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
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
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
Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,th...Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,the public network communication system is easily damaged after disasters,causing the operation center to lose control of the distribution network.In this paper,we considered using satellites to transmit the distribution network data and focus on the resource scheduling problem of the satellite emergency communication system for the distribution network.Specifically,this paper first formulates the satellite beam-pointing problem and the accesschannel joint resource allocation problem.Then,this paper proposes the Priority-based Beam-pointing and Access-Channel joint optimization algorithm(PBAC),which uses convex optimization theory to solve the satellite beam pointing problem,and adopts the block coordinate descent method,Lagrangian dual method,and a greedy algorithm to solve the access-channel joint resource allocation problem,thereby obtaining the optimal resource scheduling scheme for the satellite network.Finally,this paper conducts comparative experiments with existing methods to verify the effec-tiveness of the proposed methods.The results show that the total weighted transmitted data of the proposed algorithm is increased by about 19.29∼26.29%compared with other algorithms.展开更多
Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution netwo...Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution network planning model incorporating distributed wind turbines(DWT),distributed photovoltaics(DPV),and energy storage systems(ESS).K-means++is employed to partition the distribution network based on electrical distance.Considering the spatiotemporal correlation of distributed generation(DG)outputs in the same region,a joint output model of DWT and DPV is developed using the Frank-Copula.Due to the model’s high dimensionality,multiple constraints,and mixed-integer characteristics,bilevel programming theory is utilized to structure the model.The model is solved using a mixed-integer particle swarmoptimization algorithm(MIPSO)to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality.The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system.The results demonstrate significant improvements,with the proposedmethod reducing the annual comprehensive cost by 41.65%and 13.98%,respectively,compared to scenarios without DG and ESS or with only DG integration.Furthermore,it reduces the daily average voltage deviation by 24.35%and 10.24%and daily network losses by 55.72%and 35.71%.展开更多
The majority of water utilities,particularly public service providers such as Gidole town,are struggling to deliver a sufficient and consistent supply of water in Ethiopia's developing towns.The primary objective ...The majority of water utilities,particularly public service providers such as Gidole town,are struggling to deliver a sufficient and consistent supply of water in Ethiopia's developing towns.The primary objective of this study was to assess the hydraulic performance of water supply distribution system in Gidole Town,Ethiopia,a representative case of the challenges facing public water utilities in developing towns.The WaterGEMS v8i hydraulic model was utilized to simulate and evaluate the distribution network's performance.The system was configured as a looped network and analyzed against standard permitted pressure and velocity values in the distribution system.The model was effectively calibrated(coefficient of determination(R^(2))=0.969)using measured and observed pressure data.The model simulation run was conducted at peak and low hourly demand with 1.9 and 0.25 hourly factors,respectively.The estimated water demand of the town is 1284.3m^(3)/day(48.4 liters per capita per day),and it would be increased to 3099.77m^(3)/day(66.03 l/c/d)by the 2037 design period.The system experiences significant non-revenue water losses(75,434.11m^(3)/year),accounting for 29.9%of total water production;as a result,the present water supply coverage of the town is only 33.6%.Hydraulic simulations under peak and low demand scenarios revealed nodes with pressures outside the normal range,indicating system-wide inefficiencies.These findings highlight a combined issue of large physical losses and insufficient capacity of the water supply in the town,which is typical of many municipal systems in developing regions.The study concludes that strategic infrastructure rehabilitation,with an emphasis on pressure management and leak reduction,is not only a town necessity but a fundamental requirement for improving water security and financial sustainability for utilities in Ethiopia and similar contexts.The findings and methodology have been forwarded to town's water supply project and institutional development departments for immediate future implementation and provide a replicable framework for evidence-based investment and planning in other struggling municipalities in similar situations.展开更多
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
