TY - JOUR
T1 - Dynamic Pricing for Electric Vehicle Extreme Fast Charging
AU - Fang, Cheng
AU - Lu, Haibing
AU - Hong, Yuan
AU - Liu, Shan
AU - Chang, Jasmine
N1 - Publisher Copyright:
© 2000-2011 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - Significant developments and advancement pertaining to electric vehicle (EV) technologies, such as extreme fast charging (XFC), have been witnessed in the last decade. However, there are still many challenges to the wider deployment of EVs. One of the major barriers is its availability of fast charging stations. A possible solution is to build a fast charging sharing system, by encouraging small business owners or even householders to install and share their fast charging devices, by reselling electricity energy sourced from traditional utility companies or their own solar grid. To incentivize such a system, a smart dynamic pricing scheme is needed to facilitate those growing markets with fast charging stations. The pricing scheme is expected to take into account the dynamics intertwined with pricing, demand, and environment factors, in an effort to maximize the long-term profit with the optimal price. To this end, this paper formulates the problem of dynamic pricing for fast charging as a Markov decision process and accordingly proposes several algorithmic schemes for different applications. Experimental study is conducted with useful and interesting insights.
AB - Significant developments and advancement pertaining to electric vehicle (EV) technologies, such as extreme fast charging (XFC), have been witnessed in the last decade. However, there are still many challenges to the wider deployment of EVs. One of the major barriers is its availability of fast charging stations. A possible solution is to build a fast charging sharing system, by encouraging small business owners or even householders to install and share their fast charging devices, by reselling electricity energy sourced from traditional utility companies or their own solar grid. To incentivize such a system, a smart dynamic pricing scheme is needed to facilitate those growing markets with fast charging stations. The pricing scheme is expected to take into account the dynamics intertwined with pricing, demand, and environment factors, in an effort to maximize the long-term profit with the optimal price. To this end, this paper formulates the problem of dynamic pricing for fast charging as a Markov decision process and accordingly proposes several algorithmic schemes for different applications. Experimental study is conducted with useful and interesting insights.
KW - Fast charging
KW - XFC
KW - dynamic pricing
KW - reinforcement learning
KW - renewable energy
UR - https://www.scopus.com/pages/publications/85083437460
U2 - 10.1109/TITS.2020.2983385
DO - 10.1109/TITS.2020.2983385
M3 - 文章
AN - SCOPUS:85083437460
SN - 1524-9050
VL - 22
SP - 531
EP - 541
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 1
M1 - 9057557
ER -