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
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
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 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
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
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
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
Flexible interconnection devices(FIDs)significantly enhance the regulation and management of complex power flows in distribution networks.Voltage source converter(VSC)-based FIDs,in particular,are pivotal for increasi...Flexible interconnection devices(FIDs)significantly enhance the regulation and management of complex power flows in distribution networks.Voltage source converter(VSC)-based FIDs,in particular,are pivotal for increasing system reliability and operational efficiency.These devices are crucial in supporting the extensive incorporation of electric vehicles(EVs)and renewable energy sources(RESs)into new,load-centric environments.This study evaluates four unique FID-based configurations for distribution network interconnections,revealing their distinctive features.We developed a comprehensive evaluation framework and tool by integrating the analytic hierarchy process(AHP)and fuzzy comprehensive evaluation(FCE),which includes five key performance indicators to assess these configurations.The study identifies the optimal application scenarios for each configuration and discusses their roles in enabling the seamless integration of EVs and RESs.The findings provide essential insights and guidelines for the design and implementation of adaptable,interconnected distribution networks that are equipped to meet the growing demands of future urban environments.展开更多
This study proposes a method for analyzing the security distance of an Active Distribution Network(ADN)by incorporating the demand response of an Energy Hub(EH).Taking into account the impact of stochastic wind-solar ...This study proposes a method for analyzing the security distance of an Active Distribution Network(ADN)by incorporating the demand response of an Energy Hub(EH).Taking into account the impact of stochastic wind-solar power and flexible loads on the EH,an interactive power model was developed to represent the EH’s operation under these influences.Additionally,an ADN security distance model,integrating an EH with flexible loads,was constructed to evaluate the effect of flexible load variations on the ADN’s security distance.By considering scenarios such as air conditioning(AC)load reduction and base station(BS)load transfer,the security distances of phases A,B,and C increased by 17.1%,17.2%,and 17.7%,respectively.Furthermore,a multi-objective optimal power flow model was formulated and solved using the Forward-Backward Power Flow Algorithm,the NSGA-II multi-objective optimization algo-rithm,and the maximum satisfaction method.The simulation results of the IEEE33 node system example demonstrate that after opti-mization,the total energy cost for one day is reduced by 0.026%,and the total security distance limit of the ADN’s three phases is improved by 0.1 MVA.This method effectively enhances the security distance,facilitates BS load transfer and AC load reduction,and contributes to the energy-saving,economical,and safe operation of the power system.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
The volatility introduced by the integration of renewable energy poses challenges to the reliability of power supply,increasing the demand for energy storage in distribution networks.Shared energy storage in distribut...The volatility introduced by the integration of renewable energy poses challenges to the reliability of power supply,increasing the demand for energy storage in distribution networks.Shared energy storage in distribution networks can participate in energy storage allocation as a provider of reliability ancillary services.This paper proposes a novel Nash bargaining based energy storage coordinated allocation method to fully incentivize shared energy storage to participate in reliability services within the distribution network.First,an analytical reliability assessment model is constructed and embedded into the energy storage allocation model,where the impact of renewable energy uncertainty is described using chance constraints.Considering the interests of both the distribution network and shared energy storage operators,a Nash bargaining based energy storage coordinated allocation and benefit sharing mechanism is established,which is then transformed into a mixed-integer linear programming(MILP)model for efficient solution.Case studies show that the proposed method,through cooperation between the distribution system operator and shared energy storage operators,signif-icantly reduces investment cost of energy storage and ensures a rational distribution of the benefits obtained.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
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
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
