With the prevalence of renewable distributed energy resources(DERs)such as photovoltaics(PVs),modern active distribution networks(ADNs)suffer from voltage deviation and power quality issues.However,traditional voltage...With the prevalence of renewable distributed energy resources(DERs)such as photovoltaics(PVs),modern active distribution networks(ADNs)suffer from voltage deviation and power quality issues.However,traditional voltage control methods often face a trade-off between efficiency and effectiveness,and rarely ensure robust voltage safety under typical state perturbations in practical distribution grids.In this paper,a robust model-free voltage regulation approach is proposed which simultaneously takes security and robustness into account.In this context,the voltage control problem is formulated as a constrained Markov decision process(CMDP).A safety-augmented multiagent deep deterministic policy gradient(MADDPG)algorithm is the trained to enable real-time collaborative optimization of ADNs,aiming to maintain nodal voltages within safe operational limits while minimizing total line losses.Moreover,a robust regulation loss is introduced to ensure reliable performance under various state perturbations in practical voltage controls.The proposed regulation algorithm effectively balance efficiency,safety,and robustness,and also demonstrates potential for generalizing these characteristics to other applications.Numerical studies vali-date the robustness of the proposed method under varying state perturbations on the IEEE test cases and the optimal integrated control performance when compared to other benchmarks.展开更多
Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energ...Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.展开更多
In order to cope with the global environmental crisis caused by energy generation and achieve carbon neutrality,it is imperative to promote a new power system dominated by renewable energy sources(RESs).This paper foc...In order to cope with the global environmental crisis caused by energy generation and achieve carbon neutrality,it is imperative to promote a new power system dominated by renewable energy sources(RESs).This paper focuses on the uncertainty of RESs and the distribution characteristics of carbon emission flows(CEFs),and studies the low-carbon operation and power system planning problem.Firstly,this paper extends the uncertainty of RES to the meteorological field and establishes meteorological robust constraints of photovoltaic(PV)generation.Based on the CEF theory,the carbon transmission trajectory is accurately delineated to improve the operation of power system.Considering further constraints from the power flow,CEF,and component operation characteristics of the active distribution network(ADN),this paper formulates a low-carbon joint planning model of ADN with PV,battery energy storage system(BESS),and distributed gas generator(DGG),taking into account economy and carbon reduction.In the case study,the low-carbon planning and operation scheme are analyzed in detail across multiple dimensions including time and space.The solution results show that the planning model can effectively leverage the low-carbon performance of PV and BESS,and improve the distribution of CEF.Through case comparison,the model can also efficiently reduce the total cost of the system and enhance carbon emission reduction benefits by 35.10 to 41.04%.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
Large-scale access of distributed photovoltaic(PV)in distribution networks(DNs),if not properly evaluated,brings several operational problems.Uncertainties arising from both PV outputs and load demand significantly im...Large-scale access of distributed photovoltaic(PV)in distribution networks(DNs),if not properly evaluated,brings several operational problems.Uncertainties arising from both PV outputs and load demand significantly impact evaluation results.To address this issue,this paper proposes a possibilistic approach to evaluate PV hosting capacity(PVHC).First,possibility distribution is used to model load demand in order to reflect uncertainties associated with human factor,whereas the interval model is applied to deal with uncertainties of PV outputs.Second,a voltage deterioration index is proposed considering overvoltage risk of entire system on time scale.After that,possibilistic PVHC evaluation method based on this index is proposed.A 6-bus system is used to illustrate advantages of the proposed method,followed by a discussion of role of PVHC possibility distribution in actual decision-making of utilities.Moreover,sensitivity of simulation parameters is analyzed to reduce computational burden.Finally,the proposed method is tested on the IEEE 123-bus DN to validate adaptability to a larger system and to analyze impact of PVHC results against different acceptable values set by utilities.展开更多
