Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variat...Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition(VMD)and Channel Attention Mechanism.First,Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power.Second,the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition(VMD).Finally,the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM model.In this model,the convolutional neural network(CNN)and channel attention mechanism dynamically adjust the weights while capturing the spatial features of the input data to improve the discriminative ability of key features.The extracted data is then fed into the bidirectional long short-term memory network(BiLSTM)to capture the time-series features,and the final output is the prediction result.The verification is conducted using a dataset from a distributed photovoltaic power station in the Northwest region of China.The results show that compared with other prediction methods,the method proposed in this paper has a higher prediction accuracy,which helps to improve the proportion of distributed PV access to the grid,and can guarantee the safe and stable operation of the power grid.展开更多
To reduce the time required to complete the regeneration process of erasure codes, we propose a Tree-structured Parallel Regeneration (TPR) scheme for multiple data losses in distributed storage systems. Under the sch...To reduce the time required to complete the regeneration process of erasure codes, we propose a Tree-structured Parallel Regeneration (TPR) scheme for multiple data losses in distributed storage systems. Under the scheme, two algorithms are proposed for the construction of multiple regeneration trees, namely the edge-disjoint algorithm and edge-sharing algorithm. The edge-disjoint algorithm constructs multiple independent trees, and is simple and appropriate for environments where newcomers and their providers are distributed over a large area and have few intersections. The edge-sharing algorithm constructs multiple trees that compete to utilize the bandwidth, and make a better utilization of the bandwidth, although it needs to measure the available band-width and deal with the bandwidth changes; it is therefore difficult to implement in practical systems. The parallel regeneration for multiple data losses of TPR primarily includes two optimizations: firstly, transferring the data through the bandwidth optimized-paths in a pipe-line manner; secondly, executing data regeneration over multiple trees in parallel. To evaluate the proposal, we implement an event-based simulator and make a detailed comparison with some popular regeneration methods. The quantitative comparison results show that the use of TPR employing either the edge-disjoint algorithm or edge-sharing algorithm reduces the regeneration time significantly.展开更多
To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution scheme.However,the distributed machine learning is difficult to train due to that the ...To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution scheme.However,the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly,which highly demand on computing and memory resources.To overcome the challenges,we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method,which is a lightweight,few additional cost and parallelized scheme for the model training process.To validate the claims,we have conducted several experiments on multiple classical datasets.Results show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.展开更多
The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels.This paper focuses on the multi-time-scale regulation model of distribute...The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels.This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions.The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales.展开更多
In this paper, we consider a consensus tracking problem of a class of networked multi-agent systems(MASs)in non-affine pure-feedback form under a directed topology. A distributed adaptive tracking consensus control sc...In this paper, we consider a consensus tracking problem of a class of networked multi-agent systems(MASs)in non-affine pure-feedback form under a directed topology. A distributed adaptive tracking consensus control scheme is constructed recursively by the backstepping method, graph theory,neural networks(NNs) and the dynamic surface control(DSC)approach. The key advantage of the proposed control strategy is that, by the DSC technique, it avoids "explosion of complexity"problem along with the increase of the degree of individual agents and thus the computational burden of the scheme can be drastically reduced. Moreover, there is no requirement for prior knowledge about system parameters of individual agents and uncertain dynamics by employing NNs approximation technology.We then further show that, in theory, the designed control policy guarantees the consensus errors to be cooperatively semi-globally uniformly ultimately bounded(CSUUB). Finally, two examples are presented to validate the effectiveness of the proposed control strategy.展开更多
