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Dynamic pricing for fast charging stations with deep reinforcement learning

Author

Listed:
  • Cui, Li
  • Wang, Qingyuan
  • Qu, Hongquan
  • Wang, Mingshen
  • Wu, Yile
  • Ge, Le

Abstract

With the rapid development of electric vehicles (EVs) and charging infrastructures, the unbalanced utilization rate of fast charging stations (FCSTs) and the long waiting time for charging have aroused considerable attention. The incurred low operation profit of FCSTs and low satisfaction of EVs impose difficulties on the further development of EV industry. Existing literature ignored the influence of real-time charging price changes on traffic flow variation and EV charging determination during the dynamic price regulating process. This paper focuses on solving these crucial issues in the dynamic pricing for FCSTs with deep reinforcement learning (DRL). Firstly, considering the spatial–temporal interactions of different roads, a traffic flow prediction model is proposed based on the LSTM combined with the GNN-FiLM. Then, the Origin-Destination (OD) estimation is used to estimate the charging requirements of EVs based on the predicted traffic flow, and a charging demand prediction method for FCSTs is developed by converting the EV satisfaction into economic costs with different dimensions. Then, the vehicle–road learning environment is built with the Markov decision process (MDP), and a dynamic pricing strategy based on the Deep Deterministic Policy Gradient (DDPG) learning is proposed to achieve the optimal charging prices of FCSTs with maximum operation profit. Moreover, during the learning process, the real-time charging price is renewed based on the predicted charging demand, and the future charging demand is further predicted under the renewed charging price until the optimal price is achieved. Finally, simulation results validate that the proposed dynamic pricing strategy effectively improves the profit of FCSTs, alleviates the road congestion, and improves the users’ satisfaction.

Suggested Citation

  • Cui, Li & Wang, Qingyuan & Qu, Hongquan & Wang, Mingshen & Wu, Yile & Ge, Le, 2023. "Dynamic pricing for fast charging stations with deep reinforcement learning," Applied Energy, Elsevier, vol. 346(C).
    • Handle: RePEc:eee:appene:v:346:y:2023:i:c:s0306261923006980
    DOI: 10.1016/j.apenergy.2023.121334
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    References listed on IDEAS

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    1. 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.
    2. 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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    1. 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.
    2. 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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