Aiming at the coordination of interests between electric vehicles and vehicle to grid（V2G）charging robots connected to virtual power plants

and to improve the consumption of new energy and reduce the impact of electric vehicles on the power grid

a virtual power plant optimization scheduling model based on carbon peaking and carbon neutrality goals is proposed after analyzing the scheduling system of virtual power plants containing V2G charging robots and carbon trading. This model aims to establish an optimal scheduling model with the comprehensive optimization of the cost of electric vehicle charging and discharging

carbon trading

carbon emissions

virtual power plant revenue

virtual power plant management

and output prediction deviation compensation. The model is solved by using the improved whale optimization algorithm and the improved particle swarm optimization algorithm. The performance is analyzed through a numerical example. The results indicate that

the proposed optimization scheduling method for virtual power plants can effectively reduce carbon trading costs and CO2 emissions

and optimize the charging and discharging status of electric vehicles

which can effectively improve the overall revenue of the virtual power plant.