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TransCarbonNet:Multi-Day Grid Carbon Intensity Forecasting Using Hybrid Self-Attention and Bi-LSTM Temporal Fusion for Sustainable Energy Management 认领 引用 被引量：1
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作者 Amel Ksibi Hatoon Albadah +1 位作者 Ghadah Aldehim Manel Ayadi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期812-847,共36页
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese... Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid. 展开更多
关键词 Carbon intensity forecasting self-attention mechanism bidirectional LSTM temporal fusion sustainable energy management smart grid optimization deep learning
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Design of Flexible Buoyancy Modules for Deep-Sea Bionic Robots Via Structured Grid-Based Contour Adaptation 认领 引用
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作者 Ruilong Luo Jiawei Lin +1 位作者 Biao Wang Fang Wang 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期701-715,共15页
Due to their lightweight and flexibility,soft bionic robots are popular in deep-sea exploration.However,existing buoyancy materials lack optimal compatibility.This study proposes a flexible,pressure-resistant,multi-me... Due to their lightweight and flexibility,soft bionic robots are popular in deep-sea exploration.However,existing buoyancy materials lack optimal compatibility.This study proposes a flexible,pressure-resistant,multi-medium buoy-ancy module comprising a flexible cavity filled with a Hollow Glass Microsphere(HGM)-water mixture and introduces structured-grid thinking,which enables contour adaptation to complex bionic robot morphologies.The density and pressure resistance of the buoyancy modules were experimentally tested,and the effects of varying silicone hardness,wall thickness,and volume percentage of HGM in the mixture on the performance of the buoyancy modules were compared.The results indicate that the density of the buoyancy modules ranges from 0.751 to 0.964 g/cm3.Under a pressure of 30 MPa,the volume change rate of the buoyancy modules is between 1.74%and 2.13%.The effect of air content in the flexible cavity on buoyancy modules under high pressure was examined by comparing experimental findings with simulations. 展开更多
关键词 Flexibility Buoyancy module Pressure-resistant Structured grid thinking
FedGNN:Federated Graph Neural Networks for Privacy-Preserving Cyber-Resilient Energy Optimization in IoT-Based Smart Grids 认领 引用
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作者 Alanoud Al Mazroa Fahad Masood +3 位作者 Bakri Hussain Awaji Mohammad Alhefdi Abeer Aljohani Jawad Ahmad 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1462-1483,共22页
The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and priv... The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and privacy risks,as increased connectivity and data exchange can significantly disrupt energy management and system stability.Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks.This study proposes a privacy-preserving and cyber-resilient energy-optimization framework,FedGNN,for IoT-enabled smart grids that jointly integrates federated learning,graph neural network-based trust inference,and trust-aware energy dispatch.The framework dynamically learns node-level trust scores from multifeature measurements,including load,voltage,frequency,renewable generation,and battery storage,and incorporates them into real-time energy optimization.Results demonstrate that the proposed approach improves system resilience up to 12%,mitigates the impact of compromised nodes,and maintains operational reliability,while preserving the privacy of distributed data.A comparative analysis with baseline methods shows the proposed framework's superior performance in energy deviation,resilience,and trust-aware decision-making.The results highlight the potential of integrating AI-driven trust mechanisms with federated learning for secure and efficient energy management in future IoT-enabled smart grids. 展开更多
关键词 Cyber security energy optimization graph neural networks IoT smart grids
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An ethical assessment framework for AI security and ethics in smart grid 认领 引用
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作者 Yiying Zhang Congcong Zhao +2 位作者 Ziang Meng Chun Sing Lai Xi Chen 《Global Energy Interconnection》 EI CSCD 2026年第2期298-314,共17页
