IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i6p2506-d1359024.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/6/2506/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/6/2506/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Bin & Hu, Weihao & Ghias, Amer M.Y.M. & Xu, Xiao & Chen, Zhe, 2022. "Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings," Applied Energy, Elsevier, vol. 328(C).
    2. Duan, Pengfei & Zhao, Bingxu & Zhang, Xinghui & Fen, Mengdan, 2023. "A day-ahead optimal operation strategy for integrated energy systems in multi-public buildings based on cooperative game," Energy, Elsevier, vol. 275(C).
    3. Zhou, Yuekuan, 2023. "A dynamic self-learning grid-responsive strategy for battery sharing economy—multi-objective optimisation and posteriori multi-criteria decision making," Energy, Elsevier, vol. 266(C).
    4. Liao, Wei & Xiao, Fu & Li, Yanxue & Zhang, Hanbei & Peng, Jinqing, 2024. "A comparative study of demand-side energy management strategies for building integrated photovoltaics-battery and electric vehicles (EVs) in diversified building communities," Applied Energy, Elsevier, vol. 361(C).
    5. Wenya Xu & Yanxue Li & Guanjie He & Yang Xu & Weijun Gao, 2023. "Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control," Energies, MDPI, vol. 16(13), pages 1-19, June.
    6. Ayas Shaqour & Aya Hagishima, 2022. "Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types," Energies, MDPI, vol. 15(22), pages 1-27, November.
    7. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.
    8. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    9. Xu, Wenjie & Svetozarevic, Bratislav & Di Natale, Loris & Heer, Philipp & Jones, Colin N., 2024. "Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach," Applied Energy, Elsevier, vol. 358(C).
    10. Wang, Zixuan & Xiao, Fu & Ran, Yi & Li, Yanxue & Xu, Yang, 2024. "Scalable energy management approach of residential hybrid energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 367(C).
    11. Jiang, Yuliang & Zhu, Shanying & Xu, Qimin & Yang, Bo & Guan, Xinping, 2023. "Hybrid modeling-based temperature and humidity adaptive control for a multi-zone HVAC system," Applied Energy, Elsevier, vol. 334(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:6:p:2506-:d:1359024. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.