Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning
The transition to sustainable transportation is imperative in mitigating environmental impacts, with electric vehicles (EVs) at the forefront of this shift. Despite their environmental benefits, the global adoption of EVs is curtailed by challenges such as nascent battery technology, high costs, and...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024051843 |
| _version_ | 1797215939777789952 |
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| author | Serdar Çelik Şeyda Ok |
| author_facet | Serdar Çelik Şeyda Ok |
| author_sort | Serdar Çelik |
| collection | DOAJ |
| description | The transition to sustainable transportation is imperative in mitigating environmental impacts, with electric vehicles (EVs) at the forefront of this shift. Despite their environmental benefits, the global adoption of EVs is curtailed by challenges such as nascent battery technology, high costs, and insufficient charging infrastructure. This study addresses the optimizing electric vehicle charging station (EVCS) locations as a critical step toward enhancing EV adoption rates. Thus, establishing efficient charging stations is critical to meet the increasing demand. By integrating location modeling with demand forecasts and market penetration, we propose a comprehensive approach to determine optimal locations and capacities for EVCS. Firstly, review existing literature, highlighting the significance of facility location models in optimizing EV charging infrastructure and identifying gaps in addressing demand and market penetration. Our methodology uses a genetic algorithm to solve the p-median problem for location selection and Arena 14 simulation software to model station traffic and optimize charging unit types and quantities. The model prioritizes public areas, considering potential demand points and station locations to propose optimal charging areas. Results indicate that our model minimizes travel distances and waiting times, offering a scalable solution adaptable to future EV market growth. This study contributes to the field by presenting a sustainable and economical model for EVCS placement and capacity planning, underlining the importance of a robust charging network in the broader adoption of electric transportation. The findings suggest that proactive infrastructure development, guided by accurate demand predictions and optimized location strategies, can significantly enhance the feasibility and attractiveness of EVs, supporting global efforts towards a cleaner, more sustainable transportation system. |
| first_indexed | 2024-04-24T11:38:02Z |
| format | Article |
| id | doaj.art-2435a3eaf4b34818a1e9e43fdbd452bf |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T11:38:02Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-2435a3eaf4b34818a1e9e43fdbd452bf2024-04-10T04:29:14ZengElsevierHeliyon2405-84402024-04-01107e29153Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planningSerdar Çelik0Şeyda Ok1Department of Management Information Systems, Ostim Technical University, Ankara, Turkey; Corresponding author.Department of Marketing, Ostim Technical University, Ankara, TurkeyThe transition to sustainable transportation is imperative in mitigating environmental impacts, with electric vehicles (EVs) at the forefront of this shift. Despite their environmental benefits, the global adoption of EVs is curtailed by challenges such as nascent battery technology, high costs, and insufficient charging infrastructure. This study addresses the optimizing electric vehicle charging station (EVCS) locations as a critical step toward enhancing EV adoption rates. Thus, establishing efficient charging stations is critical to meet the increasing demand. By integrating location modeling with demand forecasts and market penetration, we propose a comprehensive approach to determine optimal locations and capacities for EVCS. Firstly, review existing literature, highlighting the significance of facility location models in optimizing EV charging infrastructure and identifying gaps in addressing demand and market penetration. Our methodology uses a genetic algorithm to solve the p-median problem for location selection and Arena 14 simulation software to model station traffic and optimize charging unit types and quantities. The model prioritizes public areas, considering potential demand points and station locations to propose optimal charging areas. Results indicate that our model minimizes travel distances and waiting times, offering a scalable solution adaptable to future EV market growth. This study contributes to the field by presenting a sustainable and economical model for EVCS placement and capacity planning, underlining the importance of a robust charging network in the broader adoption of electric transportation. The findings suggest that proactive infrastructure development, guided by accurate demand predictions and optimized location strategies, can significantly enhance the feasibility and attractiveness of EVs, supporting global efforts towards a cleaner, more sustainable transportation system.http://www.sciencedirect.com/science/article/pii/S2405844024051843Electric vehicleCharging stationGenetic algorithmSimulationP-medianFacility location |
| spellingShingle | Serdar Çelik Şeyda Ok Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning Heliyon Electric vehicle Charging station Genetic algorithm Simulation P-median Facility location |
| title | Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning |
| title_full | Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning |
| title_fullStr | Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning |
| title_full_unstemmed | Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning |
| title_short | Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning |
| title_sort | electric vehicle charging stations model algorithm simulation location and capacity planning |
| topic | Electric vehicle Charging station Genetic algorithm Simulation P-median Facility location |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024051843 |
| work_keys_str_mv | AT serdarcelik electricvehiclechargingstationsmodelalgorithmsimulationlocationandcapacityplanning AT seydaok electricvehiclechargingstationsmodelalgorithmsimulationlocationandcapacityplanning |