improving the mpc performance of the model in order to improve the frequency stability of the two-area microgrid

: in the context of frequency stability in a two-area microgrid, it is crucial to address the fluctuations in frequency caused by load disturbances. to achieve this, an effective load-frequency control (lfc) system, which serves as the secondary control, must be implemented. however, the presence of renewable energy sources such as wind turbines and photovoltaic systems adds complexity to the operation of the lfc system due to their inherent uncertainty. to enhance the performance of the lfc system in the two-area microgrid, this paper proposes a reduction in the number of controllers employed, aiming for a less complex structure. specifically, model predictive control (mpc) is utilized for lfc, and the weight parameters of the mpc controller are determined using craziness-based particle swarm optimization (crpso). the proposed method is compared with alternative approaches, including pid controller optimized with social spider optimization (sso), fractional order fuzzy pi (fofpi), and conventional mpc. the effectiveness of the proposed method is evaluated in various scenarios, considering load variations and the presence of distributed microgrid generation resources. the results demonstrate that the proposed method outperforms the other controllers in terms of speed of response, reduction of overshoot and undershoot, and overall complexity. importantly, the proposed method significantly improves the frequency stability of the two-area microgrid. the simulation and analysis are conducted using matlab software, providing a comprehensive understanding of the system dynamics and the performance of the proposed controller.