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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 |
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Title of host publication | Proceedings of 11th International Conference on Power, Energy and Electrical Engineering |
Publisher | IEEE |
Publication date | 2021 |
Pages | 43-49 |
ISBN (Print) | 978-1-7281-7717-5 |
DOIs | |
Publication status | Published - 2021 |
Event | 11th International Conference on Power, Energy and Electrical Engineering - Shiga, Japan Duration: 26 Feb 2021 → 28 Feb 2021 |
Conference
Conference | 11th International Conference on Power, Energy and Electrical Engineering |
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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
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- 1 Active
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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