The integration of renewable energy sources (RESs) into power systems, particularly in microgrids, is becoming a prominent trend aimed at reducing dependence on traditional energy sources. Replacing conventional synchronous generators with grid-connected RESs through power electronic converters has significantly reduced the inertia of microgrids. This reduction negatively impacts the dynamics and operational performance of microgrids when confronted with uncertainties, posing challenges to frequency and voltage stability, especially in a standalone operating mode. To address this issue, this research proposes enhancing microgrid stability through frequency control based on virtual inertia (VI). Additionally, the Iterative Learning Control (ILC) method is employed, leveraging iterative learning strategies to improve the quality of output response control. Accordingly, the ILC-VI control method is introduced, integrating the iterative learning mechanism into the virtual inertia controller to simultaneously enhance the system’s inertia and damping coefficient, thereby improving frequency stability under varying operating conditions. The effectiveness of the ILC-VI method is evaluated in comparison with the conventional VI (C-VI) control method through simulations conducted on the MATLAB/Simulink platform. Simulation results demonstrate that the ILC-VI method significantly reduces the frequency nadir, the rate of change of frequency (RoCoF), and steady-state error across iterations, while also enhancing the system’s robustness against substantial variations from renewable energy sources. Furthermore, this study analyzes the effects of varying virtual inertia values, shedding light on their role in influencing response quality and convergence speed. This research underscores the potential of the ILC-VI control method in providing effective support for low-inertia microgrids.

The inertia in power systems plays a very important role in stabilizing the power system. With the growth of distributed generation (DG)/renewable energy source (RES) penetration in the power grid, there has been a concern about the lack of inertia in the power system. One of the solutions to solve the problem is to use the Virtual Synchronous Generator (VSG) control method. For energy storage, the Vehicle-to-grid (V2G) storage system can replace the traditional hybrid energy storage which consists of batteries and supercapacitors. This application will be implemented in Microgrid. The article will focus on simulating the Microgrid with the V2G system and solar energy as a renewable energy source, the inverters are controlled by the method of VSG control. The models will be simulated and the results will be shown on MATLAB/SIMULINK software.

The proportion of renewable energy system (RES) in electricity production has increased significantly in recent years, thanks to strict environmental regulations, fossil fuel sources is limited and the global demand for electricity is rising. In Vietnam, rooftop solar power is developing rapidly, however, because of challenges in operating power systems with a high penetration of RES, these systems were not allowed to feed into the grid in 2022. This has created difficulties for managers and operators in forecasting sources and loads, since the capacity of these systems cannot be controlled. To address this issue, the paper proposes an deep learning-based solution, such as the YOLOv8 model, to analyze data and automatically detect solar power systems captured by drones accurately and quickly in some places in Quang Nam Province and Da Nang. From the analysis result, the problem of forecasting distributed sources from rooftop solar systems in Vietnam in the current context will be solved in our next work.