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An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning

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  • Yuxin Wen

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Peixiao Fan

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Jia Hu

    (State Grid Hubei Electric Power Co., Ltd., Wuhan 430072, China)

  • Song Ke

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Fuzhang Wu

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Xu Zhu

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

In recent years, the access of various distributed power sources and electric vehicles (EVs) has brought more and more randomness and uncertainty to the operation and regulation of microgrids. Therefore, an optimal scheduling strategy for microgrids with EVs based on Deep Q-learning is proposed in this paper. Firstly, a vehicle-to-grid (V2G) model considering the mobility of EVs and the randomness of user charging behavior is proposed. The charging time distribution model, charging demand model, state-of-charge (SOC) dynamic model and the model of travel location are comprehensively established, thereby realizing the construction of the mathematical model of the microgrid with EVs: it can obtain the charging/discharging situation in the EV station, so as to obtain the overall output power of the EV station. Secondly, based on Deep Q-learning, the state space and action space are set up according to the actual microgrid system, and the design of the optimal scheduling reward function is completed with the goal of economy. Finally, the calculation example results show that compared with the traditional optimization algorithm, the strategy proposed in this paper has the ability of online learning and can cope with the randomness of renewable resources better. Meanwhile, the agent with experience replay ability can be trained to complete the evolution process, so as to adapt to the nonlinear influence caused by the mobility of EVs and the periodicity of user behavior, which is feasible and superior in the field of optimal scheduling of microgrids with renewable resources and EVs.

Suggested Citation

  • Yuxin Wen & Peixiao Fan & Jia Hu & Song Ke & Fuzhang Wu & Xu Zhu, 2022. "An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10351-:d:892811
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Shulei & Jia, Runda & Pan, Hengxin & Cao, Yankai, 2023. "A safe reinforcement learning-based charging strategy for electric vehicles in residential microgrid," Applied Energy, Elsevier, vol. 348(C).
    2. Jiashun Li & Aixing Li, 2024. "Optimizing Electric Vehicle Integration with Vehicle-to-Grid Technology: The Influence of Price Difference and Battery Costs on Adoption, Profits, and Green Energy Utilization," Sustainability, MDPI, vol. 16(3), pages 1-19, January.
    3. Jianhong Hao & Ting Huang & Qiuming Xu & Yi Sun, 2023. "Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game," Sustainability, MDPI, vol. 15(24), pages 1-15, December.
    4. Xiaoqing Bai & Chun Wei & Peijie Li & Dongliang Xiao, 2023. "Editorial for the Special Issue on Sustainable Power Systems and Optimization," Sustainability, MDPI, vol. 15(6), pages 1-3, March.

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