To reduce the carbon footprint in the transportation sector and improve overall vehicle efficiency,a large number of electric vehicles are being manufactured.This is due to the fact that environmental concerns and the...To reduce the carbon footprint in the transportation sector and improve overall vehicle efficiency,a large number of electric vehicles are being manufactured.This is due to the fact that environmental concerns and the depletion of fossil fuels have become significant global problems.Lithium-ion batteries(LIBs)have been distinguished themselves from alternative energy storage technologies for electric vehicles(EVs) due to superior qualities like high energy and power density,extended cycle life,and low maintenance cost to a competitive price.However,there are still certain challenges to be solved,like EV fast charging,longer lifetime,and reduced weight.For fast charging,the multi-stage constant current(MSCC) charging technique is an emerging solution to improve charging efficiency,reduce temperature rise during charging,increase charging/discharging capacities,shorten charging time,and extend the cycle life.However,there are large variations in the implementation of the number of stages,stage transition criterion,and C-rate selection for each stage.This paper provides a review of these problems by compiling information from the literature.An overview of the impact of different design parameters(number of stages,stage transition,and C-rate) that the MSCC charging techniques have had on the LIB performance and cycle life is described in detail and analyzed.The impact of design parameters on lifetime,charging efficiency,charging and discharging capacity,charging speed,and rising temperature during charging is presented,and this review provides guidelines for designing advanced fast charging strategies and determining future research gaps.展开更多
Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance ...Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.展开更多
In this paper,a sensorless control strategy of a permanent magnet synchronous machine(PMSM)based on an improved rotor flux observer(IFO)is proposed.Due to the unknown integral initial value and the high harmonics caus...In this paper,a sensorless control strategy of a permanent magnet synchronous machine(PMSM)based on an improved rotor flux observer(IFO)is proposed.Due to the unknown integral initial value and the high harmonics caused by current sampling and inverter nonlinearities,the flux linkage estimated by traditional rotor flux observer may be inaccurate.In order to address these issues,a self-adaptive band-pass filter(SABPF)is designed to eliminate the DC component and high-frequency harmonics of the estimated equivalent rotor flux linkage.Furthermore,in order to avoid that the design of PI parameter is influenced by the amplitude of equivalent rotor flux linkage,an improved phase-locked loop(IPLL)is employed to obtain the rotor speed and to normalize the estimated equivalent rotor flux linkage.In addition,angle shift caused by an SABPF is compensated to improve the accuracy of the estimated flux linkage angle.Besides,the parameter robustness of this method is analyzed in detail.Finally,simulation and experimental results demonstrate the effectiveness and parameter robustness of the proposed method.展开更多
This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation(DSSE)against anomalous real-time measurements,as well as a deep auto-encoder(DAE)-based detector and...This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation(DSSE)against anomalous real-time measurements,as well as a deep auto-encoder(DAE)-based detector and a Gaussian process-aided residual learning(GARL)to deal with challenges arising from topology changes.A global-scanning jumping knowledge network(GSJKN)is first designed to establish the regression rule between the measurement data and state variables.The structural information of distribution system(DS)and a global-scanning module are incorporated to guide the propagation of scarce measurements in the graph topology,contributing to valid estimation precision in sparsely measured DSs.To monitor the topology changes of the network,a DAE network is employed to learn an efficient representation of the measurements of the system under a certain topology,which can achieve online monitoring of the network structure by observing the variation tendency of the reconstruction error.When the topology change occurs,a Gaussian process with a composite kernel is applied to the modeling of the pre-trained GSJKN residual to adapt to the new topology.The embedding of the physical structural knowledge enables the proposed GSJKN method to restore the missing/noisy values utilizing the adjacent measurements,which enhances the robustness to typical data acquisition errors.The adopted DAE network and special GARL-based transfer method further allow the DSSE method to rapidly detect and adapt to the topology change,as well as achieve effective quantification of the estimation uncertainties.Comparative tests on balanced and unbalanced systems demonstrate the accuracy,robustness,and adaptability of the proposed DSSE method.展开更多
Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated.Improvement in the efficiency of energy conversion systems is highly restricted because of th...Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated.Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity.Thus,inspired by the successful utilization of robotic chemists,we demonstrate a pioneering concept of artificial intelligence(AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge(DAB)converter as an example.An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform.Specifically,the DAB experimental platform operated autonomously around the clock for approximately 71 h.It performed 120,000 consecutive experiments(12,000 episodes)within a six-variable experimental space driven by a deep deterministic policy gradient(DDPG)algorithm.The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply.Consequently,zero carbon emissions may be obtained in the future.展开更多
In power electronics applications,the selection of condition monitoring methods significantly affects both the precision and complexity of the junction temperature evaluation,which is essential for the reliability ass...In power electronics applications,the selection of condition monitoring methods significantly affects both the precision and complexity of the junction temperature evaluation,which is essential for the reliability assessment of power semiconductor devices.This study begins with a failure mechanism analysis of state-of-the-art power semiconductor devices.Junction temperature measurement methods can be categorized into three distinct approaches:thermal image-based,thermal model-based,and temperature-sensitive electrical parameter(TSEP)-based methods.Their respective advantages and disadvantages are comprehensively compared.Moreover,condition monitoring of the ON-state voltage drop is summarized and benchmarked.ON-state voltage and junction temperature measurements are experimentally demonstrated in a standard three-phase converter,which provides superior measurement accuracy and rapid dynamic response characteristics.Additionally,this investigation is extended to measurement methods for TSEP in wide-bandgap semiconductors.展开更多
This paper presents control methods for hybrid AC/DC microgrid under islanding operation condition.The control schemes for AC sub-microgrid and DC sub-microgrid are investigated according to the power sharing requirem...This paper presents control methods for hybrid AC/DC microgrid under islanding operation condition.The control schemes for AC sub-microgrid and DC sub-microgrid are investigated according to the power sharing requirement and operational reliability.In addition,the key control schemes of interlinking converter with DC-link capacitor or energy storage,which will devote to the proper power sharing between AC and DC sub-microgrids to maintain AC and DC side voltage stable,is reviewed.Combining the specific control methods developed for AC and DC sub-microgrids with interlinking converter,the whole hybrid AC/DC microgrid can manage the power flow transferred between sub-microgrids for improving on the operational quality and efficiency.展开更多
Along with the increasing penetration of distributed generation with voltage-source converters(VSCs),there are extensive concerns over the potential virtual rotor angle stability, which is characterized by oscillation...Along with the increasing penetration of distributed generation with voltage-source converters(VSCs),there are extensive concerns over the potential virtual rotor angle stability, which is characterized by oscillations of power and frequency during the dynamic process of synchronization in the grid. Several control strategies have been developed for VSCs to emulate rotating inertia as well as damping of oscillations. This paper classifies these strategies and provides a small-signal modeling framework including all kinds of VSCs in different applications for virtual rotor angle stability. A unified perspective based on the famous Phillips–Heffron model is established for various VSCs. Thus, the concepts of equivalent inertia and the synchronizing and damping coefficients in different VSCs are highlighted, based on the similarities with the synchronous generator(SG) system in both physical mechanisms and mathematical models. It revealed the potentiality of various VSCs to achieve equivalence with the SG. This study helps promote the unity of VSCs and traditional SGs in both theories and methods for analyzing the dynamic behavior and enhancing the stability. Finally,future research needs and new perspectives are addressed.展开更多
With the growing integration of distributed energy resources(DERs),flexible loads,and other emerging technologies,there are increasing complexities and uncertainties for modern power and energy systems.This brings gre...With the growing integration of distributed energy resources(DERs),flexible loads,and other emerging technologies,there are increasing complexities and uncertainties for modern power and energy systems.This brings great challenges to the operation and control.Besides,with the deployment of advanced sensor and smart meters,a large number of data are generated,which brings opportunities for novel data-driven methods to deal with complicated operation and control issues.Among them,reinforcement learning(RL)is one of the most widely promoted methods for control and optimization problems.This paper provides a comprehensive literature review of RL in terms of basic ideas,various types of algorithms,and their applications in power and energy systems.The challenges and further works are also discussed.展开更多
