Artificial Intelligence-Driven Power Management System for Enhanced Efficiency in Smart Grids

Smart grids are crucial in modern energy systems, integrating sensors, communication networks, and control mechanisms to improve energy distribution. However, conventional methods like Time-Based Scheduling (TBS), Demand Response Mechanism (DRM), and Static Load Distribution (SLD) struggle with inefficiencies such as poor real-time load balancing, limited adaptability, and inaccurate power usage predictions. These drawbacks hinder the overall effectiveness of smart grid systems.To address these challenges, the proposed AI-Driven Power Management System (AI-PMS) leverages machine learning to optimize energy distribution and predict demand more accurately. By using historical and real-time data, AI-PMS dynamically adjusts power flow, ensuring better load distribution and efficiency. This method surpasses the limitations of TBS, DRM, and SLD. The implementation of AI-PMS has resulted in a $0.25 \%$ improvement in energy efficiency and a $0.15 \%$ boost in grid reliability, significantly reducing power losses. The system also enhances the integration of renewable energy sources, making it a more sustainable and adaptive solution for modern smart grids. These advancements promise better power management and lower operational costs for grid operators.