Energy Conversion and Management-X最新文献

{"title":"First-principles-computational quantum insights on enhanced thermophysical performance of ThC:Mg for clean thermoelectric and nuclear energy","authors":"Azmat Iqbal Bashir ,&nbsp;M.H. Sahafi","doi":"10.1016/j.ecmx.2025.101222","DOIUrl":"10.1016/j.ecmx.2025.101222","url":null,"abstract":"<div><div>Besides being a matured energy technology, nuclear energy is the second cleanest energy source after hydropower regarding the emission of greenhouse gases. As such, the role of nuclear energy as a key player to achieve sustainable clean energy to solve the future energy crisis cannot be underestimated. To harness the nuclear energy via the fission process, the routine fuel materials in the nuclear power plants are uranium and uranium-based compounds. However, thorium-based materials have some advantages for advanced breeder power plants. This owes to the abundance, peculiar mechanical, and thermodynamic properties of thorium (Th), such as high melting temperature (1750 °C), density, and thermal conductivity, and less radioactive byproducts. Th makes many refractory materials with melting points above 1800°C, which include carbides, nitrides, phosphides, and silicides, holding promising potential for diverse applications such as clean thermoelectric and nuclear energy. This study is the first attempt to explore comparative analysis on the phonon dynamics, thermodynamic, and thermoelectric performance and potential of ThC and Mg-doped ThC carbides using density-functional theoretical formalism. For the first-principle quantun insights and computation of thermodynamic characteristics of the materials, the Debye Model based on the Quasi Harmonic approximations is utilized. The computed results are interpreted considering novel prospects and implications, which hold great potential for fundamental and practical applications regarding thermal management and sustainable thermoelectric and clean nuclear energy via advanced breeder power plants.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101222"},"PeriodicalIF":7.6,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921676","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":"Recurrent neural network strategies for decoupling energy consumption and greenhouse gas emissions in Hungary’s industrial sector","authors":"Mohamad Ali Saleh Saleh ,&nbsp;Mutaz AlShafeey","doi":"10.1016/j.ecmx.2025.101219","DOIUrl":"10.1016/j.ecmx.2025.101219","url":null,"abstract":"<div><div>This study addresses the critical challenge facing Hungary’s industrial sector by focusing on the need to decouple economic growth from greenhouse gas (GHG) emissions to meet EU climate targets while maintaining industrial productivity. Although Hungary has achieved significant emission reductions, its industrial sector remains heavily reliant on carbon-intensive energy sources, underscoring the need for advanced analytical approaches to identify effective decoupling strategies. To address this gap, the study utilizes a Recurrent Neural Network (RNN), which is effective for modeling complex, non-linear, and temporal relationships, to analyze the interactions among industrial energy consumption, economic performance, and GHG emissions from 1995 to 2020. The results indicate that reducing coal and heat consumption by 2.5 petajoules yields significant GHG emission decreases of 4.4 percent and 4.3 percent, respectively, while a similar reduction in renewables and waste leads to a 3.5 percent drop in emissions. A 2.5 petajoule reduction in natural gas consumption results in just over a 1 percent decrease in GHG emissions, highlighting its lower emissions intensity and role as a viable transitional fuel. These findings provide critical insights for designing targeted policy interventions prioritizing coal and heat reduction and scaling up low-emission renewables to meet Hungary’s climate commitments. The methodological contribution of using RNN offers a scalable and replicable framework for other countries aiming to balance industrial productivity with sustainable development objectives.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101219"},"PeriodicalIF":7.6,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903623","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":"A review of next-generation thermoelectric generators: Advanced geometric and nanomaterial optimization and intelligent performance enhancement","authors":"Wei-Hsin Chen ,&nbsp;Argel A. Bandala ,&nbsp;Ding Luo ,&nbsp;Aristotle T. Ubando ,&nbsp;Manuel Carrera Uribe ,&nbsp;Maxine Camille O. Mallari","doi":"10.1016/j.ecmx.2025.101221","DOIUrl":"10.1016/j.ecmx.2025.101221","url":null,"abstract":"<div><div>The demand for sustainable energy solutions has accelerated the development of thermoelectric generators (TEGs) as an effective technology for harvesting waste heat. TEGs employ the Seebeck effect to convert thermal energy into electricity. They offer advantages such as solid-state operation, scalability, and minimal maintenance. However, their widespread implementation is hindered by low energy conversion efficiency. This review comprehensively analyzes the state-of-the-art in TEG technology, focusing on geometric optimization, material enhancements, and AI-driven performance improvements. Innovations in TEG designs, including segmented, variable-geometry, asymmetrical, and multistage architectures, are examined in relation to their impact on power output and efficiency. In addition, the integration of artificial intelligence (AI), computational fluid dynamics (CFD), and nanomaterials in predictive modeling and real-time optimization is discussed. AI-driven strategies such as evolutionary computation and machine learning have demonstrated significant potential in optimizing TEG configurations for maximum efficiency. Despite recent breakthroughs, challenges persist in large-scale implementation, fabrication complexity, and cost-effectiveness. Future research should prioritize the development of high-performance thermoelectric materials, advanced manufacturing techniques, and multi-physics simulation models to facilitate the next generation of TEG applications in waste heat recovery, automotive applications, and renewable energy generation. This review highlights emerging trends and outlines strategic research directions to accelerate the adoption of TEGs in sustainable energy generation.