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

Andrea Mannelli, Francesco Papi, George Pechlivanoglou, Giovanni Ferrara and Alessandro Bianchini
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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

Energies, 2021, vol. 14, issue 8, 1-32

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.

Keywords: power smoothing; wavelet transform; Li-Ion battery; hybrid energy storage systems; wind turbine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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