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Multiple hydrogen-based hybrid storage systems operation for microgrids: A combined TOPSIS and model predictive control methodology

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  • Li, Bei
  • Miao, Hongzhi
  • Li, Jiangchen

Abstract

Hydrogen-based hybrid storage system has a high energy density, which can operate as the long-term storage system, and play an important role in future smart cities. In the hydrogen storage system, fuel cell, hydrogen tanks, and electrolyzer are often combined together and operating with complex electrochemical reactions. How to efficiently operate the hydrogen storage system and considering the convoluted electrochemical reactions is a problem. In addition, multiple hydrogen storage systems are often grouped together to supply the demands. Thus, cooperating the dispatching of these storage systems is another complicated problem. In this paper, we first present a two-dimension model considering temperature influences for hydrogen-based microgrid, where a regression method is adopted. Moreover, a combined allocating-and-dispatching methodology involving two layers is proposed to cooperate the multiple storage systems. Specifically, both TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and fuzzy logic are adopted as the first-layer allocating algorithm. Then, the model predictive control (MPC) is utilized as the second-layer dispatching algorithm. Based on the combined method, power is firstly allocated to hybrid storage system considering each hybrid storage system health conditions, and secondly scheduled to battery storage and hydrogen storage based on MPC method. The simulation results showed that with the combined Dematel-TOPSIS and MPC algorithm, the degradation index and operation cost were the smallest among three algorithms, and can further extend the lifetime of hybrid hydrogen storage systems in microgrids.

Suggested Citation

  • Li, Bei & Miao, Hongzhi & Li, Jiangchen, 2021. "Multiple hydrogen-based hybrid storage systems operation for microgrids: A combined TOPSIS and model predictive control methodology," Applied Energy, Elsevier, vol. 283(C).
  • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316871
    DOI: 10.1016/j.apenergy.2020.116303
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    3. Shen, Xiaojun & Li, Xingyi & Yuan, Jiahai & Jin, Yu, 2022. "A hydrogen-based zero-carbon microgrid demonstration in renewable-rich remote areas: System design and economic feasibility," Applied Energy, Elsevier, vol. 326(C).
    4. Zaixun Ling & Yibo Cui & Jingwen Zheng & Yu Guo & Wanli Cai & Xiaofei Chen & Jiaqi Yuan & Wenjie Gang, 2021. "Design Optimization and Comparative Analysis of 100% Renewable Energy Systems for Residential Communities in Typical Areas of China When Considering Environmental and Economic Performance," Sustainability, MDPI, vol. 13(19), pages 1-24, September.
    5. Chen, Xiaofei & Xiao, Jinmei & Yuan, Jiaqi & Xiao, Ziwei & Gang, Wenjie, 2021. "Application and performance analysis of 100% renewable energy systems serving low-density communities," Renewable Energy, Elsevier, vol. 176(C), pages 433-446.
    6. Yue, Meiling & Lambert, Hugo & Pahon, Elodie & Roche, Robin & Jemei, Samir & Hissel, Daniel, 2021. "Hydrogen energy systems: A critical review of technologies, applications, trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    7. Fan, Guangyao & Liu, Zhijian & Liu, Xuan & Shi, Yaxin & Wu, Di & Guo, Jiacheng & Zhang, Shicong & Yang, Xinyan & Zhang, Yulong, 2022. "Two-layer collaborative optimization for a renewable energy system combining electricity storage, hydrogen storage, and heat storage," Energy, Elsevier, vol. 259(C).
    8. Tomasz Neumann, 2021. "Comparative Analysis of Long-Distance Transportation with the Example of Sea and Rail Transport," Energies, MDPI, vol. 14(6), pages 1-13, March.
    9. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Fernández-Lobato, Lázuli & Jurado, Francisco, 2023. "Robust energy management in isolated microgrids with hydrogen storage and demand response," Applied Energy, Elsevier, vol. 345(C).

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