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101221"},"PeriodicalIF":7.6,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917550","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":"Decomposition and decoupling of carbon footprint pressure from economic growth under energy system transition in China’s urban agglomerations: Insights from prefecture-level cities (2000–2023)","authors":"Yushan Liu ,&nbsp;Zhuang Shao ,&nbsp;Jing Zhao","doi":"10.1016/j.ecmx.2025.101214","DOIUrl":"10.1016/j.ecmx.2025.101214","url":null,"abstract":"<div><div>The accelerating imbalance between surging carbon emissions and vegetation carbon sink exacerbates climate vulnerabilities, threatening the achievement of Sustainable Development Goal 13 (Climate Action). As the world’s largest carbon emitter, China’s urban agglomerations significantly contribute to its total emissions. Assessing carbon footprint pressure (CFP) and the environmental influence brought by economic activities is critical to China’s carbon reduction policies. By integrating land-based emissions (from cropland, water, and barren land) and activity-based emissions with vegetation carbon sequestration, this study systematically evaluates the CFP dynamics of five major Chinese urban agglomerations from 2000 to 2023. Our research reveals three key findings: (1) Through Logarithmic Mean Divisia Index (LMDI) decomposition, rapid economic growth is the primary driver of surging CFP, with urban agglomerations along the Yangtze River Economic Belt experiencing disproportionately higher impacts. (2) The decomposition further demonstrates that energy consumption and carbon emission intensities have mitigated CFP growth during 2000–2020 and 2021–2023, respectively. (3) Tapio decoupling shows a predominant coexistence of strong and weak decoupling states between CFP and GDP. While Beijing-Tianjin-Hebei faced persistent decoupling challenges, the Middle Reaches of Yangtze River Urban Agglomeration exhibited fluctuating patterns with localized declines. These findings provide a theoretical foundation for optimizing low-carbon strategies and advancing sustainable urban planning in China, through dual approaches of emission reduction (via energy efficiency improvements and industrial restructuring) and enhanced carbon sinks (targeted vegetation restoration and ecological conservation).</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101214"},"PeriodicalIF":7.6,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045585","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":"Energy-efficient strategies for net-zero buildings in the UAE: a climate-resilient blueprint","authors":"Mahmoud Z. Mistarihi ,&nbsp;Mohamad Kharseh ,&nbsp;Essam M. Abo-Zahhad ,&nbsp;Kadhim Alamara ,&nbsp;Mohamed Elasy ,&nbsp;Khadija Aldhuhoori","doi":"10.1016/j.ecmx.2025.101215","DOIUrl":"10.1016/j.ecmx.2025.101215","url":null,"abstract":"<div><div>From its Net-Zero 2050 strategic plan and United Nations Vision 2030 agenda, the United Arab Emirates (UAE) dedicated itself to the sustainability of its built environment and energy, specifically the electrical consumption. Nevertheless, the major portion of the national electricity is consumed by buildings, and the major portion of this is consumed by cooling systems; there’s a need for energy-efficient approaches suited specifically to the region’s intensive climate. The current review addresses this need by establishing a comprehensive review of research conducted between 2013 and 2025 to evaluate and categorize energy-saving techniques for buildings in the hot-climate region with a special focus on the UAE. The strategies fall into four domains: (1) optimizing HVAC with 20 to 40% energy reductions by means of Variable Refrigerant Flow (VRF) systems, adaptive setpoints, and AI-based controls; (2) enhancing the building envelope with 15 to 48% reductions in cooling demand by means of phase change materials, high-reflectivity coatings, and high-quality insulation; (3) integration with renewable energy sources, specifically rooftop PV and hybrid systems, with up to 40% reductions in grid dependence; and (4) smart building technology in the form of IoT sensors, BIM-based digital twins, and predictive maintenance systems with an operational saving of 10 to 30%. This review highlights key knowledge gaps pertaining to small-scale real-world UAE validation, economic feasibility studies, and multi-strategy integrated frameworks. The current review concludes by suggesting a scaling-up roadmap for energy-efficient methods in accordance with national policy targets and providing actionable recommendations to researchers, developers, and policymakers in the hot-climate region.