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Distribution network data asset protection considering multiple topology and multi-dimensional knowledge graph 认领 引用
1
作者 Junfeng Yang Li Liu +5 位作者 Nawaraj Kumar Mahato Luhan Li Jiaxuan Yang Gangjun Gong Jun Lu Chao Yang 《Global Energy Interconnection》 EI CSCD 2026年第3期585-610,共26页
The proliferation of diverse entities within distribution network has led to an increase in the scale and complexity of data asset interactions,exacerbating security risks,such as unauthorized access,data tampering,an... The proliferation of diverse entities within distribution network has led to an increase in the scale and complexity of data asset interactions,exacerbating security risks,such as unauthorized access,data tampering,and forgery.In response to these challenges,this study introduces a novel framework that enhances the protection of data assets.It incorporates a multi-dimensional knowledge graph(MDKG)to refine access control and overcome current limitations by integrating a comprehensive set of data asset attributes,roles,policies,and permissions.This approach fosters the development of a nuanced and adaptable access-control mechanism.Furthermore,the framework integrates multiple topology(MTP)for holistic security risk detection,leveraging attention mechanisms,and cross-fusion to adapt to the dynamic data security landscape.Empirical evaluations affirm the effectiveness of MDKG-based access control,whereas comparative experiments demonstrate the superiority of the MTP-based security risk model over existing models.The framework was proven to be effective in countering security risks.This study provides innovative perspectives on data asset protection and establishes a solid foundation for the advancement of smart grid technology. 展开更多
关键词 Distribution network Data asset protection Security access control Multi-dimensional knowledge graph Multiple topology Security risk detection
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Optimal Allocation of Distributed Generation and Energy Storage Considering Line Vulnerability under Extreme Weather in Distribution Networks 认领 引用
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作者 Yangjun Zhou Chenying Yi +10 位作者 Wei Zhang Juntao Pan Ke Zhou Weixiang Huang Like Gao Shan Li Yuanchao Zhou Ling Li Liwen Qin Hongwen Wu Lijuan Yan 《Energy Engineering》 EI 2026年第6期567-583,共17页
The increasing integration of distributed generation(DG)and energy storage systems(ESS)has significantly enhanced the flexibility and efficiency of distribution networks.However,the growing frequency of extreme weathe... The increasing integration of distributed generation(DG)and energy storage systems(ESS)has significantly enhanced the flexibility and efficiency of distribution networks.However,the growing frequency of extreme weather events has exposed the vulnerability of distribution lines,posing serious challenges to the reliability and resilience of such systems.Existing DG and ESS planning models often neglect this vulnerability dimension,leading to suboptimal siting decisions and reduced system robustness.To address this issue,this paper proposes a comprehensive multi-objective optimization framework that coordinates the allocation of DG and ESS and explicitly incorporates line vulnerability under extreme weather conditions.The vulnerability index of each distribution line is first evaluated through Monte Carlo simulations that capture the probabilistic influence of micro-climatic and terrain factors.This assessment serves as a pre-processing stage that screens out high-risk lines and thereby constrains the optimization decision space to more reliable nodes for DG and ESS deployment.Building upon this filtered network,a multi-objective optimization model is established to determine the optimal siting and capacities of DG and ESS.The optimization simultaneously minimizes the total annual cost,which includes investment,operation,and maintenance expenses,as well as network power losses,while improving overall system resilience.A case study on a modified IEEE 33-bus distribution system verifies the effectiveness of the proposed method.The results demonstrate that vulnerability-aware planning achieves a better balance between cost and reliability compared with conventional approaches.Specifically,the proposed strategy reduces annual network losses and outage durations while maintaining voltage stability with respect to climate-adjusted line failure rates.Furthermore,the integration of ESS enables effective peak shaving and valley filling,improving system efficiency and operational flexibility.These findings confirm that incorporating line vulnerability into DG and ESS planning provides a practical and scalable pathway for enhancing the resilience and economy of distribution networks. 展开更多
关键词 Distributed generation energy storage systems distribution networks extreme weather line vulnerability multi-objective optimization
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Distribution Network Partitioning for Voltage Regulation Using Heterogeneous Graph Neural Networks Considering Cyber-Attacks Risk 认领 引用
