This study analyzes the Ground-Level Solar Thermal Accelerated Turbine (GSTAT) system, a novel solar thermal technology that eliminates tall chimney structures while maintaining efficient energy conversion. The system features a 244 m diameter main collector, 117 m secondary collector, 10 m horizontal turbine, and 5 m crushed gravel thermal storage layer. Mathematical modeling employs Navier-Stokes equations, energy equations with Boussinesq approximation, and k-ε turbulence modeling. Under 1000 W/m² solar insolation, the system achieves 420 K ground surface temperature (112 K rise), accelerating airflow from 1 m/s inlet velocity to 14 m/s at the turbine. The crushed gravel storage layer provides 89.73% collector efficiency and enables extended operation beyond peak solar hours. CFD analysis confirms smooth velocity transitions and optimal thermal gradients throughout the system. Net electrical output reaches 163.35 kW, accounting for turbine efficiency (85%), generator efficiency (92%), and overall system losses (95% efficiency). Economic analysis yields an LCOE of $0.22-0.33/kWh, competitive with conventional solar chimney systems ($0.15-0.30/kWh) while offering reduced construction complexity and land use requirements. The GSTAT system shows particular promise for distributed power generation where tall structures are impractical, providing a scalable alternative to conventional solar updraft towers.
The increasing demand for energy and the imperative to reduce greenhouse gas emissions have heightened the need for renewable energy sources. Hence, there has been a notable surge in research efforts focused on advancing solar energy forecasting. The aim of this study is to forecast solar radiation using Deep Neural Network models, including Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU), based on historical solar radiation values recorded at half-hour intervals in a semi-desert climate over a two-year period starting in January 2020. To assess and compare the accuracy of the two Deep Neural Networks (DNNs), various evaluation metrics were used, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Maximum Error, and R-squared (R²). Solar radiation predictions were carried out based on the values recorded over the previous twenty-four hours at half-hour intervals. The results obtained indicate that both forecasting models achieve exceptional accuracy in one-step-ahead solar radiation prediction, with correlation coefficients exceeding 97.2%. This highlights the strong potential of Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) models for optimizing and estimating solar radiation. Consequently, these models can be effectively used to estimate solar energy production, thereby enhancing the control and efficiency of solar energy systems. However, the study also highlights challenges in forecasting under partial cloud cover, where sudden fluctuations in solar radiation adversely affect prediction accuracy. Addressing these complexities could further improve the robustness of forecasting models and enhance their practical applicability in semi-desert climates.
Effective energy management is necessary for reliable power distribution and the best use of resources in DC microgrid hybrid energy systems. This study outlines an energy management method for an autonomous DC microgrid that incorporates a photovoltaic (PV) generator and a battery li-ion coupled to a DC-linkby converters DC/DC, accommodating both DC and AC loads. The photovoltaic generator is linked to a boost converter. This converter is controlled via a MPPT algorithm that uses perturbation and observation (P&O) to find the best way to get solar power when the amount of sunlight changes. The battery is connected to a buck-boost bidirectional converter at the same time, and a fuzzy logic-based energy management system (EMS) controls how it works.This controller regulates the selection of the battery operating mode (charge/discharge) based on load demand and the state of charge (SOC) of the battery, there by maintaining the stability of the DC-link voltage. The incorporation of MPPT with EMS facilitates efficient load distribution and energy equilibrium, consequently enhancing system performance and reliability.
This study presents a MATLAB-based computational model for the thermochemical and electrochemical analysis of low-temperature technologies. The model accurately predicts thermodynamic limits and key performance indicators, including reversible cell voltage and hydrogen production, as a function of operating temperature. Findings consistently demonstrate that increasing temperature reduces the reversible cell potential and Gibbs free energy change (ΔG), As a result, it lowers the electrical energy consumption for water splitting and improves the rate of hydrogen production. This analysis has validated that operating the Electrolyser at higher temperatures results in consistently higher performing AEM (50-80C), AWE (60-90C), and PEM (50-100C) systems by lowering cell voltage, boosting the amount of hydrogen produced per minute and enhancing thermodynamic efficiency. It’s worth noting that PEMs provided the greatest yield at 9.52 L/min and AWEs gained the highest efficiency of 61.74%.