The increasing proportion of distributed photovoltaics(DPVs)and electric vehicle charging stations in low-voltage distribution networks(LVDNs)has resulted in challenges such as distribution transformer overloads and v...The increasing proportion of distributed photovoltaics(DPVs)and electric vehicle charging stations in low-voltage distribution networks(LVDNs)has resulted in challenges such as distribution transformer overloads and voltage violations.To address these problems,we propose a coordinated planning method for flexible interconnections and energy storage systems(ESSs)to improve the accommodation capacity of DPVs.First,the power-transfer characteristics of flexible interconnection and ESSs are analyzed.The equipment costs of the voltage source converters(VSCs)and ESSs are also analyzed comprehensively,considering the differences in installation and maintenance costs for different installation locations.Second,a bilevel programming model is established to minimize the annual comprehensive cost and yearly total PV curtailment capacity.Within this framework,the upper-level model optimizes the installation locations and capacities of the VSCs and ESSs,whereas the lower-level model optimizes the operating power of the VSCs and ESSs.The proposed model is solved using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-II).The effectiveness of the proposed planning method is validated through an actual LVDN scenario,which demonstrates its advantages in enhancing PV accommodation capacity.In addition,the economic benefits of various planning schemes with different flexible interconnection topologies and different PV grid-connected forms are quantitatively analyzed,demonstrating the adaptability of the proposed coordinated planning method.展开更多
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
Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power o...Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.展开更多
Typhoons can cause large-area blackouts or partial outages of distribution networks.We define a partial outage state in the distribution network as a gray state and propose a gray-start strategy and two-stage distribu...Typhoons can cause large-area blackouts or partial outages of distribution networks.We define a partial outage state in the distribution network as a gray state and propose a gray-start strategy and two-stage distribution network emergency recovery framework.A phase-space reconstruction and stacked integrated model for predicting wind and photovoltaic generation during typhoon disasters is proposed in the first stage.This provides guidance for second-stage post-disaster emergency recovery scheduling.The emergency recovery scheduling model is established in the second stage,and this model is supported by a thermal power-generating unit,mobile emergency generators,and distributed generators.Distributed generation includes wind power generation,photovoltaics,fuel cells,etc.Simultaneously,we con-sider the gray-start based on the pumped storage unit to be an important first step in the emergency recovery strategy.This model is val-idated on the improved IEEE 33 node system,which utilizes data from the 2022 super typhoon“Muifa”in Zhoushan,Zhejiang,China.Simulations indicate the superiority of a gray start with a pumped storage unit and the proposed emergency recovery strategy.展开更多
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
In the context of clean and Low-carbon energy transformation and new power system,China^photovoltaic power generation will usher in great development.Its large-scale access impacts the safe and stable operation of the...In the context of clean and Low-carbon energy transformation and new power system,China^photovoltaic power generation will usher in great development.Its large-scale access impacts the safe and stable operation of the power grid with increasing significance.In order to strengthen the support and Leading roles of the standards,it is urgent to revise the national standard GB/T 29319-2012,Technical requirements for connecting photovoltaic power system to distribution network,based on the current development trend of photovoltaic power generation and power grid transformation needs.This paper firstly interprets the important technical provisions of the standard,then analyzes the problems in its implementation and finally proposes some revision suggestions in terms of grid adaptability,power control and fault crossing,to facilitate safe and orderly development of photovoltaic power generation in China.展开更多
A protection system using a multi-agent concept for power distribution networks is proposed.Every digital over current relay(OCR)is developed as an agent by adding its own intelligence,self-tuning and communication ab...A protection system using a multi-agent concept for power distribution networks is proposed.Every digital over current relay(OCR)is developed as an agent by adding its own intelligence,self-tuning and communication ability.The main advantage of the multi-agent concept is that a group of agents work together to achieve a global goal which is beyond the ability of each individual agent.In order to cope with frequent changes in the network operation condition and faults,an OCR agent,proposed in this paper,is able to detect a fault or a change in the network and find its optimal parameters for protection in an autonomous manner considering information of the whole network obtained by communication between other agents.Through this kind of coordination and information exchanges,not only a local but also a global protective scheme is completed.Simulations in a simple distribution network show the effectiveness of the proposed protection system.展开更多
