Supplementary information files for "Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning and integration"

Supplementary files for article "Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning integration"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 optimisations, 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 summarises 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 Revised Manuscript with no changes marked Click here to view linked References 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.© The Authors, CC BY 4.0

《可再生氢系统建模与优化：系统性方法学综述与机器学习集成》论文的补充文件。可再生氢经济被视为能源行业脱碳的核心解决方案。然而，高昂的成本阻碍了其大规模部署。降低成本的一条极具前景的途径便是优化。优化通常指寻找可再生发电与氢系统组件的配置方案，以实现投资回报率最大化。既往研究已将诸多维度纳入优化范畴，包括技术参数、各类成本与社会经济目标函数，但目前尚无清晰的模型开发最佳实践框架。为填补这一研究空白，本批判性综述梳理了可再生氢微电网模型的最新进展，并总结了最佳建模实践。研究结果表明，机器学习集成技术的进步正在改善太阳能发电预测、氢系统模拟以及负荷曲线构建，在数据稀缺地区尤为如此。此外，相较于使用固定性能参数，考虑电解槽与燃料电池的动态特性至关重要。本综述还证实，典型气象年（Typical Meteorological Year, TMY）数据集在模拟太阳辐射方面优于第一性原理计算。同时，本研究评估了社会经济目标函数的实用性，提出更全面的水平增值（Levelized Value Addition, LVA）最适合纳入模型。本文还讨论了在全球南方等地区构建负荷曲线的最佳实践，并评估了基于人工智能与传统的优化方法及软件工具。最后，本文提出了一种融合机器学习洞察的新型循证多准则决策框架（本修订稿未标注任何修改内容，点击此处查看关联参考文献），以指导决策者基于多属性选择最优解决方案，为可再生氢系统优化提供更全面且自适应的方法。© 作者，CC BY 4.0