Journal article
An online reinforcement learning approach for dynamic pricing of electric vehicle charging stations
IEEE Access, Vol.8, pp.130305-130313
2020
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.
Details
- Title
- An online reinforcement learning approach for dynamic pricing of electric vehicle charging stations
- Authors/Creators
- V. Moghaddam (Author/Creator) - Deakin UniversityA. Yazdani (Author/Creator) - Murdoch UniversityH. Wang (Author/Creator) - Murdoch UniversityD. Parlevliet (Author/Creator) - Murdoch UniversityF. Shahnia (Author/Creator) - Murdoch University
- Publication Details
- IEEE Access, Vol.8, pp.130305-130313
- Publisher
- IEEE
- Identifiers
- 991005544084807891
- Copyright
- © 2020 IEEE
- Murdoch Affiliation
- School of Engineering and Energy
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.18 Power Systems & Electric Vehicles
- 4.18.788 Electric Vehicles
- Web Of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Telecommunications
- ESI research areas
- Engineering