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Virtual Inertia Control-based Model Predictive Control for Microgrid Frequency Stabilization Considering High Renewable Energy Integration

ABSTRACT

Renewable energy sources (RESs), such as wind and solar generations, equip inverters to connect to the microgrids. These inverters do not have any rotating mass, thus lowering the overall system inertia. This low system inertia issue could affect the microgrid stability and resiliency in the situation of uncertainties. Today’s microgrids will become unstable if the capacity of RESs become larger and larger, leading to the weakening of microgrid stability and resilience. This paper addresses a new concept of a microgrid control incorporating a virtual inertia system based on the model predictive control (MPC) to emulate virtual inertia into the microgrid control loop, thus stabilizing microgrid frequency during high penetration of RESs.

The additional controller of virtual inertia is applied to the microgrid, employing MPC with virtual inertia response. System modeling and simulations are carried out using MATLAB/Simulink® software. The simulation results confirm the superior robustness and frequency stabilization effect of the proposed MPC-based virtual inertia control in comparison to the fuzzy logic system and conventional virtual inertia control in a system with high integration of RESs. The proposed MPC-based virtual inertia control is able to improve the robustness and frequency stabilization of the microgrid effectively.

SYSTEM OVERVIEW AND MODELING

Figure 1. The studied microgrid system

Figure 1. The studied microgrid system

The microgrid system employed in this research is displayed in Figure 1. System details are shown as follows: thermal power plant with a peak power of 20 MW, wind farm 1 with a peak power of 2.5 MW, wind farm 2 with a peak power of 8 MW, residential load with a peak power of 5 MW, and industrial load with a peak power of 10 MW. The system base is 20 MW. Due to the penetration of RESs such as wind turbine generation in the microgrid, these power electronics-based RESs reduce overall system inertia and are negatively affecting frequency and voltage stabilization of the microgrid.

Figure 4. The virtual inertia power control using the model predictive control (MPC)

Figure 4. The virtual inertia power control using the model predictive control (MPC)

In this study, KVI is the virtual inertia variable gain due to the RESs/load changes. The value of 0.08 is used, obtained using trial-and-error method. It yielded a good dynamic stability during the transients even when the total system inertia is reduced to 50% of its system. Figure 4 shows the virtual inertia power control using the MPC control signal. In the virtual inertia controller model, the first-order derivative transfer function with the gain and time delay.

MODEL PREDICTIVE CONTROL DESIGN

Figure 5. The general concept of MPC

Figure 5. The general concept of MPC

The MPC method is based on an explicit use of a prediction model of the system response in order to obtain the control actions by minimizing an objective function. The effectiveness of the MPC is demonstrated to be equivalent to the optimum control. The objective of the MPC is to evaluate a sequence of control movements to the set point in an optimum manner. The general concept of MPC is shown in Figure 5.

FUZZY LOGIC SYSTEM FOR VIRTUAL INERTIA CONTROL (COMPARATIVE METHOD)

Figure 7. The framework of virtual inertial control using the fuzzy logic design

Figure 7. The framework of virtual inertial control using the fuzzy logic design

This controller aims to change the frequency deviation input signal to inertia power deviation for compensation of load/RES changes and various rates of microgrid rotation inertia as shown in Figure 7. In this study, the fuzzy system-based virtual inertia control is designed as a comparative intelligent controller to validate the effectiveness of the proposed virtual inertia controller-based MPC method.

SIMULATION RESULTS AND DISCUSSIONS

Figure 9. Frequency deviation of the microgrid during the sudden load change and default system inertia

Figure 9. Frequency deviation of the microgrid during the sudden load change and default system inertia

Figure 10. Frequency deviation of the microgrid during the sudden load change and reduction of half of default system inertia

Figure 10. Frequency deviation of the microgrid during the sudden load change and reduction of half of default system inertia

Figures 9 and 10 show the simulation results for Scenario 1 under default system inertia and half of the default system inertia, respectively. It is obvious that the virtual inertia system (i.e., blue line) improves the frequency response and reduces transient excursions compared with the microgrid system without the virtual inertia control (i.e., black dotted line). The frequency performance is clearly improved by using the fuzzy system-based virtual inertia controller (i.e., green dotted line). The best frequency performance among all simulated control method is obtained by using MPC-based virtual inertia control (i.e., red line). In addition, the transient frequency performance is significantly improved when the microgrid utilizes MPC-based virtual inertia control.

CONCLUSIONS

In a system with high integration of RESs and continuous load disturbances, the virtual inertia system might not stable and cannot maintain and stabilize the frequency deviation within the desirable frequency performance, leading to instability and system collapse. In this paper, MPC is applied for the virtual inertia control for microgrid frequency stabilization during high integration of RES and fluctuating load disturbances. The proposed MPC-based virtual inertia control has been tested for several mismatched parameters of the microgrid, wind power, and load disturbances.

Simulation results show that the proposed MPC-based virtual inertia control is robust against the parameter perturbation of the system and has desirable performance in comparison to fuzzy logic and conventional virtual inertia control designs in all of the performed test scenarios. It is concluded that the proposed MPC-based virtual inertia control is able to reduce and stabilize frequency deviation of the microgrid and gives robustness to the system subjected to uncertainties and disturbances over the fuzzy logic and conventional virtual inertia systems, thus significantly enhancing the stability and resiliency of the microgrid.

Such a promising result provides a clear perspective on utilizing robust but simple methods for virtual inertia control. For further work, as the virtual inertia system is designed by utilizing the energy storage systems, the analysis of energy storage sizes and costs based on the existing technologies will be evaluated for optimum microgrid investments and operations.

Source: Kasetsart University
Authors: Thongchart Kerdphol | Fathin S. Rahman | Yasunori Mitani | Komsan Hongesombut | Sinan Kufeo glu

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