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作者 Lei Xu Bo Zhang +1 位作者 Chunxia Dou Dong Yue 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期995-997,共3页
Dear Editor,The integration of distributed energy resources(DERs)and communication infrastructures makes distribution networks increasingly cyber-physical,requiring resilient and real-time voltage regulation.Network p... Dear Editor,The integration of distributed energy resources(DERs)and communication infrastructures makes distribution networks increasingly cyber-physical,requiring resilient and real-time voltage regulation.Network partitioning enables scalable control,yet existing methods often ignore communication and security constraints or rely on costly optimization,limiting practicality under dynamic and adversarial conditions. 展开更多
关键词 scalable controlyet distribution network partitioning heterogeneous graph neural networks distributed energy resources communication infrastructures cyber attacks risk distributed energy resources ders voltage regulation
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Distribution Network Partitioning and Distributed Voltage Coordinated Optimization Method under High-Proportion Photovoltaic Penetration 认领 引用
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作者 Jian Wang Gongqiang Yang +2 位作者 Yufeng Sun Gangui Yan Jie Long 《Energy Engineering》 EI 2026年第6期410-442,共33页
Given that the power grid partitioning method relying mainly on line reactive power flow information sees frequent changes in partitioning results with reactive power flow fluctuations under high-proportion fixed-powe... Given that the power grid partitioning method relying mainly on line reactive power flow information sees frequent changes in partitioning results with reactive power flow fluctuations under high-proportion fixed-powerfactor PV-connected distribution networks,and traditional distributed PV collaborative optimization fails to adapt due to such changes,a stable partitioning and distributed PV collaborative optimization method for this scenario is proposed.Firstly,the Gaussian mixture model(GMM)is used to characterize the characteristics of PV reactive power output,obtaining the typical curve of PV reactive power output.Secondly,the Monte Carlo Simulation(MCS)probabilistic power flow calculation is performed to obtain the node voltage distribution of the distribution network.Thirdly,based on the node voltage distribution,the Earth Mover’s Distance(EMD)is used to obtain the statistical distance between any two nodes,and this statistical distance is combined with the electrical distance defined by node voltage sensitivity to form a comprehensive electrical distance.Then,the affinity propagation clustering algorithm is applied,and considering the dynamic reactive power margin requirement,the reactive power/voltage partitioning result is obtained.Based on the reactive power partitioning result,a reactive power optimization model is established with the minimum active power loss of the system as the objective function.The optimization model is convexified using the LinDistFlow equation,and the Alternating Direction Multiplier Method(ADMM)is adopted to coordinate the reactive power output of PV inverters in each partition,achieving global optimal voltage control in the distribution network.Finally,the proposed method is verified using the IEEE 33-bus system.The application of this method reduces the system power loss by 35.94%.Compared with the traditional partitioning method,the partitioning variation rate under Scenario 1 is reduced by 54.17%and that under Scenario 2 is reduced by 70.85%when this method is adopted.This fully demonstrates that the partitioning results of the proposed method are stable,and the collaborative optimization method can improve the system voltage stability and reduce the system power loss. 展开更多
关键词 Gaussian mixture model Monte Carlo trend distribution network partition distributed voltage control synchronous ADMM
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Optimal scheduling of active distribution networks based on multi-scenario fuzzy set based charging station resource prediction 认领 引用
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作者 Zhang Maosong Zhang Chunyu +3 位作者 Hao Shi Yang Jie Yang Lingxiao Wang Xiuqin 《High Technology Letters》 EI CAS 2026年第1期97-108,共12页
With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),po... With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy. 展开更多
关键词 charging station resource prediction subtractive optimizer algorithm multi-scenario fuzzy set two-stage optimal scheduling distribution network cost optimization
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Bi-Objective Optimization of Distribution Network Reliability Enhancement Using Quantitative Decomposition 认领 引用
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作者 Chenying Yi Yangjun Zhou +8 位作者 Wei Zhang Like Gao Hongwen Wu Yuanchao Zhou Ke Zhou Weixiang Huang Juntao Pan Shan Li Bin Feng 《Energy Engineering》 EI 2026年第8期262-276,共15页
