Networked microgrid (NMG) is a novel conceptual paradigm that can bring multiple advantages to the distributed system. Increasing renewable energy utilization, reliability and efficiency of system operation and flexibility of energy sharing amongst several microgrids (MGs) are some specific privileges of NMG. In this paper, residential MGs, commercial MGs, and industrial MGs are considered as a community of NMG. The loads’ profiles are split into multiple sections to evaluate the maximum load demand (MLD). Based on the optimal operation of each MG, the operating reserve (OR) of the MGs is calculated for each section. Then, the self-organizing map as a supervised and a k-means algorithm as an unsupervised learning clustering method is utilized to cluster the MGs and effective energy-sharing. The clustering is based on the maximum load demand of MGs and the operating reserve of dispatchable energy sources, and the goal is to provide a more efficient system with high reliability. Eventually, the performance of this energy management and its benefits to the whole system is surveyed effectively. The proposed energy management system offers a more reliable system due to the possibility of reserved energy for MGs in case of power outage variation or shortage of power.

This paper presents a novel single-switch soft-switching high step-up DC-DC boost converter. By utilizing a coupled inductor and series capacitors in the output stage, the converter achieves soft switching performance for the power switch during turn-on and turn-off, along with a high step-up voltage gain. The design of the coupled inductors enables simultaneous achievement of soft switching and high voltage gain without the need for additional auxiliary circuits, ensuring circuit simplicity and high efficiency. Furthermore, the converter maintains continuous input current, similar to a boost converter, improving battery lifespan and renewable input power source operation. The proposed converter addresses diode reverse recovery issues and incorporates a common ground between input and output stages, simplifying the feedback circuit. Comparative analysis with similar high-efficiency high step-up boost converters is performed, and a 200W laboratory prototype is implemented.

This paper presents a non-isolated high step-up DC-DC converter that achieves a high voltage gain through the application of coupled inductor and multiplier techniques. Also, in this topology continuous input current and a shared ground between input and output is obtained which enhances reliability of the proposed converter. Moreover, by utilizing ZVT technique, soft switching performance for both switches are achieved across the entire range of output power which reduces switching losses and improves efficiency significantly. Furthermore, the output diodes of this converter are turned off under ZCS condition, considerably reducing reverse-recovery losses. The paper includes an analysis of the proposed converter’s operation, a comparative study with existing high step-up converters and experimental results for 200 W laboratory prototype.

To improve the voltage gain of step-up converters, the cascaded technique is considered as a possible solution in this paper. By considering the concept of cascading two Z-source networks in a conventional boost converter, the proposed topology takes the advantages of both impedance source and cascaded converters. By applying some modifications, the proposed converter provides high voltage gain while the voltage stress of the switch and diodes is still low. Moreover, the low input current ripple of the converter makes it absolutely appropriate for photovoltaic applications in expanding the lifetime of PV panels. After analyzing the operation principles of the proposed converter, we present the simulation and experimental results of a 100 W prototype to verify the proposed converter performance.

In recent years, the photovoltaic (PV) system was designed to supply solar power through photovoltaic arrays. The PV generator exhibits nonlinear voltage–current characteristics and its maximum power point tracking (MPPT), which varies with temperature and radiation. In the event of non-uniform solar insolation, several multiple maximum power points (MPPs) appear in the power–voltage characteristic of the PV module. Thus, a hybrid combination of binary particle swarm optimization (BPSO) and grey wolf optimization (GWO) is proposed herein to handle multiple MPPs. This combination is nowhere found in the literature, so the author chose this hybrid technique; and the main advantage of the proposed method is its ability to predict the global MPP (GMPP) in a very short time and to maintain accurate performance, even under different environmental conditions. Moreover, a 31-level multilevel inverter (MLI) was designed with a lower blocking voltage process to reduce the complexity of the circuit design. The entire system was executed in the MATLAB platform to examine the performance of the PV system, which was shown to extract a maximum power of 92.930 kW. The simulation design clearly showed that the proposed method with a 31-level MLI achieved better results in terms of total harmonic distortion (THD) at 1.60%, which is less when compared to the existing genetic algorithm (GA) and artificial neural networks (ANNs).