The advancement of smart grid,facilitated by the extensive integration of information communication,automated control,and artificial intelligence(AI)technologies,signifies a significant transformation of the power sys... The advancement of smart grid,facilitated by the extensive integration of information communication,automated control,and artificial intelligence(AI)technologies,signifies a significant transformation of the power system towards holistic perception,intelligent management,and secure operation.This article focuses on the security and ethical compliance of smart grid,intending to offer guiding insights for this new technological domain.This study initially delineates the potential applications,technical attributes,and design of smart grid,followed by a thorough examination of the security threats and ethical dilemmas arising from technological advancements.This study examines the pivotal role of AI in smart grid and its intricate interplay with security and ethical concerns.It performs a comprehensive analysis of the possible technical deficiencies and ethical challenges of AI systems in smart grid and assesses the extensive repercussions that these difficulties may entail.This study presents a security ethics evaluation methodology for smart grid,which thoroughly examines the ethical implications of AI technology in power grid applications and identifies existing obstacles and threats.This paper conducts a thorough policy analysis to evaluate the present security and ethical conditions of smart grid,with the objective of offering substantive theoretical support to enhance their security and ethical advancement,thereby fostering their healthy and sustainable development. 展开更多
关键词 Smart grid Artificial intelligence technology Ethics of science and technology Assessment framework
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A Double-Time-Scale Dynamic Reactive Power Optimization Method for the AC/DC Hybrid Power Grid Incorporating UPFC 认领 引用
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作者 Wei Yin Jun Wang +5 位作者 Ming Tong Zhijun Chen Ke Zhang Meiqing Huang Ran Gu Keman Lin 《Energy Engineering》 EI 2026年第7期123-149,共27页
With the high penetration of renewable energy and the rapid development of AC/DC(Alternating Current/Direct Current)hybrid power grid,the power grid is confronted with challenges such as frequent voltage fluctuations ... With the high penetration of renewable energy and the rapid development of AC/DC(Alternating Current/Direct Current)hybrid power grid,the power grid is confronted with challenges such as frequent voltage fluctuations and insufficient dynamic reactive power reserves.Full utilization of unified power flow controller(UPFC)in dynamic voltage regulation is of great significance for mitigating voltage excursions of the power grid.This paper proposes a double-time-scale dynamic reactive power optimization method for the AC/DC hybrid power grid with UPFC.A control framework for reactive power optimization of slow-time-scale and fast-time-scale is constructed incorporating the LCC-HVDC and UPFC.In this method,the slow-time-scale aims to improve the voltage profiles and reduce the system cost by setting the voltage regulation weight coefficients based on trajectory sensitivity to preserve reactive power regulation capability.The fast-time-scale adopts an adaptive feedback control mechanism.When slowtime-scale optimization is insufficient to keep the voltage within a safe range,it adjusts the real-time reactive power output of the UPFC,and damps rapid voltage swings accordingly.By implementing the additional fast-time-scale control method,the frequent variations of both the Photovoltaic(PV)and load are managed for the reactive power compensation.Case studies on a modified IEEE-30 bus system demonstrate that compared with the conventional control method,the proposed method reduces the maximum voltage deviation by 3.17%compared to the baseline,while ensuring the economic efficiency. 展开更多
关键词 AC/DC hybrid power grid UPFC double-time-scale dynamic reactive power optimization trajectory sensitivity
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Erratum to“False Data Injection Attacks on Data-Driven Algorithms in Smart Grids Utilizing Distributed Power Supplies”[Engineering 51(2025)62-74] 认领 引用
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作者 Zengji Liu Mengge Liu +1 位作者 Qi Wang Yi Tang 《Engineering》 SCIE EI CSCD 2026年第5期362-362,共1页
The publisher regrets the CRediT authorship contribution statement was inserted incorrectly and the correct statement should be updated as below:Zengji Liu:Writing-review&editing,Writing-original draft,Visualizati... The publisher regrets the CRediT authorship contribution statement was inserted incorrectly and the correct statement should be updated as below:Zengji Liu:Writing-review&editing,Writing-original draft,Visualization,Validation,Supervision,Software,Resources,Project administration,Methodology,Investigation,Funding acquisition,Formal analysis,Data curation,Conceptualization.Mengge Liu:Writing-review&editing,Writing-original draft,Investigation.Qi Wang:Writing-review&editing,Writing-original draft.Yi Tang:Writing-review&editing,Writing-original draft. 展开更多
