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

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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 languageEnglish
Title of host publicationProceedings of 11th International Conference on Power, Energy and Electrical Engineering
PublisherIEEE
Publication date2021
Pages43-49
ISBN (Print)978-1-7281-7717-5
DOIs
Publication statusPublished - 2021
Event11th International Conference on Power, Energy and Electrical Engineering - Shiga, Japan
Duration: 26 Feb 202128 Feb 2021

Conference

Conference11th International Conference on Power, Energy and Electrical Engineering
CountryJapan
CityShiga
Period26/02/202128/02/2021
Series2021 11th International Conference on Power, Energy and Electrical Engineering (cpeee)

Keywords

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

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