The distribution networks sometimes suffer from excessive losses and voltage violations in densely populated areas. The aim of the present study is to improve the performance of a distribution network by successively ...The distribution networks sometimes suffer from excessive losses and voltage violations in densely populated areas. The aim of the present study is to improve the performance of a distribution network by successively applying mono-capacitor positioning, multiple positioning and reconfiguration processes using GA-based algorithms implemented in a Matlab environment. From the diagnostic study of this network, it was observed that a minimum voltage of 0.90 pu induces a voltage deviation of 5.26%, followed by active and reactive losses of 425.08 kW and 435.09 kVAR, respectively. Single placement with the NSGAII resulted in the placement of a 3000 kVAR capacitor at node 128, which proved to be the invariably neuralgic point. Multiple placements resulted in a 21.55% reduction in losses and a 0.74% regression in voltage profile performance. After topology optimization, the loss profile improved by 65.08% and the voltage profile improved by 1.05%. Genetic algorithms are efficient and effective tools for improving the performance of distribution networks, whose degradation is often dynamic due to the natural variability of loads.展开更多
In contemporary medium-voltage distribution networks heavily penetrated by distributed energy resources(DERs),the harmonic components injected by power-electronic interfacing converters,together with the inherently in...In contemporary medium-voltage distribution networks heavily penetrated by distributed energy resources(DERs),the harmonic components injected by power-electronic interfacing converters,together with the inherently intermittent output of renewable generation,distort the zero-sequence current and continuously reshape its frequency spectrum.As a result,single-line-to-ground(SLG)faults exhibit a pronounced,strongly non-stationary behaviour that varies with operating point,load mix and DER dispatch.Under such circumstances the performance of traditional rule-based algorithms—or methods that rely solely on steady-state frequency-domain indicators—degrades sharply,and they no longer satisfy the accuracy and universality required by practical protection systems.To overcome these shortcomings,the present study develops an SLG-fault identification scheme that transforms the zero-sequence currentwaveforminto two-dimensional image representations and processes themwith a convolutional neural network(CNN).First,the causes of sample-distribution imbalance are analysed in detail by considering different neutralgrounding configurations,fault-inception mechanisms and the statistical probability of fault occurrence on each phase.Building on these insights,a discriminator network incorporating a Convolutional Block Attention Module(CBAM)is designed to autonomously extract multi-layer spatial-spectral features,while Gradient-weighted Class Activation Mapping(Grad-CAM)is employed to visualise the contribution of every salient image region,thereby enhancing interpretability.A comprehensive simulation platform is subsequently established for a DER-rich distribution system encompassing several representative topologies,feeder lengths and DER penetration levels.Large numbers of realistic SLG-fault scenarios are generated—including noise and measurement uncertainty—and are used to train,validate and test the proposed model.Extensive simulation campaigns,corroborated by field measurements from an actual utility network,demonstrate that the proposed approach attains an SLG-fault identification accuracy approaching 100 percent and maintains robust performance under severe noise conditions,confirming its suitability for real-world engineering applications.展开更多
Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and pea...Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and peak demand incidents.While battery and hydrogen storage are commonly used for peak shaving,ice-based thermal energy storage systems(TESSs)offer a direct way to reduce cooling loads without electrical conversion.This paper presents a multi-objective planning framework that optimizes TESS dispatch,network topology,and photovoltaic(PV)inverter reactive power support to address operational issues in active distribution networks.The objectives of the proposed scheme include minimizing peak demand,voltage deviations,and PV inverter VAr dependency.The mixed-integer nonlinear programming problem is solved using a Pareto-based multi-objective particle swarm optimization(MOPSO)method.The MATLAB-OpenDSS simulations for a modified IEEE-123 bus system show a 7.1%reduction in peak demand,a 13%reduction in voltage deviation,and a 52%drop in PV inverter VAr usage.The obtained solutions confirm minimal operational stress on control devices such as switches and PV inverters.Thus,unlike earlier studies,this work combines all three strategies to offer an effective solution for the operational planning of the active distribution network.展开更多
Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MG...Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MGs. This paper proposes a distributedco-optimization method for peer-to-peer (P2P) energy trading and network operation for an ADN integrated with multiple microgrids(MMGs). A framework that optimizes P2P energy trading among MMGs and ADN operations was first established. Subsequently, anenergy management model that aims to minimize the operation and energy trading costs was constructed for each MG. Accordingly, theMMGs’ cooperative game model was established based on Nash bargaining theory to incentivize each stakeholder to participate in P2Penergy trading, and a distributed solution method based on the alternating direction method of multipliers was developed. Moreover, analgorithm that adjusts the amount of energy trading between the ADN and MG is proposed to ensure safe operation of the distributionnetwork. With the communication between the MG and ADN, the MMGs’ P2P trading and ADN operations are optimized in a coordinated manner. Finally, numerical simulations were conducted to verify the accuracy and effectiveness of the proposed method.展开更多
