Maximum Power Point Tracking (MPPT) control is an essential part of every photovoltaic (PV) system, in order to overcome any change in ambient environmental conditions and ensure operation at maximum power.. Recently, micro-inverters have gained a lot of attention due to their ability to track the true MPP for each individual PV module, which is considered a powerful solution to overcome the partial shading and power mismatch problems which exist in series-connected panels. Although the LLC resonant converter has high efficiency and high boosting ability, traditional MPPT techniques based on Pulse Width Modulation (PWM) do not work well with it. In this paper, a fixed frequency predictive MPPT technique is presented for the LLC resonant converter to be used as the first-stage in a PV micro-inverter. Using predictive control enhances the tracking efficiency and reduces the steady state oscillation. Operation with fixed switching frequency for the LLC resonant converter improves the total harmonic distortion profile of the system and ease the selection of circuit magnetic component. To demonstrate the effectiveness of the proposed MPPT technique, the system is simulated using MATLAB/Simulink platform. Furthermore, a 150 W hardware prototype is developed and tested. Both simulation and experimental results are consistent and validate the proper operation of the developed system.

No abstract

Solving the energy management (EM) problem in microgrids with the incorporation of demand response programs helps in achieving technical and economic advantages and enhancing the load curve characteristics. The EM problem, with its large number of constraints, is considered as a nonlinear optimization problem. Artificial rabbits optimization has an exceptional performance, however there is no single algorithm can solve all engineering problem. So, this paper proposes a modified version of artificial rabbits optimization algorithm, called QARO, by quantum mechanics based on Monte Carlo method to determine the optimal scheduling for MG resources effectively. The main objective is minimization of the daily operating cost with the maximization of MG operator (MGO) benefit. The operating cost includes the conventional diesel generator operating cost and the cost of power transactions with the grid. The performance of the proposed algorithm is assessed using different standard benchmark test functions. A ranking order for the test function based on the average value and Tied rank technique, Wilcoxon's rank test based on median value, and Anova Kruskal–Wallis test showed that QARO achieved best results on the most functions and outperforms all other compared technique. The obtained results of the proposed QARO are compared with those obtained by employing well-known and newly-developed algorithms. Moreover, the proposed QARO is used to solve two case studies of day-ahead EM problem in MG, then the obtained results are also compared with other well-known optimization techniques, the results demonstrate the effectiveness of QARO in reducing the operating cost and maximization the MGO benefit.