind turbine has been developed by calculating wake effects in accordance with specific spatial location and distribution of correlated wind generators. Then, discretization of differential equation at each grid point derives spatial correlation matrix of wind speed through a finite volume method （FVM）, and wind velocity of each turbine corresponding to the solution of differential equation above is solved under given boundary conditions. Finally, the short term wind power prediction is made through a practical wind power curve. Forecasting results show that the spatial correlation model of wind farm can be practically used for calculating output power in wind farm quantitatively and eliminating the uncertainty in short term wind power prediction.