Hybrid Renewable Energy Sources (HRES) integrated into a microgrid (MG) are a cost-effective and convenient solution to supply energy to off-grid and rural areas in developing countries. This research paper focuses on the optimization of an HRES connected to a stand-alone microgrid system consisting of photovoltaics (PV), wind turbines (WT), batteries (BT), diesel generators (DG), and inverters to meet the energy demand of fifteen residential housing units in the city of Djelfa, Algeria. In this context, the multiobjective salp swarm algorithm (MOSSA), which is among the latest nature-inspired metaheuristic algorithms recently introduced for hybrid microgrid system (HMS) optimization, has been proposed in this paper for solving the optimization of an isolated HRES. The proposed multiobjective optimization problem takes into account the cost of energy (COE) and loss of power supply probability (LPSP) as objective functions. The proposed approach is applied to determine three design variables, which are the nominal power of photovoltaic, the number of wind turbines, and the number of battery autonomy days considering higher reliability and minimum COE. In order to perform the optimum size of HMG, MOSSA is combined with a rule-based energy management strategy (EMS). The role of EMS is the coordination of the energy flow between different system components. The effectiveness of using MOSSA in addressing the optimization issue is investigated by comparing its performance with that of the multiobjective dragonfly algorithm (MODA), multiobjective grasshopper optimization algorithm (MOGOA), and multiobjective ant lion optimizer (MOALO). The MATLAB environment is used to simulate HMS. Simulation results confirm that MOSSA achieves the optimum system size as it contributed 0.255 USD/kW h of COE and LPSP of 27.079% compared to MODA, MOGOA, and MOALO. In addition, the optimization results obtained using the proposed method provided a set of design solutions for the HMS, which will help designers select the optimal solution for the HMS.

The Energy Management Strategy (EMS) in Fuel Cell Hybrid Electric Vehicles (FCHEVs) is the key part to enhance optimal power distribution. Indeed, the most recent works are focusing on optimizing hydrogen consumption, without taking into consideration the degradation of embedded energy sources. In order to overcome this lack of knowledge, this paper describes a new health-conscious EMS algorithm based on Model Predictive Control (MPC), which aims to minimize the battery degradation to extend its lifetime. In this proposed algorithm, the health-conscious EMS is normalized in order to address its multi-objective optimization. Then, weighting factors are assigned in the objective function to minimize the selected criteria. Compared to most EMSs based on optimization techniques, this proposed approach does not require any information about the speed profile, which allows it to be used for real-time control of FCHEV. The achieved simulation results show that the proposed approach reduces the economic cost up to 50% for some speed profile, keeping the battery pack in a safe range and significantly reducing energy sources degradation. The proposed health-conscious EMS has been validated experimentally and its online operation ability clearly highlighted on a PEMFC delivery postal vehicle.

Over the last decade, flexible alternating current transmission systems (FACTS) have been crucial in ensuring optimal power distribution within modern power systems. A vital component of FACTS devices is the distribution static compensator (DSTATCOM), which is essential for maintaining a reliable power supply. It is commonly used for reactive power compensation, voltage regulation, and harmonic reduction. Determining the appropriate size and placement of DSTATCOMs is vital to ensuring their efficiency. This study introduces the improved gray wolf optimizer (I-GWO), a refined version of the classical gray wolf optimization (GWO) method. The I-GWO incorporates a dimension learning-based hunting (DLH) strategy to preserve population diversity, balance exploration and exploitation, and prevent the premature convergence of classical GWO. In this research, the I-GWO was applied to determine the optimum allocation and sizing of the DSTATCOMs, considering system constraints, including those presented by the intermittent and stochastic nature of the load and renewable energy resources, specifically wind and solar energy. The suggested approach was successfully tested on 33-, 69-, and 85-bus distribution systems and then compared with existing studies. The results demonstrated the I-GWO-based approach’s superiority in terms of reducing power losses, improving voltage profiles, and enhancing voltage stability.

Nowadays, research on electric drive control has become popular because hybrid methods to collect the advantages of individual controllers. Moreover, these methods aim to reach better performance and robust control even in the presence of uncertainties and disturbances which are typically evident in high-speed dynamics where the influence of external disturbances and modeling errors are more evident. Therefore, in this paper, a novel hybrid controller is proposed between the super-twisting algorithm based on high order design (HO-STA) and terminal sliding mode control (T-SMC) applied on a permanent magnet synchronous motor PMSM. Whereas, it accounts to deal with the weaknesses of both terminal sliding mode control and super twisting algorithm (STA) and at the same time combining their advantages; furthermore, it provides exceptional characteristics, including fast finite-time convergence, stabilization of the performance and its reaching law developer based on new design which contributes to reducing the chattering problems afflicted by C-SMC. This proposed hybrid technique contributes to gaining robust control under variation between slow, medium, and high speed levels, no matter what load torque is applied or whatever PMSM parameters change. Moreover, it also offers optimum performance characteristics such as smaller settling time and steady state error. Whereas, the control efficiency is demonstrated by Matlab/Simulink simulation to confirm our design parameters.

The low on-current and direct source-to-drain tunneling (DSDT) issues are the main drawbacks in the ultrascaled tunneling field-effect transistors based on carbon nanotube and ribbons. In this article, the performance of nanoscale junctionless carbon nanotube tunneling field-effect transistors (JL CNTTFETs) is greatly improved by using the synergy of electrostatic and chemical doping engineering. The computational investigation is conducted via a quantum simulation approach, which solves self-consistently the Poisson equation and the non-equilibrium Green’s function (NEGF) formalism in the ballistic limit. The proposed high-performance JL CNTTFET is endowed with a particular doping approach in the aim of shrinking the band-to-band tunneling (BTBT) window and dilating the direct source-to-drain tunneling window, while keeping the junctionless paradigm. The obtained improvements include the on-current, off-current, ambipolar behavior, leakage current, I₆₀ metric, subthreshold swing, current ratio, intrinsic delay, and power-delay product. The scaling capability of the proposed design was also assessed, where greatly improved switching performance and sub-thermionic subthreshold swing were recorded by using JL CNTTFET with 5 nm gate length. Moreover, a ferroelectric-based gating approach was employed for more enhancements, where further improvements in terms of switching performance were recorded. The obtained results and the conducted quantum transport analyses indicate that the proposed improvement approach can be followed to improve similar cutting-edge ultrascaled junctionless tunnel field-effect transistors based on emerging atomically thin nanomaterials.