In China, with the complex terrain and the numerous microclimate , the renewable energy output curve is difficult to predict. The increasingly mature market rules and regional climate differences are difficult to support the regular and safe operation of the power market in China. Therefore, this paper proposes an optimal dispatching strategy for a virtual power plant with multiple complementary sources included power supply,networks, loads and storage. First, it is proposed to increase the renewable energy day ahead market considering the penetration rate of renewable energy, so that renewable energy and load can enter into bilateral transactions to share the risks brought by the uncertainty of renewable energy participation in the day ahead market. At the same time, which is modeled that the whole process of virtual power plant participating in the day ahead market , and the clearing model is added to the day ahead market to optimize the market clearing price and the declared price of each source side. Secondly, considering the load fluctuation and the volatility of the renewable energy power generation that permeates the day ahead market, a standby clearing model is proposed to compensate for the opportunity costs incurred by each source side to deal with these uncertainties.Besides,such costs shall be apportioned according to the principle of "who produces the costs, who is responsible" to restore the source of costs. Finally, build a carbon trading model inside the virtual power plant to relieve the pressure on the performance of the carbon emission trading market on the power generation side. Particle swarm optimization (PSO) algorithm is used to solve the model. Finally, IEEE 30 node numerical example is used to verify the effectiveness of the method described in this paper. The results show that the multi energy complementary model can effectively cope with the change of reserve demand under different wind photovoltaic power generation conditions and penetration rates in the day ahead market, and finally achieve the maximum benefit.