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Electric vehicles

Real-Time Coordinated Operation of Electric Vehicle Fast Charging Stations Enhances Grid Efficiency

Strategic Insights

— 1 min read

Research from the University of Macau has shed light on the integration of battery energy storage systems in fast charging stations to mitigate the challenges posed by the intermittent and impulsive nature of fast charging. The study proposes a two-layer approach for real-time stochastic scheduling of multiple BES-equipped FCSs in a distribution grid, leveraging dynamic programming and consensus-based power allocation strategies. This innovative method enables direct training of value functions and sequential, distributed scheduling decisions, outperforming state-of-the-art benchmarks.

Key Takeaways:

  • The proposed two-layer approach integrates dynamic programming and consensus-based power allocation strategies to coordinate power dispatch among individual FCSs.
  • The upper layer determines the total power of all BESs at FCSs based on observed real-time fast charging loads and electricity price, using analytical expressions for efficient offline training and online scheduling.
  • The lower layer designs a consensus-based power allocation strategy to coordinate power dispatch among individual FCSs following the reference power determined in the upper layer.
  • The research found that integrating battery energy storage systems in FCSs presents a promising option to mitigate the challenges of fast charging, including reduced wear on infrastructure and increased grid efficiency.
  • The superiority of the proposed method was validated via numerical simulations in comparison with state-of-the-art benchmarks.
  • The study has significant implications for the development of smart grids and the efficient operation of electric vehicle fast charging stations.
  • Researchers from the University of Macau, led by Hongcai Zhang, proposed the innovative approach, which has been peer-reviewed and published in the Ieee Transactions On Smart Grid journal.

Statistics:

  • 16(3) is the issue number of the Ieee Transactions On Smart Grid journal where the research was published.
  • 2464-2477 are the page numbers where the research was featured.
  • The research used numerical simulations to validate the superiority of the proposed method, outperforming state-of-the-art benchmarks.
  • 2025 is the year in which the research was conducted and published.

Sources:

  • Ieee Transactions On Smart Grid, 2025;16(3):2464-2477. (Ieee-inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA)
  • University of Macau, Dept. of Electrical and Computer Engineering, State Key Lab Internet Things Smart City, Macau, People's Republic of China. (Hongcai Zhang)

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