Pablo Morcillo-Valdivia

The increasing frequency of high-impact, low-probability climate events has highlighted the limitations of conventional reliability criteria, including N-1 planning assumptions, and the need for dynamic resilience architectures in electrical systems. This article analyzes the evolution, trends, and technological challenges associated with microgrid resilience under extreme-weather disruptions. The study adopts a hybrid research design that combines quantitative bibliometric mapping of 283 Scopus-indexed article records for 2016–2025 using CiteSpace version 7 with a structured technical synthesis of the selected literature. The structural analysis identified nine thematic clusters and indicated a transition from service restoration and component-level recovery toward multi-energy energy-management systems, resilient distribution-system planning, and mobile restoration resources. The technical synthesis shows that modern resilience is increasingly associated with hierarchical control architectures: optimization and forecasting methods support tertiary-level energy management, while grid-forming inverters can provide primary-layer voltage references that support islanded operation and black-start sequences under appropriate design, protection, and validation conditions. The article concludes that future research should bridge stochastic planning and real-time physical operation through standardized interoperability frameworks, reproducible dynamic metrics, and experimentally validated control strategies for autonomous critical microgrid operation under extreme-weather conditions.

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.

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.

This study presents a scientometric analysis of renewable energy applications in low-temperature regions, focusing on green hydrogen production, carbon storage, and emerging trends. Using bibliometric tools such as RStudio and VOSviewer, the research evaluates publication trends from 1988 to 2024, revealing an exponential growth in renewable energy studies post-2021, driven by global policies promoting carbon neutrality. Life cycle assessment (LCA) plays a crucial role in evaluating the environmental impact of energy systems, underscoring the need to integrate renewable sources for emission reduction. Hydrogen production via electrolysis has emerged as a key solution in decarbonizing hard-to-abate sectors, while carbon storage technologies, such as bioenergy with carbon capture and storage (BECCS), are gaining traction. Government policies, including carbon taxes, fossil fuel phase-out strategies, and renewable energy subsidies, significantly shape the energy transition in cold regions by incentivizing low-carbon alternatives. Multi-objective optimization techniques, leveraging artificial intelligence (AI) and machine learning, are expected to enhance decision-making processes, optimizing energy efficiency, reliability, and economic feasibility in renewable energy systems. Future research must address three critical challenges: (1) strengthening policy frameworks and financial incentives for large-scale renewable energy deployment, (2) advancing energy storage, hydrogen production, and hybrid energy systems, and (3) integrating multi-objective optimization approaches to enhance cost-effectiveness and resilience in extreme climates. It is expected that the research will contribute to the field of knowledge regarding renewable energy applications in low-temperature regions.

This study presents a scientometric analysis of renewable energy applications in low-temperature regions, focusing on green hydrogen production, carbon storage, and future trends. The research covers three distinct periods from 1988 to 2024, using bibliometric tools such as R-Studio and VOSviewer to evaluate scientific publications and keyword co-occurrence. The study highlights an exponential growth in renewable energy research post-2021, driven by global initiatives to achieve carbon neutrality. Life Cycle Assessment (LCA) plays a crucial role in identifying the environmental impacts of energy systems, emphasizing the need to integrate renewable energy sources to reduce carbon emissions. The analysis reveals that hydrogen production via electrolysis has emerged as a key topic in reducing carbon footprints, particularly in hard-to-abate sectors. Carbon storage technologies, including bioenergy with carbon capture and storage (BECCS), have gained traction as essential components of future energy systems. The results indicate a growing interest in optimizing renewable energy systems for low-temperature environments, with significant progress in solar, wind, and hydrogen technologies. The study underscores the importance of local and regional adaptations, integrating energy storage solutions, and diversifying energy matrices to enhance energy security. Future trends suggest a focus on multi-objective optimization using deep learning and AI to improve energy system performance. The findings contribute to understanding the global research landscape on renewable energy applications in cold regions, offering valuable insights into sustainable solutions for extreme climates.

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.