Energy Transition through AI-Enabled Green Hydrogen Smart Grids

This paper explores the integration of Artificial Intelligence into Green Hydrogen-based smart microgrids, aiming to optimize their design, operation, and management. Green Hydrogen, produced fromrenewable sources, presents a clean and versatile energy solution. Smart micro-grids, incorporating variousenergy resources, offer localized and sustainable power supply. By leveraging AI, these systems can enhanceefficiency, reliability, and sustainability.The study reviews the application of AI in simulating and optimizing the design of Green Hydrogen-basedsmart micro-grids, controlling power quality and flow, forecasting and mitigating renewable energyvariability, and facilitating sustainable development and energy transition. Methodologically, a systematicreview approach is employed to analyze and consolidate relevant research findings.Key findings indicate that AI plays a crucial role in optimizing various aspects of Green Hydrogen-basedsmart micro-grid operations. It improves system design, energy production, storage, and consumptionefficiency, as well as resilience to grid disruptions. However, challenges such as intermittent renewableenergy sources, grid stability, and economic optimization need to be addressed.The paper suggests future directions for research, including integrating AI and machine learning techniquesfor optimization, enhancing system resilience and security, managing uncertainties, ensuring gridinteroperability, and addressing social acceptance and behavioral change. Overall, the integration of AI intoGreen Hydrogen-based smart micro-grids holds promise for advancing sustainable energy solutions andmitigating climate change