The markedly increased integration of renewable energy in the power grid is of significance in the transition to a sustainable energy future.The grid integration of renewables will be continuously enhanced in the futu...The markedly increased integration of renewable energy in the power grid is of significance in the transition to a sustainable energy future.The grid integration of renewables will be continuously enhanced in the future.According to the International Renewable Energy Agency(IRENA),renewable technology is the main pathway to reach zero carbon dioxide(CO_(2))emissions by 2060.Power electronics have played and will continue to play a significant role in this energy transition by providing efficient electrical energy conversion,distribution,transmission,and utilization.Consequently,the development of power electronics technologies,i.e.,new semiconductor devices,flexible converters,and advanced control schemes,is promoted extensively across the globe.Among various renewables,wind energy and photovoltaic(PV)are the most widely used,and accordingly these are explored in this paper to demonstrate the role of power electronics.The development of renewable energies and the demands of power electronics are reviewed first.Then,the power conversion and control technologies as well as grid codes for wind and PV systems are discussed.Future trends in terms of power semiconductors,reliability,advanced control,grid-forming operation,and security issues for largescale grid integration of renewables,and intelligent and full user engagement are presented at the end.展开更多
Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems.However,traditional intelligent methods limit the use of the physical structures...Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems.However,traditional intelligent methods limit the use of the physical structures and data information of power networks.To this end,this study proposes a fault diagnostic model for distribution systems based on deep graph learning.This model considers the physical structure of the power network as a significant constraint during model training,which endows the model with stronger information perception to resist abnormal data input and unknown application conditions.In addition,a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability.This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults.In addition,a multi-task learning framework is constructed for fault location and fault type analysis,which improves the performance and generalization ability of the model.The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model.Finally,different fault conditions,topological changes,and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model.Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods.展开更多
Microgrids(MGs)are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC ...Microgrids(MGs)are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads,distributed renewable energy sources,and energy storage systems,as well as a more resilient and economical on/off-grid control,operation,and energy management.However,MGs,as newcomers to the utility grid,are also facing challenges due to economic deregulation of energy systems,restructuring of generation,and marketbased operation.This paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution techniques.First,MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy management.Second,energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy management.Mathematical programming,adaptive dynamic programming,and deep reinforcement learning-based solution methods are investigated accordingly,together with their implementation schemes.Finally,problems for future energy management systems with dynamics-captured critical component models,stability constraints,resilience awareness,market operation,and emerging computational techniques are discussed.展开更多
This study proposes a deep reinforcement learning(DRL)based approach to analyze the optimal power flow(OPF)of distribution networks(DNs)embedded with renewable energy and storage devices.First,the OPF of the DN is for...This study proposes a deep reinforcement learning(DRL)based approach to analyze the optimal power flow(OPF)of distribution networks(DNs)embedded with renewable energy and storage devices.First,the OPF of the DN is formulated as a stochastic nonlinear programming problem.Then,the multi-period nonlinear programming decision problem is formulated as a Markov decision process(MDP),which is composed of multiple single-time-step sub-problems.Subsequently,the state-of-the-art DRL algorithm,i.e.,proximal policy optimization(PPO),is used to solve the MDP sequentially considering the impact on the future.Neural networks are used to extract operation knowledge from historical data offline and provide online decisions according to the real-time state of the DN.The proposed approach fully exploits the historical data and reduces the influence of the prediction error on the optimization results.The proposed real-time control strategy can provide more flexible decisions and achieve better performance than the pre-determined ones.Comparative results demonstrate the effectiveness of the proposed approach.展开更多
