Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution netwo...Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution network planning model incorporating distributed wind turbines(DWT),distributed photovoltaics(DPV),and energy storage systems(ESS).K-means++is employed to partition the distribution network based on electrical distance.Considering the spatiotemporal correlation of distributed generation(DG)outputs in the same region,a joint output model of DWT and DPV is developed using the Frank-Copula.Due to the model’s high dimensionality,multiple constraints,and mixed-integer characteristics,bilevel programming theory is utilized to structure the model.The model is solved using a mixed-integer particle swarmoptimization algorithm(MIPSO)to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality.The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system.The results demonstrate significant improvements,with the proposedmethod reducing the annual comprehensive cost by 41.65%and 13.98%,respectively,compared to scenarios without DG and ESS or with only DG integration.Furthermore,it reduces the daily average voltage deviation by 24.35%and 10.24%and daily network losses by 55.72%and 35.71%.展开更多
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ...Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.展开更多
This study discusses a yard planning system,which considers various resources such as storage space,yard cranes,and traffic areas in container terminals.The system is based on the function for estimating resource requ...This study discusses a yard planning system,which considers various resources such as storage space,yard cranes,and traffic areas in container terminals.The system is based on the function for estimating resource requirements of yard plans.For a given yard plan,the proposed system allows planners to check the feasibility of the plan which requires a certain amount of workload of resources in related blocks during a planning horizon.The yard planning system in this study is aimed at balancing workloads among the blocks and providing the ability to modify current yard plans by detecting blocks and periods with overloaded workloads.The system implements its planning function in a distributed manner in which planners construct yard plans under their individual control and send and receive only limited necessary information for the negotiation.展开更多
A distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm （NGA） is proposed. By using blackboard-based communication paradigm and shared data structure, multiple...A distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm （NGA） is proposed. By using blackboard-based communication paradigm and shared data structure, multiple decision-makers （DMs） can collaboratively solve the tasks-platforms allocation scheduling problems dynamically through the coordinator. This methodo- logy combined with NGA maximizes tasks execution accuracy, also minimizes the weighted total workload of the DM which is measured in terms of intra-DM and inter-DM coordination. The intra-DM employs an optimization-based scheduling algorithm to match the tasks-platforms assignment request with its own platforms. The inter-DM coordinates the exchange of collaborative request information and platforms among DMs using the blackboard architecture. The numerical result shows that the proposed black- board DM framework based on NGA can obtain a near-optimal solution for the tasks-platforms collaborative planning problem. The assignment of platforms-tasks and the patterns of coordination can achieve a nice trade-off between intra-DM and inter-DM coordination workload.展开更多
Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delay...Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delays, which is unable to ensure the integrity and timeliness of the information on decision making for satellites. And the optimization of the planning result is affected. Therefore, the effect of communication delay is considered during the multi-satel ite coordinating process. For this problem, firstly, a distributed cooperative optimization problem for multiple satellites in the delayed communication environment is formulized. Secondly, based on both the analysis of the temporal sequence of tasks in a single satellite and the dynamically decoupled characteristics of the multi-satellite system, the environment information of multi-satellite distributed cooperative optimization is constructed on the basis of the directed acyclic graph（DAG）. Then, both a cooperative optimization decision making framework and a model are built according to the decentralized partial observable Markov decision process（DEC-POMDP）. After that, a satellite coordinating strategy aimed at different conditions of communication delay is mainly analyzed, and a unified processing strategy on communication delay is designed. An approximate cooperative optimization algorithm based on simulated annealing is proposed. Finally, the effectiveness and robustness of the method presented in this paper are verified via the simulation.展开更多
Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing o...Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.展开更多
An approach for the integrated optimization of the construction/expansion capacity of high-voltage/ medium-voltage (HV/MV) substations and the configuration of MV radial distribution network was presented using plant ...An approach for the integrated optimization of the construction/expansion capacity of high-voltage/ medium-voltage (HV/MV) substations and the configuration of MV radial distribution network was presented using plant growth simulation algorithm (PGSA). In the optimization process, fixed costs correspondent to the investment in lines and substations and the variable costs associated to the operation of the system were considered under the constraints of branch capacity, substation capacity and bus voltage. The optimization variables considerably reduce the dimension of variables and speed up the process of optimizing. The effectiveness of the proposed approach was tested by a distribution system planning.展开更多
