This paper presents a reasonable gridding-parameters extraction method for setting the optimal interpolation nodes in the gridding of scattered observed data. The method can extract optimized gridding parameters based...This paper presents a reasonable gridding-parameters extraction method for setting the optimal interpolation nodes in the gridding of scattered observed data. The method can extract optimized gridding parameters based on the distribution of features in raw data. Modeling analysis proves that distortion caused by gridding can be greatly reduced when using such parameters. We also present some improved technical measures that use human- machine interaction and multi-thread parallel technology to solve inadequacies in traditional gridding software. On the basis of these methods, we have developed software that can be used to grid scattered data using a graphic interface. Finally, a comparison of different gridding parameters on field magnetic data from Ji Lin Province, North China demonstrates the superiority of the proposed method in eliminating the distortions and enhancing gridding efficiency.展开更多
Recent studies have demonstrated the importance of LUCC change with climate and ecosystem simulation, but the result could only be determined precisely if a high-resolution underlying land cover map is used. While the...Recent studies have demonstrated the importance of LUCC change with climate and ecosystem simulation, but the result could only be determined precisely if a high-resolution underlying land cover map is used. While the efforts based satellites have provided a good baseline for present land cover, what the next advancement in the research about LUCC change required is the development of reconstruction of historical LUCC change especially spatially-explicit historical dataset. Being different from other similar studies, this study is based on the analysis of historical land use patterns in the traditional cultivated region of China. Taking no account of the less important factors, altitude, slope and population patterns are selected as the major drivers of reclamation in ancient China, and used to design the HCGM （Historical Cropland Gridding Model, at a 60 km×60 km resolution）, which is an empirical model for allocating the historical cropland inventory data spatially to grid cells in each political unit. Then we use this model to reconstruct cropland distribution of the study area in 1820, and verify the result by prefectural cropland data of 1820, which is from the historical documents. The statistical analyzing result shows that the model can simulate the patterns of the cropland distribution in the historical period in the traditional cultivated region efficiently.展开更多
Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of vis...Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms,and introduces the gridding and fusion idea of different texture fea-tures to realize fast target detection.It first grids the original SAR imagery,yielding a set of grids to be classified into clutter grids and target grids,and then calculates the texture features in each grid.By fusing the calculation results,the target grids containing potential maneuvering targets are determined.The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest.The fused texture features,including local statistics features and Gray-Level Co-occurrence Matrix(GLCM),are investigated.The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast de-tection algorithms using real SAR data.The results obtained from the experiments indicate the promising practical application val-ue of our study.展开更多
The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is studied and implemented with gridding algorithm in this paper. In this paper, the sensitivity map profile, field ...The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is studied and implemented with gridding algorithm in this paper. In this paper, the sensitivity map profile, field map information and the spiral k-space data collected from an array of receiver coils are used to reconstruct un-aliased images from under-sampled data. The gridding algorithm is implemented with SENSE due to its ability in evaluating forward and adjoins operators with non-Cartesian sampled data. This paper also analyzes the performance of SENSE with real data set and identifies the computational issues that need to be improved for further research.展开更多
The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by phy...The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.展开更多
To adapt to the uncertainty of new energy,increase new energy consumption,and reduce carbon emissions,a high-voltage distribution network energy storage planning model based on robustness-oriented planning and distrib...To adapt to the uncertainty of new energy,increase new energy consumption,and reduce carbon emissions,a high-voltage distribution network energy storage planning model based on robustness-oriented planning and distributed new energy consumption is proposed.Firstly,the spatio-temporal correlation of large-scale wind-photovoltaic energy is modeled based on the Vine Copula model,and the spatial correlation of the generated wind-photovoltaic power generation is corrected to get the spatio-temporal correlation of wind-photovoltaic power generation scenarios.Finally,considering the subsequent development of new energy on demand for high-voltage distribution network peaking margin and the economy of the system peaking,we propose the optimization model of high-voltage distribution network energy storage plant siting and capacity setting for source-storage cooperative peaking.The simulation results show that the proposed energy storage plant planning method can effectively alleviate the branch circuit blockage,promote new energy consumption,reduce the burden of the main grid peak shifting,and leave sufficient peak shifting margin for the subsequent development of a new energy distribution network while ensuring the economy.展开更多
Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Ext...Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.展开更多
As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and use...As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.展开更多
Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart ...Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.展开更多
The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespre...The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespread use of smart grid technologies and outlines the specific roles and obligations of different stakeholders,such as government entities,utility companies,technology suppliers,and consumers.Government activities and regulations are crucial in facilitating the implementation of smart grid technology by offering financial incentives,regulatory assistance,and strategic guidance.Utility firms have the responsibility of implementing and integrating smart grid infrastructure,with an emphasis on improving the dependability of the grid,minimizing losses in transmission and distribution,and integrating renewable energy sources.Technology companies offer the essential hardware and software solutions,which stimulate creativity and enhance efficiency.Consumers actively engage in the energy ecosystem by participating in demand response,implementing energy saving measures,and adopting distributed energy resources like solar panels and electric vehicles.This study examines the difficulties and possibilities in India’s smart grid industry,highlighting the importance of cooperation among stakeholders to build a strong,effective,and environmentally friendly energy future.展开更多
The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow ...The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.展开更多
Ice-going ships play a crucial role in polar transportation and resource extraction.Different from the existing modeling approach which assumes that ships remain stationary,dynamic overset grid technology and DFBI(Dyn...Ice-going ships play a crucial role in polar transportation and resource extraction.Different from the existing modeling approach which assumes that ships remain stationary,dynamic overset grid technology and DFBI(Dynamic Fluid-Body Interaction)method are employed in this paper to enable the free-running motion of the ship in modeling.A numerical model capable of simulating a ship navigating through pack ice area is proposed,which uses Computational Fluid Dynamics(CFD)method to solve the flow field and applies the Discrete Element Method(DEM)to simulate ship-ice and ice-ice interactions.Besides,the proposed high-precision method for generating pack ice area can be used in conjunction with the proposed numerical model.By comparing the numerical results with the available model test data and experimental observations,the effectiveness of the numerical model is validated,demonstrating its strong capability of predicting resistance and simulating ship navigation in pack ice,as well as its significant potential and applicability for further studies.展开更多
Lithium nickel oxide(Li_(2)NiO_(2)),as a sacrificial cathode prelithiation additive,has been used to compensate for the lithium loss for improving the lifespan of lithium-ion batteries(LIBs).However,high-cost Li_(2)Ni...Lithium nickel oxide(Li_(2)NiO_(2)),as a sacrificial cathode prelithiation additive,has been used to compensate for the lithium loss for improving the lifespan of lithium-ion batteries(LIBs).However,high-cost Li_(2)NiO_(2)suffers from inferior delithiation kinetics during the first cycle.Herein,we investigated the effects of the cost-effective copper substituted Li_(2)Ni_(1-x)Cu_(x)O_(2)(x=0,0.2,0.3,0.5,0.7)synthesized by a high-temperature solid-phase method on the structure,morphology,electrochemical performance of graphite‖LiFePO_(4)battery.The X-ray diffraction(XRD)refinement result demonstrated that Cu substitution strategy could be favorable for eliminating the NiO_(x)impurity phase and weakening Li-O bond.Analysis on density of states(DOS)indicates that Cu substitution is good for enhancing the electronic conductivity,as well as reducing the delithi-ation voltage polarization confirmed by electrochemical characterizations.Therefore,the optimal Li_(2)Ni_(0.7)Cu_(0.3)O_(2)delivered a high delithiation capacity of 437 mAh·g^(-1),around 8%above that of the pristine Li_(2)NiO_(2).Furthermore,a graphite‖LiFePO_(4)pouch cell with a nominal capacity of 3000 mAh demonstrated a notably improved reversible capacity,energy density and cycle life through introducing 2 wt%Li_(2)Ni_(0.7)Cu_(0.3)O_(2)additive,delivering a 6.2 mAh·g^(-1)higher initial discharge capacity and achieving around 5%improvement in capacity retentnion at 0.5P over 1000 cycles.Additionally,the post-mortem analyses testified that the Li_(2)Ni_(0.7)Cu_(0.3)O_(2)additive could suppress solid electrolyte interphase(SEI)decomposition and homogenize the Li distribution,which benefits to stabilizing interface between graphite and electrolyte,and alleviating dendritic Li plating.In conclusion,the Li_(2)Ni_(0.7)Cu_(0.3)O_(2)additive may offer advantages such as lower cost,lower delithiation voltage and higher prelithiation capacity compared with Li_(2)NiO_(2),making it a promising candidate of cathode prelithiation additive for next-generation LIBs.展开更多
