PSO-based optimal placement of electric vehicle charging stations in a distribution network in smart grid environment incorporating backward forward sweep method

被引：5

作者：

Altaf, Mishal ^{[1
]}

Yousif, Muhammad ^{[1
,7
]}

Ijaz, Haris ^{[2
]}

Rashid, Mahnoor ^{[1
]}

Abbas, Nasir ^{[1
]}

Khan, Muhammad Adnan ^{[3
]}

Waseem, Muhammad ^{[4
]}

Saleh, Ahmed Mohammed ^{[5
,6
]}

机构：

[1] Natl Univ Sci & Technol NUST, US Pakistan Ctr Adv Studies Energy USPCAS E, Islamabad, Pakistan

[2] Natl Univ Sci & Technol NUST, Sch Elect Engn & Comp Sci SEECS, Islamabad, Pakistan

[3] HITEC Univ, Dept Elect Engn, Rawalpindi, Pakistan

[4] Zhejiang Univ, Sch Elect Engn, Hangzhou, Peoples R China

[5] Univ Aden, Fac Engn, Dept Elect Engn, Aden, Yemen

[6] Univ Aden, Elect Engn Dept, Aden, Yemen

[7] Natl Univ Sci & Technol NUST, US Pakistan Ctr Adv Studies Energy USPCAS E, Islamabad 44000, Pakistan

来源：

IET RENEWABLE POWER GENERATION | 2024年 / 18卷 / 15期

关键词：

distributed power generation; electric vehicle charging; electric vehicles; energy management systems; particle swarm optimization;

D O I：

10.1049/rpg2.12916

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The transition from conventional fossil-fuel vehicles to electric vehicles (EVs) is critical for mitigating environmental pollution. The placement of electric vehicle charging stations (EVCS) significantly impacts the utility operator and electrical network. Inappropriately placed EVCS lead to challenges such as increased load, unbalanced generation, power losses, and reduced voltage stability. Incorporating distributed generation (DG) helps mitigate these issues by maximizing EV usage. This study focuses on optimizing EVCS and DG placement in radial distribution networks. The methodology employs a backward and forward sweep method for load flow analysis and utilizes the particle swarm optimization (PSO) algorithm to determine optimal EVCS and DG locations and sizes. This approach, validated on the IEEE-33 bus system, outperforms existing methods. Results indicate a 2.5 times greater power loss reduction compared to simulated annealing (SA), 1.6 times better than artificial bee colony, and parity with genetic algorithm (GA). Overall, the PSO algorithm demonstrates superior optimization effectiveness and computational efficiency, showcasing 1-2.5 times better performance than other methodologies. Employing this approach yields significantly improved results, making it a promising technique for optimizing EVCS and DG placement in distribution networks. Electric vehicles (EVs) are incorporated into the transport sector to reduce the harmful effects of fossil fuel vehicles. Here, the optimal placement of electric vehicle charging stations (EVCS) and distributed energy sources (DGs) on the radial distribution network is performed using the particle swarm optimization method. For the load flow analysis, the backward and forward sweep method is used.image

引用

页码：3173 / 3187

页数：15