Modeling of Power Demands of Electric Vehicles in Correlated Probabilistic Load Flow Studies

被引：0

作者：

Bhat, Nitesh Ganesh ^{[1
]}

Prusty, B. Rajanarayan ^{[1
]}

Jena, Debashisha ^{[1
]}

机构：

[1] Natl Inst Technol Karnataka, Dept Elect & Elect Engn, Surathkal, India

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON POWER ELECTRONICS, DRIVES AND ENERGY SYSTEMS (PEDES) | 2016年

关键词：

Battery electric vehicle; extended cumulant method; plug-in hybrid electric vehicle; probabilistic load flow; radial distribution system; CUMULANT;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In this paper, extended cumulant method (ECM) is applied to probabilistic load flow analysis. Input uncertainties pertaining to plug-in hybrid electric vehicle and battery electric vehicle charging demands in residential community as well as charging stations are probabilistically modeled. Probability distributions of the result variables such as bus voltages and branch power flows pertaining to these inputs are accurately approximated; and at the same time, multiple input correlation cases are incorporated. The performance of ECM is demonstrated on the modified IEEE 69-bus radial distribution system. The results of ECM are compared with Monte-Carlo simulation.

引用

页数：6

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