M. Sc. Marcel Dipp (Research assistant) - EECS

Research assistant

2010 - 2015: Bachelor's degree in Electrical Engineering with a focus on "Electrical Energy Systems" (B.Sc.) at the University of Kassel
2015: Bachelor's thesis "Evaluation of (n-1) security and optimal resupply for selected topologies of the medium-voltage grid"
2015 - 2018: Master's degree in Electrical Engineering with a focus on "Electrical Energy Systems" (M.Sc.) at the University of Kassel
2018: Master's thesis "Validation of methods for estimating distribution grid expansion needs using cluster analysis"
2018: Semester abroad at the AGH - Akademia Górniczo-Hutnicza, University of Science and Technology in Krakow
Since 2018: Research assistant at the Department of Energy Management and Operation of Electrical Grids (e²n) at the University of Kassel

Planning and operational management of electrical grids (summer semester 2019, summer semester 2020)

2020

M. Dipp, J.-H. Menke, S. Wende-von Berg, A. Maurus, T. Kerber, M. Braun, “Monitoring at the Medium-Voltage Level Using Artificial Neural Networks—A Validation of the Methodology Based on Measured Local Network Stations,” 16th Symposium on Energy Innovation, Graz, Austria, 2020.

2019

M. Dipp, J.-H. Menke, S. Wende-von Berg, M. Braun, “Training of Artificial Neural Networks Based on Feed-in Time Series of Photovoltaics and Wind Power for Active and Reactive Power Monitoring in Medium-Voltage Grids,” INFORMATIK 2019: 50 Years of the German Informatics Society, Kassel, 2019.

C. J. Holzhüter, P. Lytaev, M. Dipp, M. Hassouna, K. Brendlinger, J. Viebahn, W. Gegelman, und C. Merz, „Graph Neural Networks for Grid Control: Prospects in AI-assisted Transmission Grid Operation“, in ETG Kongress 2025 : voller Energie - heute und morgen : 21.- 22. Mai 2025 in Kassel, VDE ETG, Hrsg. Berlin: VDE-Verlag, 2025, S. TBD.

J.-H. Menke, M. Dipp, Z. Liu, C. Ma, F. Schäfer, und M. Braun, „Applications of Artificial Neural Networks in the Context of Power Systems“, in Artificial Intelligence Techniques for a Scalable Energy Transition, M. Sayed-Mouchaweh, Hrsg. Cham: Springer International Publishing, 2020, S. 345–373.

M. Dipp, J.-H. Menke, S. Wende - von Berg, und M. Braun, „Training of Artificial Neural Networks Based on Feed-in Time Series of Photovoltaics and Wind Power for Active and Reactive Power Monitoring in Medium-Voltage Grids“, in INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik–Informatik für Gesellschaft, K. David, K. Geihs, M. Lange, und G. Stumme, Hrsg. Bonn: Gesellschaft für Informatik eV, 2019, S. 545–557.