August 30,2007,Shenzhen,China-ZTE Corporation ("ZTE"),a leading global provider of telecommunications equipment and network solutions,announced that it has recently signed an agreement with Telkom Indonesia...August 30,2007,Shenzhen,China-ZTE Corporation ("ZTE"),a leading global provider of telecommunications equipment and network solutions,announced that it has recently signed an agreement with Telkom Indonesia ("Telkom"),the largest InfoCom company and full-service network provider in Indonesia,to help expand the CDMA2000 network capacity for national coverage. Under the contract,the network expansion will cover the four main cities of the eastern part of Sulawesi,Indonesia.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
This paper uses a novel scenario generation method for tackling the uncertainties of wind power in the transmission network expansion planning(TNEP)problem.A heuristic moment matching(HMM)method is first applied to ge...This paper uses a novel scenario generation method for tackling the uncertainties of wind power in the transmission network expansion planning(TNEP)problem.A heuristic moment matching(HMM)method is first applied to generate the typical scenarios for capturing the stochastic features of wind power,including expectation,standard deviation,skewness,kurtosis,and correlation of multiple wind farms.Then,based on the typical scenarios,a robust TNEP problem is presented and formulated.The solution of the problem is robust against all the scenarios that represent the stochastic features of wind power.Three test systems are used to verify the HMM method and is compared against Taguchi’s Orthogonal Array(OA)method.The simulation results show that the HMM method has better performance than the OA method in terms of the trade-off between robustness and economy.Additionally,the main factors influencing the planning scheme are studied,including the number of scenarios,wind farm capacity,and penalty factors,which provide a reference for system operators choosing parameters.展开更多
Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and...Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation.This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation.The proposed model includes distributionally robust joint chance constraints,which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set.The proposed formulation yields a twostage robust optimization model with variable bounds of the uncertain sets,which is hard to solve.By applying the affine decision rule,second-order conic reformulation,and duality,we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers.Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.展开更多
Road network expansion can result in the fragmentation of ecological landscapes due to the transformation of landscape processes and patterns.However,knowledge about these processes and patterns is scarce.In this stud...Road network expansion can result in the fragmentation of ecological landscapes due to the transformation of landscape processes and patterns.However,knowledge about these processes and patterns is scarce.In this study,the road network and landscape patterns in the Dongzhi tableland of the Chinese Loess Plateau(CLP) between 2005 and 2020 were characterized,and their spatial relationships were analyzed.The results showed that(1) the kernel density estimation(KDE) method is useful in characterizing road network density.When the bandwidth value is four,the boundary of the road network kernel can be distinguished clearly.(2) The road network in the tableland expanded greatly over the past 15 years,and the total area of road kernels in the Dongzhi tableland increased from 55.73 km~2 in 2005 to 223.55 km~2 in 2020.(3) High-density road networks were generally distributed on cultivated and constructed lands where the slopes were generally 0°–5°,while low-and medium-density road networks were mostly distributed in grassland areas where the slopes were greater than 5°.(4) Road network density is closely related to the coverage of cultivated and constructed lands.The results of this study are helpful in understanding the potential impact of road network evolution on the landscape at a regional scale.展开更多
We propose in this paper a data driven state estimation scheme for generating nonlinear reduced models for parametric families of PDEs, directly providingdata-to-state maps, represented in terms of Deep Neural Network...We propose in this paper a data driven state estimation scheme for generating nonlinear reduced models for parametric families of PDEs, directly providingdata-to-state maps, represented in terms of Deep Neural Networks. A major constituentis a sensor-induced decomposition of a model-compliant Hilbert space warranting approximation in problem relevant metrics. It plays a similar role as in a ParametricBackground Data Weak framework for state estimators based on Reduced Basis concepts. Extensive numerical tests shed light on several optimization strategies that areto improve robustness and performance of such estimators.展开更多
文摘August 30,2007,Shenzhen,China-ZTE Corporation ("ZTE"),a leading global provider of telecommunications equipment and network solutions,announced that it has recently signed an agreement with Telkom Indonesia ("Telkom"),the largest InfoCom company and full-service network provider in Indonesia,to help expand the CDMA2000 network capacity for national coverage. Under the contract,the network expansion will cover the four main cities of the eastern part of Sulawesi,Indonesia.
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supported in part by the National Natural Science Foundation of China under Grant No.51377027The National Basic Research Program of China under Grant No.2013CB228205by Innovation Project of Guangxi Graduate Education under Grant No.YCSZ2015053.
文摘This paper uses a novel scenario generation method for tackling the uncertainties of wind power in the transmission network expansion planning(TNEP)problem.A heuristic moment matching(HMM)method is first applied to generate the typical scenarios for capturing the stochastic features of wind power,including expectation,standard deviation,skewness,kurtosis,and correlation of multiple wind farms.Then,based on the typical scenarios,a robust TNEP problem is presented and formulated.The solution of the problem is robust against all the scenarios that represent the stochastic features of wind power.Three test systems are used to verify the HMM method and is compared against Taguchi’s Orthogonal Array(OA)method.The simulation results show that the HMM method has better performance than the OA method in terms of the trade-off between robustness and economy.Additionally,the main factors influencing the planning scheme are studied,including the number of scenarios,wind farm capacity,and penalty factors,which provide a reference for system operators choosing parameters.
基金supported by the National Natural Science Foundation of China(No.52077136)。
文摘Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation.This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation.The proposed model includes distributionally robust joint chance constraints,which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set.The proposed formulation yields a twostage robust optimization model with variable bounds of the uncertain sets,which is hard to solve.By applying the affine decision rule,second-order conic reformulation,and duality,we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers.Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.
基金National Natural Science Foundation of China,No.41790444Strategic Priority Research Program of Chinese Academy of Sciences,No.XDB40000000National Key Research and Development Program,No.2018YFC1504701。
文摘Road network expansion can result in the fragmentation of ecological landscapes due to the transformation of landscape processes and patterns.However,knowledge about these processes and patterns is scarce.In this study,the road network and landscape patterns in the Dongzhi tableland of the Chinese Loess Plateau(CLP) between 2005 and 2020 were characterized,and their spatial relationships were analyzed.The results showed that(1) the kernel density estimation(KDE) method is useful in characterizing road network density.When the bandwidth value is four,the boundary of the road network kernel can be distinguished clearly.(2) The road network in the tableland expanded greatly over the past 15 years,and the total area of road kernels in the Dongzhi tableland increased from 55.73 km~2 in 2005 to 223.55 km~2 in 2020.(3) High-density road networks were generally distributed on cultivated and constructed lands where the slopes were generally 0°–5°,while low-and medium-density road networks were mostly distributed in grassland areas where the slopes were greater than 5°.(4) Road network density is closely related to the coverage of cultivated and constructed lands.The results of this study are helpful in understanding the potential impact of road network evolution on the landscape at a regional scale.
基金This work was supported by National Science Foundation under grant DMS-2012469.
文摘We propose in this paper a data driven state estimation scheme for generating nonlinear reduced models for parametric families of PDEs, directly providingdata-to-state maps, represented in terms of Deep Neural Networks. A major constituentis a sensor-induced decomposition of a model-compliant Hilbert space warranting approximation in problem relevant metrics. It plays a similar role as in a ParametricBackground Data Weak framework for state estimators based on Reduced Basis concepts. Extensive numerical tests shed light on several optimization strategies that areto improve robustness and performance of such estimators.