Battery Health-Informed and Policy-Aware Deep Reinforcement Learning for EV-Facilitated Distribution Grid Optimal Policy

IF 8.6 1区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Smart Grid Pub Date : 2024-09-13 DOI:10.1109/tsg.2024.3460486

Jiahang Xie, Petr Vorobev, Rufan Yang, Hung Dinh Nguyen

引用次数: 0

查看原文本刊更多论文

针对电动汽车促进配电网优化策略的电池健康信息和政策感知深度强化学习

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Smart Grid ENGINEERING, ELECTRICAL & ELECTRONIC-

CiteScore

22.10

自引率

9.40%

发文量

526

审稿时长

6 months

期刊介绍： The IEEE Transactions on Smart Grid is a multidisciplinary journal that focuses on research and development in the field of smart grid technology. It covers various aspects of the smart grid, including energy networks, prosumers (consumers who also produce energy), electric transportation, distributed energy resources, and communications. The journal also addresses the integration of microgrids and active distribution networks with transmission systems. It publishes original research on smart grid theories and principles, including technologies and systems for demand response, Advance Metering Infrastructure, cyber-physical systems, multi-energy systems, transactive energy, data analytics, and electric vehicle integration. Additionally, the journal considers surveys of existing work on the smart grid that propose new perspectives on the history and future of intelligent and active grids.