基于拟蒙特卡洛的半不变量法概率潮流计算

随着风电并网容量的增加,概率潮流计算方法在计及风电出力不确定性的同时,还需考虑邻近风电场由于风速相关性导致的风电出力相关性问题。针对风电出力波动范围较大且存在相关性的特点,提出一种可考虑输入变量相关性的基于拟蒙特卡洛的半不变量法概率潮流计算方法。该方法利用基于Nataf变换的拟蒙特卡洛法产生具有相关性的风电出力样本,在各样本点处进行半不变量法概率潮流计算,基于各风电出力样本下的状态变量正态分布特性,依全概率公式整合所得正态分布得到最终的概率潮流结果。基于IEEE 30节点系统的算例分析表明,所提方法在较小采样规模下具有很高的计算精度,能够较精确地得到系统状态变量的概率分布。

Quasi Monte Carlo Based Cumulant Method for Probabilistic Load Flow Calculation

With the increasing penetration of wind sources, not only the uncertainty of wind power output, but also the correlation of wind power output due to wind speed correlations among adjacent wind farms should be considered in probabilistic load flow ( PLF) methods. In allusion to the correlation and large fluctuation range of wind power output, this paper proposes a quasi Monte Carlo ( QMC ) based cumulant method of PLF considering the correlation between input variables. QMC based on Nataf transformation is used to generate wind power output samples with correlation, and the cumulant method is used to calculate PLF at each sample point. With the state variables satisfying the normal distribution at each sample point, the final probability distributions are obtained by integrating all normal distributions according to the total probability formula. The comparative tests in the IEEE 30-bus system demonstrate that the proposed method has high computational precision under the small sampling size, and can obtain the probability distribution of the system state variables accurately.