A REVIEW OF METAHEURISTIC OPTIMIZATION ALGORITHMS FOR POWER SYSTEM APPLICATIONS

Abstract

Modern electric power systems are becoming increasingly complex due to rising electricity demand, renewable-energy integration, distributed generation, energy storage, electric vehicles and smart-grid technologies. These developments create challenging optimization problems involving nonlinearities, nonconvexity, multiple objectives, uncertainty and operational constraints. This paper presents a review of metaheuristic optimization algorithms for power-system applications, focusing on their principles, classifications, applications, comparative characteristics, challenges and future directions. Major algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) and Harris Hawks Optimization (HHO), are examined. Their applications in optimal power flow, economic dispatch, unit commitment, renewable-energy integration, distributed-generation allocation, transmission planning, microgrid management and power-quality improvement are discussed. The review highlights the growing importance of hybrid, multi-objective, uncertainty-aware, adaptive and machine-learning-assisted approaches for robust and efficient power-system optimization.