Distributed photovoltaic(PV)is one of the important power sources for building a new power system with new energy as the main body.The rapid development of distributed PV has brought new challenges to the operation of...Distributed photovoltaic(PV)is one of the important power sources for building a new power system with new energy as the main body.The rapid development of distributed PV has brought new challenges to the operation of distribution networks.In order to improve the absorption ability of large-scale distributed PV access to the distribution network,the AC/DC hybrid distribution network is constructed based on flexible interconnection technology,and a coordinated scheduling strategy model of hydrogen energy storage(HS)and distributed PV is established.Firstly,the mathematical model of distributed PV and HS system is established,and a comprehensive energy storage system combining seasonal hydrogen energy storage(SHS)and battery(BT)is proposed.Then,a flexible interconnected distribution network scheduling optimization model is established to minimize the total active power loss,voltage deviation and system operating cost.Finally,simulation analysis is carried out on the improved IEEE33 node,the NSGA-II algorithm is used to solve specific examples,and the optimal scheduling results of the comprehensive economy and power quality of the distribution network are obtained.Compared with the method that does not consider HS and flexible interconnection technology,the network loss and voltage deviation of this method are lower,and the total system cost can be reduced by 3.55%,which verifies the effectiveness of the proposed method.展开更多
The introduction of international,Chinese and foreign standards for distributed resources integration is presented in this paper.The typical standards in North America,Europe and China are compared in several key aspe...The introduction of international,Chinese and foreign standards for distributed resources integration is presented in this paper.The typical standards in North America,Europe and China are compared in several key aspects,展开更多
In distributed systems independent agents need to interact with each other to accomplish their task. Modern peer-to-peer computing technologies always concern with enabling interaction among agents and help them coope...In distributed systems independent agents need to interact with each other to accomplish their task. Modern peer-to-peer computing technologies always concern with enabling interaction among agents and help them cooperate with each other. But in fact, access control should also be considered to limit interaction to make it harmless. This paper proposed a proxy based rule regulated interaction (PBRRI) model. Role based access control is introduced for security concerns. Regulation rules are enforced in a distributed manner so that PBRRI can be applied to the open distributed systems such as Internet.展开更多
Along with the implementation of electricity sales-side reform and incremental power distribution investment liberalization under China’s new round of electricity market reform, the main subjects of distributed gener...Along with the implementation of electricity sales-side reform and incremental power distribution investment liberalization under China’s new round of electricity market reform, the main subjects of distributed generation investment, construction, and operations have become more various, and the approaches as well as the ways that distributed generation takes part in the market have been more flexible. How to operate distributed generation economically and efficiently in this market environment has become an urgent issue to be addressed. Based on the domestic development situation of China’s distributed generation, this paper introduces typical existing business models of distributed generation, and elaborates the correlation between electricity market reform and business models of distributed generation in depth in combination with the contents of electricity market reform. At last, several business models of distributed generation which are feasible to be implemented in China have been proposed under the new electricity market reform based on successful international distributed generation operation experiences in the terms of stimulating stakeholders’ participation in the investment and operations of distributed generation. Characteristics of these recommended models are compared and analyzed as well.展开更多
Significant progress has been made in distributed unmanned aerial vehicle(UAV)swarm exploration.In complex scenarios,existing methods typically rely on shared trajectory information for collision avoidance,but communi...Significant progress has been made in distributed unmanned aerial vehicle(UAV)swarm exploration.In complex scenarios,existing methods typically rely on shared trajectory information for collision avoidance,but communication timeliness issues may result in outdated trajectories being referenced when making collision avoidance decisions,preventing timely responses to the motion changes of other UAVs,thus elevating the collision risk.To address this issue,this paper proposes a new distributed UAV swarm exploration framework.First,we introduce an improved global exploration strategy that combines the exploration task requirements with the surrounding obstacle distribution to plan an efficient and safe coverage path.Secondly,we design a collision risk prediction method based on relative distance and relative velocity,which effectively assists UAVs in making timely collision avoidance decisions.Lastly,we propose a multi-objective local trajectory optimization function that considers the positions of UAVs and static obstacles,thereby planning safe flight trajectories.Extensive simulations and real-world experiments demonstrate that this framework enables safe and efficient exploration in complex environments.展开更多