With the era of diversified automotive technologies, petrol and diesel motor vehicles have ruled supreme as the main modes of transportation for years. However, their adverse environmental impacts have propelled the world toward electrified mobility at a faster pace. Such a shift highlights the importance of a comprehensive study of critical building blocks of Electric Vehicles (EVs), more specifically vehicle charging stations. This paper presents a novel low-ripple Alternating Current (AC) – Direct Current (DC) conversion architecture for Electric Vehicle (EV) charging applications, integrating a Rippleless Power Factor Correction (PFC) stage with high-frequency galvanic isolation and synchronous rectification. The front-end PFC circuit employs a dual-loop control mechanism combining a hysteresis current control strategy with voltage and current PI controllers. This approach ensures accurate input current shaping, reduced Total Harmonic Distortion (THD), and near-unity power factor, while providing stable and ripple-free DC output ideal for battery charging systems. The intermediate high-frequency inverter and isolation transformer enable compact design and safe voltage level adaptation, meeting safety and performance standards required in EV infrastructures. On the secondary side, a synchronous rectifier with closed-loop control minimizes switching losses and enhances energy transfer efficiency. The coordinated control structure ensures excellent dynamic response and robust output voltage regulation under varying input and load. Conditions, which are common in real-world EV charging scenarios MATLAB simulation results validate that the proposed system attains superior power quality, enhanced efficiency of 97.5%, and effective ripple suppression reduced THD of 1.17% compared to conventional converter topologies
In hot and arid desert climates, the thermal performance of passive buildings is strongly influenced by external climatic factors such as solar radiation, air temperature, humidity, and wind speed. However, these challenges can be mitigated through a judicious selection of construction materials and the optimization of their properties to ensure occupant thermal comfort. This study aims to identify the optimal combinations of insulation and wall thickness in straw-reinforced adobe structures to enhance the energy performance of buildings in a Moroccan desert context, specifically in the city of Errachidia. To achieve this aim, the study employs a validated energy model to investigate two key parameters: (1) the addition of natural fiber insulation (0.10 m) and (2) the variation of wall thickness (0.3 m to 0.5 m). The thermal simulation results indicate that adding 0.10 m of insulation significantly enhances thermal performance compared to non-insulated walls. Without insulation, wall thicknesses ranging from 0.4 m to 0.5 m reduce thermal fluctuations by 2°C. However, with insulation, a 0.3 m thick wall achieves a reduction of 3.7°C in summer indoor temperature peaks and maintains winter indoor temperatures as high as 12.1°C, even under extreme outdoor conditions. The integration of eco-friendly insulation panels also leads to a 23.18% reduction in cooling energy demand and a 40% decrease in heating needs compared to uninsulated walls.
In this paper an optimal control methodology for electric vehicles using Artificial Hummingbird Algorithm (AHA) is proposed. The main objective is to improve the performance of EV in terms of different critical parameters to meet the increasing demand for efficient and intelligent control systems in automotive industry. The proposed control strategy uses an AHA tuned Proportional-Integral controller to optimize the controller parameters for the best performance. The performance indicators such as vehicle speed, drive cycle, distance travel, overall vehicle efficiency, State of Charge, and torque are evaluated on a test case in MATLAB/Simulink environment. To validate the proposed approach, its performance is benchmarked with a Particle Swarm Optimization (PSO) algorithm. Results show that the AHA tuned PI controller performs better than the PSO algorithm. The AHA based strategy shows better efficiency, better SoC management, and better responsiveness in acceleration and torque delivery than PSO based control strategy. The results of this study indicate that the Artificial Hummingbird Algorithm could be a very powerful tool to optimize EV control systems to make electric vehicles more efficient, reliable, and high performance. By simultaneously optimizing control of three different road situations, the AHA decreases the tracking delay of conventional PSO based proportional-integral-controller by an order of 31.6 %. At the same time, AHA provides a 4 % extended driving range and a 4.8 % enhanced total energy efficiency, in comparison to particle swarm optimization (PSO).
In this paper, a detailed investigation of the energetic characteristics of a photovoltaic thermal collector taking into consideration water and air as heat transfer fluids, circulating through the flow channel at the collector under examination. The present research takes as its main purpose the evaluation of the differences in performance between both tested transfer flows and to identify the impact of each fluid on energy efficiency. A mathematical model governing heat transfer between the principal elements of the investigated device is defined. The numerical resolution of this model is carried out using MATLAB software, taking into consideration air and water as heat transfer fluids, with similar flow conditions and meteorological parameters. The numerical model allowed us to make a detailed evaluation of the energy properties of the analyzed system. The findings obtained showed significant differences between both coolants examined. The cooling effect of water demonstrated superior heat transfer capacity, with higher heat transfer rates and better overall energy efficiency than air, and a positive effect on electrical efficiency was observed. The use of water demonstrated a better overall energy efficiency of around 67.84%, compared with the case of air, which was no more than 57%.
