This paper proposes a stochastic and distributed optimal energy management approach for active distribution networks(ADNs)within office buildings.The proposed approach aims at scheduling office buildings fitted with h...This paper proposes a stochastic and distributed optimal energy management approach for active distribution networks(ADNs)within office buildings.The proposed approach aims at scheduling office buildings fitted with heating ventilation and air conditioning(HVAC)systems,and electric vehicle(EV)charging piles,to participate in the ADN optimization.First,an energy management approach for the ADN with aggregated office buildings is proposed.And the ADN optimization model is formulated considering the detailed building thermal dynamics and the mobile behaviors of workers.Then,to consider un-certainties of photovoltaic(PV)power,scenario-based stochastic programming is integrated into the ADN optimization model.To further realize the stochastic energy management of the ADN within office buildings in a distributed manner,the alternating direction method of multipliers(ADMM)is used to solve the ADN optimization model.The original ADN optimization problem is divided into the network-side and the building-side sub-problems to effectively protect the privacy of the ADN and the office buildings.Finally,the ADN optimization model incorporating office buildings is validated in the winter by numerical studies.In addition,the impacts of comfort temperature range and expected state of charge(SOC)at departure are analyzed.Index Terms—ADN,EV,HVAC system,Office building,Stochastic and distributed energy management.展开更多
DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of c...DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of complex converter networks gets complicated.Because the reference frames of different converters might not fully align,depending on the structure.Thus,in order to find an accurate impedance model of a complex network for stability analysis,converting the impedances of different converters into a common reference frame is required.This paper presents a comprehensive investigation on the transformation of dq impedances to a common reference frame in complex converter networks.Four different methods are introduced and analyzed in a systematic way.Moreover,a rigorous comparison among these approaches is carried out,where the method with the simplest transformation procedure is finally suggested for the modeling of complex converter networks.The performed analysis is verified by injecting two independent small-signal perturbations into the d and the q axis,and doing a point-by-point impedance measurement.展开更多
Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.Ho...Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.展开更多
The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coup...The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.展开更多
A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system(ESS) in active distribution systems(ADS). The proposed model enables us to take into account how optimal...A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system(ESS) in active distribution systems(ADS). The proposed model enables us to take into account how optimal operation strategy of ESS in the lower level can affect and be affected by the optimal allocation of ESS in the upper level. The power characteristic model of micro-grid(MG)and typical daily scenarios are established to take full consideration of time-variable nature of renewable energy generations(REGs) and load demand while easing the burden of computation. To solve the bi-level mixed integer problem, a multi-subgroup hierarchical chaos hybrid algorithm is introduced based on differential evolution(DE) and particle swarm optimization(PSO). The modified IEEE-33 bus benchmark distribution system is utilized to investigate the availability and effectiveness of the proposed model and the hybrid algorithm. Results indicate that the planningmodel gives an adequate consideration to the optimal operation and different roles of ESS, and has the advantages of objectiveness and reasonableness.展开更多
Microgrids are networked control systems with multiple distributed generators(DGs).Microgrids are associated with many problems,such as communication delays,high sampling rates,and frequent controller updates,which ma...Microgrids are networked control systems with multiple distributed generators(DGs).Microgrids are associated with many problems,such as communication delays,high sampling rates,and frequent controller updates,which make it challenging to realize coordination control among the DGs.Therefore,finite-time consensus algorithms and event-triggered control methods are combined to propose a distributed coordination control method for microgrid systems.The DG in the microgrid system serves as an agent node in the control network,and a distributed secondary controller is designed using finite-time consensus algorithm,such that the frequency and voltage restoration control has a faster convergence time and better anti-interference performance.The event-triggered function was designed based on the state information of the agents.The controller exchanges the state information at the trigger instants.System stability is analyzed using the Lyapunov stability theory,and it is verified that the controller cannot exhibit the Zeno phenomenon in the event-triggered process.A simulation platform was developed in Matlab/Simulink to verify that the proposed control method can effectively reduce the frequency of controller updates during communication delays and the burden on the communication network.展开更多
Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-wo...Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies,which are difficult to distinguish from faults.A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this paper.Symplectic geometry mode decomposition(SGMD)is introduced to obtain the components characterizing battery states,and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of batteries.The 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of values.The proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal runaway.And the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness,high reliability,and long time scale warning,and the method is easy to implement online.展开更多
Development of power electronics transformers(PETs)has made dc distribution and hybrid ac/dc systems a competitive solution for future network expansion.This study focuses on the functions of a multiport PET interfaci...Development of power electronics transformers(PETs)has made dc distribution and hybrid ac/dc systems a competitive solution for future network expansion.This study focuses on the functions of a multiport PET interfacing medium-voltage and low-voltage networks.A comprehensive power flow control scheme based on a globalised multiport transmission model of the PET and droop control is derived.This allows for a coordinated energy exchange between the ports of the PET,which enables autonomous operation.Simulations of the proposed approach in a hybrid ac/dc distribution network with different levels of distributed generation and loads verify the effectiveness of the method.展开更多
Calculation of eigen-solutions plays an important role in the small signal stability analysis of power systems.In this paper,a novel approach based on matrix perturbation theory is proposed for the calculation of eige...Calculation of eigen-solutions plays an important role in the small signal stability analysis of power systems.In this paper,a novel approach based on matrix perturbation theory is proposed for the calculation of eigen-solutions in a perturbed system.Rigorous theoretical analysis is conducted on the solution of distinct,multiple,and close eigen-solutions,respectively,under perturbations of parameters.The computational flowchart of the unified solution of eigen-solutions is then proposed,aimed toward obtaining eigen-solutions of a perturbed system directly with algebraic formulas without solving an eigenvalue problem repeatedly.Finally,the effectiveness of the matrix perturbation based approach for eigen-solutions’calculation in power systems is verified by numerical examples on a two-area four-machine system.展开更多
基金supported in part by the Fundamental Research Funds for the Central Universities(2021YJS148)the National Natural Science Foundation of China(Grant No.51677004).
文摘This paper proposes a stochastic and distributed optimal energy management approach for active distribution networks(ADNs)within office buildings.The proposed approach aims at scheduling office buildings fitted with heating ventilation and air conditioning(HVAC)systems,and electric vehicle(EV)charging piles,to participate in the ADN optimization.First,an energy management approach for the ADN with aggregated office buildings is proposed.And the ADN optimization model is formulated considering the detailed building thermal dynamics and the mobile behaviors of workers.Then,to consider un-certainties of photovoltaic(PV)power,scenario-based stochastic programming is integrated into the ADN optimization model.To further realize the stochastic energy management of the ADN within office buildings in a distributed manner,the alternating direction method of multipliers(ADMM)is used to solve the ADN optimization model.The original ADN optimization problem is divided into the network-side and the building-side sub-problems to effectively protect the privacy of the ADN and the office buildings.Finally,the ADN optimization model incorporating office buildings is validated in the winter by numerical studies.In addition,the impacts of comfort temperature range and expected state of charge(SOC)at departure are analyzed.Index Terms—ADN,EV,HVAC system,Office building,Stochastic and distributed energy management.
基金The support of the first and fourth authors is given by National Key R&D Program of China,2018YFB0905200.The support for the second and third authors is coming from BIRD171227/17 project of the University of Padova.
文摘DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of complex converter networks gets complicated.Because the reference frames of different converters might not fully align,depending on the structure.Thus,in order to find an accurate impedance model of a complex network for stability analysis,converting the impedances of different converters into a common reference frame is required.This paper presents a comprehensive investigation on the transformation of dq impedances to a common reference frame in complex converter networks.Four different methods are introduced and analyzed in a systematic way.Moreover,a rigorous comparison among these approaches is carried out,where the method with the simplest transformation procedure is finally suggested for the modeling of complex converter networks.The performed analysis is verified by injecting two independent small-signal perturbations into the d and the q axis,and doing a point-by-point impedance measurement.
基金supported by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China[Grant No.52222708]the Natural Science Foundation of Beijing Municipality[Grant No.3212033]。
文摘Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.
