With the rapid development of smart logistics,the integration of digital twin technology into warehousing and distribution network optimization has emerged as a promising solution to improve operational efficiency,red...With the rapid development of smart logistics,the integration of digital twin technology into warehousing and distribution network optimization has emerged as a promising solution to improve operational efficiency,reduce costs,and enhance decision-making capabilities.This study explores how a digital twin framework can be constructed for smart warehousing and how it can be leveraged to optimize distribution networks.We review existing literature on digital twin foundations,smart warehousing systems,and distribution optimization methods,highlighting research gaps and practical challenges.A conceptual three-layer architecture is proposed,comprising the perception layer,data layer,and application layer,along with data flow and functional modules including warehousing simulation,dynamic routing optimization,and predictive maintenance.The discussion elaborates on the advantages,limitations,and managerial implications of the proposed framework.Future research directions include empirical validation,integration with blockchain,and applications in green logistics.展开更多
With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),po...With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy.展开更多
With the increasing penetration of large-scale renewable energy sources into the power grid,distribution networks are facing significant challenges,including intensified voltage fluctuations and increased network loss...With the increasing penetration of large-scale renewable energy sources into the power grid,distribution networks are facing significant challenges,including intensified voltage fluctuations and increased network losses.Although deep reinforcement learning has made considerable advancements in addressing optimi-zation problems compared to traditional algorithms,there has been limited focus on enhancing convergence and safety in cooperative optimization scenarios,particularly those involving topological reconstruction.To overcome these challenges,this paper proposes a distribution network optimization model that incorporates topological security-constrained integrated reinforcement learning.The model improves the encoding of topologies by representing them in a multi-dimensional discrete space and introduces a topological masking mechanism to achieve high safety and computational efficiency.Addi-tionally,an ensemble strategy is utilized to develop an action network group,improving action prediction and screening,thereby achieving better training stability.Experiments conducted on an enhanced IEEE33-node distribution network system indicate that the proposed improvements significantly enhance training stability and support the safe and efficient operation of the system.展开更多
In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation pr...In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation problems are jointly optimized.In order to make the resource allocation suitable for large scale networks,the optimization problem is decomposed first based on an effective decomposition algorithm named optimal condition decomposition(OCD) algorithm.Furthermore,aiming at reducing implementation complexity,the subcarriers are divided into chunks and are allocated chunk by chunk.The simulation results show that the proposed algorithm achieves more superior performance than uniform power allocation scheme and Lagrange relaxation method,and then the proposed algorithm can strike a balance between the complexity and performance of the multi-carrier Ultra-Dense Networks.展开更多
A distibuted optimal local double loop(DOLDL) network is presented. Emphasis is laid on the topology and distributed routing algorithms for the DOLDL. On the basis of building an abstract model, a set of definitions a...A distibuted optimal local double loop(DOLDL) network is presented. Emphasis is laid on the topology and distributed routing algorithms for the DOLDL. On the basis of building an abstract model, a set of definitions and theorems are described and proved. An algorithm which can optimize the double loop networks is presented. The optimal values of the topologic parameters for the DOLDL have been obtained by the algorithm, and these numerical results are analyzed. The study shows that the bounds of the optimal diameter (d) and average hop distance (a) for this class of networks are [square-root 3N -2] less-than-or-equal-to d less-than-or-equal-to [square-root 3N+1] and (5N/9(N-1)) (square-root 3N-1.8) < a < (5N/9 (N-1)). (square-root 3N - 0.23), respectively (N is the number of nodes in the network. (3 less-than-or-equal-to N less-than-or-equal-to 10(4)). A class of the distributed routing algorithms for the DOLDL and the implementation procedure of an adaptive fault-tolerant algorithm are proposed. The correctness of the algorithm has been also verified by simulating.展开更多
Development of power electronic devices and their implementation in power distribution networks significantly improves not only the easier integration of distributed generation into distribution networks but also the ...Development of power electronic devices and their implementation in power distribution networks significantly improves not only the easier integration of distributed generation into distribution networks but also the further refinement of control algorithms specialized in the application of power management procedures in distribution systems.Implementation of DC Soft Open Points(DCSOP)for closing loops in distribution networks could serve as one of the most representative examples of the use of power electronic devices as management resources in modern distribution networks.The paper describes the possibilities of the application of DCSOP used for loops closure and power flow control in medium voltage distribution networks with integrated distributed generators.Considering all the benefits of loops closure and the controllability of the DCSOP,it is considered as a management resource capable of achieving the desired/optimal state in the distribution network.The management strategy proposed in this paper covers two cases:the first case is where an adequate communication infrastructure is available when all DCSOP operations are based on Optimal Power Flow(OPF)calculations,and the second case is when OPF results are unavailable for some reason(most often due to communication interruptions)and DCSOP operations are based on processing available data using a neural network-based algorithm.The proposed management strategy has been implemented and tested on an IEEE 33 distribution test network with integrated distributed generators.The simulation results,described in this paper,can serve not only as a basis for further scientific research but also as a basis for the practical implementation of real controllers based on the proposed algorithm and its application in practice,and in real distribution networks.展开更多
