Dynamic pricing for fast charging stations with deep reinforcement learning
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DOI: 10.1016/j.apenergy.2023.121334
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- Xiang, Yue & Liu, Junyong & Li, Ran & Li, Furong & Gu, Chenghong & Tang, Shuoya, 2016. "Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates," Applied Energy, Elsevier, vol. 178(C), pages 647-659.
- Athanasios Paraskevas & Dimitrios Aletras & Antonios Chrysopoulos & Antonios Marinopoulos & Dimitrios I. Doukas, 2022. "Optimal Management for EV Charging Stations: A Win–Win Strategy for Different Stakeholders Using Constrained Deep Q-Learning," Energies, MDPI, vol. 15(7), pages 1-24, March.
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- Elinor Ginzburg-Ganz & Itay Segev & Alexander Balabanov & Elior Segev & Sivan Kaully Naveh & Ram Machlev & Juri Belikov & Liran Katzir & Sarah Keren & Yoash Levron, 2024. "Reinforcement Learning Model-Based and Model-Free Paradigms for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions," Energies, MDPI, vol. 17(21), pages 1-54, October.
- Chen, Sheng & Cheng, Hao & Zhang, Hongcai & Lv, Si & Wei, Zhinong & Jin, Yuyang, 2025. "Privacy-preserving coordination of power and transportation networks using spatiotemporal GAT for predicting EV charging demands," Applied Energy, Elsevier, vol. 377(PA).
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Keywords
Electric vehicle (EV); Fast charging station (FCST); Dynamic pricing; User satisfaction; deep reinforcement learning (DRL);All these keywords.
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