Virtual power plants (VPP) efficiently aggregate small-capacity and large-volume distributed energy resources through advanced control technologies to participate in electricity market transactions. With the increase in the number of distributed energy sources

the volatility of their power output and the problem of their returns after aggregation still need to be solved. Based on this

a cooperative game scheduling model is proposed for multi-type distributed energy sources aggregated in a virtual power plant under the day-ahead power market. Firstly

the operation framework of multi-type distributed energy aggregation in virtual power plant is proposed. Then

a combined prediction model based on variational modal decomposition (VMD) and improved bidirectional multi gated long short-term memory (Bi-MGLSTM) network is established because the uncertainty of wind power output seriously affects the operation of the system. Secondly

the same type of distributed energy sources form alliances and aim to maximize the revenue from power sales

and construct a cooperative game scheduling model for multiple alliances of virtual power plants. In order to realize the fairness of revenue distribution among alliances and members

a multifactor improvement shapley value method and a two-stage refinement of the revenue distribution scheme based on the parity cycle kernel method are designed. Finally

the example results show that the proposed method effectively improves the prediction accuracy of wind power

realizes the cooperative and complementary operation among alliances within the virtual power plant

and ensures the fairness and reasonableness of the revenue distribution among multiple subjects.