Optimization of a Micro Grid Operation under Uncertainty Using Model Predictive Controller

This paper presents the Optimization of a microgrid operation under Uncertainty using Model Predictive Controller. Instability in power supply in our society and the country at large has led to the liquidation of many establishments that solely depended on power for their daily activities. This instability in power supply observed in the country can be overcome by optimization of a microgrid operation under uncertainty using model predictive control. This was done in this manner, characterizing the microgrid operation, determining the threats in microgrid operation, designing a model predictive controller rule base that will eradicate the threats in microgrid operation thereby enhancing its operation, training ANN in this rule base for effective eradication of its operational threats thereby enhancing its operational efficiency, designing a Simulink model for optimization of a microgrid operation under uncertainty using model predictive control and validating and justifying the operational efficiency of a microgrid with and without MPC. The stability of the conventional approach occurred at a coordinate of (0.4, 5) through (0.4, 10), while that of using fuzzy controller occurred at a coordinate of (1.109, 5) through (1.109, 5). On the other hand using ANN controller stabilities at coordinates of (1.16, 5) through (1.16, 5) and that when MPC is used stabilizes at a coordinate of (1.223, 5) through (1.223, 5). With these results, it showed that optimization of a microgrid operation under uncertainty using model predictive control (MPC) gave the highest power system stability when compared with the other three like conventional, fuzzy, and Artificial Neural Network (ANN).