论文标题
电动汽车舰队的自动充电以增强可再生生成性能
Autonomous Charging of Electric Vehicle Fleets to Enhance Renewable Generation Dispatchability
论文作者
论文摘要
PV单元提供了加利福尼亚州发电能力的总计19％，几个月来，超过10％的能量受到了减少。在这项研究中，代表了一种新颖的方法来减少可再生产生的减少和通过电动汽车的充电协调来提高系统灵活性。提出的问题是一个顺序的决策过程，可以通过拟合的Q泰式算法来解决，该算法与其他强化学习方法不同，需要更少的学习情节。提出了三个案例研究，以验证所提出的方法的有效性。这些案例包括关注器负载，坡道服务和非确定性PV生成的利用。结果表明，通过此框架，EVS成功地学习了如何在其旅行时间以及太阳能发电的随机场景中调整其充电时间表。
A total 19% of generation capacity in California is offered by PV units and over some months, more than 10% of this energy is curtailed. In this research, a novel approach to reduce renewable generation curtailments and increasing system flexibility by means of electric vehicles' charging coordination is represented. The presented problem is a sequential decision making process, and is solved by fitted Q-iteration algorithm which unlike other reinforcement learning methods, needs fewer episodes of learning. Three case studies are presented to validate the effectiveness of the proposed approach. These cases include aggregator load following, ramp service and utilization of non-deterministic PV generation. The results suggest that through this framework, EVs successfully learn how to adjust their charging schedule in stochastic scenarios where their trip times, as well as solar power generation are unknown beforehand.
