Risk Identification and Analysis for PPP Projects of Electric Vehicle Charging Infrastructure Based on 2-Tuple and the DEMATEL Model

Zhang, Lihui; Zhao, Zhenli; Chai, Jianxue; Kan, Zhinan

doi:10.3390/wevj10010004

Cited by 24 publications

(15 citation statements)

References 54 publications

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“…Although the fuzzy TOPSIS method has similarities with the public charging infrastructure index evaluation system, it only considers the subjective weight of risk and does not consider its objective weight. [ 20 ] also studied the risks of electric vehicle charging infrastructure PPP projects, using 2-Tuple and the DEMATEL Model, but this method is not suitable for fuzzy information and incomplete information. [ 21 ] used 4 different methods to evaluate 18 public infrastructures of EVs in Lithuania.…”

Section: Related Reviewmentioning

confidence: 99%

Application of the entropy-DEMATEL-VIKOR multicriteria decision-making method in public charging infrastructure

Dong

Yang

2021

PLoS ONE

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As an energy-saving and environmentally friendly means of transportation, electric vehicles have been advocated and promoted by various countries, resulting in an increase in the number of electric vehicles. The improvement of public charging infrastructure not only drives the development of the electric vehicle industry but also solves the problems of user difficulty in charging and the low utilization rate of charging piles. From the perspective of electric vehicle (EV) user experience, this research establishes a framework of indicators, including the reputation level, service quality, convenience, economy and safety. Second, the objective entropy weight method and the subjective decision-making trial and evaluation laboratory (DEMATEL) method are combined to weight the indicators. Among the indicators, the comprehensive weights of market share (C2), app operation interface (C3), and charging mode (C5) are 0.107, 0.088, and 0.090, respectively, ranking in the top three. These three indicators should be given more attention by public charging infrastructure operators. Finally, three alternative public charging infrastructures are sorted by using the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method. Since the positive ideal solution Si of h1 (state grid) is 0.084, the negative ideal solution Ri is 0.248, and the comprehensive index Qi is 0.000. All ranking first, this finding indicates that the public charging infrastructure of this operator has strong competitiveness in the market. In addition, the results are consistent with actual news reports, which also proves the effectiveness of the index system and model.

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Section: Related Reviewmentioning

confidence: 99%

Application of the entropy-DEMATEL-VIKOR multicriteria decision-making method in public charging infrastructure

Dong

Yang

2021

PLoS ONE

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“…In this article, the time of use was selected as the time-based program to supply the demand service provision. The vehicles participating in the program were required with different costs at various times (e.g., load peak or low load) [17][18][19][20].…”

Section: Studied Systemmentioning

confidence: 99%

Risk management and participation of electric vehicle considering transmission line congestion in the smart grids for demand response

Beyzaee

Marvi

2020

FACTA U EE

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Demand response (DR) could serve as an effective tool to further balance the electricity demand and supply in smart grids. It is also defined as the changes in normal electricity usage by end-use customers in response to pricing and incentive payments. Electric cars (EVs) are potentially distributed energy sources, which support the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes, and their participation in time-based (e.g., time of use) and incentive-based (e.g., regulation services) DR programs helps improve the stability and reduce the potential risks to the grid. Moreover, the smart scheduling of EV charging and discharging activities supports the high penetration of renewable energies with volatile energy generation. This article was focused on DR in the presence of EVs to assess the effects of transmission line congestion on a 33-bit grid. A random model from the standpoint of an independent system operator was used to manage the risk and participation of EVs in the DR of smart grids. The main risk factors were those caused by the uncertainties in renewable energies (e.g., wind and solar), imbalance between demand and renewable energy sources, and transmission line congestion. The effectiveness of the model in a 33-bit grid in response to various settings (e.g., penetration rate of EVs and risk level) was evaluated based on the transmission line congestion and system exploitation costs. According to the results, the use of services such as time-based DR programs was effective in the reduction of the electricity costs for independent system operators and aggregators. In addition, the results demonstrated that the participation of EVs in incentive-based DR programs with the park model was particularly effective in this regard.

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“…covariance matrix was ( ) 3 10 P eye = × , the measurement noise covariance matrix was ( ) 4 10 Q eye = × .…”

Section: Case Study 1: Double Lane Changes Maneuvermentioning

confidence: 99%

“…With the development of the automobile industry, people have higher and higher requirements for active safety and ride comfort of automobiles [1][2][3], and many advanced electronic control systems, such as electronic stabilization system, anti-lock braking system, traction control system, and anti-skid drive system, have been widely used in vehicles [4,5]. In these electronic control systems, accurate and reliable vehicle driving state measurement signal is one of the necessary conditions for closed-loop feedback control [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Sideslip Angle Fusion Estimation Method of an Autonomous Electric Vehicle Based on Robust Cubature Kalman Filter with Redundant Measurement Information

Chen

et al. 2019

WEVJ

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Accurate and reliable estimation information of sideslip angle is very important for intelligent motion control and active safety control of an autonomous vehicle. To solve the problem of sideslip angle estimation of an autonomous vehicle, a sideslip angle fusion estimation method based on robust cubature Kalman filter and wheel-speed coupling relationship is proposed in this paper. The vehicle dynamics model, tire model, and wheel speed coupling model are established and discretized, and a robust cubature Kalman filter is designed for vehicle running state estimation according to the discrete vehicle model. An adaptive measurement-update solution of the robust cubature Kalman filter is presented to improve the robustness of estimation, and then, the wheel-speed coupling relationship is introduced to the measurement update equation of the robust cubature Kalman filter and an adaptive sideslip angle fusion estimation method is designed. The simulations in the CarSim-Simulink co-simulation platform and the actual vehicle road test are carried out, and the effectiveness of the proposed estimation method is validated by corresponding comparative analysis results.

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Risk Identification and Analysis for PPP Projects of Electric Vehicle Charging Infrastructure Based on 2-Tuple and the DEMATEL Model

Cited by 24 publications

References 54 publications

Application of the entropy-DEMATEL-VIKOR multicriteria decision-making method in public charging infrastructure

Application of the entropy-DEMATEL-VIKOR multicriteria decision-making method in public charging infrastructure

Risk management and participation of electric vehicle considering transmission line congestion in the smart grids for demand response

Sideslip Angle Fusion Estimation Method of an Autonomous Electric Vehicle Based on Robust Cubature Kalman Filter with Redundant Measurement Information

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