Fires and human casualties caused by single phase-to-ground faults in distribution networks are frequent.However,existing ground fault suppression methods are affected by ground fault resistance.Thus,an adaptive suppr...Fires and human casualties caused by single phase-to-ground faults in distribution networks are frequent.However,existing ground fault suppression methods are affected by ground fault resistance.Thus,an adaptive suppression method that seamlessly combines principles of current and voltage suppression is proposed,which has good adaptability to different ground fault resistance.Meanwhile,a multi-criteria ground fault suppression exit strategy matched to adaptive suppression method is proposed to avoid damage of device caused by power backflow,which provides the possibility for reliable and fast exit of the fault suppression device.Experimental results demonstrate effectiveness and advantages of the adaptive suppression method and its exit strategy.展开更多
The dense integration of residential distributed photovoltaic(PV)systems into three-phase,four-wire low-voltage(LV)distribution networks results in reverse power flow and three-phase imbalance,leading to voltage viola...The dense integration of residential distributed photovoltaic(PV)systems into three-phase,four-wire low-voltage(LV)distribution networks results in reverse power flow and three-phase imbalance,leading to voltage violations that hinder the growth of rural distributed PV systems.Traditional voltage droop-based control methods regulate PV power output solely based on local voltage measurements at the point of PV connection.Due to a lack of global coordination and optimization,their efficiency is often subpar.This paper presents a centralized coordinated active/reactive power control strategy for PV inverters in rural LV distribution feeders with high PV penetration.The strategy optimizes residential PV inverter reactive and active power control to enhance voltage quality.It uses sensitivity coefficients derived from the inverse Jacobian matrix to assign adjustment weights to individual PV units and iteratively optimize their power outputs.The control sequence prioritizes reactive power increases;if the coefficients are below average or the inverters reach capacity,active power is curtailed until voltage issues are resolved.A simulation based on a real 37-node rural distribution network shows that the proposed method significantly reduces PV curtailment.Typical daily results indicate a curtailment rate of 1.47%,which is significantly lower than the 15.4%observed with the voltage droop-based control method.The total daily PV power output(measured every 15 min)increases from 5.55 to 6.41 MW,improving PV hosting capacity.展开更多
Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN mode...Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN models are limited in their ability to actively suppress noise,which constrains their robustness and precision in solving time-varying problems.This paper introduces a novel active noise rejection ZNN(ANR-ZNN)design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour.Through rigorous theoretical analysis,we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise.As a case study,the ANR-ZNN model is specifically applied to time-varying matrix inversion.Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness,emphasising the proposed design's superiority and potential for solving time-varying problems.展开更多
Addressing the critical detection range limitation in active electrosensing(AES)for underwater sensing,this study proposes an enhanced AES system via novel array optimization.While AES offers advantages like interfere...Addressing the critical detection range limitation in active electrosensing(AES)for underwater sensing,this study proposes an enhanced AES system via novel array optimization.While AES offers advantages like interference immunity,acoustic stealth detection,and low cost,its short range restricts applicability.A target perturbation model under differential signal acquisition reveals that signal strength increases with local electric field intensity,target size,differential channel spacing,and conductivity contrast,but decreases with target-electrode distance.To extend detection,novel array configurations were explored.Simulations demonstrate that both rectangular and offset arrays significantly outperform the traditional collinear layout.Specifically,an offset array(with 8 m transmitting–receiving spacing)achieved an effective detection range enhancement exceeding 83%under the same distortion threshold while maintaining simplified electrode structure.Experimental validation confirmed a 100%increase in maximum detection distance to 5 m under identical noise thresholds compared to the collinear array.Furthermore,a fully connected neural network-based localization model achieved a mean positioning error of 14.12 cm at 3.15 m in static scenarios.In dynamic scenarios within 1–3 m,mean errors were controlled between 13.19 cm and 27.56 cm.Mechanistic analysis indicates that increasing the array baseline enhances the signal-to-noise ratio by simultaneously suppressing near-field environmental noise and amplifying far-field signal reception.Structural innovations in array design enabled this study to significantly expand the detection range of AES systems without compromising cost efficiency.These advancements directly promote the engineering application of AES technology,offering critical technical support for underwater defense security monitoring,long-range early warning systems,and maritime rights protection.展开更多
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
