A comprehensive review of hydrogen integrated hybrid renewable energy systems: Configurations, models, simulation and optimization with artificial intelligence

Abstract

This work presents a comprehensive review of hydrogen-based hybrid renewable energy systems (HRESs), covering mathematical models, simulation and artificial intelligence (AI)-driven optimization approaches. Emphasizing the potential of hydrogen as an energy carrier to deepen renewable energy integration, especially in solar and wind HRESs, this review systematically details mathematical models for various renewable generation and storage systems, serving as a structured reference for researchers. Given the complexity of HRES modeling, this work provides insights into different modeling software and optimization algorithms, with a particular focus on artificial intelligence methods. The integration of software and artificial intelligence promises to solve complex modeling and optimization challenges with potential applications in different environments. Future directions suggest that the physical model-assisted AI framework, which embeds physical principles within AI models, holds promise for enhancing prediction accuracy and reliability in HRES applications. This framework, especially when combined with stochastic optimization, offers a potential pathway to address challenges in data availability and computational complexity, supporting the effective design and optimization of hydrogen-based HRESs for real-world applications. The overall findings will help improve the design and optimization of hydrogen-based hybrid renewable energy systems for practical implementation.

Keywords

Hybrid renewable energy system / Solar / Wind / Hydrogen / Optimization / Artificial intelligence

CRediT authorship contribution statement

Chenglong Li: Writing – original draft, Methodology, Conceptualization. Tianqi Yang: Writing – review & editing, Methodology, Funding acquisition. Wenchao Cai: Software, Formal analysis. Kodjo Agbossou: Writing – review & editing, Investigation, Conceptualization. Pierre Bénard: Visualization, Supervision, Resources. Richard Chahine: Supervision, Conceptualization. Yi Zong: Writing – review & editing, Supervision. Yaze Li: Visualization, Software, Resources. Shenglin Su: Visualization, Investigation, Data curation. Guodong Li: Visualization, Software, Investigation. Xianglin Yan: Resources, Investigation, Data curation. Jin Li: Investigation, Data curation. Jinsheng Xiao: Writing – review & editing, Validation, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The Hydrogen Research Institute at Université du Québec à Trois-Rivières has initialized a new domain of research collaboration with the School of Automotive Engineering at Wuhan University of Technology on renewable energy system simulation and optimization. This research is related to hydrogen purification, energy storage and safety projects funded by the National Natural Science Foundation of China (52176191, 51476120) and battery energy storage and safety projects funded by the Research Programme of Wuhan University of Technology Chongqing Research Institute (YF2021-08), the Science and Technology Development Foundation of CMVR (China Merchants Vehicle Research Institute) (20AKC3). Thanks to the International Network Programme supported by the Danish Agency for Higher Education and Science for the PRESS project “Proactive Energy Management Systems for Power-to-Heat and Power-to-Gas Solutions” (No. 8073-00026B). Thanks to the support from CSC (China Scholarship Council) and FRQNT (Fonds de Recherche du Québec - Nature et Technologies) for the PBEEE fellowships to students Wenchao Cai, Yaze Li, Shenglin Su, Jin Li and Xianglin Yan (308465, 322579, 322578, 338541 and 338545).

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