Sizing Energy Storage to Mitigate Wind Power Forecast Error Impacts by Signal Processing Techniques

This paper proposes to use discrete Fourier transform (DFT) and discrete wavelet transform (DWT) methods to schedule grid-scale energy storage systems to mitigate wind power forecast error impacts while considering energy storage properties. This is accomplished by decomposing the wind forecast error signal to different time-varying periodic components to schedule sodium sulfur (NaS) batteries, compressed air energy storage (CAES), and conventional generators. The advantage of signal processing techniques is that the resultant decomposed components are appropriate for cycling of each energy storage technology. It is also beneficial for conventional generators, which are more efficient to operate close to rated capacity. The tradeoff between installing more energy storage units and decreasing the wind spillage, back-up energy, and the standard deviation of residual forecast error signal is analyzed. The NaS battery life cycle analysis and CAES contribution on increasing NaS battery lifetime are studied. The impact of considering the frequency bias constant to allow small frequency deviations is also investigated. To showcase the applicability of the proposed approach, a simulation case study based on a real-world 5-min interval wind data from Bonneville Power Administration (BPA) in 2013 is presented.