Optimizing Cascade-Loop Controller Parameters of Two-Area Autonomous Microgrid for Load Frequency Control Using Genetic Algorithm

This paper suggests the use of a cascade control system combining a proportional integral derivative (PID) and a fractional order proportional integral derivative (FOPID) controller as a secondary frequency control strategy. The optimal parameters of this proposed controller are adjusted by minimizing the time-weighted integral of absolute error (ITAE) using the genetic algorithm (GA). The main objective is to improve the Load Frequency Control (LFC) of the two-area autonomous Microgrid (MG) by getting better disturbance rejection. In this study, a dynamic model of a two-area autonomous Microgrid (MG) is adopted as a studied system which will be investigated under two scenarios. The first scenario includes a step variation of the different systems perturbations (load, solar photovoltaic (PV) and Wind power in Area 1 and Area 2). The second scenario involves changing the system parameters to evaluate the robustness under uncertainties. Using MATLAB/Simulink, the simulation results show that the suggested cascade PIDFOPID controller is characterized by a better swift correction of frequency and tie-line power deviations with a minimal cost than the other controllers.