Energy Reports最新文献

{"title":"Enhancing energy security in Bangladesh through small modular reactor integration: A geographical, industrial and demand-based perspective","authors":"Afroza Shelley,&nbsp;H. Rainad Khan Rohan","doi":"10.1016/j.egyr.2025.08.030","DOIUrl":"10.1016/j.egyr.2025.08.030","url":null,"abstract":"<div><div>Small modular reactors (SMRs) can play a pivotal role in fulfilling Bangladesh’s ever-rising energy demands while addressing critical environmental concerns. This study seeks to explore the possibility of deploying SMRs in Bangladesh, with a focus on geographic, industrial and electricity demand factors. Analysis of division-wise power demand and load-shedding data from 2022 to 2023 indicates that SMRs with an electrical output of approximately 300 MW, either individually or in parallel, could be deployed in various administrative divisions, where daily load-shedding ranged from 110 to 500 MW (with the highest demand in the capital Dhaka). Additionally, the eight export processing zones (EPZs) in the country, which currently consume around 297 MW electricity, could also benefit from the installation of small and micro-modular reactors tailored to their energy needs. The country currently imports 2656 MW power from India, a portion of which could be replaced with domestically generated electricity from SMRs, thus enhancing national energy self-sufficiency. Furthermore, local industrial hubs and remote areas disconnected from the grid, such as coastal islands, wetlands and the Chittagong hill tracts, stand to benefit significantly from SMR deployment. The selection of specific SMR technologies will hinge on factors such as availability, technological advancements, national infrastructural development, financing/costs and the active engagement of both government and stakeholders.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 1917-1935"},"PeriodicalIF":5.1,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability and condition assessment techniques for oil-immersed power equipment under varying temperatures: A review","authors":"Raheel Ahmed ,&nbsp;Ji Liu ,&nbsp;Mingze Zhang ,&nbsp;Xianhao Fan","doi":"10.1016/j.egyr.2025.08.016","DOIUrl":"10.1016/j.egyr.2025.08.016","url":null,"abstract":"<div><div>Oil-immersed power transformers are important components in modern power grids, relying heavily on the integrity of their insulation systems. It consists of cellulose-based solid insulation and mineral or ester based insulating fluids. The insulation materials degrade over time due to electrical, thermal, mechanical and environmental pressures. Researchers have conducted accelerated aging tests in the lab to replicate real-life conditions to investigate how oil-immersed equipment degrades over time. The valuable techniques to reduce the lifespan of the insulation system enable the study of the material behavior and predict how long the insulation system will withstand. This review examines conventional and advanced transformer insulation assessment techniques, particularly emphasizing efficacy in cold-climate environments. It includes the degree of polymerization (DP), furfural analysis, and dissolved gas analysis (DGA) as well as advanced dielectric response methodologies, including FDS and PDC testing. The review also discusses low-temperature insulating material precision, challenges, and dependability issues. Emerging diagnostic approaches incorporating artificial intelligence, machine learning and optical sensing technologies offer promising improvements in early fault detection and condition monitoring. The acceleration of the deterioration process can lead to premature insulation failure and increased operational risks. Additionally, it discusses the integration of AI and machine learning for enhanced monitoring and the development of cold-resistant materials and fluids for improved transformer reliability in extreme environments.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 1896-1916"},"PeriodicalIF":5.1,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced maximum power point estimation algorithm using quantum particle swarm optimization for solar photovoltaic micro inverter systems","authors":"Ganesh Moorthy Jagadeesan ,&nbsp;Arivoli Sundaramurthy ,&nbsp;M. Vijayakumar ,&nbsp;Birhanu Belete","doi":"10.1016/j.egyr.2025.08.010","DOIUrl":"10.1016/j.egyr.2025.08.010","url":null,"abstract":"<div><div>Photovoltaic systems are increasingly integrated into distributed energy networks, demanding compact and efficient inverter solutions that can maintain stable operation under variable environmental conditions. To address this, a single-stage micro-inverter architecture is developed using a quantum-behaved particle swarm optimization algorithm for enhanced maximum power point tracking. The motivation lies in improving the convergence speed and tracking accuracy over conventional methods while minimizing system complexity. The