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

González-Ordiano, Jorge Ángel; Mühlpfordt, Tillmann 1; Braun, Eric 1; Liu, Jianlei 1; Çakmak, Hüseyin ORCID iD icon 1; Kühnapfel, Uwe 1; Düpmeier, Clemens 1; Waczowicz, Simon ORCID iD icon 1; Faulwasser, Timm 1; Mikut, Ralf ORCID iD icon 1; Hagenmeyer, Veit 1; Appino, Riccardo Remo 1
1 Karlsruher Institut für Technologie (KIT)


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.

Verlagsausgabe §
DOI: 10.5445/IR/1000136314
Veröffentlicht am 06.09.2021
DOI: 10.1016/j.apenergy.2021.117498
Zitationen: 5
Web of Science
Zitationen: 5
Zitationen: 5
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 15.11.2021
Sprache Englisch
Identifikator ISSN: 0306-2619
KITopen-ID: 1000136314
HGF-Programm 37.12.03 (POF IV, LK 01) Smart Areas and Research Platforms
Erschienen in Applied energy
Verlag Elsevier
Band 302
Seiten Art.-Nr.: 117498
Nachgewiesen in Dimensions
Web of Science
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