Distributed energy resources have been proven to be an effective and promising solution to enhance power system resilience and improve household-level reliability.In this paper,we propose a method to evaluate the reli...Distributed energy resources have been proven to be an effective and promising solution to enhance power system resilience and improve household-level reliability.In this paper,we propose a method to evaluate the reliability value of a photovoltaic(PV)energy system with a battery storage system(BSS)by considering the probability of grid outages causing household blackouts.Considering this reliability value,which is the economic profit and capital cost of PV+BSS,a simple formula is derived to calculate the optimal planning strategy.This strategy can provide household-level customers with a simple and straightforward expression for invested PV+BSS capacity.Case studies on 600 households located in eight zones of the US for the period of 2006 to 2015 demonstrate that adding the reliability value to economic profit allows households to invest in a larger PV+BSS and avoid loss of load caused by blackouts.Owing to the differences in blackout hours,households from the 8 zones express distinct willingness to install PV+BSS.The greater the probability of blackout,the greater revenue that household can get from the PV+BSS.The simulation example shows that the planning strategy obtained by proposed model has good economy in the actual operation and able to reduce the economic risk of power failure of the household users.This model can provide household with an easy and straightforward investment strategy of PV+BSS capacity.展开更多
Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.Th...Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.展开更多
In order to form an algorithm for distribution network routing,an automatic routing method of distribution network planning was proposed based on the shortest path.The problem of automatic routing was divided into two...In order to form an algorithm for distribution network routing,an automatic routing method of distribution network planning was proposed based on the shortest path.The problem of automatic routing was divided into two steps in the method:the first step was that the shortest paths along streets between substation and load points were found by the basic ant colony algorithm to form a preliminary radial distribution network,and the second step was that the result of the shortest path was used to initialize pheromone concentration and pheromone updating rules to generate globally optimal distribution network.Cases studies show that the proposed method is effective and can meet the planning requirements.It is verified that the proposed method has better solution and utility than planning method based on the ant colony algorithm.展开更多
The models, methods and their application experiences of a practical GIS(geographic information system)-based computer decision-making support system of urban power distribution network planning with seven subsystems,...The models, methods and their application experiences of a practical GIS(geographic information system)-based computer decision-making support system of urban power distribution network planning with seven subsystems,termed CNP,are described.In each subsystem there is at least one or one set of practical mathematical methobs.Some new models and mathematical methods have been introduced.In the development of CNP the idea of cognitive system engineering has been insisted on,which claims that human and computer intelligence should be combined together to solve the complex engineering problems cooperatively.Practical applications have shown that not only the optimal plan can be automatically reached with many complicated factors considered, but also the computation,analysis and graphic drawing burden can be released considerably.展开更多
For facing the challenges brought by large-scale renewable energy having access to the system and considering the key technologies of energy Internet,it is very necessary to put forward the location method of distribu...For facing the challenges brought by large-scale renewable energy having access to the system and considering the key technologies of energy Internet,it is very necessary to put forward the location method of distribution network equipment and capacity from the perspective of life cycle cost.Compared with the traditional energy network,the equipment capacity problem of energy interconnected distribution network which involves in electricity network,thermal energy network and natural gas network is comprehensively considered in this paper.On this basis,firstly,the operation architecture of energy interconnected distribution network is designed.Secondly,taking the grid connection location and configuration capacity of key equipment in the system as the control variables and the operation cost of system comprehensive planning in the whole life cycle as the goal,the equipment location and capacity optimization model of energy interconnected distribution network is established.Finally,an IEEE 33 bus energy mutual distribution grid system is taken for example analysis,and the improved chaotic particle swarmoptimization algorithm is used to solve it.The simulation results show that the method proposed in this paper is suitable for the equipment location and capacity planning of energy interconnected distribution network,and it can effectively improve the social and economic benefits of system operation.展开更多