Fingerprint has been widely used in a variety of biometric identification systems in the past several years due to its uniqueness and immutability.With the rapid development of fingerprint identification techniques,ma...Fingerprint has been widely used in a variety of biometric identification systems in the past several years due to its uniqueness and immutability.With the rapid development of fingerprint identification techniques,many fingerprint identification systems are in urgent need to deal with large-scale fingerprint storage and high concurrent recognition queries,which bring huge challenges to the system.In this circumstance,we design and implement a distributed and load-balancing fingerprint identification system named Pegasus,which includes a distributed feature extraction subsystem and a distributed feature storage subsystem.The feature extraction procedure combines the Hadoop Image Processing Interface(HIPI)library to enhance its overall processing speed;the feature storage subsystem optimizes MongoD B's default load balance strategy to improve the efficiency and robustness of Pegasus.Experiments and simulations are carried out,and results show that Pegasus can reduce the time cost by 70%during the feature extraction procedure.Pegasus also balances the difference of access load among front-end mongos nodes to less than 5%.Additionally,Pegasus reduces over 40%of data migration among back-end data shards to obtain a more reasonable data distribution based on the operation load(insertion,deletion,update,and query)of each shard.展开更多
This paper briefly analyzed some problems and limitations of existing evaluation indicators for distributed energy system performance. Based on the diversity and difference of the energy form and grade of cooling, hea...This paper briefly analyzed some problems and limitations of existing evaluation indicators for distributed energy system performance. Based on the diversity and difference of the energy form and grade of cooling, heating and power in the distributed combined cooling, heating, and power(CCHP) system, this paper proposed three-index evaluation system composed of key indicators including relative energy saving rate, exergy efficiency and thermoelectric ratio. In order to further prove the reasonableness and scientificity of the evaluation index system applied in performance evaluation of distributed CCHP system, it enumerated, calculated, and evaluated the energy conservation of three engineering cases. The evaluation results showed that the high energy-saving rate of the system did not mean good energy-saving, but also the efficiency of the system should be examined. Only when the energy saving rate and exergy efficiency were both high, can the energy saving performance of the system be demonstrated. When the energy-saving rate was high and the efficiency was not high, it is shown that the energy-saving of the system had great room for improvement.展开更多
Sparse bundle adjustment(SBA) is a key but time-and memory-consuming step in three-dimensional(3 D) reconstruction. In this paper, we propose a 3 D point-based distributed SBA algorithm(DSBA) to improve the speed and ...Sparse bundle adjustment(SBA) is a key but time-and memory-consuming step in three-dimensional(3 D) reconstruction. In this paper, we propose a 3 D point-based distributed SBA algorithm(DSBA) to improve the speed and scalability of SBA. The algorithm uses an asynchronously distributed sparse bundle adjustment(A-DSBA)to overlap data communication with equation computation. Compared with the synchronous DSBA mechanism(SDSBA), A-DSBA reduces the running time by 46%. The experimental results on several 3 D reconstruction datasets reveal that our distributed algorithm running on eight nodes is up to five times faster than that of the stand-alone parallel SBA. Furthermore, the speedup of the proposed algorithm(running on eight nodes with 48 cores) is up to41 times that of the serial SBA(running on a single node).展开更多
The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning problems.However,the communication overhead is a major bottleneck that hampers ...The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning problems.However,the communication overhead is a major bottleneck that hampers the scalability of distributed machine learning systems.In this paper,we design two communication-efficient algorithms for distributed learning tasks.The first one is named EF-SIGNGD,in which we use the 1-bit(sign-based) gradient quantization method to save the communication bits.Moreover,the error feedback technique,i.e.,incorporating the error made by the compression operator into the next step,is employed for the convergence guarantee.The second algorithm is called LE-SIGNGD,in which we introduce a well-designed lazy gradient aggregation rule to EF-SIGNGD that can detect the gradients with small changes and reuse the outdated information.LE-SIGNGD saves communication costs both in transmitted bits and communication rounds.Furthermore,we show that LE-SIGNGD is convergent under some mild assumptions.The effectiveness of the two proposed algorithms is demonstrated through experiments on both real and synthetic data.展开更多
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
A new solar energy and biomass-based distributed energy system using H_(2)O/CO_(2)hybrid gasification is proposed,and their complementarity to enhance the system's energy efficiency is investigated and shown.In th...A new solar energy and biomass-based distributed energy system using H_(2)O/CO_(2)hybrid gasification is proposed,and their complementarity to enhance the system's energy efficiency is investigated and shown.In the system,concentrated solar energy is used to provide heat for biomass gasification;two gasifying agents(H_(2)O and CO_(2))are adopted to enhance syngas yields,and the produced solar fuel is further burned for power production in a combined cycle plant.Results show that CO share in gasification products is remarkably increased with the increment of CO_(2)/H_(2)O mole ratio caused by the boudouard reaction with the consumption of fixed carbon,while the H_(2)share is decreased;the optimal solar-to-fuel efficiency,27.88%,is achieved when the temperature and CO_(2)/H_(2)O mole ratio are 1050℃and 0.45,respectively.The emission reduction rate of CO_(2)in the system under design conditions is reduced by 2.31%compared with that using only H_(2)O agent.The annual power production of the system is increased by 1.39%,and the thermodynamic and environmental performances are significantly improved.Moreover,an economic assessment is conducted to forecast the technical feasibility of the hybrid gasification technology.This work provides a promising route to improving the thermochemical utilization efficiency of solar energy and solid fuel.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
