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：本文提出了一种考虑现实约束的电动汽车充电站（EVCS）模型，包括充电桩功率限制、合约阈值超限惩罚以及电动汽车（EV）的提前断开等情况。我们构建了不确定性条件下EVCS控制问题的数学表述，并基于用户提供的信息实现了两种多阶段随机规划方法：模型预测控制与两阶段随机规划。该模型针对充电会话起止时间及能量需求的不确定性进行建模。通过建立基于停留时间相关随机过程的用户行为模型，在保障用户满意度的同时有效降低了运营成本。基于真实数据集开展的22天仿真实验表明，两种方法相较两种基准策略均展现出显著优势。两阶段方法通过优化时考虑更广泛的不确定性场景，对电动汽车提前断开情况表现出良好的鲁棒性。优先保障用户满意度的算法相较于行业标准基线，在两项用户满意度指标上分别实现了20%和36%的提升。而在成本与用户满意度间取得最佳平衡的算法，相较于放松非预期性约束的理论最优基线，仅产生3%的相对成本增幅，同时在两项满意度指标上分别达到理论最优性能的94%和84%。