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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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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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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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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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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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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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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 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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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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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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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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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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Resilience Investment for Distribution Networks Facing Urban Waterlogging:A Two-Stage Method Combining AHP Zoning and Mixed-Integer Optimization 认领 引用
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作者 Weiwei Wu Panfei Shi +2 位作者 Luo Li Han Lin Kun Hua 《Journal of Electronic Research and Application》 2026年第4期184-190,共7页
Against the background of frequent urban waterlogging disasters caused by global climate change,the vulnerability of distribution systems has become increasingly prominent.To address the blindness and inefficiency of ... Against the background of frequent urban waterlogging disasters caused by global climate change,the vulnerability of distribution systems has become increasingly prominent.To address the blindness and inefficiency of traditional protective equipment layout,this paper proposes a two-stage optimization method based on risk zoning,aiming to scientifically guide the layout of protective equipment represented by waterproof transformers.First,the method integrates six core indicators including rainfall,elevation,and slope,and uses the Analytic Hierarchy Process(AHP)to conduct refined waterlogging risk zoning of the distribution network,dividing nodes into three risk levels:high,medium,and low.Taking this risk assessment result as a key input,a mixed-integer programming model with the goal of maximizing comprehensive benefits is constructed.Finally,the model is verified through a 25-node numerical example.The results show that through strategic layout,the model protects most loads including high-risk nodes with limited costs,and makes a strategic abandonment of low-value nodes in line with the optimization goal.This study has important practical significance for improving investment efficiency and urban power grid resilience. 展开更多
关键词 Urban waterlogging Distribution network resilience Facility layout optimization Waterproof transformer
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Multi-Timescale Optimization Scheduling of Distribution Networks Based on the Uncertainty Intervals in Source-Load Forecasting 认领 引用 被引量：1
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作者 Huanan Yu Chunhe Ye +3 位作者 Shiqiang Li He Wang Jing Bian Jinling Li 《Energy Engineering》 EI 2025年第6期2417-2448,共32页
With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ... With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system. 展开更多
关键词 Renewable energy distribution networks source-load uncertainty interval flexible scheduling soft actor-critic algorithm optimization model
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Digital twin-based resilience evaluation and intelligent strategies of smart urban water distribution networks for emergency management 认领 引用 被引量：3
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作者 Hongyan Dui Taiyu Cao Fan Wang 《Resilient Cities and Structures》 EI CSCD 2025年第1期41-52,共12页
Resilient smart urban water distribution networks are essential to ensure smooth urban operation and maintain daily water services.However,the dynamics and complexity of smart water distribution networks make its re-s... Resilient smart urban water distribution networks are essential to ensure smooth urban operation and maintain daily water services.However,the dynamics and complexity of smart water distribution networks make its re-silience study face many challenges.The introduction of digital twin technology provides an innovative solution for the resilience study of smart water distribution networks,which can more effectively support the network’s real-time monitoring and intelligent control.This paper proposes a digital twin architecture of smart water dis-tribution networks,laying the foundation for the resilience assessment of water distribution networks.Based on this,a performance evaluation model based on user satisfaction is proposed,which can more intuitively and effectively reflect the performance of urban water supply services.Meanwhile,we propose a method to quantify the importance of water distribution pipes’residual resilience,considering the time value to optimize the re-covery sequence of failed pipes and develop targeted preventive maintenance strategies.Finally,to validate the effectiveness of the proposed method,this paper applies it to a water distribution network.The results show that the proposed method can significantly improve the resilience and enhance the overall resilience of smart urban water distribution networks. 展开更多
关键词 Digital twin Smart water distribution network Resilience evaluation Importance measure
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Analyzing the Resilience of Active Distribution Networks to Hazardous Weather Considering Cyber-Physical Interdependencies 认领 引用 被引量：1
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作者 Zhiyi Li Xutao Han +2 位作者 Mohammad Shahidehpour Ping Ju Qun Yu 《Engineering》 SCIE EI CSCD 2025年第8期15-36,共22页