Ensuring reliability in distribution networks is essential under increasing operational and economic constraints.Traditional planning models rely on power flow calculations,leading to high computational costs and poor... Ensuring reliability in distribution networks is essential under increasing operational and economic constraints.Traditional planning models rely on power flow calculations,leading to high computational costs and poor scalability.This study proposes a quantitative decomposition framework that establishes a direct linkage among reliability improvement measures,reliability parameters,and reliability indices,enabling fast and analytical reliability evaluation without power flow analysis.A bi-objective optimization model is developed to minimize both reliability indices(SAIDI)and investment costs,solved using Pareto-based multi-objective PSO combined with the TOPSIS method.Case studies on a 519-node distribution network demonstrate that the proposed approach achieves significant reliability improvement with superior computational efficiency,offering a practical and scalable tool for reliabilityoriented distribution planning. 展开更多
关键词 Distribution network reliability improvement measures quantitative analysis optimization decision
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Multi-Timescale Coordinated Optimal Dispatch of Active Distribution Networks Incorporating Thermal Storage Electric Heating Clusters 认领 引用
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作者 Song Zhang Yang Yu +1 位作者 Shuguang Li Xue Li 《Energy Engineering》 EI 2026年第3期459-480,共22页
Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energ... Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs. 展开更多
关键词 Active distribution network thermal storage electric heating distributed energy resources rolling optimization multiple time scales
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An Upgrade Strategy for Active Distribution Networks Considering Probabilistic Power Flow 认领 引用
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作者 Xiaotong Li Mingquan Qiu +7 位作者 Wenjun Zhang Pengfei Li Chong Liu Chong Yang Han Xiao Feng Hu Binghui Liu He Wang 《Energy Engineering》 EI 2026年第8期133-157,共25页
To address the operational uncertainties and power quality challenges brought by high-penetration distributed energy integration into distribution networks,this paper proposes a precision renovation optimization metho... To address the operational uncertainties and power quality challenges brought by high-penetration distributed energy integration into distribution networks,this paper proposes a precision renovation optimization method for active distribution networks(ADNs)that considers probabilistic power flow.First,based on probabilistic power flow analysis,a comprehensive power quality evaluation index system is constructed to accurately quantify the impact of uncertainties caused by photovoltaic fluctuations on distribution network power quality under high-penetration distributed photovoltaic(PV)scenarios.On this basis,a precision renovation optimization model is established with the goals of minimizing renovation cost,maximizing renewable energy hosting capacity,and optimizing the comprehensive power quality index,thereby achieving coordinated optimization of economic and operational performance.To address the challenges of solving this high-dimensional,mixed-variable,and strongly constrained model,an Adaptive and Feedback-enhanced Multi-strategy Particle Swarm Optimization(AFM-PSO)algorithm is proposed.This algorithm incorporates structured particle encoding,a dynamic information entropy feedback mechanism,and a local perturbation strategy,significantly improving search efficiency and convergence accuracy,making it suitable for rapidly solving complex distribution system renovation problems.Finally,the effectiveness of the proposed model and algorithm is verified using an IEEE 33-node distribution system with high-penetration PV integration as a simulation platform.The results demonstrate that the proposed method significantly enhances system voltage stability and renewable energy hosting capacity while controlling renovation costs,validating its superiority in achieving refined renovation and efficient operation of distribution networks. 展开更多
关键词 Active distribution networks distribution network upgrade probabilistic powerffiow power quality evaluation index multi-strategy particle swarm algorithm
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Fault Self-Healing Cooperative Strategy of New Energy Distribution Network Based on Improved Ant Colony-Genetic Hybrid Algorithm 认领 引用 被引量：1
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作者 Fengchao Chen Aoqi Mei +2 位作者 Zheng Liu Ruhao Wu Qiwei Li 《Energy Engineering》 EI 2026年第4期247-267,共21页
With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper... With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm.Firstly,the graph theory adjacency matrix is used to characterize the topology of the distribution network,and the dynamic positioning of new energy nodes is realized.Secondly,based on the output model and load characteristic model of wind,photovoltaic,and energy storage,a two-layer cooperative self-healing model of the distribution network is constructed.The upper layer is based on the improved depth-breadth hybrid search(DFS-BFS)to divide the island,with the maximum weight load recovery and the minimum number of switching actions as the goal,combined with the load priority to dynamically restore the key load.The lower layer uses the improved ant colony-genetic hybrid algorithm to solve the fault recovery path with the minimum total power loss load and the minimum network loss as the goal,generate the optimal switching sequence,and verify the power flow constraints.Finally,the simulation results based on the IEEE 33-bus system show that the proposed method can guarantee the power supply of key loads in the distribution network with high-tech energy penetration,restore the power supply of more load nodes with the least switching operation,and effectively reduce the line loss,which verifies the effectiveness and superiority of the method. 展开更多
