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作者机构:The Ministry of Education Key Laboratory of Control of Power Transmission and ConversionDepartment of Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Power SystemsDepartment of Electrical EngineeringTsinghua UniversityBeijing 100084China
出 版 物:《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报(英文))
年 卷 期:2016年第4卷第4期
页 面:690-701页
核心收录:
学科分类:080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学]
基 金:supported by National Natural Science Foundation of China(No.51377103) the technology project of State Grid Corporation of China:Research on Multi-Level Decomposition Coordination of the Pareto Set of Multi-Objective Optimization Problem in Bulk Power System(No.SGSXDKYDWKJ2015-001) the support from State Energy Smart Grid R&D Center(SHANGHAI)
主 题:Adaptive simplified human learning optimization algorithm Optimal power flow AC/DC hybrid power system Valve-point loading effects of generators Carbon tax Prohibited operating zones
摘 要:This paper employs an efficacious analytical tool,adaptive simplified human learning optimization(ASHLO)algorithm,to solve optimal power flow(OPF)problem in AC/DC hybrid power system,considering valve-point loading effects of generators,carbon tax,and prohibited operating zones of generators,*** algorithm,involves random learning operator,individual learning operator,social learning operator and adaptive *** compare and analyze the computation performance of the ASHLO method,the proposed ASHLO method and other heuristic intelligent optimization methods are employed to solve OPF problem on the modified IEEE 30-bus and 118-bus AC/DC hybrid test *** results indicate that the ASHLO method has good convergent property and ***,the impacts of wind speeds and locations of HVDC transmission line integrated into the AC network on the OPF results are systematically analyzed.