Sustainable energy depends on the reliability and efficiency of wind turbines, making preventive maintenance a vital factor in ensuring their optimal performance. This paper introduces an innovative methodology utilizing Monte Carlo simulation to optimize maintenance intervals, focusing on a balanced approach that reduces both maintenance costs and energy losses. It advocates for a grouped maintenance strategy, demonstrating its superior impact on production efficiency compared to individualized approaches. Through an analysis of cost distribution and energy losses, the research establishes an optimized equilibrium for preventive maintenance, verified through a numerical case study of wind turbine with 2MW. This example shows a 3.20% increase in energy production, emphasizing the effectiveness of the proposed strategy in maximizing both cost-effectiveness and energy efficiency. By balancing these factors, the framework contributes to advancements in sustainable energy solutions, offering a promising path toward enhanced wind turbine reliability and greater operational efficiencies, ultimately facilitating a smoother transition to sustainable energy worldwide.
BaTiO3 is a model perovskite oxide that holds promise for high efficiency photovoltaic devices and next-generation 3D printing because it can have tunable optoelectronic and mechanical properties. In this work, we utilized density functional theory calculations with the CASTEP code and explored BaTiO3 under hydrostatic pressure. Our simulations illustrate that pressure is accompanied by a strong tendency for significant structural contractions that are characterized by lattice parameter shrinkage, and bond lengths reductions. The electronic band gap is highly dependent upon pressure; 1.71 eV (indirect, M–G) at 0 GPa, followed by 1.94 eV at 100 GPa, then reduced to 1.64 eV at 200 GPa, and consistently lowered to 1.34 eV at 300 GPa and then narrowed to around 0.91 eV at 400–500GPa. These band gap alterations pushed the optical absorption edge to wavelengths which are more favorable for photovoltaic applications. Device simulations using SCAPS-1D of photovoltaic devices under pressure, also demonstrated improved performance under pressure. At 0 GPa, the device has a power conversion efficiency (PCE) of 15.23% with an open-circuit voltage (Voc) of 0.7664 V, a short-circuit current density (Jsc) of 24.73 mA/cm², and a fill factor (FF) of 80.34%. As pressure is applied, these parameters significantly increased: at 300 GPa, Voc improved to 0.7729 V, Jsc increased to 30.40 mA/cm², FF is 79.39%, PCE improved to 18.65%; at 400 GPa, Voc improved to 0.7840 V, Jsc was 45.61 mA/cm², FF was 84.53%, and PCE improved to 30.23%, and similar values were observed at 500 GPa. With the elastic constants determined, it can be seen that the measured stiffness increased, and the favorable trend toward higher ductility increased as pressure was applied and is in support of incorporating BaTiO3 into mechanically resilient 3D-printed parts. All of this points to pressure engineering as a viable method for optimizing BaTiO3 for multifunctional optoelectronic and advanced manufacturing applications.
Energy and climate challenges have driven significant growth in solar power generation. However, solar power production is intermittent and unstable, which complicates its integration into power grids. Techniques of forecasting Photovoltaic (PV) energy are needed to ensure its security and cost-effectiveness. This paper deals with the use of artificial intelligence techniques to predict photovoltaic power. The proposed techniques are Weighted Linear Regression (WLR) and Long Short-Term Memory neural networks (LSTM). The investigated data in this study was obtained from a photovoltaic solar power plant located in a specific geographical area. The studied parameters are wind speed (WS), ambient temperature (T), relative humidity (RH), irradiance (GHI) and wind direction (WD). The accuracy and ability of the LSTM model to explain data variation were examined by comparing its predictions with those of the WLR model, based on three performance measures: RMSE, MAE and R2. The obtained results show that weighted linear regression produces an acceptable estimation of photovoltaic power. However, the LSTM model performs better and has the potential to improve forecast reliability and accuracy.
