Cloud Computing for Optimizing Sustainable Energy Networks - Mwafaq, Lara; Meftin, Noor Kadhim; Rasheed, Ali Abdulameer; Al-Dulaimi, Mohammed K. H.; Hasan, Talib Kalefa; Batumalay, M. (International Journal of Engineering, Science and Information Technology, 2025)
The increasing integration of renewable energy sources into power systems creates significant challenges for grid stability, efficiency, and scalability. This study investigates cloud computing as a strategic control layer for optimizing these sustainable energy networks. We designed and deployed a cloud-based energy management system that utilizes intelligent data processing, real-time load balancing, and predictive analytics to enhance the performance of decentralized grids. The methodology combines virtualized monitoring with adaptive fault detection and dynamic energy routing, allowing the system to respond autonomously to fluctuations in supply and demand. Our empirical evaluation demonstrates that cloud integration significantly improves transmission efficiency, reduces system downtime, and enables higher utilization of renewable energy, thereby lowering reliance on fossil-fuel backups. Key performance metrics, including data latency and machine learning inference time, were also enhanced, accelerating overall decision-making. These findings validate the hypothesis that cloud platforms are not merely computational tools but essential instruments for the global energy transition. The study concludes by discussing limitations related to cybersecurity and interoperability and proposes future research into hybrid cloud-edge architectures for energy efficiency.