A scenario-based stochastic programming approach for the public charging station location problem

This paper presents an integrated framework for the optimal planning of public charging stations for plug-in electric vehicles (PEVs) in urban areas. The framework consists of two main components: (i) an out-of-home charging demand model based on an activity-based travel demand model, and (ii) a pub...

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Vydané v:Transportmetrica. (Abingdon, Oxfordshire, UK) Ročník 10; číslo 1; s. 340 - 367
Hlavní autori: Kim, Seheon, Rasouli, Soora, Timmermans, Harry J. P., Yang, Dujuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Abingdon Taylor & Francis 31.12.2022
Taylor & Francis Ltd
Predmet:
activity-based charging demand
Decision trees
dynamic charging behaviour
Electric power demand
Electric vehicle charging
Electrical plugs
Integer programming
Location optimization under uncertainty
Mixed integer
model-based scenario generation
Stochastic programming
Traffic models
Travel demand
Uncertainty
Urban areas
ISSN:2168-0566, 2168-0582
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Shrnutí:This paper presents an integrated framework for the optimal planning of public charging stations for plug-in electric vehicles (PEVs) in urban areas. The framework consists of two main components: (i) an out-of-home charging demand model based on an activity-based travel demand model, and (ii) a public charging station location-allocation model using a scenario-based stochastic programming (SP) approach. In order to capture the dynamic charging behaviour of PEV users, a chi-squared automatic interaction detector (CHAID)-based mixed effects decision tree is induced from multi-day activity diaries. Moreover, because the stochastic error of the micro-simulation approach brings about uncertainty, we adopted a two-stage stochastic mixed-integer programming (TSMIP) model, which measures uncertainty by means of a finite set of scenarios obtained from the derived decision rules underlying PEV charging. The proposed approach is demonstrated for the City of Eindhoven, The Netherlands, and benefits of the stochastic solution are discussed.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2168-0566
2168-0582
DOI:10.1080/21680566.2021.1997672