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Modelling electrochemical energy storage devices in insular power network applications supported on real data

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  • Rodrigues, E.M.G.
  • Godina, R.
  • Catalão, J.P.S.

Abstract

This paper addresses different techniques for modelling electrochemical energy storage (ES) devices in insular power network applications supported on real data. The first contribution is a comprehensive performance study between a set of competing electrochemical energy storage technologies: Lithium-ion (Li-ion), Nickel–Cadmium (NiCd), Nickel–Metal Hydride (NiMH) and Lead Acid (PbA) batteries. As a second contribution, several key engineering parameters with regards to the PbA battery-based storage solution are examined, such as cell charge distribution, cell string configuration and battery capacity fade. Finally, as a third contribution, an ES system operating criterion is discussed and proposed to manage the inherent rapid aging of the batteries due to their cycling activity. The simulation results are supported on real data from two non-interconnected power grids, namely Crete (Greece) and São Miguel (Portugal) Islands, for demonstration and validation purposes.

Suggested Citation

  • Rodrigues, E.M.G. & Godina, R. & Catalão, J.P.S., 2017. "Modelling electrochemical energy storage devices in insular power network applications supported on real data," Applied Energy, Elsevier, vol. 188(C), pages 315-329.
  • Handle: RePEc:eee:appene:v:188:y:2017:i:c:p:315-329
    DOI: 10.1016/j.apenergy.2016.12.007
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    References listed on IDEAS

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    2. Mehdi Bagheri & Venera Nurmanova & Oveis Abedinia & Mohammad Salay Naderi & Noradin Ghadimi & Mehdi Salay Naderi, 2019. "Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance," Energies, MDPI, Open Access Journal, vol. 12(3), pages 1-18, January.
    3. Das, Choton K. & Bass, Octavian & Kothapalli, Ganesh & Mahmoud, Thair S. & Habibi, Daryoush, 2018. "Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm," Applied Energy, Elsevier, vol. 232(C), pages 212-228.
    4. Das, Choton K. & Bass, Octavian & Mahmoud, Thair S. & Kothapalli, Ganesh & Mousavi, Navid & Habibi, Daryoush & Masoum, Mohammad A.S., 2019. "Optimal allocation of distributed energy storage systems to improve performance and power quality of distribution networks," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    5. Mehdi Tavakkoli & Jafar Adabi & Sasan Zabihi & Radu Godina & Edris Pouresmaeil, 2018. "Reserve Allocation of Photovoltaic Systems to Improve Frequency Stability in Hybrid Power Systems," Energies, MDPI, Open Access Journal, vol. 11(10), pages 1-19, September.
    6. Tianfei Sun & Bizhong Xia & Yifan Liu & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2019. "A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries," Energies, MDPI, Open Access Journal, vol. 12(19), pages 1-22, September.

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