Optimal Planning of Parking Lots of PEVs Incorporating V2G for Reliability Improvement of Distribution Systems

被引:0
|
作者
AlNahhal, Rana H. H. [1 ]
Naiem, Adel F. F. [1 ]
Shaaban, Mostafa F. F. [2 ]
Ismail, Muhammad [3 ]
机构
[1] German Univ Cairo, Elect Circuits & Elect Dept, New Cairo 16482, Egypt
[2] Amer Univ Sharjah, Dept Elect Engn, Sharjah, U Arab Emirates
[3] Tennessee Technol Univ, Comp Sci Dept, Cookeville, TN USA
来源
IEEE ACCESS | 2022年 / 10卷
关键词
Plug-in electric vehicles; Vehicle-to-grid; Costs; Monte Carlo methods; Uncertainty; Markov processes; Probabilistic logic; Monte Carlo simulation; planning; plug-in electric vehicles; reliability; vehicle-to-grid; ELECTRIC VEHICLES; CHARGING STATIONS; OPTIMIZATION;
D O I
10.1109/ACCESS.2022.3224753
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The number of plug-in electric vehicles (PEV) increased significantly over the past few years and is expected to increase further in the next years due to environmental concerns and fossil fuel depletion. Distribution systems were not originally designed to accommodate this continuous prevalence of PEVs, challenging system planners to install parking lots (PLs) that support PEV charging. The literature has presented many studies for planning PLs, although it did not consider the vehicle-to-grid (V2G) contribution to improving system reliability. This paper proposes an optimal planning framework for allocating and sizing the PLs of PEVs incorporating V2G to improve the reliability of distribution systems. A combined probabilistic model of Markov Chain Monte Carlo simulation (MCMCS) and Monte Carlo simulation (MCS) is applied to capture the stochastic existence of PEVs in PLs. Reliability is evaluated based on a group of energy-oriented indices and the customer's annual cost of interruption. A comprehensive investigation is presented to test the proposed framework on a distribution system for grid-to-vehicle (G2V) and V2G case studies. The results show the efficiency of the proposed framework in improving the reliability indices by 12% and reducing the customer cost of interruption by 10.06% while prioritizing critical customers.
引用
收藏
页码:123521 / 123533
页数:13
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