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
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
This review explores the research activities surrounding the development and integration of smart electricity grids in Burkina Faso, a landlocked and arid territory in West Africa and one of the poorest countries in t...This review explores the research activities surrounding the development and integration of smart electricity grids in Burkina Faso, a landlocked and arid territory in West Africa and one of the poorest countries in the world with significant energy challenges. It examines the current state of energy infrastructure in Burkina Faso, focusing on the integration of renewable energy sources, particularly solar photovoltaics. It highlights the role of smart grid technologies in enabling the efficient integration of renewable energy, improving grid stability and facilitating rural electrification. Additionally, the review addresses key challenges such as inadequate infrastructure, regulatory gaps and financial constraints that hinder the deployment of smart grids in the country. By analysing existing research and ongoing projects, this paper provides a comprehensive overview of the opportunities and barriers to implementing a smart electricity grid in Burkina Faso and offers recommendations for future development and policy frameworks.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
This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as ru...This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.展开更多
The process of including renewable energy sources in power networks is moving quickly,so the need for innovative configuration solutions for grid-side ESS has grown.Among the new methods presented in this paper is GA-...The process of including renewable energy sources in power networks is moving quickly,so the need for innovative configuration solutions for grid-side ESS has grown.Among the new methods presented in this paper is GA-OCESE,which stands for Genetic Algorithm-based Optimization Configuration for Energy Storage in Electric Networks.This is one of the methods suggested in this study,which aims to enhance the sizing,positioning,and operational characteristics of structured ESS under dynamic grid conditions.Particularly,the aim is to maximize efficiency.A multiobjective genetic algorithm,the GA-OCESE framework,considers all these factors simultaneously.Besides considering cost-efficiency,response time,and energy use,the system also considers all these elements simultaneously.This enables it to effectively react to load uncertainty and variations in inputs connected to renewable sources.Results of an experimental assessment conducted on a standardized grid simulation platform indicate that by increasing energy use efficiency by 17.6%and reducing peak-load effects by 22.3%,GA-OCESE outperforms previous heuristic-based methods.This was found by contrasting the outcomes of the assessment with those of the evaluation.The results of the assessment helped to reveal this.The proposed approach will provide utility operators and energy planners with a decision-making tool that is both scalable and adaptable.This technology is particularly well-suited for smart grids,microgrid systems,and power infrastructures that heavily rely on renewable energy.Every technical component has been carefully recorded to ensure accuracy,reproducibility,and relevance across all power systems engineering software uses.This was done to ensure the program’s relevance.展开更多
Isolated power systems,such as those on islands,face acute challenges in balancing energy demand with limited generation resources,making them particularly vulnerable to disruptions.This paper addresses these challeng...Isolated power systems,such as those on islands,face acute challenges in balancing energy demand with limited generation resources,making them particularly vulnerable to disruptions.This paper addresses these challenges by proposing a novel control and simulation framework based on a holonic multi-agent architecture,specifically developed as a digital twin for the Mayotte island grid.The primary contribution is a multi-objective optimization model,driven by a genetic algorithm,designed to enhance grid resilience through intelligent,decentralized decisionmaking.The efficacy of this architecture is validated through three distinct simulation scenarios:(1)a baseline scenario establishing nominal grid operation;(2)a critical disruption involving the failure of a major power plant;and(3)a localized fault resulting in the complete disconnection of a regional sub-grid.The major results demonstrate the system’s dual resilience mechanisms.In the plant failure scenario,the top-level holon successfully managed a global energy deficit by optimally reallocating shared resources,prioritizing grid stability over complete demand satisfaction.In the disconnection scenario,the affected holon demonstrated true autonomy,transitioning seamlessly into a selfsufficient islanded microgrid to prevent a cascading failure.Collectively,these findings validate the holonic model as a robust decision-support tool capable of managing both systemic and localized faults,thereby significantly enhancing the operational resilience and stability of isolated smart grids.展开更多