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
This paper provides a systematic review on the resilience analysis of active distribution networks(ADNs)against hazardous weather events,considering the underlying cyber-physical interdependencies.As cyber-physical sy...This paper provides a systematic review on the resilience analysis of active distribution networks(ADNs)against hazardous weather events,considering the underlying cyber-physical interdependencies.As cyber-physical systems,ADNs are characterized by widespread structural and functional interdependen-cies between cyber(communication,computing,and control)and physical(electric power)subsystems and thus present complex hazardous-weather-related resilience issues.To bridge current research gaps,this paper first classifies diverse hazardous weather events for ADNs according to different time spans and degrees of hazard,with model-based and data-driven methods being utilized to characterize weather evolutions.Then,the adverse impacts of hazardous weather on all aspects of ADNs’sources,physical/cyber networks,and loads are analyzed.This paper further emphasizes the importance of situational awareness and cyber-physical collaboration throughout hazardous weather events,as these enhance the implementation of preventive dispatches,corrective actions,and coordinated restorations.In addition,a generalized quantitative resilience evaluation process is proposed regarding additional considerations about cyber subsystems and cyber-physical connections.Finally,potential hazardous-weather-related resilience challenges for both physical and cyber subsystems are discussed.展开更多
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
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
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
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
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
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
Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored ...Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored the spatiotemporal distribution characteristics of ground-level O_(3) and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021.Then,a high-performance convolutional neural network(CNN)model was established by expanding the moment and the concentration variations to general factors.Finally,the response mechanism of O_(3) to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables.The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern.When the wind direction(WD)ranges from east to southwest and the wind speed(WS)ranges between 2 and 3 m/sec,higher O_(3) concentration prone to occur.At different temperatures(T),the O_(3) concentration showed a trend of first increasing and subsequently decreasing with increasing NO_(2) concentration,peaks at the NO_(2) concentration around 0.02mg/m^(3).The sensitivity of NO_(2) to O_(3) formation is not easily affected by temperature,barometric pressure and dew point temperature.Additionally,there is a minimum IRNO_(2) at each temperature when the NO_(2) concentration is 0.03 mg/m^(3),and this minimum IRNO_(2) decreases with increasing temperature.The study explores the response mechanism of O_(3) with the change of driving variables,which can provide a scientific foundation and methodological support for the targeted management of O_(3) pollution.展开更多
基金supported in part by the National Natural Science Foundation of China(No.52177109)Key R&D Program of Hubei Province,China(No.2020BAB109).
文摘With the prevalence of renewable distributed energy resources(DERs)such as photovoltaics(PVs),modern active distribution networks(ADNs)suffer from voltage deviation and power quality issues.However,traditional voltage control methods often face a trade-off between efficiency and effectiveness,and rarely ensure robust voltage safety under typical state perturbations in practical distribution grids.In this paper,a robust model-free voltage regulation approach is proposed which simultaneously takes security and robustness into account.In this context,the voltage control problem is formulated as a constrained Markov decision process(CMDP).A safety-augmented multiagent deep deterministic policy gradient(MADDPG)algorithm is the trained to enable real-time collaborative optimization of ADNs,aiming to maintain nodal voltages within safe operational limits while minimizing total line losses.Moreover,a robust regulation loss is introduced to ensure reliable performance under various state perturbations in practical voltage controls.The proposed regulation algorithm effectively balance efficiency,safety,and robustness,and also demonstrates potential for generalizing these characteristics to other applications.Numerical studies vali-date the robustness of the proposed method under varying state perturbations on the IEEE test cases and the optimal integrated control performance when compared to other benchmarks.
基金supported by Integrated Distribution Network Planning and Operational Enhancement Using Flexibility Domains Under Deep Human-Vehicle-Charger-Road-Grid Coupling(U22B20105).
文摘Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.
基金supported by the Key Program of National Natural Science Foundation of China under Grant 52130702.
