Artificial Intelligence-Driven Smart Grid Management for Efficient Power Distribution and Renewable Energy Integration

DOI

No Doi

Published In

Year 2025 · Volume 2 · Issue 1

Published Online

January-April 2025

Pages

28-32

Article Type

Original Article

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The rapid growth of renewable energy sources and increasing electricity demand have created significant challenges for conventional power grids. Traditional grid management systems often struggle to accommodate intermittent renewable energy generation, resulting in inefficiencies, power quality issues, and increased operational costs. Smart grids have emerged as an advanced solution by integrating digital communication technologies, sensors, and automated control systems to enhance power distribution and system reliability. However, the massive volume of real-time data generated within smart grids necessitates intelligent decision-making mechanisms. Artificial Intelligence (AI) has become a transformative technology for modern smart grid management by enabling predictive analytics, demand forecasting, fault detection, energy optimization, and autonomous control. This study presents an AI-driven smart grid management framework designed to improve power distribution efficiency while maximizing renewable energy integration. The proposed framework employs machine learning algorithms, deep learning models, and real-time monitoring systems to analyze grid conditions and optimize energy flow. Simulation results demonstrate significant improvements in load balancing, energy utilization, fault prediction accuracy, and renewable energy penetration compared to conventional grid management approaches. The findings indicate that AI-driven smart grids can reduce transmission losses, enhance grid stability, and support sustainable energy transitions. The proposed framework offers a scalable and intelligent solution for future power systems and smart city infrastructures.