IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v214y2023icp216-232.html
   My bibliography  Save this article

Coordinated energy management strategy for multi-energy hub with thermo-electrochemical effect based power-to-ammonia: A multi-agent deep reinforcement learning enabled approach

Author

Listed:
  • Xiong, Kang
  • Hu, Weihao
  • Cao, Di
  • Li, Sichen
  • Zhang, Guozhou
  • Liu, Wen
  • Huang, Qi
  • Chen, Zhe

Abstract

Power-to-ammonia (P2A) technology has attracted more and more attention since ammonia is recognized as a natural zero-carbon fuel. In this context, this paper constructs a renewable energy powered multi-energy hub (MEH) system which integrates with a thermo-electrochemical effect based P2A facility. Subsequently, the energy management of proposed MEH system is casted to a multi-agent coordinated optimization problem, which aims to minimize operating cost and carbon dioxide emissions while satisfying constraints. Then, a novel multi-agent deep reinforcement learning method called CommNet is applied to solve this problem to obtain the optimal coordinated energy management strategy of each energy hub by achieving the distributed computation of global information. Finally, the simulation results show that the proposed method can achieve better performance on reducing operating cost and carbon emissions than other benchmark methods.

Suggested Citation

  • Xiong, Kang & Hu, Weihao & Cao, Di & Li, Sichen & Zhang, Guozhou & Liu, Wen & Huang, Qi & Chen, Zhe, 2023. "Coordinated energy management strategy for multi-energy hub with thermo-electrochemical effect based power-to-ammonia: A multi-agent deep reinforcement learning enabled approach," Renewable Energy, Elsevier, vol. 214(C), pages 216-232.
    • Handle: RePEc:eee:renene:v:214:y:2023:i:c:p:216-232
    DOI: 10.1016/j.renene.2023.05.067
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123006900
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.05.067?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Hanfei & Wang, Ligang & Van herle, Jan & Maréchal, François & Desideri, Umberto, 2020. "Techno-economic comparison of green ammonia production processes," Applied Energy, Elsevier, vol. 259(C).
    2. Zhang, Bin & Hu, Weihao & Xu, Xiao & Li, Tao & Zhang, Zhenyuan & Chen, Zhe, 2022. "Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 433-448.
    3. Bisengimana, Emmanuel & Zhou, Jinzhi & Binama, Maxime & Yuan, Yanping, 2022. "Numerical investigation on the factors influencing the temperature distribution of photovoltaic/thermal (PVT) evaporator/condenser for heat pump systems," Renewable Energy, Elsevier, vol. 194(C), pages 885-901.
    4. Zhou, Yanting & Ma, Zhongjing & Zhang, Jinhui & Zou, Suli, 2022. "Data-driven stochastic energy management of multi energy system using deep reinforcement learning," Energy, Elsevier, vol. 261(PA).
    5. Lu, Xinhui & Liu, Zhaoxi & Ma, Li & Wang, Lingfeng & Zhou, Kaile & Yang, Shanlin, 2020. "A robust optimization approach for coordinated operation of multiple energy hubs," Energy, Elsevier, vol. 197(C).
    6. Zhu, Dafeng & Yang, Bo & Liu, Qi & Ma, Kai & Zhu, Shanying & Ma, Chengbin & Guan, Xinping, 2020. "Energy trading in microgrids for synergies among electricity, hydrogen and heat networks," Applied Energy, Elsevier, vol. 272(C).
    7. Magadley, Esther & Kabha, Ragheb & Yehia, Ibrahim, 2021. "Outdoor comparison of two organic photovoltaic panels: The effect of solar incidence angles and incident irradiance," Renewable Energy, Elsevier, vol. 173(C), pages 721-732.
    8. Fasihi, Mahdi & Weiss, Robert & Savolainen, Jouni & Breyer, Christian, 2021. "Global potential of green ammonia based on hybrid PV-wind power plants," Applied Energy, Elsevier, vol. 294(C).
