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An Online Reinforcement Learning Approach for Dynamic Pricing of Electric Vehicle Charging Stations

Moghaddam, Valeh, Yazdani, Amirmehdi, Wang, Hai, Parlevliet, David and Shahnia, Farhad 2020, An Online Reinforcement Learning Approach for Dynamic Pricing of Electric Vehicle Charging Stations, IEEE Access, vol. 8, pp. 130305-130313, doi: 10.1109/ACCESS.2020.3009419.

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Title An Online Reinforcement Learning Approach for Dynamic Pricing of Electric Vehicle Charging Stations
Author(s) Moghaddam, Valeh
Yazdani, Amirmehdi
Wang, Hai
Parlevliet, David
Shahnia, Farhad
Journal name IEEE Access
Volume number 8
Start page 130305
End page 130313
Total pages 9
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Place of publication Piscataway, N.J.
Publication date 2020
ISSN 2169-3536
2169-3536
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Cascading style sheets
Pricing
Electric vehicle charging
Load modeling
Charging stations
Mathematical model
Learning (artificial intelligence)
Electric vehicles
pricing strategy
reinforcement learning
MODEL
Summary The global market share of electric vehicles (EVs) is on the rise, resulting in a rapid increase in their charging demand in both spatial and temporal domains. A remedy to shift the extra charging loads at peak hours to off-peak hours, caused by charging EVs at public charging stations, is an online pricing strategy. This paper presents a novel combinatorial online pricing strategy that has been established upon a reward-based model to prevent network instability and power outages. In the proposed solution, the utility provides incentives to the charging stations for their contributions in the EVs charging load shifting. Then, a constraint optimization problem is developed to minimize the total charging demand of the EVs during peak hours. To control the EVs charging demands in supporting utility’s stability and increasing the total revenue of the charging stations, treated as a multi-agent framework, an online reinforcement learning model is developed which is based on the combination of an adaptive heuristic critic and recursive least square algorithm. The effective performance of the proposed model is validated through extensive simulation studies such as qualitative, numerical, and robustness performance assessment tests. The simulation results indicate significant improvement in the robustness and effectiveness of the proposed solution in terms of utility’s power saving and charging stations’ profit.
Language eng
DOI 10.1109/ACCESS.2020.3009419
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30141053

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.