A time-varying optimization strategy for battery cluster power allocation is proposed to minimize energy loss in battery energy storage systems(BESS).First,the time-dependent loss characteristics of both storage and n...A time-varying optimization strategy for battery cluster power allocation is proposed to minimize energy loss in battery energy storage systems(BESS).First,the time-dependent loss characteristics of both storage and non-storage components in BESS are ana-lyzed.Based on this analysis,steady-state and transient methods for evaluating battery loss are proposed.Second,considering the distinct time-varying characteristics of various BESS components,the load-rate vs.equivalent-efficiency curve and the current-loss power component gradient field are introduced as analytical tools.These tools facilitate the derivation of optimization path for both time-varying and time-invariant energy compo-nents of BESS.Building on this foundation,a time-varying optimization strategy for battery cluster power allocation is developed,aiming to minimize energy loss while fully accounting for the dynamic characteristics of BESS.Compared to real-time optimization,this strategy prioritizes global optimality in the time domain,mitigates the risk of dimensionality curse,and enhances BESS efficiency.Finally,a Simulink/Simscape model is established based on real-world data to simulate internal component losses within BESS.The effectiveness of the proposed strategy is validated under a peak shaving scenario.Results indicate that,after optimization,the annual operational loss of BESS is reduced by 2.40%,while the energy round-trip efficiency is improved by 0.59%.展开更多
Preventive maintenance(PM)enhances the reliability of an ultra-high-voltage DC(UHVDC)line.However,it introduces new states and complicates the task of determining the reliability parameters of the component with PM.Th...Preventive maintenance(PM)enhances the reliability of an ultra-high-voltage DC(UHVDC)line.However,it introduces new states and complicates the task of determining the reliability parameters of the component with PM.The hierarchical connection(HC)of the inverter improves the flexibility of UHVDC,but the state spaces of the inverter and UHVDC-HC are determined by capacity distribution between the high terminal(HT)and low terminal(LT),which has not been studied yet.In this paper,the reliability of UHVDC-HC with the PM is studied.First,with the impact of PM described by a nonlinear coefficient,the state space of the components with PM is aggregated.It is the same size as without PM.The only difference is the transition rate.Second,considering power distribution between the HT/LT and its dependence on the state of the rectifier,new capacity levels of HT/LT,e.g.,75%/2,50%/3,are defined.Third,with capacity levels of the HT/LT differentiated,2 existing reliability indices are extended,and 2 are newly defined.Finally,a sensitivity model of the reliability of the UHVDC-HC to its parameters and the PM period is proposed.展开更多
The complex working environment of distribution networks tends to cause impermanent single-phase-to-ground(SPG)fault,and high-temperature ground fault arc is prone to endanger lives and power equipment,resulting in la...The complex working environment of distribution networks tends to cause impermanent single-phase-to-ground(SPG)fault,and high-temperature ground fault arc is prone to endanger lives and power equipment,resulting in large-scale power outages and fire accidents.Thus,fault arc should be extinguished in time.Meanwhile,stable operation conditions of distribution networks and reliable load power supply should be guaranteed to provide high-quality customer service.This paper proposes an active mitigation strategy for SPG fault,and provide active and reactive power compensation at the same time by utilizing an improved flexible power electronic equipment(FPEE)with dc-link sources.These controls are decoupled from each other,so utilization of FPEE is maximized as much as possible.When a SPG fault occurs in distribution networks,FPEE can output,simultaneously,active power,reactive power,and SPG fault compensation current by controlling output current on the d,q,0 coordinate system,respectively.During normal operation of distribution networks,the FPEE can be used as a virtual synchronous generator to compensate load power and its fluctuation.The proposed simultaneous multi-function can also be applied in other cases.Simulation cases are implemented to verify principles and practicability.展开更多
Rapid development of power-to-gas technology provides a potential solution for virtual power plants(VPP)to achieve near-zero carbon emissions.In this paper,a bi-level hybrid stochastic/robust optimization model is pro...Rapid development of power-to-gas technology provides a potential solution for virtual power plants(VPP)to achieve near-zero carbon emissions.In this paper,a bi-level hybrid stochastic/robust optimization model is proposed for low-carbon VPP day-ahead dispatch considering uncertainties from renewable generation and market prices.First,Karush-Kuhn-Tucker optimality conditions are employed to convert the bi-level model to a single level one.Next,the single level problem is decomposed into a master problem in the base case and several subproblems in extreme cases,which can then be solved by using the column-and-constraint generation algorithm iteratively.Numerical results indicate the proposed approach can effectively satisfy system operation constraints including the carbon emission limit,enhance computational efficiency and algorithm robustness compared with the stochastic method,and improve VPP revenue compared with the robust method.展开更多
With increasing interdependence among electricity,district heating,and natural gas systems in economy and physics,this paper focuses on the optimal bidding problem of a dominant gas-fired CHP unit in synchronized elec...With increasing interdependence among electricity,district heating,and natural gas systems in economy and physics,this paper focuses on the optimal bidding problem of a dominant gas-fired CHP unit in synchronized electricity-heat-gas markets with real-life step-wise energy offer format.Gas-fired CHP generators act as price makers and submit price-quantity offering curves in independently cleared electricity and district heating markets.A novel loss-embedded power flow model is proposed for market clearing which accounts for active power loss,congestion,reactive power flow,and voltage constraints.Adding penalty terms into the objective function eliminates additional binary variables,which eases computation burden.A two-stage trading mechanism is designed for gas-fired CHP generators to simultaneously participate in the multi-energy market.Based on a mathematical program with equilibrium constraints,an optimal bidding model is established in which the bilinear terms are eliminated by applying the binary expansion method.A diagonalization algorithm can be nested in the proposed trading mechanism if we intend to study the Nash equilibrium of the Nperson Cournot oligopoly market.Numerical tests with different scales are carried out to validate the proposed methodology in detail.展开更多