基金supported by the National Science Fund for Excellent Youth Scholars of China(52222708)the National Natural Science Foundation of China(51977007)。
文摘The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.
基金supported by Application Technology Research and Engineering Demonstration Program of National Energy Administration in China (No. NY20150301)
文摘A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system(ESS) in active distribution systems(ADS). The proposed model enables us to take into account how optimal operation strategy of ESS in the lower level can affect and be affected by the optimal allocation of ESS in the upper level. The power characteristic model of micro-grid(MG)and typical daily scenarios are established to take full consideration of time-variable nature of renewable energy generations(REGs) and load demand while easing the burden of computation. To solve the bi-level mixed integer problem, a multi-subgroup hierarchical chaos hybrid algorithm is introduced based on differential evolution(DE) and particle swarm optimization(PSO). The modified IEEE-33 bus benchmark distribution system is utilized to investigate the availability and effectiveness of the proposed model and the hybrid algorithm. Results indicate that the planningmodel gives an adequate consideration to the optimal operation and different roles of ESS, and has the advantages of objectiveness and reasonableness.
基金National Natural Science Foundation of China(62063016).
文摘Microgrids are networked control systems with multiple distributed generators(DGs).Microgrids are associated with many problems,such as communication delays,high sampling rates,and frequent controller updates,which make it challenging to realize coordination control among the DGs.Therefore,finite-time consensus algorithms and event-triggered control methods are combined to propose a distributed coordination control method for microgrid systems.The DG in the microgrid system serves as an agent node in the control network,and a distributed secondary controller is designed using finite-time consensus algorithm,such that the frequency and voltage restoration control has a faster convergence time and better anti-interference performance.The event-triggered function was designed based on the state information of the agents.The controller exchanges the state information at the trigger instants.System stability is analyzed using the Lyapunov stability theory,and it is verified that the controller cannot exhibit the Zeno phenomenon in the event-triggered process.A simulation platform was developed in Matlab/Simulink to verify that the proposed control method can effectively reduce the frequency of controller updates during communication delays and the burden on the communication network.
基金the National Natural Science Foundation of China[No.51977007,No.52007006]the Natural Science Foundation of Beijing under grant 3212033.
文摘Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies,which are difficult to distinguish from faults.A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this paper.Symplectic geometry mode decomposition(SGMD)is introduced to obtain the components characterizing battery states,and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of batteries.The 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of values.The proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal runaway.And the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness,high reliability,and long time scale warning,and the method is easy to implement online.
基金National key R&D plan:Research and Demonstration of Key Technology of Charging Facilities Network Combined with Renewable Energy Power Generation(Project No.2016YFB0900505).
文摘Development of power electronics transformers(PETs)has made dc distribution and hybrid ac/dc systems a competitive solution for future network expansion.This study focuses on the functions of a multiport PET interfacing medium-voltage and low-voltage networks.A comprehensive power flow control scheme based on a globalised multiport transmission model of the PET and droop control is derived.This allows for a coordinated energy exchange between the ports of the PET,which enables autonomous operation.Simulations of the proposed approach in a hybrid ac/dc distribution network with different levels of distributed generation and loads verify the effectiveness of the method.
基金supported in part by the National Science Foundation of United States(NSF)(Grant No.0844707)in part by the International S&T Cooperation Program of China(ISTCP)(Grant No.2013DFA60930)
文摘Calculation of eigen-solutions plays an important role in the small signal stability analysis of power systems.In this paper,a novel approach based on matrix perturbation theory is proposed for the calculation of eigen-solutions in a perturbed system.Rigorous theoretical analysis is conducted on the solution of distinct,multiple,and close eigen-solutions,respectively,under perturbations of parameters.The computational flowchart of the unified solution of eigen-solutions is then proposed,aimed toward obtaining eigen-solutions of a perturbed system directly with algebraic formulas without solving an eigenvalue problem repeatedly.Finally,the effectiveness of the matrix perturbation based approach for eigen-solutions’calculation in power systems is verified by numerical examples on a two-area four-machine system.
