Stackelberg game-based optimal scheduling of two-level virtual power plant

In order to rationally solve the problem of distributing the interest of each decision-making body in the transaction of virtual power plant (VPP) participating in the energy market, this paper introduces game theory into the VPP energy system (VPPES) and makes a more in-depth study on its joint scheduling on the basis of economy and low carbon. First, this paper constructs a bi-level VPP system with a new type of VPP and studies its optimal operation strategy under the Stackelberg game framework. The strategy takes the energy seller as the leader and the VPP supplier and the loader as the followers, and the upper and lower layers optimize the seller’s pricing strategy, the VPP’s output plan, and the user’s demand at the same time through real-time information exchange and loop iteration. Second, the energy trading process and mathematical model of the bi-level VPP system are introduced, and it is proved that there exists a unique Stackelberg equilibrium in the proposed game model, which is optimally solved by using an improved coyote optimization algorithm combined with a solver. Finally, it is verified through examples that the proposed operation strategy can significantly reduce the generation cost of the VPP and maximize the benefit of the seller and the utility of the loader, so as to realize economic energy supply and scientific energy use, which can provide a new paradigm for the economic and environmental development of the new energy system.