The high dimensionality and uncertainty of new energy output restrict the absorption capacity of distribution network to new energy. Therefore

it is necessary to fully consider the daily and periodic new energy outputs in distribution network planning. For this purpose

a multi-objective stochastic programming method of distribution network considering the uncertainties in source and load outputs is designed. Firstly

the K-means clustering algorithm is used to cluster the historical scenes into K daily state types and the corresponding probability distribution is obtained. Then

the scene set considering high-dimensional correlation between source and load is generated with the daily state types as condition and in combination with scenario generation method based on SN-CWGAN. Further-more

a multi-objective two-stage stochastic programming model is constructed for the distribution network

in which the upper layer determines the location and capacity of energy storage and plans for new energy

while the lower layer optimizes the operational plans of the distribution network for various typical scenarios. Finally

the RVEA algorithm is used to solve the proposed model. The CRITIC weight method is used to obtain objective weight and the to obtain the compromise solution by using a multi-criteria compromise solution sorting method. The planning method proposed in this paper is verified by using the IEEE33 node example system. The results show that the method not only accurately describes the probability distribution and high-dimensional correlation of source and load

but also get the planning result concurrently considering both economic factors and life cycle carbon emissions.