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

Gyeongmin Kim,Jin Hur

doi:10.1109/access.2021.3075072

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.

Highlights

The United Nations Framework Convention on Climate Change (UNFCCC) aims to reduce global warming caused by greenhouse gases (GHGs), such as carbon dioxide
With several countries exploring the development of renewable energy source-based power generation, the resulting uncertainty in power demand and supply can significantly unbalance the power supply and demand
To address the system uncertainty that may occur in future power systems, the prediction model to decentralize the ELECTRIC VEHICLE (EV) charging demand is needed

Summary

INTRODUCTION

The United Nations Framework Convention on Climate Change (UNFCCC) aims to reduce global warming caused by greenhouse gases (GHGs), such as carbon dioxide. The average mileage per vehicle is the calculated mileage based on the number of gasoline vehicles and gasoline consumption This is calculated without considering the characteristics of EVs such as charging time and frequency, so the data for EV charging demand prediction may be distorted. It can be used to establish a stable power system operation plan by applying wind power output data estimated using the ARIMAX model and predicting EV charging demand to the real-time market. The EV charging frequency by season and time was extracted based on the random sampling of predicted data for 2030 using the calculated weights. EV charging frequency sampling data was used to predict EV charging demand during low, intermediate, and peak load times (Fig. 4) and the EV charging demand forecasting in 2030 (Table 4 ), extracted by considering the weights based on time. The 154 kV bus in each group was designated as the representative bus of the corresponding defense

ENHANCED ARIMAX MODEL

PROPOSED METHODOLOGY FOR SECURITY ANALYSIS

Findings

CONCLUSION