Improved Ant Colony Optimization Framework for Efficient Power Equipment Maintenance Scheduling in Smart Grids

The growing demand for energy system reliability and operational efficiency highlights the critical need for optimized maintenance scheduling of power equipment. Conventional scheduling approaches often lack the adaptability and precision to effectively address the dynamic, multi-constraint nature of power grid operations. To overcome these limitations, this study introduces an Improved Ant Colony Optimization (IACO) based framework for intelligent scheduling of power equipment maintenance. The proposed approach integrates multiple operational factors, including equipment priority, load balance, and system stability requirements, to generate maintenance plans that enhance both reliability and resource utilization. The IACO algorithm incorporates adaptive pheromone updating, and heuristic guidance to efficiently explore the solution space and produce high-quality schedules. Experimental analysis shows that the proposed IACO algorithm achieves a scheduling precision of 98.4% and a resource utilization rate of 97.8%. Compared to ACO, it improves scheduling efficiency by 15.4%, reduces downtime by 19.6%, and shortens convergence time by 20%. This research offers a scalable, adaptive solution for power system maintenance planning, supporting enhanced reliability, reduced operational risks, and strategic decision-making in modern energy infrastructure management.