Economic Scheduling of Virtual Power Plants Based on Master-Slave Games

With the continued advancement of power market reforms, particularly on the power sales side, studying the economic optimization of multi-virtual power plant systems has become increasingly significant. For multi-objective virtual power plant systems, this paper proposes an economic optimization method based on a master-slave game framework. The method is divided into two phases: In the lower layer, the operation cost and environmental pollution cost of the virtual power plants are set as the optimization objectives. An optimization model is established for various micro-sources, such as wind power, photovoltaics, micro-gas turbines, and fuel cells, among others. In the upper layer, a master-slave game model is developed to regulate the optimal internal electricity price, facilitating electricity trading between the system operator and the virtual power plant group, thereby enhancing the level of energy sharing. The results of case studies demonstrate that the proposed methodology effectively incorporates the interests of the multi-objective modeling of virtual power plants, system operators, and multiple decision-making entities within the distribution network. It achieves mutual benefits through electricity and resource sharing, thereby significantly improving the economic performance of both virtual power plant groups and system operators.