Next Energy最新文献

{"title":"Water relocation in hydrogen energy transport: A prospective study","authors":"Marcelo D. Cabezas ,&nbsp;Lucía M. Cabezas ,&nbsp;Héctor J. Fasoli","doi":"10.1016/j.nxener.2026.100598","DOIUrl":"10.1016/j.nxener.2026.100598","url":null,"abstract":"<div><div>Hydrogen is widely considered a central energy carrier for future decarbonized systems; however, an important physical implication remains largely overlooked: the large-scale relocation of water inherently embedded in hydrogen production and transport. This study presents a global prospective assessment quantifying water redistribution associated with hydrogen-based energy trade using energy consumption datasets from 200 countries, combined with stoichiometric constraints and conservative efficiency assumptions. Unlike previous studies that evaluate water use in hydrogen production or energy flows separately, this approach explicitly quantifies the interregional relocation of water driven by hydrogen transport at the planetary scale. Results indicate that hydrogen-driven energy exchanges could generate measurable regional imbalances in water availability. Under conservative scenarios, the total relocated water volume may reach up to 23 times the amount required for direct human drinking water consumption and could approach regional requirements for health and hygiene based on a reference threshold of 50 L inhabitant⁻¹ day⁻¹. These findings reveal a previously unquantified constraint within the water–energy nexus and highlight the need to incorporate water relocation effects into long-term hydrogen trade planning. A sensitivity analysis using updated global datasets for the period 2019–2024 confirms the robustness of the results, with variations below 7% across all scenarios and no change in comparative behavior, reinforcing the structural nature of the identified water relocation effect. The results provide quantitative support for the development of water-positive hydrogen strategies, particularly in water-stressed exporting regions, contributing to more robust environmental and sustainability assessments of future global energy transitions.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100598"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147612446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models in renewable energy systems: A comprehensive review of forecasting, control, policy, and fault diagnosis","authors":"Napa Chandana ,&nbsp;Selika Narasimha Rao ,&nbsp;Madavena Kumaraswamy ,&nbsp;Archana Pallakonda ,&nbsp;Rayappa David Amar Raj","doi":"10.1016/j.nxener.2026.100586","DOIUrl":"10.1016/j.nxener.2026.100586","url":null,"abstract":"<div><div>The change toward a sustainable energy future demands intelligent solutions for planning, forecasting, and optimization in renewable energy systems. Recent advances in Artificial Intelligence (AI), particularly Large Language Models (LLMs) such as generative pretrained transformer, bidirectional encoder representations from transformers, large language model meta AI, and Falcon, provide new opportunities by enabling robust analysis of structured and unstructured data. LLMs can interpret technical documents, make heterogeneous datasets, and support applications like forecasting, fault detection, smart grid control, policy analysis, and public engagement. This paper systematically reviews the role of LLMs across renewable energy domains, classifies applications, and highlights their potential to bridge gaps in data-driven decision-making. We propose a taxonomy of LLM applications, analyze use cases, assess limitations, and outline future research trends. By integrating cutting-edge AI methods with sustainability goals, this work highlights the role of LLMs in building resilient, intelligent, and climate-conscious energy ecosystems.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100586"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147713454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and control of a nuclear pressurized water reactor using advanced model predictive control strategies","authors":"Mohamed A Rahim ,&nbsp;Ghassan Murad ,&nbsp;MB Shams ,&nbsp;Mohamad Badii ,&nbsp;Mohamed Jaafar ,&nbsp;Ayman Saeed","doi":"10.1016/j.nxener.2026.100597","DOIUrl":"10.1016/j.nxener.2026.100597","url":null,"abstract":"<div><div>The nonlinear dynamics and multiple interacting loops in pressurized water reactors (PWRs) make control design challenging, particularly when load-following and safety constraints must be handled simultaneously. Building on the detailed 27-state PWR model developed by Vajpayee et al. (2020), this paper presents a MATLAB/Simulink implementation of the complete reactor-steam-generator-pressurizer system, utilizing it as a benchmark to compare several standard control strategies. The model includes point-kinetics, thermal–hydraulic, piping, plenum, steam-generator, and pressurizer dynamics, and is validated against the transient responses reported in Vajpayee et al. (2020) for rod, heater, turbine-valve, and feedwater