Kouider Laroussi

Amid the global shift to renewable energy, fault diagnosis systems are crucial for stabilizing and managing solar and wind systems. This work presents a nonlinear, observer-based fault diagnostic system for photovoltaic systems using a voltage source converter (VSC). By employing an extended...

This paper presents a comprehensive techno-economic analysis of three molten salt Concentrated Solar Power (CSP) tower plants located in the regions of Mechria, Adrar, and Tindouf in Algeria. The study evaluates the thermal efficiency, economic feasibility, and performance of these CSP using the System Advisor Model (SAM) software, which accurately models Direct Normal Irradiance (DNI), a critical factor influencing plant performance. Key parameters analyzed include Solar Multiple (SM), Thermal energy storage (TES) hours, capacity factor (CF), and the Levelized Cost of Energy (LCOE). The results demonstrate that an optimal heliostat field configuration with a SM of 1.8 and 10 h of TES achieves a capacity factor of 51.49%, with a minimum LCOE of 0.097 $/kWh. In Mechria, with operational and maintenance costs projected at 2.51 million dollars. For the Adrar region, a SM of 1.6 and TES of 2 h yield an LCOE of 0.18 $/kWh at a capacity factor of 24.03%. Similarly, in Tindouf, a SM of 1.6 and TES of 8 h result in a capacity factor of 18.95% and an LCOE of 0.17 $/kWh. The analysis reveals that the design of CSP systems, particularly the combination of solar Multiple and TES, plays a pivotal role in optimizing the economic performance of the plants, This approach enables researchers to save time and costs by using satellite-derived DNI estimations, enhancing data accuracy and optimizing CSP deployment.

This study addresses the critical challenges of voltage stabilization in DC microgrids, where the inherent variability of renewable energy sources significantly complicates reliable operation. The focus is on optimizing the fractional-order proportional-integral (FO-PI) controller using four advanced techniques a whale optimization algorithm (WOA), grey wolf optimizer (GWO), genetic algorithm (GA), and sine cosine algorithm (SCA). Voltage instability poses substantial risks to the reliability and efficiency of DC microgrids, making the optimization of the FO-PI controller an essential task. Through comparative analysis, the study demonstrates that WOA outperforms the other methods, achieving superior voltage stability, resilience, and overall system performance. Notably, WOA achieves the lowest average cost function at 0.0004, compared to 0.892 for GWO, 0.659 for GA, and 0.096 for SCA, showcasing its effectiveness in fine-tuning the controller’s parameters. These findings highlight WOA robustness as a powerful tool for enhancing microgrid performance, especially in voltage regulation. The study underscores WOA potential in ensuring the reliable and efficient integration of renewable energy systems into DC microgrids and lays the groundwork for further research into its application in more complex and dynamic grid scenarios. By optimizing the FO-PI controller, WOA significantly contributes to the long-term stability and efficiency of DC microgrids.

This study proposes an advanced optimization technique for maximum power point tracking (MPPT) in wind turbines (WTs) based on a permanent magnet synchronous generator (PMSG), which is crucial for maximizing energy extraction under varying wind conditions. Several MPPT strategies have been evaluated and compared, including neural networks (NNs), sliding mode control (SMC), the Whale Optimization Algorithm (WOA), and the Cuckoo Search Algorithm (CSA), to determine the most effective approach for optimizing power output and improving system efficiency. Emphasis is placed on identifying techniques that not only enhance energy capture but also reduce the complexity and cost of wind energy systems. To achieve this, the study introduces a novel hybrid algorithm that integrates the strengths of both WOA and CSA, leveraging their complementary exploration and exploitation capabilities. The proposed method aims to deliver improved tracking accuracy and faster convergence to the optimal power point. The algorithms were tested using a real wind profile from Djelfa, Algeria, a region characterized by semiarid climate and varied topography, to simulate realistic operational scenarios, providing accurate assessments of each MPPT strategy under true environmental conditions. The results obtained through MATLAB/Simulink simulations demonstrate that the newly developed hybrid WO–CSA strategy consistently outperformed others, delivering approximately 140 W more power than CSA and about 230 W more than WOA and NN at a wind speed of 10 m/s, while the SMC strategy exhibited the lowest performance, generating roughly 750 W less power compared to WOA and NN. By developing the new algorithm, the study contributes to the development of more efficient and reliable WT technologies.

