用于互联和自主车辆充电的风险对抗学习系统
Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging
摘要：
本文研究了互联自主汽车充电基础设施（CAV-CI）的合理决策支持系统（RDSS）的设计。在所考虑的CAV-CI中，配电系统运营商（DSO）部署电动汽车供电设备（EVSE），为人类驾驶的联网车辆（CV）和自动驾驶车辆（AV）提供EV充电设施。当电动汽车需要的能量和充电时间超过其实际需要时，其充电要求变得不合理。因此，每个电动汽车的调度策略必须自适应地累积不合理的充电请求，以满足CV和AV的充电需求。为了解决这个问题，我们制定了RDSS问题的DSO，其目标是通过满足松弛风险的DSO充电容量利用率最大化。因此，我们设计了一个合理的奖励最大化问题，以适应非理性的行为，CV在一个数据知情的方式。我们提出了一种新的风险对抗多智能体学习系统（RAMALS）CAV-CI解决制定RDSS问题。在RAMALS中，DSO充当集中式风险对抗代理（RAA），用于向每个EVSE通知松弛风险。随后，每个EVSE扮演自学习代理的角色，通过应对来自RAA的建议来自适应地安排自己的EV会话。实验结果表明，所提出的RAMALS提供约46.6%的充电速率的改善，约28.6%的EVSE的主动充电时间的改善和至少33.3%以上的能源利用率，相比，目前部署的ACN EVSE系统，和其他基线。
英文摘要：
In this paper, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an EV charging facility for human-driven connected vehicles (CVs) and autonomous vehicles (AVs). The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its actual need. Therefore, the scheduling policy of each EVSE must be adaptively accumulated the irrational charging request to satisfy the charging demand of both CVs and AVs. To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO. Thus, we devise a rational reward maximization problem to adapt the irrational behavior by CVs in a data-informed manner. We propose a novel risk adversarial multi-agent learning system (RAMALS) for CAV-CI to solve the formulated RDSS problem. In RAMALS, the DSO acts as a centralized risk adversarial agent (RAA) for informing the laxity risk to each EVSE. Subsequently, each EVSE plays the role of a self-learner agent to adaptively schedule its own EV sessions by coping advice from RAA. Experiment results show that the proposed RAMALS affords around 46.6% improvement in charging rate, about 28.6% improvement in the EVSE's active charging time and at least 33.3% more energy utilization, as compared to a currently deployed ACN EVSE system, and other baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
