The relentlessly depleting fossil-fuel-based energy resources worldwide have forbidden an imminent energy crisis that could severely impact the general population. This dire situation calls for the immediate exploitation of renewable energy resources to redress the balance between power consumption and generation. This manuscript confers about energy management tactics to optimize the methods of power production and consumption. Furthermore, this paper also discusses the solutions to enhance the reliability of the electrical power system. In order to elucidate the enhanced reliability of the electrical system, microgrids consisting of different energy resources, load types, and optimization techniques are comprehensively analyzed to explore the significance of energy management systems (EMSs) and demand response strategies. Subsequently, this paper discusses the role of EMS for the proper consumption of electrical power considering the advent of electric vehicles (EVs) in the energy market. The main reason to integrate EVs is the growing hazards of climate change due to carbon emissions. Moreover, this paper sheds light on the growing importance of artificial intelligence (AI) in the technological realm and its incorporation into electrical systems with the notion of strengthening existing smart grid technologies and to handle the uncertainties in load management. This paper also delineates the different methodologies to effectively mitigate the probability of facing cyber-attacks and to make the smart grids invulnerable.

The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

This paper proposes an efficient load frequency control (LFC) technique based on a fractional-order proportional–integral–derivative–accelerator with a low-pass filter compensator (FOPIDA-LPF) controller, which can also be accurately referred to as the PIλDND2N2 controller. A trustworthy metaheuristic optimization algorithm, known as the gray wolf optimizer (GWO), is used to fine-tune the suggested PI^λDND²N² controller parameters. Moreover, the proposed PI^λDND²N² controller is designed for the LFC of a self-contained hybrid maritime microgrid system (HMμGS) containing solid oxide fuel cell energy units, a marine biodiesel generator, renewable energy sources (RESs), non-sensitive loads, and sensitive loads. The proposed controller enables the power system to deal with random variations in load and intermittent renewable energy sources. Comparisons with various controllers used in the literature demonstrate the excellence of the proposed PI^λDND²N² controller. Additionally, the proficiency of GWO optimization is checked against other powerful optimization techniques that have been extensively researched: particle swarm optimization and ant lion optimization. Finally, the simulation results performed by the MATLAB software prove the effectiveness and reliability of the suggested PI^λDND²N² controller built on the GWO under several contingencies of different load perturbations and random generation of RESs. The proposed controller can maintain stability within the system, while also greatly decreasing overshooting and minimizing the system’s settling time and rise time.

The electric vehicle (EV) is one of the most important and common parts of modern life. Recently, EVs have undergone a big development thanks to the advantages of high efficiency, negligible pollution, low maintenance, and low noise. Charging stations are very important and mandatory services for electric vehicles. Nevertheless, they cause high stress on the electric utility grid. Therefore, renewable energy-sourced charging stations have been introduced. They improve the environmental issues of the electric vehicles and support remote area operation. This paper proposes the application of fuzzy control to an isolated charging station supplied by photovoltaic power. The system is modeled and simulated using Matlab/Simulink. The simulation results indicate that the disturbances in the solar insolation do not affect the electric vehicle charging process at all. Moreover, the controller perfectly manages the stored energy to compensate for the solar energy variations. Additionally, the system response with the fuzzy controller is compared to that with the PI controller. The comparison shows that the fuzzy controller provides an improved response.

Lack of synchronization between high voltage DC systems linking offshore wind farms and the onshore grid is a natural consequence owing to the stochastic nature of wind energy. The poor synchronization results in increased system disturbances, grid contingencies, power loss, and frequency instability. Emphasizing frequency stability analysis, this research investigates a dynamic coordination control technique for a Double Fed Induction Generator (DFIG) consisting of OWFs integrated with a hybrid multi-terminal HVDC (MTDC) system. Line commutated converters (LCC) and voltage source converters (VSC) are used in the suggested control method in order to ensure frequency stability. The adaptive neuro-fuzzy inference approach is used to accurately predict wind speed in order to further improve frequency stability. The proposed HVDC system can integrate multiple distributed OWFs with the onshore grid system, and the control strategy is designed based on this concept. In order to ensure the transient stability of the HVDC system, the DFIG-based OWF is regulated by a rotor side controller (RSC) and a grid side controller (GSC) at the grid side using a STATCOM. The devised HVDC (MTDC) is simulated in MATLAB/SIMULINK, and the performance is evaluated in terms of different parameters, such as frequency, wind power, rotor and stator side current, torque, speed, and power. Experimental results are compared to a conventional optimal power flow (OPF) model to validate the performance.