关键词 data driven algorithms credit authorship contribution statement false data injection attacks smart grids distributed power supplies
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Spanish Blackout Reveals Another Grid Weakness 认领 引用
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作者 Mitch Leslie 《Engineering》 SCIE EI CSCD 2026年第1期7-9,共3页
Early in the afternoon of 28 April 2025,as far as more than 50million residents of Spain and Portugal knew,their electrical grid was working fine.But at 12:33 pm Central European Time(CET),the power went out across th... Early in the afternoon of 28 April 2025,as far as more than 50million residents of Spain and Portugal knew,their electrical grid was working fine.But at 12:33 pm Central European Time(CET),the power went out across the two countries and also in a sliver of southern France served by the same grid(Fig.1)[1].One writer living in Madrid likened the ensuing events to"the beginning of the end of the world"(Fig.2)[2].Traffic gridlocked,trains and subways halted,cell phone and internet service went down,stores and businesses shuttered[1-3].In just the first few hours of the blackout,Madrid's firefighters responded to more than 200 emergencies,many involving people stuck in elevators[4].Airlines canceled around 500 flights,stranding about 80000 travelers[5].In Spain,as many as 167 people,mainly elderly women,died because of the effects of the blackout,according to estimates based on historical mortality rates[6]. 展开更多
关键词 blackout electrical grid France Spain transportation disruption infrastructure failure Portugal emergency response
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Adaptive Grid-Interface Control for Power Coordination in Multi-Microgrid Energy Networks 认领 引用
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作者 Sk.A.Shezan 《Energy Engineering》 EI 2026年第1期91-114,共24页
Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency devia... Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency deviations,voltage fluctuations,and poor reactive power coordination,posing serious challenges to grid stability.Conventional Interconnection FlowControllers(IFCs)primarily regulate active power flowand fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks.To overcome these limitations,this study proposes an enhanced Interconnection Flow Controller(e-IFC)that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller(IRFC)within a unified adaptive control structure.The proposed e-IFC is implemented and analyzed in DIgSILENT PowerFactory to evaluate its performance under various grid disturbances,including frequency drops,load changes,and reactive power fluctuations.Simulation results reveal that the e-IFC achieves 27.4% higher active power sharing accuracy,19.6% lower reactive power deviation,and 18.2% improved frequency stability compared to the conventional IFC.The adaptive controller ensures seamless transitions between grid-connected and islanded modes and maintains stable operation even under communication delays and data noise.Overall,the proposed e-IFCsignificantly enhances active-reactive power coordination and dynamic stability in renewable-integrated multi-microgrid systems.Future research will focus on coupling the e-IFC with tertiary-level optimization frameworks and conducting hardware-in-the-loop validation to enable its application in large-scale smart microgrid environments. 展开更多
关键词 Active power flow control interconnection flow controller(IFC) frequency response micro grid stability reactive power management
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Grid-XAJ与Grid-GA模型在小流域洪水模拟中的适用性对比验证 认领 引用
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作者 杜若愚 程龙 +3 位作者 蒲博文 张璇 杜志水 王盼 《华北水利水电大学学报（自然科学版）》 北大核心 2026年第1期95-104,共10页
【目的】构建适合抽水蓄能电站所在小流域的洪水预报模型,提高洪水预报精度,为具有相似产汇流特性的小流域洪水预报提供新的预报模式。【方法】选取陕西省镇安抽水蓄能电站下水库坝址以上流域为研究对象,分别应用网格新安江(Grid-XAJ)... 【目的】构建适合抽水蓄能电站所在小流域的洪水预报模型,提高洪水预报精度,为具有相似产汇流特性的小流域洪水预报提供新的预报模式。【方法】选取陕西省镇安抽水蓄能电站下水库坝址以上流域为研究对象,分别应用网格新安江(Grid-XAJ)模型和网格格林-安普特(Grid-GA)模型进行洪水模拟,对比分析两模型在不同洪水场景下的模拟性能。【结果】①Grid-XAJ模型在洪水模拟方面的表现优于Grid-GA模型,率定期与验证期的合格率分别为74.36%、80.94%,达到乙级预报精度;②Grid-GA模型对久旱之后的第一场洪水的模拟效果优于Grid-XAJ模型的;③对于历年最大洪水,两模型的模拟精度相当,Grid-XAJ模型在过程拟合方面更优,Grid-GA模型在洪峰流量捕捉方面更好。【结论】Grid-XAJ模型更适用于该流域常态洪水模拟,而Grid-GA模型更适用于干旱初期的洪水预报。建议根据前期土壤含水量选择合适的水文模型进行组合预报,可为水文资料匮乏的小流域洪水预报提供有效技术途径。 展开更多
