Smart grids are considered as key solutions to solve current power security issues. Among these suggestions, microgrid is proposed to integrate distributed generations (DGs) such as photovoltaic (PV) system into the network and the control of DGs output power is getting more attention. The output power of PV arrays with nonlinear characteristics is affected by temperature, solar irradiation and load. Various maximum power point tracking (MPPT) methods for photovoltaic (PV) power systems have been considered and developed to maximize the delivered possible power. In this paper, a modelized photovoltaic source is introduced, based on the Thevenin equivalent circuit. An ordinarily employed solar system model is linearized into simple Thevenin source-resistance representation. Next, a control algorithm associated with the relationship between controller's PWM duty cycle of the MPPT boost converter and solar array output power, namely proposed MPPT algorithm, is introduced. This proposed method is compared with an existing popular MPPT algorithm to confirm its superior performance by using the MATLAB/SIMULINK^® simulation. The results show an improvement in the power generation from a PV array in any weather condition, and also help to reduce the impact of rapid change of solar irradiation on the output power variation within the time duration of change. Therefore, the proposed algorithm reduces the effect on grid frequency and motivate the PV generation penetration into the microgrids. Finally, a 50W DC-DC boost converter prototype is implemented and tested to verify the feasibility of the proposed control scheme.

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.

In microgrids, distributed generators that cannot be dispatched, such as a photovoltaic system, need to control their output power at the maximum power point. The fluctuation of their output power should be minimized with the support of the maximum power point tracking algorithm under the variation of ambient conditions. In this paper, a new maximum power point tracking method based on the parameters of power deviation ($\Delta P_{PV}$), voltage difference ($\Delta V_{PV}$), and duty cycle change ($\Delta D$) is proposed for photovoltaic systems. The presented algorithm achieves the following good results: (i) when the solar radiance is fixed, the output power is stable around the maximum power point; (ii) when the solar radiance is rapidly changing, the generated power is always in the vicinity of maximum power points; (iii) the effectiveness of energy conversion is comparable to that of intelligent algorithms. The proposed algorithm is presented and compared with traditional and intelligent maximum power point tracking algorithms on the simulation model by MATLAB/Simulink under different radiation scenarios to prove the effectiveness of the proposed method.

A Combined Strategy to Improve Operational Efficiency of Microgrids with High Integration of Solar And Wind Energy