In renewable energy systems, fluctuating outputs from energy sources and variable power demand may deteriorate the voltage quality. In this paper, a model predictive control strategy without using any proportional-integral-derivative (PID) regulators is proposed. The proposed strategy consists of a model predictive current and power (MPCP) control scheme and a model predictive voltage and power (MPVP) control method. By controlling the bidirectional dc-dc converter of the battery energy storage system based on the MPCP algorithm, the fluctuating output from the renewable energy sources can be smoothed while stable dc-bus voltage can be maintained. Meanwhile, the ac/dc interlinking converter is controlled by using the MPVP scheme to ensure stable ac voltage supply and proper power flow between the microgrid and the utility grid. Then, a system-level energy management scheme is developed to ensure stable operation under different operation modes by considering fluctuating power generation, variable power demand, battery state of charge, and electricity price. Compared with the traditional cascade control, the proposed method is simpler and shows better performance, which is validated in simulation based on a 3.5-MW PV-wind-battery system with real-world solar and wind profiles.

As the electronic interfaces between distributed energy resources and the electrical network, power converters play a vital role in voltage stabilization and power conversion. So far, various power converter control methods have been developed. Now it is urgently needed to compare and understand these approaches to support the smart microgrid pyramid. This article provides an overview of the state-of-the-art of parallel power converter control in microgrid applications. The most important control schemes to address existing challenges, including concentrated control, master–slave control, droop mechanism, virtual synchronous generators, virtual oscillator control, distributed cooperative control, and model predictive control, are highlighted and analyzed in detail. In addition, the hierarchical control structure, as well as future trends, are reviewed and discussed.

Dynamic wireless power transfer is a promising solution to address electric vehicle range anxiety and to reduce the ca-pacity and the cost of the on-board batteries. In the traditional DWPT system, the mutual inductances among transmitters make the design of compensation networks very complex. Besides, the power null phenomenon and the power pulsation phenomenon cause a fluctuating DC output voltage or current on the receiver side. To address these issues, this paper proposes a new magnetic coupler. Unipolar and bipolar coils are laid alternately to form segmented transmitters, which are turned ON or OFF according to the position of the overhead receiver coil. LCC compensations, whose inputs are connected in parallel to a common inverter for cost reduction, are adopted. At the receiver side, unipolar and bipolar coils are overlapped in the same plate in order to effec-tively smooth out the mutual inductance variations and hence, reduce the output voltage or current fluctuations. Also, a con-figurable resonant circuit is designed on the receiver side to achieve constant voltage (CV) charging and constant current (CC) charging. Moreover, an optimization design by using Fi-nite-element analysis software Maxwell is developed to choose the best turns of the receiver coil to further improve the output quality. A laboratory prototype with 4-A charging current and 96-V charging voltage, using 85kHz operation frequency, is con-structed to verify the proposed DWPT system. The experimental results show that constant output voltage and current can be achieved within 2% fluctuation, and the overall efficiency is 90.37%.

This paper presents a model predictive direct power control strategy for a grid-connected inverter used in a photovoltaic system as found in many distributed generating installations. The controller uses a system model to predict the system behavior at each sampling instant. The voltage vector that generates the least power ripple is selected using a cost function and applied during the next sampling period; thus, flexible power regulation can be achieved. In addition, the influence of a one-step delay in the digital implementation is investigated and compensated for using a model-based prediction scheme. Furthermore, a two-step horizon prediction algorithm is developed to reduce the switching frequency, which is a significant advantage in higher power applications. The effectiveness of the proposed model predictive control strategy was verified numerically by using MATLAB/Simulink and validated experimentally using a laboratory prototype.