Journal of Renewable Energies (Revue des Energies Renouvelables)

Experimental Study on Enhancing Lead Oxide Photoelectrochemical Efficiency with Tin Substrate Modifications for Renewable Energy Systems

2025-04-02T06:28:35+00:00

This work focuses on the photoelectrochemical and semicondictrise study of the corrosion layer formed in the dark on Lead and lead-tin alloys in a 0.5M sulfuric acid solution: Electrochemical Impedance Spectroscopy (EIS), Linear Sweeping Voltage (LSV), Mott-Schottky plots and photocurrent measurements. The composition was determined respectively by XRD diffraction and SEM electron microscopy. The findings indicate that the incorporation of tin leads to a reduction in the corrosion layer thickness while significantly enhancing its electrical conductivity. This effect is attributed to the formation of conductive, non-photoactive tin oxides and the development of a compact interfacial the layer separating the grid from the positive active material.

Simulation of Doubly Fed Induction generator (DFIG) for Steady state analysis when connected to a wind farm for power system stability

2025-04-02T06:34:58+00:00

In this paper, a 2MW variable speed, pitch-regulated Doubly-fed Induction Generator (DFIG) with a speed range of 800-2000 rpm was studied for steadystate analysis. The DFIG was modelled in the Matlab/Simulink environment. The rotor-side converter utilized closed- loop stator flux-oriented vector control for managing the DFIG model. This method allows for rapid control and experimenting of grid- connected, variable speed DFIG wind turbines to examine their steady-state and energetic characteristics beneath ordinary and disturbed wind conditions when connected to a wind farm. The steady-state behavior of the wind turbine generator was derived at two different magnetizing levels: one with the reactive power of the stator equal to zero (Qs = 0), and the other with the direct current of the rotor equal to zero (Idr = 0). Simulation results show that the machine has higher efficiency when magnetized through the stator as compared with magnetization of the machine through the rotor. To come out with the DFIG transitory stability simulation results traditional controllers' for active and reactive power were compared with an adaptive adaptive tracking, self-tuned feedforward proportional integral regulating model for peak performance. Additionally, stability and instability were studied by solving the Swing equation using the Runge-Kutta method of order four. In a steady-state condition for the generator, the acceleration torque (Ta) reaches zero, which signifies that the mechanical torque (Tm) matches the electrical torque (Te). In the stability investigation, Tm is assumed to be constant. The findings provide valuable insights into the control strategies required for enhancing the reliability and efficiency of wind turbines in variable wind conditions.

Quantifying Measurement Uncertainty in Photovoltaic Module Performance at Standard Test Conditions: A Machine-Based Approach

2025-04-02T06:56:51+00:00

This paper presents an in-depth analysis of measurement uncertainty in the output parameters of photovoltaic (PV) devices, focusing specifically on measurements conducted under standard test conditions (STC). Accurate characterization of module I-V performance is crucial for PV manufacturers, researchers, and investors alike. The study provides a comprehensive overview of the measurement procedures for both performance and temperature coefficient of PV modules, with a strong emphasis on the detailed calculation of associated uncertainties. Notably, the research was conducted under realworld sunlight conditions, with special attention given to reference devices such as reference cells, modules, or pyranometers, which play a pivotal role in determining overall uncertainty components. By utilizing a diverse array of machines from various sources, the results obtained are applicable across a wide range of measurement configurations. Furthermore, adherence to the ISO/IEC 17025 series of standards ensures a standardized and reliable approach. The novelty of this research lies in its comprehensive approach to uncertainty analysis, encompassing both performance and temperature coefficient measurements under real-world conditions. By providing valuable guidelines for PV module characterization and reliability assessment, particularly in uncertainty estimation, this study contributes significantly to the advancement of photovoltaic technology.

