TY - GEN
T1 - Virtual power plant for EV coordination at higher EV penetration level for better grid performance
AU - Ravikumar, Adithya
AU - Taghizadeh, Foad
AU - Deilami, Sara
PY - 2024
Y1 - 2024
N2 - The rise in plug-in electric vehicle (EV) has been a cause for concern to the disruption in the distribution grid. To manage this disruption, EV coordination strategies are used to charge/discharge EVs without causing any grid issues. The coordination strategies are generally implemented through a platform called Virtual Power Plant (VPP). But, the VPP has issues, when handling higher EV penetration the voltage deviation and power losses are higher. Additionally, the customer satisfaction at higher penetration is also reduced and EV owners are needed to be satisfied. To improve the grid performance of the VPP, this paper proposes a state of the art multi-objective optimization based EV charging coordination strategy. Using the proposed method, both the grid performance and the customer satisfaction can be maintained at higher EV penetration level. The results of the proposed method are demonstrated using MATLAB and Open-DSS.
AB - The rise in plug-in electric vehicle (EV) has been a cause for concern to the disruption in the distribution grid. To manage this disruption, EV coordination strategies are used to charge/discharge EVs without causing any grid issues. The coordination strategies are generally implemented through a platform called Virtual Power Plant (VPP). But, the VPP has issues, when handling higher EV penetration the voltage deviation and power losses are higher. Additionally, the customer satisfaction at higher penetration is also reduced and EV owners are needed to be satisfied. To improve the grid performance of the VPP, this paper proposes a state of the art multi-objective optimization based EV charging coordination strategy. Using the proposed method, both the grid performance and the customer satisfaction can be maintained at higher EV penetration level. The results of the proposed method are demonstrated using MATLAB and Open-DSS.
UR - https://www.scopus.com/pages/publications/85215547577
U2 - 10.1109/AUPEC62273.2024.10807548
DO - 10.1109/AUPEC62273.2024.10807548
M3 - Conference proceeding contribution
AN - SCOPUS:85215547577
SN - 9798350377958
BT - 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC)
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Piscataway, NJ
T2 - 34th IEEE Australasian Universities Power Engineering Conference, AUPEC 2024
Y2 - 20 November 2024 through 22 November 2024
ER -