文章基本信息

标题：Multi-objective Interval Optimization of Virtual Power Plant Considering the Uncertainty of Source and Load
本地全文：下载
作者：Shuai Han ; Leping Sun ; Xiaoxuan Guo 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2021
卷号：299
页码：1-8
DOI：10.1051/e3sconf/202129901011
语种：English
出版社：EDP Sciences
摘要：As the proportion of electric vehicles and distributed power sources connected to the power grid continues to increase, virtual power plants provide new ideas for effectively solving electric vehicles and distributed power sources connected to the grid. Considering that there are obvious uncertainties in the number of dispatchable electric vehicles and the output of distributed power sources, this paper focuses on the multi-objective interval optimization problem of virtual power plants considering the uncertainty of source load. Based on the analysis of the virtual power plant architecture, aiming at the uncertainty of the source load, a multi-objective interval optimization model of the virtual power plant was established using the interval number theory; in order to verify the validity of the established model, a virtual power plant in a certain area was selected as an example for analysis. The results show that the uncertainty of distributed power sources and electric vehicles can be better avoided in the interval optimization process, and the proposed scheme has strong robustness.