At the present stage, the distribution network has been in a relatively low voltage position, has become the main medium between power supply companies, customers and distribution system users, the working conditions ...At the present stage, the distribution network has been in a relatively low voltage position, has become the main medium between power supply companies, customers and distribution system users, the working conditions of distribution network system and the distribution equipment quality has high requirements, so by improving the grid requirements to improve the security of the distribution network, become Chinas current power development transition stage must attach great importance to the development goals, is also the key measures to ensure user safety. With the continuous growth of the national economy, Chinas domestic power industry development has been improved accordingly, its level is gradually to developed countries and local governments, this is mainly due to the distribution network construction, construction, management operation accumulated valuable experience, learned a lot of lessons and made significant progress, but over time, Chinas domestic distribution network operation calculation specification also need to be modified with the process of time, it is an important measure to improve the level of distribution network management, but also to reduce the negative impact and play a good role.展开更多
This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, ener...This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified by the 18-node typical system.展开更多
Unmanned aerial vehicle(UAV)swarms,with their superior maneuverability and efficient coordination capabilities,are widely applied in complex missions such as inspection,search and rescue.In dense and crowded low-altit...Unmanned aerial vehicle(UAV)swarms,with their superior maneuverability and efficient coordination capabilities,are widely applied in complex missions such as inspection,search and rescue.In dense and crowded low-altitude environments,the coupled planning of position and orientation is critical to ensuring the safe navigation of UAV swarms.To address this challenge,we develop a cooperative motion planning framework based on graph optimization for generating trajectories in SE(3) space.Each UAV is modeled as an ellipsoid for collision detection,allowing it to safely pass through narrow passages smaller than its own diameter.To ensure the smoothness and safety of the trajectories,the planning process integrates feasibility constraints arising from dynamics and geometry,resulting in a multi-UAV pose planning formulation.A distributed trajectory planning framework,called Pose Graph-based ADMM(PG-ADMM),is subsequently developed for the UAV swarm by leveraging the alternating direction method of multipliers(ADMM)within the pose graph optimization framework.Finally,the effectiveness and practicality of the PG-ADMM algorithm are systematically evaluated through a series of simulation scenarios.展开更多
Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited reso...Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.展开更多
A large proportion of distributed photovoltaic(DPV)and energy storage equipment is gradually being integrated into distribution networks,which increases the complexity of distribution network planning.To achieve flexi...A large proportion of distributed photovoltaic(DPV)and energy storage equipment is gradually being integrated into distribution networks,which increases the complexity of distribution network planning.To achieve flexible and efficient utilization of energy storage and DPV,a distributionally robust optimization expansion planning method for distribution networks based on Kullback-Leibler(KL)divergence is proposed.First,considering the power-flow,radial network,and energy storage constraints,a stochastic optimization planning model is established to minimize the cost of distribution network planning.Subsequently,the fuzzy sets of the DPV output based on KL divergence are embedded into the stochastic optimization planning model.This transforms it into a min-max-min three-level two-stage distributionally robust optimization model that can better balance economy and stability.Finally,the model is solved using the column-and-constraint generation(C&CG)method.The effectiveness and feasibility of the proposed model and algorithm are validated using an improved IEEE 33-node system.展开更多
In order to cope with the global environmental crisis caused by energy generation and achieve carbon neutrality,it is imperative to promote a new power system dominated by renewable energy sources(RESs).This paper foc...In order to cope with the global environmental crisis caused by energy generation and achieve carbon neutrality,it is imperative to promote a new power system dominated by renewable energy sources(RESs).This paper focuses on the uncertainty of RESs and the distribution characteristics of carbon emission flows(CEFs),and studies the low-carbon operation and power system planning problem.Firstly,this paper extends the uncertainty of RES to the meteorological field and establishes meteorological robust constraints of photovoltaic(PV)generation.Based on the CEF theory,the carbon transmission trajectory is accurately delineated to improve the operation of power system.Considering further constraints from the power flow,CEF,and component operation characteristics of the active distribution network(ADN),this paper formulates a low-carbon joint planning model of ADN with PV,battery energy storage system(BESS),and distributed gas generator(DGG),taking into account economy and carbon reduction.In the case study,the low-carbon planning and operation scheme are analyzed in detail across multiple dimensions including time and space.The solution results show that the planning model can effectively leverage the low-carbon performance of PV and BESS,and improve the distribution of CEF.Through case comparison,the model can also efficiently reduce the total cost of the system and enhance carbon emission reduction benefits by 35.10 to 41.04%.展开更多
