Abstract: With the large-scale integration of electric vehicles into the power grid, how to guide users to participate in vehicle-to-grid (V2G) interaction through reasonable incentive mechanisms has become an urgent problem to be solved. To address the limitations in existing studies, including relatively single incentive forms, insufficient characterization of user response behaviors, and inadequate analysis of adaptability to electricity price fluctuations, this paper constructs a stackelberg game model between the electric vehicle aggregator (EVA) and EV users, and designs three V2G incentive mechanisms, namely fixed subsidy, dynamic subsidy, and charging fee discount. In the upper-level model, the EVA formulates incentive strategies with the objective of maximizing its profit. In the lower-level model, EV users optimize their charging and discharging decisions with the objective of minimizing user costs. The depth-of-discharge-based battery degradation cost is introduced to characterize the asset loss caused by EV participation in V2G. The proposed model is solved using the grey wolf optimization algorithm. Case study results show that all three mechanisms can effectively reduce the system peak-valley difference, with reductions of 37.23%, 35.00%, and 35.70%, respectively, compared with uncoordinated charging. Among them, the fixed-subsidy mechanism is more effective in improving EVA profit, the dynamic-subsidy mechanism performs better in reducing user costs; and the charging-fee-discount mechanism provides stronger inducing effect on user's discharging response. The electricity price sensitivity analysis and algorithm comparison further verify the adaptability and solution effectiveness of the proposed strategy.