Methodology for Security Analysis of Grid- Connected Electric Vehicle Charging Station with Wind Generating Resources

Gyeongmin Kim, Jin Hur

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The project Carbon-Free Island Jeju by 2030 promoted by the Republic of Korea aims to expand the renewable energy sources centered on wind power in Jeju Island and supply electric vehicles for eco-friendly mobility. However, the increased penetration rate of electric vehicles and expansion of variable renewable energy sources can accelerate the power demand and uncertainty in the power generation output. In this paper, power system analysis is performed through electric vehicle charging demand and wind power outputs prediction, and an electric vehicle charging decentralization algorithm is proposed to mitigate system congestion. In order to predict electric vehicle charging demand, the measurement data were analyzed, and random sampling was performed by applying the weight of charging frequency for each season and time. In addition, wind power outputs prediction was performed using the ARIMAX model. Input variables are wind power measurement data and additional explanatory variables (wind speed). Wind power outputs prediction error (absolute average error) is about 9.6%, which means that the prediction accuracy of the proposed algorithm is high. A practical power system analysis was performed for the scenario in which electric vehicle charging is expected to be higher than the wind power generation due to the concentration of electric vehicle charging. The proposed algorithm can be used to analyze power system problems that may occur due to the concentration of electric vehicle charging demand in the future, and to prepare a method for decentralizing electric vehicle charging demand to establish a stable power system operation plan.

Original languageEnglish
Article number9410546
Pages (from-to)63905-63914
Number of pages10
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021

Keywords

  • Electric vehicle
  • charging demand
  • charging station
  • security analysis
  • wind generating resources
  • wind power forecasting

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