Navigating Power System Congestion: An In-Depth Review of Traditional and Artificial Intelligence-Driven Approaches

Congestion and overloading of transmission lines are critical issues in modern decentralized electrical grids, jeopardizing power systems' stability. Various solutions such as load shedding, generator rescheduling, optimal distributed generation placement, nodal pricing, and cost-free methods have been proposed, utilizing optimization models like particle swarm optimization, genetic algorithms, shuffled frog leaping algorithm, mixed integer non-linear programming, and fuzzy logic systems. This study thoroughly assesses these strategies' effectiveness in reducing congestion and operational costs, considering implementation feasibility, computational requirements, and impact on power system stability. Valuable insights will aid power system designers, researchers, educators, and utility architects. The research examines congestion management methods, their pros and cons, regional case studies showcasing real-world results, and explores future technologies and potential research directions.