Determination of Optimal Location of Electrical Vehicle Charging Stations in Istanbul with Genetic Algorithm and Geographical Systems

Onur Yenigün; Tülin Boyar; Kazım Yıldız

Makale detayları

Determination of Optimal Location of Electrical Vehicle Charging Stations in Istanbul with Genetic Algorithm and Geographical Systems

Yazarlar:

Onur Yenigün | Tülin Boyar | Kazım Yıldız

Yayıncı:

Gazi University

Ana Başlıklar:

Yapay Zeka (Diğer)

Dergi:

Bilişim Teknolojileri Dergisi Vol.17 Issue 1, pp.1-9

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001310692

pISSN:

1307-9697

Anahtar kelimeler:

Özet:

 The use of electric vehicles in Turkey is increasing from one day to the next. This study focuses on determining the efficient locations for the positioning of electric vehicle charging stations to be established in Istanbul. Geographic Information System was used in the study; a location map was created for the traffic density map of Istanbul and the existing electric vehicle charging stations The genetic algorithm was used to determine the points of highest charging inqury in the city and to calculate the charging demand satisfaction situation of the existing charging stations. The constraints are that the estimated charging demand is not met to a certain extent at the charging demand points and that the locations are at a maximum distance of 500 metres from the charging demand points. With the solution sets generated according to the genetic algorithm, 10 efficient locations were determined from the 223 Istanbul Parking Lot Operations datasets, representing the locations where the charging station is planned to be installed. The identified locations are the areas with high traffic in Istanbul. In this study, electric vehicle charging network operators can calculate the time to tolerate this cost for the installation of charging stations, which is quite costly; it suggests an approach to location planning so that electric vehicle users can satisfy their charging needs as quickly as possible without creating long queues.

 The use of electric vehicles in Turkey is increasing from one day to the next. This study focuses on determining the efficient locations for the positioning of electric vehicle charging stations to be established in Istanbul. Geographic Information System was used in the study; a location map was created for the traffic density map of Istanbul and the existing electric vehicle charging stations The genetic algorithm was used to determine the points of highest charging inqury in the city and to calculate the charging demand satisfaction situation of the existing charging stations. The constraints are that the estimated charging demand is not met to a certain extent at the charging demand points and that the locations are at a maximum distance of 500 metres from the charging demand points. With the solution sets generated according to the genetic algorithm, 10 efficient locations were determined from the 223 Istanbul Parking Lot Operations datasets, representing the locations where the charging station is planned to be installed. The identified locations are the areas with high traffic in Istanbul. In this study, electric vehicle charging network operators can calculate the time to tolerate this cost for the installation of charging stations, which is quite costly; it suggests an approach to location planning so that electric vehicle users can satisfy their charging needs as quickly as possible without creating long queues.

 The use of electric vehicles in Turkey is increasing from one day to the next. This study focuses on determining the efficient locations for the positioning of electric vehicle charging stations to be established in Istanbul. Geographic Information System was used in the study; a location map was created for the traffic density map of Istanbul and the existing electric vehicle charging stations The genetic algorithm was used to determine the points of highest charging inqury in the city and to calculate the charging demand satisfaction situation of the existing charging stations. The constraints are that the estimated charging demand is not met to a certain extent at the charging demand points and that the locations are at a maximum distance of 500 metres from the charging demand points. With the solution sets generated according to the genetic algorithm, 10 efficient locations were determined from the 223 Istanbul Parking Lot Operations datasets, representing the locations where the charging station is planned to be installed. The identified locations are the areas with high traffic in Istanbul. In this study, electric vehicle charging network operators can calculate the time to tolerate this cost for the installation of charging stations, which is quite costly; it suggests an approach to location planning so that electric vehicle users can satisfy their charging needs as quickly as possible without creating long queues.

 Türkiyede elektrikli araç kullanımı her geçen gün artmaktadır. Bu çalışmada, İstanbulda kurulacak elektrikli araç şarj istasyonu konumlandırması için en verimli lokasyonların belirlenmesine odaklanılmıştır. Çalışmada Coğrafi Bilgi Sistemi kullanılmış; İstanbul trafik yoğunluk haritası ve mevcut elektrikli araç şarj istasyonları için lokasyon haritası oluşturulmuştur. Genetik algoritma kullanılarak, şehirde şarj talebinin en yüksek olduğu noktalar belirlenerek mevcut şarj istasyonlarının şarj talebini karşılama durumu hesaplanmıştır. Şarj talep noktalarında tahmini şarj ihtiyacının belirli bir oranda karşılanmaması ve lokasyonların şarj talep noktalarına maksimum 500 metre uzaklıkta olması koşulları aranmıştır. Genetik algoritmaya göre oluşturulan çözüm setleri ile şarj istasyonu kurulması planlanan lokasyonları temsil eden 223 İstanbul Otopark İşletmeciliği veri setinden en verimli 10 lokasyon belirlenmiştir. Belirlenen lokasyonlar İstanbul trafiğinin yoğun olduğu bölgelerdir. Bu çalışmada elektrikli araç kullanıcılarının uzun kuyruklar oluşturmadan şarj taleplerini mümkün olan en kısa sürede karşılayabilmeleri için konum planlamasına yönelik bir yaklaşım önerilmektedir.

Makale detayları

Determination of Optimal Location of Electrical Vehicle Charging Stations in Istanbul with Genetic Algorithm and Geographical Systems

Yazarlar:

Onur Yenigün | Tülin Boyar | Kazım Yıldız

Yayıncı:

Gazi University

Ana Başlıklar:

Yapay Zeka (Diğer)

Dergi:

Bilişim Teknolojileri Dergisi Vol.17 Issue 1, pp.1-9

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001310692

pISSN:

1307-9697

electric vehicle charging station

electric vehicle charging station positioning

determination of electric vehicle charging station locationsElektrikli araç şarj istasyonu

elektrikli araç şarj istasyonu konumlandırma

elektrikli araç şarj istasyonu yerlerinin belirlenmesi

genetik algoritma

Sayı

Makale detayları

Determination of Optimal Location of Electrical Vehicle Charging Stations in Istanbul with Genetic Algorithm and Geographical Systems

Yazarlar: Onur Yenigün | Tülin Boyar | Kazım Yıldız

Yayıncı: Gazi University

Ana Başlıklar: Yapay Zeka (Diğer)

Dergi: Bilişim Teknolojileri Dergisi Vol.17 Issue 1, pp.1-9

Belge Tipi: Makale

Yayın Tarihi: 1/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001310692

pISSN: 1307-9697

electric vehicle charging station

electric vehicle charging station positioning

determination of electric vehicle charging station locationsElektrikli araç şarj istasyonu

elektrikli araç şarj istasyonu konumlandırma

elektrikli araç şarj istasyonu yerlerinin belirlenmesi

genetik algoritma

Sayı

Yazarlar:

Onur Yenigün | Tülin Boyar | Kazım Yıldız

Yayıncı:

Gazi University

Ana Başlıklar:

Yapay Zeka (Diğer)

Dergi:

Bilişim Teknolojileri Dergisi Vol.17 Issue 1, pp.1-9

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001310692

pISSN:

1307-9697