We study virtual energy storage services based on the aggregation of EV batteries in parking lots under time-varying, uncertain EV departures and state-of-charge limits. We propose a convex data-driven scheduling framework in which a parking lot manager provides storage services to a prosumer community while interacting with a retailer. The framework yields finite-sample, distribution-free guarantees on constraint violations and allows the parking lot manager to explicitly tune the trade-off between economic performance and operational safety. To enhance reliability under imperfect data, we extend the formulation to adversarial perturbations of the training samples and Wasserstein distributional shifts, obtaining robustness certificates against both corrupted data and out-of-distribution uncertainty. Numerical studies confirm the predicted profit-risk trade-off and show consistency between the theoretical certificates and the observed violation levels.

翻译：研究了电动汽车电池聚合在停车场景中，考虑时变、不确定的电动汽车驶离状态及荷电状态限制下的虚拟储能服务。提出了一种凸数据驱动调度框架，使停车场管理者在参与零售商交互的同时为产消者群体提供储能服务。该框架提供有限样本、无分布假设的约束违反保证，并允许管理者显式调节经济效益与运行安全性之间的权衡。为增强非完美数据下的可靠性，将框架扩展至训练样本的对抗扰动和Wasserstein分布偏移，获得同时针对数据污染和分布外不确定性的鲁棒性认证。数值实验验证了预测的利润-风险权衡关系，并展示了理论认证与观测违反水平之间的一致性。