The water distribution network is an important part of the plain water environment improvement system. To make efficient use of the regional water diversion source, scientifically distribute the water diversion flow a...The water distribution network is an important part of the plain water environment improvement system. To make efficient use of the regional water diversion source, scientifically distribute the water diversion flow and improve the water environment carrying capacity of Haishu Plain, the river network hydrodynamic model is used in this paper to simulate the water intake location, reasonable water quantity and influence range of water transfer in Haishu Plain. The simulation results have high accuracy, which can provide a scientific basis for the scale, water transfer mechanism and project layout of water transfer construction in Haishu Plain and show a strong reference value for the study of water diversion and distribution scheme of coastal plain river network.展开更多
With increasing electricity demand and large-scale stochastic charging of electric vehicles(EVs),distribution networks face inevitable shortage of transfer capability,bringing new challenges to distribution network pl...With increasing electricity demand and large-scale stochastic charging of electric vehicles(EVs),distribution networks face inevitable shortage of transfer capability,bringing new challenges to distribution network planning(DNP).Dynamic thermal rating(DTR),which evaluates the equipment rating based on actual meteorological conditions and equipment thermal state,can enhance the equipment transfer capability to meet the increasing load demand.In this paper,we propose a model considering the active response of EVs,and a bi-level DNP model incorporating the DTR of cables and transformers,in the upper level,the Prim algorithm is embedded into the particle swarm optimisation(PSO)algorithm to obtain an initial grid topology;in the lower level,types of cables and transformers as well as the installation of DTR equipment are determined,second-order cone(SOC)relaxation and linearisation of the variables product are then carried out to meet the non-linear constraints of cables and transformers,and the upper and lower models are solved in an iterative manner.Case studies demonstrate that the implementation of DTR effectively enhances the transfer capability of cables and transformers,saving 4.8%investment cost while ensuring 96%uplift of power supply.Besides,with 90%active response rate of EVs,total cost can be further reduced.展开更多
基金This research was funded by“Chunhui Program”Collaborative Scientific Research Project of the Ministry of Education of the People’s Republic of China(Project No.HZKY20220242)the S&T Program of Hebei(Project No.225676163GH).
文摘Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution network planning model incorporating distributed wind turbines(DWT),distributed photovoltaics(DPV),and energy storage systems(ESS).K-means++is employed to partition the distribution network based on electrical distance.Considering the spatiotemporal correlation of distributed generation(DG)outputs in the same region,a joint output model of DWT and DPV is developed using the Frank-Copula.Due to the model’s high dimensionality,multiple constraints,and mixed-integer characteristics,bilevel programming theory is utilized to structure the model.The model is solved using a mixed-integer particle swarmoptimization algorithm(MIPSO)to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality.The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system.The results demonstrate significant improvements,with the proposedmethod reducing the annual comprehensive cost by 41.65%and 13.98%,respectively,compared to scenarios without DG and ESS or with only DG integration.Furthermore,it reduces the daily average voltage deviation by 24.35%and 10.24%and daily network losses by 55.72%and 35.71%.
文摘Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.
基金Project supported by the Korean Ministry of Education,Science and Technology Grant (the Regional Core Research Program/Institute of Logistics Information Technology)
文摘This study discusses a yard planning system,which considers various resources such as storage space,yard cranes,and traffic areas in container terminals.The system is based on the function for estimating resource requirements of yard plans.For a given yard plan,the proposed system allows planners to check the feasibility of the plan which requires a certain amount of workload of resources in related blocks during a planning horizon.The yard planning system in this study is aimed at balancing workloads among the blocks and providing the ability to modify current yard plans by detecting blocks and periods with overloaded workloads.The system implements its planning function in a distributed manner in which planners construct yard plans under their individual control and send and receive only limited necessary information for the negotiation.
基金supported by the National Aerospace Science Foundation of China(20138053038)the Graduate Starting Seed Fund of Northwestern Polytechnical University(Z2015111)
文摘A distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm （NGA） is proposed. By using blackboard-based communication paradigm and shared data structure, multiple decision-makers （DMs） can collaboratively solve the tasks-platforms allocation scheduling problems dynamically through the coordinator. This methodo- logy combined with NGA maximizes tasks execution accuracy, also minimizes the weighted total workload of the DM which is measured in terms of intra-DM and inter-DM coordination. The intra-DM employs an optimization-based scheduling algorithm to match the tasks-platforms assignment request with its own platforms. The inter-DM coordinates the exchange of collaborative request information and platforms among DMs using the blackboard architecture. The numerical result shows that the proposed black- board DM framework based on NGA can obtain a near-optimal solution for the tasks-platforms collaborative planning problem. The assignment of platforms-tasks and the patterns of coordination can achieve a nice trade-off between intra-DM and inter-DM coordination workload.
