To propel us toward a greener and more resilient future, it is imperative that we adopt renewable sources and implement innovative sustainable solutions in response to the escalating energy crisis. Thus, renewable energies have emerged as a viable solution to the global energy crisis, with photovoltaic energy being one of the prominent sources in this regard. This paper represents a significant step in the desired direction by focusing on detailed, comprehensive dynamic modeling and efficient control of photovoltaic (PV) systems as grid-connected energy sources. The ultimate goal is to enhance system reliability and ensure high power quality. The behavior of the suggested photovoltaic system is tested under varying sun radiation conditions. The PV system is complemented by a boost converter and a three-phase pulse width modulation (PWM) inverter, with MATLAB software employed for system investigation. This research paper enhances photovoltaic (PV) system performance through the integration of model-predictive control (MPC) with a high-gain DC–DC converter. It improves maximum power point tracking (MPPT) efficiency in response to the variability of solar energy by combining MPC with the traditional incremental conductance (IN-C) method. Additionally, the system incorporates a DC–AC converter for three-phase pulse width modulation, which is also controlled by predictive control technology supported by Particle Swarm Optimization (PSO) to further enhance performance. PSO was selected due to its capability to optimize complex systems and its proficiency in handling nonlinear functions and multiple variables, making it an ideal choice for improving MPC control performance. The simulation results demonstrate the system’s ability to maintain stable energy production despite variations in solar irradiation levels, thus highlighting its effectiveness.

This work demonstrates that a control strategy like MPC optimized by the PSO algorithm is more optimal than traditional controllers like PI controllers. Comparing the active power or torque electromagnetic obtained through these control strategies to the active power reference provided by DFIG, it was observed that this plan can significantly enhance the system’s productivity. Through constant modification of the regulation, these strategies can maintain optimal operation even under varying wind conditions. This is not only ensuring maximum power generation but also reduces the impact of disturbances on the system. Overall, the use of control strategies like MPC-PSO has proven to be effective in enhancing the performance of wind energy systems, making them a promising technology for future renewable energy systems. The used system in this article is simulated by using MATLAB/SIMULINK software.