With the widespread implementation of distributed generation(DG)and the integration of soft open point(SOP)into the distribution network(DN),the latter is steadily transitioning into a flexible distribution network(FD...With the widespread implementation of distributed generation(DG)and the integration of soft open point(SOP)into the distribution network(DN),the latter is steadily transitioning into a flexible distribution network(FDN),the calculation of carbon flow distribution in FDN is more difficult.To this end,this study constructs a model for low-carbon optimal operations within the FDN on the basis of enhanced carbon emission flow(CEF).First,the carbon emission characteristics of FDNs are scrutinized and an improved method for calculating carbon flow within these networks is proposed.Subsequently,a model for optimizing low-carbon operations within FDNs is formulated based on the refined CEF,which merges the specificities of DG and intelligent SOP.Finally,this model is scrutinized using an upgraded IEEE 33-node distribution system,a comparative analysis of the cases reveals that when DG and SOP are operated in a coordinated manner in the FDN,with the cost of electricity generation was reduced by 40.63 percent and the cost of carbon emissions by 10.18 percent.The findings indicate that the judicious optimization of areas exhibiting higher carbon flow rates can effectively enhance the economic efficiency of DN operations and curtail the carbon emissions of the overall network.展开更多
Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing o...Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.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 integration of distributed generation brings in new challenges for the operation of distribution networks,including out-of-limit voltage and power flow control.Soft open points(SOP)are new power electronic devices...The integration of distributed generation brings in new challenges for the operation of distribution networks,including out-of-limit voltage and power flow control.Soft open points(SOP)are new power electronic devices that can flexibly control active and reactive power flows.With the exception of active power output,photovoltaic(PV)devices can provide reactive power compensation through an inverter.Thus,a synergetic optimization operation method for SOP and PV in a distribution network is proposed.A synergetic optimization model was developed.The voltage deviation,network loss,and ratio of photovoltaic abandonment were selected as the objective functions.The PV model was improved by considering the three reactive power output modes of the PV inverter.Both the load fluctuation and loss of the SOP were considered.Three multi-objective optimization algorithms were used,and a compromise optimal solution was calculated.Case studies were conducted using an IEEE 33-node system.The simulation results indicated that the SOP and PVs complemented each other in terms of active power transmission and reactive power compensation.Synergetic optimization improves power control capability and flexibility,providing better power quality and PV consumption rate.展开更多
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.
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.
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.
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.
基金supported by the Scientific and Technological Research Council of Turkey(TUBITAK)under Grant No.124E002(1001-Project).
文摘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.
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 Project of the China Southern Power Grid Co.,Ltd.(Project number:SZKJXM20230085).
文摘Flexible interconnection devices(FIDs)significantly enhance the regulation and management of complex power flows in distribution networks.Voltage source converter(VSC)-based FIDs,in particular,are pivotal for increasing system reliability and operational efficiency.These devices are crucial in supporting the extensive incorporation of electric vehicles(EVs)and renewable energy sources(RESs)into new,load-centric environments.This study evaluates four unique FID-based configurations for distribution network interconnections,revealing their distinctive features.We developed a comprehensive evaluation framework and tool by integrating the analytic hierarchy process(AHP)and fuzzy comprehensive evaluation(FCE),which includes five key performance indicators to assess these configurations.The study identifies the optimal application scenarios for each configuration and discusses their roles in enabling the seamless integration of EVs and RESs.The findings provide essential insights and guidelines for the design and implementation of adaptable,interconnected distribution networks that are equipped to meet the growing demands of future urban environments.
基金supported in part by the National Nat-ural Science Foundation of China(No.51977012,No.52307080).
文摘This study proposes a method for analyzing the security distance of an Active Distribution Network(ADN)by incorporating the demand response of an Energy Hub(EH).Taking into account the impact of stochastic wind-solar power and flexible loads on the EH,an interactive power model was developed to represent the EH’s operation under these influences.Additionally,an ADN security distance model,integrating an EH with flexible loads,was constructed to evaluate the effect of flexible load variations on the ADN’s security distance.By considering scenarios such as air conditioning(AC)load reduction and base station(BS)load transfer,the security distances of phases A,B,and C increased by 17.1%,17.2%,and 17.7%,respectively.Furthermore,a multi-objective optimal power flow model was formulated and solved using the Forward-Backward Power Flow Algorithm,the NSGA-II multi-objective optimization algo-rithm,and the maximum satisfaction method.The simulation results of the IEEE33 node system example demonstrate that after opti-mization,the total energy cost for one day is reduced by 0.026%,and the total security distance limit of the ADN’s three phases is improved by 0.1 MVA.This method effectively enhances the security distance,facilitates BS load transfer and AC load reduction,and contributes to the energy-saving,economical,and safe operation of the power system.
