The increasing penetration rate of distributed PV in the distribution network entails higher requirements for its full absorption. To improve the carrying capacity of distributed PV in a distribution network, a method to achieve that considering correlation is proposed. First, the correlation between distributed PV output and load is described by the Frank-Copula function, and the correlation sample matrix of each random variable is obtained based on the Nataf transform, and the probabilistic power flow is calculated. Then, with the target of maximum distributed PV access capacity and the constraint of the power grid operation safety index, a distributed PV capacity enhancement model is established. Finally, the nonlinear reverse learning whale algorithm is proposed to analyze the model, and the IEEE33-node system is taken as an example for simulation analysis. The results show that the proposed method can effectively improve the distributed photovoltaic carrying capacity of the network.