Optimal location and sizing of renewable distributed generations and electric vehicle charging stations

被引:0
|
作者
Guindi, Marina [1 ]
Kamel, Rashad M. [2 ]
机构
[1] Amer Univ Kuwait, Coll Engn & Appl Sci, Dept Engn, POB 3323, Safat 13034, Kuwait
[2] Kuwait Univ, Coll Engn & Petr, Elect Engn Dept, POB 5969, Safat 13060, Kuwait
关键词
Electric vehicle charging station; Renewable distributed generation; Particle swarm optimization; Cuckoo search algorithm; OPTIMAL PLACEMENT; ENERGY-RESOURCES; OPTIMIZATION; INTEGRATION; ALLOCATION; REDUCTION; SYSTEMS; DGS;
D O I
10.1016/j.renene.2024.121272
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Many countries are aiming to replace gasoline-based vehicles with electric vehicles (EVs). The increasing adoption of EVs has led to a surge in the number of charging stations, significantly impacting the electrical grid with issues such as power quality degradation, increased losses, and voltage fluctuations. In response to these challenges, there is a growing interest in integrating distributed generation from unconventional and renewable sources into the grid to power EV Charging Stations (EVCSs). However, this integration poses new complexities, including increased power losses and voltage instability. Consequently, the optimal allocation and sizing of Renewable Distributed Generations (RDGs) and EVCSs have become critical planning considerations. This paper addresses these issues by formulating a multi-objective optimization problem aimed at minimizing power losses and improving the voltage profile of distribution systems. The study introduces several constraints, including the placement of EVCSs and RDGs at separate buses, and employs particle swarm optimization and cuckoo search algorithm to simultaneously determine the optimal locations and sizes of the RDGs and the optimal locations of the EVCSs. The effectiveness of the proposed techniques is demonstrated through simulations on IEEE 33 radial and meshed distribution systems, illustrating their capability to identify optimal configurations for integrating RDGs and EVCSs.
引用
收藏
页数:22
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