Wind Power Scenario Generation based on the Generalized Dynamic Factor Model and Generative Adversarial Network

For conducting resource adequacy studies, we synthesize multiple long-term wind power scenarios of distributed wind farms by using the spatio-temporal features: spatial and temporal correlation, waveforms, marginal and ramp-rate distributions, power spectral densities, and statistical characteristics. Generating the spatial correlation in scenarios requires designing common factors for neighboring wind farms and antithetical factors for distant wind farms. The generalized dynamic factor model (GDFM) can extract the common factors through cross spectral density analysis, but it cannot closely replicate waveform patterns. The GAN can synthesize plausible samples representing the temporal correlation by verifying samples through a fake sample discriminator. To combine the advantages of GDFM and GAN, we use the GAN to provide a filter that extracts dynamic factors with temporal information from the observation data, and we then apply this filter in the GDFM to represent both spatial and frequency correlations of plausible waveforms. Numerical tests on the combined GDFM–GAN approach demonstrate performance improvements over competing alternatives in synthesizing wind power scenarios from Australia. The proposed method better reproduces the statistical characteristics of actual wind power compared with alternatives such as (i) GDFM with filters synthesized from distributions of actual dynamic filters and (ii) GAN with direct synthesis without dynamic factors.