The study of plant diversity is often hindered by the challenge of integrating data from different sources and different data types.A standardized data system would facilitate detailed exploration of plant distributio...The study of plant diversity is often hindered by the challenge of integrating data from different sources and different data types.A standardized data system would facilitate detailed exploration of plant distribution patterns and dynamics for botanists,ecologists,conservation biologists,and biogeographers.This study proposes a gridded vector data integration method,combining grid-based techniques with vectorization to integrate diverse data types from multiple sources into grids of the same scale.Here we demonstrate the methodology by creating a comprehensive 1°×1°database of western China that includes plant distribution information and environmental factor data.This approach addresses the need for a standardized data system to facilitate exploration of plant distribution patterns and dynamic changes in the region.展开更多
Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constr...Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constrained Optimal Power Flow(SCOPF)using the Line Outage Distribution Factor(LODF)to enhance resilience in a renewable energy-integrated microgrid.The research examines a 30-bus system with 14 generators and an 8669 MW load demand,optimizing both single-objective and multi-objective scenarios.The single-objective opti-mization achieves a total generation cost of$47,738,while the multi-objective approach reduces costs to$47,614 and minimizes battery power output to 165.02 kW.Under contingency conditions,failures in transmission lines 1,22,and 35 lead to complete power loss in those lines,requiring a redistribution strategy.Implementing SCOPF mitigates these disruptions by adjusting power flows,ensuring no line exceeds its capacity.Specifically,in contingency 1,power in channel 4 is reduced from 59 to 32 kW,while overall load shedding is minimized to 0.278 MW.These results demonstrate the effectiveness of SCOPF in maintaining stability and reducing economic losses.Unlike prior studies,this work integrates LODF into SCOPF for large-scale microgrid applications,offering a computationally efficient contingency management framework that enhances grid resilience and supports renewable energy adoption.展开更多
The extraction characteristics of multi-charged ions produced by ion sources are important for some useful applications.In this paper,the extraction process of Cu^(+),Cu^(2+),Cu^(3+),and Cu^(4+)mixed ions is simulated...The extraction characteristics of multi-charged ions produced by ion sources are important for some useful applications.In this paper,the extraction process of Cu^(+),Cu^(2+),Cu^(3+),and Cu^(4+)mixed ions is simulated by setting ideal physical parameters in a two-dimensional particle-in-cell(PIC)code,and the evolution characteristics of density and velocity distributions of different charged ions during plasma(density about 10^(15)m^(-3))motion and extraction are presented.Besides,the effects of grid thickness and grid aperture on the motion behavior of different charged ions and the extracted ion current are analyzed.The results showed that the ion diffusion increases with the increase of the ion charge,and higher charged ions are more likely to be affected by the grid.This provides support for further understanding of the extraction characteristics of multi-charged mixed ion beams.展开更多
This research presents an analysis of smart grid units to enhance connected units’security during data transmissions.The major advantage of the proposed method is that the system model encompasses multiple aspects su...This research presents an analysis of smart grid units to enhance connected units’security during data transmissions.The major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and losses.In addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid networks.Moreover,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced complexities.The suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate measurements.This results in the provision of only the original data values,hence enhancing security.Both power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case studies.The proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.展开更多
In China,electric vehicle(EV)fast-charging power has quadrupled in the past five years,progressing toward 10-minute ultrafast charging.This rapid increase raises concerns about the impact on the power grid including i...In China,electric vehicle(EV)fast-charging power has quadrupled in the past five years,progressing toward 10-minute ultrafast charging.This rapid increase raises concerns about the impact on the power grid including increased peak power demand and the need for substantial upgrades to power infrastruc-ture.Here,we introduce an integrated model to assess fast and ultrafast charging impacts for represen-tative charging stations in China,combining real-world charging patterns and detailed station optimization models.We find that larger stations with 12 or more chargers experience modest peak power increases of less than 30%when fast-charging power is doubled,primarily because shorter charg-ing sessions are less likely to overlap.For more typical stations(e.g.,8-9 chargers and 120 kW·charger^(−1)),upgrading chargers to 350-550 kW while allowing managed dynamic waiting strategies(of∼1 minute)can reduce overall charging times to∼9 minutes.At stations,deploying battery storage and/or expanding transformers can help manage future increases in station loads,yet the primary device cost of the former is∼4 times higher than that of the latter.Our results offer insights for charging infrastructure planning,EV-grid interactions,and associated policymaking.展开更多
