Advanced optimization models for the location of charging stations in e-mobility

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Advanced optimization models for the location of charging stations in e-mobility. / Staněk, Rostislav; Greistorfer, Peter; Kastner, Anna Elisabeth.
in: Central European Journal of Operations Research, Jahrgang 32.2024, Nr. September, 07.12.2023, S. 737-761.

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)

Vancouver

Staněk R, Greistorfer P, Kastner AE. Advanced optimization models for the location of charging stations in e-mobility. Central European Journal of Operations Research. 2023 Dez 7;32.2024(September):737-761. Epub 2023 Dez 7. doi: 10.1007/s10100-023-00878-w

Author

Staněk, Rostislav ; Greistorfer, Peter ; Kastner, Anna Elisabeth. / Advanced optimization models for the location of charging stations in e-mobility. in: Central European Journal of Operations Research. 2023 ; Jahrgang 32.2024, Nr. September. S. 737-761.

Bibtex - Download

@article{f80b38fdae2c48fe88c1da4f87652001,
title = "Advanced optimization models for the location of charging stations in e-mobility",
abstract = "For a reduction in environmental pollution and dependency on petroleum, electric vehicles (EV) present an advantageous alternative to traditionally fossil-fuel powered automobiles. Rapid growth in the number of EVs requires an urgent need to develop an adequate charging station infrastructure to stimulate and facilitate their usage. Due to restricted investments in the development of a sufficient infrastructure, locations have to be chosen deliberately. In this paper, two extensions considering different objectives and further constraints to the deterministic flow refuelling location problem (DFRLP), described by Vries and Duijzer (Omega 69:102–114, 2017), are introduced. First, our research shows that location-dependent construction costs significantly influence the charging infrastructure obtained. Tests for different cost scenarios are carried out and policy implications are discussed. The original DFRLP assumes an unlimited capacity, meaning it is always possible to refuel all EVs at all charging stations, where they stop. Hence, for a more realistic modelling, we assume a limited capacity of charging stations in our second extension. Finally, both extensions are evaluated using benchmark instances based on test instances from the literature.",
keywords = "electric vehicle, recharging, flow refuelling, facility location, Electric vehicles, Recharging, Flow refuelling, Facility location",
author = "Rostislav Stan{\v e}k and Peter Greistorfer and Kastner, {Anna Elisabeth}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s).",
year = "2023",
month = dec,
day = "7",
doi = "10.1007/s10100-023-00878-w",
language = "English",
volume = "32.2024",
pages = "737--761",
journal = "Central European Journal of Operations Research",
issn = "1435-246X",
publisher = "Springer Berlin",
number = "September",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Advanced optimization models for the location of charging stations in e-mobility

AU - Staněk, Rostislav

AU - Greistorfer, Peter

AU - Kastner, Anna Elisabeth

N1 - Publisher Copyright: © 2023, The Author(s).

PY - 2023/12/7

Y1 - 2023/12/7

N2 - For a reduction in environmental pollution and dependency on petroleum, electric vehicles (EV) present an advantageous alternative to traditionally fossil-fuel powered automobiles. Rapid growth in the number of EVs requires an urgent need to develop an adequate charging station infrastructure to stimulate and facilitate their usage. Due to restricted investments in the development of a sufficient infrastructure, locations have to be chosen deliberately. In this paper, two extensions considering different objectives and further constraints to the deterministic flow refuelling location problem (DFRLP), described by Vries and Duijzer (Omega 69:102–114, 2017), are introduced. First, our research shows that location-dependent construction costs significantly influence the charging infrastructure obtained. Tests for different cost scenarios are carried out and policy implications are discussed. The original DFRLP assumes an unlimited capacity, meaning it is always possible to refuel all EVs at all charging stations, where they stop. Hence, for a more realistic modelling, we assume a limited capacity of charging stations in our second extension. Finally, both extensions are evaluated using benchmark instances based on test instances from the literature.

AB - For a reduction in environmental pollution and dependency on petroleum, electric vehicles (EV) present an advantageous alternative to traditionally fossil-fuel powered automobiles. Rapid growth in the number of EVs requires an urgent need to develop an adequate charging station infrastructure to stimulate and facilitate their usage. Due to restricted investments in the development of a sufficient infrastructure, locations have to be chosen deliberately. In this paper, two extensions considering different objectives and further constraints to the deterministic flow refuelling location problem (DFRLP), described by Vries and Duijzer (Omega 69:102–114, 2017), are introduced. First, our research shows that location-dependent construction costs significantly influence the charging infrastructure obtained. Tests for different cost scenarios are carried out and policy implications are discussed. The original DFRLP assumes an unlimited capacity, meaning it is always possible to refuel all EVs at all charging stations, where they stop. Hence, for a more realistic modelling, we assume a limited capacity of charging stations in our second extension. Finally, both extensions are evaluated using benchmark instances based on test instances from the literature.

KW - electric vehicle

KW - recharging

KW - flow refuelling

KW - facility location

KW - Electric vehicles

KW - Recharging

KW - Flow refuelling

KW - Facility location

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VL - 32.2024

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EP - 761

JO - Central European Journal of Operations Research

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