P2P trading is driving the decentralization of the electricity market,the autonomy and privacy requirements of prosumers may intro-duce safety risks such as voltage violations.Existing security management methods base...P2P trading is driving the decentralization of the electricity market,the autonomy and privacy requirements of prosumers may intro-duce safety risks such as voltage violations.Existing security management methods based on price guidance may face unsolvable situa-tions in trading scenarios and have difficulty assessing the impact of P2P transactions on voltage security.To this end,this paper proposes a novel distribution system operator(DSO)-prosumers bi-level optimization framework incorporating the dynamic operating envelope(DOE)and risk coefficient-based network usage charge(RC-NUC).In the upper-level,the DOE is employed for dynamic voltage man-agement to prevent violations while the RC-NUC further guides prosumers to engage in grid-friendly transactions.The lower-level decen-tralized market enables prosumers to optimize trading decisions autonomously.Only price signals and energy quantities are exchanged between the two levels,ensuring the privacy of both parties.Additionally,an alternating direction method of multipliers(ADMM)with adaptive penalty factor is introduced to improve computational efficiency.Case studies on a modified IEEE 33-bus system demonstrate that the proposed method reduces voltage violation risks by 18.31%and enhances trading efficiency by 32.3%.These results highlight the feasibility and effectiveness of the approach in advancing secure and efficient distributed energy transactions.展开更多
With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reductio...With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.展开更多
基金supported by the Science and Technology Project of Sichuan Electric Power Company“Power Supply Guarantee Strategy for Urban Distribution Networks Considering Coordination with Virtual Power Plant during Extreme Weather Event”(No.521920230003).
文摘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.
文摘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.
文摘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.
基金supported by the Science and Technology Project of the State Grid Corporation of China(5400-202255158A-1-1-ZN).
文摘Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,the public network communication system is easily damaged after disasters,causing the operation center to lose control of the distribution network.In this paper,we considered using satellites to transmit the distribution network data and focus on the resource scheduling problem of the satellite emergency communication system for the distribution network.Specifically,this paper first formulates the satellite beam-pointing problem and the accesschannel joint resource allocation problem.Then,this paper proposes the Priority-based Beam-pointing and Access-Channel joint optimization algorithm(PBAC),which uses convex optimization theory to solve the satellite beam pointing problem,and adopts the block coordinate descent method,Lagrangian dual method,and a greedy algorithm to solve the access-channel joint resource allocation problem,thereby obtaining the optimal resource scheduling scheme for the satellite network.Finally,this paper conducts comparative experiments with existing methods to verify the effec-tiveness of the proposed methods.The results show that the total weighted transmitted data of the proposed algorithm is increased by about 19.29∼26.29%compared with other algorithms.
基金This research was funded by“Chunhui Program”Collaborative Scientific Research Project of the Ministry of Education of the People’s Republic of China(Project No.HZKY20220242)the S&T Program of Hebei(Project No.225676163GH).
文摘Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution network planning model incorporating distributed wind turbines(DWT),distributed photovoltaics(DPV),and energy storage systems(ESS).K-means++is employed to partition the distribution network based on electrical distance.Considering the spatiotemporal correlation of distributed generation(DG)outputs in the same region,a joint output model of DWT and DPV is developed using the Frank-Copula.Due to the model’s high dimensionality,multiple constraints,and mixed-integer characteristics,bilevel programming theory is utilized to structure the model.The model is solved using a mixed-integer particle swarmoptimization algorithm(MIPSO)to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality.The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system.The results demonstrate significant improvements,with the proposedmethod reducing the annual comprehensive cost by 41.65%and 13.98%,respectively,compared to scenarios without DG and ESS or with only DG integration.Furthermore,it reduces the daily average voltage deviation by 24.35%and 10.24%and daily network losses by 55.72%and 35.71%.
