Due to the lack of inertia and uncertainty in the selection of optimal Proportional Integral (PI) controller gains, the voltage and frequency variations are higher in the islanded mode of the operation of a Microgrid (MG) compared to the grid-connected mode. This study, as such, develops an optimal control strategy for the voltage and frequency regulation of Photovoltaic (PV) based MG systems operating in islanding mode using Grasshopper Optimization Algorithm (GOA). The intelligence of the GOA is utilized to optimize the PI controller parameters. This ensures an enhanced dynamic response and power quality of the studied MG system during Distributed Generators (DG) insertion and load change conditions. A droop control is also employed within the control architecture, alongside the voltage and current control loops, as a power-sharing controller. In order to validate the performance of the proposed control architecture, its effectiveness in regulating MG voltage, frequency, and power quality is compared with the precedent Artificial Intelligence (AI) based control architectures for the same control objectives. The effectiveness of the proposed GOA based parameter selection method is also validated by analyzing its performance with respect to the improved transient response and power quality of the studied MG system in comparison with that of the Particle Swarm Optimization (PSO) and Whales Optimization Algorithm (WOA) based parameter selection methods. The simulation results establish that the GOA provides a faster and better solution than PSO and WOA which resulted in a minimum voltage and frequency overshoot with minimum output current and Total Harmonic Distortion (THD).

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Despite the vast benefits of integrating renewable energy sources (RES) with the utility grid, they pose stability and power quality problems when interconnected with the existing power system. This is due to the production of high voltages and current overshoots/undershoots during their injection or disconnection into/from the power system. In addition, the high harmonic distortion in the output voltage and current waveforms may also be observed due to the excessive inverter switching frequencies used for controlling distributed generator’s (DG) power output. Hence, the development of a robust and intelligent controller for the grid-connected microgrid (MG) is the need of the hour. As such, this paper aims to develop a robust and intelligent optimal power flow controller using a grasshopper optimization algorithm (GOA) to optimize the dynamic response and power quality of the grid-connected MG while sharing the desired amount of power with the grid. To validate the effectiveness of proposed GOA-based controller, its performance in achieving the desired power sharing ratio with optimal dynamic response and power quality is compared with that of its precedent particle swarm optimization (PSO)-based controller under MG injection and abrupt load change conditions. The proposed controller provides tremendous system’s dynamic response with minimum current harmonic distortion even at higher DG penetration levels.

Summary To alleviate the overloads in the power system and to reduce the exponential growth in carbon dioxide (CO2) emissions, deployment of the renewable energy sources (RES) into the power system is the need of the hour. However, injecting these RES into the current power system network causes large voltage and power overshoots hence deteriorate the transient response and power quality of the overall power system. In this paper, an efficient solution of the above‐mentioned issues is explored by developing an optimal microgrid (MG) controller using one of the most modern and intelligent artificial intelligence (AI) techniques named the salp swarm optimization algorithm (SSA). The intelligence of the SSA is exploited to select the optimal controller gains and dc‐link capacitance value by minimizing a time integrating error fitness function (FF) which in‐turn enhances the dynamic response and power quality of the studied MG system. The proposed grid‐tied MG controller is designed to achieve the preset active and reactive power sharing ratio between distributed generator (DG) and utility grid during DG and load switching conditions. To validate the superiority of the proposed controller, its performance is compared with that of its precedent grasshopper optimization algorithm (GOA)‐based controller for the identical operating conditions and system configuration. The outcomes of the study show that the proposed MG controller outperforms its competitor in terms of transient response and quality of power.

The increasing penetration of Microgrids (MGs) into existing power systems and ''plug and play'' capability of Distributed Generators (DGs) causes large overshoots and settling times along with various power quality issues such as voltage and frequency flickers, current harmonics and short current transients. In this context, over the past few years, considerable research has been undertaken to investigate and address the mentioned issues using different control schemes in conjunction with soft computational techniques. The recent trends and advancements in the field of Artificial Intelligence (AI) have led the development of Swarm Intelligence (SI) based optimized controllers for smooth Renewable Energy Sources (RES) penetration and optimal voltage, frequency, and power-sharing regulation. Moreover, the recent studies have proved that the SI-based controllers provide enhanced dynamic response, optimized power quality and improved the dynamic stability of the MG systems as compared to the conventional control methods. Their importance in modern AC MG architectures can be judged from the growing number of publications in the recent past. However, literature, pertaining to SI applications to AC MG, is scattered with no comprehensive review on this significant development. As such, this study provides an overview of 15 different SI optimization techniques as applied to AC MG controls from 43 research publications including a detailed review of one of the elementary and most widely used SI based metaheuristic optimization algorithms called Particle Swarm Optimization (PSO) algorithm. This comprehensive review provides a valuable one-stop source of knowledge for the researchers and experts working on SI controller's applications for AC MG dynamic response and power quality improvements.