Intelligent Optimization-Based Load Frequency Control for Multi-Source Hybrid Power Systems

The rapid rise in energy needs has put immense pressure on our power systems, particularly due to the significant reliance on renewable energy sources. It's essential to manage this increasing pressure effectively to guarantee the continuous and smooth functioning of our power grids. The balance between power generation and demand significantly influences the system's frequency, impacting its stability. Any variations in this frequency can result in over-voltage issues, grid damage, and in severe cases, complete blackouts. Consequently, this study introduced a robust method to alleviate the load frequency issues stemming from fluctuating demand with a significant increase in the use of renewable energy. The Load Frequency Control (LFC) system depends on a Proportional Integral (PI) regulator. To optimize this controller, the genetic algorithm (GA) is initially employed, followed by refinement through the firefly algorithm (FA). Improvements in how the system reacts are achieved by integrating a Fractional Order Proportional Integral Controller (FOPID) adjusted through the dragonfly search algorithm (DSA). Also, to enhance the system's effectiveness, a layered Proportional Integral-Proportional Derivative (PI-PD) controller is suggested, utilizing Grey Wolf Optimization (GWO) to determine its parameters. The GWO-PI-PD controller provides satisfactory results in mitigating the LFC issue as compared to the other techniques.