The microgrid has two main steady-state modes: grid-connected mode and islanded mode. The microgrid needs a high-performance controller to reduce the overshoot value that affects the efficiency of the network. However, the high voltage value causes the inverter to stop. Thus, an improved power-sharing response to the transfer between these two modes must be insured. More important points to study in a microgrid are the current sharing and power (active or reactive) sharing, besides the match percentage of power sharing among parallel inverters and the overshoot of both active and reactive power. This article aims to optimize the power response in addition to voltage and frequency stability, in order to make this network’s performance more robust against external disturbance. This can be achieved through a self-tuning control method using an optimization algorithm. Here, the optimized droop control is provided by the H-infinity (H∞) method improved with the artificial bee colony algorithm. To verify the results, it was compared with different algorithms such as conventional droop control, conventional particle swarm optimization, and artificial bee colony algorithms. The implementation of the optimization algorithm is explained using the time domain MATLAB/SIMULINK simulation model.

The synchronization and power-sharing among parallel inverters beside the voltage stability and frequency are important functions to be achieved in the microgrid. This paper investigates power sharing and controller is applied to limit voltage and frequency deviation. The voltage and frequency stability can be improved using a droop control with optimization technique for power (active and reactive) sharing of parallel distributed generation (DG) units. Where conventional droop control is not accurate share of output current and power among inverters. Therefore, particle swarm optimization (PSO) is a smart control strategy to reduce the frequency and voltage deviation, reduce the harmonic current, and it can accurately power sharing with elimination a mismatched among inverters or DG. The results of the control strategies are verified in simulation using Matlab/Simulink.

Although composite high-pressure tanks are a subject of growing interest, especially for hydrogen storage applications, a detailed structural reliability analysis still needs to be improved. This work aims to provide a probabilistic investigation of the mechanical response of composite high-pressure hydrogen storage tanks using the Monte Carlo Simulation method. A performance function based on the circumferential model of composite pressure cylinders is employed with five random design variables. According to the results, the internal pressure and the helical layer thickness are the foremost parameters significantly impacting the structural reliability of the tank, whereas, the helical layer thickness and winding angles have a minor influence. In addition, high coefficients of variation values cause the contraction of the safety margin potentially leading to the failure of the composite hydrogen high-pressure tank. The obtained results were validated with experimental tests available in the literature

The increasing deployment of Renewable Energy Sources (RESs), particularly wind and solar power, plays a critical role in reducing carbon emissions and supporting sustainable energy transitions. However, the large-scale integration of RESs into smart grids introduces significant technical challenges related to frequency stability, voltage regulation, rotor angle stability, power quality, inertia reduction, harmonic distortion, reverse power flow, Sub-Synchronous Interactions (SSIs), and protection coordination. Although numerous review studies have examined renewable energy integration, most focus on high-level frameworks, bibliometric analyses, optimization techniques, or isolated applications of artificial intelligence (AI) while lacking a comprehensive synthesis that bridges AI-driven solutions with the physical dynamics, control mechanisms, and protection requirements of highly renewable power systems. To address this gap, this review provides a comprehensive technical assessment of wind generator topologies, solar inverter architectures, grid-forming and grid-following control strategies, virtual inertia and virtual Synchronous Generator (SG) technologies, adaptive load-frequency control, energy storage integration, protection coordination, and real-time stability enhancement techniques for high-RES smart grids. Furthermore, the review systematically examines the role of AI in frequency regulation, voltage control, harmonic mitigation, predictive operation, parameter optimization, and system resilience. Unlike previous reviews, this study integrates physical-layer perspectives by connecting AI-driven decision-making with practical grid control mechanisms, inverter dynamics, wide-area monitoring, microgrid operation, High Voltage Direct Current (HVDC) interconnections, EV/Vehicle-to-Grid (V2G) integration, and multi-resource energy management. The review identifies key research priorities, including the development of real-time AI-assisted frequency control, adaptive protection schemes for low-inertia systems, coordinated grid-forming inverter control, resilient autonomous grid operation, and scalable multi-energy management frameworks. The findings provide actionable guidance for researchers, utilities, policymakers, and industry stakeholders seeking to enhance stability, reliability, and operational flexibility in future smart grids with very highly renewable energy penetration.