Coordinated Autonomous Vehicle Parking for Vehicle-to-Grid Services: Formulation and Distributed Algorithm

TL;DR

This paper presents a distributed algorithm for coordinated autonomous vehicle parking to support vehicle-to-grid services, achieving near-optimal solutions efficiently and robustly under communication constraints.

Contribution

It formulates the Coordinated Parking Problem and develops a distributed dual decomposition algorithm for practical, efficient solutions in AV parking management for V2G.

Findings

Distributed algorithm achieves near-optimal solutions

Significantly reduces computational time compared to ILP solver

Performs well even with communication loss

Abstract

Autonomous vehicles (AVs) will revolutionarize ground transport and take a substantial role in the future transportation system. Most AVs are likely to be electric vehicles (EVs) and they can participate in the vehicle-to-grid (V2G) system to support various V2G services. Although it is generally infeasible for EVs to dictate their routes, we can design AV travel plans to fulfill certain system-wide objectives. In this paper, we focus on the AVs looking for parking and study how they can be led to appropriate parking facilities to support V2G services. We formulate the Coordinated Parking Problem (CPP), which can be solved by a standard integer linear program solver but requires long computational time. To make it more practical, we develop a distributed algorithm to address CPP based on dual decomposition. We carry out a series of simulations to evaluate the proposed solution methods. Our results show that the distributed algorithm can produce nearly optimal solutions with substantially less computational time. A coarser time scale can improve computational time but degrade the solution quality resulting in possible infeasible solution. Even with communication loss, the distributed algorithm can still perform well and converge with only little degradation in speed.