This work presents the operation of an autonomous direct current (DC) DC microgrid for residential house controlled by an energy management system based on low complexity fuzzy logic controller of only 25-rules to manage the power flow that supply house load demand. The microgrid consists of photovoltaic (PV), wind turbine, fuel cell, battery energy storage and diesel generator. The size of the battery energy storage is determined based on the battery sizing algorithm depending on the generation of renewables during all seasons of the year in the eastern region of Saudi Arabia. Two scenarios are considered in this work. In the first scenario: the microgrid consists of solar PV, wind turbine, battery energy storage and fuel cell. The fuzzy logic controller is optimized using an artificial bee colony technique in order to increase the system energy saving efficiency and to reduce the cost. In the second scenario: wind turbine is replaced by a diesel generator, also the rated power of the fuel cell is reduced. In this scenario, a new method is proposed to reduce the generation cost of the dispatchable sources in the microgrid by considering economic dispatch within the optimized fuzzy logic energy management system. To obtain the most suitable technique for solving the economic dispatch problem, three optimization techniques were used which are particle swarm optimization, genetic algorithm and artificial bee colony based on real environmental data and real house load demand. A comparison in terms of energy saving between the two scenarios and a comparison in terms of cost reduction between conventional economic dispatch method and the proposed method are presented.

The shift toward electric vehicles (EVs) is inspired by the commitment to mitigate emissions and reliance on fossil fuels, with drive systems at the core of the transformation. EVs replace the traditionally internal combustion engines (ICE) by adopting electric motors powered by batteries. However, there are major issues that may complicate the adoption of EVs including battery limitations, energy density, charging speed, insufficient charging stations, cost, and reliance on critical materials for instance, lithium and cobalt. This paper provides a holistic review about powertrain technologies for EVs discussing the key elements to be considered to ensure efficient charging system, high output power to increase the charging speed, etc. These can be achieved by incorporating cutting-edge technologies in switching power electronic devices (i.e., utilizing silicon-carbide and gallium-nitride), broadly efficient power topologies, leveraging solid-state and silicon anode batteries, thermal management, etc. It also explores several critical technological challenges that impede the performance of EVs, including battery degradation, charging infrastructure, and other related issues along with solutions to address these challenges, such as advancements in battery chemistry, the development of higher energy-density materials, and the implementation of advanced thermal management systems to enhance overall efficiency and longevity. In top of that, this review clearly highlights the classification of the recently adopted power train technologies, broadly adopted converters, as well as future trends on breakthroughs in next-generation solid-state batteries with higher energy density and faster charging, and charging station accessibility improvement. It also offers a constructive insight on integrating some renewable energy sources to enhance EV accessibility and sustainability. Overall, while challenges remain, innovations in batteries, electronics, and infrastructure are paving the way for broader EV adoption.

Electrical grounding is an indispensable part of the power system network. The grounding system is mainly affected by grounding resistance and the nature of the soil. High ground resistance produces the phenomenon of soil ionization, surface arching, and back flashover. A conventional grounding system requires the deep digging of electrodes, thus creating maintenance difficulties. This research work focuses on the safe operation of an electric power system from external and internal impulses arising due to lightning strikes or short circuits. The study proposes an application of mineral samples as grounding materials, and bentonite is used as backfilling material in portable grounding systems. A detailed experimental analysis was conducted under controlled conditions to evaluate the performance of selected materials in high-resistance soil. The problem of a deeply driven electrode is addressed by designing the portable grounding system. The study results demonstrate that the proposed portable grounding system could be installed in troubled environments such as forests, deserts, and rocky terrains. To measure the breakdown voltages of the proposed samples, X-ray Diffraction (XRD) analysis and other laboratory tests were conducted. The electric field intensities are extracted through Finite Element Analysis (FEA). The experimental and simulation findings show the expected performance of mineral samples under various operating conditions. The findings of this study can guide the practitioners for safe and efficient operations of portable electrical grounding systems.

In this paper, economic feasibility of installing small-scale solar photovoltaic (PV) system is studied at the residential and commercial buildings from an end-user perspective. Based on given scenarios, the best sizing methodology of solar PV system installation has been proposed focusing primarily on the minimum payback period under given (rooftop) area for solar PV installation by the customer. The strategy is demonstrated with the help of a case study using real-time monthly load profile data of residential as well as commercial load/customers and current market price for solar PVs and inverters. In addition, sensitivity analysis has also been carried out to examine the effectiveness of net metering scheme for fairly high participation from end users. Since Saudi Arabia’s Electricity and Co-generation Regulatory Authority (ECRA) has recently approved and published the net metering scheme for small-scale solar PV systems allowing end users to generate and export energy surplus to the utility grid, the proposed scheme has become vital and its practical significance is justified with figures and graphs obtained through computer simulations.

This paper proposes an advanced control approach to controlling a DC-DC buck converter for a proton exchange membrane (PEM) electrolyzer within the framework of a direct current (DC) microgrid. The proposed adaptive backstepping terminal sliding mode control (ABTSMC) leverages a physics-informed neural network (PINN) to accurately estimate and compensate for system uncertainty. The composite controller achieves finite-time convergence of the tracking error by combining backstepping control and terminal sliding mode control (TSMC). The proposed PINN aims to optimize the unconstrained parameters by utilizing observed training points from the solution, ensuring the network accurately interpolates a limited portion of the solution. The efficacy of the proposed hybrid control method is validated using a hardware-in-the-loop (HIL) implementation under various test settings, ensuring the preservation of the actual performance of the PEM electrolyzer during testing. The experimental verification results demonstrate that the proposed control method exhibits greater benefits, such as a faster dynamic response and greater robustness against parameter uncertainties than improved sliding mode-based controllers. In situations where operational conditions change, a rapid response is achieved within a mere $\mathbf{0.025}\mathit{s}$ of settling time, exhibiting a minimal percentage overshoot of about $\mathbf{17.5}\%$ and presenting minimal fluctuations.