A dual carbon reward mechanism for electric vehicle charging scheduling based on multi-level Stackelberg game

The rapid expansion of electric vehicles (EVs) poses increasing challenges for grid stability, peak load management, and rebound carbon emissions during night time. Existing studies of EV scheduling strategies often pursue single objectives such as cost minimization or load balancing, while overlooking the interactions among government, charging station operator (CSO), and EV users. Moreover, conventional models are usually built upon static carbon reward mechanisms, which fail to incorporate the regulatory role of government and cannot dynamically adapt to renewable energy fluctuations, resulting in carbon-inefficient scheduling outcomes. To address these gaps, this study proposes a novel three-level Stackelberg game-based charging scheduling model, in which the government acts as a top-level leader to strategically align environmental goals with economic incentives. A differentiated dual carbon reward mechanism is designed to couple real-time carbon signals with electricity pricing, providing targeted incentives for both CSO and EV users while minimizing government fiscal expenditure. The proposed model is solved using an enhanced genetic algorithm (GA), and simulation results demonstrate its effectiveness to reduce carbon emissions, improve renewable energy utilization, and enhance overall economic benefits, without compromising charging service quality. In this regard, the presented work provides a practical and scalable solution for sustainable EV charging management, meanwhile offering valuable insights for policymakers and operators in the context of low-carbon transportation.