The integration of variable renewable energy sources (RES), such as solar and wind, into the microgrid through energy storage systems and controllable loads can destabilize the network. These factors cause power supply/demand imbalances, leading to voltage fluctuations and outages. In this article, a new optimization approach is proposed to address these challenges and to effectively manage the energy balance in microgrids. The study propose an Artificial Immune System inspired algorithm to identify optimal solutions and improve the power quality through power dispatch within the microgrid. Using the predicted renewable energy production data, the T-Cell algorithm executes calculations and sends them to a MATLAB environment for real-time simulation, which will make the system flexible in terms of dynamics and optimization of the power distribution using real-time data
The significant developments in solar photovoltaic (PV) technology have led to a strong push toward the development of new Maximum Power Point Tracking (MPPT) methods. Hence, various MPPT techniques have been applied to enhance the efficiency of PV energy. In addition, metaheuristic algorithms are widely used in various scientific and technical fields for problem solving purposes. Indeed, a majority of these techniques are inspired by natural phenomena, such as physical laws or biological processes. In the present study, the effectiveness of a smart MPPT technique utilizing Particle Swarm Optimization (PSO) has been evaluated, to enhance the efficiency of the photovoltaic system. To achieve this, a mathematical model was developed and implemented within the MATLAB/SIMULINK environment. PSIM tools were then used to verify and analyze the results. The findings obtained indicated that the high similarity of optimization in terms of maximum photovoltaic generator power, with an error of less than 1.8%. Recent advancements in solar photovoltaic (PV) technology have led to a growing focus on improving Maximum Power Point Tracking (MPPT) techniques. Various MPPT methods have been developed to enhance the efficiency of PV systems. Additionally, metaheuristic algorithms, inspired by natural phenomena such as biological processes or physical laws, are widely used across scientific and engineering fields for optimization tasks. In this study, the performance of a novel MPPT technique based on Particle Swarm Optimization (PSO) was evaluated to improve the efficiency of photovoltaic systems. A mathematical model was developed and implemented in the MATLAB/SIMULINK environment, and the results were further validated using PSIM tools. The findings show that the proposed PSO-based MPPT technique achieves a high degree of optimization, with an error of less than 1.8% in terms of maximum photovoltaic power output.
This study proposes an enhanced control strategy to address critical challenges in microgrids with distributed energy sources, particularly voltage stability and power-sharing issues. The approach utilizes a Microgrid Central Controller (MGCC) in conjunction with droop control techniques. Evaluations using real-world meteorological data and MATLAB/Simulink demonstrate significant improvements in microgrid performance. The effectiveness of this strategy highlights the importance of advanced control in optimizing renewable energy microgrid performance, paving the way for advancements in sustainable energy management.
This study presents a numerical investigation of a solar desalination system enhanced with water film cooling and a flat plate collector. Solar still systems are increasingly recognized as effective solutions for reducing reliance on conventional energy sources in water treatment and for addressing the growing challenge of water scarcity. To this end, three system configurations are analyzed and compared: a conventional solar still (CSS), a solar still with film cooling (SSC), and a solar still incorporating both film cooling and a flat plate collector (SSP). Simulations are carried out under the climatic conditions of Errachidia, Morocco (latitude: 31.91°, longitude: –4.42°). The results demonstrate notable performance enhancements, with daily freshwater yields of 3.48, 4.51, and 8.45 kg/m²·day for CSS, SSC, and SSP, respectively.
This study investigates how pore geometry and porosity modulate the thermal conductivity and heat transfer characteristics of porous silicon. Leveraging OpenBTE—an open-source computational tool based on the Boltzmann Transport Equation (BTE)—the research analyzes three distinct pore geometries (circular, rectangular, and hexagonal) with porosity ranging from 5% to 45% in order to quantify their impact on phonon-mediated thermal transport.The results shown a clear dependence of thermal conductivity on pore shape and porosity. Rectangular pores showed the highest thermal conductivity, ranging from 64.4 W/(m·K) at 5% porosity to 26.7 W/(m·K) at 45%. Circular pores yielded intermediate thermal conductivity values, varying from 56.8 W/(m·K) at 5% to 9.5 W/(m·K) at 45. Hexagonal pores show the lowest thermal conductivity, ranging from 54.6 W/(m·K) to 7.2 W/(m·K). These insights demonstrate the critical role of pore architecture in tailoring heat dissipation pathways, providing actionable guidelines for engineering optimized pore networks. Experimental results advance the understanding of structure-property relationships in porous materials, enabling precise control over thermal performance for applications in thermoelectric, microelectronics, and energy-efficient systems.
