基于离散傅里叶变换矩阵的概率最优潮流计算方法

随着大规模可再生能源并网,不确定性源增多,现代电力系统规模扩大,导致其电力系统概率最优潮流计算更加耗时。其中,网络规模变大将导致单次确定性最优潮流的求解变得更为复杂,而另一方面不确定源变多的潜在后果是必须增加确定性最优潮流的求解次数才能保证输出结果的精确性。针对上述问题,引入离散傅里叶变换法(Discrete Fourier transformation matrix,DFTM)对概率最优潮流进行分析与计算,并对其样本点的选取策略进行了深入分析。DFTM法采点数量灵活,同时可以精确地处理具有相关性的随机变量,能够较好地兼顾目前概率最优潮流计算中的精度与速度问题。最后基于改进的IEEE 118节点算例,以传统蒙特卡洛模拟法的结果作为参考,验证了DFTM法在非对称分布变量所占比例不同场景下的概率最优潮流计算精度与速度优势。并通过与无迹变换法进行对比,进一步展现了DFTM法的优越性能。

Probabilistic optimal power flow calculation method based on a discrete Fourier transformation matrix

With the vast integration of renewable energy,modern power systems are becoming large-scale networks with a greater number of uncertainty sources,which makes Probabilistic Optimal Power Flow(POPF)analysis quite time-consuming.On the one hand,the larger scale of a network makes the implementation of the Deterministic Optimal Power Flow(DOPF)more complicated;on the other hand,to obtain an accurate output,a heavier computation burden on DOPF is unavoidable due to the more uncertainty sources.Correspondingly,a Discrete Fourier Transformation Matrix(DFTM)is adopted to implement a probabilistic optimal power flow calculation,and the characteristics of DFTM samples are further investigated.The DFTM method is flexible in sampling point and can accurately handle the correlation amongst variables.Therefore,the DFTM method is able to balance the accuracy and efficiency of POPF analysis.Finally,the modified IEEE 118-bus system is adopted and the Monte Carlo simulation method is used as a reference to verify the effectiveness and superiority of the DFTM method in different proportion of asymmetrically distributed random variables.Compared with the unscented transformation method,the superiority of DFTM method is shown further.