A simultaneous approach implementing wind-powered electric vehicle charging stations for charging demand dispersion

Abstract Electric vehicle charging demand is highly variable depending on the charging pattern of consumers. If electric vehicle charging time is converged, the charging demand connected to power grids will increase and system instability may be induced. Wind Generating Sources (WGRs) can provide as much of the charging energy as possible. In this paper, we propose a simultaneous approach implementing wind-powered electric vehicle charging stations in order to distribute the charging demand of the electric vehicle with wind generating resources. The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model is applied to predict hourly wind power outputs. The augmented ARIMAX model is extended from Autoregressive Integrated Moving Average (ARIMA) model by adding exogenous such as a wind speed including grid integration analysis simulation processes. To validate the proposed approach for electric vehicle charging dispersion, we use the empirical data from the Jeju Island's wind farms in South Korea.

[1]  Sungwoo Bae,et al.  Electric vehicle charging demand forecasting model based on big data technologies , 2016 .

[2]  Wiwik Anggraeni,et al.  Performance Comparisons between Arima and Arimax Method in Moslem Kids Clothes Demand Forecasting: Case Study , 2015 .

[3]  Qun Liu,et al.  The Impact of Electric Vehicle Charging Station on the Grid , 2015 .

[4]  Ming Cheng,et al.  Optimal Scheduling of PEV Charging/Discharging in Microgrids with Combined Objectives , 2016 .

[5]  T. K. Saha,et al.  Impact of electric vehicle load on power system oscillatory stability , 2013, 2013 Australasian Universities Power Engineering Conference (AUPEC).

[6]  Zhigang Cao,et al.  Charging Scheduling of Electric Vehicles With Local Renewable Energy Under Uncertain Electric Vehicle Arrival and Grid Power Price , 2013, IEEE Transactions on Vehicular Technology.

[7]  Janaina G. de Oliveira,et al.  An alternative to charging stations for electric vehicles , 2017, 2017 IEEE 8th International Symposium on Power Electronics for Distributed Generation Systems (PEDG).

[8]  Juha Kiviluoma,et al.  Managing wind power variability and uncertainty through increased power system flexibility , 2013 .

[9]  Adel Nasiri,et al.  EV charging station integrating renewable energy and second-life battery , 2013, 2013 International Conference on Renewable Energy Research and Applications (ICRERA).

[10]  Dimitri N. Mavris,et al.  Real-time Scheduling Techniques for Electric Vehicle Charging in Support of Frequency Regulation , 2013, CSER.

[11]  Jian Wang,et al.  Electric Vehicle Load Characteristic Analysis and Impact of Regional Power Grid , 2012, EMEIT 2012.

[12]  Anibal T. de Almeida,et al.  Integration of renewable energy generation with EV charging strategies to optimize grid load balancing , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[13]  Mark Z. Jacobson,et al.  Variability and uncertainty of wind power in the California electric power system , 2013 .

[14]  Bo Wang,et al.  Coordinated charging strategy of electric vehicle charging station based on combination of linear power flow and genetic algorithm , 2016, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[15]  Tim J. LaClair,et al.  Control strategies for electric vehicle (EV) charging using renewables and local storage , 2014, 2014 IEEE Transportation Electrification Conference and Expo (ITEC).

[16]  S. Avdakovic,et al.  Impact of charging a large number of electric vehicles on the power system voltage stability , 2014 .

[17]  Charles W. Chase.  Measuring Forecast Accuracy , 1995 .

[18]  Xingping Zhang,et al.  Charge Pricing Optimization Model for Private Charging Piles in Beijing , 2017 .

[19]  Jin Hur,et al.  Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method , 2017, Energy.