Affected by the pandemic coronavirus-19(COVID-19),significant changes have taken place in all aspects of social production and residents’lives,as well as in the energy supply and consumption characteristics of the po...Affected by the pandemic coronavirus-19(COVID-19),significant changes have taken place in all aspects of social production and residents’lives,as well as in the energy supply and consumption characteristics of the power system.COVID-19 has brought unpredictable uncertainties to the power grid.These changes and uncertainties pose a challenge to conventional electric load forecasting.Therefore,aiming to load forecasting under the background of the pandemic,this paper proposes a power load segmented forecasting method based on the pandemic stage division method,attention mechanism,and bi-directional long and short-term memory artificial neural network quantile regression model(ESD-ABiLSTMQR).According to the development degree of the pandemic,considering characteristics of different development stages of the pandemic,the pandemic is divided into four stages by using the analytic hierarchy process method(AHP):initial stage,outbreak stage,control stage,and recovery stage.A segmented load forecasting model based on LSTM and attention mechanism is established to forecast load in different time series.Cases used data from the pandemic in Wuhan,China,for verification.Results show the segmented forecasting method can analyze load characteristics of each stage and can effectively improve the accuracy of load forecasting.展开更多
Distributed generation(DG)systems with renewable energy are often connected to weak grids.However,there may be large background harmonics in weak grids,which can easily cause power quality issues at the point of commo...Distributed generation(DG)systems with renewable energy are often connected to weak grids.However,there may be large background harmonics in weak grids,which can easily cause power quality issues at the point of common coupling(PCC).For this reason,DG-grid interfacing inverters are expected to have the ability to suppress harmonics while achieving power transmission with the grid.To this end,a collaborative control method with feedforward multiple secondorder generalized integrator(FMSOGI)harmonic extraction and harmonic weighting control(HWC)are proposed in this paper to improve voltage quality at PCC.Compared with traditional control methods,the proposed collaborative control is simpler and has better harmonic suppression ability due to direct suppression.On the basis of the proposed collaborative control,system stability is analyzed for DG-grid interfacing inverters to set proper parameters.Finally,simulation and experimental results from Matlab and HIL StarSim,respectively,are presented to verify effectiveness of the proposed control method.展开更多
Knowledge graph,which is a rapidly developing technology,provides strong support in business and engineering.Knowledge graph plays an important role in recommendations and decision-making,while in the electric power i...Knowledge graph,which is a rapidly developing technology,provides strong support in business and engineering.Knowledge graph plays an important role in recommendations and decision-making,while in the electric power industry,there would be more possibilities for knowledge graph to be utilized.However,as a complex cause-and-effect network,the electric power domain knowledge graph has massive nodes,heterogeneous edges,and sparse structures.Thus,it requires human effort to process data,while quality and accuracy cannot be guaranteed.We propose a novel graph computing-based knowledge reasoning method that takes into account the sparsity of the electric power domain knowledge graph to solve the aforementioned problems and achieve improved accuracy of graph classification and knowledge reasoning tasks.The Haar basis is constructed to realize fast calculation,while the multiscale network structure is introduced to assure classification accuracy and generalization.We evaluate the proposed algorithm on the NCI-1,CEPRI UHVP,and CEPRI EQUIP databases.Simulation results demonstrate its superior performance in terms of accuracy and loss.展开更多
A regional electricity-heating integrated energy system(REH-IES)can make extensive use of renewable energy sources,realize complementary and coordinated operation of multiple energy sources,reduce carbon emissions and...A regional electricity-heating integrated energy system(REH-IES)can make extensive use of renewable energy sources,realize complementary and coordinated operation of multiple energy sources,reduce carbon emissions and promote the development of zero/low carbon systems.This paper proposes a risk-averse stochastic optimal scheduling model for REHIES.An energy flow framework for the REH-IES is proposed considering energy interaction between the electric-heating microgrids(EHMs)and electricity distribution network and the heating network.Then,considering the uncertainties of power output of renewable energy sources,dynamic characteristics of pipelines in the heating network,and thermal inertia of smart buildings,a stochastic optimal scheduling model for the REH-IES is established.Uncertainties of renewable energy sources bring financial risks to optimal scheduling of the REH-IES.Therefore,conditional value-at-risk(CVaR)theory is adopted to measure the risk and to limit the risk within an acceptable range,to achieve minimum expected scheduling cost of the REH-IES.The stochastic programming-based problem is transformed into a second-order cone programming(SOCP)model through secondorder cone relaxation method.Case studies verify the stochastic optimal scheduling model can reduce expected scheduling cost of the REH-IES,promote consumption of renewable energy sources and reduce carbon emissions.展开更多
