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Charging station layout planning for electric vehicles based on power system flexibility requirements

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  • Jiang, Ziyue
  • Han, Jingzuo
  • Li, Yetong
  • Chen, Xinyu
  • Peng, Tianduo
  • Xiong, Jianliang
  • Shu, Zhan

Abstract

Under the ambitious commitment of reaching carbon neutrality by 2060, China promotes both the deployment of renewable energy and the development of electric vehicles. Renewable fluctuations in the supply side and EV charging burden in the demand side propose higher requirements for the system flexibility. Optimized station layout and charging schedule could coordinate the load curve, provide system flexibility and accommodate the variable renewables. However, previous work only focuses on maximizing the profit of station holders and vehicle owners, lacking the consideration of the broader impact on power system. Here, we propose an EV charging station layout optimization methodology considering not only the EV charging behavior, sequential charging demand, but also its further impact on power system. The station layout and charging schedule are co-optimized with an integrated power system model. Applying the proposed methodology to Jiangxi in 2025, a cumulative charging station capacity of 1412, 1092, and 1415 MW is recommended in workplaces, residences, and shopping centers, respectively. Increased renewable energy integration and decreased thermal power generation are realized, achieving reduced carbon emissions of 800 kilotons. This work provides an effective methodology for slow charging station layout from the perspective of the integrated power system.

Suggested Citation

  • Jiang, Ziyue & Han, Jingzuo & Li, Yetong & Chen, Xinyu & Peng, Tianduo & Xiong, Jianliang & Shu, Zhan, 2023. "Charging station layout planning for electric vehicles based on power system flexibility requirements," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223023770
    DOI: 10.1016/j.energy.2023.128983
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    References listed on IDEAS

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    Cited by:

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    2. Sepehrzad, Reza & Khodadadi, Amin & Adinehpour, Sara & Karimi, Maede, 2024. "A multi-agent deep reinforcement learning paradigm to improve the robustness and resilience of grid connected electric vehicle charging stations against the destructive effects of cyber-attacks," Energy, Elsevier, vol. 307(C).
    3. Du, Zhili & Zheng, Lirong & Lin, Boqiang, 2024. "Influence of charging stations accessibility on charging stations utilization," Energy, Elsevier, vol. 298(C).
    4. Wang, Song & Shi, Lefeng, 2024. "EV diffusion promotion analysis under different charging market structure," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    5. Güven, Aykut Fatih, 2024. "Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management," Energy, Elsevier, vol. 303(C).

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