  We develop a probabilistic framework for joint simulation of short-term
electricity generation from renewable assets. In this paper we describe a
method for producing hourly day-ahead scenarios of generated power at
grid-scale across hundreds of assets. These scenarios are conditional on
specified forecasts and yield a full uncertainty quantification both at the
marginal asset-level and across asset collections. Our simulation pipeline
first applies asset calibration to normalize hourly, daily and seasonal
generation profiles, and to Gaussianize the forecast--actuals distribution. We
then develop a novel clustering approach to stably estimate the covariance
matrix across assets; clustering is done hierarchically to achieve scalability.
An extended case study using an ERCOT-like system with nearly 500 solar and
wind farms is used for illustration.