In a press-pack insulated gate bipolar transistor(IGBT),a compact packaging structure forms a strong electromagnetic coupling,thermal coupling,and stress coupling,threatening current sharing,temperature sharing,and st...In a press-pack insulated gate bipolar transistor(IGBT),a compact packaging structure forms a strong electromagnetic coupling,thermal coupling,and stress coupling,threatening current sharing,temperature sharing,and stress sharing of paralleled chips.Optimized layouts are proposed based on the inductance analytical model to improve the performance and reliability of Press-Pack IGBT devices.What’s more,transient and steady-state co-simulation using an improved behavioral model is performed to verify the proposed layout.In the test,the PCB Rogowski coil,direct thermocouple,and force-sensitive parameters fittings are used to measure the current distribution,temperature distribution,and stress distribution.The simulation and test results indicate that a rotationally symmetrical layout with IGBT surrounding the FRD mode can achieve uniform current,temperature,and stress.展开更多
Accurate topology information is crucial to management and application in an active low-voltage distribution network(LVDN).Existing topology identification(TI)methods mostly lack a systematic framework to obtain preci...Accurate topology information is crucial to management and application in an active low-voltage distribution network(LVDN).Existing topology identification(TI)methods mostly lack a systematic framework to obtain precise hierarchical relations and consumers’segment locations.Their performances are usually deteriorated by introduction of incomplete and tampered smart meter data.To address the problem of TI with penetration of PV prosumers,non-consumption users,and electricity thieves,a data-driven algorithm is proposed via measurements of nodal voltage magnitude and active power,without any prior network information.Inspired by engineering applications of graph theory knowledge,we cast connection problems of LVDN into the solution of adjacency matrices.Up-down and parallel relations of branches are first identified using active power,based on feature extraction of frequency domain filtering and correlation.Correlation factor analysis is subsequently adopted to assign multiple consumers to specific subnetworks,and then consumers’segments are precisely located by combining regression analysis and association strategy.The proposed algorithm is successfully examined on in a complex LVDN,and results show higher robustness under different scenarios.展开更多
Recognition methods of electromagnetic transients(EMT)have been widely used in power systems with the assumption that training and testing data are drawn from the same probability distribution.However,that assumption ...Recognition methods of electromagnetic transients(EMT)have been widely used in power systems with the assumption that training and testing data are drawn from the same probability distribution.However,that assumption is hard to satisfy in industrial applications because the distribution of measured EMT testing data generally changes over time.The performance of these methods gradually deteriorates with the distribution shift.The phenomenon limits application of EMT recognition methods.Therefore,this paper proposes a transfer learning-based recognition network(TLRN)for EMT to break the limitation.It consists of a feature extractor,EMT recognizer,domain recognizer,and maximum mean discrepancy(MMD).The feature extractor is constructed to learn features of EMT automatically.The domain recognizer and MMD make features learned by the feature extractor domain invariant.Based on domain invariant features,the EMT recognizer achieves accurate EMT recognition,despite the distribution discrepancy between EMT training and testing data.TLRN maintains satisfactory EMT recognition performance by updating periodically with an unsupervised learning strategy.Using EMT datasets measured from different substations,scenario experiments,and experiment comparisons are conducted,and the recognition performance of the proposed TLRN is demonstrated.展开更多
This paper proposes a multi-agent cooperative operation optimization strategy for regional power grids considering the uncertainty of renewable energy output and flexibility of electric vehicle(EV)scheduling,which not...This paper proposes a multi-agent cooperative operation optimization strategy for regional power grids considering the uncertainty of renewable energy output and flexibility of electric vehicle(EV)scheduling,which not only improves the economy of networked microgrid(NMG)scheduling but also reduces the impact on active distribution network(ADN).EV condition matrix and model of the adjustable charge-anddischarge capacity of the EV may be built up by simulating the trip rule of an EV using the driving behavior of the vehicle model.In the day-ahead stage,by taking into account NMG operating cost,distribution network loss,and EV owners’payment cost,a multi-objective optimal scheduling model is developed,and the day-ahead scheduling contract for EV is obtained.Generative Adversarial Network(GAN)generates a significant number of intraday scenarios of photovoltaic(PV),load,and EV based on historical scheduling data as training data for the intra-day scheduling model multi-agent PPO(MAPPO).In the intra-day scheduling stage,intra-day ultra-short-term forecast data is input into the intra-day scheduling model,and the trained multi-agent model realizes NMG distributed real-time optimal scheduling.Finally,the economy and effectiveness of the proposed strategy are verified by Day-after optimal scheduling results.展开更多
With deregulation of the energy market,the pricing strategy of energy sellers in a regional integrated energy system(RIES)can affect the interests of all participants in the market and the operation of the system.This...With deregulation of the energy market,the pricing strategy of energy sellers in a regional integrated energy system(RIES)can affect the interests of all participants in the market and the operation of the system.This paper proposes a pricing strategy for integrated energy service providers in RIES based on a deep reinforcement learning(DRL)algorithm considering privacy protection.The transaction process between the integrated energy service provider(IESP)and user aggregators(UAs)in RIES is modeled as a Stackelberg game.IESP serves as the leader in making retail prices,and different UAs serve as followers in optimizing their energy consumption strategies.Considering UAs’strategies are temporally coupled,a Markov decision process(MDP)is designed differently from existing studies.Case studies demonstrate that the proposed method is accurate and stable when solving a Stackelberg equilibrium without privacy leakage.The obtained pricing strategy avoids unreasonable pricing and guarantees the revenue of IESP and the energy demand of UAs.展开更多