disturbances. Four controllers are implemented for the same multivariable control problem (reactor power, pressurizer pressure, steam pressure, and pressurizer level): a proportional-integral (PI) controller, a linear model predictive controller (MPC), a gain-scheduled MPC (GSMPC), and a nonlinear MPC (NMPC). These controllers were tested under 2 scenarios: power ramping (up and down) to evaluate their regulatory performance and feed-water disturbances to evaluate disturbance rejection. For each controller, we document the design workflow, tuning parameters, and constraints. Closed-loop performance is evaluated using standard integral error indices and selected load-following and disturbance-rejection scenarios, together with average computation time per sampling instant. The results show that, with fair tuning, MPC-based strategies significantly reduce power-tracking integral time absolute error and settling time compared with PI control, while GSMPC can approximate NMPC performance over a wide operating range at lower computational cost, which proves its feasibility for real-time applications. The implementation details and benchmark scenarios provided here are intended to support reproducible studies and further development of advanced control methods for PWRs.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100597"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147612445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental validation of high-performance commercial thermal interface materials in PV-TEG hybrid systems","authors":"Sevgi Altınkök","doi":"10.1016/j.nxener.2026.100604","DOIUrl":"10.1016/j.nxener.2026.100604","url":null,"abstract":"<div><div>The \"Efficiency Dilemma\" caused by high photovoltaic (PV) cell temperatures and ineffective heat transfer significantly limits the performance of photovoltaic-thermoelectric (PV-TEG) hybrid systems. A critical bottleneck in these systems is the thermal contact resistance (TCR) arising from microscopic roughness at the PV-TEG interface. Recent literature has largely focused on mitigating TCR through the laboratory synthesis of complex nanocarbon-based composites. However, these methods often present challenges regarding scalability, cost, and complexity. This study proposes a practical engineering solution by experimentally validating the efficacy of a commercially available, high-performance thermal interface material (TIM) with a thermal conductivity of 6.0 W m⁻¹K⁻¹, significantly exceeding typical lab-synthesized values. Experimental tests were conducted using a calibrated halogen-tungsten solar simulator to replicate the thermal characteristics of solar radiation under controlled laboratory conditions with active cooling support. The study comparatively analyzed 2 scenarios: (i) a reference \"dry contact\" setup and (ii) an improved interface using the commercial high-performance TIM. Thermal imaging analysis and electrical measurements demonstrated that the commercial TIM effectively eliminated interfacial air gaps, reducing the PV surface temperature by 10.4°C and enhancing the TEG open-circuit voltage <span><math><mrow><msub><mrow><mo>(</mo><mi>V</mi></mrow><mrow><mi>oc</mi></mrow></msub><mo>)</mo></mrow></math></span> by 75.8% compared to the dry contact scenario. These findings confirm that accessible, off-the-shelf high-performance thermal pastes offer a viable and superior alternative to complex synthesized composites for the thermal management of PV-TEG systems.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100604"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147709905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Green hydrogen: Reassessing priorities for sustainable deployment","authors":"Shin-ichi Inage ,&nbsp;Akira Yanagisawa","doi":"10.1016/j.nxener.2026.100592","DOIUrl":"10.1016/j.nxener.2026.100592","url":null,"abstract":"<div><div>Green hydrogen is increasingly promoted as a key pillar in the transition toward decarbonized energy systems. However, its dual role—as both a clean fuel and an essential industrial feedstock—calls for a more differentiated analysis. This study presents a comprehensive techno-economic evaluation of using green hydrogen to fuel a 1 MW combined cycle gas turbine, comparing its performance and lifecycle costs against conventional liquefied natural gas-fired systems and battery energy storage solutions. The findings suggest that, under current technological and economic conditions, hydrogen-fueled power generation faces significant challenges in terms of efficiency and capital recovery, primarily due to low volumetric energy density and substantial conversion losses. In contrast, industrial applications such as ammonia production and low-carbon steelmaking offer contexts in which green hydrogen’s unique properties deliver irreplaceable value. These results indicate that deployment strategies for green hydrogen may benefit from prioritizing high-impact industrial sectors, while fuel-related applications should be approached selectively, with careful consideration of infrastructure demands and opportunity costs. This work aims to support more balanced and pragmatic hydrogen policies that align with both long-term climate goals and near-term economic realities.