关键词 Fault recovery identification of topology improved ant colony-genetic hybrid algorithm distribution network self-healing
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Optimal partitioning and operation of water distribution networks under intermittent conditions:A case study of Modena network 认领 引用
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作者 Rui Gabriel Souza Thomaz Anchieta +2 位作者 Gustavo Meirelles Daniel Barros Bruno Brentan 《Water Science and Engineering》 EI CAS CSCD 2026年第1期120-131,共12页
With over 1.3 billion people worldwide facing irregular water access,efficient water management is a global priority.This study presented a comprehensive approach for optimizing the operation of intermittent water dis... With over 1.3 billion people worldwide facing irregular water access,efficient water management is a global priority.This study presented a comprehensive approach for optimizing the operation of intermittent water distribution networks through the creation of district metered areas(DMAs).It advanced traditional DMA design by integrating network partitioning with optimized operational schedules,offering a practical framework for managing intermittent water supply systems.The proposed methodology aims to reduce water losses while improving service equity and quality.First,the network is partitioned using the fast-greedy community detection algorithm based on modularity from graph theory,enabling DMAs to operate independently at different times of a day.Flow control valves are installed at DMA entry points,while isolation valves isolate remaining boundary pipes,enhancing operational flexibility.Second,the particle swarm optimization algorithm optimizes the operational schedule of each DMA and determines the optimal start time and water supply duration for each DMA.This step minimizes total daily distributed volume while ensuring adequate service.This approach reduced the daily distributed volume of the Modena network by approximately 720.0 m3 and significantly decreased the leakage rate from 30.5% to 18.7%,demonstrating its effectiveness. 展开更多
关键词 Intermittent water distribution network Sustainable operation Optimal control Complex network theory Leakage
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Two-stage wind-solar-storage capacity allocation method research for active distribution network based on whale migrating algorithm 认领 引用
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作者 Wu Zheng Ting Tang +1 位作者 Xianggang He Jierui Yang 《Global Energy Interconnection》 EI CSCD 2026年第3期555-566,共12页
With the integration of clean energy,the increasing penetration of distributed power sources,controllable loads,and energy-storage resources in smart grids is causing substantial difficulties in the safe,stable,and co... With the integration of clean energy,the increasing penetration of distributed power sources,controllable loads,and energy-storage resources in smart grids is causing substantial difficulties in the safe,stable,and cost-effective operation of power systems.Existing research often fails to consider the interconnections among optimal dispatching and the distribution capacities of wind,solar,and energy-storage systems(ESSs).This increases the costs and dispatching difficulties.In response to this situation,a two-stage capacity-allocation approach for wind and solar power and storage in an active distribution network(ADN)is proposed in this paper.This approach is founded based on the whale migration algorithm(WMA).First,an optimization dispatch model that considers controllable loads and energy storage is formulated to minimize the dispatch operation costs of the ADN.In this optimization model,the overall cost of the ADN is taken as the objective function.The optimal configuration for wind–solar–storage capacities is obtained through the WMA.Simulation results confirm that the WMA effectively balances the solution accuracy and computational efficiency,while the proposed scheme enhances the economic performance of active distribution grids. 展开更多
关键词 Active distribution network(ADN) Optimization dispatching Capacity allocation Two-stage Whale migration algorithm(WMA)
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Centralized PV Coordination Control Strategy for Unbalanced LV Distribution Networks Based on Sensitivity Coefficient Weights 认领 引用
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作者 Xuming Hu Nan Hu +3 位作者 Na Li Xinsong Zhang Xiaocen Xue Xiuyong Yu 《Energy Engineering》 EI 2026年第3期391-410,共20页
