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 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
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
Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity...Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid.The design of any DSM system using a wireless network must consider the wireless link impairments,which is missing in existing literature.In this paper,we propose a DSM system using a Real-Time Pricing(RTP)mechanism and a wireless Neighborhood Area Network(NAN)with data transfer uncertainty.A Zigbee-based Internet of Things(IoT)model is considered for the communication infrastructure of the NAN.A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link.The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users,decision-makers,and energy providers.A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices.Simulation results indicate that the proposed system benefits users and energy providers.Furthermore,experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN,which can substantially impact the performance of the proposed DSM system.Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price,user welfare,and provider welfare.展开更多
To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installa...To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.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
Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enha...Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enhance their practicality.Data privacy and security are widely discussed,and many solutions are proposed in this area.Energy theft attacks by greedy customers are another difficulty demanding immediate solutions to decrease the economic losses caused by these attacks.The tremendous amount of data generated in smart grid systems is also considered a struggle in these systems,which is commonly solved via fog computing.This work proposes an energytheft detection method for smart grid systems employed in a fog-based network infrastructure.This work also proposes and analyzes Zero-day energy theft attack detection through a multi-layered approach.The detection process occurs at fog nodes via five machine-learning classification models.The performance of the classifiers is measured,validated,and reported for all models at fog nodes,as well as the required training and testing time.Finally,the measured results are compared to when the detection process occurs at a central processing unit(cloud server)to investigate and compare the performance metrics’goodness.The results show comparable accuracy,precision,recall,and F1-measure performance.Meanwhile,the measured execution time has decreased significantly in the case of the fog-based network infrastructure.The fog-based model achieved an accuracy and recall of 98%,F1 score of 99%,and reduced detection time up to around 85%compared to the cloud-based approach.展开更多
The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,dist...The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.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
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
The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serv...The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serves as an effective emergency solution.This study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity constraints.Due to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop approach.Since the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory design.The first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)technique.By iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop issue.Simulation results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.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 phasor data concentrator placement(PDCP)in wide area measurement systems(WAMS)is an optimization problem in the communication network planning for power grid.Instead of using the traditional integer linear program...The phasor data concentrator placement(PDCP)in wide area measurement systems(WAMS)is an optimization problem in the communication network planning for power grid.Instead of using the traditional integer linear programming(ILP)based modeling and solution schemes that ignore the graph-related features of WAMS,in this work,the PDCP problem is solved through a heuristic graphbased two-phase procedure(TPP):topology partitioning,and phasor data concentrator(PDC)provisioning.Based on the existing minimum k-section algorithms in graph theory,the k-base topology partitioning algorithm is proposed.To improve the performance,the“center-node-last”pre-partitioning algorithm is proposed to give an initial partition before the k-base partitioning algorithm is applied.Then,the PDC provisioning algorithm is proposed to locate PDCs into the decomposed sub-graphs.The proposed TPP was evaluated on five different IEEE benchmark test power systems and the achieved overall communication performance compared to the ILP based schemes show the validity and efficiency of the proposed method.展开更多
Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it mus...Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions.We cast the offloading problem as aMarkov Decision Process(MDP)and solve it with Deep Reinforcement Learning(DRL).Specifically,we present a three-tier architecture—end devices,edge nodes,and a cloud server—and enhance Proximal Policy Optimization(PPO)to learn adaptive,energy-aware policies.A Convolutional Neural Network(CNN)extracts high-level features from system states,enabling the agent to respond continually to changing conditions.Extensive simulations show that the proposed method reduces task latency and energy consumption far more than several baseline algorithms,thereby improving overall system performance.These results demonstrate the effectiveness and robustness of the framework for real-time task offloading in dynamic smart-grid environments.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
The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastr...The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.展开更多
The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.Thi...The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.展开更多
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.
