Proactive Energy Management for Renewable-Integrated Smart Grids with Real-Time Optimization using a Novel Hybrid PSO-GA Algorithm

The increasing integration of renewable energy sources (RES) into smart grids presents significant challenges related to variability, stability, and energy management. This research proposes a novel hybrid PSO-GA algorithm for real-time decision-making and optimization in smart grid systems. The proposed PSO-GA combines the exploration capabilities of Particle Swarm Optimization (PSO) with the capabilities of Genetic Algorithm (GA), enabling it to effectively address the dynamic and challenging nature of smart grid environments. The hybrid PSO-GA algorithm demonstrates significant improvements in transmission and distribution losses, reducing them by up to 6%. Additionally, it enhances energy efficiency, achieving an increase of 88% compared to traditional and non-hybrid optimized systems. The proposed system offers lower operational costs and improved power system stability, facilitating the integration of high levels of renewable power. Furthermore, the system's ability to respond to real-time demands and proactively address dynamic grid situations ensures reliable and efficient power delivery. This research contributes to the development of advanced energy management strategies for smart grids, promoting the increased adoption of renewable energy and paving the way for a more sustainable and efficient energy future.