A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle(EV)owners.Considering the uncertainty of price fluctuation and the randomness of EV owner’s commuting behavi...A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle(EV)owners.Considering the uncertainty of price fluctuation and the randomness of EV owner’s commuting behavior,we propose a deep reinforcement learning based method for the minimization of individual EV charging cost.The charging problem is first formulated as a Markov decision process(MDP),which has unknown transition probability.A modified long short-term memory(LSTM)neural network is used as the representation layer to extract temporal features from the electricity price signal.The deep deterministic policy gradient(DDPG)algorithm,which has continuous action spaces,is used to solve the MDP.The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner.Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2%compared with other benchmark methods.展开更多
As more and more power electronic based generation units are integrated into power systems, the stable operation of power systems has been challenged due to the lack of system inertia. In order to solve this issue, th...As more and more power electronic based generation units are integrated into power systems, the stable operation of power systems has been challenged due to the lack of system inertia. In order to solve this issue, the virtual synchronous generator(VSG), in which the power electronic inverter is controlled to mimic the characteristics of traditional synchronous generators, is a promising strategy. In this paper, the representation of the synchronous generator in power systems is firstly presented as the basis for the VSG. Then the modelling methods of VSG are comprehensively reviewed and compared.Applications of the VSG in power systems are summarized as well. Finally, the challenges and future trends of the VSG implementation are discussed.展开更多
The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We pr...The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.展开更多
The increasing trend for integrating renewable energy sources into the grid to achieve a cleaner energy system is one of the main reasons for the development of sustainable microgrid(MG)technologies.As typical power-e...The increasing trend for integrating renewable energy sources into the grid to achieve a cleaner energy system is one of the main reasons for the development of sustainable microgrid(MG)technologies.As typical power-electronized power systems,MGs make extensive use of power electronics converters,which are highly controllable and flexible but lead to a profound impact on the dynamic performance of the whole system.Compared with traditional large-capacity power systems,MGs are less resistant to perturbations,and various dynamic variables are coupled with each other on multiple timescales,resulting in a more complex system instability mechanism.To meet the technical and economic challenges,such as active and reactive power-sharing,voltage,and frequency deviations,and imbalances between power supply and demand,the concept of hierarchical control has been introduced into MGs,allowing systems to control and manage the high capacity of renewable energy sources and loads.However,as the capacity and scale of the MG system increase,along with a multi-timescale control loop design,the multi-timescale interactions in the system may become more significant,posing a serious threat to its safe and stable operation.To investigate the multi-timescale behaviors and instability mechanisms under dynamic inter-actions for AC MGs,existing coordinated control strategies are discussed,and the dynamic stability of the system is defined and classified in this paper.Then,the modeling and assessment methods for the stability analysis of multi-timescale systems are also summarized.Finally,an outlook and discussion of future research directions for AC MGs are also presented.展开更多
文摘To reduce the carbon footprint in the transportation sector and improve overall vehicle efficiency,a large number of electric vehicles are being manufactured.This is due to the fact that environmental concerns and the depletion of fossil fuels have become significant global problems.Lithium-ion batteries(LIBs)have been distinguished themselves from alternative energy storage technologies for electric vehicles(EVs) due to superior qualities like high energy and power density,extended cycle life,and low maintenance cost to a competitive price.However,there are still certain challenges to be solved,like EV fast charging,longer lifetime,and reduced weight.For fast charging,the multi-stage constant current(MSCC) charging technique is an emerging solution to improve charging efficiency,reduce temperature rise during charging,increase charging/discharging capacities,shorten charging time,and extend the cycle life.However,there are large variations in the implementation of the number of stages,stage transition criterion,and C-rate selection for each stage.This paper provides a review of these problems by compiling information from the literature.An overview of the impact of different design parameters(number of stages,stage transition,and C-rate) that the MSCC charging techniques have had on the LIB performance and cycle life is described in detail and analyzed.The impact of design parameters on lifetime,charging efficiency,charging and discharging capacity,charging speed,and rising temperature during charging is presented,and this review provides guidelines for designing advanced fast charging strategies and determining future research gaps.
基金supported by the National Natural Science Foundation of China (No.62173281,52377217,U23A20651)Sichuan Science and Technology Program (No.24NSFSC0024,23ZDYF0734,23NSFSC1436)+2 种基金Dazhou City School Cooperation Project (No.DZXQHZ006)Technopole Talent Summit Project (No.KJCRCFH08)Robert Gordon University。
文摘Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.
