Forecasting the High Penetration of Wind Power on Multiple Scales Using Multi-to-Multi Mapping

Abstract

Highly wind penetrated future power system will couple to the variabilities and nonlinear correlations of wind. Reliable wind power forecasting (WPF) for a region is critical to the security and economics of the power system operation. Therefore, this paper proposes a multiscale WPF method by establishing a multi-to-multi (m2m) mapping network and the use of stacked denoising autoencoder (SDAE). The concerned forecast time horizon is 24-72 hours. First, multi-NWPs in a region are corrected based on SDAE to generate better inputs for the following regional WPF. Second, a number of SDAEs with diverse model parameters and input features are integrated into ensemble SDAE for predicting the wind power generated from various wind farms in a region. Two sets of data are utilized in this case study to validate the proposed method. The results show that the proposed m2m mapping and SDAE are able to capture the real correlations of wind at multiple sites, and outperform the other counterparts in terms of multi-NWPs correction as well as the WPF for both the region and individual concerned wind farm. Moreover, the ensemble SDAE performs better than any other individual regional WPF model.