Robust Energy Management for Uncertain Microgrid Using Modified Grey Wolf Optimizer

被引：0

作者：

Cao, Yuhao ^{[1
]}

Chen, Tengpeng ^{[1
]}

Sun, Lu ^{[2
]}

Sun, Yuhao ^{[3
,4
]}

Wei, Zhongbao ^{[5
]}

Amaratunga, Gehan A. J. ^{[6
]}

机构：

[1] Xiamen Univ, Dept Instrumental & Elect Engn, Xiamen, Peoples R China

[2] Nanyang Technol Univ, Expt Power Grid Ctr EPGC, Singapore, Singapore

[3] CTC Intelligence Shenzhen Tech Co Ltd, Shenzhen, Peoples R China

[4] Yunnan Univ, Natl Ctr Int Res Photoelect & Energy Mat, Kunming, Peoples R China

[5] Beijing Inst Technol, Sch Mech Engn, Beijing, Peoples R China

[6] Univ Cambridge, Dept Engn, Cambridge CB3 0FA, England

来源：

PROCEEDINGS OF THE 15TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2020) | 2020年

基金：

中国国家自然科学基金;

关键词：

robust optimization; energy management; uncertainty; modified grey wolf optimizer; microgrid; SYSTEM;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Uncertainties of renewable energy sources (RFS) power generation and load demand have detrimental effects on the microgrid operation. In this paper, a robust optimization approach based on modified grey wolf optimizer is proposed to determine the optimal energy management for a typical microgrid with regard to uncertainties. Furthermore, the influence of uncertainty budget for RES power generation and load demand on operation cost and pollutant gas emissions are studied. Simulation results show a good reduction both in operation cost and pollution emissions as well verify the effectiveness of our proposed approach.

引用

页码：1515 / 1519

页数：5

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