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Renewable energy generators are widely used in distribution networks to help meet consumption needs. Photovoltaic systems are common renewable generators, widely used in distribution networks and microgrids; however, due to the uncertainties of renewable generation, this leads to a possibility that the network may have problems in terms of generating the power required by the network at a given moment. To better control distribution networks, algorithms may thus be used to predict and model work conditions throughout the day and night. Model action control methods offer one of the more effective ways of controlling networks under uncertainties, which is important where, in addition to photovoltaic generation uncertainty, the network load is also subject to uncertainty. In this article, the optimal control of photovoltaic resources was done using the Model Predictive Control (MPC) method, a multivariable control method that offers dynamic control as opposed to direct control. The implementation of the predictive control controller of the model was done using MATLAB simulation of the microgrid, and the results highlight the advantages of the proposed method in terms of improving the total harmonic distortion indices and reducing rise time and settling time. Overall, the steady-state control method is confirmed to reduce steady-state errors. [ABSTRACT FROM AUTHOR]