A Novel Model Predictive Control for Stability Improvement of Small Scaled Zero-inertia Multiple DGs Micro-grid

A zero-inertia micro-grid is a power system consisting of multiple renewable energy power sources and energy storage systems without the presence of conventional synchronous generators. In such a system, a large variation of the load or source sides during the islanded mode of operation extremely degrades the micro-grid's voltage and frequency stability. This study presents a virtual inertia-based predictive control strategy for a small-scale zero-inertia multiple distributed generators (DGs) micro-grid. In islanded mode, Voltage Model Predictive Control (VMPC) was implemented to control and maintain the voltage and frequency of the micro-grid. However, instabilities in frequency and voltage may rise at the Point of Common Coupling (PCC) due to large variations at both source and load sides. Therefore, the proposed virtual inertia loop calculates the amount of active power to be delivered or absorbed by each DG, and its effect is reflected in the estimated d current component of the VMPC, thus providing better frequency regulation. In grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. The control approach also enables the DG plug and play characteristics. The performance of the control strategy was investigated and verified using the PSCAD/EMTDC software platform.

An Improved Virtual Inertia Control Strategy for Low Voltage AC Microgrids with Hybrid Energy Storage Systems

This paper proposes a novel virtual inertia control (VIC) method based on a feedforward decoupling strategy to address the low inertia issue of power-converter-interfaced microgrids. The feedforward control scheme is employed to eliminate the coupling between active and reactive power caused by line impedance. The active power-voltage droop can be applied to the battery converter in the hybrid energy storage system (HESS). A novel VIC method is developed for the supercapacitor (SC) converter of HESS to increase the inertia of the microgrid. Detailed small-signal modeling of the SC converter with the proposed VIC was conducted, and the transfer function model was obtained. Parameter analysis of the virtual inertia and virtual damping was carried out with the pole-zero map method, and the step response characteristic of output voltage amplitude with power variation was analyzed in detail, deriving the parameter design principle. The simulation study verifies the effectiveness and validity of the proposed control strategy. The proposed feedforward decoupling method and VIC can be widely applied in microgrids to enhance inertia and improve their power quality.

Virtual Synchronous Generator Using an Intelligent Controller for Virtual Inertia Estimation

Virtual synchronous generators (VSGs) with inertia characteristics are generally adopted for the control of distributed generators (DGs) in order to mimic a synchronous generator. However, since the amount of virtual inertia in VSG control is usually constant and given by trial and error, the real power and frequency oscillations of a battery energy storage system (BESS) occurring under load variation result in the degradation of the control performance of the DG. Thus, in this study, a novel virtual inertia estimation methodology is proposed to estimate suitable values of virtual inertia for VSGs and to suppress the real power output and frequency oscillations of the DG under load variation. In addition, to improve the function of the proposed virtual inertia estimator and the transient responses of the real power output and frequency of the DG, an online-trained Petri probabilistic wavelet fuzzy neural network (PPWFNN) controller is proposed to replace the proportional integral (PI) controller. The network structure and the online learning algorithm using backpropagation (BP) of the proposed PPWFNN are represented in detail. Finally, on the basis of the experimental results, it can be concluded that superior performance in terms of real power output and frequency response under load variation can be achieved by using the proposed virtual inertia estimator and the intelligent PPWFNN controller.

Predictive Control-Based NADIR-Minimizing Algorithm for Solid-State Transformer

Solid-state transformers (SSTs) are becoming an important solution to control active distribution systems. Their significant flexibility in comparison with traditional magnetic transformers is essential to ensure power quality and protection coordination at the distribution level in scenarios of large penetration of distributed energy resources such as renewables, electric vehicles and energy storage. However, the power electronic interface of SSTs decouples the nature of the inertial and frequency responses of distribution loads, deteriorating the frequency stability, especially under the integration of large-scale solar and wind power plants. Despite the virtual inertia/voltage sensitivity-based algorithms that have been proposed, the frequency sensitivity of loads and the capability of guaranteeing optimal control, considering the operating restrictions, have been overlooked. To counteract this specific issue, this work proposes a predictive control-driven approach to provide SSTs with frequency response actions by a strategy that harnesses the voltage and frequency sensibility of distribution loads and considers the limitations of voltage and frequency given by grid codes at distribution grids. In particular, the control strategy is centered in minimizing the NADIR of frequency transients. Numerical results are attained employing an empirically-validated model of the power system frequency dynamics and a dynamic model of distribution loads. Through proportional frequency control, the results of the proposed algorithm are contrasted. It is demonstrated that the NADIR improved about 0.1 Hz for 30% of SST penetration.

An Adaptive Backstepping Sliding Mode Cascade-Control Method for a DC Microgrid Based on Nonlinear Virtual Inertia

In order to improve the bus voltage robustness of distributed multi-source DC microgrid, a new cascade control method based on nonlinear virtual inertia and adaptive backstepping sliding mode is proposed. Firstly, the mathematical model of distributed multi-source DC microgrid with a buck–boost interface converter is analyzed and established. A nonlinear virtual inertia control method based on a variable droop coefficient is given by introducing the converter output voltage variation rate feedback term and a saturation function equation. Secondly, the voltage and current double closed-loop cascade controller is designed by using backstepping sliding mode control and adaptive algorithms. Finally, the system and cascade control models are built in MATLAB/Simulink for multi-case simulation. The feasibility and effectiveness of the proposed method is verified by comparing the results with traditional control methods.