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.

中文翻译：

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电力和自主电动乘车系统的实时协调运行

电动汽车中自动驾驶功能的集成正在从根本上改变交通系统，并为电力公司提供了开发创新解决方案以利用自动电动汽车 (AEV) 时空充电灵活性的机会。本文开发了一种多智能体强化学习模型，用于相互依赖的自主电动乘车系统 (AERS) 和配电系统 (PDS) 的智能实时协调运行，在确保 AEV 的质量的同时实现 AEV 的协调路由和充电。通过满足时空乘客需求和定期为电池充电来提供服务。所提出的模型采用改进的深度 Q 网络方法和多代理部署来确定协调操作问题的近似最优解，该协调操作问题实时确定大量 AEV 的路由和充电。所提出的模型在盐湖城交通系统采用的 13 节点交通网络和 IEEE 33 总线测试配电系统上进行了测试，以展示所提出模型在确定实时路由和充电决策方面的效率用于 AEV，同时满足 AERS 和 PDS 的操作限制。