Optimal power flow calculation in AC/DC hybrid power system based on adaptive simplified human learning optimization algorithm

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• 作者：Jia CAO ; Zheng YAN ; Xiaoyuan XU ; Guangyu HE…
• 关键词：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
• 刊名：Journal of Modern Power Systems and Clean Energy
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：4
• 期：4
• 页码：690-701
• 全文大小：1,643 KB
• 刊物主题：Energy Systems; Renewable and Green Energy; Power Electronics, Electrical Machines and Networks;
• 出版者：Springer Berlin Heidelberg
• ISSN：2196-5420
• 卷排序：4

文摘

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, respectively. ASHLO algorithm, involves random learning operator, individual learning operator, social learning operator and adaptive strategies. To 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 system. Numerical results indicate that the ASHLO method has good convergent property and robustness. Meanwhile, the impacts of wind speeds and locations of HVDC transmission line integrated into the AC network on the OPF results are systematically analyzed.