Interconnection planning involving bi-directional converters(BdCs)is crucial for enhancing the reliability and robustness of hybrid alternating current(AC)/direct current(DC)microgrid clusters with high penetrations o...Interconnection planning involving bi-directional converters(BdCs)is crucial for enhancing the reliability and robustness of hybrid alternating current(AC)/direct current(DC)microgrid clusters with high penetrations of renewable energy resources(RESs).However,challenges such as the non-convex nature of BdC efficiency and renewable energy uncertainty complicate the planning process.To address these issues,this paper proposes a tri-level BdC-based planning framework that incorporates dynamic BdC efficiency and a data-correlated uncertainty set(DcUS)derived from historical data patterns.The proposed framework employs a least-squares approximation to linearize BdC efficiency and constructs the DcUS to balance computational efficiency and solution robustness.Additionally,a fully parallel column and constraint generation algorithm is developed to solve the model efficiently.Numerical simulations on a practical hybrid AC/DC microgrid system demonstrate that the proposed method reduces interconnection costs by up to 21.8%compared to conventional uncertainty sets while ensuring robust operation under all considered scenarios.These results highlight the computational efficiency,robustness,and practicality of the proposed approach,making it a promising solution for modern power systems.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
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
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
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
Fibonacci sequence,generated by summing the preceding two terms,is a classical sequence renowned for its elegant properties.In this paper,leveraging properties of generalized Fibonacci sequences and formulas for conse...Fibonacci sequence,generated by summing the preceding two terms,is a classical sequence renowned for its elegant properties.In this paper,leveraging properties of generalized Fibonacci sequences and formulas for consecutive sums of equidistant sub-sequences,we investigate the ratio of the sum of numbers along main-diagonal and sub-diagonal of odd-order grids containing generalized Fibonacci sequences.We show that this ratio is solely dependent on the order of the grid,providing a concise and splendid identity.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
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
In conventional fi nite diff erence numerical simulation of seismic waves,regular grids in Cartesian coordinates are used to divide the calculated region.When simulating seismic wave fi elds under an irregular surface...In conventional fi nite diff erence numerical simulation of seismic waves,regular grids in Cartesian coordinates are used to divide the calculated region.When simulating seismic wave fi elds under an irregular surface,such grids are unsuitable to realize the free boundary condition.They also easily generate false scattered waves at the corners of the grids owing to the approximation of the stepped grids.These issues affect the simulation accuracy.This study introduces an orthogonal body-fitted grid generation technique in computational fl uid dynamics for generating grids in transversely isotropic(TI)media under an irregular surface.The fi rst-order velocity-stress equation in curvilinear coordinates is calculated using the optimized nonstaggered grids finite difference method.The point oscillation generated by the nonstaggered grids difference is eliminated by selective filtering.The orthogonal body-fitted grids can accurately describe the irregular surface.Further,the orthogonality of the grids allows the implementation of free boundary conditions without complicated coordinate transformation and interpolation operations.Numerical examples show that the numerical solutions obtained by this method agree well with the analytical solutions.By comparing the simulation results of the proposed method with those of the regular grid difference method,the proposed method can eff ectively eliminate the false scattered waves caused by the stepped grids under the condition of the same grid spacing.Thus,the accuracy of the numerical simulation is improved.In addition,the simulation results of the three-layer TI media model on an irregular surface show that the proposed method is also suitable for complex models.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
The primary factor contributing to frequency instability in microgrids is the inherent intermittency of renewable energy sources.This paper introduces novel dual-backup controllers utilizing advanced fractional order ...The primary factor contributing to frequency instability in microgrids is the inherent intermittency of renewable energy sources.This paper introduces novel dual-backup controllers utilizing advanced fractional order proportional integral derivative(FOPID)controllers to enhance frequency and tie-line power stability in microgrids amid increasing renewable energy integration.To improve load frequency control,the proposed controllers are applied to a two-area interconnectedmicrogrid system incorporating diverse energy sources,such as wind turbines,photovoltaic cells,diesel generators,and various storage technologies.A