AI-Enabled Integration of Smart Grids and Green Hydrogen: A System-Level Review of Flexibility, Control, and Cyber-Physical Energy Systems
人工知能を活用したスマートグリッドとグリーン水素の統合：柔軟性、制御、サイバーフィジカルエネルギーシステムのシステムレベルレビュー (AI 翻訳)
M. Bibih, K. Choukri, Mohamed El Khaili, H. Chakir
🤖 gxceed AI 要約
日本語
本レビューは、スマートグリッドとグリーン水素のAIによる統合について、142件の研究を系統的に分析。需要側柔軟性、ICT基盤、サイバーセキュリティ、制御性能、AI最適化の5つの柱に分類し、システム統合の課題として相互運用性、通信遅延、市場適合性を指摘。水素電解装置の柔軟性ポテンシャルと経済性、環境制約の重要性を明らかにした。
English
This systematic review synthesizes 142 studies on AI-enabled integration of smart grids and green hydrogen, classifying them into five pillars: demand flexibility, ICT infrastructure, cybersecurity, control performance, and AI optimization. It finds that while components are mature, system integration is limited by interoperability, latency, and market barriers. Electrolyzers can provide flexibility services, but economic viability depends on market design. Environmental constraints like water and material criticality are emerging as binding factors.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本は第6次エネルギー基本計画で水素導入を推進中。スマートグリッドと水素の連携は、FIT後の電力市場改革や系統安定化に貢献する可能性が高い。本レビューの知見は、国内の水素戦略とデジタルグリッド政策の統合に示唆を与える。
In the global GX context
Globally, the integration of green hydrogen with smart grids is critical for sector coupling and grid flexibility. This review provides a unified framework linking AI, markets, and sustainability, relevant to IEA hydrogen reports, EU hydrogen strategy, and ISSB's emerging disclosure requirements for energy infrastructure investments.
👥 読者別の含意
🔬研究者:Provides a comprehensive taxonomy and identifies interoperability and environmental constraints as key research gaps for AI-hydrogen-grid integration.
🏢実務担当者:Highlights that electrolyzers can offer fast-response flexibility, but market design and latency must be addressed for commercial deployment.
🏛政策担当者:Regulatory frameworks for hydrogen market participation and grid codes need to account for multi-timescale flexibility and environmental resource limits.
📄 Abstract（原文）
The rapid digitalization of power systems and the growing penetration of variable renewable energy sources have intensified the need for flexible and resilient smart-grid architectures capable of coordinating cross-sector energy flows. This review aims to provide a system-level synthesis of the artificial-intelligence-enabled integration of smart grids and green hydrogen, explicitly addressing coordination across physical infrastructure, digital control layers, market mechanisms, and environmental constraints. Following the PRISMA 2020 framework, 142 high-relevance studies published between 2010 and 2025 were systematically screened and classified into five interdependent thematic pillars: demand-side flexibility, ICT and IoT infrastructures, cybersecurity and resilience, communication and control performance, and AI-based optimization and decision-making. The synthesis reveals three principal findings. First, while core technologies such as photovoltaics, battery storage, and proton exchange membrane electrolyzers exhibit high component-level maturity, system-integration readiness remains limited by interoperability, communication latency, cybersecurity compliance, and market eligibility constraints. Second, electrolyzers can technically provide fast-response and multi-timescale flexibility services, yet their economic viability depends strongly on market product granularity, settlement intervals, and regulatory frameworks. Third, environmental and resource constraints, including water availability and material criticality, are emerging as binding factors that must be embedded directly into planning and optimization models. Overall, the review positions artificial intelligence as a cross-layer coordination mechanism that links operational control, digital observability, market participation, and sustainability boundaries, providing an integrated architecture to guide scalable and resilient smart grid–hydrogen deployment.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/app16052504first seen 2026-05-15 19:26:12
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