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Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game

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Listed:
  • Xiu Ji

    (Future Industrial Technology Innovation Institute, Changchun Institute of Technology, Changchun 130000, China)

  • Mingge Li

    (Future Industrial Technology Innovation Institute, Changchun Institute of Technology, Changchun 130000, China)

  • Zheyu Yue

    (Future Industrial Technology Innovation Institute, Changchun Institute of Technology, Changchun 130000, China)

  • Haifeng Zhang

    (Power Science Research Institute of State Grid Jilin Electric Power Co., Changchun 130000, China)

  • Yizhu Wang

    (Future Industrial Technology Innovation Institute, Changchun Institute of Technology, Changchun 130000, China)

Abstract

Rapid advances in renewable energy technologies offer significant opportunities for the global energy transition and environmental protection. However, due to the fluctuating and intermittent nature of their power generation, which leads to the phenomenon of power abandonment, it has become a key challenge to efficiently consume renewable energy sources and guarantee the reliable operation of the power system. In order to address the above problems, this paper proposes an electric vehicle aggregator (EVA) scheduling strategy based on a two-layer game by constructing a two-layer game model between renewable energy generators (REG) and EVA, where the REG formulates time-sharing tariff strategies in the upper layer to guide the charging and discharging behaviors of electric vehicles, and the EVA respond to the price signals in the lower layer to optimize the large-scale electric vehicle scheduling. For the complexity of large-scale scheduling, this paper introduces the A2C (Advantage Actor-Critic) reinforcement learning algorithm, which combines the value network and the strategy network synergistically to optimize the real-time scheduling process. Based on the case study of wind power, photovoltaic, and wind–solar complementary data in Jilin Province, the results show that the strategy significantly improves the rate of renewable energy consumption (up to 97.88%) and reduces the cost of power purchase by EVA (an average saving of RMB 0.04/kWh), realizing a win–win situation for all parties. The study provides theoretical support for the synergistic optimization of the power system and renewable energy and is of great practical significance for the large-scale application of electric vehicles and new energy consumption.

Suggested Citation

  • Xiu Ji & Mingge Li & Zheyu Yue & Haifeng Zhang & Yizhu Wang, 2024. "Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game," Energies, MDPI, vol. 18(1), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:18:y:2024:i:1:p:80-:d:1555341
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    References listed on IDEAS

    as
    1. Xiangchu Xu & Zewei Zhan & Zengqiang Mi & Ling Ji, 2023. "An Optimized Decision Model for Electric Vehicle Aggregator Participation in the Electricity Market Based on the Stackelberg Game," Sustainability, MDPI, vol. 15(20), pages 1-26, October.
    2. Liu, Laibao & Wang, Zheng & Wang, Yang & Wang, Jun & Chang, Rui & He, Gang & Tang, Wenjun & Gao, Ziqi & Li, Jiangtao & Liu, Changyi & Zhao, Lin & Qin, Dahe & Li, Shuangcheng, 2020. "Optimizing wind/solar combinations at finer scales to mitigate renewable energy variability in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    3. Yuchen Yang & Kavan Javanroodi & Vahid M. Nik, 2022. "Climate Change and Renewable Energy Generation in Europe—Long-Term Impact Assessment on Solar and Wind Energy Using High-Resolution Future Climate Data and Considering Climate Uncertainties," Energies, MDPI, vol. 15(1), pages 1-19, January.
    4. Hosseini Dolatabadi, Sayed Hamid & Bhuiyan, Tanveer Hossain & Chen, Yang & Morales, Jose Luis, 2024. "A stochastic game-theoretic optimization approach for managing local electricity markets with electric vehicles and renewable sources," Applied Energy, Elsevier, vol. 368(C).
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