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 demonstrates robustness against early disconnections by considering a wider range 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：本文提出了一种电动汽车充电站（EVCS）模型，该模型融合了实际约束条件，如车位功率限制、合同阈值超限惩罚以及电动汽车（EV）提前断开连接。我们提出了在不确定性条件下EVCS控制问题的数学表述，并实现了两种利用用户提供信息的多阶段随机规划方法，即模型预测控制和两阶段随机规划。该模型处理了充电会话开始时间、结束时间以及能量需求中的不确定性。基于逗留时间相关随机过程的用户行为模型有助于在保持客户满意度的同时降低电力成本。通过基于真实数据集的22天模拟，将所提出的两种方法的优势与两个基线方法进行了对比验证。两阶段方法通过考虑更广泛的不确定性场景进行优化，展现出对提前断开连接的鲁棒性。与行业标准基线相比，优先考虑用户满意度而非电力成本的算法在两个用户满意度指标上分别实现了20%和36%的提升。此外，在成本和用户满意度之间取得最佳平衡的算法，与理论上最优的基线（即放宽了非预期约束条件）相比，仅带来3%的相对成本增加，同时在两个满意度指标上分别达到了用户满意度的94%和84%。