Many proposed P2P networks are based on traditional interconnection topologies. Given a static topology, the maintenance mechanism for node join/departure is critical to designing an efficient P2P network. Kautz graph...Many proposed P2P networks are based on traditional interconnection topologies. Given a static topology, the maintenance mechanism for node join/departure is critical to designing an efficient P2P network. Kautz graphs have many good properties such as constant degree, low congestion and optimal diameter. Due to the complexity in topology maintenance, however, to date there have been no effective P2P networks that are proposed based on Kautz graphs with base ~ 2. To address this problem, this paper presents the ＂distributed Kautz （D-Kautz） graphs＂, which adapt Kautz graphs to the characteristics of P2P networks. Using the D-Kautz graphs we further propose SKY, the first effective P2P network based on Kautz graphs with arbitrary base. The effectiveness of SKY is demonstrated through analysis and simulations.展开更多
Many machine learning and data mining （MLDM] problems like recommendation, topic modeling, and medical diagnosis can be modeled as computing on bipartite graphs. However, inost distributed graph-parallel systems are ...Many machine learning and data mining （MLDM] problems like recommendation, topic modeling, and medical diagnosis can be modeled as computing on bipartite graphs. However, inost distributed graph-parallel systems are oblivious to the unique characteristics in such graphs and existing online graph partitioning algorithms usually cause excessive repli- cation of vertices as well as significant pressure on network communication. This article identifies the challenges and oppor- tunities of partitioning bipartite graphs for distributed MLDM processing and proposes BiGraph, a set of bipartite-oriented graph partitioning algorithms. BiGraph leverages observations such as the skewed distribution of vertices, discriminated computation load and imbalanced data sizes between the two subsets of vertices to derive a set of optimal graph partition- ing algorithms that result in minimal vertex replication and network communication. BiGraph has been implemented on PowerGraph and is shown to have a performance boost up to 17.75X （from 1.16X） for four typical MLDM algorithnls, due to reducing up to 80% vertex replication, and up to 96% network traffic.展开更多
In addition to increasing penetration of distributed generation(DG),the distribution system power flow may be significantly impacted by direction and magnitude.This paper proposes a method for optimal placement of win...In addition to increasing penetration of distributed generation(DG),the distribution system power flow may be significantly impacted by direction and magnitude.This paper proposes a method for optimal placement of wind DG considering the unbalanced operation of distribution systems.The objective function includes static voltage stability index,three-phase unbalance index,system reliability index,and DG investment cost.The untransposed distribution lines and unbalanced load are modelled,and corresponding static voltage stability index and system reliability considering DG penetrations are derived.The expected and stochastic daily distributed generation and demand profiles in four seasons are calculated to improve the accuracy.To solve this multi-objective optimization model,a fuzzy membership function is used to integrate the four individual objectives,and a sensitivity-based method is proposed to solve the model efficiently.Case study on IEEE 13-bus distribution 3-phase networks and 123-node test feeder successfully verifies the performance of the proposed approach.展开更多
基金supported by the Inner Mongolia Power Company 2024 Staff Innovation Studio Innovation Project“Research on Cluster Output Prediction and Group Control Technology for County-Wide Distributed Photovoltaic Construction”.
文摘Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition(VMD)and Channel Attention Mechanism.First,Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power.Second,the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition(VMD).Finally,the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM model.In this model,the convolutional neural network(CNN)and channel attention mechanism dynamically adjust the weights while capturing the spatial features of the input data to improve the discriminative ability of key features.The extracted data is then fed into the bidirectional long short-term memory network(BiLSTM)to capture the time-series features,and the final output is the prediction result.The verification is conducted using a dataset from a distributed photovoltaic power station in the Northwest region of China.The results show that compared with other prediction methods,the method proposed in this paper has a higher prediction accuracy,which helps to improve the proportion of distributed PV access to the grid,and can guarantee the safe and stable operation of the power grid.