The dense integration of residential distributed photovoltaic(PV)systems into three-phase,four-wire low-voltage(LV)distribution networks results in reverse power flow and three-phase imbalance,leading to voltage viola... The dense integration of residential distributed photovoltaic(PV)systems into three-phase,four-wire low-voltage(LV)distribution networks results in reverse power flow and three-phase imbalance,leading to voltage violations that hinder the growth of rural distributed PV systems.Traditional voltage droop-based control methods regulate PV power output solely based on local voltage measurements at the point of PV connection.Due to a lack of global coordination and optimization,their efficiency is often subpar.This paper presents a centralized coordinated activeeactive power control strategy for PV inverters in rural LV distribution feeders with high PV penetration.The strategy optimizes residential PV inverter reactive and active power control to enhance voltage quality.It uses sensitivity coefficients derived from the inverse Jacobian matrix to assign adjustment weights to individual PV units and iteratively optimize their power outputs.The control sequence prioritizes reactive power increases;if the coefficients are below average or the inverters reach capacity,active power is curtailed until voltage issues are resolved.A simulation based on a real 37-node rural distribution network shows that the proposed method significantly reduces PV curtailment.Typical daily results indicate a curtailment rate of 1.47%,which is significantly lower than the 15.4%observed with the voltage droop-based control method.The total daily PV power output(measured every 15 min)increases from 5.55 to 6.41 MW,improving PV hosting capacity. 展开更多
关键词 Low-voltage distribution network PV inverter voltage violation centralized iterative optimization control curtailment rate
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Dual Layer Source Grid Load Storage Collaborative Planning Model Based on Benders Decomposition: Distribution Network Optimization Considering Low-Carbon and Economy 认领 引用
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作者 Jun Guo Maoyuan Chen +2 位作者 Yuyang Li Sibo Feng Guangyu Fu 《Energy Engineering》 EI 2026年第2期104-133,共30页
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ... Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability. 展开更多
关键词 Benders decomposition source grid load storage distribution network planning low-carbon economy optimization model
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Optimal Dispatch of Urban Distribution Networks Considering Virtual Power Plant Coordination under Extreme Scenarios 认领 引用
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作者 Yong Li Yuxuan Chen +4 位作者 Jiahui He Guowei He Chenxi Dai Jingjing Tong Wenting Lei 《Energy Engineering》 EI 2026年第1期204-220,共17页
Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the... Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study. 展开更多
关键词 Urban distribution network virtual power plant power supply support leader-follower optimization game extreme weather scenarios
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A Multi-Stage Expansion Planning Method for Rural Distribution Networks with Flexible Interconnection 认领 引用
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作者 Yueyang Ji Yaohui Peng +4 位作者 Haoran Ji Xinran Na Yuxuan Chen Wei Li Shengbin Chen 《Energy Engineering》 EI 2026年第8期17-31,共15页
With the increasing penetration of distributed generations and continuous growth of loads,traditional rural distribution networks face severe challenges in both hosting capacity and reliability.Addressing these issues... With the increasing penetration of distributed generations and continuous growth of loads,traditional rural distribution networks face severe challenges in both hosting capacity and reliability.Addressing these issues requires planning approaches that strike a balance between economic efficiency in infrastructure development and resilience in operation.Considering the dynamic growth of distributed generations and rural loads over the planning horizon,this paper presents a multi-stage expansion planning approach that coordinates flexible interconnection devices(FIDs)with substation and line construction to improve both economic performance and system reliability.The proposed method account for the time-varying growth of DGs and loads,as well as the declining investment cost of power electronic devices across multiple planning stages.The model holistically considers both economic efficiency and operational reliability,formulating the problem as a mixed-integer second-order cone programming(MISOCP)model to ensure computational efficiency.Case studies conducted on a practical 138-node rural distribution network in Guangxi,China,demonstrate the effectiveness of the proposed method.Compared to traditional single-stage or singleresource planning strategies,results indicate that the proposed multi-stage coordinated strategy achieves a significant reduction in total annualized cost while simultaneously enhancing system reliability,effectively mitigating voltage violations,and achieving a 100%PV accommodation rate without curtailment.This work provides a practical and adaptive planning framework for rural distribution networks,offering valuable insights for achieving cost-effective and resilient network development under rural energy transition. 展开更多
