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.
@article{arxiv.2205.04736,
title = {Large Scale Probabilistic Simulation of Renewables Production},
author = {Mike Ludkovski and Glen Swindle and Eric Grannan},
journal= {arXiv preprint arXiv:2205.04736},
year = {2022}
}