Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 22, Issue 1, Pages 531-541Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.2983385
Keywords
Pricing; Charging stations; Learning (artificial intelligence); Electric vehicle charging; Elasticity; Vehicle dynamics; Fast charging; dynamic pricing; reinforcement learning; XFC; renewable energy
Categories
Funding
- State Grid Corporation of China [5418-201958524A-0-0-00]
- First Class Discipline of Zhejiang-A (Zhejiang University of Finance and Economics-Statistics)
- National Natural Science Foundation (NSFC) Programs of China [71722014]
- Youth Innovation Team of Shaanxi Universities Big data and Business Intelligent Innovation Team
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Significant developments in electric vehicle technologies, such as extreme fast charging, have been witnessed in the past decade. However, the lack of fast charging stations remains a major barrier to wider EV deployment. To address this issue, establishing a fast charging sharing system and implementing a smart dynamic pricing scheme are crucial steps forward.
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.
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