    9. Zhu, Dafeng & Yang, Bo & Liu, Yuxiang & Wang, Zhaojian & Ma, Kai & Guan, Xinping, 2022. "Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park," Applied Energy, Elsevier, vol. 311(C).
    10. Mulleriyawage, U.G.K. & Shen, W.X., 2021. "Impact of demand side management on optimal sizing of residential battery energy storage system," Renewable Energy, Elsevier, vol. 172(C), pages 1250-1266.
    11. Behzadi, Amirmohammad & Habibollahzade, Ali & Ahmadi, Pouria & Gholamian, Ehsan & Houshfar, Ehsan, 2019. "Multi-objective design optimization of a solar based system for electricity, cooling, and hydrogen production," Energy, Elsevier, vol. 169(C), pages 696-709.
    12. Heidari, A. & Mortazavi, S.S. & Bansal, R.C., 2020. "Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies," Applied Energy, Elsevier, vol. 261(C).
    13. Zakaria, A. & Ismail, Firas B. & Lipu, M.S. Hossain & Hannan, M.A., 2020. "Uncertainty models for stochastic optimization in renewable energy applications," Renewable Energy, Elsevier, vol. 145(C), pages 1543-1571.
    14. Cao, Di & Zhao, Junbo & Hu, Weihao & Ding, Fei & Yu, Nanpeng & Huang, Qi & Chen, Zhe, 2022. "Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    15. Lee, Joseph C.Y. & Draxl, Caroline & Berg, Larry K., 2022. "Evaluating wind speed and power forecasts for wind energy applications using an open-source and systematic validation framework," Renewable Energy, Elsevier, vol. 200(C), pages 457-475.
    16. Chen, Yuzhu & Hu, Xiaojian & Xu, Wentao & Xu, Qiliang & Wang, Jun & Lund, Peter D., 2022. "Multi-objective optimization of a solar-driven trigeneration system considering power-to-heat storage and carbon tax," Energy, Elsevier, vol. 250(C).
    17. Zhong, Like & Yao, Erren & Zou, Hansen & Xi, Guang, 2022. "Thermodynamic and economic analysis of a directly solar-driven power-to-methane system by detailed distributed parameter method," Applied Energy, Elsevier, vol. 312(C).
    18. Zhu, Dafeng & Yang, Bo & Ma, Chengbin & Wang, Zhaojian & Zhu, Shanying & Ma, Kai & Guan, Xinping, 2022. "Stochastic gradient-based fast distributed multi-energy management for an industrial park with temporally-coupled constraints," Applied Energy, Elsevier, vol. 317(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xu, Guanxin & Wu, Yan & Tang, Shuo & Wang, Yufei & Yu, Xinhai & Ma, Mingyan, 2024. "Optimal design of hydrogen production processing coupling alkaline and proton exchange membrane electrolyzers," Energy, Elsevier, vol. 302(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Innocent Kamwa & Leila Bagherzadeh & Atieh Delavari, 2023. "Integrated Demand Response Programs in Energy Hubs: A Review of Applications, Classifications, Models and Future Directions," Energies, MDPI, vol. 16(11), pages 1-21, May.
    2. Yifan Wang & Laurence A. Wright, 2021. "A Comparative Review of Alternative Fuels for the Maritime Sector: Economic, Technology, and Policy Challenges for Clean Energy Implementation," World, MDPI, vol. 2(4), pages 1-26, October.
    3. Zhou, Yuekuan & Liu, Xiaohua & Zhao, Qianchuan, 2024. "A stochastic vehicle schedule model for demand response and grid flexibility in a renewable-building-e-transportation-microgrid," Renewable Energy, Elsevier, vol. 221(C).
    4. Ullah, Zia & Elkadeem, M.R. & Kotb, Kotb M. & Taha, Ibrahim B.M. & Wang, Shaorong, 2021. "Multi-criteria decision-making model for optimal planning of on/off grid hybrid solar, wind, hydro, biomass clean electricity supply," Renewable Energy, Elsevier, vol. 179(C), pages 885-910.
    5. Carlo James Cunanan & Carlos Andrés Elorza Casas & Mitchell Yorke & Michael Fowler & Xiao-Yu Wu, 2022. "Design and Analysis of an Offshore Wind Power to Ammonia Production System in Nova Scotia," Energies, MDPI, vol. 15(24), pages 1-23, December.