hypothesis is that embedding a probabilistic optimization algorithm within the control loop can improve adaptability to irradiance fluctuations and achieve better regulatory compliance. The proposed design employs a flyback converter topology that combines voltage step-up, galvanic isolation, and direct current to alternating current inversion into a unified platform. The control strategy integrates a quantum-behaved particle swarm optimization-based maximum power point estimation mechanism and a proportional-integral controller, both validated through simulation and real-time hardware testing using a programmable controller and solar simulator. The quantitative results show the system achieves maximum power tracking efficiency of 95–98 percent, with voltage and current total harmonic distortion levels of 3.6 percent and 4.9 percent, respectively, meeting grid compliance requirements. The proposed solution ensures system stability with a phase margin of 58.7 degrees and responds well under dynamically varying irradiance conditions. This work demonstrates that integrating intelligent control with a compact flyback-based architecture results in a reliable, efficient, and grid-compliant solar micro-inverter suitable for low-power residential and standalone solar applications.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 1877-1895"},"PeriodicalIF":5.1,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aligned interpretations? Comparing energy modeller and policymaker perspectives on model development and use","authors":"Chris Foulds,&nbsp;Aled Jones,&nbsp;Sarah Royston,&nbsp;Roberto Pasqualino","doi":"10.1016/j.egyr.2025.08.025","DOIUrl":"10.1016/j.egyr.2025.08.025","url":null,"abstract":"<div><div>Models are not neutral. Yet, there has been insufficient attention given to the possible divergence in interpretations between those producing energy models and those using the related modelling evidence. This paper investigates how various actors involved in an energy policymaking-modelling system imagine key components of model design and development. We focus on four model components that energy-economic modellers have identified as essential for advancing the relevance and value of energy-economic models in the context of net-zero transitions: (1) uncertainties; (2) technological innovation and radical transformation; (3) diversity of actor responses; and (4) long-time horizons. We adopted a case study approach that focused on the EU energy policymaking-modelling system. We conducted 16 interviews; 6 with European Commission policy officers, and 10 with those developing Commission-funded energy-economic models. We found that the four model components exhibited little Interpretative Flexibility; specifically, only one component (long-time horizons) was interpreted differently between EU policymakers and energy-economic modellers. Indeed, the policymakers’ understanding of model development meant that they did little interpretative work on energy models altogether. We call for modeller-policymaker dialogue to prioritise what sits behind interpretations (e.g. theories, ideologies) and to more constructively discuss model boundaries and scope (e.g. via systems thinking, use of intermediaries).</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 1866-1876"},"PeriodicalIF":5.1,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power system planning using optimally designed transmission lines","authors":"Bhuban Dhamala,&nbsp;Mona Ghassemi","doi":"10.1016/j.egyr.2025.08.026","DOIUrl":"10.1016/j.egyr.2025.08.026","url":null,"abstract":"<div><div>Over the past several decades, transmission lines, the primary infrastructure for transporting electric energy across power systems, have largely maintained their traditional designs, despite significant shifts in supply and demand dynamics. The inherent operational limitations of these conventional designs continue to be a major constraint on grid loadability and are expected to remain so in the foreseeable future. To address this challenge, we propose the use of unconventional transmission lines optimally designed with enhanced power transfer capabilities, known as high-surge-impedance loading (HSIL) lines. The proposed HSIL configuration achieves a surge impedance loading of 1354.72 MW, representing a 41.04 % increase over that of conventional 500 kV lines with equivalent conductor weight and cross-sectional area. The impact of these lines on the planning of large-scale power systems is assessed through detailed transmission expansion planning (TEP) studies. Using a 17-bus 500 kV test system in three different loading scenarios, each analyzed under normal and N-1 contingency conditions, we compare the performance of conventional versus HSIL lines to connect a new load to a new location. Comprehensive economic analysis reveals that the implementation of HSIL lines results in substantial cost savings, thus presenting a technically robust and economically attractive solution for future power system planning.