Amid ASEAN’s accelerating energy transition,the Advanced Energy Storage Industry Technology and Innovation Alliance(AESIA)drives cross-border collaboration to address grid fragility,aging infrastructure,and investmen...Amid ASEAN’s accelerating energy transition,the Advanced Energy Storage Industry Technology and Innovation Alliance(AESIA)drives cross-border collaboration to address grid fragility,aging infrastructure,and investment gaps.By leveraging China’s tropical-tested solutions(e.g.,grid-stabilizing storage systems)and aligning with ASEAN’s 2030 renewable targets,AESIA focuses on three pillars:adaptive technology(localized storage for solar/wind integration),regional grid interconnection(via the ASEAN Power Grid to share renewable surpluses),and blended finance(mitigating risks for long-duration storage projects).Key initiatives include standardized tropical storage protocols,training ASEAN engineers in microgrid management,and pilot cross-border projects reducing curtailment.By 2030,AESIA aims to scale affordable storage and integrate emerging tech,balancing energy security with decarbonization.This model bridges technical expertise with ASEAN’s dynamic needs,fostering a resilient,inclusive energy future.展开更多
The rapid evolution and expanding scale of AI(artificial intelligence)technologies exert unprecedented energy demands on global electrical grids.Powering computationally intensive tasks such as large-scale AI model tr...The rapid evolution and expanding scale of AI(artificial intelligence)technologies exert unprecedented energy demands on global electrical grids.Powering computationally intensive tasks such as large-scale AI model training and widespread real-time inference necessitates substantial electricity consumption,presenting a significant challenge to conventional power infrastructure.This paper examines the critical need for a fundamental shift towards smart energy grids in response to AI’s growing energy footprint.It delves into the symbiotic relationship wherein AI acts as a significant energy consumer while offering the intelligence required for dynamic load management,efficient integration of renewable energy sources,and optimized grid operations.We posit that advanced smart grids are indispensable for facilitating AI’s sustainable growth,underscoring this synergy as a pivotal advancement toward a resilient energy future.展开更多
基金partly supported by the Public Geological Survey Project(No.201011039)the National High Technology Research and Development Project of China(No.2007AA06Z134)the 111 Project under the Ministry of Education and the State Administration of Foreign Experts Affairs,China(No.B07011)
文摘This paper presents a reasonable gridding-parameters extraction method for setting the optimal interpolation nodes in the gridding of scattered observed data. The method can extract optimized gridding parameters based on the distribution of features in raw data. Modeling analysis proves that distortion caused by gridding can be greatly reduced when using such parameters. We also present some improved technical measures that use human- machine interaction and multi-thread parallel technology to solve inadequacies in traditional gridding software. On the basis of these methods, we have developed software that can be used to grid scattered data using a graphic interface. Finally, a comparison of different gridding parameters on field magnetic data from Ji Lin Province, North China demonstrates the superiority of the proposed method in eliminating the distortions and enhancing gridding efficiency.
基金Natiional Natural Science Foundation of China,No.40471007Innovation Knowledge Project of CAS,No.KZCX2-YW-315
文摘Recent studies have demonstrated the importance of LUCC change with climate and ecosystem simulation, but the result could only be determined precisely if a high-resolution underlying land cover map is used. While the efforts based satellites have provided a good baseline for present land cover, what the next advancement in the research about LUCC change required is the development of reconstruction of historical LUCC change especially spatially-explicit historical dataset. Being different from other similar studies, this study is based on the analysis of historical land use patterns in the traditional cultivated region of China. Taking no account of the less important factors, altitude, slope and population patterns are selected as the major drivers of reclamation in ancient China, and used to design the HCGM （Historical Cropland Gridding Model, at a 60 km×60 km resolution）, which is an empirical model for allocating the historical cropland inventory data spatially to grid cells in each political unit. Then we use this model to reconstruct cropland distribution of the study area in 1820, and verify the result by prefectural cropland data of 1820, which is from the historical documents. The statistical analyzing result shows that the model can simulate the patterns of the cropland distribution in the historical period in the traditional cultivated region efficiently.
