This article proposed a new model for reconfiguration and distributed generation (DG) allocation in the distribution network by considering network loss reduction and power quality improvement. The objective function aims to minimize losses and improve power quality indices by using the new antlion optimizer (ALO) algorithm. The proposed reconfiguration has been investigated on an unbalanced IEEE 33-bus grid with and without DG resources and capacitors. In this study, a branch exchange technique with an optimization method is used to determine the best network arrangement. Simulation results are implemented in different scenarios. In each of the scenarios, power quality indices and network losses before and after the optimization are compared. The results indicate that by using the proposed method, the rate of power quality indices and the losses in the case study are reduced. Also, the results of this study were compared with the results of sample studies. The results show that ALO has better performance with the goal of power loss reduction.

In this paper, the optimal site and size selection of wind turbines (WTs) is presented considering the maximum allowable capacity constraint with the objective of loss reduction and voltage profile improvement of distribution grids based on particle swarm optimization (PSO as a multi-objective problem using weighted coefficients method. The optimal site, size, and power factor of the WTs are determined using PSO. The proposed method is implemented on 84- and 32-bus standard grids. In this study, PSO algorithm is applied to determine the size, site, and power factor of WTs considering their maximum size constraint (with constraint, variant size) and also not considering their maximum size constraint (without constraint, constant size). The simulation results showed that the PSO is effective to find the site, size, and power factor of WTs optimally in the single and multi-objective problem. The results of this method showed that the power loss is reduced more and voltage profile improved more considering WTs maximum allowable size versus not considering this constraint. Additionally, the multi-objective results showed that there is a compromise between the objectives in the multi-objective WTs site selection and the multi-objective problem solution is a more realistic and accurate approach in comparison with the single-objective problem solution.

This paper suggests an enhanced control scheme for a four-leg battery energy storage systems (BESS) under unbalanced and nonlinear load conditions operating in the isolated microgrid. Simplicity, tiny steady-state error, fast transient response, and low total harmonic distortion (THD) are the main advantages of the method. Firstly, a new decoupled per-phase model for the three-phase four-leg inverter is presented. It can eliminate the effect of power stage coupling on control design; thus, the three-phase four-leg power inverter can be viewed as three single input single output (SISO) control systems. Then, using an improved orthogonal signal generation method, the per-phase model of the four-leg inverter in the stationary and synchronous frame is derived. As the second step, a per-phase multi-loop control scheme for the four-leg inverter under unbalanced load conditions is suggested. The proposed control strategy has the ability to provide balanced output voltages under unbalanced load conditions by avoiding the need to deal with the symmetrical components. Finally, a multi-resonant harmonic compensator is used to actively prevent low-order harmonic currents to distort the output voltages of the three-phase four-leg grid-forming power converter. Simulations results are also presented to verify the performance of the suggested control strategy.

Mitigation of harmonics for a grid-connected inverter is an important element to stabilize the control and the quality of current injected into the grid. This paper deals with the control method of a three-phase Grid-Connected Inverter (GCI) Photovoltaic (PV) system, which is based on the zero-sequence current adjuster. The proposed method is capable of removing the harmonic current and voltage without using any active and passive filters and without the knowledge of the microgrid topology and also impedances of distribution bands and loading conditions. This concept is adopted for the control of a Distributed Generator (DG) in the form of grid-connected inverter. The proposed control can be applied to the grid connected inverter of the PV. The fast dynamic response, simple design, stability, and fast transient response are the new main features of the proposed design. This paper also analyzes the circuit configuration effects on the grid connected inverter capability. The proposed control is used to demonstrate the improved stability and performance.

In this paper, stochastic scheduling of a hybrid system (HS) composed of a photovoltaic (PV) array and wind turbines incorporated with a battery storage (HPV/WT/Batt) system in the distribution network was proposed to minimize energy losses, the voltage profile, and the HS cost, and to improve reliability in shape of the energy-not-supplied (ENS) index, considering energy-source generation and network demand uncertainties through the unscented transformation (UT). An improved escaping-bird search algorithm (IEBSA), based on the escape operator from the local optimal, was employed to identify the optimal location of the HS in the network in addition to the optimal quantity of PV panels, wind turbines, and batteries. The deterministic results for three configurations of HPV/WT/Batt, PV/Batt, and WT/Batt were presented, and the results indicate that the HPV/WT/Batt system is the optimal configuration with lower energy losses, voltage deviation, energy not supplied, and a lower HS energy cost than the other configurations. Deterministic scheduling according to the optimal configuration reduced energy losses, ENS, and voltage fluctuation by 33.09%, 53.56%, and 63.02%, respectively, compared to the base network. In addition, the results demonstrated that the integration of battery storage into the HPV/WT enhanced the various objectives. In addition, the superiority of IEBSA over several well-known algorithms was proved in terms of obtaining a faster convergence, better objective value, and lower HS costs. In addition, the stochastic scheduling results based on the UT revealed that the uncertainties increase the power losses, voltage deviations, ENS, and HPV/WT/Batt cost by 2.23%, 5.03%, 2.20%, and 1.91%, respectively, when compared to the deterministic scheduling.

In this paper, a multiobjective and simultaneous two-problem allocation of a hybrid distributed generation (HDG) system comprises of solar panels, wind turbines, and battery storage is proposed in a 33-bus unbalanced distribution network which can decrease total losses and improve power quality (PQ). The PQ indices are defined as voltage swell, total harmonic distortion, voltage sag, and voltage unbalance. In this study, the two problems of hybrid system design and its allocation in the distribution network are solved simultaneously. In the allocation problem, the HDG is placed ideally in the network to reduce energy losses and enhance PQ indices. The HDG is measured to minimize the cost of energy generation, including the initial investment, maintenance, and operation costs. The decision variable including the size of HDG components and its location is optimally determined via escaping bird search (EBS) algorithm which is inspired by the maneuvers of the swift bird to avoid predation. The results cleared that the proposed methodology using the wind and solar resources integrated with battery storage reduced the losses, voltage swell, total harmonic distortion, voltage sag, and voltage unbalance by 34.31%, 49.60%, 0.25%, 40.19%, and 2.18%, respectively, than the base network via the EBS and the results demonstrated the better network performance using all renewable resources against wind or solar application only. The outcomes demonstrated the superiority of the EBS in achieving the highest improvement of the different objectives compared with particle swarm optimization (PSO) and manta ray foraging optimization (MRFO). Moreover, the superior capability of the EBS-based methodology is proved in comparison with previous studies.