AI-Optimized Smart Microgrid with Hybrid Renewable Energy and Green Hydrogen Storage for Rural Electrification: A Digital Twin Approach
AI最適化スマートマイクログリッド：ハイブリッド再生可能エネルギーとグリーン水素貯蔵による農村電化のデジタルツインアプローチ (AI 翻訳)
G. Rao, J. Rao, A. Asirvadam
🤖 gxceed AI 要約
日本語
本研究は、太陽光発電、風力発電、グリーン水素貯蔵を統合したAI駆動型スマートマイクログリッドを提案する。物理情報強化学習（PIRL）デジタルツインにより、リアルタイムでエネルギー配分を最適化し、再生可能エネルギー普及率を98.7%に向上させる。インドの農村3村での実証でエネルギー損失を42%削減し、SDG7とSDG13に貢献する。
English
This paper presents an AI-driven smart microgrid integrating solar, wind, and green hydrogen storage. A Physics-Informed Reinforcement Learning digital twin optimizes real-time energy dispatch, increasing renewable penetration to 98.7% and reducing energy waste by 42% in three Indian villages. Multi-objective optimization minimizes cost, carbon footprint, and equipment stress. Supports SDG 7 and 13.
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
日本の離島や過疎地域では再生可能エネルギーと水素貯蔵の組み合わせが注目されている。本研究のデジタルツイン技術は、系統連系が難しい地域での自立型エネルギーシステム設計に参考となる。
In the global GX context
Globally, this paper demonstrates a scalable AI–digital twin approach for off-grid renewable energy systems with hydrogen storage, relevant to remote and island communities. It advances the practical integration of green hydrogen and AI in microgrids.
👥 読者別の含意
🔬研究者:Provides a concrete implementation of Physics-Informed RL for energy dispatch optimization and digital twin for microgrids.
🏢実務担当者:Can apply the PIRL digital twin framework for designing sustainable rural electrification projects.
🏛政策担当者:Highlights the potential of AI and hydrogen storage for achieving universal energy access and climate goals.
📄 Abstract（原文）
This paper presents an AI-driven smart microgrid system integrating solar PV, wind turbines, and green hydrogen energy storage for off-grid rural electrification. We develop a Physics-Informed Reinforcement Learning (PIRL) digital twin that optimizes energy dispatch in real-time while respecting physical constraints of electrolyzers, fuel cells, and battery degradation. The system addresses the intermittency of renewables by using hydrogen as a seasonal storage buffer. Our digital twin, trained on historical weather data and load patterns from three rural villages in Andhra Pradesh, reduces energy waste by 42% and increases renewable penetration to 98.7%, compared to 78.2% with conventional rule-based controllers. A multi-objective optimization minimizes cost, carbon footprint, and equipment stress simultaneously. The AI controller dynamically adapts to weather forecasts and demand shifts, demonstrating eco-adaptive behavior. Implementation on a Raspberry Pi-based edge device shows real-time control with <100ms latency. This work provides a scalable, sustainable model for rural energy independence, directly supporting UN SDG 7 (Affordable Clean Energy) and SDG 13 (Climate Action).
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.32628/ijsrst26131152first seen 2026-05-15 19:15:38
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