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Primary Frequency Controller with Prediction-Based Droop Coefficient for Wind-Storage Systems under Spot Market Rules

Author

Listed:
  • Shengqi Zhang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Yateendra Mishra

    (School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia)

  • Bei Yuan

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Jianfeng Zhao

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Mohammad Shahidehpour

    (Illinois Institute of Technology, Chicago, IL 60616, USA)

Abstract

Increasing penetration levels of asynchronous wind turbine generators (WTG) reduce the ability of the power system to maintain adequate frequency responses. WTG with the installation of battery energy storage systems (BESS) as wind-storage systems (WSS), not only reduce the intermittency but also provide a frequency response. Meanwhile, many studies indicate that using the dynamic droop coefficient of WSS in primary frequency control (PFC) based on the prediction values, is an effective way to enable the performance of WSS similar to conventional synchronous generators. This paper proposes a PFC for WSS with a prediction-based droop coefficient (PDC) according to the re-bid process under real-time spot market rules. Specifically, WSS update the values of the reference power and droop coefficient discretely at every bidding interval using near-term wind power and frequency prediction, which enables WSS to be more dispatchable in the view of transmission system operators (TSOs). Also, the accurate prediction method in the proposed PDC-PFC achieves the optimal arrangement of power from WTG and BESS in PFC. Finally, promising simulation results for a hybrid power system show the efficacy of the proposed PDC-PFC for WSS under different operating conditions.

Suggested Citation

  • Shengqi Zhang & Yateendra Mishra & Bei Yuan & Jianfeng Zhao & Mohammad Shahidehpour, 2018. "Primary Frequency Controller with Prediction-Based Droop Coefficient for Wind-Storage Systems under Spot Market Rules," Energies, MDPI, vol. 11(9), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2340-:d:167886
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    References listed on IDEAS

    as
    1. Tiejiang Yuan & Jinjun Wang & Yuhang Guan & Zheng Liu & Xinfu Song & Yong Che & Wenping Cao, 2018. "Virtual Inertia Adaptive Control of a Doubly Fed Induction Generator (DFIG) Wind Power System with Hydrogen Energy Storage," Energies, MDPI, vol. 11(4), pages 1-16, April.
    2. Zhang, S. & Mishra, Y. & Shahidehpour, M., 2017. "Utilizing distributed energy resources to support frequency regulation services," Applied Energy, Elsevier, vol. 206(C), pages 1484-1494.
    3. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    4. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    5. Yuyang Gao & Chao Qu & Kequan Zhang, 2016. "A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(10), pages 1-28, September.
    6. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
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    Cited by:

    1. Changgang Li & Zhi Hang & Hengxu Zhang & Qi Guo & Yihua Zhu & Vladimir Terzija, 2020. "Evaluation of DFIGs’ Primary Frequency Regulation Capability for Power Systems with High Penetration of Wind Power," Energies, MDPI, vol. 13(23), pages 1-19, November.
    2. Pablo Fernández-Bustamante & Oscar Barambones & Isidro Calvo & Cristian Napole & Mohamed Derbeli, 2021. "Provision of Frequency Response from Wind Farms: A Review," Energies, MDPI, vol. 14(20), pages 1-24, October.

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