Automatic generation control optimization for power system resilience under real world load variations using genetic algorithm

Modern power systems must be resilient to sudden load variations in order to keep the system stable. For Automatic Generation Control (AGC), single load change is impractical and need further analysis. This study comprehensively explore the performance of AGC in a two-area interconnected power system, focusing on a wide range load variations that can exists in realistic power systems consisting from 100 to 300 MW in both increments and decrements. The performance of three control strategies-Conventional AGC (CAGC), Tie-Line Bias (TLB) Control, and Genetic Algorithm-Optimized PID (GA-PID)-is assessed across 12 distinct cases, each tested under these three scenarios. A total of 360 tests are conducted, with performance measured by key metrics, including overshoot, undershoot, settling time, and steady-state accuracy for both areas. The results demonstrate that GA-PID consistently outperforms CAGC and TLB in minimizing transient deviations, ensuring faster stabilization, and maintaining steady-state accuracy. For load increases, GA-PID reduces overshoot by up to 90% and eliminates undershoot in several cases. In comparison, CAGC and TLB show notable weaknesses when dealing with larger disturbances, such as extended oscillations and bigger deviations. The results highlight how effective GA-PID is as a strong and flexible control method, which is crucial for today’s power systems that need to manage unpredictable changes in load.