灵活性(工程)
相互依存
电力系统
布线(电子设计自动化)
强化学习
计算机科学
节点(物理)
服务(商务)
功率(物理)
工程类
运筹学
实时计算
计算机网络
人工智能
统计
物理
数学
经济
结构工程
量子力学
政治学
法学
经济
作者
Avishan Bagherinezhad,Mohammad Mehdi Hosseini,Masood Parvania
出处
期刊:IEEE Transactions on Smart Grid
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:14 (3): 2214-2225
标识
DOI:10.1109/tsg.2023.3247780
摘要
Integration of self-driving functions in electric vehicles is radically changing the transportation systems, and representing an opportunity for power utilities to develop innovative solutions for harnessing the spatio-temporal charging flexibility of autonomous electric vehicles (AEVs). This paper develops a multi-agent reinforcement learning model for intelligent real-time coordinated operation of interdependent autonomous electric ride-hailing system (AERS) and power distribution system (PDS), which enables the coordinated routing and charging of AEVs while ensuring the quality of service by meeting spatio-temporal passenger demand and regularly charging the batteries. The proposed model adopts a modified deep Q-network method with multi-agent rollout to determine the near-optimal solution for the coordinated operation problem that determines the routing and charging of numerous AEVs in real-time. The proposed model is tested on a 13-node transportation network, adopted from the Salt Lake City transportation system, and the IEEE 33-bus test power distribution system to showcase the efficiency of the proposed model in determining the real-time routing and charging decisions for AEVs while meeting the operational constraints of both AERS and PDS.
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