PID Controller Trained Using Ant Colony Algorithm for Load Frequency Control Problems on Three Equal-Area Interconnected Thermal Power with Renewable Energy Sources

The load frequency control (LFC) of a triple equal-area thermal power scheme using renewable energy sources is suggested in this work using a proportional-integral-derivative regulator. The Ant Colony Optimization (ACO) algorithm design parameters, such as pheromone strength, ants, evaporation rate, and iteration, are adjusted for a three-area power system devoid of any renewable energy sources using an Integral of Time multiplied Absolute Error (ITAE) criterion. For the same networked power system, the results of the proposed method are compared to some recently published heuristic methods, such as cohort intelligence (CIO), genetic algorithms (GA), teaching learning-based optimization (TLBO), differential evolution (DE), and PID controllers trained using particle swarm optimization (PSO). Studies show that the suggested ACO-PID controllers offer a better dynamic response in comparison to existing soft computing method-based PID controllers for following one percentage step disturbances. This approach also incorporates a battery energy storage system and renewable energy sources. Further, the recommended controllers’ dependability and compatibility with renewable energy sources are addressed.