The aim of this bibliometric analysis was to evaluate the evolution of scientific research in hydrogen, focusing on green hydrogen production, storage, and utilization. Articles from prominent databases were analyzed, revealing a strong emphasis on sustainable hydrogen technologies through keywords like “hydrogen production”, “green hydrogen”, and “solar power generation”. Mature research areas include production methods and electrolysis, while emerging topics such as energy efficiency and policy are gaining traction. The most-cited papers, from Energy Conversion and Management to the International Journal of Hydrogen Energy, cover techno-economic assessments and case studies on deploying hydrogen technologies. Key findings highlight the variability of the Levelized Cost of Hydrogen (LCOH) across technologies and regions. Deep learning applications, including Fast Fourier Transform-based forecasting and explainable AI models, are transforming production optimization, while Life Cycle Assessment (LCA) emphasizes renewable energy’s role in reducing carbon intensity and resource consumption. Diverse strategies, such as fiscal incentives for wind energy and use of urban waste, underline the importance of local solutions. This analysis reflects the rapid growth of hydrogen research, driven by international collaboration and innovations in sustainable production, storage, and optimization. It is hoped that this paper will help to shed more light on the current and future understanding of green hydrogen.

Hydrogen, as a clean energy source, has enormous potential in addressing global climate change and energy security challenges. This paper discusses different hydrogen production methodologies (steam methane reforming and water electrolysis), focusing on the electrolysis process as the most promising method for industrial-scale hydrogen generation. The review delved into three main electrolysis methods, including alkaline water electrolysis, proton exchange membrane electrolysis, and anion exchange membrane electrolysis cells. Also, the production of hydrogen as a by-product by means of membrane cells and mercury cells. The process of reforming natural gas (mainly methane) using steam is currently the predominant technique, comprising approximately 96% of the world’s hydrogen synthesis. However, it is carbon intensive and therefore not sustainable over time. Water, as a renewable resource, carbon-free and rich in hydrogen (11.11%), offers one of the best solutions to replace hydrogen production from fossil fuels by decomposing water. This article highlights the fundamental principles of electrolysis, recent membrane studies, and operating parameters for hydrogen production. The study also shows the amount of pollutant emissions (g of CO₂/g of H₂) associated with a hydrogen color attribute. The integration of water electrolysis with renewable energy sources constitutes an efficient and sustainable strategy in the production of green hydrogen, minimizing environmental impact and optimizing the use of clean energy resources.

Photovoltaic (PV) power forecasting combined with energy storage systems (ESS) is critical for grid stability and renewable energy optimization. Machine learning (ML) techniques have shown promise in improving PV forecast accuracy and ESS operation. However, the intersection of PV forecasting and ESS control remains underexplored, warranting a systematic review of recent advances and evaluation of ML effectiveness in PV–ESS integration. To assess research trends in ML-based PV forecasting with ESS (scientometric analysis), synthesize state-of-the-art ML approaches for PV–ESS forecasting (systematic review), and quantify their overall predictive performance via meta-analysis of the coefficient of determination (R²). A comprehensive search of Scopus (2010–2025) was conducted following PRISMA 2020 guidelines. Studies focusing on ML-based PV power forecasting integrated with ESS were included. Multiple reviewers screened the records and extracted data. Study quality was appraised using Joanna Briggs Institute checklists. A random-effects meta-analysis of R² was performed to aggregate model performance across studies. The search identified 227 records; 50 studies were included in the review and 5 in the meta-analysis. Publications grew rapidly after 2018, indicating increased interest in PV–ESS forecasting. Deep learning models and hybrid architectures were the most frequently studied and outperformed traditional methods, while integrating PV forecasts with ESS control consistently improved operational outcomes. Common methodological limitations were noted, such as limited external validation and non-standardized evaluation metrics. The meta-analysis found a pooled R² ~0.95 (95% CI) with no heterogeneity (I² = 0), and no evidence of publication bias. ML-based forecasting significantly improves PV–ESS performance, underscoring AI as a key enabler for effective PV–ESS integration. Future research should address remaining gaps and explore advanced approaches to further enhance PV–ESS outcomes.

This study aims to map the evolution of pico-hydropower and Michell–Banki (cross-flow) turbine research from 2000 to 2025 through a combined bibliometric analysis and qualitative mini-review. In total, 1036 Scopus-indexed records were initially identified and refined to 922 relevant publications for analysis. Bibliometric mapping with CiteSpace, VOSviewer, and Bibliometrix identified publication trends and seven major thematic clusters (dominated by topics such as cross-flow turbine design, renewable energy integration, and asynchronous generators), while a qualitative mini-review of key studies provided contextual depth. The analysis detected 25 keywords with strong citation bursts, indicating a shift in focus over the last decade from traditional electrical regulation toward digitalization and additive manufacturing. The mini-review distilled three dominant lines of inquiry geometric design optimization, hydraulic performance characterization, and socio-economic evaluation and highlighted critical knowledge gaps, including the absence of standardized flow–head–efficiency (Q–H–η) performance data, sparse reporting of economic metrics like levelized cost of energy (LCOE), and limited high-altitude (above 3000 m) validation of pico-hydro systems. This study’s integrative approach is unique compared to prior bibliometric or technical reviews, providing a comprehensive overview of the pico-hydropower landscape and outlining a future research agenda to standardize experimental protocols, integrate economic analysis, and extend cross-flow turbine deployments to high-Andean regions.

Optimal sizing and design of renewable microgrids and hybrid renewable energy systems require balancing renewable resource variability, demand uncertainty, storage operation, reliability, and techno-economic constraints. Artificial intelligence and metaheuristic optimization strategies have been increasingly used to address these challenges; however, the evidence remains methodologically heterogeneous. This scoping review maps the literature on artificial intelligence, learning-based, metaheuristic, heuristic, and optimization-based strategies for renewable microgrid sizing and design. The review followed PRISMA-ScR guidelines. Searches were conducted in Scopus and the Web of Science Core Collection for research articles published between 2009 and March 2026. A total of 69 studies were included. Metaheuristics dominated the field, appearing in 63 studies, with particle swarm optimization and genetic algorithm-based strategies as the most frequent methodological families. Artificial intelligence and learning-based strategies were mainly used to support forecasting, surrogate modeling, uncertainty handling, and energy management. The most recurrent configurations involved photovoltaic, wind, and battery storage systems, often with diesel backup in stand-alone or off-grid contexts. The literature is strongly oriented toward metaheuristic sizing of PV–wind–battery microgrids, with emerging integration of AI-assisted prediction and decision-support strategies. Future studies should address reproducibility, uncertainty modeling, real-world validation, degradation assessment, explainability, and scalability.

The use of renewable energy sources in microgrids is broad and fragmented owing to the contributions of various approaches and disciplines. A bibliometric review contributes to a critical and comprehensive understanding of this field. The SciMAT software was used to determine the evolution of this important field of research. To achieve this, 842 research articles were collected from the Scopus database for 2001-2021. Recently, the thematic interest of researchers has focused on the concepts of life-cycle analyses of solar and wind energy systems and the efficiency of photovoltaic systems as a method of evaluating renewable energy sources. The results illustrate the relevance of renewable energy sources with a gradual increase in the number of studies published in international journals since 2010. The period overlay plots present several new and transient topics between sub-periods; it is a research field that undergoes constant evolution and has not yet reached a stage of scientific maturity.