关键词 网格新安江模型 网格格林-安普特模型 蓄满产流 超渗产流 小流域洪水模拟
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Emittance optimization of gridded thermionic‑cathode electron gun for high‑quality beam injectors 认领 引用 被引量：1
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作者 Xiao‑Yu Peng Hao Hu +3 位作者 Tong‑Ning Hu Jian Pang Jian‑Jun Deng Guang‑Yao Feng 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第1期119-129,共11页
Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced... Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced by the injector critically influences the performance of the entire accelerator-based scientific research apparatus.The injectors of such facilities usually use photocathode and thermionic-cathode electron guns.Although the photocathode injector can produce electron beams of excellent quality,its associated laser system is massive and intricate.The thermionic-cathode electron gun,especially the gridded electron gun injector,has a simple structure capable of generating numerous electron beams.However,its emittance is typically high.In this study,methods to reduce beam emittance are explored through a comprehensive analysis of various grid structures and preliminary design results,examining the evolution of beam phase space at different grid positions.An optimization method for reducing the emittance of a gridded thermionic-cathode electron gun is proposed through theoretical derivation,electromagnetic-field simulation,and beam-dynamics simulation.A 50%reduction in emittance was achieved for a 50 keV,1.7 A electron gun,laying the foundation for the subsequent design of a high-current,low-emittance injector. 展开更多
关键词 Electron gun Gridded Beam injector Beam dynamics Emittance optimization
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基于Griddler模拟的晶硅异质结太阳电池金属化图形优化 认领 引用
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作者 李晓彤 贾晓洁 +3 位作者 赵雷 彭长涛 辛科 王文静 《太阳能学报》 EI CAS CSCD 北大核心 2026年第4期606-613,共8页
针对基于n型晶体硅(c-Si)的异质结(SHJ)太阳电池,通过Griddler软件模拟电池的电流-电压(I-V)性能,对电极金属化图案进行了优化,以实现高转换效率。采用实验验证了模拟结果,并确认了仿真模型的准确性。模拟对比分析了电镀制备的铜电极与... 针对基于n型晶体硅(c-Si)的异质结(SHJ)太阳电池,通过Griddler软件模拟电池的电流-电压(I-V)性能,对电极金属化图案进行了优化,以实现高转换效率。采用实验验证了模拟结果,并确认了仿真模型的准确性。模拟对比分析了电镀制备的铜电极与丝网印刷制备的银电极在由垂直排列的细栅和主栅组成的梳状图形中的优化效果。主要分析了电镀铜主栅数量、电镀铜与丝网印刷银细栅数量的电性能差异,以及铜电极高度的影响。提出在SHJ太阳电池中镀铜电极实现高效率的最佳参数:主栅数目为12,间距为13.85 mm,细栅间距为1.410 mm、线宽为20µm时,电镀铜电极比丝网印刷银电极的最佳效率提高0.17%。理论上,电极高度越高,效率越高。通过优化主栅与细栅间的间距,栅线图形的设计可有效适用于不同尺寸的太阳电池。 展开更多
关键词 硅太阳电池 异质结 电镀铜 Griddler模拟 栅线设计
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Dual Layer Source Grid Load Storage Collaborative Planning Model Based on Benders Decomposition: Distribution Network Optimization Considering Low-Carbon and Economy 认领 引用 被引量：1
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作者 Jun Guo Maoyuan Chen +2 位作者 Yuyang Li Sibo Feng Guangyu Fu 《Energy Engineering》 EI 2026年第2期104-133,共30页
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ... Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability. 展开更多
关键词 Benders decomposition source grid load storage distribution network planning low-carbon economy optimization model
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A dynamic partition weight strategy for accelerating parallel adaptive Cartesian grid generation 认领 引用
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作者 Hang CHEN Zhenming WANG +3 位作者 Linlin TIAN Jianming LIU Ning QIN Ning ZHAO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第7期302-312,共11页
The generation of meshes and the Adaptive Mesh Refinement(AMR)have presented considerable challenges in computational fluid dynamics.This paper presents a strategy for automatic adaptive Cartesian grid generation with... The generation of meshes and the Adaptive Mesh Refinement(AMR)have presented considerable challenges in computational fluid dynamics.This paper presents a strategy for automatic adaptive Cartesian grid generation within a multicore parallel framework based on the Dynamic Partition Weight(DPW)method.It integrates the unique features of cells generated before and after each AMR and predicts the number of iterations for each cell.The partition weight of the cell is set in proportion to the number of iterations,and the grid-parallel repartition that considers the partition weight is performed before executing computations that require geometric information retrieval.A number of configurations,including a wing-body,are selected for analysis to evaluate the strategy's effectiveness.The results indicate that the computational load imbalance is alleviated during the Cartesian grid generation process,significantly reducing time consumption,with an improvement rate exceeding 50%.For the wing-body case,a 1.37-billion-cell grid is generated in 44.49 s by using 1024 cores with the DPW strategy,demonstrating DPW's efficiency and strong parallel scalability for Cartesian mesh generation. 展开更多
