面向信息物理融合能源系统的大规模风电场发电功率随机特性分析模型与算法

Estimation and Characteristic Analysis of Large-scale Wind Farm Generation in Cyber Physical Energy System

随着风电并网容量的迅猛增长,对风电高度不确定性和间歇特性的准确估计与实时分析成为了风电入网迫切需要解决的难题。该文以信息物理融合系统为支撑,利用其多源信息感知与融合的特点,以随机Burgers方程为基础,建立了描述近地风场动态特性及其风电转换过程的随机系统模型。利用粒子滤波算法,实现了大规模风电场发电功率波动性的提前τ步估计与随机特性分析。最后,利用一个30风机风电场算例说明了算法的有效性。

The variability and uncertainty of wind energy is becoming a huge threat to the security and stability of the grid with high wind penetration. This paper focused on the stochastic dynamic model for aggregated generation of large-scale wind farm in cyher physical energy system, and presented a dynamic system description aggregated generation of wind turbines based on stochastic Burgers equation from near-surface wind field＇s dynamics and wind-generation function with uncertainties. Then we developed a particle filter based solutions to estimate the z-step-ahead generation of this nonlinear dynamic system. Finally, a 30-turbine study case shows that this method is efficient and effective.