Resilience assessment of electrified road networks subject to charging station failures

Abstract

The number of EVs and charging facilities is expected to increase significantly in the near future, further coupling the existing transportation system with the power system. This may bring new stresses and risks to such system of systems. This paper presents a mathematical framework to analyze the resilience of an electrified road network (ERN) subject to potential failures of its supporting fast-charging stations (FCSs). Within this framework, a novel linear optimization model is proposed for the first time to solve the system optimal dynamic traffic assignment problem of ERN. The characteristics considered in the modeling framework include the location, capacity, and charging speed of FCSs, as well as the driving range, charging time and state of charge (SoC) of EVs. The linear model is proposed based on the cell transmission model. It is used as the first stage model to assign the traffic under normal FCS operations. A second stage model is, then, extended to minimize the total travel time after the stochastic occurrence of FCS failures, i.e., in the failure and recovery phases. Two metrics are considered to quantify the ERN performance and the impacts of FCS failures. A numerical example is studied to illustrate the usefulness of the proposed framework for analyzing ERN resilience. The results show that deploying FCSs near the highway entrances and maintaining their operation are relevant factors to enhance the system's resilience. The analysis can provide guidelines to the system operators for effective management of the ERN operation and identify resilience-critical FCSs for system resilience improvement.

Optimal planning of flood‐resilient electric vehicle charging stations

“…However, the integration of flood hazard considerations into charging station planning is still lacking. A large number of impact factors were considered by previous studies, such as EV battery capacities (Sassi & Oulamara, 2017; Wang et al., 2022; M. Xu et al., 2020), traffic volume (Sathaye & Kelley, 2013), total energy consumption (Bie et al., 2021; Rominger & Farkas, 2017; Yi & Bauer, 2016), EV penetration levels (Lin & Hua, 2015; Xi et al., 2013), installation cost (Tan & Lin, 2014), drivers’ travel behaviors (T. D. Chen et al., 2013; Kavianipour et al., 2021; Shahraki et al., 2015), users’ waiting time (Uslu & Kaya, 2021), and drive range (Kchaou‐Boujelben & Gicquel, 2020; Yıldız et al., 2019). While a large body of literature exists, none of them considers the impact of flood hazards on charging station deployments.…”

Complex belief interval‐based distance measure with its application in pattern recognition

“…[28][29][30][31] Moreover, these methods have been widely used in various tasks, such as group decision-making (multiple attribute decision making-MADM), [32][33][34] reliability optimization, 35 medical system, 36,37 decision making, [38][39][40] fault diagnosis, [41][42][43] engineering system, 44 risk analysis, 45,46 and so on. [47][48][49][50] Regarding the processing of uncertain information, the most representative one is the D-S evidence theory. D-S evidence theory, as a typical tool for uncertain information processing and information fusion, has been extensively studied.…”

“…According to the measurement of uncertain information, many methods are used to identify the target and achieve the desired effect, such as evidential reasoning, 24 information fusion, 25 multidata fusion, 26,27 and so on 28 – 31 . Moreover, these methods have been widely used in various tasks, such as group decision‐making (multiple attribute decision making—MADM), 32 – 34 reliability optimization, 35 medical system, 36,37 decision making, 38 – 40 fault diagnosis, 41 – 43 engineering system, 44 risk analysis, 45,46 and so on 47 – 50 …”

Maximum Entropy of Random Permutation Set

“…In order to model and process the information with uncertainty, many theories have been developed, such as probability theory (Lee, 1980), fuzzy set theory (Zadeh, 1965), Dempster-Shafer evidence theory (evidence theory) (Dempster, 1967;Shafer, 1976), rough set theory (Pawlak, 1982), and Z-numbers (Zadeh, 2011), which have been further used in various fields, such as information fusion (Lai, Chang, Ang, & Cheong, 2020;Pan, Zhang, Wu, & Skibniewski, 2020), risk assessment (X. Gao, Su, Qian, & Pan, 2021;Wang, Abdin, Fang, & Zio, 2021;, game theory (Babajanyan, Allahverdyan, Cheong, Koh, & Jones, 2019), fault diagnosis (X. Chen, Wang, Ying, & Cao, 2021;Huang et al, 2021;Wang, Liu, Zhao, Guo, & Terzijae, 2020;Wang, Wei, et al, 2020), complex networks (T. Wen & Cheong, 2021), target recognition (Z.…”