This study focuses on the crystalline lithium-based perovskite material, LiGeCl₃, with a view to improving its structural, elastic, electronic and optical properties by exploiting the effect of hydrostatic pressure. Combining density of states (DOS and PDOS) analysis with DFT and GGA approximation results, it is shown that the application of pressure reduces the lattice parameter, enhancing self-cohesion and stabilising the atomic structure. At ambient pressure, LiGeCl₃ exhibits semiconducting properties with a direct band gap, dominated by the p-orbitals of Cl atoms in the valence band and Ge in the conduction band. Under increasing pressure (0 to 6 GPa), the band gap is progressively reduced until it disappears at 6 GPa, leading to an electronic transition from a semiconducting to a metallic state. This transition results from the compression of the crystal lattice, which intensifies orbital interactions and causes the valence and conduction bands to overlap. In addition, pressure significantly enhances the optoelectronic properties of LiGeCl₃, including absorption in the visible spectrum, spectral reflectivity and refractive index, making the material more suitable for photovoltaic applications. These results highlight the potential of LiGeCl₃ in engineering advanced materials for semiconductor and optoelectronic devices, while demonstrating the crucial role of hydrostatic pressure as a tool for modulating material properties
Accurate prediction of aerodynamic coefficients is essential for the design and control of smart blades featuring morphing airfoils. This study presents a data-driven metamodel based on Artificial Neural Networks (ANNs) developed to predict the aerodynamic behavior of airfoils within the NACA series. The model accepts geometric descriptors of airfoils along with the angle of attack (AoA) as input and outputs corresponding lift (Cl) and drag (Cd) coefficients. A high-fidelity aerodynamic database was generated through systematic simulations across a wide range of AoAs and NACA profiles to train and validate the ANN. The model is trained on NACA 4-digit series profiles covering a wide range of AoA and geometric parameters. The ANN model achieved a mean squared error of 2.10805 e-3 and an R² above 0.997 on test data. The trained metamodel demonstrates excellent generalisation accuracy while drastically reducing computational requirements compared to conventional CFD or BEM-based methods. The model is particularly suited for integration into larger simulation frameworks, such as Blade Element Momentum (BEM) codes or adaptive control systems, enabling real-time performance estimation for morphing smart blades. This work contributes a scalable and efficient surrogate modelling approach for aerodynamic prediction across diverse airfoil geometries.
Solar drying technologies (SDTs) are increasingly used to reduce post-harvest losses and extend the shelf life of agricultural produce. However, despite their benefits, studies have noted potential socio-economic and environmental impacts on humans and ecosystems. This study applied the Social Life Cycle Assessment (SLCA) framework to evaluate the social impacts of SDTs in Morogoro and Arusha regions, Tanzania. Using a participatory approach, focus group discussions with stakeholders identified 16 core social issues expanded into 56 SLCA sub-indicators. A structured questionnaire was administered to 244 respondents, including farmers and entrepreneurs. Results show SDTs contribute significantly to food security, reduced post-harvest losses, and improved market access. However, concerns include poor end-of-life management of SDT components, weak policy and legal frameworks, unequal working conditions, and limited social protection. Regionally, Morogoro outperformed Arusha across most indicators, with Arusha requiring targeted interventions. This is the first comprehensive SLCA study on SDTs in Africa, introducing two new indicators “improved food quality” and “food security” into SLCA methodology. The study enhances traditional life cycle assessments by focusing on social sustainability, providing critical insights for policymakers and planners seeking to promote sustainable agricultural technologies.
This paper proposes an enhanced fuzzy logic controller (FLC) for photovoltaic (PV) systems, featuring a novel reduced-order design. It introduces a significantly simplified FLC for MPPT in a 10-kW grid-connected PV system. The proposed controller minimizes both the number of input variables and the number of membership functions (MFs). Specifically, it utilizes only a single input, the sum of conductance and its increment, and employs just five rules, a substantial reduction compared to the 25-49 rules typical in standard FLCs. Integrated within a system architecture featuring a DC-to-DC converter and a 3-level voltage source converter (VSC) for grid power transfer via duty cycle control, this highly reduced FLC maintains robust MPPT performance through adaptive responses to varying weather conditions. Consequently, it achieves considerable simplification in implementation complexity without sacrificing operational efficiency. To our knowledge, this FLC is among the few controllers capable of such significant rule reduction while maintaining performance. Key results show that at 1 kW/m², incremental conductance (IC) achieves 99% efficiency compared to FLC’s 96%. Under medium irradiance (0.5 kW/m²), FLC outperforms IC by 5% (93% vs. 88%). For low irradiance (0.2 kW/m²), both reach 95.2%. Under large irradiance steps (0.4 to 1 kW/m²), the FLC achieves 22× faster convergence than IC (0.015 s vs. 0.33 s), demonstrating superior dynamic response to abrupt solar variations. This highlights the proposed algorithm’s robustness for dynamic weather scenarios while maintaining competitiveness in steady operation.
