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Intelligent energy management system of hydrogen based microgrid empowered by AI optimization technique

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  • Qamar, Hafiz Ghulam Murtza
  • Guo, Xiaoqiang
  • Ahmad, Fareed

Abstract

Microgrids powered by hydrogen often face challenges in effectively managing energy over an extended duration due to the intermittent nature of renewable energy sources (RES) and fluctuating loads. This research emphasizes the use of AI technologies, including machine learning to improve the efficiency of intelligent energy management system (IEMS), hydrogen storage, fuel cell consumption, and power system quality improvement. A novel approach integrating Isolation Forest Ensemble (IFE) and Modified Water Wave Optimization (MWWO) has been proposed to enhance energy management and to improve the power quality. This method combines the population size with precision and accuracy. The effectiveness of this study has been assessed against established methods (AVO, PSO & IPSO, MFO, SAG & XGBoost, MILP, and QTLBO) concerning numerous factors including component sizing, economics, reliability, and efficiency of microgrid. The precision level of the proposed technique is approximately 83 %. The primary benefit of this research is the improvement of power quality and cost-efficiency in hydrogen based microgrids, attained through optimized and intelligent energy management system. This article furnishes evidence indicating the superiority of the anticipated approach. This study utilizes MATLAB/Simulink and PyCharm framework for its investigation and projection.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pb:s0960148124018068
    DOI: 10.1016/j.renene.2024.121738
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    References listed on IDEAS

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    1. 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).
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    4. Ren, Kezheng & Liu, Jun & Wu, Zeyang & Liu, Xinglei & Nie, Yongxin & Xu, Haitao, 2024. "A data-driven DRL-based home energy management system optimization framework considering uncertain household parameters," Applied Energy, Elsevier, vol. 355(C).
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    1. Lei Zhang & Yuxing Yuan & Su Yan & Hang Cao & Tao Du, 2025. "Advances in Modeling and Optimization of Intelligent Power Systems Integrating Renewable Energy in the Industrial Sector: A Multi-Perspective Review," Energies, MDPI, vol. 18(10), pages 1-50, May.

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