Abstract: 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．

Key words: distributed power supply, latin hypercube model Carroll simulation, radial basis neural network, probabilistic power flow