Simon Stock

M.Sc.
Research Assistant

Contact

Simon Stock, M. Sc.
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Office Hours
Jederzeit
Harburger Schloßstraße 36,
21079 Hamburg
Building HS36, Room C3 0.006
Phone: +49 40 42878 2378
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Research Projects

Applications of AI in distribution system operation

Applications of AI in distribution system operation

Hamburg University of Technology (TUHH); Duration: 2020 to 2024

VeN²uS
Networked grid protection systems - Adaptive and interconnected

VeN²uS

Networked grid protection systems - Adaptive and interconnected

Federal Ministry for Economic Affairs and Climate Action (BMWK); Duration: 2021 to 2024

Research Focus

Optimal operation and energy managment in electrical distribution grids (Smart Grids) using artifical intelligence

Publications

TUHH Open Research (TORE)

2023

2022

2021

Courses

Stud.IP
link to course in Stud.IP Studip_icon
Abgesagt! Seminare.EIM: From linear to non-convex optimization (CSBS, DSBS, IIWBS, TMBS, CSMS, IIWMS)
Semester:
WiSe 23/24
Course type:
Seminar
Lecturer:
Dr. Anna Katharina Kirf, Prof. Dr. Martin Kliesch
Description:
Optimization problems are ubiquitous in most scientific fields and engineering. The easiest such problems are linear and already have numerous applications. They are special cases of convex optimization problems, which are often efficiently solvable with many software implementations available. Many optimization problems are non-convex and are often hard to solve. This seminar aims to provide a basic understanding of general principles of `easy' optimization problems (convex optimization). We will see selected examples for the theoretical analysis of standard algorithms for these problems. Then, we will also see example classes of non-convex optimization problems and heuristic ways to solve them. Throughout, there are opportunities to do numerical experiments with standard optimization packages as well as to look into examples for important applications.
Area classification:
Studiendekanat Elektrotechnik, Informatik und Mathematik
Stud.IP informationen about this course:
Home institute: Studiendekanat Elektrotechnik, Informatik und Mathematik (E)
Registered participants in Stud.IP: 4
Documents: 1

Supervised Theses

ongoing
completed

2021

  • Hund, P. (2021). Modellierung eines elektrischen Netzes zur Demonstration des Einflusses von virtueller Trägheit durch umrichterbasierte Energieanlagen.

  • Hund, P. (2021). Koordinierte Bereitstellung von virtueller Trägheit durch erneuerbare umrichterbasierte Energieanlagen in Verteilnetzen mithilfe von künstlicher Intelligenz.

  • Möller, P. (2021). Erfassung der Knotenspannung in Niederspannungsnetzen auf Basis von dezentralen Messeinrichtungen mithilfe von Machine learning.

  • Plant, R. (2021). Estimation of Power System Inertia in an Inverter-Dominated Distribution Grid Using Machine Learning.

2020

  • Dressel, M. (2020). Modellierung der Zustandsschätzung eines elektrischen Netzes mit Hilfe von Graph neuronalen Netzen.

  • Schmidt, M. (2020). Vorhersage von zuverlässig bereitstellbarer Regelleistung aus Erneuerbaren Energien mithilfe von neuronalen Netzen.