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.

这篇论文介绍了一种电动汽车充电站（EVCS）模型，它考虑到现实世界的约束条件，如插槽功率限制、合同阈值超过惩罚或电动汽车（EVs）的早期断开连接。我们提出了一个对 EVCS 控制问题的不确定性建模，并实施了两种多级随机规划方法，利用用户提供的信息，即模型预测控制和两阶段随机规划。该模型解决了充电会话开始和结束时间以及能源需求的不确定性。基于停留时间相关的随机过程的用户行为模型提高了成本降低同时保持客户满意度。通过使用真实数据集进行为期 22 天的模拟，展示了两种提出的方法与两种基准的优势。考虑到更多的不确定性情景进行优化，两阶段方法表现出对早期断开连接的鲁棒性。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相对于行业标准基准提高了 20% 和 36%。此外，在实现理论上的最佳基准时，该算法在成本和用户满意度之间达到了 94% 和 84% 的用户满意度表现，并仅出现 3% 的相对成本增加 - 放宽了无先见性约束。

通过用户行为建模和随机规划控制大型电动汽车充电站

Controlling Large Electric Vehicle Charging Stations via User Behavior  Modeling and Stochastic Programming

Convex composition optimization is an emerging topic that covers a wide range
of applications arising from stochastic optimal control, reinforcement learning
and multi-stage stochastic programming. Existing algorithms suffer from
unsatisfactory sample complexity and practical issues since they ignore the
convexity structure in the algorithmic design. In this paper, we develop a new
stochastic compositional variance-reduced gradient algorithm with the sample
complexity of $O((m+n)\log(1/\epsilon)+1/\epsilon^3)$ where $m+n$ is the total
number of samples. Our algorithm is near-optimal as the dependence on $m+n$ is
optimal up to a logarithmic factor. Experimental results on real-world datasets
demonstrate the effectiveness and efficiency of the new algorithm.

本文提出了一种新的随机组合减少方差的梯度算法来解决现有算法在算法设计中忽略凸性结构而导致的样本复杂度和实践问题，实验结果表明了该算法的有效性和效率。