论文标题
太阳能驱动的EV充电优化通过深度加固学习
Solar Power driven EV Charging Optimization with Deep Reinforcement Learning
论文作者
论文摘要
电力部门脱碳在即将到来的能源过渡到更可持续的未来中起着至关重要的作用。分散的能源（例如电动汽车（EV）和太阳能光伏系统（PV））不断整合到住宅电力系统中，从而增加了电源分配网络中瓶颈的风险。本文旨在解决国内电动汽车充电的挑战，同时优先考虑清洁，太阳能消耗。实时关税被视为基于价格的需求响应（DR）机制，可以激励最终用户通过使用深度加固学习（DRL）在高太阳能PV生成小时内最佳地转移EV充电负载。分析了山核桃街数据集的历史测量值，以塑造灵活性潜在的奖励，以描述最终用户充电偏好。实验结果表明，拟议的DQN EV最佳充电策略能够通过实现太阳能的平均利用88.4来平均减少11.5％的电费。
Power sector decarbonization plays a vital role in the upcoming energy transition towards a more sustainable future. Decentralized energy resources, such as Electric Vehicles (EV) and solar photovoltaic systems (PV), are continuously integrated in residential power systems, increasing the risk of bottlenecks in power distribution networks. This paper aims to address the challenge of domestic EV charging while prioritizing clean, solar energy consumption. Real Time-of-Use tariffs are treated as a price-based Demand Response (DR) mechanism that can incentivize end-users to optimally shift EV charging load in hours of high solar PV generation with the use of Deep Reinforcement Learning (DRL). Historical measurements from the Pecan Street dataset are analyzed to shape a flexibility potential reward to describe end-user charging preferences. Experimental results show that the proposed DQN EV optimal charging policy is able to reduce electricity bills by an average 11.5\% by achieving an average utilization of solar power 88.4
