TY - GEN
T1 - Optimal Coordination of Electric Vehicles for Voltage Support in Distribution Networks
AU - Chen, Xiaoli
AU - Parisio, Alessandra
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2023/1/11
Y1 - 2023/1/11
N2 - The increase in electric vehicles (EVs) and distributed generations (DGs) has brought significant voltage fluctuation issues to distribution networks. Since simultaneous active and reactive power coordination can make voltage regulation more efficient, optimal coordination of active/reactive EV charging/discharging is an attractive solution. This work contributes to addressing the challenge above by devising an optimisation-based coordination framework for EV charging/discharging for voltage support in distribution networks. The proposed framework is based on model predictive control (MPC), and includes reactive/active power management, as well as EV behaviour modelling. The proposed framework minimises network voltage deviation and electricity cost of electric vehicle charging stations (EVCSs). An EV behaviour modelling is integrated into the MPC scheme by using a Markov Chain Monte Carlo (MCMC) method. A case study is conducted on a 33-bus distribution network with photovoltaic (PV) generation and multiple EVCSs, demonstrating effective voltage regulation and cost-saving of the proposed MPC-based optimisation framework.
AB - The increase in electric vehicles (EVs) and distributed generations (DGs) has brought significant voltage fluctuation issues to distribution networks. Since simultaneous active and reactive power coordination can make voltage regulation more efficient, optimal coordination of active/reactive EV charging/discharging is an attractive solution. This work contributes to addressing the challenge above by devising an optimisation-based coordination framework for EV charging/discharging for voltage support in distribution networks. The proposed framework is based on model predictive control (MPC), and includes reactive/active power management, as well as EV behaviour modelling. The proposed framework minimises network voltage deviation and electricity cost of electric vehicle charging stations (EVCSs). An EV behaviour modelling is integrated into the MPC scheme by using a Markov Chain Monte Carlo (MCMC) method. A case study is conducted on a 33-bus distribution network with photovoltaic (PV) generation and multiple EVCSs, demonstrating effective voltage regulation and cost-saving of the proposed MPC-based optimisation framework.
KW - demand side management
KW - electric vehicle charging
KW - optimization
KW - power distribution networks
KW - reactive power
KW - vehicle-to-grid
KW - voltage control
UR - http://www.scopus.com/inward/record.url?scp=85146898021&partnerID=8YFLogxK
U2 - 10.1109/ISGTAsia54193.2022.10003554
DO - 10.1109/ISGTAsia54193.2022.10003554
M3 - Conference contribution
AN - SCOPUS:85146898021
SN - 9798350399677
T3 - Proceedings of the International Conference on Innovative Smart Grid Technologies
SP - 650
EP - 654
BT - Proceedings of the 11th International Conference on Innovative Smart Grid Technologies - Asia (ISGT-Asia 2022)
PB - IEEE
T2 - 11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022
Y2 - 1 November 2022 through 5 November 2022
ER -