Charge carrier mobility and the recombination processes within a bulk heterojunction organic solar cell exhibiting disordered hopping

2025-04-02T07:22:32+00:00

This paper studies the interplay between charge carrier mobility and the related recombination processes exhibited within a bulk heterojunction-disordered hopping organic solar cell, using drift-diffusion simulations. The investigation focuses on the recombination order, the current-voltage properties and the charge carrier mobility’s active involvement in the recombination processes within an organic solar cell. The outcome of the investigation based on the drift diffusion simulation highlights the fact that the recombination characteristics are altered by charge carrier mobility. There exists a normalised mobility, which averages the progression of slow to fast charge carriers transforming the electrons and holes mobilities into an optimal mobility, which significantly increases the efficiency for a variety of bulk heterojunction structure types by significantly lowering the extent of recombination.

Applying the Generalized Pareto Principle to Predict Peak Temperatures in Northeast Algeria

2025-04-02T07:30:55+00:00

This study employs the Generalized Pareto Distribution (GPD) to model high temperature events at the Batna station, strategically applying varying thresholds to enhance accuracy and mitigate tail distribution bias. This was done by analyzing historical rainfall data from 1981 to 202. Using maximum likelihood estimation and an innovative approach to fitting multi-threshold GPDs, stable thresholds were established, particularly identifying the Pareto II type as the most suitable model for temperatures exceeding 25°C. Analysis revealed a consistent average temperature of 27.52°C, with a negative skewness indicating a bias toward lower values within extreme temperatures. The study also predicts return levels for different periods, providing critical temperature thresholds expected to be exceeded every 2, 20, 50, and 100 years. These findings contribute to understanding extreme temperature dynamics in the region, supporting urban planning, infrastructure design, and climate resilience strategies.

Analysis of a simple vapor compression and ejector refrigeration system working with eco-friendly refrigerants

2025-04-02T07:53:04+00:00

Transitioning to alternative refrigerants with low Global Warming Potential (GWP) in both vapor compression and ejector refrigeration systems emerges as a viable strategy to address the environmental impact associated with refrigeration technologies. This shift necessitates a thorough examination of factors such as thermodynamic performance, safety considerations, and optimization of system design. The outcomes of this study contribute to the advancement of sustainable refrigeration systems, aligning with global initiatives to curb greenhouse gas emissions and preserve the environment. The study adopts a thermodynamic approach to numerically investigate several eco-friendly refrigerants with GWP below 150, including R1234yf, R1234ze, R1270, R152a, R290, and R600a, as potential alternatives for vapor compression and ejector refrigeration systems. Thermodynamic models, developed in MATLAB using refrigerant properties, reveal that R600a and R290 exhibit promising potential as replacements for R134a in vapor compression refrigeration systems. These alternatives demonstrate a noteworthy improvement in the thermodynamic coefficient of performance, with percentages of 2.47% and 2.12%, respectively, under similar working conditions. For ejector refrigeration systems, R152a, R717, and R1270 exhibit enhanced coefficients of performance, contributing to significant savings in generator heat load. The results highlight the ability of these refrigerants to improve both the efficiency and sustainability of refrigeration systems in diverse applications.

IoT and AI for Real-time Water Monitoring and Leak Detection

2025-04-02T08:09:29+00:00

Water is essential for ecological sustainability and human survival, necessitating effective management to meet rising global demands and address climate change. Traditional water supply monitoring methods are labor-intensive and slow, limiting real-time data acquisition and issue resolution. This paper presents QoW-Pro, an IoT-based water monitoring system that leverages AI algorithms to significantly enhance water quality assessments and leak detection. QoW-Pro enables realtime data collection, predictive modeling, and anomaly detection, leading to improved decision-making in water resource management. The system demonstrates quantitative improvements in leak detection accuracy and water quality prediction, offering a scalable solution adaptable to both urban and agricultural settings. By combining IoT and AI, this research contributes to the sustainable management of water resources, ensuring their availability and quality for future generations.

