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Probabilistic real-time grid operation management of future distribution grids with high penetration of renewable generators and electrical vehicles based on artificial intelligence

  • In this paper, we propose a novel method for real-time control of electric distribution grids with a limited number of measurements. The method copes with the changing grid behaviour caused by the increasing number of renewable energies and electric vehicles. Three AI based models are used. Firstly, a probabilistic forecasting estimates possible scenarios at unobserved grid nodes. Secondly, a state estimation is used to detect grid congestion. Finally, a grid control suggests multiple possible solutions for the detected problem. The best countermeasures are then detected by evaluating the systems stability for the next time-step.

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Metadaten
Author:Marcel ArpogausORCiD, Jasmin Montalbano, Manuela Linke, Gunnar Schubert
DOI:https://doi.org/10.1049/icp.2022.0681
ISBN:978-1-83953-705-9
Parent Title (English):CIRED Porto Workshop 2022: E-mobility and power distribution systems, 2-3 June 2022, Hybrid Conference, Porto, Portugal
Publisher:The Institution of Engineering and Technology (IET)
Document Type:Conference Proceeding
Language:English
Year of Publication:2022
Release Date:2023/01/12
Tag:Power system state estimation; Load forecasting; Artificial intelligence; Power distribution control; Probablistic forecasting
First Page:147
Last Page:151
Article Number:1162
Note:
Volltext im Campusnetz der Hochschule Konstanz abrufbar.
Institutes:Fakult├Ąt Elektrotechnik und Informationstechnik
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (German):License LogoUrheberrechtlich gesch├╝tzt