Paper Info
Title
Deep Reinforcement Learning-Based Charging Pricing for Autonomous Mobility-on-Demand System
Abstract
The autonomous mobility-on-demand (AMoD) system plays an important role in the urban transportation system. The charging behavior of AMoD fleet becomes a critical link between charging system and transportation system. In this paper, we investigate a strategic charging pricing scheme for charging station operators (CSOs) based on a non-cooperative Stackelberg game framework. The Stackelberg equilibrium investigates the pricing competition among multiple CSOs, and explores the nexus between the CSOs and AMoD operator. In the proposed framework, the responsive behavior of AMoD operator (order-serving, repositioning, and charging) is formulated as a multi-commodity network flow model to solve an energy-aware traffic flow problem. Meanwhile, a soft actor-critic based multi-agent deep reinforcement learning algorithm is developed to solve the proposed equilibrium framework while considering privacy-conservation constraints among CSOs. A numerical case study with city-scale real-world data is used to validate the effectiveness of the proposed framework.
Year
DOI
Venue
2022
10.1109/TSG.2021.3131804
IEEE Transactions on Smart Grid
Keywords
DocType
Volume
EV charging pricing,deep reinforcement learning,power and transportation system,autonomous mobility-on-demand,soft actor-critic
Journal
13
Issue
ISSN
Citations 
2
1949-3053
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Ying Lu100.34
Yanchang Liang240.76
Zhaohao Ding3164.78
Qiuwei Wu46019.97
Tao Ding5158.48
Wei-Jen Lee69431.94