基金supported by the National Grand Fundamental Research of China (973 Program) under Grant No. 2011CB302601the National High Technology Research and Development of China (863 Program) under GrantNo. 2013AA01A213+2 种基金the National Natural Science Foundation of China under Grant No. 60873215the Natural Science Foundation for Distinguished Young Scholars of Hunan Province under Grant No. S2010J5050Specialized Research Fund for the Doctoral Program of Higher Education under Grant No. 20124307110015
文摘To reduce the time required to complete the regeneration process of erasure codes, we propose a Tree-structured Parallel Regeneration (TPR) scheme for multiple data losses in distributed storage systems. Under the scheme, two algorithms are proposed for the construction of multiple regeneration trees, namely the edge-disjoint algorithm and edge-sharing algorithm. The edge-disjoint algorithm constructs multiple independent trees, and is simple and appropriate for environments where newcomers and their providers are distributed over a large area and have few intersections. The edge-sharing algorithm constructs multiple trees that compete to utilize the bandwidth, and make a better utilization of the bandwidth, although it needs to measure the available band-width and deal with the bandwidth changes; it is therefore difficult to implement in practical systems. The parallel regeneration for multiple data losses of TPR primarily includes two optimizations: firstly, transferring the data through the bandwidth optimized-paths in a pipe-line manner; secondly, executing data regeneration over multiple trees in parallel. To evaluate the proposal, we implement an event-based simulator and make a detailed comparison with some popular regeneration methods. The quantitative comparison results show that the use of TPR employing either the edge-disjoint algorithm or edge-sharing algorithm reduces the regeneration time significantly.
基金partly supported by National Key Basic Research Program of China(2016YFB1000100)partly supported by National Natural Science Foundation of China(NO.61402490)。
文摘To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution scheme.However,the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly,which highly demand on computing and memory resources.To overcome the challenges,we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method,which is a lightweight,few additional cost and parallelized scheme for the model training process.To validate the claims,we have conducted several experiments on multiple classical datasets.Results show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.
文摘The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels.This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions.The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales.
基金supported in part by the National Natural Science Foundation of Chin(61503194,61533010,61374055)the Ph.D.Programs Foundation of Ministry of Education of China(20110142110036)+6 种基金the Natural Science Foundation o Jiangsu Province(BK20131381,BK20140877)China Postdoctoral Scienc Foundation(2015M571788)Jiangsu Planned Projects for Postdoctoral Re search Funds(1402066B)the Foundation of the Key Laboratory of Marin Dynamic Simulation and Control for the Ministry of Transport(DMU)(DMU MSCKLT2016005)Jiangsu Government Scholarship for Overseas Studie(2017-037)the Key University Natural Science Research Project of Jiangsu Province(17KJA120003)the Scientific Foundation of Nanjing University of Posts and Telecommunications(NUPTSF)(NY214076)
文摘In this paper, we consider a consensus tracking problem of a class of networked multi-agent systems(MASs)in non-affine pure-feedback form under a directed topology. A distributed adaptive tracking consensus control scheme is constructed recursively by the backstepping method, graph theory,neural networks(NNs) and the dynamic surface control(DSC)approach. The key advantage of the proposed control strategy is that, by the DSC technique, it avoids "explosion of complexity"problem along with the increase of the degree of individual agents and thus the computational burden of the scheme can be drastically reduced. Moreover, there is no requirement for prior knowledge about system parameters of individual agents and uncertain dynamics by employing NNs approximation technology.We then further show that, in theory, the designed control policy guarantees the consensus errors to be cooperatively semi-globally uniformly ultimately bounded(CSUUB). Finally, two examples are presented to validate the effectiveness of the proposed control strategy.
文摘Distributed photovoltaic(PV)is one of the important power sources for building a new power system with new energy as the main body.The rapid development of distributed PV has brought new challenges to the operation of distribution networks.In order to improve the absorption ability of large-scale distributed PV access to the distribution network,the AC/DC hybrid distribution network is constructed based on flexible interconnection technology,and a coordinated scheduling strategy model of hydrogen energy storage(HS)and distributed PV is established.Firstly,the mathematical model of distributed PV and HS system is established,and a comprehensive energy storage system combining seasonal hydrogen energy storage(SHS)and battery(BT)is proposed.Then,a flexible interconnected distribution network scheduling optimization model is established to minimize the total active power loss,voltage deviation and system operating cost.Finally,simulation analysis is carried out on the improved IEEE33 node,the NSGA-II algorithm is used to solve specific examples,and the optimal scheduling results of the comprehensive economy and power quality of the distribution network are obtained.Compared with the method that does not consider HS and flexible interconnection technology,the network loss and voltage deviation of this method are lower,and the total system cost can be reduced by 3.55%,which verifies the effectiveness of the proposed method.
