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

Chunyang Zhao; Seyedmostafa Hashemi; Peter Bach Andersen; Chresten Traholt

doi:10.1109/CPEEE51686.2021.9383356

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

Chunyang Zhao, Seyedmostafa Hashemi, Peter Bach Andersen, Chresten Traholt

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

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.

Original language	English
Title of host publication	Proceedings of 11^th International Conference on Power, Energy and Electrical Engineering
Publisher	IEEE
Publication date	2021
Pages	43-49
ISBN (Print)	978-1-7281-7717-5
DOIs	https://doi.org/10.1109/CPEEE51686.2021.9383356
Publication status	Published - 2021
Event	11^th International Conference on Power, Energy and Electrical Engineering - Shiga, Japan Duration: 26 Feb 2021 → 28 Feb 2021

Conference

Conference	11^th International Conference on Power, Energy and Electrical Engineering
Country/Territory	Japan
City	Shiga
Period	26/02/2021 → 28/02/2021

Keywords

Data-driven model
Battery energy storage system
State of Health
Grid-connected application
Battery service

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CPEEE51686.2021.9383356

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BOSS: Bornholm Smartgrid Secured- by grid connected battery systems
Hashemi Toghroljerdi, S., Træholt, C., Østergaard, J., Frost, F., Andersen, P. B., Klintström, M., Jakobsen, P. M., Sørensen, T. M., Jensen, M. B., Jensen, R. H., Due, A., Rabjerg, M. F., Hameed, Z., Kharlamova, N. & Zhao, C.
01/03/2019 → 31/12/2022
Project: Research

Cite this

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title = "Data-driven State of Health Modeling of Battery Energy Storage Systems Providing Grid Services",

abstract = "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.",

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author = "Chunyang Zhao and Seyedmostafa Hashemi and Andersen, {Peter Bach} and Chresten Traholt",

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pages = "43--49",

booktitle = "Proceedings of 11th International Conference on Power, Energy and Electrical Engineering",

publisher = "IEEE",

address = "United States",

note = "11<sup>th</sup> International Conference on Power, Energy and Electrical Engineering, CPEEE 2021 ; Conference date: 26-02-2021 Through 28-02-2021",

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Zhao, C , Hashemi, S , Andersen, PB & Traholt, C 2021, Data-driven State of Health Modeling of Battery Energy Storage Systems Providing Grid Services. in Proceedings of 11^th International Conference on Power, Energy and Electrical Engineering. IEEE, pp. 43-49, 11^th International Conference on Power, Energy and Electrical Engineering, Shiga, Japan, 26/02/2021. https://doi.org/10.1109/CPEEE51686.2021.9383356

Data-driven State of Health Modeling of Battery Energy Storage Systems Providing Grid Services. / Zhao, Chunyang ; Hashemi, Seyedmostafa ; Andersen, Peter Bach ; Traholt, Chresten.

Proceedings of 11^th International Conference on Power, Energy and Electrical Engineering. IEEE, 2021. p. 43-49.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Data-driven State of Health Modeling of Battery Energy Storage Systems Providing Grid Services

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AU - Hashemi, Seyedmostafa

AU - Andersen, Peter Bach

AU - Traholt, Chresten

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Data-driven model

KW - Battery energy storage system

KW - State of Health

KW - Grid-connected application

KW - Battery service

U2 - 10.1109/CPEEE51686.2021.9383356

DO - 10.1109/CPEEE51686.2021.9383356

M3 - Article in proceedings

SN - 978-1-7281-7717-5

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BT - Proceedings of 11th International Conference on Power, Energy and Electrical Engineering

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ER -

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

Abstract

Conference

Keywords

UN SDGs

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Projects

BOSS: Bornholm Smartgrid Secured- by grid connected battery systems

Cite this