Robust Game Optimization Considering the Dispatchable Potential of Electric Vehicle Clusters

[Objective] To exploit the dispatchable potential of electric vehicle （EV） clusters and address the insufficient consideration of renewable energy output uncertainty and user behavior heterogeneity， a Stackelberg game-based regulation strategy considering dispatchable potential is proposed for EV clusters and charging stations. [Methods] A dispatchable potential model for EV clusters is constructed across three dimensions： time， power， and energy. An aggregation model is then established based on the consistency principle to reflect the true regulation boundaries. On this basis， a bi-level game model with the charging station as the leader and the EV cluster as the follower is formulated. The upper level adopts two-stage robust optimization to handle photovoltaic output uncertainty， while the lower level establishes an optimal response model incorporating user response willingness and off-station charging demand. The bi-level model is equivalently solved using Karush-Kuhn-Tucker （KKT） conditions and robust decomposition. [Results] Simulations based on real-world charging data demonstrate that， compared with scenarios without cluster aggregation and without vehicle-to-grid （V2G）， the proposed strategy increases the charging station’s revenue by 9.6% and 10.5%， and reduces user costs by 11.3% and 29.1%， respectively. [Conclusions] The proposed strategy can effectively unlock the large-scale regulation capability of EV clusters， achieving a win-win situation for both the charging station and users. Furthermore， two-stage robust optimization enhances scheduling robustness under PV output uncertainty， while the V2G mode， combined with user behavior feedback， ensures the feasibility of regulation and user satisfaction.