Load frequency control for two-area hybrid microgrids using model predictive control optimized by grey wolf-pattern search algorithm

Load-frequency control for an islanded microgrid is very important. If a disturbance is applied to an islanded microgrid, the frequency of the system starts to fluctuate and these fluctuations must be damped by the load-frequency control system. The load-frequency control system uses different controllers to improve the damping of frequency fluctuations related to the islanded microgrid. In this article, the number of controllers used in the load-frequency control system related to an islanded two-area microgrid has been reduced, which means less complexity and less cost in the control structure of microgrids. Also, a new control method has been used in the load-frequency control structure related to the two-area microgrid, which is used to dampen the frequency fluctuations of each of the areas related to the two-area microgrid and to dampen the power deviations between the two microgrids. For this purpose, a hybrid Grey Wolf Optimizer and Pattern Search Algorithm (HGWO-PS)-based model predictive controller (new control method) is employed for load frequency control of an islanded two-area microgrid. The numerical simulations are conducted to verify the performance of the presented controller. Different scenarios such as changes in loads and/or variations in the generated power of the wind turbine generator and the photovoltaic system are studied in the MATLAB/Simulink environment, and the performance of the presented HGWO-PS-based MPC controller, the GWO-based MPC, and social-spider optimizer-based proportional-integral-derivative controller are compared using some criteria including the settling time, the peak overshoot and the peak undershoot. The results show the effectiveness of the presented controller.