novelmeta-heuristic algorithm is adopted to select the optimal parameters of the proposed controllers.The efficacy of the advanced FOPID controllers is demonstrated through comparative analyses against traditional proportional integral derivative(PID)and FOPID controllers,showcasing superior performance inmanaging systemfluctuations.The optimization algorithm is also evaluated against other artificial intelligent methods for parameter optimization,affirming the proposed solution’s efficiency.The robustness of the intelligent controllers against system uncertainties is further validated under extensive power disturbances,proving their capability to maintain grid stability.The dual-controller configuration ensures redundancy,allowing them to operate as mutual backups,enhancing system reliability.This research underlines the importance of sophisticated control strategies for future-proofing microgrid operations against the backdrop of evolving energy landscapes.展开更多
False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work u...False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.展开更多
To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article com...To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article combines wind turbine monitoring data with numerical weather prediction(NWP)data to create a suitable wind power prediction framework for distributed grids.First,high-precision NWP of the turbine range is achieved using weather research and forecasting models(WRF),and Kriging interpolation locates predicted meteorological data at the turbine site.Then,a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve,and historical power is reconstructed using variational mode decomposition(VMD)filtering to form input variables in chronological order.Finally,input variables of a single turbine enter the temporal convolutional network(TCN)to complete initial feature extraction,and then integrate the outputs of all TCN layers using Long Short Term Memory Networks(LSTM)to obtain power prediction sequences for all turbine positions.The proposed method was tested on a wind farm connected to a distributed power grid,and the results showed it to be superior to existing typical methods.展开更多
The global water demand and supply situation is becoming increasingly severe due to water shortage and uneven distribution of water resources.The highest water demand in the energy sector is attributable to power gene...The global water demand and supply situation is becoming increasingly severe due to water shortage and uneven distribution of water resources.The highest water demand in the energy sector is attributable to power generation.With cross-country and cross-continental power grid interconnections becoming a reality,electricity trading across countries and the creation of new opportunities for re-allocation of water resources are possible.This study expands the concept of virtual water and proposes a generalized virtual water flow in an interconnected power grid system to accurately estimate water resource benefits of clean power transmission from both the production and the consumption sides.By defining the water scarcity index as a price mechanism indicator,the benefits of water resources allocation through power grid interconnections are evaluated.Taking the Africa-Asia-Europe interconnection scenario as an example,the total water saving would amount to 88.95 million m^3 by 2030 and 337.8 million m^3 by 2050.This result shows that grid interconnections could promote the development of renewable energy and expand the benefits of available water resources.展开更多
This paper presents an analysis of the power flow within the Northern Interconnected Grid of Cameroon. The Newton-Raphson method has been performed, known for its accuracy, under MATLAB software, to model and solve co...This paper presents an analysis of the power flow within the Northern Interconnected Grid of Cameroon. The Newton-Raphson method has been performed, known for its accuracy, under MATLAB software, to model and solve complex power flow equations. This study simulates a series of outage scenarios to evaluate the responsiveness of the grid. The results obtained underline the crucial importance of reactive power management and highlight the urgent need to consolidate the grid infrastructure of North Cameroon. To increase grid resilience and stability, the paper recommends the strategic integration of renewables and the development of interconnections with other power grids. These measures are presented as viable solutions to meet current and future energy distribution challenges, ensuring a reliable and sustainable power supply for Cameroon.展开更多
Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the hig...Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.展开更多
Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical a...Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.展开更多
Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightene...Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.展开更多