文摘In order to cope with the global environmental crisis caused by energy generation and achieve carbon neutrality,it is imperative to promote a new power system dominated by renewable energy sources(RESs).This paper focuses on the uncertainty of RESs and the distribution characteristics of carbon emission flows(CEFs),and studies the low-carbon operation and power system planning problem.Firstly,this paper extends the uncertainty of RES to the meteorological field and establishes meteorological robust constraints of photovoltaic(PV)generation.Based on the CEF theory,the carbon transmission trajectory is accurately delineated to improve the operation of power system.Considering further constraints from the power flow,CEF,and component operation characteristics of the active distribution network(ADN),this paper formulates a low-carbon joint planning model of ADN with PV,battery energy storage system(BESS),and distributed gas generator(DGG),taking into account economy and carbon reduction.In the case study,the low-carbon planning and operation scheme are analyzed in detail across multiple dimensions including time and space.The solution results show that the planning model can effectively leverage the low-carbon performance of PV and BESS,and improve the distribution of CEF.Through case comparison,the model can also efficiently reduce the total cost of the system and enhance carbon emission reduction benefits by 35.10 to 41.04%.
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 by the National Key R&D Program of China(2018YFB0904700).
文摘Large-scale access of distributed photovoltaic(PV)in distribution networks(DNs),if not properly evaluated,brings several operational problems.Uncertainties arising from both PV outputs and load demand significantly impact evaluation results.To address this issue,this paper proposes a possibilistic approach to evaluate PV hosting capacity(PVHC).First,possibility distribution is used to model load demand in order to reflect uncertainties associated with human factor,whereas the interval model is applied to deal with uncertainties of PV outputs.Second,a voltage deterioration index is proposed considering overvoltage risk of entire system on time scale.After that,possibilistic PVHC evaluation method based on this index is proposed.A 6-bus system is used to illustrate advantages of the proposed method,followed by a discussion of role of PVHC possibility distribution in actual decision-making of utilities.Moreover,sensitivity of simulation parameters is analyzed to reduce computational burden.Finally,the proposed method is tested on the IEEE 123-bus DN to validate adaptability to a larger system and to analyze impact of PVHC results against different acceptable values set by utilities.
基金supported by the National Natural Science Foundation of China(51677030).
文摘Fires and human casualties caused by single phase-to-ground faults in distribution networks are frequent.However,existing ground fault suppression methods are affected by ground fault resistance.Thus,an adaptive suppression method that seamlessly combines principles of current and voltage suppression is proposed,which has good adaptability to different ground fault resistance.Meanwhile,a multi-criteria ground fault suppression exit strategy matched to adaptive suppression method is proposed to avoid damage of device caused by power backflow,which provides the possibility for reliable and fast exit of the fault suppression device.Experimental results demonstrate effectiveness and advantages of the adaptive suppression method and its exit strategy.
基金supported by the Provincial Industrial Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.of China,grant number JC2024118.
文摘The dense integration of residential distributed photovoltaic(PV)systems into three-phase,four-wire low-voltage(LV)distribution networks results in reverse power flow and three-phase imbalance,leading to voltage violations that hinder the growth of rural distributed PV systems.Traditional voltage droop-based control methods regulate PV power output solely based on local voltage measurements at the point of PV connection.Due to a lack of global coordination and optimization,their efficiency is often subpar.This paper presents a centralized coordinated active/reactive power control strategy for PV inverters in rural LV distribution feeders with high PV penetration.The strategy optimizes residential PV inverter reactive and active power control to enhance voltage quality.It uses sensitivity coefficients derived from the inverse Jacobian matrix to assign adjustment weights to individual PV units and iteratively optimize their power outputs.The control sequence prioritizes reactive power increases;if the coefficients are below average or the inverters reach capacity,active power is curtailed until voltage issues are resolved.A simulation based on a real 37-node rural distribution network shows that the proposed method significantly reduces PV curtailment.Typical daily results indicate a curtailment rate of 1.47%,which is significantly lower than the 15.4%observed with the voltage droop-based control method.The total daily PV power output(measured every 15 min)increases from 5.55 to 6.41 MW,improving PV hosting capacity.
基金supported by the National Science and Technology Major Project(2022ZD0119901)the National Natural Science Foundation of China under Grant(U2141234,62463004 and U24A20260)+1 种基金the Hainan Province Science and Technology Special Fund(ZDYF2024GXJS003)the Scientific Research Fund of Hainan University(KYQD(ZR)23025).
