Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning integration

• Machine learning enhances renewable hydrogen system modeling and optimization • TMY or ML better for solar irradiation modeling over first-principle calculations • Dynamic electrolyzer and fuel cell models offer realistic performance predictions • Levelized Value Addition integrates socio-economic benefits into system models • An MCDM framework with machine learning supports multi-attribute decision-making The renewable hydrogen economy is recognized as an integral solution for decarbonizing energy sectors. However, high costs have hindered widespread deployment. One promising way of reducing the costs is optimization. Optimization generally involves finding the configuration of the renewable generation and hydrogen system components that maximizes return on investment. Previous studies have included many aspects into their optimizations, including technical parameters and different costs/socio-economic objective functions, however there is no clear best-practice framework for model development. To address these gaps, this critical review examines the latest development in renewable hydrogen microgrid models and summarizes the best modeling practice. The findings show that advances in machine learning integration are improving solar electricity generation forecasting, hydrogen system simulations, and load profile development, particularly in data-scarce regions. Additionally, it is important to account for electrolyzer and fuel cell dynamics, rather than utilizing fixed performance values. This review also demonstrates that typical meteorological year datasets are better for modeling solar irradiation than first-principle calculations. The practicability of socio-economic objective functions is also assessed, proposing that the more comprehensive Levelized Value Addition (LVA) is best suited for inclusion into models. Best practices for creating load profiles in regions like the Global South are discussed, along with an evaluation of AI-based and traditional optimization methods and software tools. Finally, a new evidence-based multi-criteria decision-making framework integrated with machine learning insights, is proposed to guide decision-makers in selecting optimal solutions based on multiple attributes, offering a more comprehensive and adaptive approach to renewable hydrogen system optimization.