文摘The introduction of international,Chinese and foreign standards for distributed resources integration is presented in this paper.The typical standards in North America,Europe and China are compared in several key aspects,
文摘In distributed systems independent agents need to interact with each other to accomplish their task. Modern peer-to-peer computing technologies always concern with enabling interaction among agents and help them cooperate with each other. But in fact, access control should also be considered to limit interaction to make it harmless. This paper proposed a proxy based rule regulated interaction (PBRRI) model. Role based access control is introduced for security concerns. Regulation rules are enforced in a distributed manner so that PBRRI can be applied to the open distributed systems such as Internet.
基金supported by SGCC Scientific and Technological Project(PDB17201600043)National Key R&D Program of China(2016YFB0900400)
文摘Along with the implementation of electricity sales-side reform and incremental power distribution investment liberalization under China’s new round of electricity market reform, the main subjects of distributed generation investment, construction, and operations have become more various, and the approaches as well as the ways that distributed generation takes part in the market have been more flexible. How to operate distributed generation economically and efficiently in this market environment has become an urgent issue to be addressed. Based on the domestic development situation of China’s distributed generation, this paper introduces typical existing business models of distributed generation, and elaborates the correlation between electricity market reform and business models of distributed generation in depth in combination with the contents of electricity market reform. At last, several business models of distributed generation which are feasible to be implemented in China have been proposed under the new electricity market reform based on successful international distributed generation operation experiences in the terms of stimulating stakeholders’ participation in the investment and operations of distributed generation. Characteristics of these recommended models are compared and analyzed as well.
基金supported in part by the National Key Research and Development Program of China under Grant No.2022YFA1004700in part by the National Natural Science Foundation of China under Grant Nos.62301308,62305019,62371342,and 62071334+4 种基金in part by the Shanghai Municipal Science and Technology Major Project under Grant No.2021SHZDZX0100in part by the Shanghai Municipal Commission of Science and Technology Project under Grant No.19511132101in part by the Aeronautical Science Foundation of China under Grant No.20230007308001in part by the Fundamental Research Funds for the Central Universities under Grant No.22120210543in part by Tianjin Transportation Science and Technology Project under Grant No.2025-76.
文摘Significant progress has been made in distributed unmanned aerial vehicle(UAV)swarm exploration.In complex scenarios,existing methods typically rely on shared trajectory information for collision avoidance,but communication timeliness issues may result in outdated trajectories being referenced when making collision avoidance decisions,preventing timely responses to the motion changes of other UAVs,thus elevating the collision risk.To address this issue,this paper proposes a new distributed UAV swarm exploration framework.First,we introduce an improved global exploration strategy that combines the exploration task requirements with the surrounding obstacle distribution to plan an efficient and safe coverage path.Secondly,we design a collision risk prediction method based on relative distance and relative velocity,which effectively assists UAVs in making timely collision avoidance decisions.Lastly,we propose a multi-objective local trajectory optimization function that considers the positions of UAVs and static obstacles,thereby planning safe flight trajectories.Extensive simulations and real-world experiments demonstrate that this framework enables safe and efficient exploration in complex environments.
基金Project supported by the National Basic Research Program(973)of China(No.2014CB340303)the National Natural Science Foundation of China(Nos.61222205 and 61402490)+1 种基金the Program for New Century Excellent Talents in University,China(No.141066)the Fok Ying-Tong Education Foundation
文摘Fingerprint has been widely used in a variety of biometric identification systems in the past several years due to its uniqueness and immutability.With the rapid development of fingerprint identification techniques,many fingerprint identification systems are in urgent need to deal with large-scale fingerprint storage and high concurrent recognition queries,which bring huge challenges to the system.In this circumstance,we design and implement a distributed and load-balancing fingerprint identification system named Pegasus,which includes a distributed feature extraction subsystem and a distributed feature storage subsystem.The feature extraction procedure combines the Hadoop Image Processing Interface(HIPI)library to enhance its overall processing speed;the feature storage subsystem optimizes MongoD B's default load balance strategy to improve the efficiency and robustness of Pegasus.Experiments and simulations are carried out,and results show that Pegasus can reduce the time cost by 70%during the feature extraction procedure.Pegasus also balances the difference of access load among front-end mongos nodes to less than 5%.Additionally,Pegasus reduces over 40%of data migration among back-end data shards to obtain a more reasonable data distribution based on the operation load(insertion,deletion,update,and query)of each shard.
