Focused Review of Recent Modelling Strategies in Power-to-X Systems for Renewable Energy Storage in Smart Grids

Abstract

The variability of renewable energy sources presents a major challenge for maintaining power system stability and long-duration energy storage. Power-to-Hydrogen (PtH₂) systems provide a viable solution by converting surplus renewable  into hydrogen, which can be stored and used across different sectors. This review focuses on focuses on modelling strategies applied to three core PtH₂ processes: hydrogen production via electrolysis, storage, and integration into smart grids. Traditional modelling approaches including computational fluid dynamics (CFD), techno-economic analysis (TEA), process simulation, and linear programming (LP) remain essential for system design but are limited in handling dynamic, real-time operations. In contrast, emerging methods including machine learning (ML), reinforcement learning (RL), surrogate modelling, digital twins, and augmented/virtual reality (AR/VR) platforms offer improved adaptability, predictive control, and operator interaction. However, these tools face limitations related to data availability, computational cost, model interpretability, and integration with existing simulation environments. The review identifies a growing shift toward hybrid modelling frameworks that combine physical accuracy with data-driven adaptability. Future research should focus on building standardised datasets, developing interoperable modelling platforms, expanding the role of real-time visualisation technologies, and must be supported not only by technical innovation but also by evolving policy for scalable and resilient PtH₂-integrated smart grid.