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Energy-storage system sizing and operation strategies based on discrete Fourier transform for reliable wind-power generation

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  • Oh, Eunsung
  • Son, Sung-Yong

Abstract

The use of wind-power generation (WPG) increases supply-side variability, and hence reduces the reliability of power generation. Even if WPG is forecasted to deal with such a problem, the uncertainty of WPG cannot be fully resolved because of its randomness and intermittent nature. This paper proposes a two-step energy-storage system (ESS) sizing and operation strategy for enhancing the WPG reliability. From the analysis of WPG data, it is determined that the uncertainty that leads to deterioration in reliability affects the instantaneous changes in WPG significantly. In this regard, ESS sizing and operation strategies are designed based on the frequency-domain analysis using the discrete Fourier transform (DFT). The ESS sizing is firstly determined to filter out high-frequency components, and the ESS operation is determined considering the ESS sizing constraints for reducing the difference between the actual WPG and its forecasting. A case study is presented using the data from a large-scale wind farm with 4782 MW total capacity of 42 plants located in Columbia River Gorge, United States. The numerical results obtained demonstrate that the proposed ESS method can reduce the root-mean-squared error by up to 26% and further reduce by about 3% compared to the conventional method with the ESS.

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  • Oh, Eunsung & Son, Sung-Yong, 2018. "Energy-storage system sizing and operation strategies based on discrete Fourier transform for reliable wind-power generation," Renewable Energy, Elsevier, vol. 116(PA), pages 786-794.
    • Handle: RePEc:eee:renene:v:116:y:2018:i:pa:p:786-794
    DOI: 10.1016/j.renene.2017.10.028
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    References listed on IDEAS

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    Cited by:

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    5. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    6. Heggarty, Thomas & Bourmaud, Jean-Yves & Girard, Robin & Kariniotakis, Georges, 2020. "Quantifying power system flexibility provision," Applied Energy, Elsevier, vol. 279(C).
    7. Teng, Sin Yong & Máša, Vítězslav & Touš, Michal & Vondra, Marek & Lam, Hon Loong & Stehlík, Petr, 2022. "Waste-to-energy forecasting and real-time optimization: An anomaly-aware approach," Renewable Energy, Elsevier, vol. 181(C), pages 142-155.
    8. Oh, Eunsung & Son, Sung-Yong, 2020. "Theoretical energy storage system sizing method and performance analysis for wind power forecast uncertainty management," Renewable Energy, Elsevier, vol. 155(C), pages 1060-1069.
    9. Tong, Shuiguang & Cheng, Zhewu & Cong, Feiyun & Tong, Zheming & Zhang, Yidong, 2018. "Developing a grid-connected power optimization strategy for the integration of wind power with low-temperature adiabatic compressed air energy storage," Renewable Energy, Elsevier, vol. 125(C), pages 73-86.

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