After the integration of large-scale DistributedGeneration(DG)into the distribution network,the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and de...After the integration of large-scale DistributedGeneration(DG)into the distribution network,the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network,exacerbating the phenomenon of wind and solar power wastage.As a novel power system model,the fundamental concept of Regional Autonomous Power Grids(RAPGs)is to achieve localized management and energy autonomy,thereby facilitating the effective consumption of DGs.Therefore,this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple evaluation indexes for autonomy.First,a regional Energy Storage(ES)configuration strategy is proposed.This strategy can select a suitable reference value for the upper limit of ES configuration based on the regional load andDGoutput to maximize the elimination of source load deviations in the region as the upper limit constraint of ES capacity.Then,a control strategy for regional ES is proposed,the charging and discharging reference line of ES is set,and multiple autonomy and economic indexes are used as objective functions to select different proportions of ES to control the distributed resources of the regional power grid and establish evaluation indexes of the internal regional generation and load power ratio,the proportion of power supply matching hours,new energy consumption rate and tie line power imbalance outside the region to evaluate changes in the regional autonomy capabilities.The final simulation results showthat in the real regional grid example,the planning method in the planning year in the region of the overall power supply matching hour ratio and new energy consumption rate increased by 3.9%and 4.8%on average,and the power imbalance of the tie line decreased by 7.8%on average.The proposed planning approach enables the maximization of regional autonomy while effectively smoothing the fluctuation of power exchange between the regional grid and the higher-level grid.This presents a rational and effective planning solution for the regional grid,facilitating the coordinated development between the region and the distribution network.展开更多
This paper presents an experimental investigation on the flexural behavior of cross-connected brick masonry infill wall panels supported on reinforced concrete beam grids above and below the walls.The experimental pro...This paper presents an experimental investigation on the flexural behavior of cross-connected brick masonry infill wall panels supported on reinforced concrete beam grids above and below the walls.The experimental program was comprised of six wall systems.The effect of change in lower beam stiffness relative to the wall and the geometry of the main walls were investigated.From the results of the experimental tests,the increase in the depth of the lower beam grid reduces the deflection,resulting in an increase in the load carrying capacity of the wall.Further,the stiffness of the main walls affects the deflection and the failure load of the cross walls.展开更多
基金supported by the National Natural Science Foundation of China(72271213)the Shenzhen Science and Technology Program(JCYJ20220530143800001 and RCYX20221008092927070)+1 种基金the Guangdong Basic and Applied Basic Research Foundation(2024A1515240024)the National Key Research and Development Program of China(2022YFB2403500).
文摘Interconnection planning involving bi-directional converters(BdCs)is crucial for enhancing the reliability and robustness of hybrid alternating current(AC)/direct current(DC)microgrid clusters with high penetrations of renewable energy resources(RESs).However,challenges such as the non-convex nature of BdC efficiency and renewable energy uncertainty complicate the planning process.To address these issues,this paper proposes a tri-level BdC-based planning framework that incorporates dynamic BdC efficiency and a data-correlated uncertainty set(DcUS)derived from historical data patterns.The proposed framework employs a least-squares approximation to linearize BdC efficiency and constructs the DcUS to balance computational efficiency and solution robustness.Additionally,a fully parallel column and constraint generation algorithm is developed to solve the model efficiently.Numerical simulations on a practical hybrid AC/DC microgrid system demonstrate that the proposed method reduces interconnection costs by up to 21.8%compared to conventional uncertainty sets while ensuring robust operation under all considered scenarios.These results highlight the computational efficiency,robustness,and practicality of the proposed approach,making it a promising solution for modern power systems.
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.
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.
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 National Natural Science Foundation of China(Grant No.12471298)the Shaanxi Fundamental Science Research Project for Mathematics and Physics(Grant No.23JSQ031)the Shaanxi Province College Student Innovation and Entrepreneurship Training Program(Grant Nos.S202210699481 and S202310699324X).
文摘Fibonacci sequence,generated by summing the preceding two terms,is a classical sequence renowned for its elegant properties.In this paper,leveraging properties of generalized Fibonacci sequences and formulas for consecutive sums of equidistant sub-sequences,we investigate the ratio of the sum of numbers along main-diagonal and sub-diagonal of odd-order grids containing generalized Fibonacci sequences.We show that this ratio is solely dependent on the order of the grid,providing a concise and splendid identity.
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 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.