关键词 Adaptive grid generation Cartesian mesh Dynamic grid partition k-d tree Parallel computing
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Federated anomaly detection in smart grid systems 认领 引用
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作者 Tre’R.Jeter Raed Alharbi +1 位作者 Jung Taek Seo My T.Thai 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期451-461,共11页
Smart Grid infrastructures have enhanced energy distribution efficiency,reliability,and sustainability,but their proper operation requires robust anomaly detection to mitigate risks from equipment failures,cyberattack... Smart Grid infrastructures have enhanced energy distribution efficiency,reliability,and sustainability,but their proper operation requires robust anomaly detection to mitigate risks from equipment failures,cyberattacks,and natural disasters.Federated Learning(FL)offers a privacy-preserving solution by allowing power plants and grid sectors to collaboratively train models without sharing raw data,addressing privacy concerns,regulatory compliance,and single points of failure that often emerge in centralized approaches.FL also improves real-time anomaly detection and scalability by adapting dynamically to different grid topologies while incurring minimal communication overhead.Within our FL framework,Transformer models excel in anomaly detection due to their self-attention mechanisms that capture intricate temporal dependencies in sensor data.Unlike traditional models,Transformers effectively learn long-range patterns,enhancing detection accuracy and responsiveness.This work conducts a comparative study of two state-of-the-art Tranformer models in an FL environment,evaluating their anomaly detection performance across four diverse smart grid datasets.To assess robustness,we introduce a GAN-based Anomaly Injection Attack(GAIA)that generates and injects realistic syntheitc anomalies.Our results indicate that both federated Transformer models achieve high detection performance across seven metrics,even under adversarial conditions,offering valuable insights into their capabilities in decentralized smart grid applications. 展开更多
关键词 Federated learning Transformers Anomaly detection Smart grid
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A novel unstructured-grid generator using upscaling applied to a compositional reservoir simulator 认领 引用
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作者 Marcelo Menezes Farias Pedro Salgueiro +3 位作者 Ivens da Costa Menezes Lima Francisco Marcondes Joaquim Bento Cavalcante-Neto Kamy Sepehrnoori 《Petroleum Science》 SCIE EI CAS CSCD 2026年第6期3439-3458,共20页
This work introduces a novel unstructured grid generator using an upscaling technique and the Element-based Finite Volume Method(EbFVM).The proposed grid generator can either replicate the original structured Corner P... This work introduces a novel unstructured grid generator using an upscaling technique and the Element-based Finite Volume Method(EbFVM).The proposed grid generator can either replicate the original structured Corner Point grid as an unstructured mesh or coarsen it by upscaling reservoir properties for computational efficiency.The proposed methodology enables flexible grid coarsening from structured geological models while preserving numerical accuracy in critical flow regions.The approach integrates a user-defined clustering algorithm with a Dykstra-Parsons coefficient to generate hybrid-resolution grids.A modified Cardwell-Parsons method is used for permeability upscaling and volumetric averaging for porosity.An unstructured grid option has been implemented into the UTCOMPRS simulator,and the grid generator is evaluated and validated through five case studies,including realistic reservoir models and industry benchmarks such as UNISIM-I,model 2 of the SPE10case,and the Sleipner CO2 storage project using the UTCOMPRS.Results demonstrate the approach's ability to significantly reduce computational costs while maintaining reliable production forecasts.This work presents a compelling and computationally efficient tool for reservoir simulation. 展开更多
关键词 Compositional reservoir simulation Upscaling Element-based Finite Volume Method Unstructured grids
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A novel overset grid strategy and high-order inter-grid boundaries treatment with the cell-centered finite difference method 认领 引用
