@article{Ali2026, 
author = {Tariq Ali and Muzna Sarwar and Farrukh Jamal and Mohammad Hijji and Husam S. Samkari and Mohammed F. Allehyani and M. Ammad ud Din and Muhammad Ayaz},
title = {Data-driven complex network framework for risk dynamics and stability evaluations in renewable-integrated power systems},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {6},
pages = {19177-19216},
keywords = {complex network, power system stability, energy integration, resilience assessment, machine learning, computational complexity},
url = {https://www.sciopen.com/article/10.3934/math.2026781},
doi = {10.3934/math.2026781},
abstract = {The integration of renewable energy sources into modern power systems introduces stability and resilience challenges due to their intermittent and stochastic behavior. To address these issues, this study proposes an artificial intelligence (AI)-driven statistical complex network (AI-SCN) framework for stability assessments in renewable-integrated power grids. The framework models the grid as a weighted complex network, where the nodes represent generation, storage, and load units, and the edges capture electrical and statistical dependencies. By integrating network topology metrics with data-driven AI models, AI-SCN enables accurate stability margin estimation and resilience quantification under varying renewable penetration levels. Simulations on the Institute of Electrical and Electronics Engineers (IEEE) 39-bus and IEEE 118-bus systems show that AI-SCN outperforms conventional and long short-term memory (LSTM)-based approaches, achieving root mean square error (RMSE) values of 0.0185 and 0.0219, respectively, representing improvements of 40.7% and 58.9%. Furthermore, recovery time is reduced from 12.8 s to 8.4 s, demonstrating the system's enhanced recovery efficiency. These results confirm that AI-SCN offers a scalable and adaptive framework for improving stability and resilience in renewable-dominant power systems.}
}