Solar photovoltaic (PV) generation suffers from multiple serious issues, for example shading effects directly affect the performance of installed PV arrays. By studying these issues, it can play a key role in improving the limitations and performance of PV panels. The study of PV arrays under partial shading conditions is mentioned in many papers as a part of other studies and as an independent phenomenon. Therefore, it is essential to improve these studies and update them with new research and study this problem in detail to know how to limit the effects of partial shading irradiance conditions on PV arrays performances, including economical aspects. This paper proposes to study the effects of different partial shading irradiance conditions for a PV array in various configurations. The analysis is proved in detail through the simulation of the proposed PV array in the Matlab/Simulink environment. The obtained results show the performances of interconnected PV arrays under different partial shading irradiance and temperature conditions, including the solar array I-V and P-V characteristics. In addition, different PV array configurations with and without bypass and blocking protection diodes improve and minimize the effect of partial shading conditions and avoid the reductions in output power.

This paper presents the modeling and simulation of a vehicle-to-grid (V2G) system used to regulate the frequency in a microgrid (MG) with distributed energy resources (DERs) including a distributed diesel generator (DG), renewable energy sources (RES) like photovoltaic (PV) arrays and wind turbines (WTs), and the residential and industrial loads of the electrical grid. To see the impact of the V2G system on the grid frequency, the proposed MG system is simulated using MATLAB/Simulink software in different operating conditions. Analysis results show the power profile (generation and consumption) and the impact of the V2G system on peak regulation. It has been shown that the proposed V2G system can use the available power to regulate the frequency in the MG when different events affect the grid frequency.

The urgent need to reduce greenhouse gas (GHG) emissions and dependency on fossil fuel has accelerated the global shift towards electric mobility solutions based on photovoltaic (PV) systems with battery energy storage systems. However, electric vehicles (EVs) charging infrastructure is still unevenly distributed, especially in rural and climatically sensitive regions. This paper presents an original study on technical feasibility of stand-alone PV-powered EVs charging stations (PVCSs) in Ghardaïa, Algeria, using an assessment based on MATLAB simulation and considering careful design, seasonal variation in irradiance, thermal losses, and storage autonomy. The study confirms that stand-alone PV-battery systems can satisfy the EVs charging needs in the off-grid context of Ghardaïa for most of the year. However, seasonal variations highlight potential constraints of the system. Although the current configuration is resilient during peak solar months, careful consideration of storage, load management, and adding other energy sources during periods of low solar irradiance is needed. These findings reinforce the need for sitespecific, climate-sensitive system design to ensure year-round energy reliability in the Saharan environment.

The Perovskite solar cells (PSCs) have surpassed 26% laboratory efficiencies, making them promising for nextgeneration photovoltaic (PV) technologies. However, their commercial adoptions remain challenged by rapid degradation under fluctuating environmental conditions. While conventional drift-diffusion models focus on steady-state conditions, they do not capture the dynamic interplay between temperaturehumidity cycle and material degradation mechanisms that dictate long-term device stability. This paper introduces a novel, compact MATLAB-based drift-diffusion framework that incorporates daily cyclic environmental profiles and updates material parameters over time to simulate outdoor operating conditions realistically. The model integrates Poisson's and modified continuity equations, allowing key material properties such as carrier mobility, lifetime, and trap density, to dynamically respond to sinusoidal changes in temperature $\left (\pm 10^{\circ} \mathrm{C}\right)$ and relative humidity ($\pm 20 \%$), representative of natural diurnal cycles. Full validation against recent literature shows excellent agreement with simulated current-voltage (I-V) characteristics, spatial carrier distributions, and thermal responses matching experimental triple-cation PSCs data within 5%. By optimizing absorber thickness under environmental stress, the framework uncovers design rules that improve power conversion efficiency ($P C E$) from 18.9 % to 20.2 % while enhancing longevity. The optimum thickness of $\sim 500 ~\text{nm}$ balances absorption enhancement and recombination loss maintaining performance without degrading stability. This computationally efficient platform provides for researchers and industry practitioners a valuable tool for rapid pre-prototyping and stability screening, accelerating the transition from laboratory discoveries to practical commercial PSC products.

The rise of electric vehicles (EVs) and renewable integration such as photovoltaic (PV) sources has made autonomous PV-powered battery EVs (BEVs) charging stations (PVCSs) an essential solution for sustainable electrified transportation. This work presents an original study of such PVCSs, leveraging Gaussian mixture model (GMM) scenario creation, human-in-the-loop analytics, and state-of-the-art Matlab modeling. The originality consists in combining realistic behavioral scenario generation, robust state-of-charge (SOC) and load supervision, and a suite of new visual figures for transparent, user-centric academic evaluation. The “human-in-the-loop” approach, which involves incorporating real-world user behaviors and unpredictability into the computer simulation, is a significant aspect of this research. By capturing daily/behavioral dynamics, renewable energy sources fluctuation, and storage effects across five analytics figures, it has been demonstrated how smart controls and data-driven insights can enhance self-sufficiency and robustness. Results highlight the design trade-offs, detect potential vulnerabilities, and demonstrate the viability of off-grid PVCSs even under human behavioral uncertainty.