Search Results for author: Luis Böttcher

Found 5 papers, 0 papers with code

End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability

no code implementations6 May 2024 Hinrikus Wolf, Luis Böttcher, Sarra Bouchkati, Philipp Lutat, Jens Breitung, Bastian Jung, Tina Möllemann, Viktor Todosijević, Jan Schiefelbein-Lach, Oliver Pohl, Andreas Ulbig, Martin Grohe

In the course of the energy transition, the expansion of generation and consumption will change, and many of these technologies, such as PV systems, electric cars and heat pumps, will influence the power flow, especially in the distribution grids.

Analysing the interaction of expansion decisions by end customers and grid development in the context of a municipal energy system

no code implementations22 Apr 2024 Paul Maximilian Röhrig, Nancy Radermacher, Luis Böttcher, Andreas Ulbig

The aim of this work is a holistic analysis of the staggered interactions of generation expansion and grid expansion for a future decentralized energy supply concept conditioned by the expansion in the field of self-generation.

Representation of Distribution Grid Expansion Costs in Power System Planning

no code implementations18 Oct 2023 Luis Böttcher, Christian Fröhlich, Steffen Kortmann, Simon Braun, Julian Saat, Andreas Ulbig

With the method presented in this paper, a Feasible Planning Region (FPR) is developed, which represents the operational boundaries of the distribution grids for several expansion stages and thus represents an admissible solution space for the planning of distribution grids in systemic planning approaches.

Modelling Residential Supply Tasks Based on Digital Orthophotography Using Machine Learning

no code implementations25 Oct 2022 Klemens Schumann, Luis Böttcher, Philipp Hälsig, Daniel Zelenak, Andreas Ulbig

To do this, buildings are first identified from orthophotos, then residential building types are classified, and finally the electricity demand of each building is determined.

Solving AC Power Flow with Graph Neural Networks under Realistic Constraints

no code implementations14 Apr 2022 Luis Böttcher, Hinrikus Wolf, Bastian Jung, Philipp Lutat, Marc Trageser, Oliver Pohl, Andreas Ulbig, Martin Grohe

In our approach, we demonstrate the development of a framework that uses graph neural networks to learn the physical constraints of the power flow.

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