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100592"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power-to-methanol process assessment using enhanced kinetic models","authors":"Vitória N. Silva Oliveira,&nbsp;Rafaelle Gomes Santiago,&nbsp;Moises Bastos-Neto,&nbsp;Célio L. Cavalcante Jr.,&nbsp;F. Murilo T. Luna","doi":"10.1016/j.nxener.2026.100518","DOIUrl":"10.1016/j.nxener.2026.100518","url":null,"abstract":"<div><div>In the energy transition scenario, Power-to-X processes play a crucial role by converting surplus electricity from renewable sources into fuels, chemicals, and other energy carriers. These technologies not only help to balance the supply and demand of energy but also promote decarbonization. In this study, the conversion of carbon dioxide and hydrogen into methanol (Power-to-Methanol) as a strategic solution to store and transport hydrogen was evaluated by modeling and simulation. The investigation addressed the rate laws governing the reactions in the hydrogenation of pollutant gases into methanol. Refitted and original Bussche-Froment (BF) and Graaf kinetic models were used to understand and identify the key factors in process efficiency for improving competitiveness compared to conventional processes. The sensitivity analysis revealed some similarities in both models; however, discrepancies in conversion predictions reached up to 49%, particularly at intermediate residence times, low temperatures, and high pressures. Selecting an appropriate residence time (below 0.1 h) proved critical to reducing divergences between models, providing actionable insight for reliable process design and optimization. These discrepancies between the models contribute to a broad theoretical optimal operating window for the process. Considering both models, the methanol production and CO₂ conversions were higher in temperatures between 200 and 250 °C. In this optimal temperature range, increasing the pressure contributed to higher methanol production. Increasing H₂/CO₂ ratio favored CO₂ conversion, achieving an average for both models of 44% with a ratio of 8:1. However, a ratio of 3:1 for the Graaf model and 2:1 for the BF model resulted in maximum methanol production. Finally, increasing the CO concentration raised the obtained methanol concentration but resulted in lower carbon dioxide conversion.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100518"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidential multi-model CNN integration for visual fault detection in solar panels","authors":"Nadjia Khatir ,&nbsp;Safia Nait-Bahloul","doi":"10.1016/j.nxener.2025.100509","DOIUrl":"10.1016/j.nxener.2025.100509","url":null,"abstract":"<div><div>This study proposes an evidential fusion framework for classifying visual defects in solar panels using convolutional neural networks (CNNs) and Dempster-Shafer theory (DST). Three pretrained CNN models: ResNet50, MobileNetV2, and EfficientNetB0 are fine-tuned to detect multiple defect types, and their outputs are fused at the logit level using DST. Unlike conventional ensemble strategies such as majority voting, the proposed method explicitly accounts for uncertainty and conflict among predictions by assigning belief masses to sets of hypotheses. Experimental evaluations conducted on a multiclass solar panel dataset demonstrate that DST fusion consistently outperforms individual models and majority voting across all macro-averaged metrics, particularly in underrepresented or visually ambiguous classes such as <em>Physical-Damage</em> and <em>Dusty</em>. These findings underscore the potential of uncertainty-sensitive model fusion to enhance the robustness and interpretability of automated photovoltaic inspection systems.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100509"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging the financial gap: Analysis of parity prices and policy instruments for green hydrogen applications in hard-to-abate sectors in Germany","authors":"Valerie Ziemsky ,&nbsp;Simon Pichlmaier ,&nbsp;Markus Gyetko","doi":"10.1016/j.nxener.2026.100610","DOIUrl":"10.1016/j.nxener.2026.100610","url":null,"abstract":"<div><div>Green hydrogen technologies are crucial for decarbonizing hard-to-abate sectors in Germany. This paper applies a cost-based analytical framework to quantify financial gaps between projected green hydrogen supply costs and sector-specific parity prices through 2050 in 6 key applications (chemical industry, primary steel, reconversion, heavy-duty transport, shipping, aviation) and assesses the implications for policy support instruments. Key findings include: (1) Power purchase agreement (PPA) based green hydrogen costs depend on renewable energy market trends, grid fee exemptions, and free emission allowance allocations for electrolyzers; (2) Only the heavy-duty transportation and shipping sectors achieve cost parity with PPA-based hydrogen by 2050; (3) The chemical industry and aviation sector are highly sensitive to green hydrogen costs and regulatory changes, resulting in conditional technology shifts depending on hydrogen sourcing; (4) Closing the remaining financial gaps will require supply-side subsidies, as CO₂ price increases alone are insufficient. Policymakers should implement sector-specific support mechanisms for green hydrogen technologies, considering the varying effectiveness of CO₂ pricing and the different cost structures across technologies.