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101215"},"PeriodicalIF":7.6,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004787","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":"A hybrid machine learning approach for predicting the performance of perovskite solar cells under varying temperatures","authors":"Ali Rahmani ,&nbsp;Farzin Hosseinifard ,&nbsp;Mohsen Salimi ,&nbsp;Majid Amidpour","doi":"10.1016/j.ecmx.2025.101217","DOIUrl":"10.1016/j.ecmx.2025.101217","url":null,"abstract":"<div><div>Renewable energy sources, particularly solar cells, play a crucial role in energy production, with silicon-based cells being the most common. However, perovskite solar cells have emerged as a promising alternative due to their diverse structural configurations and lower cost compared to traditional silicon cells. This study develops a unified model that integrates both classification and regression approaches to predict the optimal absorber material and assess the impact of temperature on solar cell performance. In the classification task, gradient boosting and random forest models demonstrated a higher area under the curve compared to other models. Before perovskite solar cells can be commercialized, experimental research must be conducted to better understand the factors influencing their performance. However, experiments are time-consuming and costly, and testing under varied conditions has its limitations. To overcome these challenges, machine learning is applied to improve and expand experimental data. With its high accuracy and speed, machine learning is widely used across various fields, including the development of perovskite solar cells. In this research, the impact of temperature on perovskite solar cells is examined. The goal was to gather experimental data and predict missing information. Among the three regression techniques applied, random forest regression yielded the highest accuracy at 98 %, while linear regression had the lowest at 75 %. Using the random forest approach, the power conversion efficiency at predicted temperatures of 55 °C, 75 °C, and 85 °C was found to be 99 %, 89 %, and 88 % of the initial value, respectively.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101217"},"PeriodicalIF":7.6,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895980","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":"A hybrid ANN-transfer function framework for multi-dimensional PV tilt angle optimization","authors":"Monika Božiková ,&nbsp;Vladimír Madola ,&nbsp;Matúš Bilčík ,&nbsp;Vladimír Cviklovič","doi":"10.1016/j.ecmx.2025.101216","DOIUrl":"10.1016/j.ecmx.2025.101216","url":null,"abstract":"<div><div>The manuscript focuses on the application of optimization techniques and decision-making processes in photovoltaic energy systems, supported by data analysis methods aimed at solving energy-related modelling problems. This study applies an integrated framework for the analysis, modelling, and evaluation of a photovoltaic system’s energy balance, combining transfer-function methods with neural networks with Monte Carlo simulation to optimise panel tilt, grid interaction, self-consumption, and economic payback. The work presents a mathematical model and a multi-step calculation algorithm for determining the optimum tilt angle of a photovoltaic system, with the tilt angle ranging from 0° to 90°. The model describes the system’s energy balance and enables its analytical identification under varying conditions. An extended modelling algorithm using Laplace transform was developed to validate the analytical model, with further verification carried out through the application of an artificial neural network. A complex simulation procedure was carried out for a tilt angle of 25°, including statistical evaluation of the relationship between the parameters of the analytical model and those of the model defined in a complex variable domain, across different time periods. Statistical significance of the observed differences in key quantification indices was assessed. The results confirmed a high level of validity 93.9% of the original model and demonstrated the practical applicability of the proposed modelling procedure and verification method using complex variable. Furthermore, the framework integrates<!--> <!-->economic assessment, revealed that tilt angles around 25° provide the highest energy yield (5230 kWh/year) and best net present value (€14,707), whereas vertical panels reduce energy yield by up to 16.2%.<!--> <!-->The proposed framework therefore provides<!--> <!-->a rigorous technical basis and economic justification<!--> <!-->for optimised PV system design under Central European climatic conditions.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101216"},"PeriodicalIF":7.6,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895441","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":"Electricity pinch analysis method for flexibility supply-demand matching in power systems","authors":"Yaling Mao ,&nbsp;Tiejiang Yuan ,&nbsp;Xueqin Tian ,&nbsp;Yue Teng","doi":"10.1016/j.ecmx.2025.101210","DOIUrl":"10.1016/j.ecmx.2025.101210","url":null,"abstract":"<div><div>The growing integration of renewable energy into modern power systems presents significant challenges in maintaining flexibility supply–demand balance. Traditional operation simulation-based planning approaches often fail to provide effective flexibility matching mechanisms, resulting in either insufficient resource allocation or over-provisioning, while struggling to reconcile reliability requirements with computational complexity. Leveraging the theoretical framework of pinch technology from process engineering, this paper