关键词 Rural distribution networks coordinated planning flexible interconnection device multi-stage planning reliability
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A Spatiotemporal Collaborative Framework for Dynamic Cluster Partitioning in EV/EC-Integrated Distribution Networks 认领 引用
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作者 Fukang Zhang Yang Wang +1 位作者 Runtian Tang Zhixin Yun 《Energy Engineering》 EI 2026年第5期97-118,共22页
The large-scale integration of electric vehicle(EV)and exchange stations(EC)into distribution networks introduces strong spatiotemporal load fluctuations and charging capacity constraints,leading to frequent voltage v... The large-scale integration of electric vehicle(EV)and exchange stations(EC)into distribution networks introduces strong spatiotemporal load fluctuations and charging capacity constraints,leading to frequent voltage violations and reduced control flexibility.Traditional centralized control approaches face critical limitations,including high communication latency and computational complexity.To address these challenges,this paper proposes a Hybrid Intelligence(HI)-driven framework for distribution networks,which explicitly considers EV/EC charging power limits,cluster-level resource balance,and voltage security constraints.By incorporating spatiotemporal characteristics with intelligent optimization techniques,a Variant Monte Carlo Sampling(VMCS)algorithm is developed to generate the initial node partitions.These partitions are further refined using a Capacity-Corrected K-means Extension combined with Simulated Annealing Optimization(CCE-SAO),resulting in an optimized cluster configuration.A two-layer control architecture,termed“spatiotemporal collaborative optimization—distributed iteration,”is established to effectively address the drawbacks of traditional static clustering methods in large-scale systems,such as vulnerability to local optima and limited adaptability.This enhances global optimization under complex operational scenarios.Simulation results on the IEEE 33-bus and IEEE 123-bus test systems show that the proposed HI-based method effectively improves voltage quality.In the IEEE 33-bus system,the voltage deviation at the most fluctuating node is reduced by 30%compared with conventional K-means clustering and by 40%compared with centralized control under peak load conditions,validating the effectiveness of the proposed framework for future distribution networks with large-scale EVEC integration. 展开更多
关键词 Cluster partitioning hybrid intelligent EV/EC distribution networks
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Multi-Objective and Multi-Criteria Optimization of Energy Storage Planning in Renewable Distribution Networks 认领 引用
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作者 Alireza Norouzpour Shahrbejari Nafiseh Pishbin +2 位作者 Mohammad Reza Maghami Mazlan Mohamed Mohammad Golmohammad 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期581-619,共39页
This study presents a weighted-sum multi-criteria optimization framework using PSO for the optimal siting,sizing,and scenario-based operation of energy storage systems(ESSs)in renewable-integrated distribution network... This study presents a weighted-sum multi-criteria optimization framework using PSO for the optimal siting,sizing,and scenario-based operation of energy storage systems(ESSs)in renewable-integrated distribution networks.The proposed model concurrently addresses technical,economic,and reliability objectives—minimizing active power losses(PL),voltage deviation(VD),expected energy not supplied(EENS),and short-circuit level(SCL),while maximizing voltage sensitivity index(VSI)and power-loss sensitivity factor(PLSF).A Particle Swarm Optimization(PSO)algorithm with weighted-sum scalarization is employed to solve this complex,nonlinear optimization problem and effectively balance the conflicting operational goals.The framework is validated using IEEE 69-bus and IEEE 118-bus test systems under varying load conditions(20%,50%,100%,and 150%)with time-dependent photovoltaic(PV)and wind turbine(WT)generation profiles.Results demonstrate that the proposed approach achieves significant performance enhancements,reducing power losses by up to 54%,EENS by 88%,and operational cost by 22%while maintaining SCL values within protection limits.Furthermore,the inclusion of ESS units improves system reliability and voltage stability,ensuring smooth operation during load fluctuations and fault conditions.The findings confirm that the proposed weighted-sum multi-criteria optimization framework using PSO provides a scalable and protection-aware solution for integrating ESSs into renewable-rich distribution networks.It offers a robust planning and operational tool for next-generation smart grids,enabling a more efficient,resilient,and sustainable energy ecosystem. 展开更多
关键词 Energy storage systems multi-objective optimization optimal placements distribution networks renewable integration grid reliability这篇论文的主要研究内容是什么?这篇论文的作者单位都有哪些?这篇论文的研究成果有哪些应用?
