Abstract: In order to accurately evaluate the influence of the sequential feature and correlation of photovoltaic output (PV) and load on the operation state of power system, a time-series correlation probability optimal power flow calculation method based on adaptive diffusion kernel density estimation was proposed. Firstly, using the adaptive diffusion kernel density estimation model of photovoltaic output, the Gaussian kernel function was transformed into a linear diffusion process, and the Gaussian kernel function was transformed into a linear diffusion process. In order to improve the local adaptability of the PV output model, the adaptive optimal bandwidth was selected for the diffusion kernel function by using the asymptotic mean integrated squared error (AMISE) method. Secondly, Copula theory was used to build the joint probability distribution model of time series of PV output and load, and relevant samples of time series PV output and load were obtained. Genetic algorithm was used to calculate the probabilistic optimal power flow which could accurately account for the time series and correlation of photovoltaic and load; . Finally, based on the measured data of a PV power station in China and the simulation analysis of IEEE30 system, the accuracy and validity of the proposed probabilistic optimal power flow calculation method considering the correlation between PV output and load sequence are verified.