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.
Highlights
Wind energy is one of the most environmental-friendly and promising renewable energy sources
In order to prevent possible damages, the requirements of the electrical system are very restrictive, constraining the power gradients that the network can absorb, as discussed in more detail in the following. This has led to the development of power smoothing approaches, i.e., methods capable of mitigating fluctuations, smoothing the power profile fed into the network [4]
For the purpose of power smoothing, it allows to distinguish effectively within the signal those lower-frequency fluctuations that can be delivered to the grid from the higher-frequency ones that need to be compensated with a storage, as shown recently by [21,22,23]
Summary
Wind energy is one of the most environmental-friendly and promising renewable energy sources. In order to prevent possible damages, the requirements of the electrical system are very restrictive, constraining the power gradients that the network can absorb, as discussed in more detail in the following This has led to the development of power smoothing approaches, i.e., methods capable of mitigating fluctuations, smoothing the power profile fed into the network [4]. The idea of analyzing the wind power output like a signal and smoothing out the highest frequency components is becoming one of the most investigated trends. In this perspective, the application of the discrete wavelet transform (DWT) is appreciated (e.g., [19]). For the purpose of power smoothing, it allows to distinguish effectively within the signal those lower-frequency fluctuations that can be delivered to the grid from the higher-frequency ones that need to be compensated with a storage, as shown recently by [21,22,23]
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