This article presents a comprehensive framework for advancing sustainable transportation through the integration of next-generation energy technologies.It explores the convergence of Vernova green energy,nuclear fissi...This article presents a comprehensive framework for advancing sustainable transportation through the integration of next-generation energy technologies.It explores the convergence of Vernova green energy,nuclear fission from ARCs(advanced reactor concepts)and SMRs(small modular reactors),and future-focused nuclear fusion methods-MCF(magnetic confinement fusion)and ICF(inertial confinement fusion).Central to this integration is the use of AI(artificial intelligence)to enhance smart grid efficiency,enable real-time optimization,and ensure resilient energy delivery.The synergy between these zero-carbon energy sources and AI-driven infrastructure promises a transformative impact on electric mobility,hydrogen-powered systems,and autonomous transport.By detailing the architecture of an AI-augmented,carbon-neutral transport ecosystem,this paper contributes to the roadmap for future global mobility.展开更多
To improve the fault diagnosis accuracy of a PV grid-connected inverter,a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed.Firstly,unlike the traditional VMD algorithm which rel...To improve the fault diagnosis accuracy of a PV grid-connected inverter,a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed.Firstly,unlike the traditional VMD algorithm which relies on manual experience to set parameters(e.g.,noise tolerance,penalty parameter,number of decompositions),this paper achieves adaptive optimization of parameters through MPA algorithmto avoid the problemof feature information loss caused by manual parameter tuning,and adopts the improved VMD algorithm for feature extraction of DC-side voltage data signals of PV-grid-connected inverters;and then,adopts the PSO algorithm for theThen,the PSO algorithm is used to optimize the optimal batch size,the number of nodes in the hidden layer and the learning rate of the BiLSTM network,which significantly improves the model’s ability to capture the long-term dependent features of the PV inverter’s timing signals,to construct the PV grid-connected inverter prediction model of PSO-BiLSTM,and predict the capacitance value of the PVgrid-connected inverter.Finally,diagnostic experiments are carried out based on the expected capacitance value and the capacitance failure criterion.The results showthat compared with the traditional VMD algorithm,the MPA-optimised VMD improves the signal-to-noise ratio(SNR)of the signal decomposition from 28.5 to 33.2 dB(16.5%improvement).After combining with the PSO-BiLSTM model,the mean absolute percentage error(MAPE)of the fault diagnosis is reduced to 1.31%,and the coefficient of determination(R2)is up to 0.99.It is concluded that the present method has excellent diagnostic performance of PV grid-connected inverter data signals and effectively improves the accuracy of PV grid-connected inverter diagnosis.展开更多
The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solution...The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solutions are needed to improve efficiency, resilience, and environmental performance. This paper reviews the integration of Digital Twin (DT) technologies and Machine Learning (ML) for optimizing energy management in smart buildings connected to smart grids. A key enabler of this integration is the Internet of Things (IoT), which provides the sensor networks and real-time data streams that fee/d DT–ML frameworks, enabling accurate monitoring, forecasting, and adaptive control. Through this synergy, DT–ML systems enhance energy prediction, occupant comfort, and automated fault detection, while also supporting broader sustainability goals. The review examines recent advances in DT–ML energy systems, with attention to enabling technologies such as IoT sensor networks, building energy management systems, edge–cloud computing, and advanced analytics. Key challenges including data interoperability, cybersecurity, scalability, and the need for standardized frameworks are critically discussed, along with emerging solutions such as federated learning and blockchain. Special focus is given to human-centric digital twin frameworks that integrate user comfort and behavioral adaptation into energy optimization strategies. The findings suggest that DT–ML integration, enabled by IoT sensor networks, has the potential to significantly reduce energy consumption, lower operational costs, and improve resilience in urban infrastructures. The paper concludes by outlining future research priorities, including decentralized learning models, universal data standards, enhanced privacy protocols, and expanding digital twin applications for distributed renewable energy resources.展开更多
By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help s...By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.展开更多
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This review explores the research activities surrounding the development and integration of smart electricity grids in Burkina Faso, a landlocked and arid territory in West Africa and one of the poorest countries in the world with significant energy challenges. It examines the current state of energy infrastructure in Burkina Faso, focusing on the integration of renewable energy sources, particularly solar photovoltaics. It highlights the role of smart grid technologies in enabling the efficient integration of renewable energy, improving grid stability and facilitating rural electrification. Additionally, the review addresses key challenges such as inadequate infrastructure, regulatory gaps and financial constraints that hinder the deployment of smart grids in the country. By analysing existing research and ongoing projects, this paper provides a comprehensive overview of the opportunities and barriers to implementing a smart electricity grid in Burkina Faso and offers recommendations for future development and policy frameworks.