基金This work has been partly supported by National Natural Science Foundation of China(NSFC 51877093,51707079,and 51807075),National Key Research and Development Program of China(Project ID:YS2018YFGH000200),and Fund。
文摘In this paper,a sensorless control strategy of a permanent magnet synchronous machine(PMSM)based on an improved rotor flux observer(IFO)is proposed.Due to the unknown integral initial value and the high harmonics caused by current sampling and inverter nonlinearities,the flux linkage estimated by traditional rotor flux observer may be inaccurate.In order to address these issues,a self-adaptive band-pass filter(SABPF)is designed to eliminate the DC component and high-frequency harmonics of the estimated equivalent rotor flux linkage.Furthermore,in order to avoid that the design of PI parameter is influenced by the amplitude of equivalent rotor flux linkage,an improved phase-locked loop(IPLL)is employed to obtain the rotor speed and to normalize the estimated equivalent rotor flux linkage.In addition,angle shift caused by an SABPF is compensated to improve the accuracy of the estimated flux linkage angle.Besides,the parameter robustness of this method is analyzed in detail.Finally,simulation and experimental results demonstrate the effectiveness and parameter robustness of the proposed method.
基金supported in part by Fundamental Research Funds for the Central Universities(No.ZYGX2024J014)in part by the National Natural Science Foundation of China(No.52277083).
文摘This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation(DSSE)against anomalous real-time measurements,as well as a deep auto-encoder(DAE)-based detector and a Gaussian process-aided residual learning(GARL)to deal with challenges arising from topology changes.A global-scanning jumping knowledge network(GSJKN)is first designed to establish the regression rule between the measurement data and state variables.The structural information of distribution system(DS)and a global-scanning module are incorporated to guide the propagation of scarce measurements in the graph topology,contributing to valid estimation precision in sparsely measured DSs.To monitor the topology changes of the network,a DAE network is employed to learn an efficient representation of the measurements of the system under a certain topology,which can achieve online monitoring of the network structure by observing the variation tendency of the reconstruction error.When the topology change occurs,a Gaussian process with a composite kernel is applied to the modeling of the pre-trained GSJKN residual to adapt to the new topology.The embedding of the physical structural knowledge enables the proposed GSJKN method to restore the missing/noisy values utilizing the adjacent measurements,which enhances the robustness to typical data acquisition errors.The adopted DAE network and special GARL-based transfer method further allow the DSSE method to rapidly detect and adapt to the topology change,as well as achieve effective quantification of the estimation uncertainties.Comparative tests on balanced and unbalanced systems demonstrate the accuracy,robustness,and adaptability of the proposed DSSE method.
基金support from the National Natural Science Foundation of China(52277083).
文摘Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated.Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity.Thus,inspired by the successful utilization of robotic chemists,we demonstrate a pioneering concept of artificial intelligence(AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge(DAB)converter as an example.An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform.Specifically,the DAB experimental platform operated autonomously around the clock for approximately 71 h.It performed 120,000 consecutive experiments(12,000 episodes)within a six-variable experimental space driven by a deep deterministic policy gradient(DDPG)algorithm.The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply.Consequently,zero carbon emissions may be obtained in the future.
文摘In power electronics applications,the selection of condition monitoring methods significantly affects both the precision and complexity of the junction temperature evaluation,which is essential for the reliability assessment of power semiconductor devices.This study begins with a failure mechanism analysis of state-of-the-art power semiconductor devices.Junction temperature measurement methods can be categorized into three distinct approaches:thermal image-based,thermal model-based,and temperature-sensitive electrical parameter(TSEP)-based methods.Their respective advantages and disadvantages are comprehensively compared.Moreover,condition monitoring of the ON-state voltage drop is summarized and benchmarked.ON-state voltage and junction temperature measurements are experimentally demonstrated in a standard three-phase converter,which provides superior measurement accuracy and rapid dynamic response characteristics.Additionally,this investigation is extended to measurement methods for TSEP in wide-bandgap semiconductors.
文摘This paper presents control methods for hybrid AC/DC microgrid under islanding operation condition.The control schemes for AC sub-microgrid and DC sub-microgrid are investigated according to the power sharing requirement and operational reliability.In addition,the key control schemes of interlinking converter with DC-link capacitor or energy storage,which will devote to the proper power sharing between AC and DC sub-microgrids to maintain AC and DC side voltage stable,is reviewed.Combining the specific control methods developed for AC and DC sub-microgrids with interlinking converter,the whole hybrid AC/DC microgrid can manage the power flow transferred between sub-microgrids for improving on the operational quality and efficiency.
