In the context of electricity and carbon markets, with the continuous deepening of virtual power plant research and the goal of achieving energy mutual support in different regions within the same distribution network, a dynamic electricity-carbon pricing virtual power plant alliance master-slave game scheduling strategy was proposed. First, an interaction framework of the virtual power plant alliance was designed, where the alliance operator set the electricity-carbon prices, and the user entities formulate their operational plans based on the prices. Second, the information gap decision theory was employed to handle uncertainties in both generation and load sides, and a virtual power plant alliance optimization scheduling model was established based on the master-slave game. Finally, a particle swarm algorithm nested with a CPLEX solver was used to solve the model, and case studies were conducted to validate the rationality and effectiveness of the proposed strategy. Simulation results show that the virtual power plant alliance improves in terms of daily operating costs, carbon emissions, and overall benefits.