Probabilistic forecasts of the distribution grid state using data-driven forecasts and probabilistic power flow

Jorge Ángel González-Ordiano, Tillmann Mühlpfordt, Eric Braun, Jianlei Liu, Hüseyin Çakmak, Uwe Kühnapfel, Clemens Düpmeier, Simon Waczowicz, Timm Faulwasser, Ralf Mikut, Veit Hagenmeyer and Riccardo Remo Appino

Applied Energy, 2021, vol. 302, issue C, No S0306261921008837

Abstract: The uncertainty associated with renewable energies creates challenges in the operation of distribution grids. One way for Distribution System Operators to deal with this is the computation of probabilistic forecasts of the full state of the grid. Recently, probabilistic forecasts have seen increased interest for quantifying the uncertainty of renewable generation and load. However, individual probabilistic forecasts of the state defining variables do not allow the prediction of the probability of joint events, for instance, the probability of two line flows exceeding their limits simultaneously. To overcome the issue of estimating the probability of joint events, we present an approach that combines data-driven probabilistic forecasts (obtained more specifically with quantile regressions) and probabilistic power flow. Moreover, we test the presented method using data from a real-world distribution grid that is part of the Energy Lab 2.0 of the Karlsruhe Institute of Technology and we implement it within a state-of-the-art computational framework.

Keywords: Probabilistic forecasts; Probabilistic power flow; Distribution grid; Uncertainty quantification (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2021.117498

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