IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i9p2340-d167886.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/9/2340/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/9/2340/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.
    2. Xiang Ying & Keke Zhao & Zhiqiang Liu & Jie Gao & Dongxiao He & Xuewei Li & Wei Xiong, 2022. "Wind Speed Prediction via Collaborative Filtering on Virtual Edge Expanding Graphs," Mathematics, MDPI, vol. 10(11), pages 1-16, June.
    3. Pei Du & Yu Jin & Kequan Zhang, 2016. "A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed," Sustainability, MDPI, vol. 8(8), pages 1-25, August.
    4. Chen, Xue-Jun & Zhao, Jing & Jia, Xiao-Zhong & Li, Zhong-Long, 2021. "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, Elsevier, vol. 165(P1), pages 595-611.
    5. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
    6. Zhang, Zhendong & Ye, Lei & Qin, Hui & Liu, Yongqi & Wang, Chao & Yu, Xiang & Yin, Xingli & Li, Jie, 2019. "Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression," Applied Energy, Elsevier, vol. 247(C), pages 270-284.
    7. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    8. Guanjun Liu & Chao Wang & Hui Qin & Jialong Fu & Qin Shen, 2022. "A Novel Hybrid Machine Learning Model for Wind Speed Probabilistic Forecasting," Energies, MDPI, vol. 15(19), pages 1-16, September.
    9. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    10. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Yang, Qibo & Hsu, Yuan-Ming & Chen, Yudi & Lee, Jay, 2019. "A combined filtering strategy for short term and long term wind speed prediction with improved accuracy," Renewable Energy, Elsevier, vol. 136(C), pages 1082-1090.
    11. Xu, Li & Ou, Yanxia & Cai, Jingjing & Wang, Jin & Fu, Yang & Bian, Xiaoyan, 2023. "Offshore wind speed assessment with statistical and attention-based neural network methods based on STL decomposition," Renewable Energy, Elsevier, vol. 216(C).
    12. Liu, Guanjun & Wang, Yun & Qin, Hui & Shen, Keyan & Liu, Shuai & Shen, Qin & Qu, Yuhua & Zhou, Jianzhong, 2023. "Probabilistic spatiotemporal forecasting of wind speed based on multi-network deep ensembles method," Renewable Energy, Elsevier, vol. 209(C), pages 231-247.
    13. Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
    14. Wu, Xuedong & Zhu, Zhiyu & Su, Xunliang & Fan, Shaosheng & Du, Zhaoping & Chang, Yanchao & Zeng, Qingjun, 2015. "A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction," Energy, Elsevier, vol. 88(C), pages 194-201.
    15. Li, Chaoshun & Xiao, Zhengguang & Xia, Xin & Zou, Wen & Zhang, Chu, 2018. "A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 131-144.
    16. Ines Würth & Laura Valldecabres & Elliot Simon & Corinna Möhrlen & Bahri Uzunoğlu & Ciaran Gilbert & Gregor Giebel & David Schlipf & Anton Kaifel, 2019. "Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36," Energies, MDPI, vol. 12(4), pages 1-30, February.
    17. Xiong, Zhanhang & Yao, Jianjiang & Huang, Yongmin & Yu, Zhaoxu & Liu, Yalei, 2024. "A wind speed forecasting method based on EMD-MGM with switching QR loss function and novel subsequence superposition," Applied Energy, Elsevier, vol. 353(PB).
    18. Liu, Yi & Wang, Ranpeng & Gu, Yin & Li, Congjian & Wang, Gangqiao, 2024. "Physics-inspired and data-driven two-stage deep learning approach for wind field reconstruction with experimental validation," Energy, Elsevier, vol. 298(C).
    19. Jianguo Zhou & Xiaolei Xu & Xuejing Huo & Yushuo Li, 2019. "Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    20. Li, Wenzhe & Jia, Xiaodong & Li, Xiang & Wang, Yinglu & Lee, Jay, 2021. "A Markov model for short term wind speed prediction by integrating the wind acceleration information," Renewable Energy, Elsevier, vol. 164(C), pages 242-253.

    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:jeners:v:11:y:2018:i:9:p:2340-:d:167886. 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.