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Discrete Wavelet Transform for the Real-Time Smoothing of Wind Turbine Power Using Li-Ion Batteries

Author

Listed:
  • Andrea Mannelli

    (Department of Industrial Engineering, Università Degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy)

  • Francesco Papi

    (Department of Industrial Engineering, Università Degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy)

  • George Pechlivanoglou

    (Eunice Energy Group, 29, Vas. Sofias Ave, 10674 Athens, Greece)

  • Giovanni Ferrara

    (Department of Industrial Engineering, Università Degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy)

  • Alessandro Bianchini

    (Department of Industrial Engineering, Università Degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy)

Abstract

Energy Storage Systems (EES) are key to further increase the penetration in energy grids of intermittent renewable energy sources, such as wind, by smoothing out power fluctuations. In order this to be economically feasible; however, the ESS need to be sized correctly and managed efficiently. In the study, the use of discrete wavelet transform (Daubechies Db4) to decompose the power output of utility-scale wind turbines into high and low-frequency components, with the objective of smoothing wind turbine power output, is discussed and applied to four-year Supervisory Control And Data Acquisition (SCADA) real data from multi-MW, on-shore wind turbines provided by the industrial partner. Two main research requests were tackled: first, the effectiveness of the discrete wavelet transform for the correct sizing and management of the battery (Li-Ion type) storage was assessed in comparison to more traditional approaches such as a simple moving average and a direct use of the battery in response to excessive power fluctuations. The performance of different storage designs was compared, in terms of abatement of ramp rate violations, depending on the power smoothing technique applied. Results show that the wavelet transform leads to a more efficient battery use, characterized by lower variation of the averaged state-of-charge, and in turn to the need for a lower battery capacity, which can be translated into a cost reduction (up to −28%). The second research objective was to prove that the wavelet-based power smoothing technique has superior performance for the real-time control of a wind park. To this end, a simple procedure is proposed to generate a suitable moving window centered on the actual sample in which the wavelet transform can be applied. The power-smoothing performance of the method was tested on the same time series data, showing again that the discrete wavelet transform represents a superior solution in comparison to conventional approaches.

Suggested Citation

  • Andrea Mannelli & Francesco Papi & George Pechlivanoglou & Giovanni Ferrara & Alessandro Bianchini, 2021. "Discrete Wavelet Transform for the Real-Time Smoothing of Wind Turbine Power Using Li-Ion Batteries," Energies, MDPI, vol. 14(8), pages 1-32, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2184-:d:535858
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    References listed on IDEAS

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    Cited by:

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    2. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Forecasting error processing techniques and frequency domain decomposition for forecasting error compensation and renewable energy firming in hybrid systems," Applied Energy, Elsevier, vol. 313(C).
    3. Superchi, Francesco & Papi, Francesco & Mannelli, Andrea & Balduzzi, Francesco & Ferro, Francesco Maria & Bianchini, Alessandro, 2023. "Development of a reliable simulation framework for techno-economic analyses on green hydrogen production from wind farms using alkaline electrolyzers," Renewable Energy, Elsevier, vol. 207(C), pages 731-742.
    4. José Baptista & Pedro Faria & Bruno Canizes & Tiago Pinto, 2022. "Power Quality of Renewable Energy Source Systems: A New Paradigm of Electrical Grids," Energies, MDPI, vol. 15(9), pages 1-4, April.

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