An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overloa...An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overload of generators and a large deviation in frequency.In this paper,the mechanism that leads to poor transient power sharing is revealed.Then,a parameter design and a coordinated control strategy are proposed to improve transient power sharing.A coordinated enhanced power-sharing(EPS)control strategy is proposed for IIDGs,which prevents the overload of IIDGs in grid-forming mode and is compatible with the existing power sharing strategies.By using a hierarchical control structure,accurate transient power sharing is achieved without the knowledge of connecting impedance.The analysis results and the proposed control method are validated by simulation.展开更多
Voltage source converters(VSCs),equipped with Pf and Q-U droop characteristics,can support a power system from both frequency and voltage.Unfortunately,overcurrent and power angle instability are still challenging asp...Voltage source converters(VSCs),equipped with Pf and Q-U droop characteristics,can support a power system from both frequency and voltage.Unfortunately,overcurrent and power angle instability are still challenging aspects of VSCs under fault conditions.Therefore,fault current limitation and power angle stability are essential conditions for the safe operation of a VSC.Thus,the transient characteristics of a VSC are analyzed to guide transient control.Then,a transient control method for a VSC,considering both fault current limitation and power angle stability,is proposed.With the proposed method,power angle stability is realized by optimizing the P-f controller.On the basis of power angle control,the Q-U controller and inner current controller are improved to effectively suppress the fault current.Finally,relevant tests are performed to verify the proposed method.展开更多
Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate tempor...Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate temporal relationships and enhance the precision of multi-step time forecast,this paper introduces an innovative approach for ultra-short-term photovoltaic(PV)power prediction,leveraging an enhanced Temporal Convolutional Neural Network(TCN)architecture and feature modeling.First,this study introduces a method employing the Spearman coefficient for meteorological feature filtration.Integrated with three-dimensional PV panel modeling,key factors influencing PV power generation are identified and prioritized.Second,the analysis of the correlation coefficient between astronomical features and PV power prediction demonstrates the theoretical substantiation for the practicality and essentiality of incorporating astronomical features.Third,an enhanced TCN model is introduced,augmenting the original TCN structure with a projection head layer to enhance its capacity for learning and expressing nonlinear features.Meanwhile,a new rolling timing network mechanism is constructed to guarantee the segmentation prediction of future long-time output sequences.Multiple experiments demonstrate the superior performance of the proposed forecasting method compared to existing models.The accuracy of PV power prediction in the next 4 hours,devoid of meteorological conditions,increases by 20.5%.Furthermore,incorporating shortwave radiation for predictions over 4 hours,2 hours,and 1 hour enhances accuracy by 11.1%,9.1%,and 8.8%,respectively.展开更多
Current urban transport and energy systems are gradually being integrated and developed towards a state of multi-area interconnection.This paper proposes a decentralized optimization approach of multi-area power-trans...Current urban transport and energy systems are gradually being integrated and developed towards a state of multi-area interconnection.This paper proposes a decentralized optimization approach of multi-area power-transport coupled systems(PTCSs)based on this change.To begin with,models concerning optimal power flow and mixed equilibrium flow are defined to describe flow patterns,respectively.Considering the traffic assignment model is non-linear and challenging to solve,this paper converts it into an equivalent variational inequality(Ⅵ).With this foundation,a decentralized optimization model is proposed,and decoupling strategies are investigated.To solve the problem effectively,an improved algorithm applicable to the decentralized optimization of PTCSs,supported by the Ⅳ tool,is proposed.In addition,a rigorous convergence analysis of the proposed algorithm was conducted.Simulations indicate the proposed algorithm solves the problem with good results and can guarantee convergence within a reasonable time frame.展开更多
As one of the key components of a UHV converter transformer,the suitable switching condition of the vacuum onload tap changer(OLTC)is essential for the stability of the power system.It is highly desirable to interpret...As one of the key components of a UHV converter transformer,the suitable switching condition of the vacuum onload tap changer(OLTC)is essential for the stability of the power system.It is highly desirable to interpret OLTC’s switching procedure to assess OLTC’s switching condition.However,such work can’t be solved by vibration analysis alone.Therefore,this paper proposes an innovative electromechanical comprehensive analysis method.First,a joint motor drive current,vibration,and electromagnetic signals online monitoring system is established and deployed in a VRG type OLTC in±800 kV converter station.Second,independent component analysis(ICA),dynamic time warping(DTW),and wavelet transform(WT)are used for processing motor current,UHFCT,and vibration signals,respectively,to extract electromechanical pulses corresponding to OLTC’s contact actions.Third,the switching procedure of OLTC is revealed by comprehensively analyzing the corresponding time relationship between these extracted pulses,combined with OLTC’s timing sequence.The methodologies developed in this paper can help to interpret OLTC’s switching procedure for deeper and finer online condition assessment.展开更多
基金supported by Key Program of the Na-tional Natural Science Foundation of China(No.52337004).
