Optimal Location of Electric Vehicle Fast Charging Station Using Grasshopper Optimization Algorithm
The increasing prevalence of Electric Vehicles (EVs) has underscored the critical importance of establishing a comprehensive and effective charging station network. To sufficiently meet the energy demands of electric vehicles, it is imperative to establish a robust charging station infrastructure th...
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Seventh Sense Research Group
2024
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| author | Yasin Z.M. Salim N.A. Noor S.Z.M. Aziz N.F.A. Mohamad H. |
| author2 | 57211410254 |
| author_facet | 57211410254 Yasin Z.M. Salim N.A. Noor S.Z.M. Aziz N.F.A. Mohamad H. |
| author_sort | Yasin Z.M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The increasing prevalence of Electric Vehicles (EVs) has underscored the critical importance of establishing a comprehensive and effective charging station network. To sufficiently meet the energy demands of electric vehicles, it is imperative to establish a robust charging station infrastructure that can effectively cater to a substantial volume of electric automobiles. This infrastructure must be widely deployed to ensure widespread accessibility and usability. Many EVs� concurrent usage of electric charging stations may lead to potential unreliability in the distribution setup. Hence, it is imperative to strategically determine the placement and sizing of Fast Charging Stations (FCS) to achieve optimal functionality of the power grid. This paper proposes the Grasshopper Optimization Algorithm (GOA) as a technique for strategically locating FCS to minimize costs. GOA is a computational technique that addresses optimization challenges by formulating a mathematical model that emulates the collective behaviour observed in natural grasshopper swarms. The proposed methodology is evaluated on an IEEE 69-bus radial distribution system. The results indicate that the proposed methodology has successfully identified the most economically efficient location for FCS within a power distribution network compared to alternative optimization methods. � 2023 Seventh Sense Research Group�. |
| format | Article |
| id | my.uniten.dspace-34131 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2024 |
| publisher | Seventh Sense Research Group |
| record_format | dspace |
| spelling | my.uniten.dspace-341312024-10-14T11:18:05Z Optimal Location of Electric Vehicle Fast Charging Station Using Grasshopper Optimization Algorithm Yasin Z.M. Salim N.A. Noor S.Z.M. Aziz N.F.A. Mohamad H. 57211410254 36806685300 58643927500 57221906825 36809989400 Ant colony optimizer Cost minimization Distribution system Minimum voltage Power loss minimization The increasing prevalence of Electric Vehicles (EVs) has underscored the critical importance of establishing a comprehensive and effective charging station network. To sufficiently meet the energy demands of electric vehicles, it is imperative to establish a robust charging station infrastructure that can effectively cater to a substantial volume of electric automobiles. This infrastructure must be widely deployed to ensure widespread accessibility and usability. Many EVs� concurrent usage of electric charging stations may lead to potential unreliability in the distribution setup. Hence, it is imperative to strategically determine the placement and sizing of Fast Charging Stations (FCS) to achieve optimal functionality of the power grid. This paper proposes the Grasshopper Optimization Algorithm (GOA) as a technique for strategically locating FCS to minimize costs. GOA is a computational technique that addresses optimization challenges by formulating a mathematical model that emulates the collective behaviour observed in natural grasshopper swarms. The proposed methodology is evaluated on an IEEE 69-bus radial distribution system. The results indicate that the proposed methodology has successfully identified the most economically efficient location for FCS within a power distribution network compared to alternative optimization methods. � 2023 Seventh Sense Research Group�. Final 2024-10-14T03:18:05Z 2024-10-14T03:18:05Z 2023 Article 10.14445/23488379/IJEEE-V10I9P117 2-s2.0-85173993412 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173993412&doi=10.14445%2f23488379%2fIJEEE-V10I9P117&partnerID=40&md5=5e8ce0bed325d6e00960b8568f14a37b https://irepository.uniten.edu.my/handle/123456789/34131 10 9 181 189 All Open Access Hybrid Gold Open Access Seventh Sense Research Group Scopus |
| spellingShingle | Ant colony optimizer Cost minimization Distribution system Minimum voltage Power loss minimization Yasin Z.M. Salim N.A. Noor S.Z.M. Aziz N.F.A. Mohamad H. Optimal Location of Electric Vehicle Fast Charging Station Using Grasshopper Optimization Algorithm |
| title | Optimal Location of Electric Vehicle Fast Charging Station Using Grasshopper Optimization Algorithm |
| title_full | Optimal Location of Electric Vehicle Fast Charging Station Using Grasshopper Optimization Algorithm |
| title_fullStr | Optimal Location of Electric Vehicle Fast Charging Station Using Grasshopper Optimization Algorithm |
| title_full_unstemmed | Optimal Location of Electric Vehicle Fast Charging Station Using Grasshopper Optimization Algorithm |
| title_short | Optimal Location of Electric Vehicle Fast Charging Station Using Grasshopper Optimization Algorithm |
| title_sort | optimal location of electric vehicle fast charging station using grasshopper optimization algorithm |
| topic | Ant colony optimizer Cost minimization Distribution system Minimum voltage Power loss minimization |
| url_provider | http://dspace.uniten.edu.my/ |