基金supported by the Special Fund for Science and Technology Development of Guangdong Province in 2017 (2017A010104014)the National Natural Science Foundation of China (51176036, 21376052)the National Basic Research Program of China (2010CB227306 and 2016YFD04003032)
文摘This paper briefly analyzed some problems and limitations of existing evaluation indicators for distributed energy system performance. Based on the diversity and difference of the energy form and grade of cooling, heating and power in the distributed combined cooling, heating, and power(CCHP) system, this paper proposed three-index evaluation system composed of key indicators including relative energy saving rate, exergy efficiency and thermoelectric ratio. In order to further prove the reasonableness and scientificity of the evaluation index system applied in performance evaluation of distributed CCHP system, it enumerated, calculated, and evaluated the energy conservation of three engineering cases. The evaluation results showed that the high energy-saving rate of the system did not mean good energy-saving, but also the efficiency of the system should be examined. Only when the energy saving rate and exergy efficiency were both high, can the energy saving performance of the system be demonstrated. When the energy-saving rate was high and the efficiency was not high, it is shown that the energy-saving of the system had great room for improvement.
基金Project supported by the National Natural Science Foundation of China(Nos.U1435219,U1435222,and 61572515)the National Key R&D Program of China(No.2016YFB0200401)the Major Research Plan of the National Key R&D Program of China(No.2016YFC0901600)
文摘Sparse bundle adjustment(SBA) is a key but time-and memory-consuming step in three-dimensional(3 D) reconstruction. In this paper, we propose a 3 D point-based distributed SBA algorithm(DSBA) to improve the speed and scalability of SBA. The algorithm uses an asynchronously distributed sparse bundle adjustment(A-DSBA)to overlap data communication with equation computation. Compared with the synchronous DSBA mechanism(SDSBA), A-DSBA reduces the running time by 46%. The experimental results on several 3 D reconstruction datasets reveal that our distributed algorithm running on eight nodes is up to five times faster than that of the stand-alone parallel SBA. Furthermore, the speedup of the proposed algorithm(running on eight nodes with 48 cores) is up to41 times that of the serial SBA(running on a single node).
基金supported in part by the Core Electronic Devices, High-End Generic Chips, and Basic Software Major Special Projects (No. 2018ZX01028101)the National Natural Science Foundation of China (Nos. 61907034, 61932001, and 61906200)。
文摘The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning problems.However,the communication overhead is a major bottleneck that hampers the scalability of distributed machine learning systems.In this paper,we design two communication-efficient algorithms for distributed learning tasks.The first one is named EF-SIGNGD,in which we use the 1-bit(sign-based) gradient quantization method to save the communication bits.Moreover,the error feedback technique,i.e.,incorporating the error made by the compression operator into the next step,is employed for the convergence guarantee.The second algorithm is called LE-SIGNGD,in which we introduce a well-designed lazy gradient aggregation rule to EF-SIGNGD that can detect the gradients with small changes and reuse the outdated information.LE-SIGNGD saves communication costs both in transmitted bits and communication rounds.Furthermore,we show that LE-SIGNGD is convergent under some mild assumptions.The effectiveness of the two proposed algorithms is demonstrated through experiments on both real and synthetic data.
