To improve the computational efficiency of the reliability-based design optimization(RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method(PSO-AERSM) was proposed by integr...To improve the computational efficiency of the reliability-based design optimization(RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method(PSO-AERSM) was proposed by integrating particle swarm optimization(PSO) algorithm and advanced extremum response surface method(AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated. And then the RBDO model of flexible mechanism was presented based on AERSM and PSO. Finally, regarding cross-sectional area as design variable, the reliability optimization of flexible mechanism was implemented subject to reliability degree and uncertainties based on the proposed approach. The optimization results show that the cross-section sizes obviously reduce by 22.96 mm^2 while keeping reliability degree. Through the comparison of methods, it is demonstrated that the AERSM holds high computational efficiency while keeping computational precision for the RBDO of flexible mechanism, and PSO algorithm minimizes the response of the objective function. The efforts of this work provide a useful sight for the reliability optimization of flexible mechanism, and enrich and develop the reliability theory as well.展开更多
先进压缩空气储能(advanced compressed air energy storage,A-CAES)具有大容量、非补燃、寿命长、比投资小等突出优势,已成为最具潜力与发展前景的新型储能技术之一。为充分挖掘A-CAES潜能,并提升其优化规划的合理性,提出了一种考虑调...先进压缩空气储能(advanced compressed air energy storage,A-CAES)具有大容量、非补燃、寿命长、比投资小等突出优势,已成为最具潜力与发展前景的新型储能技术之一。为充分挖掘A-CAES潜能,并提升其优化规划的合理性,提出了一种考虑调峰-备用-爬坡-惯量多应用价值的大规模A-CAES多阶段优化规划策略。首先,考虑新能源与负荷增长进程,提出大规模A-CAES多阶段优化规划架构与流程;其次,研究A-CAES在削峰填谷、事故备用、灵活爬坡、惯量支撑等方面的应用价值及运行特性,最后,以多阶段经济价值与多尺度功效价值为需求导向,将上述运行特性映射为规划边界,构建大规模A-CAES多阶段优化规划模型。基于改进IEEE-118节点系统开展算例分析,结果表明:所提策略能够充分考虑大规模A-CAES多应用价值进行配置,避免因超前投资与粗略估计造成的储能资源冗余。展开更多
基金Projects(51275138,51475025)supported by the National Natural Science Foundation of ChinaProject(12531109)supported by the Science Foundation of Heilongjiang Provincial Department of Education,China+1 种基金Projects(XJ2015002,G-YZ90)supported by Hong Kong Scholars Program,ChinaProject(2015M580037)supported by Postdoctoral Science Foundation of China
文摘To improve the computational efficiency of the reliability-based design optimization(RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method(PSO-AERSM) was proposed by integrating particle swarm optimization(PSO) algorithm and advanced extremum response surface method(AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated. And then the RBDO model of flexible mechanism was presented based on AERSM and PSO. Finally, regarding cross-sectional area as design variable, the reliability optimization of flexible mechanism was implemented subject to reliability degree and uncertainties based on the proposed approach. The optimization results show that the cross-section sizes obviously reduce by 22.96 mm^2 while keeping reliability degree. Through the comparison of methods, it is demonstrated that the AERSM holds high computational efficiency while keeping computational precision for the RBDO of flexible mechanism, and PSO algorithm minimizes the response of the objective function. The efforts of this work provide a useful sight for the reliability optimization of flexible mechanism, and enrich and develop the reliability theory as well.
文摘先进压缩空气储能(advanced compressed air energy storage,A-CAES)具有大容量、非补燃、寿命长、比投资小等突出优势,已成为最具潜力与发展前景的新型储能技术之一。为充分挖掘A-CAES潜能,并提升其优化规划的合理性,提出了一种考虑调峰-备用-爬坡-惯量多应用价值的大规模A-CAES多阶段优化规划策略。首先,考虑新能源与负荷增长进程,提出大规模A-CAES多阶段优化规划架构与流程;其次,研究A-CAES在削峰填谷、事故备用、灵活爬坡、惯量支撑等方面的应用价值及运行特性,最后,以多阶段经济价值与多尺度功效价值为需求导向,将上述运行特性映射为规划边界,构建大规模A-CAES多阶段优化规划模型。基于改进IEEE-118节点系统开展算例分析,结果表明:所提策略能够充分考虑大规模A-CAES多应用价值进行配置,避免因超前投资与粗略估计造成的储能资源冗余。
