用停车场电动车电池构建抗干扰储能系统，兼顾收益与安全。
Adversarially and Distributionally Robust Virtual Energy Storage Systems via the Scenario Approach
- 基于凸优化的调度框架，数据驱动且无需假设分布
- 实测验证了收益与风险的可调平衡，理论保证与实际违例率一致
- 对数据污染和分布外扰动均有鲁棒性，适合能源社区应用
我们研究在电动汽车离场时间不确定、充电状态受限的条件下，基于停车场内电动车电池聚合提供的虚拟储能服务。提出一种凸的数据驱动调度框架，使停车场管理者在与零售商互动的同时，向产消者社区提供储能服务。该框架在有限样本下提供无分布假设的约束违反保障，并允许管理者显式调节经济性能与运行安全之间的权衡。为提升数据不完善下的可靠性，将模型扩展至对抗性样本扰动和Wasserstein分布偏移，获得对数据损坏和分布外不确定性的鲁棒性证明。数值实验验证了预测的利润-风险权衡，并显示理论证书与观测到的违反水平保持一致。
原文摘要 · Abstract (English)
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.
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