文摘A time-varying optimization strategy for battery cluster power allocation is proposed to minimize energy loss in battery energy storage systems(BESS).First,the time-dependent loss characteristics of both storage and non-storage components in BESS are ana-lyzed.Based on this analysis,steady-state and transient methods for evaluating battery loss are proposed.Second,considering the distinct time-varying characteristics of various BESS components,the load-rate vs.equivalent-efficiency curve and the current-loss power component gradient field are introduced as analytical tools.These tools facilitate the derivation of optimization path for both time-varying and time-invariant energy compo-nents of BESS.Building on this foundation,a time-varying optimization strategy for battery cluster power allocation is developed,aiming to minimize energy loss while fully accounting for the dynamic characteristics of BESS.Compared to real-time optimization,this strategy prioritizes global optimality in the time domain,mitigates the risk of dimensionality curse,and enhances BESS efficiency.Finally,a Simulink/Simscape model is established based on real-world data to simulate internal component losses within BESS.The effectiveness of the proposed strategy is validated under a peak shaving scenario.Results indicate that,after optimization,the annual operational loss of BESS is reduced by 2.40%,while the energy round-trip efficiency is improved by 0.59%.
基金supported by the National Natural Science Foundation of China(51877061).
文摘Preventive maintenance(PM)enhances the reliability of an ultra-high-voltage DC(UHVDC)line.However,it introduces new states and complicates the task of determining the reliability parameters of the component with PM.The hierarchical connection(HC)of the inverter improves the flexibility of UHVDC,but the state spaces of the inverter and UHVDC-HC are determined by capacity distribution between the high terminal(HT)and low terminal(LT),which has not been studied yet.In this paper,the reliability of UHVDC-HC with the PM is studied.First,with the impact of PM described by a nonlinear coefficient,the state space of the components with PM is aggregated.It is the same size as without PM.The only difference is the transition rate.Second,considering power distribution between the HT/LT and its dependence on the state of the rectifier,new capacity levels of HT/LT,e.g.,75%/2,50%/3,are defined.Third,with capacity levels of the HT/LT differentiated,2 existing reliability indices are extended,and 2 are newly defined.Finally,a sensitivity model of the reliability of the UHVDC-HC to its parameters and the PM period is proposed.
基金supported in part by the National Natural Science Foundation of China(No.51677030).
文摘The complex working environment of distribution networks tends to cause impermanent single-phase-to-ground(SPG)fault,and high-temperature ground fault arc is prone to endanger lives and power equipment,resulting in large-scale power outages and fire accidents.Thus,fault arc should be extinguished in time.Meanwhile,stable operation conditions of distribution networks and reliable load power supply should be guaranteed to provide high-quality customer service.This paper proposes an active mitigation strategy for SPG fault,and provide active and reactive power compensation at the same time by utilizing an improved flexible power electronic equipment(FPEE)with dc-link sources.These controls are decoupled from each other,so utilization of FPEE is maximized as much as possible.When a SPG fault occurs in distribution networks,FPEE can output,simultaneously,active power,reactive power,and SPG fault compensation current by controlling output current on the d,q,0 coordinate system,respectively.During normal operation of distribution networks,the FPEE can be used as a virtual synchronous generator to compensate load power and its fluctuation.The proposed simultaneous multi-function can also be applied in other cases.Simulation cases are implemented to verify principles and practicability.
基金supported by the Shenzhen Science and Technology Program(JCYJ20210324130811031)Tsinghua Shenzhen International Graduate School Interdisciplinary Research and Innovation Fund(JC2021004).
文摘Rapid development of power-to-gas technology provides a potential solution for virtual power plants(VPP)to achieve near-zero carbon emissions.In this paper,a bi-level hybrid stochastic/robust optimization model is proposed for low-carbon VPP day-ahead dispatch considering uncertainties from renewable generation and market prices.First,Karush-Kuhn-Tucker optimality conditions are employed to convert the bi-level model to a single level one.Next,the single level problem is decomposed into a master problem in the base case and several subproblems in extreme cases,which can then be solved by using the column-and-constraint generation algorithm iteratively.Numerical results indicate the proposed approach can effectively satisfy system operation constraints including the carbon emission limit,enhance computational efficiency and algorithm robustness compared with the stochastic method,and improve VPP revenue compared with the robust method.
基金supported in part by the National Natural Science Foundation of China(52107076)in part by the Natural Science Foundation of Jiangsu Province(BK20200013)in part by the Smart Grid Joint Fund of National Science Foundation of China&State Grid Corporation of China(U1866208).
