Cost-Effective Strategy for the Deployment of Charging Stations for Electric Vehicles Using Flower Pollination Algorithm

The adoption of electric vehicles (EVs) is increasingly prevalent due to their advantages such as reduced emissions, lower noise pollution, decreased maintenance requirements, and efficient power consumption. Many modern countries are integrating EVs as part of their initiatives to enhance environmental sustainability and energy efficiency. However, the strategic placement of EV charging stations is crucial as it can impact voltage stability, peak load, power quality, and transformer lifespan within the power system. Thus, optimizing the location and sizing of EV charging stations is essential for cost-effective distribution system planning. This paper presents an approach for determining the optimal placement and sizing of charging stations using the Flower Pollination Algorithm. The proposed method is evaluated on a distribution power system, demonstrating its efficacy in optimizing charging station locations and sizes while minimizing operational costs. The results indicate that the Flower Pollination Algorithm outperforms the Artificial Bee Colony Algorithm in terms of cost efficiency and overall performance.

Authors

Institutions

Publication Details

Journal
International Journal of Academic Research in Economics and Management Sciences
Published
2024-08-05
DOI
https://doi.org/10.6007/ijarems/v13-i3/22104
Citations
1
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cost-Effective Strategy for the Deployment of Charging Stations for Electric Vehicles Using Flower Pollination Algorithm

Siti Zaliha Mohammad Noor, Zuhaila Mat Yasin, Nofri Yenita Dahlan, Nor Salwati Othman
1 citations
International Journal of Academic Research in Economics and Management Sciences
Electric Vehicles and Infrastructure
0.21
article

Cost-Effective Strategy for the Deployment of Charging Stations for Electric Vehicles Using Flower Pollination Algorithm

Siti Zaliha Mohammad Noor, Zuhaila Mat Yasin, Nofri Yenita Dahlan, Nor Salwati Othman
article en
1 citations

Abstract

The adoption of electric vehicles (EVs) is increasingly prevalent due to their advantages such as reduced emissions, lower noise pollution, decreased maintenance requirements, and efficient power consumption. Many modern countries are integrating EVs as part of their initiatives to enhance environmental sustainability and energy efficiency. However, the strategic placement of EV charging stations is crucial as it can impact voltage stability, peak load, power quality, and transformer lifespan within the power system. Thus, optimizing the location and sizing of EV charging stations is essential for cost-effective distribution system planning. This paper presents an approach for determining the optimal placement and sizing of charging stations using the Flower Pollination Algorithm. The proposed method is evaluated on a distribution power system, demonstrating its efficacy in optimizing charging station locations and sizes while minimizing operational costs. The results indicate that the Flower Pollination Algorithm outperforms the Artificial Bee Colony Algorithm in terms of cost efficiency and overall performance.

International Journal of Academic Research in Economics and Management SciencesVol. 13(3)
Universiti Teknologi MARA System (MY), College of Accounting (SI), Universiti Tenaga Nasional (MY), Universiti Teknologi MARA (MY)
Openalex Percentile: Top 49%
Electric Vehicles and Infrastructure
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.