Abstract: Aiming at the problem of wind curtailment caused by the energy structure lacked the adjustment ability in the three north area,this paper aggregated wind farm,concentrating solar power plant（CSPP）,thermal power units and combined heat and power（CHP）plant into virtual power plant（VPP）. Using stochastic optimization to deal with the uncertainty of wind-solar,Latin hypercube sampling（LHS）was used to generated a large number of random scenes,and based on considering the random characteristics and correlation of wind-solar distribution fully,Kantorovich distance reduction and K-means clustering algorithm were used to optimized and reduced the dimension of random scenes,for obtaining typical prediction wind-solar scenes. Combined with the flexibility and energy supply inertia of CSPP,the optimal dispatching model of the VPP contained photothermal was constructed,and the objective function of minimizing the total operation cost of the system was established. Finally,an example was given to verify the superiority of the proposed stochastic optimization method in computational efficiency and prediction accuracy;The objective functions under different operation scenarios were solved to verify that the optimal dispatching model could improve the wind power consumption capacity while reducing the system operation cost effectively.