Abstract: The injection of a high percentage of renewable energy sources introduces many uncertainties into the virtual power plant. If these uncertainty sources are ignored or inaccurately characterized, it will bring great risks to the operation and scheduling of the virtual power plant. In order to solve this problem, this paper considers the correlation of wind and solar energy sources in the virtual power plant, and utilizes Frank-Copula as a “connectivity function” to solve the joint distribution function of wind and solar power, so as to generate a typical scenario of wind and solar power. In order to minimize the operating cost of the virtual power plant, this paper comprehensively considers the electricity-carbon-green certificate market and the incentive-based demand response mechanism. The interval method, stepped carbon price, and Gounod model are used to describe the uncertainties of demand response, carbon price, and green certificate price, respectively, and an optimization scheduling model for virtual power plants based on interval linear programming is constructed. The case study results show that the model is able to quantify the risk of each component’s uncertainty on the virtual power plant dispatch, and ensure the safe and reliable operation of the virtual power plant with a certain degree of economy at the same time.