TY - GEN
T1 - Optimal Deployment of Traffic Energy Router for Wireless Energy Trading
AU - Tang, Yao
AU - Chau, K. T.
AU - Liu, Wei
AU - Guo, Jian
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Traffic energy routers (TERs) are wireless power infrastructures that support the trading of electric vehicles (EVs) in the energy market without physical cables. This paper proposes a TER deployment model for wireless energy trading. The objective is to decide which road junction should be electrified into a TER and how long it is. In this paper, the ultracapacitor is utilized due to the advantages of long-life cycles, high power handling capacity and charging/discharging efficiency. Firstly, the number of EVs stopped at each road junction due to red traffic lights is estimated. Secondly, referring to the current electricity price, the charging and discharging demands are predicted. Thirdly, the predicted demand is revised according to whether the EVs are on the electrified paths and the charging state of ultracapacitors. Hence, the trading profiles in the spatial and temporal domains are obtained. Finally, a multi-objective deployment optimization model is designed, aiming to minimize the installation cost and maximize the drivers' profits. Simulations are carried out to study the energy trading behaviors and the optimal deployment strategy. Compared with the full deployment case, the optimal deployment in our paper uses 2.3% cost while earning 38.0% profit.
AB - Traffic energy routers (TERs) are wireless power infrastructures that support the trading of electric vehicles (EVs) in the energy market without physical cables. This paper proposes a TER deployment model for wireless energy trading. The objective is to decide which road junction should be electrified into a TER and how long it is. In this paper, the ultracapacitor is utilized due to the advantages of long-life cycles, high power handling capacity and charging/discharging efficiency. Firstly, the number of EVs stopped at each road junction due to red traffic lights is estimated. Secondly, referring to the current electricity price, the charging and discharging demands are predicted. Thirdly, the predicted demand is revised according to whether the EVs are on the electrified paths and the charging state of ultracapacitors. Hence, the trading profiles in the spatial and temporal domains are obtained. Finally, a multi-objective deployment optimization model is designed, aiming to minimize the installation cost and maximize the drivers' profits. Simulations are carried out to study the energy trading behaviors and the optimal deployment strategy. Compared with the full deployment case, the optimal deployment in our paper uses 2.3% cost while earning 38.0% profit.
KW - Electric vehicle
KW - infrastructure deployment
KW - optimization
KW - wireless energy trading
UR - http://www.scopus.com/inward/record.url?scp=85179501867&partnerID=8YFLogxK
U2 - 10.1109/IECON51785.2023.10311796
DO - 10.1109/IECON51785.2023.10311796
M3 - Conference article published in proceeding or book
AN - SCOPUS:85179501867
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE Computer Society
T2 - 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Y2 - 16 October 2023 through 19 October 2023
ER -