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.
文摘This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.
文摘The process of including renewable energy sources in power networks is moving quickly,so the need for innovative configuration solutions for grid-side ESS has grown.Among the new methods presented in this paper is GA-OCESE,which stands for Genetic Algorithm-based Optimization Configuration for Energy Storage in Electric Networks.This is one of the methods suggested in this study,which aims to enhance the sizing,positioning,and operational characteristics of structured ESS under dynamic grid conditions.Particularly,the aim is to maximize efficiency.A multiobjective genetic algorithm,the GA-OCESE framework,considers all these factors simultaneously.Besides considering cost-efficiency,response time,and energy use,the system also considers all these elements simultaneously.This enables it to effectively react to load uncertainty and variations in inputs connected to renewable sources.Results of an experimental assessment conducted on a standardized grid simulation platform indicate that by increasing energy use efficiency by 17.6%and reducing peak-load effects by 22.3%,GA-OCESE outperforms previous heuristic-based methods.This was found by contrasting the outcomes of the assessment with those of the evaluation.The results of the assessment helped to reveal this.The proposed approach will provide utility operators and energy planners with a decision-making tool that is both scalable and adaptable.This technology is particularly well-suited for smart grids,microgrid systems,and power infrastructures that heavily rely on renewable energy.Every technical component has been carefully recorded to ensure accuracy,reproducibility,and relevance across all power systems engineering software uses.This was done to ensure the program’s relevance.
基金funding for this study from the European Union’s Horizon 2020 research and innovation programme under grant agreement No.957843The research was supported by the MAESHA project.More information can be found at the Horizon 2020 website:https://cordis.europa.eu/project/id/957843/fr(accessed on 28 September 2025).
文摘Isolated power systems,such as those on islands,face acute challenges in balancing energy demand with limited generation resources,making them particularly vulnerable to disruptions.This paper addresses these challenges by proposing a novel control and simulation framework based on a holonic multi-agent architecture,specifically developed as a digital twin for the Mayotte island grid.The primary contribution is a multi-objective optimization model,driven by a genetic algorithm,designed to enhance grid resilience through intelligent,decentralized decisionmaking.The efficacy of this architecture is validated through three distinct simulation scenarios:(1)a baseline scenario establishing nominal grid operation;(2)a critical disruption involving the failure of a major power plant;and(3)a localized fault resulting in the complete disconnection of a regional sub-grid.The major results demonstrate the system’s dual resilience mechanisms.In the plant failure scenario,the top-level holon successfully managed a global energy deficit by optimally reallocating shared resources,prioritizing grid stability over complete demand satisfaction.In the disconnection scenario,the affected holon demonstrated true autonomy,transitioning seamlessly into a selfsufficient islanded microgrid to prevent a cascading failure.Collectively,these findings validate the holonic model as a robust decision-support tool capable of managing both systemic and localized faults,thereby significantly enhancing the operational resilience and stability of isolated smart grids.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(2019QZKK0502)the National Natural Science Foundation of China(32322006)+1 种基金the Major Program for Basic Research Project of Yunnan Province(202103AF140005 and 202101BC070002)the Practice Innovation Fund for Professional Degree Graduates of Yunnan University(ZC-22222401).
