Green Hydrogen in Integrated Multi-Energy Systems: Technological Pathways, Policy and Market Perspectives, and the Role of Artificial Intelligence
Fuels, vol.7, no.2, 2026 (ESCI, Scopus)
- Publication Type: Article / Review
- Volume: 7 Issue: 2
- Publication Date: 2026
- Doi Number: 10.3390/fuels7020037
- Journal Name: Fuels
- Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
- Keywords: artificial intelligence, electrolyzer, green hydrogen, integrated multi-energy systems
- Gazi University Affiliated: Yes
Abstract
Green hydrogen is increasingly discussed as an energy carrier that can link electricity, gas, heat, and transport sectors. However, many existing reviews address this topic from separate viewpoints, such as hydrogen production technologies, Artificial Intelligence (AI) applications, or system integration, with less attention to how policy and market conditions affect deployment. This review brings these related aspects together in one structured discussion. The paper first reviews the hydrogen supply chain, including production, storage, transport, and utilization. It then discusses an integrated multi-energy architecture in which hydrogen interacts with electricity, natural gas, heat, and cooling networks. Policy instruments in five major economies, including the European Union, the United States, China, Japan, and India, are compared. The review also summarizes the main barriers to large-scale deployment, including high production costs, limited infrastructure, technological challenges, regulatory uncertainty, and supply-chain constraints. In addition, the current market structure and selected large-scale hydrogen projects planned in the United States are reviewed. The paper also examines the role of artificial intelligence in green hydrogen systems. AI applications are grouped into four main stages of the hydrogen value chain: forecasting renewable energy generation, improving electrolyzer design and operation, optimizing storage and distribution, and supporting system-level techno-economic assessment. Recent Machine Learning (ML) studies are compared based on their methods and their contributions to operation and planning. Overall, this review highlights the role of AI in enabling green hydrogen integration within multi-energy systems.