Advanced optimization models for the location of charging stations in e-mobility
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In: Central European Journal of Operations Research, Vol. 32.2024, No. September, 07.12.2023, p. 737-761.
Research output: Contribution to journal › Article › Research › peer-review
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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
UR - https://link.springer.com/article/10.1007/s10100-023-00878-w?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20231207&utm_content=10.1007/s10100-023-00878-w
UR - http://www.scopus.com/inward/record.url?scp=85178957883&partnerID=8YFLogxK
U2 - 10.1007/s10100-023-00878-w
DO - 10.1007/s10100-023-00878-w
M3 - Article
VL - 32.2024
SP - 737
EP - 761
JO - Central European Journal of Operations Research
JF - Central European Journal of Operations Research
SN - 1435-246X
IS - September
ER -