Abstract: In view of the poor practicality of the constant scheduling model for distributed power supply site selection and constant volume configuration，considering the time-series and uncertainty of DG ( Distributed Generation) output and load demand，based on the total investment cost of the distribution network system，total voltage deviation and system network loss minimization，multi-objective scheduling model is established． Firstly，the Monte-Carlo algorithm was used to simulate the wind-load scenario all year round，and the K-means clustering method is used to reduce the year-round scenarios． To solve the problem that the number of clusters in the K-means algorithm is difficult to set，the contour factor is introduced and improved to get the optimal number of clusters． Finally，the NSGA-I and non-biased strategy are used to optimize the process． In order to verify the
validity of the method，taking the IEEE33 node distribution system as an example，the comparison experiment with the standard genetic algorithm validates the validity and superiority of the proposed algorithm．

Key words: distributed generation, Monte-Carlo algorithm, K-means clustering, silhouette coefficient, NSGA-Ⅱ, unbiased compromise strategy