基金supported by the National Key Research and Development Program of China (Grant No.2023YFC3206501 and 2022YFFO802600)the National Natural Science Foundation of China (Grant No.52369003,42262010 and 42374166)+6 种基金the Natural Science Foundation of Inner Mongolia Autonomous Region of China (Grant No.2023LHMS04011 and2022MS04009)the Application Technology Research and Development Project of Jungar Banner (Grant No.2023YY-18 and 2023YY-19)the First-class Academic Subjects Special Research Project of the Education Department of Inner Mongolia Autonomous Region (Grant No.YLXKZX-NND-010)the Inner Mongolia Autonomous Region Science and Technology Leading Talent Team (Grant No.2022LJRC0007)the Inner Mongolia Agricultural University Basic Research Project(BR22-12-04)the Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region (Grant No.NMGIRT2313)the Basic Scientific Research Project of Institutions of Higher(Grant No.JY20230090)。
文摘In conventional fi nite diff erence numerical simulation of seismic waves,regular grids in Cartesian coordinates are used to divide the calculated region.When simulating seismic wave fi elds under an irregular surface,such grids are unsuitable to realize the free boundary condition.They also easily generate false scattered waves at the corners of the grids owing to the approximation of the stepped grids.These issues affect the simulation accuracy.This study introduces an orthogonal body-fitted grid generation technique in computational fl uid dynamics for generating grids in transversely isotropic(TI)media under an irregular surface.The fi rst-order velocity-stress equation in curvilinear coordinates is calculated using the optimized nonstaggered grids finite difference method.The point oscillation generated by the nonstaggered grids difference is eliminated by selective filtering.The orthogonal body-fitted grids can accurately describe the irregular surface.Further,the orthogonality of the grids allows the implementation of free boundary conditions without complicated coordinate transformation and interpolation operations.Numerical examples show that the numerical solutions obtained by this method agree well with the analytical solutions.By comparing the simulation results of the proposed method with those of the regular grid difference method,the proposed method can eff ectively eliminate the false scattered waves caused by the stepped grids under the condition of the same grid spacing.Thus,the accuracy of the numerical simulation is improved.In addition,the simulation results of the three-layer TI media model on an irregular surface show that the proposed method is also suitable for complex models.
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The primary factor contributing to frequency instability in microgrids is the inherent intermittency of renewable energy sources.This paper introduces novel dual-backup controllers utilizing advanced fractional order proportional integral derivative(FOPID)controllers to enhance frequency and tie-line power stability in microgrids amid increasing renewable energy integration.To improve load frequency control,the proposed controllers are applied to a two-area interconnectedmicrogrid system incorporating diverse energy sources,such as wind turbines,photovoltaic cells,diesel generators,and various storage technologies.A novelmeta-heuristic algorithm is adopted to select the optimal parameters of the proposed controllers.The efficacy of the advanced FOPID controllers is demonstrated through comparative analyses against traditional proportional integral derivative(PID)and FOPID controllers,showcasing superior performance inmanaging systemfluctuations.The optimization algorithm is also evaluated against other artificial intelligent methods for parameter optimization,affirming the proposed solution’s efficiency.The robustness of the intelligent controllers against system uncertainties is further validated under extensive power disturbances,proving their capability to maintain grid stability.The dual-controller configuration ensures redundancy,allowing them to operate as mutual backups,enhancing system reliability.This research underlines the importance of sophisticated control strategies for future-proofing microgrid operations against the backdrop of evolving energy landscapes.
基金supported in part by the Research Fund of Guangxi Key Lab of Multi-Source Information Mining&Security(MIMS21-M-02).
文摘False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.
基金funded by National Key Research and Development Program of China (2021YFB2601400)。
文摘To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article combines wind turbine monitoring data with numerical weather prediction(NWP)data to create a suitable wind power prediction framework for distributed grids.First,high-precision NWP of the turbine range is achieved using weather research and forecasting models(WRF),and Kriging interpolation locates predicted meteorological data at the turbine site.Then,a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve,and historical power is reconstructed using variational mode decomposition(VMD)filtering to form input variables in chronological order.Finally,input variables of a single turbine enter the temporal convolutional network(TCN)to complete initial feature extraction,and then integrate the outputs of all TCN layers using Long Short Term Memory Networks(LSTM)to obtain power prediction sequences for all turbine positions.The proposed method was tested on a wind farm connected to a distributed power grid,and the results showed it to be superior to existing typical methods.
