Abstract-Random fluctuation in photovoltaic (PV) power plants is becoming a serious problem affecting the power quality and stability of the grid along with the increasing penetration of PVs. In order to solve this problem, by the adding of energy storage systems (ESS), a grid-connected microgrid system can be performed. To make this system feasible, this paper proposes a model predictive control (MPC) based on power/voltage smoothing strategy. With the receding horizon optimization performed by MPC, the system parameters can be estimated with high accuracy, and at the same time the optimal ESS power reference is obtained. The critical parameters, such as state of charge (SoC), are also taken into account in order to ensure the health and stability of the ESSs. In this proposed control strategy, communication between PVs and ESS is not needed, since control command can be calculated with local measured data. At the same time, MPC can make a great contribution to the accuracy and timeliness of the control. Finally, experimental results from a grid-connected lab-scale microgrid system are presented to prove effectiveness and robustness of the proposed approach. Index Terms-Energy storage, photovoltaic, power quality, model predictive control (MPC), power smoothing

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This paper proposes a novel current control method based on Model Predictive Control (MPC) for three-phase inverters. The proposed method is based on an Adaptive MPC (A-MPC) with a PWM modulation. An innovative model parameter estimation and modification method is also proposed, leading to enhanced control accuracy. Comparing with traditional current control methods, such as PI and PR control, the proposed method has better dynamic performance. The transient dynamics, i.e., recovery time and overshoot, have been considerably improved. Simulation and experimental results are presented to validate the effectiveness of the proposal.