Degradation-aware bi-level scheduling of electric vehicle clusters for enhanced renewable hosting capacity

With the increasing penetration of renewable energy, power systems face escalating challenges in maintaining operational flexibility and enhancing renewable energy hosting capacity. Electric vehicle clusters, enabled by large-scale deployment and controllable charging and discharging capability, offer substantial potential as distributed flexibility resources. However, temporal mismatches between renewable generation and electric vehicle charging demand, along with insufficient incentive mechanisms and concerns about battery degradation, constrain effective participation in vehicle-to-grid coordination. This paper proposes a degradation-aware bi-level scheduling framework that represents electric vehicle clusters as shared mobile energy storage. At the user level, battery degradation is quantified through an equivalent full-cycle cost formulation, and degradation costs are embedded in charging and discharging decisions. At the aggregator level, a bi-level dynamic pricing and coordination mechanism is established: the upper level determines system-level scheduling and issues price signals, while the lower level captures heterogeneous price-responsive behaviours under operational constraints. To solve the resulting multi-objective optimization model, an improved nondominated sorting genetic algorithm is developed, and a weighted time-slot benefit allocation method based on the Shapley value is introduced to characterize time-varying marginal contributions and allocate benefits. Case studies show that the proposed framework reduces peak-period grid power purchases, mitigates load fluctuations, and increases renewable energy utilization to approximately ninety-six percent. The results indicate that degradation-aware coordinated aggregation of electric vehicle clusters provides an effective and scalable pathway to enhance renewable hosting capacity and support the low-carbon transition of power systems.