Optimal scheduling of virtual power plants: An optimization approach combining multi-objective and game theory

To address the challenges of resource classification and aggregation, multi-objective co-optimization and energy pricing faced by virtual power plant (VPP). Firstly, this study proposes a supply and demand side resource aggregation framework to mobilize resource flexibility by integrating both sides to build a supply–demand flexibility aggregator (SDFA). Subsequently, a dual-layer optimization model based on a master–slave game between the VPP operator (VPPO) and SDFA is developed, and dynamic strategy optimization is used to achieve the maximum benefits of VPPO and the economic-low carbon bi-objective equilibrium of SDFA. Finally, for the dual-layer optimization model, a hierarchical solution strategy is designed, with the upper layer using a genetic algorithm (GA) and the lower layer using a multi-objective particle swarm algorithm (MOPSO), entropy weight method (EWM), and quadratic programming (QP) for combined solution. In this solution strategy, a dynamic weighted multi-objective solution scheme is designed by considering the impact of different energy pricing of VPPO on the importance of the economics and low carbon nature of SDFA. Simulation results show that the proposed method increased the revenue of the VPPO by 19.14% and the integrated objective of SDFA by 105.20%, with a 19.67% decrease in carbon emissions and a 7.26% increase in user utility.