文摘With increasing interdependence among electricity,district heating,and natural gas systems in economy and physics,this paper focuses on the optimal bidding problem of a dominant gas-fired CHP unit in synchronized electricity-heat-gas markets with real-life step-wise energy offer format.Gas-fired CHP generators act as price makers and submit price-quantity offering curves in independently cleared electricity and district heating markets.A novel loss-embedded power flow model is proposed for market clearing which accounts for active power loss,congestion,reactive power flow,and voltage constraints.Adding penalty terms into the objective function eliminates additional binary variables,which eases computation burden.A two-stage trading mechanism is designed for gas-fired CHP generators to simultaneously participate in the multi-energy market.Based on a mathematical program with equilibrium constraints,an optimal bidding model is established in which the bilinear terms are eliminated by applying the binary expansion method.A diagonalization algorithm can be nested in the proposed trading mechanism if we intend to study the Nash equilibrium of the Nperson Cournot oligopoly market.Numerical tests with different scales are carried out to validate the proposed methodology in detail.
基金supported by National Natural Science Foundation of China(No.52407074)Anhui Provincial Natural Science Foundation Youth Project,China(2308085QE177)Key Research Projects in Natural Sciences of Universities funded by the Department of Education in Anhui Province,China(2023AH050092).
文摘Affected by the pandemic coronavirus-19(COVID-19),significant changes have taken place in all aspects of social production and residents’lives,as well as in the energy supply and consumption characteristics of the power system.COVID-19 has brought unpredictable uncertainties to the power grid.These changes and uncertainties pose a challenge to conventional electric load forecasting.Therefore,aiming to load forecasting under the background of the pandemic,this paper proposes a power load segmented forecasting method based on the pandemic stage division method,attention mechanism,and bi-directional long and short-term memory artificial neural network quantile regression model(ESD-ABiLSTMQR).According to the development degree of the pandemic,considering characteristics of different development stages of the pandemic,the pandemic is divided into four stages by using the analytic hierarchy process method(AHP):initial stage,outbreak stage,control stage,and recovery stage.A segmented load forecasting model based on LSTM and attention mechanism is established to forecast load in different time series.Cases used data from the pandemic in Wuhan,China,for verification.Results show the segmented forecasting method can analyze load characteristics of each stage and can effectively improve the accuracy of load forecasting.
基金supported in part by the Natural Science Foundation of Hebei Province of China under Grant E2018203152in part by the National Natural Science Foundation of China under Grant 6200739.
文摘Distributed generation(DG)systems with renewable energy are often connected to weak grids.However,there may be large background harmonics in weak grids,which can easily cause power quality issues at the point of common coupling(PCC).For this reason,DG-grid interfacing inverters are expected to have the ability to suppress harmonics while achieving power transmission with the grid.To this end,a collaborative control method with feedforward multiple secondorder generalized integrator(FMSOGI)harmonic extraction and harmonic weighting control(HWC)are proposed in this paper to improve voltage quality at PCC.Compared with traditional control methods,the proposed collaborative control is simpler and has better harmonic suppression ability due to direct suppression.On the basis of the proposed collaborative control,system stability is analyzed for DG-grid interfacing inverters to set proper parameters.Finally,simulation and experimental results from Matlab and HIL StarSim,respectively,are presented to verify effectiveness of the proposed control method.
基金supported by National Key R&D Program of China(2020YFB0905900).
文摘Knowledge graph,which is a rapidly developing technology,provides strong support in business and engineering.Knowledge graph plays an important role in recommendations and decision-making,while in the electric power industry,there would be more possibilities for knowledge graph to be utilized.However,as a complex cause-and-effect network,the electric power domain knowledge graph has massive nodes,heterogeneous edges,and sparse structures.Thus,it requires human effort to process data,while quality and accuracy cannot be guaranteed.We propose a novel graph computing-based knowledge reasoning method that takes into account the sparsity of the electric power domain knowledge graph to solve the aforementioned problems and achieve improved accuracy of graph classification and knowledge reasoning tasks.The Haar basis is constructed to realize fast calculation,while the multiscale network structure is introduced to assure classification accuracy and generalization.We evaluate the proposed algorithm on the NCI-1,CEPRI UHVP,and CEPRI EQUIP databases.Simulation results demonstrate its superior performance in terms of accuracy and loss.
基金supported by the National Natural Science Foundation of China(52377080,U24B2078)the Department of Science and Technology of Jilin Province(20230101374JC).
文摘A regional electricity-heating integrated energy system(REH-IES)can make extensive use of renewable energy sources,realize complementary and coordinated operation of multiple energy sources,reduce carbon emissions and promote the development of zero/low carbon systems.This paper proposes a risk-averse stochastic optimal scheduling model for REHIES.An energy flow framework for the REH-IES is proposed considering energy interaction between the electric-heating microgrids(EHMs)and electricity distribution network and the heating network.Then,considering the uncertainties of power output of renewable energy sources,dynamic characteristics of pipelines in the heating network,and thermal inertia of smart buildings,a stochastic optimal scheduling model for the REH-IES is established.Uncertainties of renewable energy sources bring financial risks to optimal scheduling of the REH-IES.Therefore,conditional value-at-risk(CVaR)theory is adopted to measure the risk and to limit the risk within an acceptable range,to achieve minimum expected scheduling cost of the REH-IES.The stochastic programming-based problem is transformed into a second-order cone programming(SOCP)model through secondorder cone relaxation method.Case studies verify the stochastic optimal scheduling model can reduce expected scheduling cost of the REH-IES,promote consumption of renewable energy sources and reduce carbon emissions.