基金supported by the National Science Foundation for Young Scholars of China(6130123471401175)
文摘Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delays, which is unable to ensure the integrity and timeliness of the information on decision making for satellites. And the optimization of the planning result is affected. Therefore, the effect of communication delay is considered during the multi-satel ite coordinating process. For this problem, firstly, a distributed cooperative optimization problem for multiple satellites in the delayed communication environment is formulized. Secondly, based on both the analysis of the temporal sequence of tasks in a single satellite and the dynamically decoupled characteristics of the multi-satellite system, the environment information of multi-satellite distributed cooperative optimization is constructed on the basis of the directed acyclic graph（DAG）. Then, both a cooperative optimization decision making framework and a model are built according to the decentralized partial observable Markov decision process（DEC-POMDP）. After that, a satellite coordinating strategy aimed at different conditions of communication delay is mainly analyzed, and a unified processing strategy on communication delay is designed. An approximate cooperative optimization algorithm based on simulated annealing is proposed. Finally, the effectiveness and robustness of the method presented in this paper are verified via the simulation.
基金supported in part by the National Natural Science Foundation of China under Grant 52307134the Fundamental Research Funds for the Central Universities(xzy012025022)。
文摘Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.
基金the National Natural Science Foundation of China (No. 50747025)the Postdoctoral Science Foundation of China (No. 20060400648)+1 种基金the Scientific Research Foundation for the Returned Overseas Chinese Scholars (No. 2005383)the Shanghai Key Scienceand Technology Research Program (No. 041612012)
文摘An approach for the integrated optimization of the construction/expansion capacity of high-voltage/ medium-voltage (HV/MV) substations and the configuration of MV radial distribution network was presented using plant growth simulation algorithm (PGSA). In the optimization process, fixed costs correspondent to the investment in lines and substations and the variable costs associated to the operation of the system were considered under the constraints of branch capacity, substation capacity and bus voltage. The optimization variables considerably reduce the dimension of variables and speed up the process of optimizing. The effectiveness of the proposed approach was tested by a distribution system planning.
基金supported by National Natural Science Foundation of China(Project 51907064)in part by China State Key Lab.of Power System(SKLD19KM09)in part by State Grid Corporation of China(1400202024222A-0-0-00)
文摘Distributed energy resources have been proven to be an effective and promising solution to enhance power system resilience and improve household-level reliability.In this paper,we propose a method to evaluate the reliability value of a photovoltaic(PV)energy system with a battery storage system(BSS)by considering the probability of grid outages causing household blackouts.Considering this reliability value,which is the economic profit and capital cost of PV+BSS,a simple formula is derived to calculate the optimal planning strategy.This strategy can provide household-level customers with a simple and straightforward expression for invested PV+BSS capacity.Case studies on 600 households located in eight zones of the US for the period of 2006 to 2015 demonstrate that adding the reliability value to economic profit allows households to invest in a larger PV+BSS and avoid loss of load caused by blackouts.Owing to the differences in blackout hours,households from the 8 zones express distinct willingness to install PV+BSS.The greater the probability of blackout,the greater revenue that household can get from the PV+BSS.The simulation example shows that the planning strategy obtained by proposed model has good economy in the actual operation and able to reduce the economic risk of power failure of the household users.This model can provide household with an easy and straightforward investment strategy of PV+BSS capacity.
基金supported by the Science and Technology Project of China Southern Power Grid(GZHKJXM20210043-080041KK52210002).
文摘Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.
基金Project(2009CB219703) supported by the National Basic Research Program of ChinaProject(2011AA05A117) supported by the National High Technology Research and Development Program of China
文摘In order to form an algorithm for distribution network routing,an automatic routing method of distribution network planning was proposed based on the shortest path.The problem of automatic routing was divided into two steps in the method:the first step was that the shortest paths along streets between substation and load points were found by the basic ant colony algorithm to form a preliminary radial distribution network,and the second step was that the result of the shortest path was used to initialize pheromone concentration and pheromone updating rules to generate globally optimal distribution network.Cases studies show that the proposed method is effective and can meet the planning requirements.It is verified that the proposed method has better solution and utility than planning method based on the ant colony algorithm.
