  We present a method to generate renewable scenarios using Bayesian
probabilities by implementing the Bayesian generative adversarial
network~(Bayesian GAN), which is a variant of generative adversarial networks
based on two interconnected deep neural networks. By using a Bayesian
formulation, generators can be constructed and trained to produce scenarios
that capture different salient modes in the data, allowing for better diversity
and more accurate representation of the underlying physical process. Compared
to conventional statistical models that are often hard to scale or sample from,
this method is model-free and can generate samples extremely efficiently. For
validation, we use wind and solar times-series data from NREL integration data
sets to train the Bayesian GAN. We demonstrate that proposed method is able to
generate clusters of wind scenarios with different variance and mean value, and
is able to distinguish and generate wind and solar scenarios simultaneously
even if the historical data are intentionally mixed.
