基于广义多项式混沌法的含风电电力系统随机潮流

Probabilistic Load Flow for Wind Power Integrated System Based on Generalized Polynomial Chaos Methods

风电的大量接入加剧了电力系统运行的不确定性,随机潮流计算是分析电力系统不确定性的重要工具。针对风电接入,提出了基于广义多项式混沌理论的方法求解随机潮流。该算法选取最优广义多项式混沌基函数构成级数展开式来近似表示随机输入变量,再根据基函数的正交性,构造确定性方程组,进而将求解系统状态变量概率分布的问题转化为其广义多项式混沌逼近系数的求解问题。针对风电出力概率分布复杂且需计及风速相关性的情况,采用等价变换方法处理随机输入变量。对IEEE 30节点系统的随机潮流计算结果表明:所提算法的计算精度较高;与蒙特卡洛模拟法相比,计算量较小。

The large-scale integration of wind power has increased the uncertainty in power systems.Probabilistic load flow calculation is a significant probabilistic analysis tool.Based on generalized polynomial chaos（gPC）,a method is proposed to calculate probabilistic load flow with wind generation uncertainties.With this method,each random input variable is approximated by a finite-order expansion using optimal gPC basis functions.Then,a series of deterministic nonlinear equations can be built according to the orthogonality of gPC basis functions.Thus,the solution of random state variables＇ distribution is transformed into calculation of their gPC approximation coefficients.In addition,the idea of equivalent transformation is adopted to deal with the complexity of wind power probabilistic distribution and correction of wind speed.Calculation on an IEEE 30 bus system suggests that the proposed method can achieve a good degree of accuracy as well as higher computational efficiency in contrast to Monte Carlo simulation（MCS）method.