No abstract

No abstract

Battery energy storage systems (BESSs) have been widely used for microgrid control. Generally, BESS control systems are based on proportional-integral (PI) control techniques with the outer and inner control loops based on PI regulators. Recently, model predictive control (MPC) has attracted attention for application to future energy processing and control systems because it can easily deal with multivariable cases, system constraints, and nonlinearities. This study considers the application of MPC-based BESSs to microgrid control. Two types of MPC are presented in this study: MPC based on predictive power control (PPC) and MPC based on PI control in the outer and predictive current control (PCC) in the inner control loops. In particular, the effective application of MPC for microgrids with multiple BESSs should be considered because of the differences in their control performance. In this study, microgrids with two BESSs based on two MPC techniques are considered as an example. The control performance of the MPC used for the control microgrid is compared to that of the PI control. The proposed control strategy is investigated through simulations using MATLAB/Simulink software. The simulation results show that the response time, power and voltage ripples, and frequency spectrum could be improved significantly by using MPC.

Model predictive control (MPC) has been widely studied for regulating frequency in stand-alone microgrids (MGs), owing to the advantages of MPC such as fast response and robustness against the parameter uncertainties. Understanding the impacts of system parameters on the control performance of MPC could be useful for the designing process of the controller to achieve better performance. This study analyzes the impact of system parameters on the control performance of MPC for frequency regulation in a stand-alone MG. The typical stand-alone MG, which consists of a diesel engine generator, an energy storage system (ESS), a wind turbine generator, and a load, is considered in this study. The diesel engine generator is in charge of primary frequency control whereas the ESS is responsible for secondary frequency control. The stand-alone MG is linearized to obtain the dynamic model that is used for designing MPC-based secondary frequency control. The robustness of MPC against the variation of system parameters is also analyzed in this study. A comparison study of MPC and proportional-integral (PI) control is presented. Simulation results show that MPC has a faster response time and lower overshoot compared to PI control. In addition, the robustness of MPC against the system uncertainties is stronger than conventional PI control.

Microgrids are eco-friendly power systems because they use renewable sources such as solar and wind power as the main power source. However, the stochastic nature of wind and solar power is a considerable challenge for the efficient operation of microgrids. Microgrid operations have to satisfy quality requirements in terms of the frequency and voltage. To overcome these problems, energy storage systems for short-and long-term storage are used with microgrids. Recently, the use of short-term energy storage systems such as flywheels has attracted significant interest as a potential solution to this problem. Conventional flywheel energy storage systems exhibit only one control mode during operation: either smoothing wind power control or frequency control. In this paper, we propose a new flywheel energy storage system based on a doubly fed induction machine and a battery for use with microgrids. The new flywheel energy storage system can be used not only to mitigate wind power fluctuations, but also to control the frequency as well as the voltage of the microgrid during islanded operation. The performance of the proposed flywheel energy storage system is investigated through various simulations using MATLAB/Simulink software. In addition, a conventional flywheel energy storage system based on a doubly fed induction machine is simulated and its performance compared with that of the proposed one.

Variable switching frequency in the finite control set model predictive control (FCS-MPC) method causes a negative impact on the converter efficiency and the design of the output filters. Several studies have addressed the problem, but they are either complicated or require heavy computation. This study proposes a new model predictive control (MPC) method with constant switching frequency, which is simple to implement and needs only a small computation time. The proposed MPC method is based on the gradient descent (GD) method to find the optimal voltage vector. Since the cost function of the MPC method is represented in the strongly convex function, the optimal voltage vector could be found quickly by using the GD method, which reduces the computation time of the MPC method. The design of the proposed MPC method based on GD (GD-MPC) is shown in this study. The feasibility of the proposed GD-MPC is evaluated in the real-time simulation using OPAL-RT technologies. The performance of the proposed method in the case of single inverter operation or parallel inverter operation is shown. A comparison study on the proposed GD-MPC and the MPC with the concept of the virtual state vector (VSV-MPC) is presented to demonstrate the effectiveness of the proposed predictive control. Real-time simulation results show that the proposed GD-MPC method performs better with a low total harmonic distortion (THD) value of output current and short computation time, compared to the VSV-MPC method.

Abstract:A stand-alone multi-microgrid (MMG) system can be formed by connecting multiple stand-alone microgrids (MGs). In the stand-alone MMG system where the frequencies of each MG system are different, a back-to-back (BTB) converter can be used for interconnecting the adjacent MG system. The frequency control performance of the MMG system can be improved by designing the suitable controller of the BTB converter. This study proposes a multi-frequency control in the BTB converter to improve the performance of frequency regulation in the MMG system. Autonomous power sharing between each MG system is achieved by using the proposed multi-frequency control. The stand-alone MMG system where two stand-alone MG systems with different nominal frequencies are interconnected using the BTB converter is simulated in this study to show the feasibility of the proposed multi-frequency controller. Each stand-alone MG system consists of an inverter-based distributed generator (DG) that uses a grid-forming converter with a conventional frequency droop controller. The inverter-based DG is responsible for the primary frequency control in each MG system. To show the effectiveness of the proposed multi-frequency control, a comparison study of the multi-frequency control and the single frequency control is presented in this study. Simulation results show that the system stability can be improved by using the proposed multi-frequency controller.

The integration of smart grid technologies in interconnected power system networks presents multiple challenges for the power industry and the scientific community. To address these challenges, researchers are creating new methods for the validation of: control, interoperability, reliability of Internet of Things systems, distributed energy resources, modern power equipment for applications covering power system stability, operation, control, and cybersecurity. Novel methods for laboratory testing of electrical power systems incorporate novel simulation techniques spanning real-time simulation, Power Hardware-in-the-Loop, Controller Hardware-in-the-Loop, Power System-in-the-Loop, and co-simulation technologies. These methods directly support the acceleration of electrical systems and power electronics component research by validating technological solutions in high-fidelity environments. In this paper, members of the Survey of Smart Grid International Research Facility Network task on Advanced Laboratory Testing Methods present a review of methods, test procedures, studies, and experiences employing advanced laboratory techniques for validation of range of research and development prototypes and novel power system solutions.