文摘The majority of water utilities,particularly public service providers such as Gidole town,are struggling to deliver a sufficient and consistent supply of water in Ethiopia's developing towns.The primary objective of this study was to assess the hydraulic performance of water supply distribution system in Gidole Town,Ethiopia,a representative case of the challenges facing public water utilities in developing towns.The WaterGEMS v8i hydraulic model was utilized to simulate and evaluate the distribution network's performance.The system was configured as a looped network and analyzed against standard permitted pressure and velocity values in the distribution system.The model was effectively calibrated(coefficient of determination(R^(2))=0.969)using measured and observed pressure data.The model simulation run was conducted at peak and low hourly demand with 1.9 and 0.25 hourly factors,respectively.The estimated water demand of the town is 1284.3m^(3)/day(48.4 liters per capita per day),and it would be increased to 3099.77m^(3)/day(66.03 l/c/d)by the 2037 design period.The system experiences significant non-revenue water losses(75,434.11m^(3)/year),accounting for 29.9%of total water production;as a result,the present water supply coverage of the town is only 33.6%.Hydraulic simulations under peak and low demand scenarios revealed nodes with pressures outside the normal range,indicating system-wide inefficiencies.These findings highlight a combined issue of large physical losses and insufficient capacity of the water supply in the town,which is typical of many municipal systems in developing regions.The study concludes that strategic infrastructure rehabilitation,with an emphasis on pressure management and leak reduction,is not only a town necessity but a fundamental requirement for improving water security and financial sustainability for utilities in Ethiopia and similar contexts.The findings and methodology have been forwarded to town's water supply project and institutional development departments for immediate future implementation and provide a replicable framework for evidence-based investment and planning in other struggling municipalities in similar situations.
基金the financial support for this research from the Program for the Program for young backbone teachers in Universities of Henan Province(No.2021GGJS007).
文摘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.
基金supported by the Science and Technology Support Program of Guizhou Province([2022]General 012)the Key Science and Technology Project of China Southern Power Grid Corporation(GZKJXM20220043)。
文摘The increasing proportion of distributed photovoltaics(DPVs)and electric vehicle charging stations in low-voltage distribution networks(LVDNs)has resulted in challenges such as distribution transformer overloads and voltage violations.To address these problems,we propose a coordinated planning method for flexible interconnections and energy storage systems(ESSs)to improve the accommodation capacity of DPVs.First,the power-transfer characteristics of flexible interconnection and ESSs are analyzed.The equipment costs of the voltage source converters(VSCs)and ESSs are also analyzed comprehensively,considering the differences in installation and maintenance costs for different installation locations.Second,a bilevel programming model is established to minimize the annual comprehensive cost and yearly total PV curtailment capacity.Within this framework,the upper-level model optimizes the installation locations and capacities of the VSCs and ESSs,whereas the lower-level model optimizes the operating power of the VSCs and ESSs.The proposed model is solved using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-II).The effectiveness of the proposed planning method is validated through an actual LVDN scenario,which demonstrates its advantages in enhancing PV accommodation capacity.In addition,the economic benefits of various planning schemes with different flexible interconnection topologies and different PV grid-connected forms are quantitatively analyzed,demonstrating the adaptability of the proposed coordinated planning method.
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘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.
基金funded by the“Research and Application Project of Collaborative Optimization Control Technology for Distribution Station Area for High Proportion Distributed PV Consumption(4000-202318079A-1-1-ZN)”of the Headquarters of the State Grid Corporation.
文摘Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.
基金supported in part by the National Nat-ural Science Foundation of China(52177110)Key Pro-gram of the National Natural Science Foundation of China(U22B20106,U2142206)+2 种基金Shenzhen Science and Technology Program(JCYJ20210324131409026)the Science and Technology Project of the State Grid Corpo-ration of China(5200-202319382A-2-3-XG)State Grid Zhejiang Elctric Power Co.,Ltd.Science and Tech-nology Project(B311DS24001A).