This paper considers the collaborative resource allocation problem over a hybrid cloud center and edge server network, an emerging infrastructure for efficient Internet services. The cloud center acts as a pool of ine...This paper considers the collaborative resource allocation problem over a hybrid cloud center and edge server network, an emerging infrastructure for efficient Internet services. The cloud center acts as a pool of inexhaustible computation and storage powers. The edge servers often have limited computation and storage powers but are able to provide quick responses to service requests from end users. Upon receiving service requests, edge servers assign them to themselves, their neighboring edge servers, as well as the cloud center, aiming at minimizing the overall network cost. This paper first establishes an optimization model for this problem. Second, in light of the separable structure of the optimization model, we utilize the alternating direction method of multipliers （ADMM） to develop a fully collaborative resource allocation algorithm. The edge servers and the cloud center autonomously collaborate to compute their local optimization variables and prices of network resources, and reach an optimal solution. Numerical experiments demonstrate the effectiveness of the hybrid network infrastructure as well as the proposed algorithm.展开更多
A flaw of demand coverage method in solving optimal monitoring stations problem under multiple demand patterns was identified in this paper. In the demand coverage method, the demand coverage of each set of monitoring...A flaw of demand coverage method in solving optimal monitoring stations problem under multiple demand patterns was identified in this paper. In the demand coverage method, the demand coverage of each set of monitoring stations is calculated by accumulating their demand coverage under each demand pattern, and the impact of temporal distribution between different time periods or demand patterns is ignored. This could lead to miscalculation of the optimal locations of the monitoring stations. To overcome this flaw, this paper presents a Demand Coverage Index （DCI） based method. The optimization considers extended period unsteady hydrau- lics due to the change of nodal demands with time. The method is cast in a genetic algorithm framework for integration with Environmental Protection Agency Net （EPANET） and is demonstrated through example applica- tions. Results show that the set of optimal locations of monitoring stations obtained using the DCI method can represent the water quality of water distribution systems under multiple demand patterns better than the one obtained using previous methods.展开更多
文摘With the rapid development of smart logistics,the integration of digital twin technology into warehousing and distribution network optimization has emerged as a promising solution to improve operational efficiency,reduce costs,and enhance decision-making capabilities.This study explores how a digital twin framework can be constructed for smart warehousing and how it can be leveraged to optimize distribution networks.We review existing literature on digital twin foundations,smart warehousing systems,and distribution optimization methods,highlighting research gaps and practical challenges.A conceptual three-layer architecture is proposed,comprising the perception layer,data layer,and application layer,along with data flow and functional modules including warehousing simulation,dynamic routing optimization,and predictive maintenance.The discussion elaborates on the advantages,limitations,and managerial implications of the proposed framework.Future research directions include empirical validation,integration with blockchain,and applications in green logistics.
基金Supported by the Technology Project of State Grid Corporation Headquarters(No.5100-202322029A-1-1-ZN)the 2024 Youth Science Foundation Project of China (No.62303006)。
文摘With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy.
基金supported by the National Natural Science Foundation of China(No.U24B2088).
文摘With the increasing penetration of large-scale renewable energy sources into the power grid,distribution networks are facing significant challenges,including intensified voltage fluctuations and increased network losses.Although deep reinforcement learning has made considerable advancements in addressing optimi-zation problems compared to traditional algorithms,there has been limited focus on enhancing convergence and safety in cooperative optimization scenarios,particularly those involving topological reconstruction.To overcome these challenges,this paper proposes a distribution network optimization model that incorporates topological security-constrained integrated reinforcement learning.The model improves the encoding of topologies by representing them in a multi-dimensional discrete space and introduces a topological masking mechanism to achieve high safety and computational efficiency.Addi-tionally,an ensemble strategy is utilized to develop an action network group,improving action prediction and screening,thereby achieving better training stability.Experiments conducted on an enhanced IEEE33-node distribution network system indicate that the proposed improvements significantly enhance training stability and support the safe and efficient operation of the system.