基金supported by National High Technology Research and Development Program of China(No.2015AA050606)National Key Research and Development Program(No.2016YFB0900302)National Natural Science Foundation of China(No.U1510208,61273045,51361135705)
文摘Along with the increasing penetration of distributed generation with voltage-source converters(VSCs),there are extensive concerns over the potential virtual rotor angle stability, which is characterized by oscillations of power and frequency during the dynamic process of synchronization in the grid. Several control strategies have been developed for VSCs to emulate rotating inertia as well as damping of oscillations. This paper classifies these strategies and provides a small-signal modeling framework including all kinds of VSCs in different applications for virtual rotor angle stability. A unified perspective based on the famous Phillips–Heffron model is established for various VSCs. Thus, the concepts of equivalent inertia and the synchronizing and damping coefficients in different VSCs are highlighted, based on the similarities with the synchronous generator(SG) system in both physical mechanisms and mathematical models. It revealed the potentiality of various VSCs to achieve equivalence with the SG. This study helps promote the unity of VSCs and traditional SGs in both theories and methods for analyzing the dynamic behavior and enhancing the stability. Finally,future research needs and new perspectives are addressed.
基金supported by the Sichuan Science and Technology Program(Sichuan Distinguished Young Scholars)(No.2020JDJQ0037).
文摘With the growing integration of distributed energy resources(DERs),flexible loads,and other emerging technologies,there are increasing complexities and uncertainties for modern power and energy systems.This brings great challenges to the operation and control.Besides,with the deployment of advanced sensor and smart meters,a large number of data are generated,which brings opportunities for novel data-driven methods to deal with complicated operation and control issues.Among them,reinforcement learning(RL)is one of the most widely promoted methods for control and optimization problems.This paper provides a comprehensive literature review of RL in terms of basic ideas,various types of algorithms,and their applications in power and energy systems.The challenges and further works are also discussed.
文摘The markedly increased integration of renewable energy in the power grid is of significance in the transition to a sustainable energy future.The grid integration of renewables will be continuously enhanced in the future.According to the International Renewable Energy Agency(IRENA),renewable technology is the main pathway to reach zero carbon dioxide(CO_(2))emissions by 2060.Power electronics have played and will continue to play a significant role in this energy transition by providing efficient electrical energy conversion,distribution,transmission,and utilization.Consequently,the development of power electronics technologies,i.e.,new semiconductor devices,flexible converters,and advanced control schemes,is promoted extensively across the globe.Among various renewables,wind energy and photovoltaic(PV)are the most widely used,and accordingly these are explored in this paper to demonstrate the role of power electronics.The development of renewable energies and the demands of power electronics are reviewed first.Then,the power conversion and control technologies as well as grid codes for wind and PV systems are discussed.Future trends in terms of power semiconductors,reliability,advanced control,grid-forming operation,and security issues for largescale grid integration of renewables,and intelligent and full user engagement are presented at the end.
基金supported by National Natural Science Foundation of China(No.52277083)。
文摘Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems.However,traditional intelligent methods limit the use of the physical structures and data information of power networks.To this end,this study proposes a fault diagnostic model for distribution systems based on deep graph learning.This model considers the physical structure of the power network as a significant constraint during model training,which endows the model with stronger information perception to resist abnormal data input and unknown application conditions.In addition,a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability.This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults.In addition,a multi-task learning framework is constructed for fault location and fault type analysis,which improves the performance and generalization ability of the model.The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model.Finally,different fault conditions,topological changes,and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model.Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods.
基金supported in part by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources under Grant LAPS21002in part by the National Natural Science Foundation of China under Grant 52061635102in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515110583.
