AI-Driven Stability Prediction of Renewable Electrical Networks with Green Hydrogen Integration
Safaa ESSAID, Loubna LAZRAK
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.
Keywords
Green Hydrogen Integration, Energy Storage, Voltage Stability, Renewable Energy, Machine Learning, Stability prediction, Radial Basis Function Support Vector Machine RBF SVM, Random Forest, Quadratic Discriminant Analysis QDA, Naive Bayes
Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Safaa ESSAID, Loubna LAZRAK, AI-Driven Stability Prediction of Renewable Electrical Networks with Green Hydrogen Integration, Materials Research Proceedings, Vol. 64, pp 832-839, 2026
DOI: https://doi.org/10.21741/9781644904091-103
The article was published as article 103 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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