This paper introduces an Electric Vehicle Charging Station (EVCS) model that incorporates real-world constraints, such as slot power limitations, contract threshold overruns penalties, or early disconnections of electric vehicles (EVs). We propose a formulation of the problem of EVCS control under uncertainty, and implement two Multi-Stage Stochastic Programming approaches that leverage user-provided information, namely, Model Predictive Control and Two-Stage Stochastic Programming. The model addresses uncertainties in charging session start and end times, as well as in energy demand. A user's behavior model based on a sojourn-time-dependent stochastic process enhances cost reduction while maintaining customer satisfaction. The benefits of the two proposed methods are showcased against two baselines over a 22-day simulation using a real-world dataset. The two-stage approach proves robust against early disconnections, considering a more significant number of uncertainty scenarios for optimization. The algorithm prioritizing user satisfaction over electricity cost achieves a 20% and 36% improvement in two user satisfaction metrics compared to an industry-standard baseline. Additionally, the algorithm striking the best balance between cost and user satisfaction exhibits a mere 3% relative cost increase compared to the theoretically optimal baseline - for which the nonanticipativity constraint is relaxed - while attaining 94% and 84% of the user satisfaction performance in the two used satisfaction metrics.

翻译：本文介绍了一种考虑实际约束的电动汽车充电站（EVCS）模型，包括充电桩功率限制、合同阈值超限罚金以及电动汽车（EV）提前断开连接等。我们提出了不确定条件下EVCS控制问题的公式化表达，并实现了两种利用用户提供信息的多元随机规划方法，即模型预测控制和两阶段随机规划。该模型可处理充电会话开始时间、结束时间以及能量需求的不确定性。基于驻留时间相关随机过程的用户行为模型在保持客户满意度的同时降低了成本。通过使用真实数据集进行22天仿真，我们将所提出的两种方法相对于两个基线基准的性能优势进行了展示。两阶段方法在优化过程中考虑到更多不确定性场景，对电动汽车提前断开连接表现出了鲁棒性。相较于行业标准基线，优先考虑用户满意度优于用电成本的算法在两项用户满意度指标上分别实现了20%和36%的提升。此外，在成本与用户满意度之间取得最佳平衡的算法，其相对成本仅比理论最优基线（放宽了非预期性约束）高出3%，同时在两项满意度指标上分别达到了94%和84%的用户满意度表现。