文摘Microgrids(MGs)are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads,distributed renewable energy sources,and energy storage systems,as well as a more resilient and economical on/off-grid control,operation,and energy management.However,MGs,as newcomers to the utility grid,are also facing challenges due to economic deregulation of energy systems,restructuring of generation,and marketbased operation.This paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution techniques.First,MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy management.Second,energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy management.Mathematical programming,adaptive dynamic programming,and deep reinforcement learning-based solution methods are investigated accordingly,together with their implementation schemes.Finally,problems for future energy management systems with dynamics-captured critical component models,stability constraints,resilience awareness,market operation,and emerging computational techniques are discussed.
文摘This study proposes a deep reinforcement learning(DRL)based approach to analyze the optimal power flow(OPF)of distribution networks(DNs)embedded with renewable energy and storage devices.First,the OPF of the DN is formulated as a stochastic nonlinear programming problem.Then,the multi-period nonlinear programming decision problem is formulated as a Markov decision process(MDP),which is composed of multiple single-time-step sub-problems.Subsequently,the state-of-the-art DRL algorithm,i.e.,proximal policy optimization(PPO),is used to solve the MDP sequentially considering the impact on the future.Neural networks are used to extract operation knowledge from historical data offline and provide online decisions according to the real-time state of the DN.The proposed approach fully exploits the historical data and reduces the influence of the prediction error on the optimization results.The proposed real-time control strategy can provide more flexible decisions and achieve better performance than the pre-determined ones.Comparative results demonstrate the effectiveness of the proposed approach.
基金supported by the Sichuan Science and Technology Program(No.2020JDJQ0037)。
文摘A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle(EV)owners.Considering the uncertainty of price fluctuation and the randomness of EV owner’s commuting behavior,we propose a deep reinforcement learning based method for the minimization of individual EV charging cost.The charging problem is first formulated as a Markov decision process(MDP),which has unknown transition probability.A modified long short-term memory(LSTM)neural network is used as the representation layer to extract temporal features from the electricity price signal.The deep deterministic policy gradient(DDPG)algorithm,which has continuous action spaces,is used to solve the MDP.The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner.Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2%compared with other benchmark methods.
文摘As more and more power electronic based generation units are integrated into power systems, the stable operation of power systems has been challenged due to the lack of system inertia. In order to solve this issue, the virtual synchronous generator(VSG), in which the power electronic inverter is controlled to mimic the characteristics of traditional synchronous generators, is a promising strategy. In this paper, the representation of the synchronous generator in power systems is firstly presented as the basis for the VSG. Then the modelling methods of VSG are comprehensively reviewed and compared.Applications of the VSG in power systems are summarized as well. Finally, the challenges and future trends of the VSG implementation are discussed.
基金supported by Sichuan Province Innovative Talent Funding Project for Postdoctoral Fellows (No. BX202210)。
文摘The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.
基金partly supported by the National Natural Science Foundation of China(NSFC)(No.51977026)the Science and Technology Program of Sichuan Province(No.2021YFG0255)the Sichuan Pro-vincial Postdoctoral Science Foundation(No.246861).
文摘The increasing trend for integrating renewable energy sources into the grid to achieve a cleaner energy system is one of the main reasons for the development of sustainable microgrid(MG)technologies.As typical power-electronized power systems,MGs make extensive use of power electronics converters,which are highly controllable and flexible but lead to a profound impact on the dynamic performance of the whole system.Compared with traditional large-capacity power systems,MGs are less resistant to perturbations,and various dynamic variables are coupled with each other on multiple timescales,resulting in a more complex system instability mechanism.To meet the technical and economic challenges,such as active and reactive power-sharing,voltage,and frequency deviations,and imbalances between power supply and demand,the concept of hierarchical control has been introduced into MGs,allowing systems to control and manage the high capacity of renewable energy sources and loads.However,as the capacity and scale of the MG system increase,along with a multi-timescale control loop design,the multi-timescale interactions in the system may become more significant,posing a serious threat to its safe and stable operation.To investigate the multi-timescale behaviors and instability mechanisms under dynamic inter-actions for AC MGs,existing coordinated control strategies are discussed,and the dynamic stability of the system is defined and classified in this paper.Then,the modeling and assessment methods for the stability analysis of multi-timescale systems are also summarized.Finally,an outlook and discussion of future research directions for AC MGs are also presented.
