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

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.

Tuning the Discrete Wavelet Transform for Power Smoothing of Wind Turbines

“…It is then apparent that the optimization of the level of decomposition plays an important role to correctly define the power content to be split between the BESS and the grid. Previous experience showed that for the Daubechies family, often used in this kind of applications, moderate decomposition levels (2-3) are sufficient to guarantee a good smoothing of wind power values [6], as shown in Figure 3 and Figure 4.…”

“…The algorithm can work in real-time mode or in offline, and its main settings are reported in Table 1. For additional info, the readers can refer to [6]. Table 1.…”

“…The present study is aimed at further developing and tuning a power smoothing approach developed by the authors recently [6]. The proposed strategy makes use of the discrete wavelet transform (DWT) to tackle the power signal decomposition between those high frequency fluctuations that need to be smoothed out and the slower one that can be handled by the grid connection.…”

“…The distinctive feature of the use of DWT consists in the ability of processing non-stationary signals using a multi-resolution analysis (MRA) [7]. Initial studies have shown that the DWT approach is extremely efficient both in "offline" and "online" mode in comparison with more conventional approaches like direct power smoothing or the moving average [6]. More specifically, the offline mode consists into the use of DWT on a power data set, either experimentally recorded or synthetized based on wind forecast, to define the behavior of the hybrid energy system for different storage size.…”

Techno-economic study on green hydrogen production and use in hard-to-abate industrial sectors

“…The battery model simulates the behaviour of a lithium-ion Battery Energy Storage System (BESS) and is based on a previous study by some of the authors [21]. The main considered parameters are the state of charge (SOC), limited to 15-95% to avoid harmful deep cycles, and the charging rate (C-rate), limited to full charge in 1 hour (1C) and full discharge in 2 hours (2C).…”