Advanced Power System Optimization: Evaluation of Automatic Generation Control Techniques in Two-Area Networks

Automatic Generation Control (AGC) plays a critical role in ensuring frequency stability and power regulation in multi-area power systems. In this study, the application of intelligent techniques in AGC of a two-area power system was explored, and a comparative analysis of conventional AGC, Tie-Line Bias (TLB) Control, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) was provided. The performance of each method is evaluated using metrics such as rise time, settling time, overshoot, peak time, and settling minimum and maximum values. The results demonstrated that GA emerged as the best-performing technique, offering the fastest rise time (1.7140e-06 s), shortest settling time (2.5346 s), and minimal overshoot (0.000693%). PSO also shows strong performance with a rise time of 0.0022 s and an overshoot of 0.0310%. Conventional AGC and TLB Control, while effective, exhibit longer settling times and higher overshoot compared to GA and PSO. These findings highlight the superiority of GA for AGC in two-area power systems, providing rapid and precise control with minimal deviations. This research suggests that intelligent control methods such as GA and PSO offer significant improvements over conventional techniques and have potential applications in real-world power systems.