Data–model hybrid hierarchical scheduling for grid-connected PEM green hydrogen production in multi-energy systems with LLM-aided coordination
マルチエネルギーシステムにおけるLLM支援協調を用いた系統接続PEMグリーン水素製造のためのデータモデルハイブリッド階層的スケジューリング (AI 翻訳)
Houqi Chen, Mengyi Xu, Chen Shi, Zhe Wang, Da Xie
🤖 gxceed AI 要約
日本語
本論文は、系統接続されたPEMグリーン水素製造のための階層的データモデルハイブリッドスケジューリングフレームワークを提案する。物理モデルによる安全制約とLLMによる適応的最適化を統合し、欧州のデータを用いた検証では、PID制御と比較して電力追従誤差4.9%、熱安全マージン174%向上、故障復旧時間11.8%短縮を達成した。
English
This paper proposes a hierarchical data-model hybrid scheduling framework for grid-connected PEM green hydrogen production, integrating physics-based safety constraints and LLM-aided adaptive optimization. Using European data, it achieves 4.9% power tracking error (vs 7.8% for PID), 174% larger thermal safety margins, and 11.8% faster fault recovery.
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
日本は水素基本戦略を掲げ、グリーン水素の大量導入を目指している。本論文の制御技術は、日本における再エネ由来水素製造の効率化・安全運用に貢献する可能性がある。
In the global GX context
Green hydrogen is critical for global decarbonization, especially in hard-to-abate sectors. This paper advances the operational optimization of PEM electrolyzers, offering a hybrid approach that balances efficiency, safety, and adaptability—relevant for the emerging hydrogen economy worldwide.
👥 読者別の含意
🔬研究者:Novel hybrid scheduling framework combining physics-based models and LLM coordination for green hydrogen production.
🏢実務担当者:Provides a practical approach to optimize hydrogen production in multi-energy systems, improving efficiency and safety.
📄 Abstract（原文）
The integration of variable wind and photovoltaic generation with proton exchange membrane (PEM) water electrolysis is a key enabler for green hydrogen production in smart multi-energy systems, but it exposes coupled multi-physics dynamics, conflicting operational objectives, and strict safety constraints. This paper proposes a hierarchical data–model hybrid scheduling framework for grid-connected PEM green hydrogen production. At the modeling layer, a physics-informed representation with six governing equations in the main text and twenty-two supporting equations in Appendix A defines explicit “red-line” constraints for electrochemical efficiency, thermal safety, and equipment degradation. At the coordination layer, a high-level module—designed with the aid of a large language model during a 90 day design phase—maps heterogeneous operational context (renewable forecasts, electricity prices, stack health indicators) into adaptive multi-objective preference weights and operating bounds. At the execution layer, Takagi–Sugeno fuzzy controllers provide real-time set-point tracking for multiple PEM units. Numerical studies using European wind, photovoltaic, and day-ahead price data over a 7 day test period demonstrate that the proposed framework achieves ∼4.9% power tracking root mean square error [compared with 7.8% for proportional-integral-derivative (PID) control], thermal safety margins approximately 174% larger than conventional baselines, and roughly 11.8% faster fault recovery. These results indicate that the proposed physics-informed, data–model hybrid architecture effectively supports the integration of green hydrogen into smart multi-energy systems.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1063/5.0314440first seen 2026-05-15 19:27:17
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