A Spatial-Temporal Model for Locating Electric Vehicle Charging Stations

Dong, Jin; Zhao, Yaohua; Dong, Xinyang; Zhao, Mingyang; Yang, Lei; Lv, Mingsong; Chen, Gang

doi:10.1007/978-981-13-1026-3_7

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…Operating costs include labor costs, equipment maintenance costs, and other expenses, as shown in Equation (17). Where γ is the conversion factor of running cost, γ = 0.1.…”

Section: Objective Functionmentioning

confidence: 99%

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Optimal Layout of Electric Vehicle Charging Station Locations Considering Dynamic Charging Demand

Pei

Zhang

et al. 2023

Electronics

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This paper proposes an optimization method for electric vehicle charging station locations considering dynamic charging demand. Firstly, the driving characteristics and charging characteristics of the electric vehicle are obtained based on the driving trajectory of the electric vehicle, and the charging demand is predicted using a Monte Carlo simulation. Then a mathematical model with the goal of minimizing the overall cost is constructed, and the impact on carbon emissions is considered in the model. In order to better solve the location model, an improved whale optimization algorithm based on a hybrid strategy is proposed. Finally, the location problem of Shenzhen electric taxi charging stations is analyzed as an example. The results show that when the number of charging stations is set to 19, the comprehensive cost is the smallest and the energy saving and emission reduction effect is good. The improved whale optimization algorithm also has higher solution accuracy and convergence speed than other classical algorithms.

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“…Operating costs include labor costs, equipment maintenance costs, and other expenses, as shown in Equation (17). Where γ is the conversion factor of running cost, γ = 0.1.…”

Section: Objective Functionmentioning

confidence: 99%

“…Obviously, the results obtained by using the trajectory of fuel vehicles will not conform to the actual situation. With the continuous improvement of data, many scholars have also begun to use electric vehicle trajectory data for charging demand prediction and site selection planning [8,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Optimal Layout of Electric Vehicle Charging Station Locations Considering Dynamic Charging Demand

Pei

Zhang

et al. 2023

Electronics

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“…The proposals for solving the problem of electric vehicle charging stations' location take into account several criteria, which means that the issue should be approached holistically. Apart from the economic [14,24,42,[57][58][59][60][61][62][63][64][65][66][67], environmental [40,61,65,[68][69][70] and technical criteria [14,46,51,58,60,61,65,[71][72][73], there are also the social criteria that need to be considered [39][40][41]60,61,72,74,75]. Additionally, besides the single criterion proposals [24,41,46,57,74,[76][77][78] there are currently more and more multi-criteria approaches [40,43,…”

Section: State Of the Art In Evcs Locationmentioning

confidence: 99%

Multiple-Criteria-Based Electric Vehicle Charging Infrastructure Design Problem

et al. 2021

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The article presents the issue of designing a network of electric vehicle charging stations (EVCSs) for servicing passenger and commercial vehicles and taking into account the already existing locations. The authors propose a five-stage multicriteria- and GIS-based EVCS location methodology (5MAGISEV) for designing the EVCS network that includes defining potential EVCS locations (stage 1), constructing a family of evaluation criteria (stage 2), generating alternatives (stage 3), selecting an appropriate multiple criteria decision aid method (stage 4) and conducting a multicriteria evaluation of alternatives (stage 5). The methodology was verified by its experimental application for solving the problem of EVCS location in Poznan, Poland. As a result of its application, a set of over 155,000 alternatives was generated in view of different EVCS location determination policies. A family of eight criteria with subcriteria was proposed to assess the solutions, including the evaluation of the degree of integration of the considered EVCSs with such issues as public transport (C1); urbanized areas (C2); main road network (C3); industrial areas (C4); and the location with respect to transport areas (C5), shops of various types (C6), address points (C7) and DYI stores (C8). Based on the selected method of multiple criteria decision aid (LBS), a solution was selected that constitutes a compromise design of the EVCS network.

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“…Sreten et al [4] introduced an electric power system reliability check method into the optimal allocation of charging stations, taking into account the constraints of power system operation and EV owners' requirements. Three models in [5] were proposed to locate the charging stations by considering the spatial and temporal distribution of EV drivers' behaviors, and the model framework was verified by the actual geographic data and EV usage data. In [6], metro Boston data were used to obtain the movement patterns of individuals to find an efficient layout of charging stations to minimize overall energy overhead and EV drivers' driving distance to charging stations.…”

Section: ) Locating Charging Stationsmentioning

confidence: 99%

Optimizing the Locations and Sizes of Solar Assisted Electric Vehicle Charging Stations in an Urban Area

Dong

Yang

et al. 2020

IEEE Access

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With the widespread adoption of electric vehicles (EVs), introducing solar energy in building EV charging stations is promising as it can reduce carbon emissions and improve air quality. The main challenges are how to decide where to build solar assisted charging stations in a city and how to size the charging stations, as the decision is affected by a broad range of factors, such as construction cost, solar energy fluctuation, and user requirements. This paper proposed an approach to efficiently decide the locations and sizes of solar energy assisted charging stations for an urban area. Experiments are conducted on real EV history data from 297 users of an EV leasing company. The results show that the proposed method can produce high quality decisions within reasonable computation time. The work of this paper will provide important information for decision makers to integrate solar energy into the EV charging infrastructure.

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A Spatial-Temporal Model for Locating Electric Vehicle Charging Stations

Cited by 10 publications

References 9 publications

Optimal Layout of Electric Vehicle Charging Station Locations Considering Dynamic Charging Demand

Optimal Layout of Electric Vehicle Charging Station Locations Considering Dynamic Charging Demand

Multiple-Criteria-Based Electric Vehicle Charging Infrastructure Design Problem

Optimizing the Locations and Sizes of Solar Assisted Electric Vehicle Charging Stations in an Urban Area

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