The increasing demand for electric vehicles (EVs) is driven by the urgent need for environmentally friendly transportation. This paper addresses the challenge of optimizing EV drivetrain efficiency by proposing a novel single-inverter electronic differential system for distributed EV drivetrains. The research focuses on reducing system cost and complexity while maintaining high performance. The methodology involves a detailed simulation using MATLAB/Simulink to validate the theoretical soundness of the proposed connection method. The results demonstrate that the proposed system achieves a minimum accuracy rate of 97.5%, marking a significant improvement over traditional dual-inverter systems. This approach not only enhances drivetrain efficiency but also contributes to more compact and cost-effective vehicle designs. Additionally, the findings underscore the potential for further refinement and exploration, suggesting that continued advancements in ED systems could lead to even greater performance gains in the future. This research lays the groundwork for future innovations in EV technology, particularly in the areas of cost reduction and system efficiency.

This research endeavors to enhance the efficiency and functionality of electric vehicle (EV) energy storage systems by scrutinizing the state of charge (SOC) across three batteries. Utilizing Matlab Simulink, a comprehensive battery management system (BMS) integrating passive cell balancing techniques has been meticulously devised. Additionally, a pioneering protective mechanism has been proposed to prevent undue discharges and overcharges, thus ensuring the longevity and reliability of EV battery systems. The study's findings underline the significance and applicability of the developed model in facilitating prospective enhancements within the realm of EV energy storage systems. By meticulously analyzing SOC dynamics across multiple batteries, the study offers valuable insights into optimizing battery utilization, prolonging battery lifespan, and enhancing overall system performance. Moreover, the integration of passive cell balancing techniques within the BMS framework represents a noteworthy advancement in battery management strategies. This approach not only promotes uniformity in SOC distribution among battery cells but also minimizes energy losses and mitigates the risk of cell degradation, thereby bolstering the operational efficiency and durability of EV energy storage systems. Furthermore, the proposed protective mechanism serves as a proactive measure to safeguard against potential damage caused by extreme operating conditions, such as overdischarge and overcharge events. By implementing real-time monitoring and control mechanisms, the protective system ensures the safe and reliable operation of EV batteries under varying driving conditions, thereby enhancing both safety and performance aspects. In conclusion, this research contributes to the ongoing efforts aimed at advancing the state-of-the-art in EV energy storage systems, holding immense potential for fostering innovation and driving sustainable progress in the field of electric mobility.

In addressing the critical challenge of developing sustainable energy solutions for electric vehicle (EV) battery charging, this study introduces an innovative direct current (DC) microgrid system optimized for areas with high solar irradiance, such as Ain El Ibel, Djelfa. The research confronts two primary difficulties: maximizing solar energy utilization in the microgrid system and ensuring system stability and response accuracy for reliable EV charging. To tackle these challenges, the study presents two original achievements. Firstly, it develops a neural network-enhanced Maximum Power Point Tracking (MPPT) controller, which is further optimized with Particle Swarm Optimization (PSO) to increase the efficiency of solar energy capture. Secondly, it refines the system's reliability through the advanced calibration of a Fractional Order Proportional-Integral (FOPI) controller using the Grey Wolf Optimization (GWO) technique, marking a notable improvement in microgrid system stability and response accuracy. The integration of a solar panel array, battery storage, and a supercapacitor, coupled with these advanced optimization techniques, exemplifies a significant leap forward in enhancing efficiency and reliability of EV battery charging through renewable energy sources. Comprehensive simulation and evaluation of the system underscore its superiority over conventional methods, demonstrating the effectiveness of combining neural network-based optimization with PSO and GWO. This breakthrough not only advances the field of renewable energy, particularly for solar-powered EV charging stations, but also aligns with global efforts towards sustainable transportation solutions.

In this study, photovoltaic (PV) panels, lithium battery storage systems, and supercapacitors are integrated to enhance the reliability and stability of standalone microgrids. Despite their importance, standalone microgrids face challenges relating to intermittent energy sources and voltage fluctuations in remote and off-grid areas.Using supercapacitors in conjunction with batteries can enhance power delivery and protect battery systems. This study investigates how supercapacitors can enhance energy efficiency, extend battery life, and ensure a robust power supply. The advantages of supercapacitor-battery hybrid systems, as well as mechanisms that ensure seamless energy management and battery protection, are explored in this study. In energy storage and electrification applications, these hybrid systems provide enhanced performance, reliability, and longevity by utilizing supercapacitors. It has been found that voltage stability and energy management have been significantly improved, indicating the potential for sustainable energy solutions in remote locations.