基金supported by National Key R&D Program of China(2016YFB0901800).
文摘In a press-pack insulated gate bipolar transistor(IGBT),a compact packaging structure forms a strong electromagnetic coupling,thermal coupling,and stress coupling,threatening current sharing,temperature sharing,and stress sharing of paralleled chips.Optimized layouts are proposed based on the inductance analytical model to improve the performance and reliability of Press-Pack IGBT devices.What’s more,transient and steady-state co-simulation using an improved behavioral model is performed to verify the proposed layout.In the test,the PCB Rogowski coil,direct thermocouple,and force-sensitive parameters fittings are used to measure the current distribution,temperature distribution,and stress distribution.The simulation and test results indicate that a rotationally symmetrical layout with IGBT surrounding the FRD mode can achieve uniform current,temperature,and stress.
文摘Accurate topology information is crucial to management and application in an active low-voltage distribution network(LVDN).Existing topology identification(TI)methods mostly lack a systematic framework to obtain precise hierarchical relations and consumers’segment locations.Their performances are usually deteriorated by introduction of incomplete and tampered smart meter data.To address the problem of TI with penetration of PV prosumers,non-consumption users,and electricity thieves,a data-driven algorithm is proposed via measurements of nodal voltage magnitude and active power,without any prior network information.Inspired by engineering applications of graph theory knowledge,we cast connection problems of LVDN into the solution of adjacency matrices.Up-down and parallel relations of branches are first identified using active power,based on feature extraction of frequency domain filtering and correlation.Correlation factor analysis is subsequently adopted to assign multiple consumers to specific subnetworks,and then consumers’segments are precisely located by combining regression analysis and association strategy.The proposed algorithm is successfully examined on in a complex LVDN,and results show higher robustness under different scenarios.
基金supported by National Natural Science Foundation of China(51837002).
文摘Recognition methods of electromagnetic transients(EMT)have been widely used in power systems with the assumption that training and testing data are drawn from the same probability distribution.However,that assumption is hard to satisfy in industrial applications because the distribution of measured EMT testing data generally changes over time.The performance of these methods gradually deteriorates with the distribution shift.The phenomenon limits application of EMT recognition methods.Therefore,this paper proposes a transfer learning-based recognition network(TLRN)for EMT to break the limitation.It consists of a feature extractor,EMT recognizer,domain recognizer,and maximum mean discrepancy(MMD).The feature extractor is constructed to learn features of EMT automatically.The domain recognizer and MMD make features learned by the feature extractor domain invariant.Based on domain invariant features,the EMT recognizer achieves accurate EMT recognition,despite the distribution discrepancy between EMT training and testing data.TLRN maintains satisfactory EMT recognition performance by updating periodically with an unsupervised learning strategy.Using EMT datasets measured from different substations,scenario experiments,and experiment comparisons are conducted,and the recognition performance of the proposed TLRN is demonstrated.
基金supported by the Science and Technology Project of State Grid Corporation of China(5100-202155320A-0-0-00).
文摘This paper proposes a multi-agent cooperative operation optimization strategy for regional power grids considering the uncertainty of renewable energy output and flexibility of electric vehicle(EV)scheduling,which not only improves the economy of networked microgrid(NMG)scheduling but also reduces the impact on active distribution network(ADN).EV condition matrix and model of the adjustable charge-anddischarge capacity of the EV may be built up by simulating the trip rule of an EV using the driving behavior of the vehicle model.In the day-ahead stage,by taking into account NMG operating cost,distribution network loss,and EV owners’payment cost,a multi-objective optimal scheduling model is developed,and the day-ahead scheduling contract for EV is obtained.Generative Adversarial Network(GAN)generates a significant number of intraday scenarios of photovoltaic(PV),load,and EV based on historical scheduling data as training data for the intra-day scheduling model multi-agent PPO(MAPPO).In the intra-day scheduling stage,intra-day ultra-short-term forecast data is input into the intra-day scheduling model,and the trained multi-agent model realizes NMG distributed real-time optimal scheduling.Finally,the economy and effectiveness of the proposed strategy are verified by Day-after optimal scheduling results.
文摘With deregulation of the energy market,the pricing strategy of energy sellers in a regional integrated energy system(RIES)can affect the interests of all participants in the market and the operation of the system.This paper proposes a pricing strategy for integrated energy service providers in RIES based on a deep reinforcement learning(DRL)algorithm considering privacy protection.The transaction process between the integrated energy service provider(IESP)and user aggregators(UAs)in RIES is modeled as a Stackelberg game.IESP serves as the leader in making retail prices,and different UAs serve as followers in optimizing their energy consumption strategies.Considering UAs’strategies are temporally coupled,a Markov decision process(MDP)is designed differently from existing studies.Case studies demonstrate that the proposed method is accurate and stable when solving a Stackelberg equilibrium without privacy leakage.The obtained pricing strategy avoids unreasonable pricing and guarantees the revenue of IESP and the energy demand of UAs.
