The Optimal Placement of Electric Vehicle Fast Charging Stations in the Electrical Distribution System with Randomly Placed Solar Power Distributed Generations

被引:0
|
作者
Ahmad F. [1 ]
Iqbal A. [2 ]
Ashraf I. [1 ]
Marzband M. [3 ]
Khan I. [4 ]
机构
[1] Department of Electrical Engineering, Aligarh Muslim University, Uttar Pradesh
[2] Department of Electrical Engineering, Qatar University, Doha
[3] Department: Mathematics Physics and Electrical Engineering, Northumbria University
[4] Clean and Resilient Energy Systems (CARES) Lab, Texas A&M University, Galveston
关键词
Charging stations; electric vehicle population; improved bald eagle search algorithm; land cost; optimal placement;
D O I
10.13052/dgaej2156-3306.37416
中图分类号
学科分类号
摘要
The growing use of electric vehicles (EVs) in today's transport sector is gradually reducing the use of petroleum-based vehicles. However, as EV penetration grows, the EV's demand influences distribution network parameters such as power loss, voltage profile. Therefore, an improved bald eagle search (IBES) algorithm is suggested for the optimal placement of FCSs into the distribution network with high penetration of randomly distributed solar power generation (SPDG). This study suggests a two-stage approach for placing FCSs. The charging station investor decision index (CSIDI) was introduced in the first stage, taking into account the land cost index (LCI) and the electric vehicle population index (EVPI). The CSIDI was developed to decrease land costs while increasing EV population for FCS installation. In the next one, an optimization problem is constructed to minimize total active power loss while taking distribution system operator (DSO) constraints into consideration. The IEEE-34 bus distribution system is used as the proposed network. The simulation is carried out in MATLAB to integrate the EVCSs in three cases in the distribution network with SPDGs randomly placed. Therefore, The IBES found the best optimal positions with a power loss of 198.43 kW. When compared to the PSO technique, the IBES technique has a reduced average power loss of 2.02%. © 2022 River Publishers.
引用
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页码:1277 / 1304
页数:27
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