Artificial Intelligence-Optimized Hybrid Hydrogen–Battery Energy Storage for Renewable Microgrids
再生可能エネルギーマイクログリッド向け人工知能最適化ハイブリッド水素-バッテリーエネルギー貯蔵 (AI 翻訳)
Johnson O Abiola, Humbulani Simon Phuluwa, David Aborisade, Muniru Okelola, Godwin Igbinigie, Ilesanmi Daniyan
🤖 gxceed AI 要約
日本語
本研究は、再生可能エネルギーマイクログリッドにおけるハイブリッド水素-バッテリー蓄電システムの最適管理に深層強化学習（DRL）を適用。Soft Actor-Critic（SAC）アルゴリズムを用いて、太陽光発電、バッテリー、水素チェーンの運用コストを2.0%削減し、電力遷移を平滑化した。オフグリッド商用施設の実データで検証。
English
This study applies deep reinforcement learning (SAC algorithm) to optimize hybrid hydrogen-battery storage in a renewable microgrid. Using a Markov Decision Process model, it reduces operational costs by 2.0% and achieves smoother power transitions compared to rule-based control. Validated on a year-long dataset from a commercial hospitality site.
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
日本では、離島や災害時のエネルギー自立を目指すマイクログリッドに水素貯蔵の導入が検討されている。本手法はAIによる運用最適化で経済性を向上させ、国内のオフグリッドシステムへの応用が期待される。
In the global GX context
Globally, hybrid hydrogen-battery storage is key for renewable microgrid reliability. This DRL-based optimization offers a cost-effective path for commercial off-grid systems, aligning with energy transition goals worldwide.
👥 読者別の含意
🔬研究者:Demonstrates the effectiveness of SAC-based DRL for multi-energy storage coordination, providing a benchmark for future AI-driven microgrid control research.
🏢実務担当者:Commercial microgrid operators can apply this AI approach to reduce operational costs and improve system stability, especially for remote or off-grid facilities.
🏛政策担当者:Highlights the potential of AI to lower the cost of renewable microgrids, supporting policies for decentralized energy and hydrogen storage deployment.
📄 Abstract（原文）
The transition toward sustainable energy on a global scale is hindered by the intermittent nature of renewables and the high costs of achieving energy independence for off-grid commercial systems. This study develops a deep reinforcement learning (DRL) solution for the optimal management of a hybrid hydrogen-battery energy storage system in a microgrid based on renewables. Taking a study-based approach, drawing on a case study from a commercial hospitality center, the control challenge was modelled as a Markov Decision Process. For the implementation, the Soft Actor-Critic (SAC) learning algorithm was used to regulate power transitions among photovoltaic generation units, batteries, and a hydrogen chain comprising electrolyzers, storage, and fuel cells. The SAC learning-based controller was constructed for minimizing the cost of operations through a reward function that takes into account the degradation costs of batteries as well as the transition costs for seamless operational changes. The SAC-learning-based controller was validated on a high-resolution dataset for a year and was compared with a rule-based controller. It was found that the SAC controller is capable of reducing costs by 2.0%, producing a much smoother transition in power, as well as more intelligent usage of the two storage systems. These findings pave the way for exploration of the DRL for a reliable and affordable transition toward commercial microgrids.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.46793/adeletters.2026.5.2.1first seen 2026-07-13 05:02:16
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