文献类型：期刊文献

英文题名：Dynamic economic emission dispatch with wind power based on improved multi-objective brain storm optimisation algorithm

作者：Gang, Liu Yongli, Zhu Wei, Jiang

第一作者：Gang, Liu;刘刚

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 550003, 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：14

期号：13

起止页码：2526-2537

外文期刊名：IET RENEWABLE POWER GENERATION

收录：;EI(收录号：20204209367653);Scopus(收录号：2-s2.0-85092655475);WOS:【SCI-EXPANDED(收录号:WOS:000581900200024)】；

语种：英文

外文关键词：wind power plants; power generation scheduling; power generation dispatch; genetic algorithms; wind power; power generation economics; pattern clustering; probability; external archive mechanism; multiobjective BSO algorithm; emission objectives; DEED incorporating wind power model; optimal wind power output scheduling; wind power prediction interval; improved multiobjective brain storm optimisation algorithm; nonparametric kernel density estimation technique; forecast error; dynamic economic emission dispatch model; wind power penetration; DEED problem; basic brainstorm optimisation algorithm; differential mutation operation; individual crossover operation

摘要：Based on the non-parametric kernel density estimation technique, the probability distribution of wind power output and its forecast error are accurately modelled. The confidence interval and forecast error upper and lower bounds of wind power output are estimated to build a dynamic economic emission dispatch (DEED) model with wind power penetration. To effectively solve the DEED problem with multi-objective, high-dimensional, non-linear and strong constraints, based on the basic brainstorm optimisation (BSO) algorithm, three improvement mechanisms, namely random clustering centre, differential mutation operation and individual crossover operation are introduced to enhance the converging and diverging operation of BSO. Based on these improvements and external archive mechanism, an improved multi-objective BSO (IMOBSO) algorithm is proposed. Simulations on a classical test system with ten thermal units are performed, where two case studies are investigated carefully. The simulation results demonstrate that: (i) the proposed IMOBSO can optimise the cost and emission objectives simultaneously and have achieved better performance than other algorithms; (ii) the proposed DEED incorporating wind power model is reasonable and effective because it can achieve the optimal wind power output scheduling by adjusting the system's spinning reserve capacity and the confidence level of wind power prediction interval.