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Multi-stage and multi-timescale optimal energy management for hydrogen-based integrated energy systems

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  • Fang, Xiaolun
  • Dong, Wei
  • Wang, Yubin
  • Yang, Qiang

Abstract

With the technological advances of flexible multiple energy conversion and utilization, hydrogen energy has attracted increasing attention. The hydrogen-based integrated energy system (HIES) consisting of multiple microgrids (MGs) with hydrogen exchanges among MGs is considered a promising hydrogen utilization paradigm. To facilitate the coordination among multiple MGs, this paper proposed a multi-stage and multi-timescale energy management for a HIES considering the electricity-heat-hydrogen supply-demand balance and demand uncertainties. The proposed solution consists of three stages, i.e. the day-ahead scheduling stage, model predictive control (MPC) based intraday rolling dispatch stage and intraday real-time adjustment stage to participate in the electricity and hydrogen market. In the HIES, hydrogen energy can be dispatched and utilized across MGs to enable flexible energy management and improve energy utilization efficiency. The proposed solution is extensively assessed through the IEEE 33-bus test network with a HIES compared with three benchmark solutions and the numerical results confirm its effectiveness and economic benefits.

Suggested Citation

  • Fang, Xiaolun & Dong, Wei & Wang, Yubin & Yang, Qiang, 2024. "Multi-stage and multi-timescale optimal energy management for hydrogen-based integrated energy systems," Energy, Elsevier, vol. 286(C).
  • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223029705
    DOI: 10.1016/j.energy.2023.129576
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    References listed on IDEAS

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    1. Asensio, F.J. & San Martín, J.I. & Zamora, I. & Garcia-Villalobos, J., 2017. "Fuel cell-based CHP system modelling using Artificial Neural Networks aimed at developing techno-economic efficiency maximization control systems," Energy, Elsevier, vol. 123(C), pages 585-593.
    2. 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).
    3. Lahnaoui, Amin & Wulf, Christina & Heinrichs, Heidi & Dalmazzone, Didier, 2018. "Optimizing hydrogen transportation system for mobility by minimizing the cost of transportation via compressed gas truck in North Rhine-Westphalia," Applied Energy, Elsevier, vol. 223(C), pages 317-328.
    4. Panah, Payam Ghaebi & Bornapour, Mosayeb & Hemmati, Reza & Guerrero, Josep M., 2021. "Charging station Stochastic Programming for Hydrogen/Battery Electric Buses using Multi-Criteria Crow Search Algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    5. Petrollese, Mario & Valverde, Luis & Cocco, Daniele & Cau, Giorgio & Guerra, José, 2016. "Real-time integration of optimal generation scheduling with MPC for the energy management of a renewable hydrogen-based microgrid," Applied Energy, Elsevier, vol. 166(C), pages 96-106.
    6. Zhong, Xiaoqing & Zhong, Weifeng & Liu, Yi & Yang, Chao & Xie, Shengli, 2022. "Optimal energy management for multi-energy multi-microgrid networks considering carbon emission limitations," Energy, Elsevier, vol. 246(C).
    7. Wang, Longze & Jiao, Shucen & Xie, Yu & Xia, Shiwei & Zhang, Delong & Zhang, Yan & Li, Meicheng, 2022. "Two-way dynamic pricing mechanism of hydrogen filling stations in electric-hydrogen coupling system enhanced by blockchain," Energy, Elsevier, vol. 239(PC).
    8. Fang, Xiaolun & Dong, Wei & Wang, Yubin & Yang, Qiang, 2022. "Multiple time-scale energy management strategy for a hydrogen-based multi-energy microgrid," Applied Energy, Elsevier, vol. 328(C).
    9. Das, Saborni & Basu, Mousumi, 2020. "Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources," Energy, Elsevier, vol. 190(C).
    10. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    11. Huang, Yujing & Wang, Yudong & Liu, Nian, 2022. "A two-stage energy management for heat-electricity integrated energy system considering dynamic pricing of Stackelberg game and operation strategy optimization," Energy, Elsevier, vol. 244(PA).
    12. Wu, Qiong & Xie, Zhun & Ren, Hongbo & Li, Qifen & Yang, Yongwen, 2022. "Optimal trading strategies for multi-energy microgrid cluster considering demand response under different trading modes: A comparison study," Energy, Elsevier, vol. 254(PC).
    13. Li, Peng & Wang, Zixuan & Wang, Jiahao & Guo, Tianyu & Yin, Yunxing, 2021. "A multi-time-space scale optimal operation strategy for a distributed integrated energy system," Applied Energy, Elsevier, vol. 289(C).
    14. Liu, Jinhui & Xu, Zhanbo & Wu, Jiang & Liu, Kun & Guan, Xiaohong, 2021. "Optimal planning of distributed hydrogen-based multi-energy systems," Applied Energy, Elsevier, vol. 281(C).
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