Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method

Wu, Yunna; Yang, Meng; Zhang, Haobo; Chen, Kaifeng; Wang, Yang

doi:10.3390/en9030157

Cited by 110 publications

(64 citation statements)

References 84 publications

(179 reference statements)

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“…The choice in favour of multiple criteria decision aid methods was determined both because of the structure of data [31,32] and clear necessity to comprise opinions of experts on how the city should develop [33].…”

Section: Evaluation Using Mcdm Methodsmentioning

confidence: 99%

Decision-Aiding Evaluation of Public Infrastructure for Electric Vehicles in Cities and Resorts of Lithuania

et al. 2018

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Abstract:In the National Communication Development of [2014][2015][2016][2017][2018][2019][2020][2021][2022] Program and Guidelines of the Development of the Public Electric Vehicle Charging Infrastructure confirmed by the Government of the Republic of Lithuania, it is planned that, until the year of 2025, among newly registered vehicles, electric ones should make at least 10%. Analysis of the trend of electric vehicles makes evident that the target does not have a real chance to be achieved without targeted efforts. In order to improve the infrastructure of electric vehicles in major cities and resorts of Lithuania, we have carried out a comparative analysis of public infrastructure for electric vehicles in 18 Lithuanian cities and resorts. For the quantitative analysis, we proposed eight criteria describing such an infrastructure. As perception of the infrastructure by owners of electric cars depends on complex factors, we used multiple criteria evaluation methods (MCDM) for evaluation of the current state of its development by four such methods: EDAS, SAW, TOPSIS, and PROMETHEE II. Based on the evaluation results, prominent and lagging factors were understood, and proposals for effective development of public infrastructure of electric vehicles were proposed for improvement of the infrastructure.

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Section: Evaluation Using Mcdm Methodsmentioning

confidence: 99%

Decision-Aiding Evaluation of Public Infrastructure for Electric Vehicles in Cities and Resorts of Lithuania

et al. 2018

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“…In recent years, the methods to properly determine the distribution of EV charging stations has been investigated by a number of researchers. Wu et al [8] proposed a method for selecting the optimal EV charging station location through a preference ranking organization-based decision system combined with the cloud model. This method recommended the distribution of charging stations in terms of price.…”

Section: Introductionmentioning

confidence: 99%

Constraint-Aware Electricity Consumption Estimation for Prevention of Overload by Electric Vehicle Charging Station

Ahn¹,

Jo²,

Kang³

2019

Energies

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An increase in the number of electrical vehicles has resulted in an increase in the number of electrical vehicle charging stations. As a result, the electricity load consumed by charging stations has become large enough to de-stabilize the electricity supply system. Therefore, real-time monitoring of how much electricity each charging station is consuming has become very much important. However, only limited information such as charging time is available from the operators of electric vehicle charging stations. The actual electricity consumption data is not provided in real time. Conventional methods estimate the accumulated electricity consumption of charging stations using a linear regression curve. However, an estimate of the electricity consumption for each charge is needed. In this paper, we propose an advanced electricity estimation system which predicts the energy consumption for each charge. The proposed method uses a constraint-aware non-linear regression curve, and performs additional data selection processes. The experimental results show that the proposed system achieves about 73% regression accuracy. In addition, the proposed system can display the energy consumption per hour and visualize this information on a map. This makes it possible to monitor the electricity consumption of the charging stations in real-time and by location, which helps to select appropriate locations where new vehicle charging stations need to be installed.

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“…With the rapid development of the cloud model theory, successful applications were carried out in various fields, such as intelligent control [34], network security [35], and algorithm improvement [36]. In particular, since Wang and Feng [37] introduced the conversion between linguistic variables and clouds, the cloud model has also been applied to the field of decision-making [9,[38][39][40].…”

Section: Literature Reviewmentioning

confidence: 99%

A Method for Multi-Criteria Group Decision Making with 2-Tuple Linguistic Information Based on Cloud Model

Zhang

Gao

et al. 2017

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This paper presents a new approach to solve the multi-criteria group decision making (MCGDM) problem where criteria values take the form of 2-tuple linguistic information. Firstly, a 2-tuple hybrid ordered weighted geometric (THOWG) operator is proposed, which synthetically considers the importance of both individual and the ordered position so as to overcome the defects of existing operators. Secondly, combining the advantages of the cloud model and 2-tuple linguistic variable, a new generating cloud method is proposed to transform 2-tuple linguistic variables into clouds. Thirdly, we further define some new cloud algorithms, such as cloud possibility degree and cloud support degree which can be respectively used to compare clouds and determine the criteria weights. Furthermore, a new approach for 2-tuple linguistic group decision making is presented on the basis of the THOWG operator, the improved generating cloud method as well as the new cloud algorithms. Finally, an example of assessing the social effects of biomass power plants (BPPS) is illustrated to verify the application and feasible of the developed approach, and a comparative analysis is also conducted to validate the effectiveness of the proposed method.

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Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method

Cited by 110 publications

References 84 publications

Decision-Aiding Evaluation of Public Infrastructure for Electric Vehicles in Cities and Resorts of Lithuania

Decision-Aiding Evaluation of Public Infrastructure for Electric Vehicles in Cities and Resorts of Lithuania

Constraint-Aware Electricity Consumption Estimation for Prevention of Overload by Electric Vehicle Charging Station

A Method for Multi-Criteria Group Decision Making with 2-Tuple Linguistic Information Based on Cloud Model

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