    6. Zhu, Dafeng & Yang, Bo & Ma, Chengbin & Wang, Zhaojian & Zhu, Shanying & Ma, Kai & Guan, Xinping, 2022. "Stochastic gradient-based fast distributed multi-energy management for an industrial park with temporally-coupled constraints," Applied Energy, Elsevier, vol. 317(C).
    7. Wen, Du & Aziz, Muhammad, 2022. "Techno-economic analyses of power-to-ammonia-to-power and biomass-to-ammonia-to-power pathways for carbon neutrality scenario," Applied Energy, Elsevier, vol. 319(C).
    8. Kanaan, Riham & Affonso Nóbrega, Pedro Henrique & Achard, Patrick & Beauger, Christian, 2023. "Economical assessment comparison for hydrogen reconversion from ammonia using thermal decomposition and electrolysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    9. Aslani, Mehrdad & Mashayekhi, Mehdi & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2022. "Robust optimal operation of energy hub incorporating integrated thermal and electrical demand response programs under various electric vehicle charging modes," Applied Energy, Elsevier, vol. 321(C).
    10. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad & Hajizadeh, Amin & Mohammadi-ivatloo, Behnam, 2022. "A comprehensive review on optimization challenges of smart energy hubs under uncertainty factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    11. Li, Changzhi & Lin, Wei & Wu, Hangyu & Li, Yang & Zhu, Wenchao & Xie, Changjun & Gooi, Hoay Beng & Zhao, Bo & Zhang, Leiqi, 2023. "Performance degradation decomposition-ensemble prediction of PEMFC using CEEMDAN and dual data-driven model," Renewable Energy, Elsevier, vol. 215(C).
    12. Byun, Manhee & Lim, Dongjun & Lee, Boreum & Kim, Ayeon & Lee, In-Beum & Brigljević, Boris & Lim, Hankwon, 2022. "Economically feasible decarbonization of the Haber-Bosch process through supercritical CO2 Allam cycle integration," Applied Energy, Elsevier, vol. 307(C).
    13. Tan, Jinjing & Pan, Weiqi & Li, Yang & Hu, Haoming & Zhang, Can, 2023. "Energy-sharing operation strategy of multi-district integrated energy systems considering carbon and renewable energy certificate trading," Applied Energy, Elsevier, vol. 339(C).
    14. Wu, Yuxin & Yan, Haoyuan & Liu, Min & Zhao, Tianyang & Qiu, Jiayu & Liu, Shengwei, 2023. "Distributed energy trading on networked energy hubs under network constraints," Renewable Energy, Elsevier, vol. 209(C), pages 491-504.
    15. Qi, Meng & Kim, Minsu & Dat Vo, Nguyen & Yin, Liang & Liu, Yi & Park, Jinwoo & Moon, Il, 2022. "Proposal and surrogate-based cost-optimal design of an innovative green ammonia and electricity co-production system via liquid air energy storage," Applied Energy, Elsevier, vol. 314(C).
    16. Lu, Xinhui & Li, Haobin & Zhou, Kaile & Yang, Shanlin, 2023. "Optimal load dispatch of energy hub considering uncertainties of renewable energy and demand response," Energy, Elsevier, vol. 262(PB).
    17. Liu, Qian & Li, Wanjun & Zhao, Zhen & Jian, Gan, 2024. "Optimal operation of coordinated multi-carrier energy hubs for integrated electricity and gas networks," Energy, Elsevier, vol. 288(C).
    18. Matija Kostelac & Lin Herenčić & Tomislav Capuder, 2022. "Planning and Operational Aspects of Individual and Clustered Multi-Energy Microgrid Options," Energies, MDPI, vol. 15(4), pages 1-17, February.
    19. Silva, Ana R. & Pousinho, H.M.I. & Estanqueiro, Ana, 2022. "A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets," Energy, Elsevier, vol. 258(C).
    20. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:214:y:2023:i:c:p:216-232. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.