Deep-Reinforcement-Learning-Based Energy Management for Off-Grid Wind-to-Hydrogen Systems
基于深度强化学习的离网风-氢系统能源管理 (AI 翻訳)
Bo Zhou, Yuan Gao, Xiaoxu He, Yiyina Teng, Ning Wang, Baocheng Wang, Xiao-bin Song
🤖 gxceed AI 要約
日本語
本論文は、離島などの遠隔地での大規模グリーン水素生産に有望なオフグリッド風力-水素システムに焦点を当て、風力発電の変動と負荷変動に対応するための深層強化学習ベースのエネルギー管理戦略を提案。経済性と安定性を両立する目的関数を設定し、不確実性下での適応的な電力配分を実現。ケーススタディにより、負荷需要を満たしつつ再生可能エネルギー利用率と水素生産を向上させ、収益増加とシステム安定運用を確認。
English
This paper proposes a deep reinforcement learning-based energy management strategy for off-grid wind-to-hydrogen systems, addressing challenges from fluctuating wind and stochastic loads. By jointly optimizing economic performance and operational stability, the strategy enables adaptive power flow allocation under uncertainty. Case studies show improved renewable energy utilization and hydrogen production, increasing profit while ensuring stable operation.
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
日本は水素社会の実現を目指しており、離島など遠隔地でのグリーン水素生産技術は重要。本研究のDRLベースの管理手法は、日本の洋上風力と水素連携プロジェクトに応用可能。ただし、現状では日本の具体的な政策や規制との直接的な連携は薄い。
In the global GX context
This work is globally relevant as off-grid renewable hydrogen production is a key enabler for decarbonizing hard-to-abate sectors. The DRL-based energy management offers a scalable solution for integrating variable renewables with electrolysis, relevant to regions like the EU and Australia pursuing large-scale hydrogen hubs.
👥 読者別の含意
🔬研究者:Provides a novel DRL approach for energy management in off-grid wind-to-hydrogen systems, contributing to the intersection of reinforcement learning and renewable energy optimization.
🏢実務担当者:Offers a practical adaptive control method for operators of remote hydrogen production facilities to increase efficiency and profitability under uncertain conditions.
📄 Abstract（原文）
Off-grid wind-to-hydrogen systems are considered a promising solution for sustainable, large-scale green hydrogen production in remote areas. However, under the combined effects of highly fluctuating wind generation and stochastic load variations, existing energy management methods still face a challenge: in off-grid wind-to-hydrogen systems, intelligent energy management studies that jointly address economic performance and operational stability are still limited. To address these issues, this paper develops a mathematical model for an off-grid wind-to-hydrogen system to reveal the coupling characteristics of the wind–electricity–hydrogen conversion process. Building on this model, a deep-reinforcement-learning-based energy management strategy is proposed. By formulating objectives that simultaneously capture economic benefits and stability requirements, the proposed strategy enables adaptive power flow allocation and dynamic optimization under uncertainty. Case studies demonstrate that, while fully satisfying load demand, the proposed strategy can significantly improve renewable energy utilization and hydrogen production, thereby increasing profit and ensuring stable and sustainable system operation.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18052408first seen 2026-05-15 20:15:06
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。