标题： 基于Transformer的深度学习模型用于变化电动汽车数量的联合路由和调度 显示英文标题

标题： Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers

摘要： 随着可再生能源在现代电力系统中的日益融合，由于其间歇性和不确定性输出，带来了显著的运行挑战。近年来，移动储能系统（ESSs）已成为一种流行的灵活资源，用于缓解这些挑战。与固定式ESSs相比，移动ESSs提供了额外的空间灵活性，通过交通网络实现成本效益高的能源输送。然而，移动ESSs的广泛部署通常受到高投资成本的阻碍，这促使研究人员探索利用更易获得的替代方案，例如将电动汽车（EVs）作为移动储能单元。因此，在本工作中，我们探讨了这一机会，提出了一个基于MIP的日前电动汽车联合路径规划和调度问题。然而，在实际环境中求解该问题往往计算上难以处理，因为二进制变量的存在使其具有组合挑战性。因此，我们提出通过使用基于变压器的深度学习（DL）模型来简化MIP求解器的问题解决方案过程，以修剪解的搜索空间。这是通过训练模型快速预测最优二进制解来实现的。此外，与许多现有DL方法假设固定问题结构不同，所提出的模型设计用于适应任何规模的电动汽车车队的问题。这种灵活性至关重要，因为频繁的重新训练可能会引入显著的计算开销。我们通过在IEEE 33节点系统与Nguyen-Dupuis交通网络耦合的仿真中评估了该方法。 显示英文摘要

摘要： The growing integration of renewable energy sources in modern power systems has introduced significant operational challenges due to their intermittent and uncertain outputs. In recent years, mobile energy storage systems (ESSs) have emerged as a popular flexible resource for mitigating these challenges. Compared to stationary ESSs, mobile ESSs offer additional spatial flexibility, enabling cost-effective energy delivery through the transportation network. However, the widespread deployment of mobile ESSs is often hindered by the high investment cost, which has motivated researchers to investigate utilising more readily available alternatives, such as electric vehicles (EVs) as mobile energy storage units instead. Hence, we explore this opportunity with a MIP-based day-ahead electric vehicle joint routing and scheduling problem in this work. However, solving the problem in a practical setting can often be computationally intractable since the existence of binary variables makes it combinatorial challenging. Therefore, we proposed to simplify the problem's solution process for a MIP solver by pruning the solution search space with a transformer-based deep learning (DL) model. This is done by training the model to rapidly predict the optimal binary solutions. In addition, unlike many existing DL approaches that assume fixed problem structures, the proposed model is designed to accommodate problems with EV fleets of any sizes. This flexibility is essential since frequent re-training can introduce significant computational overhead. We evaluated the approach with simulations on the IEEE 33-bus system coupled with the Nguyen-Dupuis transportation network.