基金Supported by the National Natural Science Foundation of China (No. 61032001, No.61002045)
文摘Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms,and introduces the gridding and fusion idea of different texture fea-tures to realize fast target detection.It first grids the original SAR imagery,yielding a set of grids to be classified into clutter grids and target grids,and then calculates the texture features in each grid.By fusing the calculation results,the target grids containing potential maneuvering targets are determined.The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest.The fused texture features,including local statistics features and Gray-Level Co-occurrence Matrix(GLCM),are investigated.The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast de-tection algorithms using real SAR data.The results obtained from the experiments indicate the promising practical application val-ue of our study.
文摘The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is studied and implemented with gridding algorithm in this paper. In this paper, the sensitivity map profile, field map information and the spiral k-space data collected from an array of receiver coils are used to reconstruct un-aliased images from under-sampled data. The gridding algorithm is implemented with SENSE due to its ability in evaluating forward and adjoins operators with non-Cartesian sampled data. This paper also analyzes the performance of SENSE with real data set and identifies the computational issues that need to be improved for further research.
文摘The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.
基金supported by State Grid Anhui Electric Power Co.,Ltd.Research Program(B3120923000C).
文摘To adapt to the uncertainty of new energy,increase new energy consumption,and reduce carbon emissions,a high-voltage distribution network energy storage planning model based on robustness-oriented planning and distributed new energy consumption is proposed.Firstly,the spatio-temporal correlation of large-scale wind-photovoltaic energy is modeled based on the Vine Copula model,and the spatial correlation of the generated wind-photovoltaic power generation is corrected to get the spatio-temporal correlation of wind-photovoltaic power generation scenarios.Finally,considering the subsequent development of new energy on demand for high-voltage distribution network peaking margin and the economy of the system peaking,we propose the optimization model of high-voltage distribution network energy storage plant siting and capacity setting for source-storage cooperative peaking.The simulation results show that the proposed energy storage plant planning method can effectively alleviate the branch circuit blockage,promote new energy consumption,reduce the burden of the main grid peak shifting,and leave sufficient peak shifting margin for the subsequent development of a new energy distribution network while ensuring the economy.
基金the University of Transport Technology under grant number DTTD2022-12.
文摘Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.
基金supported by the National Key R&D Program of China(No.2023YFB2703700)the National Natural Science Foundation of China(Nos.U21A20465,62302457,62402444,62172292)+4 种基金the Fundamental Research Funds of Zhejiang Sci-Tech University(Nos.23222092-Y,22222266-Y)the Program for Leading Innovative Research Team of Zhejiang Province(No.2023R01001)the Zhejiang Provincial Natural Science Foundation of China(Nos.LQ24F020008,LQ24F020012)the Foundation of State Key Laboratory of Public Big Data(No.[2022]417)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01119).
文摘As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
基金Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1445)。
文摘Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.
文摘The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespread use of smart grid technologies and outlines the specific roles and obligations of different stakeholders,such as government entities,utility companies,technology suppliers,and consumers.Government activities and regulations are crucial in facilitating the implementation of smart grid technology by offering financial incentives,regulatory assistance,and strategic guidance.Utility firms have the responsibility of implementing and integrating smart grid infrastructure,with an emphasis on improving the dependability of the grid,minimizing losses in transmission and distribution,and integrating renewable energy sources.Technology companies offer the essential hardware and software solutions,which stimulate creativity and enhance efficiency.Consumers actively engage in the energy ecosystem by participating in demand response,implementing energy saving measures,and adopting distributed energy resources like solar panels and electric vehicles.This study examines the difficulties and possibilities in India’s smart grid industry,highlighting the importance of cooperation among stakeholders to build a strong,effective,and environmentally friendly energy future.
基金supported in part by Natural Science Foundation of Jiangsu Province under Grant BK20230255Natural Science Foundation of Shandong Province under Grant ZR2023QE281.
文摘The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.
文摘Ice-going ships play a crucial role in polar transportation and resource extraction.Different from the existing modeling approach which assumes that ships remain stationary,dynamic overset grid technology and DFBI(Dynamic Fluid-Body Interaction)method are employed in this paper to enable the free-running motion of the ship in modeling.A numerical model capable of simulating a ship navigating through pack ice area is proposed,which uses Computational Fluid Dynamics(CFD)method to solve the flow field and applies the Discrete Element Method(DEM)to simulate ship-ice and ice-ice interactions.Besides,the proposed high-precision method for generating pack ice area can be used in conjunction with the proposed numerical model.By comparing the numerical results with the available model test data and experimental observations,the effectiveness of the numerical model is validated,demonstrating its strong capability of predicting resistance and simulating ship navigation in pack ice,as well as its significant potential and applicability for further studies.
