Online battery-protective vehicle to grid behavior management

doi:10.1016/j.energy.2021.123083

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 互联网大数据与安全信息学研究中心

	Online battery-protective vehicle to grid behavior management
	Li, Shuangqi 3,4; Zhao, Pengfei 1; Gu, Chenghong 3; Huo, Da 2; Zeng, Xianwu 3; Pei, Xiaoze 3; Cheng, Shuang 3; Li, Jianwei 3
刊名	ENERGY
	2022-03-15
卷号	243 页码:10
关键词	Battery degradation Battery protective strategy Electric vehicle Energy management Energy storage system Transportation electrification Vehicle to grid
ISSN号	0360-5442
DOI	10.1016/j.energy.2021.123083
通讯作者	Gu, Chenghong(C.Gu@bath.ac.uk)
英文摘要	With the popularization of electric vehicles, vehicle-to-grid (V2G) has become an indispensable technology to improve grid economy and reliability. However, battery aging should be mitigated while providing V2G services so as to protect customer benefits and mobilize their positivity. Conventional battery anti-aging V2G scheduling methods mainly offline operates and can hardly be deployed online in hardware equipment. This paper proposes a novel online battery anti-aging V2G scheduling method based on a novel two-stage parameter calibration framework. In the first stage, the V2G scheduling is modeled as an optimization problem, where the objective is to reduce grid peak-valley difference and mitigate battery aging. The online deployment of the developed optimization-based V2G scheduling is realized by a rule-based V2G coordinator in the second stage, and a novel parameter calibration method is developed to adjust controller hyper-parameters. With the parameter calibration process, the global optimality and real-time performance of V2G strategies can be simultaneously realized. The effectiveness of the proposed methodologies is verified on a practical UK distribution network. Simulation results indicate that it can effectively mitigate battery aging in providing V2G services while guaranteeing algorithm real-time performance. (c) 2022 Elsevier Ltd. All rights reserved.
WOS关键词	IN ELECTRIC VEHICLES ; ENERGY MANAGEMENT ; STRATEGY ; OPTIMIZATION ; DEMAND ; COST
WOS研究方向	Thermodynamics ; Energy & Fuels
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000791916400004
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48446]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者	Gu, Chenghong
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Cranfield Univ, Sch Water Energy & Environm, Cranfield, Beds, England 3.Univ Bath, Dept Elect & Elect Engn, Bath, Somerset, United Kingdom 4.Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Li, Shuangqi,Zhao, Pengfei,Gu, Chenghong,et al. Online battery-protective vehicle to grid behavior management[J]. ENERGY,2022,243:10.
APA	Li, Shuangqi.,Zhao, Pengfei.,Gu, Chenghong.,Huo, Da.,Zeng, Xianwu.,...&Li, Jianwei.(2022).Online battery-protective vehicle to grid behavior management.ENERGY,243,10.
MLA	Li, Shuangqi,et al."Online battery-protective vehicle to grid behavior management".ENERGY 243(2022):10.