考虑风电相关系数矩阵非正定的概率潮流计算

高比例风电并网导致电力系统的随机性因素急剧增加,概率潮流计算是电力系统运行和规划的必要工具.提出一种可灵活处理相关系数矩阵非正定的概率潮流方法.该算法通过改进近似贝叶斯计算,和结合奇异值分解的Nataf变换得到考虑相关性的状态变量样本,通过误差函数快速计算和比照统计特征值,进而将状态变量概率分布求解问题转变为一种参数反演问题.基于IEEE-30节点系统的仿真结果验证了所提方法的计算精度和时效性.

Probabilistic power flow calculation considering wind power correlation coefficient matrix non-positive definite

High proportion wind power integrated power grid has increased the randomness in power systems. Probabilistic power flow calculation is a necessary tool for power system operation and planning. A new probabilistic power flow method that can flexibly deal with non-positive definite correlation matrix is proposed. With this method, improved approximate bayesian computation and singular value decomposition combined with Nataf transformation are used to obtain the state variable samples considering the correlation. Then, it uses the error function to quickly calculate and compare statistical eigenvalues. Thus, the state variable probability distribution problem is transformed into the parameter inversion problem. Simulation results based on IEEE30-bus system verify the calculation accuracy and timeliness of the proposed method.