文摘The models, methods and their application experiences of a practical GIS(geographic information system)-based computer decision-making support system of urban power distribution network planning with seven subsystems,termed CNP,are described.In each subsystem there is at least one or one set of practical mathematical methobs.Some new models and mathematical methods have been introduced.In the development of CNP the idea of cognitive system engineering has been insisted on,which claims that human and computer intelligence should be combined together to solve the complex engineering problems cooperatively.Practical applications have shown that not only the optimal plan can be automatically reached with many complicated factors considered, but also the computation,analysis and graphic drawing burden can be released considerably.
基金The authors received specific funding for State Grid Corporation Headquarters Project Support,Key Technologies and Applications of Planning and Decision-Making Based on the Full Cost Chain of the Power Grid,Grant No.5205331800001.
文摘For facing the challenges brought by large-scale renewable energy having access to the system and considering the key technologies of energy Internet,it is very necessary to put forward the location method of distribution network equipment and capacity from the perspective of life cycle cost.Compared with the traditional energy network,the equipment capacity problem of energy interconnected distribution network which involves in electricity network,thermal energy network and natural gas network is comprehensively considered in this paper.On this basis,firstly,the operation architecture of energy interconnected distribution network is designed.Secondly,taking the grid connection location and configuration capacity of key equipment in the system as the control variables and the operation cost of system comprehensive planning in the whole life cycle as the goal,the equipment location and capacity optimization model of energy interconnected distribution network is established.Finally,an IEEE 33 bus energy mutual distribution grid system is taken for example analysis,and the improved chaotic particle swarmoptimization algorithm is used to solve it.The simulation results show that the method proposed in this paper is suitable for the equipment location and capacity planning of energy interconnected distribution network,and it can effectively improve the social and economic benefits of system operation.
文摘At the present stage, the distribution network has been in a relatively low voltage position, has become the main medium between power supply companies, customers and distribution system users, the working conditions of distribution network system and the distribution equipment quality has high requirements, so by improving the grid requirements to improve the security of the distribution network, become Chinas current power development transition stage must attach great importance to the development goals, is also the key measures to ensure user safety. With the continuous growth of the national economy, Chinas domestic power industry development has been improved accordingly, its level is gradually to developed countries and local governments, this is mainly due to the distribution network construction, construction, management operation accumulated valuable experience, learned a lot of lessons and made significant progress, but over time, Chinas domestic distribution network operation calculation specification also need to be modified with the process of time, it is an important measure to improve the level of distribution network management, but also to reduce the negative impact and play a good role.
基金financial supports and the strategic platform for innovation&research provided by Danish national project iPower.
文摘This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified by the 18-node typical system.
基金sponsored by the National Key Research and Development Program of China under No.2022 YFA1004701the National Natural Science Foundation of China under Grant Nos.72271187,62373283 and 62088101+1 种基金partially by Shanghai Municipal Science and Technology Major Project No.2021 SHZDZX0100the Fundamental Research Funds for the Central Universities
文摘Unmanned aerial vehicle(UAV)swarms,with their superior maneuverability and efficient coordination capabilities,are widely applied in complex missions such as inspection,search and rescue.In dense and crowded low-altitude environments,the coupled planning of position and orientation is critical to ensuring the safe navigation of UAV swarms.To address this challenge,we develop a cooperative motion planning framework based on graph optimization for generating trajectories in SE(3) space.Each UAV is modeled as an ellipsoid for collision detection,allowing it to safely pass through narrow passages smaller than its own diameter.To ensure the smoothness and safety of the trajectories,the planning process integrates feasibility constraints arising from dynamics and geometry,resulting in a multi-UAV pose planning formulation.A distributed trajectory planning framework,called Pose Graph-based ADMM(PG-ADMM),is subsequently developed for the UAV swarm by leveraging the alternating direction method of multipliers(ADMM)within the pose graph optimization framework.Finally,the effectiveness and practicality of the PG-ADMM algorithm are systematically evaluated through a series of simulation scenarios.