文摘Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN models are limited in their ability to actively suppress noise,which constrains their robustness and precision in solving time-varying problems.This paper introduces a novel active noise rejection ZNN(ANR-ZNN)design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour.Through rigorous theoretical analysis,we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise.As a case study,the ANR-ZNN model is specifically applied to time-varying matrix inversion.Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness,emphasising the proposed design's superiority and potential for solving time-varying problems.
基金supported in part by National Natural Science Foundation of China(Grant No.62273075).
文摘Addressing the critical detection range limitation in active electrosensing(AES)for underwater sensing,this study proposes an enhanced AES system via novel array optimization.While AES offers advantages like interference immunity,acoustic stealth detection,and low cost,its short range restricts applicability.A target perturbation model under differential signal acquisition reveals that signal strength increases with local electric field intensity,target size,differential channel spacing,and conductivity contrast,but decreases with target-electrode distance.To extend detection,novel array configurations were explored.Simulations demonstrate that both rectangular and offset arrays significantly outperform the traditional collinear layout.Specifically,an offset array(with 8 m transmitting–receiving spacing)achieved an effective detection range enhancement exceeding 83%under the same distortion threshold while maintaining simplified electrode structure.Experimental validation confirmed a 100%increase in maximum detection distance to 5 m under identical noise thresholds compared to the collinear array.Furthermore,a fully connected neural network-based localization model achieved a mean positioning error of 14.12 cm at 3.15 m in static scenarios.In dynamic scenarios within 1–3 m,mean errors were controlled between 13.19 cm and 27.56 cm.Mechanistic analysis indicates that increasing the array baseline enhances the signal-to-noise ratio by simultaneously suppressing near-field environmental noise and amplifying far-field signal reception.Structural innovations in array design enabled this study to significantly expand the detection range of AES systems without compromising cost efficiency.These advancements directly promote the engineering application of AES technology,offering critical technical support for underwater defense security monitoring,long-range early warning systems,and maritime rights protection.
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.
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 National Natural Science Foundation of China(52477132 and U2066601).
文摘This paper provides a systematic review on the resilience analysis of active distribution networks(ADNs)against hazardous weather events,considering the underlying cyber-physical interdependencies.As cyber-physical systems,ADNs are characterized by widespread structural and functional interdependen-cies between cyber(communication,computing,and control)and physical(electric power)subsystems and thus present complex hazardous-weather-related resilience issues.To bridge current research gaps,this paper first classifies diverse hazardous weather events for ADNs according to different time spans and degrees of hazard,with model-based and data-driven methods being utilized to characterize weather evolutions.Then,the adverse impacts of hazardous weather on all aspects of ADNs’sources,physical/cyber networks,and loads are analyzed.This paper further emphasizes the importance of situational awareness and cyber-physical collaboration throughout hazardous weather events,as these enhance the implementation of preventive dispatches,corrective actions,and coordinated restorations.In addition,a generalized quantitative resilience evaluation process is proposed regarding additional considerations about cyber subsystems and cyber-physical connections.Finally,potential hazardous-weather-related resilience challenges for both physical and cyber subsystems are discussed.
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.
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 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 by the National Key Research and Development Program of China (Nos.2022YFC3702000 and 2022YFC3703500)the Key R&D Project of Zhejiang Province (No.2022C03146).
文摘Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored the spatiotemporal distribution characteristics of ground-level O_(3) and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021.Then,a high-performance convolutional neural network(CNN)model was established by expanding the moment and the concentration variations to general factors.Finally,the response mechanism of O_(3) to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables.The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern.When the wind direction(WD)ranges from east to southwest and the wind speed(WS)ranges between 2 and 3 m/sec,higher O_(3) concentration prone to occur.At different temperatures(T),the O_(3) concentration showed a trend of first increasing and subsequently decreasing with increasing NO_(2) concentration,peaks at the NO_(2) concentration around 0.02mg/m^(3).The sensitivity of NO_(2) to O_(3) formation is not easily affected by temperature,barometric pressure and dew point temperature.Additionally,there is a minimum IRNO_(2) at each temperature when the NO_(2) concentration is 0.03 mg/m^(3),and this minimum IRNO_(2) decreases with increasing temperature.The study explores the response mechanism of O_(3) with the change of driving variables,which can provide a scientific foundation and methodological support for the targeted management of O_(3) pollution.