proposes an Electricity Pinch Analysis (EPA) method for flexibility assessment. First, the net-load profile is decomposed by successive variational mode decomposition (SVMD) optimized with the Red-billed Blue Magpie Optimization (RBMO) algorithm to construct a flexibility demand model. Subsequently, a unified characterization method is developed to model the amplitude-frequency characteristics of flexibility resources, ensuring compatibility with pinch analysis requirements. Guided by the supply–demand matching principles inherent to pinch analysis, a graphical method is introduced that efficiently aligns flexibility resources with demand. Source and sink composite curves are constructed and horizontally shifted to locate the pinch point, thereby identifying the bottleneck in flexibility balance. Finally, chronological operation simulations are carried out within the frequency band indicated by the pinch point to validate the feasibility of the planning outcome. By relying directly on frequency-domain characteristic parameters for resource planning, the proposed approach markedly reduces dependence on high-precision sequential forecasts and significantly mitigates the impact of power-prediction uncertainty on planning results.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101210"},"PeriodicalIF":7.6,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888883","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":"Context matters: A cross-cultural analysis of drivers, barriers, and sustainability impacts of household decisions on solar PV","authors":"Gamal Alkawsi ,&nbsp;Mohammed A. Al-Sharafi ,&nbsp;Halimah Badioze Zaman ,&nbsp;Fadi Herzallah ,&nbsp;Yahia Baashar ,&nbsp;Adnan Bakather ,&nbsp;Noor Ismawati Jaafar","doi":"10.1016/j.ecmx.2025.101202","DOIUrl":"10.1016/j.ecmx.2025.101202","url":null,"abstract":"<div><div>The transition to renewable energy is critical in addressing global climate challenges, yet household adoption of photovoltaic (PV) solar systems remains limited, particularly in diverse socio-economic and cultural contexts. This study investigates the drivers and barriers influencing household decisions to install solar energy systems and their impact on sustainable consumption behaviors, focusing on Malaysia and Palestine as contrasting cultural and socio-economic contexts. The study offers a novel theoretical integration by extending Behavioral Reasoning Theory (BRT) to directly assess the decision to install—rather than mere intention—and by incorporating Innovation Resistance Theory (IRT) to examine both motivators and inhibitors. Using survey data from 768 respondents and Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-group analysis (MGA), the findings reveal cross-cultural differences. In Malaysia, environmental values strongly influence reasons for adoption and the decision to install, highlighting the role of supportive policies and incentives. Conversely, in Palestine, systemic socio-economic barriers dilute these effects. The decision to install PV systems was positively linked to sustainable consumption behaviors in both contexts, albeit more pronounced in Malaysia. These findings underscore the importance of enabling environments in enhancing the adoption and sustainability impacts of PV technology and highlight the need for tailored interventions to address barriers in resource-constrained regions.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101202"},"PeriodicalIF":7.6,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895440","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":"Economic assessment of self-consumption and energy communities: Profit distribution insights from a real case study","authors":"Jose Miguel Riquelme-Dominguez ,&nbsp;María Emilia Sempértegui ,&nbsp;Juan Manuel Roldan-Fernandez ,&nbsp;Javier Serrano-Gonzalez ,&nbsp;Jesus Manuel Riquelme-Santos","doi":"10.1016/j.ecmx.2025.101198","DOIUrl":"10.1016/j.ecmx.2025.101198","url":null,"abstract":"<div><div>The deployment of solar energy for self-consumption provides an opportunity to restructure energy systems by harnessing energy and allowing individuals to actively participate in the energy transition, resulting in more significant profits. This work compares the photovoltaic (PV) electricity production for residential prosumers under three scenarios, in which: (1) the PV systems are designed to supply the individual demands of each user optimally; (2) with the exact PV capacity of the first scenario, the users decide to form an energy community; and (3), the prosumers decide to consolidate as an energy community from the beginning, and the whole PV system is designed to cover the demand of all the users optimally. Results show that energy communities in general, and creating the community from zero in particular, are more cost-effective than when the prosumers invest and manage their own PV system individually. The paper also discusses the distribution of the additional profits considering four allocation strategies, with the sharing approach based on the optimal individual photovoltaic power capacity being the most advantageous for all prosumers of the community. Specifically, with this sharing strategy, all prosumers reduce their payback, all prosumers increase the Net Present Value of their investment, and all prosumers pay less than 50% of what they pay when they do not have a self-consumption installation.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101198"},"PeriodicalIF":7.6,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917547","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}