基金supported by National Natural Science Foundation of China(No.62072436 and No.62202449)National Key Research and Development Program of China(2021YFB2900102).
文摘Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.
基金Supported in part by the Research on Distribution Network Configuration and Multiple Collaborative Optimization Technology Supporting Large-scale Distributed Photovoltaic Development(5216A022000K)in part by the National Key Research and Development Program(2022YFB2405600)+1 种基金in part by the National Natural Science Foundation of China(U22B200134)in part by the Hunan Natural Science Foundation Funded Project(2024JJ6143).
文摘A large proportion of distributed photovoltaic(DPV)and energy storage equipment is gradually being integrated into distribution networks,which increases the complexity of distribution network planning.To achieve flexible and efficient utilization of energy storage and DPV,a distributionally robust optimization expansion planning method for distribution networks based on Kullback-Leibler(KL)divergence is proposed.First,considering the power-flow,radial network,and energy storage constraints,a stochastic optimization planning model is established to minimize the cost of distribution network planning.Subsequently,the fuzzy sets of the DPV output based on KL divergence are embedded into the stochastic optimization planning model.This transforms it into a min-max-min three-level two-stage distributionally robust optimization model that can better balance economy and stability.Finally,the model is solved using the column-and-constraint generation(C&CG)method.The effectiveness and feasibility of the proposed model and algorithm are validated using an improved IEEE 33-node system.
基金supported by the Key Program of National Natural Science Foundation of China under Grant 52130702.
文摘In order to cope with the global environmental crisis caused by energy generation and achieve carbon neutrality,it is imperative to promote a new power system dominated by renewable energy sources(RESs).This paper focuses on the uncertainty of RESs and the distribution characteristics of carbon emission flows(CEFs),and studies the low-carbon operation and power system planning problem.Firstly,this paper extends the uncertainty of RES to the meteorological field and establishes meteorological robust constraints of photovoltaic(PV)generation.Based on the CEF theory,the carbon transmission trajectory is accurately delineated to improve the operation of power system.Considering further constraints from the power flow,CEF,and component operation characteristics of the active distribution network(ADN),this paper formulates a low-carbon joint planning model of ADN with PV,battery energy storage system(BESS),and distributed gas generator(DGG),taking into account economy and carbon reduction.In the case study,the low-carbon planning and operation scheme are analyzed in detail across multiple dimensions including time and space.The solution results show that the planning model can effectively leverage the low-carbon performance of PV and BESS,and improve the distribution of CEF.Through case comparison,the model can also efficiently reduce the total cost of the system and enhance carbon emission reduction benefits by 35.10 to 41.04%.
文摘The water distribution network is an important part of the plain water environment improvement system. To make efficient use of the regional water diversion source, scientifically distribute the water diversion flow and improve the water environment carrying capacity of Haishu Plain, the river network hydrodynamic model is used in this paper to simulate the water intake location, reasonable water quantity and influence range of water transfer in Haishu Plain. The simulation results have high accuracy, which can provide a scientific basis for the scale, water transfer mechanism and project layout of water transfer construction in Haishu Plain and show a strong reference value for the study of water diversion and distribution scheme of coastal plain river network.
基金The State Grid under Project,Grant/Award Number:5700-202118195A-0-0-00。
文摘With increasing electricity demand and large-scale stochastic charging of electric vehicles(EVs),distribution networks face inevitable shortage of transfer capability,bringing new challenges to distribution network planning(DNP).Dynamic thermal rating(DTR),which evaluates the equipment rating based on actual meteorological conditions and equipment thermal state,can enhance the equipment transfer capability to meet the increasing load demand.In this paper,we propose a model considering the active response of EVs,and a bi-level DNP model incorporating the DTR of cables and transformers,in the upper level,the Prim algorithm is embedded into the particle swarm optimisation(PSO)algorithm to obtain an initial grid topology;in the lower level,types of cables and transformers as well as the installation of DTR equipment are determined,second-order cone(SOC)relaxation and linearisation of the variables product are then carried out to meet the non-linear constraints of cables and transformers,and the upper and lower models are solved in an iterative manner.Case studies demonstrate that the implementation of DTR effectively enhances the transfer capability of cables and transformers,saving 4.8%investment cost while ensuring 96%uplift of power supply.Besides,with 90%active response rate of EVs,total cost can be further reduced.
