Data-driven State of Health Modeling of Battery Energy Storage Systems Providing Grid Services

被引：11
作者：
Zhao, Chunyang [1 ]
Hashemi, Seyedmostafa [1 ]
Andersen, Peter Bach [1 ]
Traeholt, Chresten [1 ]
机构：
[1] Tech Univ Denmark, Ctr Elect Power & Energy CEE, Copenhagen, Denmark
来源：
2021 11TH INTERNATIONAL CONFERENCE ON POWER, ENERGY AND ELECTRICAL ENGINEERING (CPEEE 2021) | 2021年
关键词：
data-driven model; battery energy storage system; state of health; grid-connected application; battery service; OPERATION; ECONOMICS; SUPPORT;
D O I：
10.1109/CPEEE51686.2021.9383356
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Battery energy storage system (BESS) is key for future renewable energy systems, as it can provide various grid support functionalities, facilitate the participation of renewable energy sources in electricity markets, and increase grid stability. However, battery degradation is a major factor hindering the BESS implementation for grid applications. Battery state of health (SOH) is a key performance indicator of the BESS, and data-driven models powered by machine learning techniques are among the most promising solutions for the BESS degradation estimation. In this paper, a novel taxonomy of BESS services is proposed based on battery usage. Besides, the data-driven techniques for battery SOH modeling and data-driven SOH estimation applications for BESS providing grid services are reviewed and discussed. Further, a comprehensive discussion is presented regarding the challenges in the area of data-driven SOH modeling methods for the BESS providing grid services in practical applications.
引用
收藏
页码：43 / 49
页数：7
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