A probabilistic load flow method considering the correlation between input variables based on improved Monte Carlo simulation(MCS)is proposed. Regarding the behaviors of diversity and randomness for variable input

the method establishes the Gaussian mixture model(GMM)for variable input and performs parameter estimation by the use of measured data. Uniform design sampling(UDS)is introduced to improve the sampling efficiency

and correlated samples are generated by marginal transformation and Cholesky decomposition. Moreover

multi-linearization is applied to reduce the truncated error as well as time consumption. The simulation results of IEEE 30-bus and IEEE 118-bus test system verify the effectiveness

accuracy and practicability of the proposed method. This work is supported by National Natural Science Foundation of China(No. 51377021)

Fundamental Research Funds for the Central Universities(No. 2242016K41064)and State Grid Corporation of China.