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Optimal planning of distributed hydrogen-based multi-energy systems

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  • Liu, Jinhui
  • Xu, Zhanbo
  • Wu, Jiang
  • Liu, Kun
  • Guan, Xiaohong

Abstract

As a clean and renewable energy, hydrogen has attracted increasing attention for the replacement of fossil fuels because it is an emerging way to address the uncertainties of the renewable energy. Besides, coordination of the energy storage units, such as hydrogen storage unit, hot water storage unit and chilled water storage unit, could improve energy efficiency and reduce system cost. Thus, the optimal planning of a distributed hydrogen-based multi-energy system is very important to build a hydrogen-based distributed energy system in the demand side. This paper focuses on the system planning problem, which is formulated as a mixed-integer linear programming problem and the objective is to minimize annual capital and operation expenditure of the system. The problem is solved by a commercial solver. The case studies are performed under different energy demand profiles and solar radiations obtained by EnergyPlus in twelve typical cities around the world. It is found that multi-energy storage units in the systems can significantly reduce system capital expenditure and operating expenses. Hot water storage unit enjoys the best benefits with an average system cost reduction rate being 64.6% with the planning horizon (8760 h). And the DHME system is environmental friendly, which can even drop over 100% carbon emission in high solar radiation regions compared with the conventional electricity-driven energy system. Furthermore, with optimistic hydrogen price target of USA Department of Energy, the developed DHME system will maximum reduce 60.0% OPEX compared with the conventional electricity-driven energy system.

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  • 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).
    • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920315257
    DOI: 10.1016/j.apenergy.2020.116107
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    Cited by:

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    14. Cephas Samende & Zhong Fan & Jun Cao & Renzo Fabián & Gregory N. Baltas & Pedro Rodriguez, 2023. "Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand Using Deep Reinforcement Learning," Energies, MDPI, vol. 16(19), pages 1-20, September.
    15. Ma, Ning & Fan, Lurong, 2023. "Double recovery strategy of carbon for coal-to-power based on a multi-energy system with tradable green certificates," Energy, Elsevier, vol. 273(C).
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    17. 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).
    18. Son, Hyunsoo & Kim, Miae & Kim, Jin-Kuk, 2022. "Sustainable process integration of electrification technologies with industrial energy systems," Energy, Elsevier, vol. 239(PB).
    19. 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).
    20. Kai Zhang & Xiangxiang Dong & Chaofeng Li & Yanling Zhao & Kun Liu, 2024. "Capacity Expansion Planning of Hydrogen-Enabled Industrial Energy Systems for Carbon Dioxide Peaking," Energies, MDPI, vol. 17(14), pages 1-10, July.
    21. Azimian, Mahdi & Amir, Vahid & Mohseni, Soheil & Brent, Alan C. & Bazmohammadi, Najmeh & Guerrero, Josep M., 2022. "Optimal Investment Planning of Bankable Multi-Carrier Microgrid Networks," Applied Energy, Elsevier, vol. 328(C).
    22. Wang, Yuwei & Song, Minghao & Jia, Mengyao & Li, Bingkang & Fei, Haoran & Zhang, Yiyue & Wang, Xuejie, 2023. "Multi-objective distributionally robust optimization for hydrogen-involved total renewable energy CCHP planning under source-load uncertainties," Applied Energy, Elsevier, vol. 342(C).

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