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An online reinforcement learning approach for dynamic pricing of electric vehicle charging stations

Moghaddam, V., Yazdani, A., Wang, H.ORCID: 0000-0003-2789-9530, Parlevliet, D. and Shahnia, F.ORCID: 0000-0002-8434-0525 (2020) An online reinforcement learning approach for dynamic pricing of electric vehicle charging stations. IEEE Access, 8 . pp. 130305-130313.

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Abstract

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.

Item Type: Journal Article
Murdoch Affiliation: Engineering and Energy
Publisher: IEEE
Copyright: © 2020 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/57269
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