基金supported in part by Beijing Natural Science Foundation(4152047)the 863 project No.2014AA01A701+1 种基金111 Project of China under Grant B14010China Mobile Research Institute under grant[2014]451
文摘In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation problems are jointly optimized.In order to make the resource allocation suitable for large scale networks,the optimization problem is decomposed first based on an effective decomposition algorithm named optimal condition decomposition(OCD) algorithm.Furthermore,aiming at reducing implementation complexity,the subcarriers are divided into chunks and are allocated chunk by chunk.The simulation results show that the proposed algorithm achieves more superior performance than uniform power allocation scheme and Lagrange relaxation method,and then the proposed algorithm can strike a balance between the complexity and performance of the multi-carrier Ultra-Dense Networks.
文摘A distibuted optimal local double loop(DOLDL) network is presented. Emphasis is laid on the topology and distributed routing algorithms for the DOLDL. On the basis of building an abstract model, a set of definitions and theorems are described and proved. An algorithm which can optimize the double loop networks is presented. The optimal values of the topologic parameters for the DOLDL have been obtained by the algorithm, and these numerical results are analyzed. The study shows that the bounds of the optimal diameter (d) and average hop distance (a) for this class of networks are [square-root 3N -2] less-than-or-equal-to d less-than-or-equal-to [square-root 3N+1] and (5N/9(N-1)) (square-root 3N-1.8) < a < (5N/9 (N-1)). (square-root 3N - 0.23), respectively (N is the number of nodes in the network. (3 less-than-or-equal-to N less-than-or-equal-to 10(4)). A class of the distributed routing algorithms for the DOLDL and the implementation procedure of an adaptive fault-tolerant algorithm are proposed. The correctness of the algorithm has been also verified by simulating.
文摘Development of power electronic devices and their implementation in power distribution networks significantly improves not only the easier integration of distributed generation into distribution networks but also the further refinement of control algorithms specialized in the application of power management procedures in distribution systems.Implementation of DC Soft Open Points(DCSOP)for closing loops in distribution networks could serve as one of the most representative examples of the use of power electronic devices as management resources in modern distribution networks.The paper describes the possibilities of the application of DCSOP used for loops closure and power flow control in medium voltage distribution networks with integrated distributed generators.Considering all the benefits of loops closure and the controllability of the DCSOP,it is considered as a management resource capable of achieving the desired/optimal state in the distribution network.The management strategy proposed in this paper covers two cases:the first case is where an adequate communication infrastructure is available when all DCSOP operations are based on Optimal Power Flow(OPF)calculations,and the second case is when OPF results are unavailable for some reason(most often due to communication interruptions)and DCSOP operations are based on processing available data using a neural network-based algorithm.The proposed management strategy has been implemented and tested on an IEEE 33 distribution test network with integrated distributed generators.The simulation results,described in this paper,can serve not only as a basis for further scientific research but also as a basis for the practical implementation of real controllers based on the proposed algorithm and its application in practice,and in real distribution networks.
文摘This paper considers the collaborative resource allocation problem over a hybrid cloud center and edge server network, an emerging infrastructure for efficient Internet services. The cloud center acts as a pool of inexhaustible computation and storage powers. The edge servers often have limited computation and storage powers but are able to provide quick responses to service requests from end users. Upon receiving service requests, edge servers assign them to themselves, their neighboring edge servers, as well as the cloud center, aiming at minimizing the overall network cost. This paper first establishes an optimization model for this problem. Second, in light of the separable structure of the optimization model, we utilize the alternating direction method of multipliers （ADMM） to develop a fully collaborative resource allocation algorithm. The edge servers and the cloud center autonomously collaborate to compute their local optimization variables and prices of network resources, and reach an optimal solution. Numerical experiments demonstrate the effectiveness of the hybrid network infrastructure as well as the proposed algorithm.
文摘A flaw of demand coverage method in solving optimal monitoring stations problem under multiple demand patterns was identified in this paper. In the demand coverage method, the demand coverage of each set of monitoring stations is calculated by accumulating their demand coverage under each demand pattern, and the impact of temporal distribution between different time periods or demand patterns is ignored. This could lead to miscalculation of the optimal locations of the monitoring stations. To overcome this flaw, this paper presents a Demand Coverage Index （DCI） based method. The optimization considers extended period unsteady hydrau- lics due to the change of nodal demands with time. The method is cast in a genetic algorithm framework for integration with Environmental Protection Agency Net （EPANET） and is demonstrated through example applica- tions. Results show that the set of optimal locations of monitoring stations obtained using the DCI method can represent the water quality of water distribution systems under multiple demand patterns better than the one obtained using previous methods.
