With the increase of distributed photovoltaic (DPV) generation stations integrated into the distribution network, the uncertain fluctuation of PV output results in frequent fluctuations of the operating states of thenetwork. Thus planning of DPV generation stations integrated into the distribution network is important for secure operation. There is a problem of grid-connection buses and capacity selection of DPV generation stations integrated into a network. We take the grid-connected locations and configuration capacity as decision variables, and the uncertainty of DPV output is considered. From this a distributionally robust optimal allocation model for the grid-connection buses and capacity selection of DPV generation station integrated into the network is established. The daily photovoltaic output curve of unit capacity of DPV generation station calculated by the historical data of local illumination intensity and environmental temperature is used as data to construct an ambiguity set based on KL divergence. The uncertain fluctuation range of unit capacity PV output is obtained by the optimal calculation of the probability distributions with maximum and minimum expected values in the ambiguity set. Then the distributionally robust optimization model is transformed into a robust optimization model, and the optimal allocation scheme is obtained using the Benders decomposition method to solve the model. Finally, taking an actual 180-bus distribution network as an example, a simulation is performed and compared with the deterministic optimization and traditional robust optimization methods. It is verified that the proposed distributionally robust optimization method can not only ensure the secure operation of the distribution network under the uncertain fluctuation of PV output, but also can overcome the conservatism of the robust optimization method. Hence the obtained allocation scheme can achieve a good balance between economy and robustness. This work is supported by the National Natural Science Foundation of China (No. 51977080) and the Science and Technology Project of Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd. (No. GZHKJXM20180053).