Online Battery Protective Energy Management for Energy-Transportation Nexus | |
Li, Shuangqi1,2; Zhao, Pengfei3,4; Gu, Chenghong1; Li, Jianwei1; Cheng, Shuang1; Xu, Minghao1 | |
发表期刊 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS |
ISSN | 1551-3203 |
2022-11-01 | |
卷号 | 18期号:11页码:8203-8212 |
通讯作者 | Gu, Chenghong(c.gu@bath.ac.uk) |
摘要 | Grid-connected electric vehicles (GEVs) and energy-transportation nexus bring a bright prospect to improve the penetration of renewable energy and the economy of microgrids (MGs). However, it is challenging to determine optimal vehicle-to-grid (V2G) strategies due to the complex battery aging mechanism and volatile MG states. This article develops a novel online battery anti-aging energy management method for energy-transportation nexus by using a novel deep reinforcement learning (DRL) framework. Based on battery aging characteristic analysis and rain-flow cycle counting technology, the quantification of aging cost in V2G strategies is realized by modeling the impact of number of cycles, depth of discharge, and charge and discharge rate. The established life loss model is used to evaluate battery anti-aging effectiveness of agent actions. The coordination of GEVs charging is modeled as multiobjective learning by using a DRL algorithm. The training objective is to maximize renewable penetration while reducing MG power fluctuations and vehicle battery aging costs. The developed energy-transportation nexus energy management method is verified to be effective in optimal power balancing and battery anti-aging control on a MG in the U.K. This article provides an efficient and economical tool for MG power balancing by optimally coordinating GEVs charging and renewable energy, thus helping promote a low-cost decarbonization transition. |
关键词 | Batteries Aging Vehicle-to-grid US Department of Defense Costs Renewable energy sources Reinforcement learning Battery aging mitigation deep reinforcement learning (DRL) electric vehicle microgrid (MG) renewable energy transportation electrification vehicle-to-grid (V2G) |
DOI | 10.1109/TII.2022.3163778 |
关键词[WOS] | GRID OPERATIONS ; ION BATTERIES ; V2G ; CAPACITY ; SUPPORT ; MODEL |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
WOS类目 | Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial |
WOS记录号 | WOS:000856145200084 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50443 |
专题 | 复杂系统管理与控制国家重点实验室_互联网大数据与信息安全 |
通讯作者 | Gu, Chenghong |
作者单位 | 1.Univ Bath, Dept Elect & Elect Engn, Bath BA2 7AY, Avon, England 2.Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100045, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Shuangqi,Zhao, Pengfei,Gu, Chenghong,et al. Online Battery Protective Energy Management for Energy-Transportation Nexus[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2022,18(11):8203-8212. |
APA | Li, Shuangqi,Zhao, Pengfei,Gu, Chenghong,Li, Jianwei,Cheng, Shuang,&Xu, Minghao.(2022).Online Battery Protective Energy Management for Energy-Transportation Nexus.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,18(11),8203-8212. |
MLA | Li, Shuangqi,et al."Online Battery Protective Energy Management for Energy-Transportation Nexus".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 18.11(2022):8203-8212. |
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