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supported by the National Natural Science Foundation of China(No.52306220)Major Program of the National Natural Science Foundation of China(No.52090061)。
文摘A new solar energy and biomass-based distributed energy system using H_(2)O/CO_(2)hybrid gasification is proposed,and their complementarity to enhance the system's energy efficiency is investigated and shown.In the system,concentrated solar energy is used to provide heat for biomass gasification;two gasifying agents(H_(2)O and CO_(2))are adopted to enhance syngas yields,and the produced solar fuel is further burned for power production in a combined cycle plant.Results show that CO share in gasification products is remarkably increased with the increment of CO_(2)/H_(2)O mole ratio caused by the boudouard reaction with the consumption of fixed carbon,while the H_(2)share is decreased;the optimal solar-to-fuel efficiency,27.88%,is achieved when the temperature and CO_(2)/H_(2)O mole ratio are 1050℃and 0.45,respectively.The emission reduction rate of CO_(2)in the system under design conditions is reduced by 2.31%compared with that using only H_(2)O agent.The annual power production of the system is increased by 1.39%,and the thermodynamic and environmental performances are significantly improved.Moreover,an economic assessment is conducted to forecast the technical feasibility of the hybrid gasification technology.This work provides a promising route to improving the thermochemical utilization efficiency of solar energy and solid fuel.
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Supported partially by the National Natural Science Foundation of China (Grant Nos. 60673167 and 60703072)the Hunan Provincial Natural Science Foundation of China (Grant No. 08JJ3125)the National Basic Research Program of China (973) (Grant No. 2005CB321801)
文摘Many proposed P2P networks are based on traditional interconnection topologies. Given a static topology, the maintenance mechanism for node join/departure is critical to designing an efficient P2P network. Kautz graphs have many good properties such as constant degree, low congestion and optimal diameter. Due to the complexity in topology maintenance, however, to date there have been no effective P2P networks that are proposed based on Kautz graphs with base ~ 2. To address this problem, this paper presents the ＂distributed Kautz （D-Kautz） graphs＂, which adapt Kautz graphs to the characteristics of P2P networks. Using the D-Kautz graphs we further propose SKY, the first effective P2P network based on Kautz graphs with arbitrary base. The effectiveness of SKY is demonstrated through analysis and simulations.
基金This work was supported in part by the Doctoral Fund of Ministry of Education of China under Grant No. 20130073120040, the Program for New Century Excellent Talents in University of Ministry of Education of China, the Shanghai Science and Technology Developnmnt hinds under Grant No. 12QA1401700, a foundation for the Author of National Excellent Doctoral Dissertation of China, the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing under Grant No. 2014A05, the National Natural Science Foundation of China under Grant Nos. 61003002, 61402284, the Shanghai Science and Technology Development Fund for High-Tech Achievement Translation under Grant No. 14511100902, and the Singapore National Research Foundation under Grant No. CREATE E2S2.
文摘Many machine learning and data mining （MLDM] problems like recommendation, topic modeling, and medical diagnosis can be modeled as computing on bipartite graphs. However, inost distributed graph-parallel systems are oblivious to the unique characteristics in such graphs and existing online graph partitioning algorithms usually cause excessive repli- cation of vertices as well as significant pressure on network communication. This article identifies the challenges and oppor- tunities of partitioning bipartite graphs for distributed MLDM processing and proposes BiGraph, a set of bipartite-oriented graph partitioning algorithms. BiGraph leverages observations such as the skewed distribution of vertices, discriminated computation load and imbalanced data sizes between the two subsets of vertices to derive a set of optimal graph partition- ing algorithms that result in minimal vertex replication and network communication. BiGraph has been implemented on PowerGraph and is shown to have a performance boost up to 17.75X （from 1.16X） for four typical MLDM algorithnls, due to reducing up to 80% vertex replication, and up to 96% network traffic.
基金supported in part by the 2015 Science and Technology Project of China Southern Power Grid(WYKJ00000027)in part by funding from mid-career researcher development scheme,the Faculty of Engineering&Information Technologies,The University of Sydney.
文摘In addition to increasing penetration of distributed generation(DG),the distribution system power flow may be significantly impacted by direction and magnitude.This paper proposes a method for optimal placement of wind DG considering the unbalanced operation of distribution systems.The objective function includes static voltage stability index,three-phase unbalance index,system reliability index,and DG investment cost.The untransposed distribution lines and unbalanced load are modelled,and corresponding static voltage stability index and system reliability considering DG penetrations are derived.The expected and stochastic daily distributed generation and demand profiles in four seasons are calculated to improve the accuracy.To solve this multi-objective optimization model,a fuzzy membership function is used to integrate the four individual objectives,and a sensitivity-based method is proposed to solve the model efficiently.Case study on IEEE 13-bus distribution 3-phase networks and 123-node test feeder successfully verifies the performance of the proposed approach.
