Aiming at the scenario of strong uncertainty on both sides of source and load in active distribution network

this paper proposes a probabilistic power flow calculation method of active distribution network based on Copula theory and equal probability segmentation. Through Copula function and Rosenblatt inverse transformation

the description of the correlation of input random variables and the transformation of independence are realized. In order to make the correlation analysis based on Copula theory easy to carry out

the unit output is no longer discretized

but the unit output probability model is segmented in its output interval to obtain the conditional probability density function under the segmented interval. An interval segmentation method based on equal probability principle is proposed for interval segmentation. Through the total probability formula

the complete probability power flow calculation is decomposed into multiple high-precision conditional probability power flow calculations

and the Gram-Charlier (GC) series is used to fit the probability distribution function.