This work studies the efficiency and long-term viability of powered hydrogen production. For this purpose, a detailed exploration of hydrogen production techniques has been undertaken, involving data collection, information authentication, data organization, and analysis. The efficiency trends, environmental impact, and hydrogen production costs in a landscape marked by limited data availability were investigated. The main contribution of this work is to reduce the existing data gap in the field of hydrogen production by compiling and summarizing dispersed data. The findings are expected to facilitate the decision-making process by considering regional variations, energy source availability, and the potential for technological advancements that may further enhance the economic viability of electrolysis. The results show that hydrogen production methods can be identified that do not cause significant harm to the environment. Photolysis stands out as the least serious offender, producing 0 kg of CO₂ per kg of H₂, while thermolysis emerges as the major contributor to emissions, with 20 kg of CO₂ per kg of H₂ produced.

Solar radiation is a fundamental parameter for solar photovoltaic (PV) technology. Reliable solar radiation prediction has become valuable for designing solar PV systems, guaranteeing their performance, operational efficiency, safety in operations, grid dispatchment, and financial planning. However, high quality ground-based solar radiation measurements are scarce, especially for very short-term time horizons. Most existing studies trained machine learning (ML) models using datasets with time horizons of 1 h or 1 day, whereas very few studies reported using a dataset with a 1 min time horizon. In this study, a comprehensive evaluation of nine ensemble learning algorithms (ELAs) was performed to estimate solar radiation in Santo Domingo with a 1 min time horizon dataset, collected from a local weather station. The ensemble learning models evaluated included seven homogeneous ensembles: Random Forest (RF), Extra Tree (ET), adaptive gradient boosting (AGB), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting (LGBM), histogram-based gradient boosting (HGB); and two heterogeneous ensembles: voting and stacking. RF, ET, GB, and HGB were combined to develop voting and stacking ensembles, with linear regression (LR) being adopted in the second layer of the stacking ensemble. Six technical metrics, including mean squared error (MSE), root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2), were used as criteria to determine the prediction quality of the developed ensemble algorithms. A comparison of the results indicates that the HGB algorithm offers superior prediction performance among the homogeneous ensemble learning models, while overall, the stacking ensemble provides the best accuracy, with metric values of MSE = 3218.27, RMSE = 56.73, rRMSE = 12.700, MAE = 29.87, MAPE = 10.60, and R2 = 0.964.

This paper describes research on the harmonic-distortion capacity of a single-stage photovoltaic (PV) 3.68 kWp generator in a microgrid configuration. An overview of various harmonic compensator methods used in PV generators is presented to evaluate their advantages and disadvantages. Proportional-resonant (PR) structures with harmonic compensators (HCs) are designed, modeled, and validated through real-time tests. The modeling of harmonic compensation structures for a photovoltaic inverter using MATLAB/SIMULINK R2022a is explained in detail. The harmonic compensation capacity of a PV generator inverter is studied under voltage harmonics, grid frequency variation, and voltage unbalance. The contributions of this work are, firstly a bibliographic analysis of various strategies currently used for harmonic compensation in grid-connected inverters and secondly, a detailed explanation of the modeling of harmonic compensation structures using MATLAB/SIMULINK. Finally, a demonstration of the improvement in energy quality that results from using harmonic compensation techniques in photovoltaic generators in microgrid configurations affected by grid disturbances is performed. The obtained results show that harmonic compensation strategies based on resonant filters are a good alternative for the reduction of harmonic voltage distortion in the presence of grid disturbances such as frequency variation and voltage unbalance.

Solar radiation corresponds to fundamental parameters for solar photovoltaic (PV) technology. Reliable solar radiation prediction became valuable to design solar PV systems, performance, operatively efficient planning, safety operation, grid dispatch and financial characteristics. However, high quality ground-based solar radiation measurements are scarce, especially for very short-term time horizon. Most of the existent studies trained the machine learning (ML) model used dataset with time horizon of 1-hour or day, very fewer studies have been reported using a dataset with a 1-mitute time horizon. In this study, a comprehensive evaluation of nine ensemble learning algorithms (ELA) is performed to estimate the solar radiation in Santo Domingo with a 1-minute time horizon dataset collected from a local weather station. The ensemble learning evaluated included seven homogeneous ensembles; Random Forest (RF), Extra Tree (ET), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGBM), Histogram-based Gradient Boosting (HGB) and heterogeneous ensemble named as Voting and Stacking. RF, ET, GB, HGB were combined to develop Voting and Stacking and Linear Regression (LR) was adopted in the second layer of the Stacking. Five technical metrics, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2) were used as criteria to determine the prediction quality of the developed ensemble algorithms. Comparison of the results indicates that the HGB algorithm offers superior prediction performance among the homogeneous ensemble learning and overall, the Stacking provides the best accuracy with metric values of MSE=3218.27, RMSE= 56.73, MAE=29.87, MAPE=10.60, R2=0.964.