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Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid.The design of any DSM system using a wireless network must consider the wireless link impairments,which is missing in existing literature.In this paper,we propose a DSM system using a Real-Time Pricing(RTP)mechanism and a wireless Neighborhood Area Network(NAN)with data transfer uncertainty.A Zigbee-based Internet of Things(IoT)model is considered for the communication infrastructure of the NAN.A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link.The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users,decision-makers,and energy providers.A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices.Simulation results indicate that the proposed system benefits users and energy providers.Furthermore,experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN,which can substantially impact the performance of the proposed DSM system.Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price,user welfare,and provider welfare.
基金supported by the Fujian University of Technology under Grant GYZ20016,GY-Z18183,and GY-Z19005partially supported by the National Science and Technology Council under Grant NSTC 113-2221-E-224-056-.
文摘To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.
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.
文摘Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enhance their practicality.Data privacy and security are widely discussed,and many solutions are proposed in this area.Energy theft attacks by greedy customers are another difficulty demanding immediate solutions to decrease the economic losses caused by these attacks.The tremendous amount of data generated in smart grid systems is also considered a struggle in these systems,which is commonly solved via fog computing.This work proposes an energytheft detection method for smart grid systems employed in a fog-based network infrastructure.This work also proposes and analyzes Zero-day energy theft attack detection through a multi-layered approach.The detection process occurs at fog nodes via five machine-learning classification models.The performance of the classifiers is measured,validated,and reported for all models at fog nodes,as well as the required training and testing time.Finally,the measured results are compared to when the detection process occurs at a central processing unit(cloud server)to investigate and compare the performance metrics’goodness.The results show comparable accuracy,precision,recall,and F1-measure performance.Meanwhile,the measured execution time has decreased significantly in the case of the fog-based network infrastructure.The fog-based model achieved an accuracy and recall of 98%,F1 score of 99%,and reduced detection time up to around 85%compared to the cloud-based approach.
文摘The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.
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.
基金Supported by the Self-funded Research Project of Beijing FibrLink Communications Co.Ltd.“Research on Key Technologies forUnifiedManagement of Air-to-Earth Integrated CommunicationNetworks(546826230034).”。
文摘The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serves as an effective emergency solution.This study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity constraints.Due to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop approach.Since the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory design.The first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)technique.By iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop issue.Simulation results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.
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 National Key Research and Development Program of China(2023YFB 2906403).
文摘The phasor data concentrator placement(PDCP)in wide area measurement systems(WAMS)is an optimization problem in the communication network planning for power grid.Instead of using the traditional integer linear programming(ILP)based modeling and solution schemes that ignore the graph-related features of WAMS,in this work,the PDCP problem is solved through a heuristic graphbased two-phase procedure(TPP):topology partitioning,and phasor data concentrator(PDC)provisioning.Based on the existing minimum k-section algorithms in graph theory,the k-base topology partitioning algorithm is proposed.To improve the performance,the“center-node-last”pre-partitioning algorithm is proposed to give an initial partition before the k-base partitioning algorithm is applied.Then,the PDC provisioning algorithm is proposed to locate PDCs into the decomposed sub-graphs.The proposed TPP was evaluated on five different IEEE benchmark test power systems and the achieved overall communication performance compared to the ILP based schemes show the validity and efficiency of the proposed method.
基金supported by the National Natural Science Foundation of China(Grant No.62103349)the Henan Province Science and Technology Research Project(Grant No.232102210104).
文摘Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions.We cast the offloading problem as aMarkov Decision Process(MDP)and solve it with Deep Reinforcement Learning(DRL).Specifically,we present a three-tier architecture—end devices,edge nodes,and a cloud server—and enhance Proximal Policy Optimization(PPO)to learn adaptive,energy-aware policies.A Convolutional Neural Network(CNN)extracts high-level features from system states,enabling the agent to respond continually to changing conditions.Extensive simulations show that the proposed method reduces task latency and energy consumption far more than several baseline algorithms,thereby improving overall system performance.These results demonstrate the effectiveness and robustness of the framework for real-time task offloading in dynamic smart-grid environments.
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supported by the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.
文摘The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.
