Spatial–temporal QoS assessment of the EV charging network considering power outages

Difei Tang, Xiaofeng Liu, Tianyang Zhao, Qi Wang

Research output: Contribution to journalArticlepeer-review

Abstract

The rapid expansion of electric vehicle charging networks (EVCNs) makes them critical infrastructures bridging power and traffic systems. The EVCN could be vulnerable during power outages at fast charging stations (FCSs), which are induced by planned maintenance or emergency load shedding. This paper proposes an approach to assess the impact of power outages on the Quality-of-service of the EVCN. The Markov decision process is utilized to model the spatial–temporal randomness of EV movement in a graph-based EVCN. The decision of charging by EV drivers is estimated by a fuzzy logic inference system. The spatial–temporal EV charging load at FCSs is formulated by a queuing-based non-linear optimization problem. Yen’s algorithm is adopted to simulate the EV redistribution phenomenon of searching adjacent healthy FCSs in response to the power outage. Quality-of-service (QoS) indices are derived to assess the potential congestions in the adjacent healthy FCSs. The case studies demonstrate that power outages may cause congestion at peripheral FCSs, exacerbating the QoS of the EVCN. Partial charging may alleviate the QoS deterioration in the event of FCS outages.

Original languageEnglish
Article number1112169
JournalFrontiers in Energy Research
Volume11
DOIs
Publication statusPublished - 21 Mar 2023

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China under Grant 52277105.

Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.

Keywords

  • charging network
  • electric vehicle
  • Markov decision process
  • power outage
  • Quality-of-service
  • queuing theory

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Economics and Econometrics

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