基于深度强化学习的电动汽车充电优化调度考虑终端用户灵活性
Optimal Scheduling of Electric Vehicle Charging with Deep Reinforcement Learning considering End Users Flexibility
AI总结：
本研究利用深度Q网络（DQN）在分时电价下优化电动汽车充电调度，通过历史数据分析提出终端用户灵活性奖励，实现超过20%的电费节省。
摘要：
分布式能源资源，尤其是电动汽车（EV）的快速增长，预计在未来十年内将急剧增加，这将给现有的配电网络带来进一步的压力，从而增加对更高系统可靠性和灵活性的需求。为了避免不必要的网络投资并提高对配电网络的可控性，网络运营商开发了需求响应（DR）项目，激励终端用户转移其用电消费，以换取经济或其他利益。人工智能（AI）方法在住宅负荷调度应用研究中处于前沿，主要归因于其高精度、高计算速度以及对所开发模型物理特性依赖较低的特点。本研究的目标是在分时电价（Time-of-Use）方案下，利用深度强化学习，特别是深度Q网络（DQN），确定家庭电动汽车的降低充电成本策略。通过历史数据分析，提出了一种新颖的终端用户灵活性潜力奖励，其中具有太阳能发电的家庭被用于训练和测试所设计的算法。所提出的DQN电动汽车充电策略可为终端用户节省超过20%的电费。
英文摘要：
The rapid growth of decentralized energy resources and especially Electric Vehicles (EV), that are expected to increase sharply over the next decade, will put further stress on existing power distribution networks, increasing the need for higher system reliability and flexibility. In an attempt to avoid unnecessary network investments and to increase the controllability over distribution networks, network operators develop demand response (DR) programs that incentivize end users to shift their consumption in return for financial or other benefits. Artificial intelligence (AI) methods are in the research forefront for residential load scheduling applications, mainly due to their high accuracy, high computational speed and lower dependence on the physical characteristics of the models under development. The aim of this work is to identify households' EV cost-reducing charging policy under a Time-of-Use tariff scheme, with the use of Deep Reinforcement Learning, and more specifically Deep Q-Networks (DQN). A novel end users flexibility potential reward is inferred from historical data analysis, where households with solar power generation have been used to train and test the designed algorithm. The suggested DQN EV charging policy can lead to more than 20% of savings in end users electricity bills.
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。
