Hybrid LQR Control Optimized with PSO and TLBO Algorithms for Single Area Load Frequency Control Incorporating Renewable Energy Systems

The recent shift towards renewable energy sources, such as wind and solar, has necessitated the development of more robust and adaptive load-frequency control (LFC) techniques. This study explores the application of Particle swarm optimization (PSO), Teaching-learning-based optimization (TLBO), and Auto-tuned Linear quadratic regulator (LQR) controllers for LFC in a multi-source power system consisting of thermal, wind, and solar energy generation units. The system dynamics, given the intermittency of renewable sources and the unpredictable nature of step load disturbances, present challenges in maintaining frequency stability. In this context, a single test area combining various power-generation systems such as thermal, solar, and wind is taken. The simulation study shows that the PSO-based LQR gives more accurate results than the auto-tuned and TLBO-tuned LQR controllers regarding stability and settling time.