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.

翻译：分布式能源特别是电动汽车（EV）的快速增长——预计未来十年将急剧增加——将进一步加剧现有配电网的压力，对系统可靠性和灵活性的需求日益提高。为避免不必要的电网投资并增强对配电网的可控性，电网运营商开发需求响应（DR）计划，通过经济激励或其他补偿方式鼓励终端用户调整用电模式。人工智能方法因具有高精度、高计算速度及对模型物理特性依赖较低的优势，正成为住宅负荷调度应用的研究前沿。本文旨在通过深度强化学习（特别是深度Q网络，DQN）确定分时电价机制下的住宅电动汽车成本优化充电策略。基于历史数据分析，创新性地推导出终端用户灵活性潜力奖励函数，并利用配备太阳能发电的住宅数据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使终端用户电费节省超过20%。