High-gain bidirectional dc–dc converters are so popular in many applications, such as drives, photovoltaic systems, electric vehicles (EVs), etc. This article proposes an extendable bidirectional common-ground dc–dc converter suitable for high-gain applications, such as EVs. The proposed converter uses fewer number of switches to provide high step-up and high step-down voltage converting capabilities in both power flow directions. Since the input and output of this converter share the same ground, the electromagnetic interface is alleviated. In order to assess the versatility of the proposed dc–dc converter and to achieve a fast response, by equipping this converter with an input–output feedback linearization (IOFL) controller, its performance is analyzed in EV applications. To this end, the steady-state calculations are worked out for both charge and discharge directions, then dynamic analysis and control design are presented based on nonlinear controller by IOFL. Simulation and experimental results are provided to prove the feasibility of the proposed converter.

This paper deals with a new islanding detection method based on parallel inductive impedance (PII) switching at distributed generation (DG) connection point, and monitoring the rate of change of voltage $(dv\!/\!dt)$ at DG output. In the proposed approach, switching the PII causes variation in the $dv\!/\!dt$ . This variation is very small in the case that DG operates in parallel with the main grid, whereas the changes will be very large during the occurrence of islanding. Therefore, the $dv\!/\!dt$ is employed to identify both the islanding and nonislanding situations; however, to better analyze the $dv\!/\!dt$ changes, a fast Fourier transform (FFT) is employed to process the variation of the $dv\!/\!dt$ . To select the type of inserted impedance, the effect of switching various impedance types on the $\text{FFT}$ $(dv\!/\!dt)$ is thoroughly analyzed. It is demonstrated that the purely inductive impedance has better performance than other impedance types. The performance of the proposed method is examined through comprehensive simulation studies in MATLAB. The simulation results indicate that the proposed method retains its efficiency for both inverters- and synchronous-based DGs. Moreover, the proposed method not only has a substantial influence on the reduction of disturbances due to the small amount of PII but also eliminates the nondetection zone entirely in comparison with other available methods.

With increasing worldwide attention on environmental sustainability, microgrids that harness renewable sources have become more prominent. The changing characteristics of renewable energy sources and energy demand’s unpredictable patterns might cause disruptions in the sustainable working of microgrids. Moreover, EVs (electric vehicles), being dynamic loads, might significantly affect the security administration of the microgrid. However, the persistent problem of PSOAs (particle swarm optimization algorithms) being affected by local optima emphasizes the need for more improvements to these algorithms. In order to tackle these difficulties, a framework for dual-objective optimization was developed with the aim of improving both economic efficiency and environmental sustainability in microgrids that incorporate electric vehicles. This model employs a linear weighting strategy under a TPZSG (two-person zero-sum game) to maximize the utilization of renewables options and provide support for the load. The ultimate objective is to achieve a more efficient balance between these two goals. In addition, a more advanced approach called enhanced ASA-PSOA (adaptive simulated annealing-PSOA) is employed to find the best solutions in this context. The simulation outcomes indicate that the multi-function weighting strategy can reduce the impact of uncertainties, hence optimizing the use of renewable resources and load management. Furthermore, implementing systematic charging and discharging procedures for electric vehicles has the potential to decline both operational and environmental expenses in microgrids. The total expense of the system under the proposed algorithm (ASA-PSOA) can be reduced by 11.1%, 10.1%, 6.5%, and 4.5% compared to the PSOA, standard-PSOA, adaptive-PSOA, and simulated annealing-PSOA, respectively. Therefore, the improved optimization technique greatly enhances the economic and ecological efficiency of the microgrid.

This paper presents a new islanding detection method for synchronous Distributed Generation (DG). The proposed method is developed based on the combination of rate of change of exciter voltage (RCEV) and open-close circuit breaker (OCCB) at DG connection point. Whenever the RCEV parameter is not sufficient enough to detect islanding condition efficiently and correctly, the OCCB strategy will be triggered. The proposed OCCB strategy sends a switching command to one-phase of the three-phase circuit breaker (CB) which connects the DG to the grid. In this way, the CB will be immediately opened and then closed. This switching condition will causes a variation in the value of RCEV. This variation is small for the conditions when DG operates in parallel with the main grid, whereas it will be large during the occurrence of islanding. The implementation of the proposed strategy is cost-effective because it only uses the CB at the DG output and does not require any extra equipment. The proposed approach is tested on two different systems including a hypothetical power system and the IEEE 33-bus test system in MATLAB/Simulink environment. The simulation results show that the proposed method eliminates the non-detection zone (NDZ), and also reveals high accuracy compared with previously introduced methods. Moreover, it has been proved that the performance of the proposed approach is not affected by DG capacity, DG location, the number of DG units, as well as network configuration.