This paper provides a systematic review on the resilience analysis of active distribution networks(ADNs)against hazardous weather events,considering the underlying cyber-physical interdependencies.As cyber-physical sy... This paper provides a systematic review on the resilience analysis of active distribution networks(ADNs)against hazardous weather events,considering the underlying cyber-physical interdependencies.As cyber-physical systems,ADNs are characterized by widespread structural and functional interdependen-cies between cyber(communication,computing,and control)and physical(electric power)subsystems and thus present complex hazardous-weather-related resilience issues.To bridge current research gaps,this paper first classifies diverse hazardous weather events for ADNs according to different time spans and degrees of hazard,with model-based and data-driven methods being utilized to characterize weather evolutions.Then,the adverse impacts of hazardous weather on all aspects of ADNs’sources,physical/cyber networks,and loads are analyzed.This paper further emphasizes the importance of situational awareness and cyber-physical collaboration throughout hazardous weather events,as these enhance the implementation of preventive dispatches,corrective actions,and coordinated restorations.In addition,a generalized quantitative resilience evaluation process is proposed regarding additional considerations about cyber subsystems and cyber-physical connections.Finally,potential hazardous-weather-related resilience challenges for both physical and cyber subsystems are discussed. 展开更多
关键词 Cyber–physical interdependency Resilience analysis Active distribution network Hazardous weather event
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Advanced Nodal Pricing Strategies for Modern Power Distribution Networks:Enhancing Market Efficiency and System Reliability 认领 引用
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作者 Ganesh Wakte Mukesh Kumar +2 位作者 Mohammad Aljaidi Ramesh Kumar Manish Kumar Singla 《Energy Engineering》 EI 2025年第6期2519-2537,共19页
Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of ... Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations,traditional nodal pricing models often fall short to meet these new challenges.This research introduces a novel enhanced nodal pricing mechanism for distribution networks,integrating advanced optimization techniques and hybrid models to overcome these limitations.The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and system reliability.This study leverages cutting-edge hybrid algorithms,combining elements of machine learning with conventional optimization methods,to achieve superior performance.Key findings demonstrate that the proposed hybrid nodal pricing model significantly reduces pricing errors and operational costs compared to conventional methods.Through extensive simulations and comparative analysis,the model exhibits enhanced performance under varying load conditions and increased levels of renewable energy integration.The results indicate a substantial improvement in pricing precision and network stability.This study contributes to the ongoing discourse on optimizing electricity market mechanisms and provides actionable insights for policymakers and utility operators.By addressing the complexities of modern power distribution systems,our research offers a robust solution that enhances the efficiency and reliability of power distribution networks,marking a significant advancement in the field. 展开更多
关键词 Nodal pricing distribution networks optimization renewable energy pricing accuracy system reliability
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Fault Distance EstimationMethod for DC Distribution Networks Based on Sparse Measurement of High-Frequency Electrical Quantities 认领 引用
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作者 He Wang Shiqiang Li +1 位作者 Yiqi Liu Jing Bian 《Energy Engineering》 EI 2025年第11期4497-4521,共25页
With the evolution of DC distribution networks from traditional radial topologies to more complex multi-branch structures,the number of measurement points supporting synchronous communication remains relatively limite... With the evolution of DC distribution networks from traditional radial topologies to more complex multi-branch structures,the number of measurement points supporting synchronous communication remains relatively limited.This poses challenges for conventional fault distance estimation methods,which are often tailored to simple topologies and are thus difficult to apply to large-scale,multi-node DC networks.To address this,a fault distance estimation method based on sparse measurement of high-frequency electrical quantities is proposed in this paper.First,a preliminary fault line identification model based on compressed sensing is constructed to effectively narrow the fault search range and improve localization efficiency.Then,leveraging the high-frequency impedance characteristics and the voltage-current relationship of electrical quantities,a fault distance estimation approach based on high-frequency measurements from both ends of a line is designed.This enables accurate distance estimation even when the measurement devices are not directly placed at both ends of the faulted line,overcoming the dependence on specific sensor placement inherent in traditional methods.Finally,to further enhance accuracy,an optimization model based on minimizing the high-frequency voltage error at the fault point is introduced to reduce estimation error.Simulation results demonstrate that the proposed method achieves a fault distance estimation error of less than 1%under normal conditions,and maintains good performance even under adverse scenarios. 展开更多
关键词 DC distribution network fault location compressed sensing fault distance estimation high-frequency electrical quantities
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Pareto Multi-Objective Reconfiguration of IEEE 123-Bus Unbalanced Power Distribution Networks Using Metaheuristic Algorithms:A Comprehensive Analysis of Power Quality Improvement 认领 引用
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作者 Nisa NacarÇıkan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期3279-3327,共49页
This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss r... This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management. 展开更多
关键词 Voltage and current unbalanced index unbalanced power distribution network power quality metaheuristic algorithms reconfiguration optimization
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Multi-Stage Voltage Control Optimization Strategy for Distribution Networks Considering Active-Reactive Co-Regulation of Electric Vehicles 认领 引用 被引量：1
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作者 Shukang Lyu Fei Zeng +3 位作者 Huachun Han Huiyu Miao Yi Pan Xiaodong Yuan 《Energy Engineering》 EI 2025年第1期221-242,共22页
The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the exis... The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network. 展开更多
关键词 Electric vehicle(EV) distribution network multi-stage optimization active-reactive power regulation voltage control
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