A Mean Field Control Method for Rapid Electrolysis Power Allocation of Green Hydrogen Production
グリーン水素生産の迅速な電解電力割り当てのための平均場制御手法 (AI 翻訳)
Youxin Chen, Kuan Zhang, Nian Liu, Chi Yung Chung
🤖 gxceed AI 要約
日本語
本論文は、大規模電解槽アレイの効率的な電力配分を実現する平均場制御手法を提案。安全性と効率を考慮した粒子分割モデルと、CNN加速によるハイブリッド解法を組み合わせ、再生可能エネルギー主体の水素生産システムの安定運用と効率向上を実証した。
English
This paper proposes a mean field control method for rapid electrolysis power allocation in large-scale electrolyzer arrays, enhancing green hydrogen production efficiency and renewable energy utilization. It combines a hyper-box dynamic particle partitioning model with a CNN-accelerated hybrid solution algorithm, proving stability and efficiency through case studies.
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2050年カーボンニュートラル達成に向け水素社会の実現を掲げており、本手法のような大規模水素生産システムの制御技術は、再生可能エネルギー由来水素（グリーン水素）のコスト低減と安定供給に寄与する可能性がある。
In the global GX context
This paper addresses the operational challenge of large-scale green hydrogen production, which is critical for global decarbonization. The proposed mean field control method improves efficiency and reliability, applicable to hydrogen hubs and renewable integration worldwide.
👥 読者別の含意
🔬研究者:Offers a novel control framework for large-scale electrolyzer arrays combining mean field theory with deep learning, advancing power allocation optimization.
🏢実務担当者:Provides a computationally efficient method for real-time power allocation in electrolysis systems, potentially improving hydrogen production profitability.
🏛政策担当者:Supports the scale-up of green hydrogen infrastructure by addressing technical barriers, relevant for energy transition roadmaps.
📄 Abstract（原文）
This paper proposes a mean field control (MFC) method for rapid electrolysis power allocation to enhance the green hydrogen production efficiency of large-scale electrolyzer (EZ) array while improving the renewable energy utilization in renewable-dominated hydrogen production systems (RHPSs). Firstly, a hyper-box dynamic homogeneous particle partitioning model considering the safety electrolysis power fluctuation range and hydrogen production efficiency is formulated to reduce the control complexity. Secondly, an MFC strategy is designed for the EZ array to efficiently decompose the electrolysis power commands among a large number of EZ stacks, addressing uncertainties from random power fluctuations and cell failures through a linear-quadratic Hamilton-Jacobi-Bellman and Fokker-Planck-Kolmogorov (HJB-FPK) partial differential equations with Poisson jump processes. Additionally, a hybrid finite difference solution algorithm with convolutional neural network (CNN) acceleration is developed to numerically search for the mean field control equilibrium (MFCE), which can improve the high-order accuracy and computational efficiency. Finally, the stability, consistency and convergence of the solution algorithm are rigorously proven through the truncation error analysis rule and Lax equivalence theorem. Comparative case studies have validated the superiority of the proposed methodology on rapid electrolysis power allocation and green hydrogen production efficiency for large-scale EZ arrays.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/tste.2025.3605661first seen 2026-05-15 19:31:25
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