With the large-scale access of renewable energy sources, its randomness and volatility make it difficult to dispatch the power system and even cause the discarding of the wind. In order to reduce the impact of large-scale new energy access on the power grid, a two-layer optimal scheduling model of virtual power plant based on time sharing price is proposed. In the upper model, the wind and scenery prediction force is declared for the planned force. In view of the deviation between the actual force and the forecast force, the gas turbine and the energy storage battery are coordinated to suppress it. On the basis of the maximum wind and scenery, the minimum deviation compensation scheme is obtained, and the upper force is transferred to the lower layer model. In the lower layer model, the control strategy is set up based on the time sharing price to reduce the net load trough difference, and then the adaptive particle swarm optimization is used to optimize the output of each thermal power unit. Finally, the impact of different wind and solar forecast errors on the economy of the virtual power plant is compared and analyzed, and the change of net load curve caused by pumped storage device is studied. The results show that the model can improve the level of new energy consumption, and the virtual power plant based on TOU price can maximize the profit and ensure the balance of supply and demand in the region. This work is supported by National Natural Science Foundation of China (No. 51767023).