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作者 Xuejun DING Fei LIAO +1 位作者 Yao JIN Jinsheng CAI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期256-281,共26页
This paper presents an efficient and automated Overset Grid Assembly(OGA)method for the structured grid,and investigates high-order interpolation methods for inter-grid boundaries with the cell-centered finite differe... This paper presents an efficient and automated Overset Grid Assembly(OGA)method for the structured grid,and investigates high-order interpolation methods for inter-grid boundaries with the cell-centered finite difference method.Four enhancements are introduced:a hybrid holecutting approach integrates the efficiency of approximate hole-cutting and the accuracy and robustness of direct-cutting,effectively addressing challenges such as small gaps and thin cuts;an improved implicit hole boundary optimization method,incorporating quality comparison,can significantly reduce donor search workloads;an improved implicit interpolation cell cancellation algorithm minimizes overlap regions,particularly beneficial for high-order interpolation with large stencils;an algorithm for identifying and eliminating islands without using wall distances,effectively removes islands.The OGA results of a multi-element airfoil,a circular array of cylinders,multiple spheres,and a wing-pylon-store configuration indicate that the proposed method would be a suitable selection for multi-body problems,even in the presence of small gaps and thin geometries.Additionally,for high-order interpolation at inter-grid boundaries,this study presents an optimized interpolation method designed to minimize spectral property errors.Numerical results indicate that for periodic problems frequently crossing inter-grid boundaries,the optimized interpolation is more accurate than classical Lagrange interpolation and would be a suitable selection. 展开更多
关键词 Cell-centered finite difference method High-order Optimized interpolation Overset grid method
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Energy Efficiency Maximization for CR-NOMA Based Smart Grid Communication Network 认领 引用
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作者 Mubashar Sarfraz Sheraz Alam +1 位作者 Sajjad A.Ghauri Asad Mahmood 《China Communications》 SCIE EI CSCD 2026年第2期244-259,共16页
Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approa... Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approach that makes use of cognitive radio(CR)and non-orthogonal multiple access(NOMA)technologies.Our work focuses on using user pairing(UP)and power allocation(PA)techniques to maximize energy efficiency(EE)in SGCN,particularly within neighbourhood area networks(NANs).We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting.This problem is NP-hard,nonlinear,and nonconvex by nature.To address the computational complexity of the problem,we use the block coordinate descent(BCD)method,which breaks the problem into UP and PA subproblems.Initially,we proposed the zebra-optimization user pairing(ZOUP)algorithm to tackle the UP problem,which outperforms both orthogonal multiple access(OMA)and non-optimized NOMA(UPWO)by 78.8%and13.6%,respectively,at a SNR of 15 dB.Based on the ZOUP pairs,we subsequently proposed the PA approach,i.e.,ZOUPPA,which significantly outperforms UPWO and ZOUP by 53.2%and 25.4%,respectively,at an SNR of 15 dB.A detailed analysis of key parameters,including varying SNRs,power allocation constants,path loss exponents,user density,channel availability,and coverage radius,underscores the superiority of our approach.By facilitating the effective use of communication resources in SGCN,our research opens the door to more intelligent and energy-efficient grid systems.Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA. 展开更多
关键词 cognitive radio energy efficiency nonorthogonal multiple access smart grid communications zebra optimization algorithm
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Resilient adversarial–evolutionary multi-agent intelligence for real-time EV charging and energy trading in renewable-integrated smart grids 认领 引用
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作者 Rahul Wilson Kotla Srinivasa Rao Yarlagadda T.S.N.G.Sarada Devi 《Global Energy Interconnection》 EI CSCD 2026年第3期621-638,共18页