Evaluating the Performance of Parabolic Trough Solar Power Plants in Algerian Deserts: A Case Study of Andasol-1

2025-04-02T08:21:32+00:00

Electricity generation through renewable energy sources is essential for addressing environmental and economic challenges caused by reliance on fossil fuels. The global energy sector is rapidly transitioning towards sustainable and renewable energy sources, with concentrated solar power (CSP) emerging as a promising technology, particularly parabolic trough solar power plants. This study examines the performance of the Andasol-1 Parabolic Trough Solar Power Plant, with a 50 MWe power output, in various locations of the Algerian desert, including Bechar, Djanet, and Tamanrasset regions. A detailed overview of the technical specifications of the Andasol-1 facility is presented, and a comprehensive economic and energy analysis is carried out using the System Advisor Model (SAM) software. Our findings indicate that the Djanet region emerged as the most favorable site for CSP deployment, with a capacity factor of 53.7% and a Levelized Cost of Electricity (LCOE) of 16.84 ¢/kWh, offering the best balance of energy yield and cost efficiency. These results contribute to the global transition to clean and economically advantageous energy sources and provide valuable insights into the viability and efficiency of CSP technologies in dry climates.

Influences of ternary ethanol methanol gasoline mixtures on the performance of a spark ignition engine

2025-04-02T08:37:02+00:00

The principal aim of this study is to determine the influence of ternary mixtures of gasoline, ethanol and methanol as fuels, on a spark ignition engine performance. Four samples of fuels were prepared with varying concentrations of each constituent. The different fuels studied are: EM0 (gasoline), EM5 (2.5% ethanol, 2.5% methanol, 95% gasoline), EM10 (5% ethanol, 5% methanol, 90% gasoline) and EM15 (7.5% ethanol, 7.5% methanol, 85% gasoline). A battery of tests (appearance, color, odor, lower heating value, density, research octane number, vapor pressure, total sulfur content and distillation curve) was carried out on these mixtures in order to determine their physicochemical properties and their ability to be used as fuel. Then, the different prepared mixtures were used as fuels to determine the performance of the 4-stroke, carbureted Renault 4 engine. These tests show that these additions give rise to a fuel with a lower sulfur level, a higher RON and a lower low heating value compared to unleaded gasoline and therefore improve, depending on the circumstances, the power of the fuel engine, its specific consumption, its speed and combustion. At the end of this study, it was concluded that the use of fuel mixtures was relevant for high speeds and also more suitable for total loads.

Smart Integration of Renewable Energy into Transportation: Challenges, Innovations, and Future Research Directions

2025-04-02T09:02:33+00:00

The integration of renewable energy into transportation systems is essential for reducing greenhouse gas emissions and achieving global sustainability goals. This review explores the challenges, innovations, and future directions of incorporating renewable energy sources such as solar, wind, and bioenergy into transportation infrastructures. Key challenges include the intermittency of renewable energy, the need for advancements in energy storage systems, and the regulatory and economic barriers hindering widespread adoption. Innovations such as electric vehicles (EVs), vehicle-to-grid (V2G) technologies, and smart grids are pivotal in enabling this transition. Furthermore, artificial intelligence (AI) and machine learning (ML) offer significant potential to optimize energy management, enhance efficiency, and facilitate the smooth integration of renewable energy with transportation systems. The review also discusses successful case studies from different regions and examines policy frameworks supporting renewable energy in transportation. Future directions point toward increased collaboration between industries, technological advancements, and supportive policies to create a more sustainable, resilient transportation sector. Ultimately, this review aims to provide a comprehensive understanding of how smart integration of renewable energy into transportation can drive a cleaner, more sustainable future.

Investigation on Syngas production from forest Biomass (Sapele, Sypo and Ayous) wood in the downdraft gasifier using Aspen plus and IC engine integration

