A Stackelberg game-based approach for optimal scheduling of virtual power plant with V2G and demand response under tiered carbon trading

To achieve efficient collaborative operation of a virtual power plant (VPP) under low-carbon and economic goals, A multi-energy coordinated scheduling framework for VPP is proposed. A Stackelberg game model is established with the VPP operator (VPPO) as the leader and the energy supplier operator and user aggregator (UA) as followers. The main contributions include: an electric vehicle (EV) charging/discharging strategy was designed, which ensures its practicality through managed fixed charging/discharging windows and state of charge constraints; concurrently, integrated demand response (IDR) coordinated with Vehicle-to-Grid (V2G) technology via a real-time pricing mechanism to optimize controllable device outputs and shape user consumption. A tiered carbon trading mechanism is further introduced to analyze the game decision-making behavior of each entity under carbon constraints. A VPP in a certain park incorporating renewable energy and combined cooling, heating, and power units is selected as the research case to verify the proposed method. Case simulation results demonstrate that VPPO revenue increases by 17.15%, UA consumer surplus rises by 8.75%, system carbon emissions reduce by 7.75%, and the load peak-valley difference decreases by 57.13%. The proportion of electric demand response load and the number of EVs participating in V2G operations will impact the revenues of different entities and load stability. Studies indicate that the strong constraints of the tiered carbon trading mechanism on major carbon emitters, combined with the collaborative optimization capability of V2G and IDR, can effectively balance low-carbon economic goals and multi-stakeholder interests, providing theoretical support for the low-carbon transformation of complex energy systems.