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Evaluating the Fast Frequency Support Ability of the Generation Units in Modern Power Systems

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  • Muyang Liu

    (Department of Electrical Engineering, North China Electric Power University, Beijing 102206, China)

  • Ruo Mo

    (Department of Electrical Engineering, North China Electric Power University, Beijing 102206, China)

  • Yening Lai

    (State Grid Electric Power Research Institute, Nanjing 250003, China)

  • Zhaowei Li

    (Department of Electrical Engineering, North China Electric Power University, Beijing 102206, China)

  • Zhaohui Qie

    (State Grid Electric Power Research Institute, Nanjing 250003, China)

  • Hua Zheng

    (Department of Electrical Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

Modern power systems include synchronous generators (SGs) and inverter-based resources (IBRs) that provide fast frequency support (FFS) to the system. To evaluate the FFS ability of both SGs and IBRs under a unified framework, this paper proposes a method that evaluates the FFS ability of each generation unit via its dynamic trajectories of the active power output and the frequency following a contingency. The proposed method quantified FFS ability via two indexes, namely, the equivalent inertia constant and the equivalent droop, of each generation unit. The Tikhonov regularization algorithm is employed to estimate the FFS ability indexes. The New England 10-machine system serves to validate the feasibility and accuracy of the proposed method and illustrate the different FFS ability of the grid−forming and grid−following IBRs.

Suggested Citation

  • Muyang Liu & Ruo Mo & Yening Lai & Zhaowei Li & Zhaohui Qie & Hua Zheng, 2024. "Evaluating the Fast Frequency Support Ability of the Generation Units in Modern Power Systems," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:6:p:2506-:d:1359024
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    References listed on IDEAS

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    1. Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).
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