文摘Typhoons can cause large-area blackouts or partial outages of distribution networks.We define a partial outage state in the distribution network as a gray state and propose a gray-start strategy and two-stage distribution network emergency recovery framework.A phase-space reconstruction and stacked integrated model for predicting wind and photovoltaic generation during typhoon disasters is proposed in the first stage.This provides guidance for second-stage post-disaster emergency recovery scheduling.The emergency recovery scheduling model is established in the second stage,and this model is supported by a thermal power-generating unit,mobile emergency generators,and distributed generators.Distributed generation includes wind power generation,photovoltaics,fuel cells,etc.Simultaneously,we con-sider the gray-start based on the pumped storage unit to be an important first step in the emergency recovery strategy.This model is val-idated on the improved IEEE 33 node system,which utilizes data from the 2022 super typhoon“Muifa”in Zhoushan,Zhejiang,China.Simulations indicate the superiority of a gray start with a pumped storage unit and the proposed emergency recovery strategy.
基金funded by the State Grid Corporation Science and Technology Project(5108-202218280A-2-391-XG).
文摘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.
文摘In the context of clean and Low-carbon energy transformation and new power system,China^photovoltaic power generation will usher in great development.Its large-scale access impacts the safe and stable operation of the power grid with increasing significance.In order to strengthen the support and Leading roles of the standards,it is urgent to revise the national standard GB/T 29319-2012,Technical requirements for connecting photovoltaic power system to distribution network,based on the current development trend of photovoltaic power generation and power grid transformation needs.This paper firstly interprets the important technical provisions of the standard,then analyzes the problems in its implementation and finally proposes some revision suggestions in terms of grid adaptability,power control and fault crossing,to facilitate safe and orderly development of photovoltaic power generation in China.
文摘A protection system using a multi-agent concept for power distribution networks is proposed.Every digital over current relay(OCR)is developed as an agent by adding its own intelligence,self-tuning and communication ability.The main advantage of the multi-agent concept is that a group of agents work together to achieve a global goal which is beyond the ability of each individual agent.In order to cope with frequent changes in the network operation condition and faults,an OCR agent,proposed in this paper,is able to detect a fault or a change in the network and find its optimal parameters for protection in an autonomous manner considering information of the whole network obtained by communication between other agents.Through this kind of coordination and information exchanges,not only a local but also a global protective scheme is completed.Simulations in a simple distribution network show the effectiveness of the proposed protection system.
文摘The distribution networks sometimes suffer from excessive losses and voltage violations in densely populated areas. The aim of the present study is to improve the performance of a distribution network by successively applying mono-capacitor positioning, multiple positioning and reconfiguration processes using GA-based algorithms implemented in a Matlab environment. From the diagnostic study of this network, it was observed that a minimum voltage of 0.90 pu induces a voltage deviation of 5.26%, followed by active and reactive losses of 425.08 kW and 435.09 kVAR, respectively. Single placement with the NSGAII resulted in the placement of a 3000 kVAR capacitor at node 128, which proved to be the invariably neuralgic point. Multiple placements resulted in a 21.55% reduction in losses and a 0.74% regression in voltage profile performance. After topology optimization, the loss profile improved by 65.08% and the voltage profile improved by 1.05%. Genetic algorithms are efficient and effective tools for improving the performance of distribution networks, whose degradation is often dynamic due to the natural variability of loads.
基金supported by the Science and Technology Program of China Southern Power Grid(031800KC23120003).
