AI-Driven Stability Prediction of Renewable Electrical Networks with Green Hydrogen Integration
グリーン水素統合を伴う再生可能エネルギー送電網のAI駆動安定性予測 (AI 翻訳)
Safaa Essaid
🤖 gxceed AI 要約
日本語
本論文は、グリーン水素を長期蓄電システムとしてモデル化し、変動再エネの余剰吸収と送電網への再注入による電圧安定性向上を評価するデータ駆動型フレームワークを提案。モロッコの送電網を対象に、パワーフロー解析と機械学習（RBF-SVM等）を組み合わせ、水素統合下の安定性予測でRBF-SVMが最高性能を示した。
English
This paper proposes a data-driven framework to assess voltage stability in renewable grids with green hydrogen integration. Using Moroccan transmission network data, it models hydrogen as long-duration storage and applies machine learning (RBF-SVM, Random Forest, etc.) to predict stability. RBF-SVM outperformed other models, demonstrating the potential of AI for hydrogen-integrated grid planning.
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
日本でも水素社会実現に向けたグリーン水素の系統統合が検討されており、本手法は系統運用者の安定性評価ツールとして参考になる。ただし、日本の系統特性に合わせた調整が必要。
In the global GX context
This work contributes to the global GX context by combining green hydrogen storage modeling with machine learning for grid stability prediction, which is relevant for power system operators and hydrogen infrastructure planners worldwide. The methodology can be adapted to other grids facing high renewable penetration.
👥 読者別の含意
🔬研究者:The comparative machine learning approach for stability prediction under hydrogen integration offers a reproducible methodology for further studies.
🏢実務担当者:Grid operators can use the proposed framework to simulate hydrogen impacts on voltage stability and optimize storage operation.
🏛政策担当者:The study provides evidence that green hydrogen can support grid stability, informing policy for hydrogen storage incentives.
📄 Abstract（原文）
Abstract. This paper presents a data-driven framework to assess the impact of green hydrogen integration on the voltage stability of a part of the Moroccan electrical transmission network. The main contribution lies in modeling green hydrogen as a long-duration energy storage system capable of absorbing surplus solar and wind generation and reinjecting it into the grid to enhance voltage stability under high renewable penetration. A time-series power flow methodology, implemented in Python using the pandapower library, is developed to generate a large dataset based on voltage criteria. This dataset is then used to train and evaluate four machine learning models (RBF-SVM, Random Forest, QDA and Naive Bayes) through confusion matrices and standard performance metrics. The comparative results indicate that the RBF-SVM model clearly outperforms the other classifiers across all evaluation metrics, including accuracy, precision, recall and F1-score. Beyond model performance, the proposed approach introduces an integrated framework that combines hydrogen-based long-term energy storage modeling with machine learning–based stability prediction.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.21741/9781644904091-103first seen 2026-05-15 19:25:59
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