Large Scale Probabilistic Simulation of Renewables Production

Mike Ludkovski, Glen Swindle and Eric Grannan

Papers from arXiv.org

Abstract: 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.

Date: 2022-05
New Economics Papers: this item is included in nep-cmp and nep-ene
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