文摘In contemporary medium-voltage distribution networks heavily penetrated by distributed energy resources(DERs),the harmonic components injected by power-electronic interfacing converters,together with the inherently intermittent output of renewable generation,distort the zero-sequence current and continuously reshape its frequency spectrum.As a result,single-line-to-ground(SLG)faults exhibit a pronounced,strongly non-stationary behaviour that varies with operating point,load mix and DER dispatch.Under such circumstances the performance of traditional rule-based algorithms—or methods that rely solely on steady-state frequency-domain indicators—degrades sharply,and they no longer satisfy the accuracy and universality required by practical protection systems.To overcome these shortcomings,the present study develops an SLG-fault identification scheme that transforms the zero-sequence currentwaveforminto two-dimensional image representations and processes themwith a convolutional neural network(CNN).First,the causes of sample-distribution imbalance are analysed in detail by considering different neutralgrounding configurations,fault-inception mechanisms and the statistical probability of fault occurrence on each phase.Building on these insights,a discriminator network incorporating a Convolutional Block Attention Module(CBAM)is designed to autonomously extract multi-layer spatial-spectral features,while Gradient-weighted Class Activation Mapping(Grad-CAM)is employed to visualise the contribution of every salient image region,thereby enhancing interpretability.A comprehensive simulation platform is subsequently established for a DER-rich distribution system encompassing several representative topologies,feeder lengths and DER penetration levels.Large numbers of realistic SLG-fault scenarios are generated—including noise and measurement uncertainty—and are used to train,validate and test the proposed model.Extensive simulation campaigns,corroborated by field measurements from an actual utility network,demonstrate that the proposed approach attains an SLG-fault identification accuracy approaching 100 percent and maintains robust performance under severe noise conditions,confirming its suitability for real-world engineering applications.
基金supported by the US Appalachian Regional Commission(ARC)under Grant MU-21579-23。
文摘Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and peak demand incidents.While battery and hydrogen storage are commonly used for peak shaving,ice-based thermal energy storage systems(TESSs)offer a direct way to reduce cooling loads without electrical conversion.This paper presents a multi-objective planning framework that optimizes TESS dispatch,network topology,and photovoltaic(PV)inverter reactive power support to address operational issues in active distribution networks.The objectives of the proposed scheme include minimizing peak demand,voltage deviations,and PV inverter VAr dependency.The mixed-integer nonlinear programming problem is solved using a Pareto-based multi-objective particle swarm optimization(MOPSO)method.The MATLAB-OpenDSS simulations for a modified IEEE-123 bus system show a 7.1%reduction in peak demand,a 13%reduction in voltage deviation,and a 52%drop in PV inverter VAr usage.The obtained solutions confirm minimal operational stress on control devices such as switches and PV inverters.Thus,unlike earlier studies,this work combines all three strategies to offer an effective solution for the operational planning of the active distribution network.
基金supported by the State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks Program(grant number SGNR0000KJJS2302139).
文摘Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MGs. This paper proposes a distributedco-optimization method for peer-to-peer (P2P) energy trading and network operation for an ADN integrated with multiple microgrids(MMGs). A framework that optimizes P2P energy trading among MMGs and ADN operations was first established. Subsequently, anenergy management model that aims to minimize the operation and energy trading costs was constructed for each MG. Accordingly, theMMGs’ cooperative game model was established based on Nash bargaining theory to incentivize each stakeholder to participate in P2Penergy trading, and a distributed solution method based on the alternating direction method of multipliers was developed. Moreover, analgorithm that adjusts the amount of energy trading between the ADN and MG is proposed to ensure safe operation of the distributionnetwork. With the communication between the MG and ADN, the MMGs’ P2P trading and ADN operations are optimized in a coordinated manner. Finally, numerical simulations were conducted to verify the accuracy and effectiveness of the proposed method.
文摘P2P trading is driving the decentralization of the electricity market,the autonomy and privacy requirements of prosumers may intro-duce safety risks such as voltage violations.Existing security management methods based on price guidance may face unsolvable situa-tions in trading scenarios and have difficulty assessing the impact of P2P transactions on voltage security.To this end,this paper proposes a novel distribution system operator(DSO)-prosumers bi-level optimization framework incorporating the dynamic operating envelope(DOE)and risk coefficient-based network usage charge(RC-NUC).In the upper-level,the DOE is employed for dynamic voltage man-agement to prevent violations while the RC-NUC further guides prosumers to engage in grid-friendly transactions.The lower-level decen-tralized market enables prosumers to optimize trading decisions autonomously.Only price signals and energy quantities are exchanged between the two levels,ensuring the privacy of both parties.Additionally,an alternating direction method of multipliers(ADMM)with adaptive penalty factor is introduced to improve computational efficiency.Case studies on a modified IEEE 33-bus system demonstrate that the proposed method reduces voltage violation risks by 18.31%and enhances trading efficiency by 32.3%.These results highlight the feasibility and effectiveness of the approach in advancing secure and efficient distributed energy transactions.
文摘With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.