基金supported by the Significant Science and Technology Project in Xiamen(Future Industry Field)(Grant No.3502Z20231057).
文摘Lithium nickel oxide(Li_(2)NiO_(2)),as a sacrificial cathode prelithiation additive,has been used to compensate for the lithium loss for improving the lifespan of lithium-ion batteries(LIBs).However,high-cost Li_(2)NiO_(2)suffers from inferior delithiation kinetics during the first cycle.Herein,we investigated the effects of the cost-effective copper substituted Li_(2)Ni_(1-x)Cu_(x)O_(2)(x=0,0.2,0.3,0.5,0.7)synthesized by a high-temperature solid-phase method on the structure,morphology,electrochemical performance of graphite‖LiFePO_(4)battery.The X-ray diffraction(XRD)refinement result demonstrated that Cu substitution strategy could be favorable for eliminating the NiO_(x)impurity phase and weakening Li-O bond.Analysis on density of states(DOS)indicates that Cu substitution is good for enhancing the electronic conductivity,as well as reducing the delithi-ation voltage polarization confirmed by electrochemical characterizations.Therefore,the optimal Li_(2)Ni_(0.7)Cu_(0.3)O_(2)delivered a high delithiation capacity of 437 mAh·g^(-1),around 8%above that of the pristine Li_(2)NiO_(2).Furthermore,a graphite‖LiFePO_(4)pouch cell with a nominal capacity of 3000 mAh demonstrated a notably improved reversible capacity,energy density and cycle life through introducing 2 wt%Li_(2)Ni_(0.7)Cu_(0.3)O_(2)additive,delivering a 6.2 mAh·g^(-1)higher initial discharge capacity and achieving around 5%improvement in capacity retentnion at 0.5P over 1000 cycles.Additionally,the post-mortem analyses testified that the Li_(2)Ni_(0.7)Cu_(0.3)O_(2)additive could suppress solid electrolyte interphase(SEI)decomposition and homogenize the Li distribution,which benefits to stabilizing interface between graphite and electrolyte,and alleviating dendritic Li plating.In conclusion,the Li_(2)Ni_(0.7)Cu_(0.3)O_(2)additive may offer advantages such as lower cost,lower delithiation voltage and higher prelithiation capacity compared with Li_(2)NiO_(2),making it a promising candidate of cathode prelithiation additive for next-generation LIBs.
文摘Amid ASEAN’s accelerating energy transition,the Advanced Energy Storage Industry Technology and Innovation Alliance(AESIA)drives cross-border collaboration to address grid fragility,aging infrastructure,and investment gaps.By leveraging China’s tropical-tested solutions(e.g.,grid-stabilizing storage systems)and aligning with ASEAN’s 2030 renewable targets,AESIA focuses on three pillars:adaptive technology(localized storage for solar/wind integration),regional grid interconnection(via the ASEAN Power Grid to share renewable surpluses),and blended finance(mitigating risks for long-duration storage projects).Key initiatives include standardized tropical storage protocols,training ASEAN engineers in microgrid management,and pilot cross-border projects reducing curtailment.By 2030,AESIA aims to scale affordable storage and integrate emerging tech,balancing energy security with decarbonization.This model bridges technical expertise with ASEAN’s dynamic needs,fostering a resilient,inclusive energy future.
文摘The rapid evolution and expanding scale of AI(artificial intelligence)technologies exert unprecedented energy demands on global electrical grids.Powering computationally intensive tasks such as large-scale AI model training and widespread real-time inference necessitates substantial electricity consumption,presenting a significant challenge to conventional power infrastructure.This paper examines the critical need for a fundamental shift towards smart energy grids in response to AI’s growing energy footprint.It delves into the symbiotic relationship wherein AI acts as a significant energy consumer while offering the intelligence required for dynamic load management,efficient integration of renewable energy sources,and optimized grid operations.We posit that advanced smart grids are indispensable for facilitating AI’s sustainable growth,underscoring this synergy as a pivotal advancement toward a resilient energy future.