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 in part by the National Science Foundation of China(Grant.U24B6009)Beijing Natural Science Foundation(L243003).
文摘The volatility introduced by the integration of renewable energy poses challenges to the reliability of power supply,increasing the demand for energy storage in distribution networks.Shared energy storage in distribution networks can participate in energy storage allocation as a provider of reliability ancillary services.This paper proposes a novel Nash bargaining based energy storage coordinated allocation method to fully incentivize shared energy storage to participate in reliability services within the distribution network.First,an analytical reliability assessment model is constructed and embedded into the energy storage allocation model,where the impact of renewable energy uncertainty is described using chance constraints.Considering the interests of both the distribution network and shared energy storage operators,a Nash bargaining based energy storage coordinated allocation and benefit sharing mechanism is established,which is then transformed into a mixed-integer linear programming(MILP)model for efficient solution.Case studies show that the proposed method,through cooperation between the distribution system operator and shared energy storage operators,signif-icantly reduces investment cost of energy storage and ensures a rational distribution of the benefits obtained.
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.
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 in part by National Natural Science Foundation of China under Grant 52007026.
文摘With the widespread implementation of distributed generation(DG)and the integration of soft open point(SOP)into the distribution network(DN),the latter is steadily transitioning into a flexible distribution network(FDN),the calculation of carbon flow distribution in FDN is more difficult.To this end,this study constructs a model for low-carbon optimal operations within the FDN on the basis of enhanced carbon emission flow(CEF).First,the carbon emission characteristics of FDNs are scrutinized and an improved method for calculating carbon flow within these networks is proposed.Subsequently,a model for optimizing low-carbon operations within FDNs is formulated based on the refined CEF,which merges the specificities of DG and intelligent SOP.Finally,this model is scrutinized using an upgraded IEEE 33-node distribution system,a comparative analysis of the cases reveals that when DG and SOP are operated in a coordinated manner in the FDN,with the cost of electricity generation was reduced by 40.63 percent and the cost of carbon emissions by 10.18 percent.The findings indicate that the judicious optimization of areas exhibiting higher carbon flow rates can effectively enhance the economic efficiency of DN operations and curtail the carbon emissions of the overall network.
基金supported in part by the National Natural Science Foundation of China under Grant 52307134the Fundamental Research Funds for the Central Universities(xzy012025022)。
文摘Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.
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%.
基金supported by the Science and Technology Project of SGCC(kj2022-075).
文摘The integration of distributed generation brings in new challenges for the operation of distribution networks,including out-of-limit voltage and power flow control.Soft open points(SOP)are new power electronic devices that can flexibly control active and reactive power flows.With the exception of active power output,photovoltaic(PV)devices can provide reactive power compensation through an inverter.Thus,a synergetic optimization operation method for SOP and PV in a distribution network is proposed.A synergetic optimization model was developed.The voltage deviation,network loss,and ratio of photovoltaic abandonment were selected as the objective functions.The PV model was improved by considering the three reactive power output modes of the PV inverter.Both the load fluctuation and loss of the SOP were considered.Three multi-objective optimization algorithms were used,and a compromise optimal solution was calculated.Case studies were conducted using an IEEE 33-node system.The simulation results indicated that the SOP and PVs complemented each other in terms of active power transmission and reactive power compensation.Synergetic optimization improves power control capability and flexibility,providing better power quality and PV consumption rate.