基金supported by the State Grid GEIGC Science and Technology Project under the “Research on Global Energy Transition Scenario and Model Development and Application under the New Pattern of Global Environmental Protection” framework(Grant No.52450018000W)
文摘The global water demand and supply situation is becoming increasingly severe due to water shortage and uneven distribution of water resources.The highest water demand in the energy sector is attributable to power generation.With cross-country and cross-continental power grid interconnections becoming a reality,electricity trading across countries and the creation of new opportunities for re-allocation of water resources are possible.This study expands the concept of virtual water and proposes a generalized virtual water flow in an interconnected power grid system to accurately estimate water resource benefits of clean power transmission from both the production and the consumption sides.By defining the water scarcity index as a price mechanism indicator,the benefits of water resources allocation through power grid interconnections are evaluated.Taking the Africa-Asia-Europe interconnection scenario as an example,the total water saving would amount to 88.95 million m^3 by 2030 and 337.8 million m^3 by 2050.This result shows that grid interconnections could promote the development of renewable energy and expand the benefits of available water resources.
文摘This paper presents an analysis of the power flow within the Northern Interconnected Grid of Cameroon. The Newton-Raphson method has been performed, known for its accuracy, under MATLAB software, to model and solve complex power flow equations. This study simulates a series of outage scenarios to evaluate the responsiveness of the grid. The results obtained underline the crucial importance of reactive power management and highlight the urgent need to consolidate the grid infrastructure of North Cameroon. To increase grid resilience and stability, the paper recommends the strategic integration of renewables and the development of interconnections with other power grids. These measures are presented as viable solutions to meet current and future energy distribution challenges, ensuring a reliable and sustainable power supply for Cameroon.
基金a grant from the Center of Excellence in Information Assurance(CoEIA),KSU.
文摘Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.
基金supported by the National Nature Science Foundation of China under 62203376the Science and Technology Plan of Hebei Education Department under QN2021139+1 种基金the Nature Science Foundation of Hebei Province under F2021203043the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology under No.XTCX202203.
文摘Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.
基金The financial support from the Program for Science and Technology of Henan Province of China(Grant No.242102210148)Henan Center for Outstanding Overseas Scientists(Grant No.GZS2022011)Songshan Laboratory Pre-Research Project(Grant No.YYJC032022022).
文摘Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.
基金supported by the State Grid Henan Economic Research Institute Science and Technology Project“Calculation and Demonstration of Distributed Photovoltaic Open Capacity Based on Multi-Source Heterogeneous Data”(5217L0230013).
文摘After the integration of large-scale DistributedGeneration(DG)into the distribution network,the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network,exacerbating the phenomenon of wind and solar power wastage.As a novel power system model,the fundamental concept of Regional Autonomous Power Grids(RAPGs)is to achieve localized management and energy autonomy,thereby facilitating the effective consumption of DGs.Therefore,this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple evaluation indexes for autonomy.First,a regional Energy Storage(ES)configuration strategy is proposed.This strategy can select a suitable reference value for the upper limit of ES configuration based on the regional load andDGoutput to maximize the elimination of source load deviations in the region as the upper limit constraint of ES capacity.Then,a control strategy for regional ES is proposed,the charging and discharging reference line of ES is set,and multiple autonomy and economic indexes are used as objective functions to select different proportions of ES to control the distributed resources of the regional power grid and establish evaluation indexes of the internal regional generation and load power ratio,the proportion of power supply matching hours,new energy consumption rate and tie line power imbalance outside the region to evaluate changes in the regional autonomy capabilities.The final simulation results showthat in the real regional grid example,the planning method in the planning year in the region of the overall power supply matching hour ratio and new energy consumption rate increased by 3.9%and 4.8%on average,and the power imbalance of the tie line decreased by 7.8%on average.The proposed planning approach enables the maximization of regional autonomy while effectively smoothing the fluctuation of power exchange between the regional grid and the higher-level grid.This presents a rational and effective planning solution for the regional grid,facilitating the coordinated development between the region and the distribution network.
文摘This paper presents an experimental investigation on the flexural behavior of cross-connected brick masonry infill wall panels supported on reinforced concrete beam grids above and below the walls.The experimental program was comprised of six wall systems.The effect of change in lower beam stiffness relative to the wall and the geometry of the main walls were investigated.From the results of the experimental tests,the increase in the depth of the lower beam grid reduces the deflection,resulting in an increase in the load carrying capacity of the wall.Further,the stiffness of the main walls affects the deflection and the failure load of the cross walls.