2025-04-02T09:11:09+00:00

This study aims to evaluate the potential for syngas production from three types of equatorial forestry biomass Sapele, Sipo, and Ayous using a downdraft gasifier. The main objective is to assess the energy potential of the produced syngas when used in an internal combustion engine with a 30% efficiency, coupled with a DC generator operating at 93% efficiency. The research focuses on determining the composition and energy output of syngas from these biomass types, as well as exploring the integration of downdraft gasification with internal combustion engines for localized energy production, particularly in decentralized power systems and microgrids. The research involved modeling and simulating the gasification process using Aspen Plus software. Proximate and ultimate analyses of the biomass samples were used to simulate gasification in a downdraft gasifier. The gasifier's performance was assessed through syngas composition analysis, focusing on energy output and overall system efficiency. The integration of downdraft gasification with an internal combustion engine was simulated to determine its feasibility in decentralized power systems and microgrids. The study revealed that all three biomass types produced syngas with high concentrations of carbon monoxide (CO) and hydrogen (H2), which are critical for energy generation. The lower heating value (LHV) of the syngas remained consistent among the samples, with Sapele at 13.51 MJ/Nm³, Sipo at 13.63 MJ/Nm³, and Ayous at 13.54 MJ/Nm³. Ayous achieved the highest gasification efficiency at 79.36%, followed by Sipo at 78.89% and Sapele at 76.05%. The simulation performed in Aspen Plus showed that Ayous produced the highest electric power output at 53.55 kW, followed by Sapele at 51.86 kW and Sipo at 49.18 kW. The results highlight the potential of equatorial forestry biomass, particularly Ayous, as a renewable energy source. All three biomass types can generate syngas of comparable quality in terms of energy content, with Ayous showing the highest efficiency. This research emphasizes the feasibility of using equatorial biomass for syngas production and its integration into internal combustion engines, supporting decentralized energy generation and reducing reliance on fossil fuels.

Long Short-Term Memory Approach to Predict Battery SOC

2025-04-02T09:32:10+00:00

Estimating the ‘State of Charge’ (SOC) is a complex endeavour. Data-driven techniques for SOC estimation tend to offer higher prediction accuracy compared to traditional methods. With the progression of Artificial Intelligence (AI), machine learning has found extensive applications across various fields such as infotainment, driver assistance systems, and autonomous vehicles. This paper categorizes the machine learning techniques utilized in Battery Management System (BMS) applications and employs a modern supervised neural network approach to predict SOC. Accurate SOC estimation is crucial to prevent battery failures in critical situations, such as during heavy traffic or when traveling with limited access to charging stations. Long Short-Term Memory (LSTM) networks are particularly adept at classifying, processing, and predicting based on time series data. These models are capable of capturing and retaining features over time, making them suitable for this study. The model's predicted SOC closely matches the true SOC, and the SOC prediction error remains nearly zero even with a large sample of input data.

Brief Review of Current Research on Electrical Grids

2025-04-02T09:45:25+00:00

Electrical grids play a crucial role in the global energy transition by providing reliable energy transport. They must accommodate the integration of decentralized renewable energy systems (RESs), which vary in scale, generation rates, and intermittency. Ensuring this integration requires modern grids with enhanced performance and reliability. Today, various types of electrical grids are undergoing modernization, especially with the rise of electric vehicles. Traditional power grids are being equipped with advanced solutions across transmission, sub-transmission, and distribution networks. This modernization also extends to microgrids, high-voltage direct current (HVDC) systems, and wide-area synchronous grids, contributing to the emergence of new concepts such as supergrids and smart grids. This paper reviews rapidly the current state of research on electrical grids development, emphasizing critical concepts related to grids modernization and intellectualization. It aims to serve researchers, academics, and utility engineers interested in the latest advancements in the field. The aim of this work is unique and has not been addressed before, as the topics related to electrical grids are dispersed throughout the literature in large quantity, but they have not been reviewed as a cohesive whole.

Impact of weather variables on roof top green energy production in a multibuilding, multi-capacity ecosystem – A regression based study of 'SAI MITRA’

2025-04-02T09:50:25+00:00

The importance of green energy in the current times cannot be understated. The ill effects of traditional forms of energy generation, make solar energy one of the most environmentally friendly alternatives. SAI MITHRA – a roof top multi building, multi-capacity solar energy generation system, executed by Sri Sathya Sai Central Trust at Prasanthinilayam in South India is a prime example for promotion of green energy. Using a regression model, this study attempts to understand the impact of weather variables on solar energy production across different production capacities using high frequency daily data. In order to provide predictive insights, the impact of weather variables with t-1 and t-2 days lags on solar energy generation have also been studied. The study identifies the three important weather variables that have an impact on solar energy production – atmospheric pressure, relative humidity and dew point temperature. The insights from the paper are relevant for multi-capacity solar energy systems for improving operational efficiencies and promoting green energy ecosystems.