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Advancing performance guarantees for DNN-based OPF in topology-flexible distribution networks 认领 引用
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作者 Wei Lin Qingyue Ran +3 位作者 Zhifang Yang Juan Yu Chongyu Wang Suhan Zhang 《Cyber-Physical Energy Systems》 2026年第1期81-87,共7页
We tackled the lack of solution-quality guarantees for deep neural network(DNN)-based optimal power flow(OPF)in topology-flexible distribution networks(TFDNs),despited the black-box nature of DNN predictors.We formula... We tackled the lack of solution-quality guarantees for deep neural network(DNN)-based optimal power flow(OPF)in topology-flexible distribution networks(TFDNs),despited the black-box nature of DNN predictors.We formulated a bi-level min-max(BLMM)problem,whose inner level certifies the performance of a fixed DNN-based OPF solution and whose outer level updates the DNN to improve the certified bound.To solve the BLMM problem,we developed an iterative scheme grounded in Danskin’s theorem.In particular,we attained the exact solution by explicitly reformulating the BLMM problem through binary variables.To alleviate the computational burden from binary variables,we combined convex relaxation with pattern recognition,thereby reducing the required number of binary variables.The effectiveness of the proposed method is demonstrated on a 4-bus system and the IEEE 136-bus test system. 展开更多
关键词 Distribution network Optimal power flow Topology change Deep neural network Performance guarantee
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An Optimized Ensemble Learning Framework for Energy Efficiency Assessment in Low-Voltage Distribution Networks Using Multi-Source Data Integration 认领 引用
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作者 Yujie Shi Guoxing Wu +2 位作者 Qingwei Wang Xieli Fu Wenfeng Yang 《Energy Engineering》 EI 2026年第9期350-375,共26页
This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources.The framework integrates heterogeneous data from ... This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources.The framework integrates heterogeneous data from smart meters,SCADA systems,meteorological stations,and network topology databases,employing advanced feature engineering to extract 89 essential predictors from 147 initial features.Three gradient boosting algorithms-Random Forest,XGBoost,and LightGBM-are combined through an elastic net stacking strategy with Bayesian hyperparameter optimization.The stacking ensemble achieved superior performance with an MAE of 118.4 kWh,an RMSE of 164.2 kWh,an MAPE of 3.98%,and an R2of 0.952,representing 16.8%improvement over individual models.SHAP analysis provided model interpretability,identifying temperature,historical consumption,and temporal features as the primary drivers of efficiency.The framework demonstrated robust performance under data quality degradation and successfully generalized across diverse network configurations.Field implementation yielded an 8.3%reduction in distribution losses(95%CI:7.2%-9.4%,p<0.0001),34%decrease in transformer failure rates(95%CI:28%-40%,p=0.003),and 12%-15%operational cost reduction.The framework's ability to provide accurate predictions from 15 min to 24 h ahead while maintaining computational efficiency enables proactive distribution network management,supporting the transition toward efficient and sustainable power systems. 展开更多
关键词 Ensemble learning energy efficiency assessment low-voltage distribution networks multi-source data integration SHAP analysis
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A Missing Data Complement Method Based on 3D Convolutional Neural Network and CGAN for a Distribution Network 认领 引用
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作者 Kewen Li Xiaoyong Yu +1 位作者 Shifeng Ou Jueming Pan 《Energy Engineering》 EI 2026年第9期132-156,共25页
The increasing integration of renewable energy sources(e.g.,wind and solar power)into distribution grids and the development of new,source-grid-load-storage coordinated power systems have led to a substantial expansio... The increasing integration of renewable energy sources(e.g.,wind and solar power)into distribution grids and the development of new,source-grid-load-storage coordinated power systems have led to a substantial expansion in the volume of situational awareness data in the distribution networks.Moreover,the transmission of low-voltage distribution measurement data via a power line carrier(PLC)is often susceptible to packet loss and,consequently,data gaps.To address these issues,this paper proposes a data completion method using a conditional generative adversarial network(CGAN)integrated with a three-dimensional convolutional neural network(3D-CNN).This approach leverages the ability of CNNs to extract and fuse multidimensional spatiotemporal features and the power of GANs(generative adversarial networks)for data augmentation.Firstly,a 3D-CNN is trained to establish a mapping between the spatiotemporal context of the measured data and the target missing data.Secondly,a CGAN is practicing via adversarial training to establish a data completion model for the distribution networks.Finally,the simulations of the IEEE 14-bus and 33-bus systems demonstrate the proposed approach's performance improvement in the distribution networks compared with that of conventional methods in terms of root mean square error,spatiotemporal correlation,and maximum volatility amplitude,which are the typical measuring metrics. 展开更多
关键词 Distribution network data estimation 3D convolutional neural network conditional generative adversarial networks spatiotemporal correlation
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