文摘The study of plant diversity is often hindered by the challenge of integrating data from different sources and different data types.A standardized data system would facilitate detailed exploration of plant distribution patterns and dynamics for botanists,ecologists,conservation biologists,and biogeographers.This study proposes a gridded vector data integration method,combining grid-based techniques with vectorization to integrate diverse data types from multiple sources into grids of the same scale.Here we demonstrate the methodology by creating a comprehensive 1°×1°database of western China that includes plant distribution information and environmental factor data.This approach addresses the need for a standardized data system to facilitate exploration of plant distribution patterns and dynamic changes in the region.
文摘Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constrained Optimal Power Flow(SCOPF)using the Line Outage Distribution Factor(LODF)to enhance resilience in a renewable energy-integrated microgrid.The research examines a 30-bus system with 14 generators and an 8669 MW load demand,optimizing both single-objective and multi-objective scenarios.The single-objective opti-mization achieves a total generation cost of$47,738,while the multi-objective approach reduces costs to$47,614 and minimizes battery power output to 165.02 kW.Under contingency conditions,failures in transmission lines 1,22,and 35 lead to complete power loss in those lines,requiring a redistribution strategy.Implementing SCOPF mitigates these disruptions by adjusting power flows,ensuring no line exceeds its capacity.Specifically,in contingency 1,power in channel 4 is reduced from 59 to 32 kW,while overall load shedding is minimized to 0.278 MW.These results demonstrate the effectiveness of SCOPF in maintaining stability and reducing economic losses.Unlike prior studies,this work integrates LODF into SCOPF for large-scale microgrid applications,offering a computationally efficient contingency management framework that enhances grid resilience and supports renewable energy adoption.
基金supported by the Presidential Foundation of China Academy of Engineering Physics(Grant No.YZJJZQ2022016)the National Natural Science Foundation of China(Grant No.52207177).
文摘The extraction characteristics of multi-charged ions produced by ion sources are important for some useful applications.In this paper,the extraction process of Cu^(+),Cu^(2+),Cu^(3+),and Cu^(4+)mixed ions is simulated by setting ideal physical parameters in a two-dimensional particle-in-cell(PIC)code,and the evolution characteristics of density and velocity distributions of different charged ions during plasma(density about 10^(15)m^(-3))motion and extraction are presented.Besides,the effects of grid thickness and grid aperture on the motion behavior of different charged ions and the extracted ion current are analyzed.The results showed that the ion diffusion increases with the increase of the ion charge,and higher charged ions are more likely to be affected by the grid.This provides support for further understanding of the extraction characteristics of multi-charged mixed ion beams.
基金supported by the Researchers Supporting Project number RSP2025R244,King Saud University,Riyadh,Saudi Arabia.
文摘This research presents an analysis of smart grid units to enhance connected units’security during data transmissions.The major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and losses.In addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid networks.Moreover,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced complexities.The suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate measurements.This results in the provision of only the original data values,hence enhancing security.Both power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case studies.The proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.
基金the support of the National Natural Science Foundation of China(72325006,72488101,and 72293601)the Sze Family Foundationthe Climate Imperative Foundation(#2024-001465)
文摘In China,electric vehicle(EV)fast-charging power has quadrupled in the past five years,progressing toward 10-minute ultrafast charging.This rapid increase raises concerns about the impact on the power grid including increased peak power demand and the need for substantial upgrades to power infrastruc-ture.Here,we introduce an integrated model to assess fast and ultrafast charging impacts for represen-tative charging stations in China,combining real-world charging patterns and detailed station optimization models.We find that larger stations with 12 or more chargers experience modest peak power increases of less than 30%when fast-charging power is doubled,primarily because shorter charg-ing sessions are less likely to overlap.For more typical stations(e.g.,8-9 chargers and 120 kW·charger^(−1)),upgrading chargers to 350-550 kW while allowing managed dynamic waiting strategies(of∼1 minute)can reduce overall charging times to∼9 minutes.At stations,deploying battery storage and/or expanding transformers can help manage future increases in station loads,yet the primary device cost of the former is∼4 times higher than that of the latter.Our results offer insights for charging infrastructure planning,EV-grid interactions,and associated policymaking.
