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Probabilistic forecasts of the distribution grid state using data-driven forecasts and probabilistic power flow
Citation Link: https://doi.org/10.15480/882.9195
Publikationstyp
Journal Article
Date Issued
2021-11-15
Sprache
English
Author(s)
González-Ordiano, Jorge Ángel
Mühlpfordt, Tillmann
Braun, Eric
Liu, Jianlei
Kühnapfel, Uwe
Düpmeier, Clemens
Waczowicz, Simon
Mikut, Ralf
Hagenmeyer, Veit
Appino, Riccardo Remo
TORE-DOI
Journal
Volume
302
Article Number
117498
Citation
Applied Energy 302: 117498 (2021-11-15)
Publisher DOI
Scopus ID
Publisher
Elsevier
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.
Subjects
Distribution grid
Probabilistic forecasts
Probabilistic power flow
Uncertainty quantification
DDC Class
333.7: Natural Resources, Energy and Environment
510: Mathematics
Publication version
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1-s2.0-S0306261921008837-main.pdf
Type
Main Article
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1.87 MB
Format
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