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Cited by 225

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Energy Management for Lifetime Extension of Energy Storage System in Micro-Grid Applications

Tran, Duong, Khambadkone, Ashwin M.·2013·IEEE Transactions on Smart Grid

Abstract

Energy storage is needed in micro-grid to help solve the problem of intermittency introduced by renewable energy sources, enhance power quality and improve controllability of power flow. This paper presents an energy manager for energy storage system (ESS) in micro-grids. The objectives of the energy manager are focused on improving the energy efficiency and extending the life expectancy of ESS while ensuring constraints of energy storage modules are complied with. To this end a smart local prediction and local scheduling algorithm is proposed. A battery lifetime model that uses the proposed Peukert lifetime energy throughput based on the workload of the battery is developed. Verification shows that in the long run, the energy manger can improve overall energy efficiency of ESS from 74.1% to 85.5%, and improve estimated lifetime of 2 Battery Packs in ESS from 3.6 years and 2.4 years to 5 years and 5.7 years respectively.

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Metadata

Authors
Tran, D., & Khambadkone, A. M.
Venue
IEEE Transactions on Smart Grid
Published
2013-09
Volume
4
Issue
3
Pages
1289-1296
ISSN
  • 1949-3053 (ISSN-L)
  • 1949-3061
OpenAlex
W2018377741
Cited by
225
References
0
FWCI
20.61
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Energy Management for Lifetime Extension of Energy Storage System in Micro-Grid Applications. https://app.cochat.ai/papers/10.1109/tsg.2013.2272835
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Tran, D., & Khambadkone, A. M. (2013). Energy Management for Lifetime Extension of Energy Storage System in Micro-Grid Applications. IEEE Transactions on Smart Grid. https://doi.org/10.1109/tsg.2013.2272835

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.