标题： 电动汽车路径与调度的联合优化：一种动态车队规模的深度学习驱动方法 显示英文标题

标题： Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes

摘要： 电动汽车（EVs）如今越来越普遍，研究表明它们作为移动储能系统为电网提供支持的潜力。实现这一潜力需要有效的充电协调，这通常被表述为混合整数规划（MIP）问题。然而，MIP问题属于NP难问题，在应用于时间敏感任务时往往难以解决。为解决这一限制，我们提出了一种深度学习辅助方法，用于优化具有不同数量电动汽车的日间联合路径规划和调度问题。该问题同时优化配电网络中的电动汽车路径、充电、放电和发电机调度，该网络包含可再生能源。训练了一个卷积神经网络来预测二进制变量，从而减少解的空间并使求解器更高效地确定剩余变量。此外，还包括一种填充机制，以处理由于电动汽车数量变化导致的输入和输出大小变化，从而消除重新训练的需要。在IEEE 33节点系统和Nguyen-Dupuis交通网络的案例研究中，与未辅助的MIP求解器相比，我们的方法将运行时间减少了97.8%，同时保持了99.5%的可行性，并且与最优解的偏差小于0.01%。 显示英文摘要

摘要： Electric Vehicles (EVs) are becoming increasingly prevalent nowadays, with studies highlighting their potential as mobile energy storage systems to provide grid support. Realising this potential requires effective charging coordination, which are often formulated as mixed-integer programming (MIP) problems. However, MIP problems are NP-hard and often intractable when applied to time-sensitive tasks. To address this limitation, we propose a deep learning assisted approach for optimising a day-ahead EV joint routing and scheduling problem with varying number of EVs. This problem simultaneously optimises EV routing, charging, discharging and generator scheduling within a distribution network with renewable energy sources. A convolutional neural network is trained to predict the binary variables, thereby reducing the solution search space and enabling solvers to determine the remaining variables more efficiently. Additionally, a padding mechanism is included to handle the changes in input and output sizes caused by varying number of EVs, thus eliminating the need for re-training. In a case study on the IEEE 33-bus system and Nguyen-Dupuis transportation network, our approach reduced runtime by 97.8% when compared to an unassisted MIP solver, while retaining 99.5% feasibility and deviating less than 0.01% from the optimal solution.