Multiple domination models for placement of electric vehicle charging stations in road networks

Gagarin, Andrei; Corcoran, Padraig

doi:10.1016/j.cor.2018.03.014

Cited by 44 publications

(34 citation statements)

References 23 publications

(35 reference statements)

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“…The distance at which charging points are located from each other or from a point on a map is yet another variable of study often found in the literature [6,9,11]. The authors find different ways of approaching distances, such as using polygon overlay methods, line layer distance, and Delaunay triangulations, among others.…”

Section: Nearest Neighbour Distance and Availabilitymentioning

confidence: 99%

“…This clearly puts a threshold onto the limits of chargers to be installed per vehicle with a risk of attaining higher environmental impacts than desired. Other studies address charging point allocation phenomena, presenting models or solving optimization problems [8][9][10][11][12][13][14]. Particular attention is given to the road segment allocation of chargers [8], privileging the use of existing infrastructure such as fuel stations and rest areas or parking lots.…”

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confidence: 99%

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Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis

et al. 2018

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Electric vehicle (EV) charging infrastructure rollout is well under way in several power systems, namely North America, Japan, Europe, and China. In order to support EV charging infrastructures design and operation, little attempt has been made to develop indicator-based methods characterising such networks across different regions. This study defines an assessment methodology, composed by eight indicators, allowing a comparison among EV public charging infrastructures. The proposed indicators capture the following: energy demand from EVs, energy use intensity, charger's intensity distribution, the use time ratios, energy use ratios, the nearest neighbour distance between chargers and availability, the total service ratio, and the carbon intensity as an environmental impact indicator. We apply the methodology to a dataset from ElaadNL, a reference smart charging provider in The Netherlands, using open source geographic information system (GIS) and R software. The dataset reveals higher energy intensity in six urban areas and that 50% of energy supplied comes from 19.6% of chargers. Correlations of spatial density are strong and nearest neighbouring distances range from 1101 to 9462 m. Use time and energy use ratios are 11.21% and 3.56%. The average carbon intensity is 4.44 gCO 2eq /MJ. Finally, the indicators are used to assess the impact of relevant public policies on the EV charging infrastructure use and roll-out.Energies 2018, 11, 1869 2 of 18 incentives, and direct electric vehicle requirements. Charging infrastructure support to consumers is also a common characteristic of these markets.The main concerns addressed in the literature regarding the charging infrastructure deployment are related to cost, charging effectiveness, and ability to satisfy dynamic demand, as well as its overall environmental impact. According to the EAFO (European alternative fuels observatory) [3], the ratio of cars per charger vary widely from country to country, from 66 to 3.7 cars per charger in Iceland and Spain, respectively. Even though at first sight, factors such as the driven distance per year, and population size and density, do not change proportionally. There are no commonly accepted goals or standards for charging infrastructure density, either on a per-capita or per-vehicle basis. Different countries seem to follow different sizing and options for the public charging infrastructure development. This depends on many factors. There is no clear way to achieve an efficient deployment of EV charging infrastructure and the associated policy that will need to be addressed to help pave the way for electrification. A study on the emerging best practices for EV charging infrastructure [4] provides insights into the differences between present infrastructure roll outs. Using a multivariable regression of 350 metropolitan areas, the authors find that both level 2 and direct current (DC) charging are linked to EV acceptance, as are consumer purchase incentives. Yet the significant charging variability across hundreds of cities poin...

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Section: Nearest Neighbour Distance and Availabilitymentioning

confidence: 99%

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confidence: 99%

Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis

et al. 2018

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“…Using the Adaptive Large Neighborhood Search algorithm [49] and the Adaptive Variable Neighborhood Search algorithm [50], locations for battery swap stations and electric vehicle routes are determined to provide services with the objective of minimizing the sum of the station construction cost and routing cost. To minimize the total cost to locate electric vehicle charging stations in road networks, Gagarin and Corcoran [51] suggest a novel approach that searches for the dominating set of locations among the candidate locations whose distance is below a certain threshold from a given driver. Using a parallel computing strategy, Tran et al [52] propose an efficient heuristic algorithm for location of AF refueling stations based on the solution of a sequence of subproblems.…”

Section: Main Distinctions Of Our Research Workmentioning

confidence: 99%

An Alternative Fuel Refueling Station Location Model considering Detour Traffic Flows on a Highway Road System

Hwang

Kweon

Ventura

2020

Journal of Advanced Transportation

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With the development of alternative fuel (AF) vehicle technologies, studies on finding the potential location of AF refueling stations in transportation networks have received considerable attention. Due to the strong limited driving range, AF vehicles for long-distance intercity trips may require multiple refueling stops at different locations on the way to their destination, which makes the AF refueling station location problem more challenging. In this paper, we consider that AF vehicles requiring multiple refueling stops at different locations during their long-distance intercity trips are capable of making detours from their preplanned paths and selecting return paths that may be different from original paths for their round trips whenever AF refueling stations are not available along the preplanned paths. These options mostly need to be considered when an AF refueling infrastructure is not fully developed on a highway system. To this end, we first propose an algorithm to generate alternative paths that may provide the multiple AF refueling stops between all origin/destination (OD) vertices. Then, a new mixed-integer programming model is proposed to locate AF refueling stations within a preselected set of candidate sites on a directed transportation network by maximizing the coverage of traffic flows along multiple paths. We first test our mathematical model with the proposed algorithm on a classical 25-vertex network with 25 candidate sites through various scenarios that consider a different number of paths for each OD pair, deviation factors, and limited driving ranges of vehicles. Then, we apply our proposed model to locate liquefied natural gas refueling stations in the state of Pennsylvania considering the construction budget. Our results show that the number of alternative paths and deviation distance available significantly affect the coverage of traffic flows at the stations as well as computational time.

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“…In the past few years, the problem of locating charging stations for electric vehicles has attracted considerable attention. For instance, efficient solution approaches based on new MILP formulations were proposed in [2] and [20] while heuristic solution techniques for large-scale problems were studied in [5] and [6]. Extensions of the basic problem taking into account a limited charging capacity of the stations (see e.g.…”

Section: Position In the Literaturementioning

confidence: 99%

“…Constraints (16) state that if a trip q has a strictly positive probability of coverage (z q > 0), for each node l ∈ N q \ {O q }, there must exist a node k visited before l during trip q, where the vehicle can be refueled up to node l. Constraints (17) link the coverage probability variables z q to the binary variables w kl q . They state that variable z q is calculated as the smallest coverage probability over all segments [k, l] where node l is refueled by a station at node k. Constraints (3), (5), (6) and (8) are maintained from the deterministic formulation FRLM2.…”

Section: Expected Flow Refueling Location Model (Efrlm)mentioning

confidence: 99%

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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Multiple domination models for placement of electric vehicle charging stations in road networks

Abstract: Multiple domination models for placement of electric vehicle charging stations in road networks, Computers and Operations Research (2018),

Cited by 44 publications

References 23 publications

Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis

Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis

An Alternative Fuel Refueling Station Location Model considering Detour Traffic Flows on a Highway Road System

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

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