The rapid expansion of electric vehicles(EVs)and renewable energy resources introduces new operational stresses in modern power networks such as peak-load surges,voltage fluctuations,and quality degradation.This work ... The rapid expansion of electric vehicles(EVs)and renewable energy resources introduces new operational stresses in modern power networks such as peak-load surges,voltage fluctuations,and quality degradation.This work presents a hybrid intelligence-based multi-agent framework that combines Adversarial Reinforcement Learning(ARL)with Dynamic Grey Wolf Optimization(DGWO)to coordinate EV charging,renewable usage,and energy trading.Each entity—EVs,charging stations,renewable units,and the grid operator—acts as an adaptive agent capable of self-learning and cooperative decision-making under uncertainty.The ARL component strengthens learning under variable demand,while DGWO continuously refines control parameters to ensure fast and stable convergence.Simulation studies on a renewable-supported microgrid show a 21%reduction in peak demand,18%higher renewable energy utilization,22%less EV waiting time,and 15%greater profitability than conventional GA,PSO,GWO,and RL methods.Voltage deviation stayed within±3%,power factor exceeded 0.97,and THD remained below 4%,meeting IEEE 519/1547 standards.These results confirm that the proposed ARL–DGWO framework offers a scalable and reliable solution for next-generation EV-grid coordination. 展开更多
关键词 Electric vehicles Renewable energy Multi-agent systems Adversarial reinforcement learning Grey Wolf Optimization Smart grid
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Dynamic carbon emission flow tracking with a new optimal storage schedule for power grids 认领 引用
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作者 Rongrong Wang Jianhua Chen +4 位作者 Suhuan Li Yong Zhang Yafei Wei Pu Xie Hongtian Chen 《Journal of Automation and Intelligence》 EI CSCD 2026年第2期172-181,共10页
With the global drive toward carbon neutrality,the deep integration of variable renewable energy sources(VRES)and energy storage systems(ESS)has rendered traditional static carbon accounting methods insufficient to ca... With the global drive toward carbon neutrality,the deep integration of variable renewable energy sources(VRES)and energy storage systems(ESS)has rendered traditional static carbon accounting methods insufficient to capture the spatiotemporal dynamics of carbon flows in power grids,highlighting the critical need for accurate tracking and equitable allocation of carbon responsibility.This paper proposes a dynamic carbon emission flow tracking framework tailored to the Jibei power grid in China,integrating a dynamic generator carbon intensity model and power transfer distribution factor(PTDF)enhanced network tracking.The framework also includes an optimal carbon allocation matrix and a predictive ESS scheduling model that links the carbon intensity during charging periods to emissions during discharging.Validated using a modified IEEE 30-bus system representing five cities in the Jibei region,results show that the dynamic model achieves a 15.2%higher accuracy than static methods,optimal ESS scheduling reduces system-wide emissions by 8.7%,and the framework maintains over 93%tracking accuracy under extreme uncertainties.Moreover,the framework quantifies inter-city carbon transfers and allocates responsibilities among grid participants,thus enabling real-time monitoring.It provides a robust foundation for carbon-aware dispatch and nodal carbon pricing,supporting the transition toward carbon-neutral power systems. 展开更多
关键词 Carbon emission flow Energy storage PTDF matrix Jibei power grid Renewable energy integration Dynamic carbon intensity
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Typical Scenarios and Technical Requirements of China’s Power Grid Towards 2030 for Power System Transformation 认领 引用
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作者 Qiang Zhao Yuqiong Zhang +3 位作者 Ziwei Chen Xiaoxin Zhou Jiameng Gao Honghua Yang 《Engineering》 SCIE EI CSCD 2026年第4期16-21,共6页
As the critical milestone for China’s Nationally Determined Contributions(NDCs)[1],2030 is a pivotal benchmark year for the transformation of China’s power system.From now to 2030,the rapid growth in installed capac... As the critical milestone for China’s Nationally Determined Contributions(NDCs)[1],2030 is a pivotal benchmark year for the transformation of China’s power system.From now to 2030,the rapid growth in installed capacity and power generation of wind and solar power will lead to profound changes in the stability mechanisms and balancing characteristics of power systems[[2],[3],[4],[5]],posing new challenges to system security and reliability. 展开更多
关键词 power system transformation power systems posing stability mechanisms balancing characteristics wind power system security power grid wind solar power solar power
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