Refine
Document Type
- Conference Proceeding (2)
- Article (1)
- Master's Thesis (1)
Language
- English (4)
Keywords
- Artificial intelligence (1)
- Deep Learning (2)
- Deep Transformation Model (1)
- Generative modeling (1)
- Load forecasting (1)
- Low-Voltage (1)
- Machine Learning (1)
- Normalizing Flow (1)
- Normalizing Flows (1)
- Power and energy (1)
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.