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100610"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147709906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances in fuel cell design and modeling: A comprehensive review","authors":"Umang Bedi","doi":"10.1016/j.nxener.2026.100517","DOIUrl":"10.1016/j.nxener.2026.100517","url":null,"abstract":"<div><div>Fuel cells are gaining popularity as sustainable alternatives to traditional energy sources due to their low environmental impact and high efficiency. Among these, direct alcohol fuel cells, proton exchange membrane fuel cells, and solid oxide fuel cells are highly promising. Fuel cells convert fuels, such as alcohols and hydrogen, into electricity with considerably higher efficiency than combustion engines, producing only water as the primary by-product. This clean energy pathway supports the United Nations Sustainable Development Goal (SDG) 7 (Affordable and Clean Energy) and advances SDG 13 (Climate Action) by reducing greenhouse gas emissions and dependency on fossil fuels. However, significant challenges remain before widespread implementation of fuel cells, including financial feasibility, long-term reliability, and scalability. Therefore, this review aims to address the existing gap in understanding how recent modeling and design advancements can overcome these limitations across different types of fuel cells. This review provides a comprehensive and current synthesis of recent fuel cell modeling and design, uniquely integrating bibliometric trends, experimental advances, and computational methods across all major types of fuel cells. Emphasis is placed on numerical optimization strategies, advancements in multi-physics simulations, sustainable material innovations, and emerging approaches such as artificial intelligence-assisted modeling and integrated multi-scale frameworks. The review offers a cross-disciplinary roadmap to improve the performance, durability, and commercial viability of next-generation fuel cell technologies.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100517"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Standalone DC microgrids: Planning, operation and uncertainty management","authors":"Hasith Jayasinghe ,&nbsp;Kosala Gunawardane ,&nbsp;Md. Alamgir Hossain ,&nbsp;Ramon Zamora","doi":"10.1016/j.nxener.2026.100511","DOIUrl":"10.1016/j.nxener.2026.100511","url":null,"abstract":"<div><div>Standalone power systems in remote areas have traditionally relied on continuously operating fossil fuel generators, leading to high operational costs, reduced efficiency, and substantial carbon emissions. Standalone direct current (DC) microgrids have emerged as a promising alternative due to their lower conversion losses, improved integration of renewable energy sources (RES), and enhanced compatibility with modern DC-native loads and storage technologies. Despite these advantages, the planning, operation, and uncertainty management of standalone DC microgrids remain technically challenging. Intermittent RES generation, stochastic load behaviour, lack of mature standards, and complex control requirements introduce significant design and operational challenges. While numerous studies have proposed methods to address issues in sizing, optimisation, control, energy management, and uncertainty management, a comprehensive and structured review that connects these aspects across the full lifecycle of DC microgrid development is still lacking. This article addresses this gap by providing a systematic review of the state-of-the-art in planning methodologies, operational strategies, and uncertainty management techniques for standalone DC microgrids. The review synthesises theoretical frameworks and practical implementations, critically evaluates existing approaches by identifying their strengths and limitations, and highlights the interdependencies among planning, real-time operation, and uncertainty mitigation. Finally, the article outlines key research challenges and future opportunities to support the reliable, cost-effective, and sustainable deployment of standalone DC microgrids. The novelty of this study lies in its integrated perspective spanning planning, operational control, and uncertainty management, offering valuable guidance for researchers, system designers, and practitioners.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"11 ","pages":"Article 100511"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}