中文摘要：

针对风电出力不满足正态分布的特点,对风电出力分段离散化,并将多个风电场的离散化结果进行组合得到系统风电出力状态的所有组合,将问题转换为求解风电注入功率确定的多个条件概率分布问题。利用全概率公式整合多个条件事件的原点矩以计算系统状态变量的期望和方差。基于所有条件概率分布满足正态分布的特点,将所得的多个Gauss函数按全概率公式累加的方法得到同时考虑风电随机性和负荷随机性的概率密度函数,摒弃了传统的Gram-Charlier级数拟合概率密度函数的方法。以IEEE 33节点配电网络接入多个风电场为例对所提方法进行验证,并与多个抽样规模的蒙特卡罗模拟法进行对比,结果证明所提方法具有与千万次抽样的蒙特卡罗模拟法等同的精度。

英文摘要：

To address the problem that the output of a wind farm does not satisfy the normal distribution, the paper divided the output of a wind farm into a lot of discrete segments, and combined the discrete segments of all wind farms in a distribution networks to get all discrete states of system＇s wind power outputs, and then converted the probabilistic load flow including the distributed wind generation into some conditional probability distribution problems in which the wind power outputs are deterministic. The expectation and variance of system state variable was calculated by combining the moment of all conditional events using the total probability formula. With all conditional probability distribution satisfying the normal distribution, the probability density function considering randomness of both wind generation and load was obtained by accumulating all Gauss functions according to the total probability formula rather than using the traditional Gram-Charlier series expansion method. IEEE 33-bus sample system including some wind farms was used for testing the proposed method. The results show that the proposed method has the same accuracy as Monte Carlo method with 10e7 simulations, which verify the validity of the proposed method.