The optimal planning and design of an integrated energy system(IES)is of great significance to facilitate distributed renewable energy(DRE)technology and improve the overall energy efficiency of the energy system.With...The optimal planning and design of an integrated energy system(IES)is of great significance to facilitate distributed renewable energy(DRE)technology and improve the overall energy efficiency of the energy system.With the increased penetration of distributed generation(DG),the power supply and load sides of an IES present more increased levels of uncertainties.Demand response(DR)and the energy storage system(ESS)serve as important means to shift energy supply and use across time to counter the indeterminate variations.However,the current IES planning methods are unable to effectively deal with the uncertainties of DREs and loads,and to optimize the operations of DG-DR-ESS due to the enormous possible combinations.In this paper,a new method for the optimal planning and design of an integrated energy system has been introduced and verified.The new method consists of three integrated elements.First,the method of the probability scenario has been used to model the uncertainties of the DREs and loads so as to better characterize the impact of uncertainty on the planning and design of the IES.Secondly,the optimal operation of the IES under different probability scenarios is ensured using the second-order cone optimization for quick solutions due to the simplicity of this sub-problem,serving as the bottom-level optimization.Thirdly,the optimal planning and design of IES through optimal sizing of the power generating components and ESS are performed using a special meta-model based global optimization method due to the complex,black-box,and computation intensive nature of this top-level optimization in a nested,bi-level global optimization problem.The combined approach takes full account of the interrelated operations of DG-DR-ESS under different design configurations to support a better optimal planning and design of the IES.The simulation has been carried out on an IES system modified from the IEEE 33-node distribution system.The simulation results show that the proposed method and model are effective.展开更多
提出基于克里金(Kriging)插值的高维模型表示(high dimensional model representation,HDMR)方法,即Kriging-HDMR方法.Kriging-HDMR方法的最大优势在于:能够明确输入参数的耦合特性,将构造模型复杂度由指数级增长降阶为多项式级增长,进...提出基于克里金(Kriging)插值的高维模型表示(high dimensional model representation,HDMR)方法,即Kriging-HDMR方法.Kriging-HDMR方法的最大优势在于:能够明确输入参数的耦合特性,将构造模型复杂度由指数级增长降阶为多项式级增长,进而用有限样本确定待求问题的物理实质.为了验证算法的建模性能,采用高维非线性函数成功地验证了该算法的可行性,并将该算法初步应用于简单的非线性工程问题,同传统算法相比,其精度和效率都得到了明显提升.展开更多
Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel （DSM） which integrates advantages of both interpolation metamodel and re...Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel （DSM） which integrates advantages of both interpolation metamodel and regression metamodel is constructed. It takes regression model as the first stage to fit overall distribution of the original model, and then interpolation model of regression model approximation error is used as the second stage to improve accuracy. Under the same conditions and with the same samples, DSM expresses higher fidelity and represents physical characteristics of original model better. Besides, in order to validate DSM characteristics, three examples including Ackley function, airfoil aerodynamic analysis and wing aerodynamic analysis are investigated, In the end, airfoil and wing aerodynamic design optimizations using genetic algorithm are presented to verify the engineering applicability of DSM.展开更多
In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of sh...In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No.60473064(国家自然科学基金)the National High-Tech Research and Development Plan of China under Grant Nos.2007AA010301,2005AA112030(国家高技术研究发展计划(863))+2 种基金the National Basic Research Program of China under Grant No.2005CB321805(国家重点基础研究发展计划(973))the Key Technologies R&D Program of China under Grant No.2003BA904B02 (国家科技攻关计划)the National Key Technology R&D Program of China under Grant No.2006BAH02A02(国家科技支撑计划)
基金This work was supported by the National Natural Science Foundation of China(51607170)the Key Front Science Project of Chinese Academy of Sciences(QYZDB-SSW-JSC024)the International Collaboration Programs of the Chinese Academy of Sciences and the Foreign Expert Affairs.
文摘The optimal planning and design of an integrated energy system(IES)is of great significance to facilitate distributed renewable energy(DRE)technology and improve the overall energy efficiency of the energy system.With the increased penetration of distributed generation(DG),the power supply and load sides of an IES present more increased levels of uncertainties.Demand response(DR)and the energy storage system(ESS)serve as important means to shift energy supply and use across time to counter the indeterminate variations.However,the current IES planning methods are unable to effectively deal with the uncertainties of DREs and loads,and to optimize the operations of DG-DR-ESS due to the enormous possible combinations.In this paper,a new method for the optimal planning and design of an integrated energy system has been introduced and verified.The new method consists of three integrated elements.First,the method of the probability scenario has been used to model the uncertainties of the DREs and loads so as to better characterize the impact of uncertainty on the planning and design of the IES.Secondly,the optimal operation of the IES under different probability scenarios is ensured using the second-order cone optimization for quick solutions due to the simplicity of this sub-problem,serving as the bottom-level optimization.Thirdly,the optimal planning and design of IES through optimal sizing of the power generating components and ESS are performed using a special meta-model based global optimization method due to the complex,black-box,and computation intensive nature of this top-level optimization in a nested,bi-level global optimization problem.The combined approach takes full account of the interrelated operations of DG-DR-ESS under different design configurations to support a better optimal planning and design of the IES.The simulation has been carried out on an IES system modified from the IEEE 33-node distribution system.The simulation results show that the proposed method and model are effective.
文摘提出基于克里金(Kriging)插值的高维模型表示(high dimensional model representation,HDMR)方法,即Kriging-HDMR方法.Kriging-HDMR方法的最大优势在于:能够明确输入参数的耦合特性,将构造模型复杂度由指数级增长降阶为多项式级增长,进而用有限样本确定待求问题的物理实质.为了验证算法的建模性能,采用高维非线性函数成功地验证了该算法的可行性,并将该算法初步应用于简单的非线性工程问题,同传统算法相比,其精度和效率都得到了明显提升.
文摘Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel （DSM） which integrates advantages of both interpolation metamodel and regression metamodel is constructed. It takes regression model as the first stage to fit overall distribution of the original model, and then interpolation model of regression model approximation error is used as the second stage to improve accuracy. Under the same conditions and with the same samples, DSM expresses higher fidelity and represents physical characteristics of original model better. Besides, in order to validate DSM characteristics, three examples including Ackley function, airfoil aerodynamic analysis and wing aerodynamic analysis are investigated, In the end, airfoil and wing aerodynamic design optimizations using genetic algorithm are presented to verify the engineering applicability of DSM.
基金Supported by the Project of Ministry of Education and Finance (No.200512)the Project of the State Key Laboratory of Ocean Engineering (GKZD010053-10)
文摘In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.