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 1855-1865"},"PeriodicalIF":5.1,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward a unified framework for electric vehicle infrastructure: Insights into charging management, communication and emerging technologies","authors":"S. Zahedieh ,&nbsp;A. Oulis Rousis ,&nbsp;A. Bourazeri","doi":"10.1016/j.egyr.2025.08.011","DOIUrl":"10.1016/j.egyr.2025.08.011","url":null,"abstract":"<div><div>The transportation sector remains a major contributor to greenhouse gas emissions, and the widespread adoption of electric vehicles (EVs) is a key strategy for mitigating this impact. However, scaling EV adoption requires an equally robust expansion and integration of charging infrastructure, which currently suffers from fragmented management systems and incompatible communication protocols. This paper systematically reviews the existing landscape of EV infrastructure, identifying gaps in charging efficiency, grid integration, and user accessibility. Through an analysis of EV simulation platforms, communication standards, and central management systems, we synthesize key insights into a unified framework that leverages emerging technologies such as blockchain, AI-driven optimization, and reinforcement learning. Furthermore, we propose strategies for enhancing scalability, including EV unification, gamification, and adaptive charging mechanisms. By providing a structured roadmap, this study serves as a comprehensive guide for researchers, policymakers, and industry leaders, offering actionable insights to optimize the EV ecosystem and accelerate the shift toward sustainable transportation.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 1834-1854"},"PeriodicalIF":5.1,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental parametrical study of sectioned tilted tubular solar still equipped with hydrogel","authors":"Karima E. Amori ,&nbsp;Tabark A. Hussien","doi":"10.1016/j.egyr.2025.08.024","DOIUrl":"10.1016/j.egyr.2025.08.024","url":null,"abstract":"<div><div>Solar distillers are a sustainable and simple solution for addressing water scarcity, but their limited productivity restricts their effectiveness. This work aimed to assess the thermal performance of a novel tracked, tilted, hexagonal tubular solar still (HTSS) of four-sectioned U-channel receiver. Two identical HTSSs were side-to-side tested in Baghdad-Iraq (33.3°N, 43.3°E) from June to September 2024. The thermal evaluation of single-axis tracking solar still, tilted at (5° to 15°) with the horizontal axis and charged with and without hydrogel beads for water depth of 60 mm. The still's thermal performance is assessed by analyzing heat transfer coefficients, energy and exergy efficiencies, as well as conducting cost and environmental impact analyses. A considerable improvement in still yield is achieved as hydrogel is charged in. Results showed that the still thermal efficiency is uplifted by (87.79, 96.24, and 108.26 %) for (2000, 4000, and 6000 beads), respectively, as the tilt angle increases from 5° to 15°. It was found that the present still (with 6000 hydrogel beads, and tilted at 15°) has the best thermal and exergy efficiencies and produces 16.72 l/m², accompanied by 9.91 ton/year emission reduction of CO₂, with a competitive cost for water production of 0.039 $/liter.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 1799-1813"},"PeriodicalIF":5.1,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable AI for explaining and predicting battery state of health using PSO-enhanced deep learning models","authors":"Sadiqa Jafari ,&nbsp;Yung-Cheol Byun","doi":"10.1016/j.egyr.2025.07.027","DOIUrl":"10.1016/j.egyr.2025.07.027","url":null,"abstract":"<div><div>Accurately determining the State-of-Health (SOH) of Lithium-ion (Li-ion) batteries is important for the safe operation of Electric Vehicles (EVs); nevertheless, in practical implementations, variables such as human error and operational conditions can affect the accuracy of SOH estimates. This paper proposes a fusion neural network based on the Convolutional Neural Network, Long-term Short Memory (LSTM), and Convolutional LSTM (ConvLSTM) models with meta-heuristic optimization and eXplainable Artificial Intelligence (XAI) to accurately predict the battery SOH. Our proposed model combines CNN, LSTM, and ConvLSTM in a novel process, optimized by PSO and explained by SHAP, in contrast to previous hybrid models that often only combine two neural networks and rarely incorporate both optimization and interpretability. The suggested method integrates CNN, LSTM, and ConvLSTM models into a Deep Neural Network (DNN) framework. This framework is tuned using Particle Swarm Optimization (PSO) to improve the generality and accuracy of SOH estimations. Furthermore, in order to achieve serial data time dependency and