文献类型：期刊文献

英文题名：Dynamic Economic Dispatch with Wind Power Penetration Based on Non-Parametric Kernel Density Estimation

作者：Liu, Gang Zhu, YongLi Huang, Zheng

第一作者：刘刚;Liu, Gang

通信作者：Zhu, YL[1]

机构：[1]North China Elect Power Univ, Sch Elect & Elect Engn, Baoding 071003, Peoples R China;[2]Guizhou Inst Technol, Sch Elect & Informat Engn, Guiyang, Peoples R China

第一机构：North China Elect Power Univ, Sch Elect & Elect Engn, Baoding 071003, Peoples R China

通信机构：corresponding author), North China Elect Power Univ, Sch Elect & Elect Engn, Baoding 071003, Peoples R China.

年份：2020

卷号：48

期号：4-5

起止页码：333-352

外文期刊名：ELECTRIC POWER COMPONENTS AND SYSTEMS

收录：;EI(收录号：20202708901726);Scopus(收录号：2-s2.0-85087345922);WOS:【SCI-EXPANDED(收录号:WOS:000549063500001)】；

基金：The paper was partially sponsored by the National Natural Science Foundation of China (51677072), Joint fund project of Guizhou Province (LH[2016]7103), Foundation for High-level Talents of Guizhou Institute of Technology (XJGC20150401) and Foundation for Academic Cultivation and Inovation of Guizhou Institute of Technology ([2017] 5789-22).

语种：英文

外文关键词：kernel density estimation; wind power; dynamic economic dispatch; spinning reserve; bat algorithm; particle swarm optimization

摘要：In order to analyze the randomness of wind power in dynamic economic dispatch (DED) with wind power, based on non-parametric kernel density estimation (KDE) technology, the probability distribution of wind power output and wind power forecast error is accurately modeled. A segmented statistical method on wind power forecast data is adopted to construct the confidence interval of the wind power output, the upper and lower bounds of the forecast errors. According to the established wind power output probability model, forecast confidence interval and forecast error upper and lower bounds, a DED model with wind power is formulated in this paper. A hybrid algorithm combining the evolutionary advantages of bat algorithm (BA) and particle swarm optimization (PSO) algorithm is designed to solve the proposed model. A crossover mechanism, which can solve the problem of falling into local optimum easily existed in BA and PSO, is introduced in the evolution of the algorithm. Finally, the effectiveness of the proposed model and algorithm is verified by simulation examples.