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Real-time optimization of large-scale hydrogen production systems using off-grid renewable energy: Scheduling strategy based on deep reinforcement learning

Author

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  • Liang, Tao
  • Chai, Lulu
  • Cao, Xin
  • Tan, Jianxin
  • Jing, Yanwei
  • Lv, Liangnian

Abstract

Aiming to amplify the renewable energy consumption capacity, this study delineates the development of an off-grid Renewable Energy Large-Scale Hydrogen Production System (H2-RES). The system was optimized for economic efficiency and safety, promising a reduction in both the investment cost for grid connection and the overall cost of hydrogen production from electrolytic water. We presented a comprehensive mathematical model for each H2-RES unit and designed a control strategy to enhance energy optimization and management. An intelligent energy scheduling policy empowered by the DDPG algorithm is introduced for optimal decision-making in continuous state and action spaces. Comparative analysis with traditional control policies, PSO, and DQN algorithms underscores the superior economic efficiency, enhanced renewable energy consumption, and safe operation facilitated by DDPG. These findings underscore the academic and engineering potential of DDPG in the energy dispatch of H2-RES.

Suggested Citation

  • Liang, Tao & Chai, Lulu & Cao, Xin & Tan, Jianxin & Jing, Yanwei & Lv, Liangnian, 2024. "Real-time optimization of large-scale hydrogen production systems using off-grid renewable energy: Scheduling strategy based on deep reinforcement learning," Renewable Energy, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:renene:v:224:y:2024:i:c:s0960148124002428
    DOI: 10.1016/j.renene.2024.120177
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    References listed on IDEAS

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    1. Zhang, Bin & Hu, Weihao & Xu, Xiao & Zhang, Zhenyuan & Chen, Zhe, 2023. "Hybrid data-driven method for low-carbon economic energy management strategy in electricity-gas coupled energy systems based on transformer network and deep reinforcement learning," Energy, Elsevier, vol. 273(C).
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    2. Feng, Jianbing & Ren, Zhouyang & Li, Wenyuan, 2025. "A robust safe reinforcement learning-based operation method for hybrid electric-hydrogen energy system risk-based dispatch considering dynamic efficiency characteristics of electrolysers," Renewable Energy, Elsevier, vol. 254(C).
    3. Yasin Khalili & Sara Yasemi & Mahdi Abdi & Masoud Ghasemi Ertian & Maryam Mohammadi & Mohammadreza Bagheri, 2025. "A Review of Integrated Carbon Capture and Hydrogen Storage: AI-Driven Optimization for Efficiency and Scalability," Sustainability, MDPI, vol. 17(13), pages 1-40, June.
    4. Zhang, Rui & Yang, Bo & Wang, Jiarong & Zhang, Zijian & Shu, Hongchun & Jiang, Lin & Sang, Yiyan & Li, Hongbiao & Gao, Dengke & Chen, Yixuan, 2025. "Techno-economic-environmental design and assessment of proton exchange membrane electrolyzers for optimal off-grid wind power hydrogen production," Renewable Energy, Elsevier, vol. 252(C).
    5. Wu, Qingxia & Peng, Long & Han, Guoqing & Shu, Jin & Yuan, Meng & Wang, Bohong, 2025. "Deep-learning-based scheduling optimization of wind-hydrogen-energy storage system on energy islands," Energy, Elsevier, vol. 320(C).
    6. Wei, Ying'an & Fan, Jingjing & Meng, Qinglong & Debnath, Kumar Biswajit & Yang, Yuqin & Liu, Jiao & Lei, Yu, 2025. "EOLD: A reinforcement learning-based energy-optimised load disaggregation framework for demand-side energy management," Renewable Energy, Elsevier, vol. 252(C).
    7. Yang, Yuyan & Xu, Xiao & Luo, Yichen & Xu, Lixiong & Liu, Junyong & Hu, Weihao, 2024. "Human-safe and economic operation of renewable hydrogen-based microgrids under plateau conditions," Renewable Energy, Elsevier, vol. 231(C).
    8. Wu, Zhangxi & Li, Ye & Li, Bin & He, Jiawei & Fan, Hui & Wang, Haiyang & Hu, Xinhang & Zhang, Shouhang, 2025. "A coordinated configuration scheme for hydrogen-electric energy storage based on transient response characteristics," Renewable Energy, Elsevier, vol. 241(C).
    9. Huang, Chunjun & Torres, José Luis Rueda & Zong, Yi & You, Shi & Jin, Xin, 2025. "A review of alkaline electrolyzer technology modeling and applications for decision-making optimization in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
    10. Lan, Penghang & Chen, She & Li, Qihang & Li, Kelin & Wang, Feng & Zhao, Yaoxun, 2024. "Intelligent hydrogen-ammonia combined energy storage system with deep reinforcement learning," Renewable Energy, Elsevier, vol. 237(PB).
    11. Wang, Shiyu & Zheng, Yujing & Chen, Jing & Li, Zhaoxiang & Ji, Yuxiong & Du, Yuchuan, 2026. "Adaptive energy scheduling strategy for port logistics systems: A dual-consolidation continual reinforcement learning approach," Applied Energy, Elsevier, vol. 404(C).
    12. Ahmadi, Mehrnaz & Aly, Hamed & Gu, Jason, 2026. "A comprehensive review of AI-driven approaches for smart grid stability and reliability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PD).
    13. Borui Zhang & Bo Liu, 2025. "An Adaptive Scheduling Method for Standalone Microgrids Based on Deep Q-Network and Particle Swarm Optimization," Energies, MDPI, vol. 18(8), pages 1-19, April.
    14. Behnamnia, Mohammad & Sarvi, Hossein & Dehghan Monfared, Abolfazl, 2025. "Leveraging AI for accurate prediction of hydrogen density (in pure/mixed Form): Implications for hydrogen energy transition processes," Renewable Energy, Elsevier, vol. 251(C).
    15. Qamar, Hafiz Ghulam Murtza & Guo, Xiaoqiang & Ahmad, Fareed, 2024. "Intelligent energy management system of hydrogen based microgrid empowered by AI optimization technique," Renewable Energy, Elsevier, vol. 237(PB).
    16. Yi, Yin & Zhou, Yun & Feng, Donghan & Yin, Wenhang & Li, Hengjie & Yang, Qingliu, 2024. "Stability control and analysis of hydrogen production using a multi-terminal DC EV charging system with PV," Renewable Energy, Elsevier, vol. 234(C).
    17. Cheng, Hangyu & Chen, Jiahui & Jung, Seunghun & Kim, Young-Bae, 2025. "Hierarchical rolling optimization strategy for hybrid electric-hydrogen system based on deep reinforcement learning," Energy, Elsevier, vol. 338(C).

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