Benders-and-Price approach for electric vehicle charging station location problem under probabilistic travel range

Lee, Chungmok; Han, Jinil

doi:10.1016/j.trb.2017.10.011

Cited by 107 publications

(56 citation statements)

References 48 publications

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“…However, we note that formulation EFRLM2 seems to be more performant than formulation EFRLM1 for large values of p only (typically more than 5 stations). This might be explained by the fact that both formulations involve similar knapsack constraints (see constraints (17) linking the trip coverage variables z q with the assignment variables w kl q in EFRLM2). Hence, the only formulation Table 3: FRLM1 and FRLM2 comparison on real-life instances strengthening obtained when using formulation EFRM2 comes from the integration of a second set of constraints (constraints (16)) to link variables z q and w kl q .…”

Section: Results For the Stochastic Problemmentioning

confidence: 99%

“…Hence, the problem modeling involves computing the coverage probability of each trip as the joint probability that each portion of the trip comprised between two opened charging stations will be shorter than the driving range. To the best of our knowledge, only two recent works, [3] and [17], have studied this problem. De Vries and Duijzer [3] assume that, for a given trip, the realization of the driving range will be the same on each portion of the trip and exploit this assumption to ease the computation of the joint probability.…”

Section: Position In the Literaturementioning

confidence: 99%

“…In both cases, the problem is reformulated as a MILP and solved with a mathematical programming solver. Lee and Han [17] also seek to maximize the expected EV flow coverage but they assume that, for a given trip, the random variables representing the stochastic driving range realization on each trip portion are totally independent of one another.…”

Section: Position In the Literaturementioning

confidence: 99%

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Efficient solution approaches for locating electric vehicle fast charging stations under driving range uncertainty

Boujelben

Gicquel

2019

Computers & Operations Research

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We seek to determine the best locations for electric vehicle fast charging stations under driving range uncertainty. Two stochastic programming based models have been recently proposed to handle the resulting stochastic flow refueling location problem: a first one maximizing the expected flow coverage of the network, a second one based on joint chance constraints. However, significant computational difficulties were encountered while solving large-size instances. We thus propose two efficient solution approaches for this problem. The first one is based on a new location-allocation type model for this problem and results in a MILP formulation, while the second one is a tabu search heuristic. Our numerical experiments show that when using the proposed MILP formulation, the computation time needed to provide guaranteed optimal solutions is significantly reduced as compared to the one needed when using the previously published MILP formulation. Moreover, our results also show that the tabu search method consistently provides good quality solutions within short computation times.

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Section: Results For the Stochastic Problemmentioning

confidence: 99%

Section: Position In the Literaturementioning

confidence: 99%

Section: Position In the Literaturementioning

confidence: 99%

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Efficient solution approaches for locating electric vehicle fast charging stations under driving range uncertainty

Boujelben

Gicquel

2019

Computers & Operations Research

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“…La sostenibilidad se refiere al desarrollo a largo plazo que incluye tres pilares: crecimiento económico, desarrollo social y protección ambiental. En la dimensión social, se consideran factores como la armonización de EC para VEEs mediante la planificación del desarrollo de la red vial urbana y de la red eléctrica, la convergencia de tráfico, la capacidad de servicio y el impacto en la vida de las personas [7], [8].…”

Section: Introductionunclassified

Despliegue óptimo georreferenciado de estaciones de carga vehicular pública considerando capacidad de flujo y distancias máximas habilitantes

Campaña

Inga

2019

I+D Tecnológico

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Los desafíos modernos están orientados a la reducción significativa de gases de efecto invernadero, los cuales, deterioran la calidad del aire. El parque automotor, suministros de energía (gas, vapor, aire acondicionado, agua para uso industrial y energía eléctrica) son fuentes de que se liberan en la atmosfera. Es por ello que, el presente artículo propone un modelo heurístico orientado a la reducción significativa de la dependencia de vehículos de combustión interna. El éxito o desacierto de la penetración masiva de vehículos eléctricos enchufables (VEE) no solo dependerá de la capacidad instalada en los sistemas eléctricos de distribución, sino que, además, de la autonomía y confort que los VEE logren brindar al usuario final. Por lo tanto, con el algoritmo Mínimo Emplazamiento de Estaciones de Carga Geolocalizadas (MEECG) se propone un modelo capaz de emplazar infraestructuras de estaciones de carga pública (IECP) considerando flujo vehicular y distancias máximas habilitantes. De tal manera que, se seleccione el mínimo número de estaciones de carga pública (ECP) garantizando la posibilidad de que un VEE pueda conectarse a una ECP reduciendo al máximo la ansiedad de alcance de parte del operador del VEE.

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“…The primary challenge of EV charging technology industry is to determine the optimal capacitated and located electric charging stations [8]. There are four presumptions made for EVs charging necessities in Lee and Han's research (2017) [9]: driver selects the shortest route, at the beginning of the route, half of the battery is full, at the end of the route, the battery must not be less than half of the battery and finally the travel range is constant and deterministic.…”

Section: Introductionmentioning

confidence: 99%

A model for determining the locations of electric charge stations in Istanbul

Daşcıoğlu¹,

Tuzkaya²,

Kılıç³

2019

Pamukkale J Eng Sci

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ÖzThe studies about Electric Vehicles (EV) have gained importance and increased in the last years depending on the environmental concerns of the classic transportation systems. One of the problems to consider at this point is locating the proper points of electric charging stations for EVs. The primary objective of this research is to locate the electric charging stations in Istanbul considering the flow of the paths. The locations of electric charging stations are determined by using a mathematical model based on the flow-refuelling location model with the aim of maximizing the captured flow. The mathematical model is run for various values of "p" and the optimum locations are obtained.Klasik ulaştırma sistemlerinin çevresel kaygıları göz önünde bulundurulduğunda, elektrikli araçlarla (EA) ilgili çalışmalar önem kazanmış ve son yıllarda sayıca artmıştır. Bu noktada ele alınması gereken sorunlardan biri EA'lar için şarj istasyonlarının uygun yerlerinin belirlenmesidir. Bu çalışmanın temel amacı, İstanbul'daki elektrik şarj istasyonlarına ilişkin en uygun konumları yolların akışını değerlendirmeye alarak bulmaktır. Şarj istasyonlarının konumları, yolların kapsanan akışını maksimize etmeyi amaçlayan akış-yakıt ikmal lokasyon modeline dayanan bir model kullanılarak belirlenmiştir. Matematiksel model farklı "p" değerli için çözülmüş ve en iyi lokasyonlar belirlenmiştir.

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Benders-and-Price approach for electric vehicle charging station location problem under probabilistic travel range

Cited by 107 publications

References 48 publications

Efficient solution approaches for locating electric vehicle fast charging stations under driving range uncertainty

Efficient solution approaches for locating electric vehicle fast charging stations under driving range uncertainty

Despliegue óptimo georreferenciado de estaciones de carga vehicular pública considerando capacidad de flujo y distancias máximas habilitantes

A model for determining the locations of electric charge stations in Istanbul

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