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

TL;DR

This paper introduces a transformer-based deep learning model that efficiently predicts optimal solutions for joint routing and scheduling of electric vehicles, accommodating varying fleet sizes to improve computational efficiency in power system operations.

Contribution

The paper presents a novel transformer-based deep learning approach that predicts solutions for EV routing and scheduling with variable fleet sizes, reducing computational complexity compared to traditional methods.

Findings

Model accurately predicts optimal solutions in simulations.

Flexible to different EV fleet sizes without retraining.

Significantly reduces solution time for complex routing problems.

Abstract

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.