TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Arpogaus, Marcel
A1  - Montalbano, Jasmin
A1  - Linke, Manuela
A1  - Schubert, Gunnar
T1  - Probabilistic real-time grid operation management of future distribution grids with high penetration of renewable generators and electrical vehicles based on artificial intelligence
T2  - CIRED Porto Workshop 2022: E-mobility and power distribution systems, 2-3 June 2022, Hybrid Conference, Porto, Portugal
N2  - 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.
KW  - Power system state estimation
KW  - Load forecasting
KW  - Artificial intelligence
KW  - Power distribution control
KW  - Probablistic forecasting
Y1  - 2022
SN  - 978-1-83953-705-9
SB  - 978-1-83953-705-9
U6  - https://doi.org/10.1049/icp.2022.0681
DO  - https://doi.org/10.1049/icp.2022.0681
N1  - Volltext im Campusnetz der Hochschule Konstanz abrufbar.
SP  - 147
EP  - 151
PB  - The Institution of Engineering and Technology (IET)
ER  -