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A review of modelling approaches to characterize lithium-ion battery energy storage systems in techno-economic analyses of power systems

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  • Vykhodtsev, Anton V.
  • Jang, Darren
  • Wang, Qianpu
  • Rosehart, William
  • Zareipour, Hamidreza

Abstract

The penetration of the lithium-ion battery energy storage system (LIBESS) into the power system environment occurs at a colossal rate worldwide. This is mainly because it is considered as one of the major tools to decarbonize, digitalize, and democratize the electricity grid. The economic viability and technical reliability of projects with batteries require appropriate assessment because of high capital expenditures, deterioration in charging/discharging performance and uncertainty with regulatory policies. Most of the power system economic studies employ a simple power-energy representation coupled with an empirical description of degradation to model the lithium-ion battery. This approach to modelling may result in violations of the safe operation and misleading estimates of the economic benefits. Recently, the number of publications on techno-economic analysis of LIBESS with more details on the lithium-ion battery performance has increased. The aim of this review paper is to explore these publications focused on the grid-connected LIBESS applications and to discuss the impacts of using more sophisticated modelling approaches. First, an overview of the three most popular battery models is given, followed by a review of the applications of such models. The possible directions of future research of employing detailed battery models in power systems’ techno-economic studies are then explored.

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  • Vykhodtsev, Anton V. & Jang, Darren & Wang, Qianpu & Rosehart, William & Zareipour, Hamidreza, 2022. "A review of modelling approaches to characterize lithium-ion battery energy storage systems in techno-economic analyses of power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:rensus:v:166:y:2022:i:c:s1364032122004804
    DOI: 10.1016/j.rser.2022.112584
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    Cited by:

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    3. Zhang, Xiaohui & Li, Z. & Sajadi, S. Mohammad & Ajour, Mohammed N. & Abu-Hamdeh, Nidal H. & Salilih, Elias M. & Karimipour, Aliakbar & Viet, PMH, 2023. "Using a hybrid system to improve a lithium-ion battery in the presence of phase change material and the effect of air on the battery charge and discharge," Energy, Elsevier, vol. 284(C).
    4. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    5. Jiahuan Lu & Rui Xiong & Jinpeng Tian & Chenxu Wang & Fengchun Sun, 2023. "Deep learning to estimate lithium-ion battery state of health without additional degradation experiments," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    6. Shen, Dongxu & Yang, Dazhi & Lyu, Chao & Ma, Jingyan & Hinds, Gareth & Sun, Qingmin & Du, Limei & Wang, Lixin, 2024. "Multi-sensor multi-mode fault diagnosis for lithium-ion battery packs with time series and discriminative features," Energy, Elsevier, vol. 290(C).

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