文摘This article presents a comprehensive framework for advancing sustainable transportation through the integration of next-generation energy technologies.It explores the convergence of Vernova green energy,nuclear fission from ARCs(advanced reactor concepts)and SMRs(small modular reactors),and future-focused nuclear fusion methods-MCF(magnetic confinement fusion)and ICF(inertial confinement fusion).Central to this integration is the use of AI(artificial intelligence)to enhance smart grid efficiency,enable real-time optimization,and ensure resilient energy delivery.The synergy between these zero-carbon energy sources and AI-driven infrastructure promises a transformative impact on electric mobility,hydrogen-powered systems,and autonomous transport.By detailing the architecture of an AI-augmented,carbon-neutral transport ecosystem,this paper contributes to the roadmap for future global mobility.
基金supported by Science and Technology Projects of Jiangsu Province(No.BE2022003)Science and Technology Projects of Jiangsu Province(No.BE2022003-5).
文摘To improve the fault diagnosis accuracy of a PV grid-connected inverter,a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed.Firstly,unlike the traditional VMD algorithm which relies on manual experience to set parameters(e.g.,noise tolerance,penalty parameter,number of decompositions),this paper achieves adaptive optimization of parameters through MPA algorithmto avoid the problemof feature information loss caused by manual parameter tuning,and adopts the improved VMD algorithm for feature extraction of DC-side voltage data signals of PV-grid-connected inverters;and then,adopts the PSO algorithm for theThen,the PSO algorithm is used to optimize the optimal batch size,the number of nodes in the hidden layer and the learning rate of the BiLSTM network,which significantly improves the model’s ability to capture the long-term dependent features of the PV inverter’s timing signals,to construct the PV grid-connected inverter prediction model of PSO-BiLSTM,and predict the capacitance value of the PVgrid-connected inverter.Finally,diagnostic experiments are carried out based on the expected capacitance value and the capacitance failure criterion.The results showthat compared with the traditional VMD algorithm,the MPA-optimised VMD improves the signal-to-noise ratio(SNR)of the signal decomposition from 28.5 to 33.2 dB(16.5%improvement).After combining with the PSO-BiLSTM model,the mean absolute percentage error(MAPE)of the fault diagnosis is reduced to 1.31%,and the coefficient of determination(R2)is up to 0.99.It is concluded that the present method has excellent diagnostic performance of PV grid-connected inverter data signals and effectively improves the accuracy of PV grid-connected inverter diagnosis.
文摘The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solutions are needed to improve efficiency, resilience, and environmental performance. This paper reviews the integration of Digital Twin (DT) technologies and Machine Learning (ML) for optimizing energy management in smart buildings connected to smart grids. A key enabler of this integration is the Internet of Things (IoT), which provides the sensor networks and real-time data streams that fee/d DT–ML frameworks, enabling accurate monitoring, forecasting, and adaptive control. Through this synergy, DT–ML systems enhance energy prediction, occupant comfort, and automated fault detection, while also supporting broader sustainability goals. The review examines recent advances in DT–ML energy systems, with attention to enabling technologies such as IoT sensor networks, building energy management systems, edge–cloud computing, and advanced analytics. Key challenges including data interoperability, cybersecurity, scalability, and the need for standardized frameworks are critically discussed, along with emerging solutions such as federated learning and blockchain. Special focus is given to human-centric digital twin frameworks that integrate user comfort and behavioral adaptation into energy optimization strategies. The findings suggest that DT–ML integration, enabled by IoT sensor networks, has the potential to significantly reduce energy consumption, lower operational costs, and improve resilience in urban infrastructures. The paper concludes by outlining future research priorities, including decentralized learning models, universal data standards, enhanced privacy protocols, and expanding digital twin applications for distributed renewable energy resources.
文摘By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.
