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
