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We develop a probabilistic framework for joint simulation of short-termelectricity generation from renewable assets. In this paper we describe amethod for producing hourly day-ahead scenarios of generated power atgrid-scale across hundreds of assets. These scenarios are conditional onspecified forecasts and yield a full uncertainty quantification both at themarginal asset-level and across asset collections. Our simulation pipelinefirst applies asset calibration to normalize hourly, daily and seasonalgeneration profiles, and to Gaussianize the forecast--actuals distribution. Wethen develop a novel clustering approach to stably estimate the covariancematrix across assets; clustering is done hierarchically to achieve scalability.An extended case study using an ERCOT-like system with nearly 500 solar andwind farms is used for illustration.

Large Scale Probabilistic Simulation of Renewables Production