改进拉丁超立方蒙特卡洛模拟

电动汽车、分布式电源的并网给电网带了明显的不确定性，为了使电网分析更能贴近实际电网，通过对负荷、分布式电源出力的概率密度函数模拟其出力，借鉴已有分布式电源和电动汽车概率模型，采用拉丁超立方蒙特卡洛模拟与径向基神经网络相结合的方式计算概率潮流。该方法充分考虑了电动汽车和分布式电源的随机性、间歇性和相关性，利用拉丁超立方蒙特卡洛模拟对比传统蒙特卡洛模拟方法，降低了采样规模，提高了采样覆盖率。径向基神经网络用于求解潮流计算方程，避免了传统方法中计算雅可比矩阵和偏导，大幅度减少了运算时间。通过仿真，该算法在改进的IEEE14 和IEEE118 节点系统的计算结果表明，在保证精度的同时， 极大地加快了算法运行速度，适用于大规模电力系统概率潮流的求解，在改进的IEEE118 节点系统中，运行时间比传统蒙特卡洛模拟降低99．9%。

Improvement of Application of Latin Hypercubic Monte Carlo Simulation

The grid connection of electric vehicles and distributed power supplies brings significant uncertainty to the grid． In order to make grid analysis closer to the actual grid． It simulates output through the probability density function of load and distributed power output． It proposes to use Latin Hypercube Monte Carlo simulation and radial basis neural network to deal with distributed power supply and electric vehicle probability model． This method takes full account of the randomness，intermittency and correlation of EVs and distributed power supplies． Using Latin hypercube MCS ( Monte Carlo Simulations) to compare with traditional Monte Carlo simulation methods，the sampling scale is reduced and sampling coverage is improved． The radial basis neural network is used to solve the power flow calculation equation，which avoids the calculation of Jacobian matrix and partial guide in the traditional method． Through simulation，the calculation results of the proposed algorithm in the improved IEEE14 and IEEE118 node systems show that while ensuring accuracy，the algorithm speeds up the algorithm greatly，and is suitable for the solution of probabilistic power flow in large-scale power systems． The improved IEEE118 node in the system，the running time is reduced by 99． 9% compared to the traditional MCS．