Please note this is a comparison between Version 1 by Oludamilare Bode Adewuyi and Version 2 by Jason Zhu.

In recent times, microgrids (MG) have emerged as solution approach to establishing resilient power systems. However, the integration of renewable energy resources (RERs) comes with a high degree of uncertainties due to heavy dependency on weather conditions. Hence, improper modeling of these uncertainties can have adverse effects on the performance of the microgrid operations. Due to this effect, more advanced algorithms need to be explored to create stability in MGs’. The model predictive control (MPC) technique has gained sound recognition due to its flexibility in executing controls and speed of processors.

- Microgrids
- Renewable Energy Resources
- Model Predictive Control

Managing the optimal planning of a microgrid is a very difficult task due to the fact that they are small decentralized low voltage systems with small demand and a high rate of disturbances from intense penetration of renewable energy resources (RERs). Model predictive control (MPC) has been applied to both grid-connected and isolated microgrids(MG) systems to help deal with several parameters as seen in **Figure 1**. Many scholarly works have been done to minimize the operating cost or maximize the revenue generation of microgrids but accurately implementing the mentioned objectives has been difficult due to numerous factors. These factors could be as a result of the intermittency posed by nature (weather conditions), error in trying to predict the situation of nature, and the computational complexities that are associated with optimizing the situation to get an optimal plan. According to ^{[1]}[61], there are two standard methods or approaches used to solve the problem of uncertainty in MGs: Reactive approach and preventive approach.

The reactive method depends on a priori information or historical or predefined deterministic data (MPC and rolling horizon approaches) and the preventive method depends on scenario generations (stochastic and robust optimizations). Majority of both the reactive and preventive optimization techniques carried out in MGs are centered on grid-connected systems compared to isolated MGs ^{[2]}[62]. The preventive approaches have proven to be ineffective and not reliable for uncertainty considerations.

The stochastic optimization approach requires assigning probabilities for scenario generations, which is sometimes computationally demanding with the static assumption of uncertainty. The robust optimization becomes over-conservative for measurements and requires different algorithms for different uncertainty sets. This is not the case of MPC; it works on inputs of a system considering the internal dynamics to give or predict an output by capturing forecast error to compensate for unforeseen initial forecasting, making it ideal for uncertainty consideration ^{[3][4][5]}[63,64,65].

Many times, MPC has been combined with either of the two preventive methods to prevent or reduce uncertainties in many scholarly articles. When MPC is combined with stochastic optimization to give the Stochastic Model Predictive Control (SMPC), the stochastic scenarios are used to execute the optimization process by assigning probabilities without much or totally considering the disturbances in the process. The MPC technique helps to reduce the computational time and takes account of the uncertainties without assuming by implementing a feedback scenario where compensation is done to eradicate the external influences of the integration of renewable technologies.

Thus, combining model predictive control with robust optimization gives a better result compared to robust optimization because instead of employing different algorithms which require time and more expertise, MPC does a single consideration of all the uncertainties or disturbances acting on the system. The optimizer in the MPC algorithm has the ability to trace errors made by the process model in predicting future outputs based on the dynamics of the system. Conservatism is highly reduced by the action of the MPC compensation process.