Optimizing Frequency Control in Isolated Microgrids with Renewable Integration Using Genetic Algorithm-Tuned PID Controllers

Microgrids that utilize renewable energy sources may encounter stability issues and subpar performance due to insufficient dampening during sudden increases in load. It is widely understood that a microgrid operates as a non-linear system. Preventing disruptions and outages in electric power necessitates the implementation of suitable control mechanisms for the microgrid's automated generation control. For this study, we employ a Genetic Algorithm (GA) optimization technique to enhance the frequency control of a two-area microgrid. This is achieved by utilizing a basic proportional-integral-derivation (PID) controller. Through the incorporation of renewable sources, the microgrid is capable of operating autonomously. The GA optimized the controller's gain while considering the criteria of integral time absolute error (ITAE). In this analysis, we will be examining the dynamic response of the GA-tuned controller in comparison to a conventional controller. Our focus will be on how the GA-tuned controller enhances the operational performance and stability of the microgrid.