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.
| 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: | Fakultät Elektrotechnik und Informationstechnik |
| Relevance: | Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband) |
| Open Access?: | Nein |
| Licence (German): |  Urheberrechtlich geschützt |
