A dynamic wavelet-based robust wind power smoothing approach using hybrid energy storage system

In this paper, a new robust dynamic-wavelet-enabled approach is proposed for wind power smoothing by using the hybrid energy storage system (HESS) consisting of batteries and super-capacitors. The developed approach is able to decompose wind power time series into self-adaptively optimized wavelet parameters without violating the physical constraints. The latter includes those for power injection regulation, state-of-charge (SOC) of HESS, and allowable charge/discharge depth. By doing that, batteries and super-capacitors can be coordinated in an optimal manner, yielding high efficiency. To address wind power uncertainty, a box-type uncertainty set that describes the probability of wind power prediction error is developed. The uncertainty set is further leveraged by the robust model predictive control (MPC) strategy and the robust coefficient to assess the trade-off between robustness and economic benefits. The advantages of the method are validated through the realistic wind farm data and the comparisons with other approaches. The results indicate that the proposed method can be implemented online to determine the robust smoothing strategy for HESS, yielding the highly-qualified integration of renewable energy into the power grid.