International Journal of Research in Advanced Electronics Engineering

The increasing integration of renewable energy sources and distributed generation in modern smart grids has introduced new challenges for optimizing power flow, particularly in handling the variability of renewable energy and ensuring system stability. This research investigates the application of the Artificial Bee Colony (ABC) algorithm to solve the Optimal Power Flow (OPF) problem in smart grids. The proposed ABC-based approach is compared with traditional optimization methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in terms of convergence speed, solution quality, and feasibility. The results show that the The ABC algorithm demonstrated superior performance both PSO and GA, achieving superior objective function values, faster convergence, and fewer constraint violations, especially under high renewable energy penetration. Additionally, the computational efficiency of ABC makes it highly suitable for real-time optimization in dynamic smart grid environments. The study also emphasizes the importance of constraint handling in maintaining voltage stability and line flow limits, where ABC demonstrates its robustness in meeting these constraints. The findings suggest that ABC is an effective tool for optimizing smart grid operations, particularly in minimizing power loss, reducing generation costs, and enhancing the reliability of power systems. Future research is recommended to explore adaptive parameter tuning, multi-objective optimization, and the integration of demand-side management and energy storage for more comprehensive solutions.