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Hybrid lithium-ion battery and hydrogen energy storage systems for a wind-supplied microgrid

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  • Giovanniello, Michael Anthony
  • Wu, Xiao-Yu

Abstract

Microgrids with high shares of variable renewable energy resources, such as wind, experience intermittent and variable electricity generation that causes supply–demand mismatches over multiple timescales. Lithium-ion batteries (LIBs) and hydrogen (H2) are promising technologies for short- and long-duration energy storage, respectively. A hybrid LIB-H2 energy storage system could thus offer a more cost-effective and reliable solution to balancing demand in renewable microgrids. Recent literature has modeled these hybrid storage systems; however, it remains unknown how anticipated, but uncertain, cost reductions and performance improvements will impact overall system cost and composition in the long term. Here, we developed a mixed integer linear programming (MILP) model for sizing the components (wind turbine, electrolyser, fuel cell, hydrogen storage, and lithium-ion battery) of a 100% wind-supplied microgrid in Canada. Compared to using just LIB or H2 alone for energy storage, the hybrid storage system was found to provide significant cost reductions. A sensitivity analysis showed that components of the H2 subsystem meaningfully impact the total microgrid cost, while the impact of the LIB subsystem is dominated by its energy storage capacity costs. Regarding efficiency, decreased electrolyzer efficiency causes the greatest increase in total system cost, whereas increased fuel cell efficiency has the greatest potential to reduce total system cost. As technologies evolve, the H2 subsystem assumes a greater role (i.e., it is larger and receives/supplies more energy over more hours) compared to the LIB subsystem, but LIB continues to provide frequent intra-day balancing in the microgrid.

Suggested Citation

  • Giovanniello, Michael Anthony & Wu, Xiao-Yu, 2023. "Hybrid lithium-ion battery and hydrogen energy storage systems for a wind-supplied microgrid," Applied Energy, Elsevier, vol. 345(C).
    • Handle: RePEc:eee:appene:v:345:y:2023:i:c:s030626192300675x
    DOI: 10.1016/j.apenergy.2023.121311
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    References listed on IDEAS

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    1. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    2. Zatti, Matteo & Gabba, Marco & Freschini, Marco & Rossi, Michele & Gambarotta, Agostino & Morini, Mirko & Martelli, Emanuele, 2019. "k-MILP: A novel clustering approach to select typical and extreme days for multi-energy systems design optimization," Energy, Elsevier, vol. 181(C), pages 1051-1063.
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    5. Kourougianni, Fanourios & Arsalis, Alexandros & Olympios, Andreas V. & Yiasoumas, Georgios & Konstantinou, Charalampos & Papanastasiou, Panos & Georghiou, George E., 2024. "A comprehensive review of green hydrogen energy systems," Renewable Energy, Elsevier, vol. 231(C).
    6. Hasan Dinçer & Serhat Yüksel & Bijan Abadi, 0000. "Techno-economic Assessment of Wind Energy Storage Technologies via Decision-Making Modelling," Proceedings of Economics and Finance Conferences 14716414, International Institute of Social and Economic Sciences.
    7. Motalleb Miri & Ivan Tolj & Frano Barbir, 2024. "Review of Proton Exchange Membrane Fuel Cell-Powered Systems for Stationary Applications Using Renewable Energy Sources," Energies, MDPI, vol. 17(15), pages 1-26, August.
    8. Yang, Guoming & Yang, Dazhi & Liu, Bai & Zhang, Hao, 2024. "The role of short- and long-duration energy storage in reducing the cost of firm photovoltaic generation," Applied Energy, Elsevier, vol. 374(C).
    9. Xu, Xuesong & Xu, Kai & Zeng, Ziyang & Tang, Jiale & He, Yuanxing & Shi, Guangze & Zhang, Tao, 2024. "Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 375(C).

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