基金supported in part by National Natural Science Foundation of China under Grant 52125705in part by National Natural Science Foundation of China under Grant 52107194.
文摘An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overload of generators and a large deviation in frequency.In this paper,the mechanism that leads to poor transient power sharing is revealed.Then,a parameter design and a coordinated control strategy are proposed to improve transient power sharing.A coordinated enhanced power-sharing(EPS)control strategy is proposed for IIDGs,which prevents the overload of IIDGs in grid-forming mode and is compatible with the existing power sharing strategies.By using a hierarchical control structure,accurate transient power sharing is achieved without the knowledge of connecting impedance.The analysis results and the proposed control method are validated by simulation.
基金supported in part by the National Natural Science Foundation of China(51907057 and 52077072)Technological Leading Talent of Hunan province(2019RS3014).
文摘Voltage source converters(VSCs),equipped with Pf and Q-U droop characteristics,can support a power system from both frequency and voltage.Unfortunately,overcurrent and power angle instability are still challenging aspects of VSCs under fault conditions.Therefore,fault current limitation and power angle stability are essential conditions for the safe operation of a VSC.Thus,the transient characteristics of a VSC are analyzed to guide transient control.Then,a transient control method for a VSC,considering both fault current limitation and power angle stability,is proposed.With the proposed method,power angle stability is realized by optimizing the P-f controller.On the basis of power angle control,the Q-U controller and inner current controller are improved to effectively suppress the fault current.Finally,relevant tests are performed to verify the proposed method.
基金supported by National Key Research and Development Program of China(Key Techniques of Adaptive Grid Integration and Active Synchronization for Extremely High Penetration Distributed Photovoltaic Power Generation,2022YFB2402900).
文摘Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate temporal relationships and enhance the precision of multi-step time forecast,this paper introduces an innovative approach for ultra-short-term photovoltaic(PV)power prediction,leveraging an enhanced Temporal Convolutional Neural Network(TCN)architecture and feature modeling.First,this study introduces a method employing the Spearman coefficient for meteorological feature filtration.Integrated with three-dimensional PV panel modeling,key factors influencing PV power generation are identified and prioritized.Second,the analysis of the correlation coefficient between astronomical features and PV power prediction demonstrates the theoretical substantiation for the practicality and essentiality of incorporating astronomical features.Third,an enhanced TCN model is introduced,augmenting the original TCN structure with a projection head layer to enhance its capacity for learning and expressing nonlinear features.Meanwhile,a new rolling timing network mechanism is constructed to guarantee the segmentation prediction of future long-time output sequences.Multiple experiments demonstrate the superior performance of the proposed forecasting method compared to existing models.The accuracy of PV power prediction in the next 4 hours,devoid of meteorological conditions,increases by 20.5%.Furthermore,incorporating shortwave radiation for predictions over 4 hours,2 hours,and 1 hour enhances accuracy by 11.1%,9.1%,and 8.8%,respectively.
基金supported by National Natural Science Foundation of China under grant 52307087.
文摘Current urban transport and energy systems are gradually being integrated and developed towards a state of multi-area interconnection.This paper proposes a decentralized optimization approach of multi-area power-transport coupled systems(PTCSs)based on this change.To begin with,models concerning optimal power flow and mixed equilibrium flow are defined to describe flow patterns,respectively.Considering the traffic assignment model is non-linear and challenging to solve,this paper converts it into an equivalent variational inequality(Ⅵ).With this foundation,a decentralized optimization model is proposed,and decoupling strategies are investigated.To solve the problem effectively,an improved algorithm applicable to the decentralized optimization of PTCSs,supported by the Ⅳ tool,is proposed.In addition,a rigorous convergence analysis of the proposed algorithm was conducted.Simulations indicate the proposed algorithm solves the problem with good results and can guarantee convergence within a reasonable time frame.
基金supported by the Science and Technology Project of State Grid Corporation of China(NO.5500-202016076A-0-0-00).
文摘As one of the key components of a UHV converter transformer,the suitable switching condition of the vacuum onload tap changer(OLTC)is essential for the stability of the power system.It is highly desirable to interpret OLTC’s switching procedure to assess OLTC’s switching condition.However,such work can’t be solved by vibration analysis alone.Therefore,this paper proposes an innovative electromechanical comprehensive analysis method.First,a joint motor drive current,vibration,and electromagnetic signals online monitoring system is established and deployed in a VRG type OLTC in±800 kV converter station.Second,independent component analysis(ICA),dynamic time warping(DTW),and wavelet transform(WT)are used for processing motor current,UHFCT,and vibration signals,respectively,to extract electromechanical pulses corresponding to OLTC’s contact actions.Third,the switching procedure of OLTC is revealed by comprehensively analyzing the corresponding time relationship between these extracted pulses,combined with OLTC’s timing sequence.The methodologies developed in this paper can help to interpret OLTC’s switching procedure for deeper and finer online condition assessment.