correlation, the suggested data-driven approach exploits the voltage distribution and capacity changes in the derived battery discharge curve. The results of the experiment show that when compared to CNN, LSTM, ConvLSTM, and other deep learning models, the proposed model achieves good performance for battery SOH prediction. Furthermore, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are limited to within 0.009% and 0.044%, accordingly, on the battery dataset. This study utilizes XAI techniques, namely SHapley Additive exPlanations (SHAP), to clarify the predictions made by the fusion DNN model. This approach aims to enhance clarity and instill trust in the system, and the findings indicate that the fusion DNN model outperforms conventional approaches, optimized using PSO and including CNN, LSTM, and ConvLSTM components, and it achieves higher <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> scores, smaller mean residuals, and improved XAI outcomes.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 1779-1798"},"PeriodicalIF":5.1,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical review of hybrid modular multilevel converters for HVDC transmission in offshore wind energy","authors":"Ratna Raj ,&nbsp;Abdellatif Elmouatamid ,&nbsp;Akhyurna Swain ,&nbsp;Philip W.T. Pong","doi":"10.1016/j.egyr.2025.07.017","DOIUrl":"10.1016/j.egyr.2025.07.017","url":null,"abstract":"<div><div>For the integration of wind energy into the transmission system, High Voltage DC (HVDC) is the most efficient and cost-effective option. Modular multilevel converters have emerged as the most suitable converters for the required AC/DC conversions. To maintain power availability, stability, and reliability in the HVDC transmission, the converters’ ability to offer fault ride-through (FRT) and work as static compensators (STATCOM) during DC faults have become important requirements. The conventional half bridge-based MMC is the most cost-effective, efficient, and widely used topology, but it cannot meet these requirements. Though full-bridge MMC has this ability, it doubles the converter cost, device count, and losses. In the present MMC topology, the capacitors occupy 50 % of the volume and 80 % weight of the converter. Reducing energy storage requirements is needed for their efficient use in offshore wind energy substations. Many Hybrid MMC (HMMC) topologies have been introduced in the last few years to offer a solution to meet these requirements, but no topology has been found yet that can fulfill all the required criteria. A comprehensive review of the existing topologies, their features, strengths, and drawbacks are needed to identify research gaps and guide future works. This paper provides a thorough and systematic review of the topology, fault handling, and STATCOM mode of all hybrid MMCs (HMMCs) for HVDC offshore wind energy transmission found in the literature. It compares these topologies' structure, losses, and functionality to guide future research. A case study calculates these topologies' losses and energy requirements by using time-domain analysis of a switching-based model in MATLAB of these converters, with a rating of 1500MVA with 1050KV DC voltage. The results present a quantitative comparison of losses and energy storage requirements across these topologies which would be useful to give directions to future works.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 1761-1778"},"PeriodicalIF":5.1,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do climate policy uncertainty, green finance, and the energy market matter for carbon price? Evidence from China","authors":"Lian Xiong ,&nbsp;Mohan Chen ,&nbsp;Wen Luo ,&nbsp;Wenhua Yang","doi":"10.1016/j.egyr.2025.08.027","DOIUrl":"10.1016/j.egyr.2025.08.027","url":null,"abstract":"<div><div>Using quantile Granger causality test and Quantile Vector Autoregression (QVAR) dynamic time and frequency, this paper investigates the driving factor of China carbon pricing and provide risk hedging strategies. We examine the relationship between the carbon trading market, clean energy market, traditional energy market, green finance market, and climate policy uncertainty under different quantiles and periods. Our study reveals that the overall relationship between China's carbon market and other markets, such as the clean energy market, traditional energy market, and green financial market, follows a U-shaped curve. During financial stress periods (τ=0.05 and τ=0.95), the total connectedness of the system reaches 85.78 % and 84.18 %, respectively, compared to 22.13 % under normal conditions (τ=0.50). Meanwhile, we also find incorporating energy market and the green stock market into a portfolio can significantly hedge systemic risks, irrespective of the level of climate policy uncertainty.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 1814-1823"},"PeriodicalIF":5.1,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}