Logo Institute of Electrical Power and Energy Technology
Institute of Electrical Power and Energy Technology
Logo Institute of Electrical Power and Energy Technology
Institute of Electrical Power and Energy Technology
DE
EN
Search
DE
EN
Search
IEET > Staff > Research Assistants > Simon Stock, M.Sc.

Simon Stock

M.Sc.
Research Assistant

Contact

Simon Stock, M. Sc.
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Office Hours
Jederzeit
Harburger Schloßstraße 22a,
21079 Hamburg
Building HS22a, Room 2.002
Phone: +49 40 42878 2378
Logo

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

to the project page
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

to the project page

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
zur Veranstaltung in Stud.IP Studip_icon
Simulation of Communication Networks (PBL)
Untertitel:
This course is part of the module: Communication Networks II - Simulation and Modeling, Simulation of Communication Networks
Semester:
SoSe 24
Veranstaltungstyp:
PBL -Projekt-/problembasierte Lehrveranstaltung (Lehre)
Veranstaltungsnummer:
lv887_s24
DozentIn:
Dr.Ing- Koojana Kuladinithi
Beschreibung:

In the course necessary basic stochastics and the discrete event simulation are introduced. Also simulation models for communication networks, for example, traffic models, mobility models and radio channel models are presented in the lecture. Students work with a simulation tool, where they can directly try out the acquired skills, algorithms and models. At the end of the course increasingly complex networks and protocols are considered and their performance is determined by simulation.
Voraussetzungen:
Understanding of basic principles of communication networks and their protocols as presented in 'Communication Networks' or 'Computer Networks' Lectures. Basic Knowledge in Stochastics. Basic programming knowledge, especially C++ (to work with OMNeT++ networking simulator)
Lernorganisation:
605 - Communication Networks II - Simulation and Modeling<ul><li>605 - Communication Networks II - Simulation and Modeling: mündlich</li></ul><br>606 - Simulation of Communication Networks<ul><li>606 - Simulation of Communication Networks: mündlich</li></ul>
Leistungsnachweis:
605 - Communication Networks II - Simulation and Modeling<ul><li>605 - Communication Networks II - Simulation and Modeling: mündlich</li></ul><br>606 - Simulation of Communication Networks<ul><li>606 - Simulation of Communication Networks: mündlich</li></ul>
Sonstiges:
Publications about this Course:

The concept and structure of this course was published in our paper "Teaching Modelling and Analysis of Communication Networks using OMNeT++ Simulator", for which we received the "Best Scientific Contribution Award" of the 5th OMNeT++ Summit in 2018. Parallel to the paper, we released the exercises and the final task of this year as open teaching material.
https://easychair.org/publications/paper/13ck

In 2020, we also published our experiences in teaching this course online in our paper "Online Teaching of Project-based Learning Courses - Issues, Challenges and Outcomes" as part of the "On-line Networking Education Community Discussion" at SIGCOMM 2020.
http://gaia.cs.umass.edu/sigcomm_education_workshop_2020/papers/sigcommedu20-final22.pdf
ECTS-Kreditpunkte:
6
Weitere Informationen aus Stud.IP zu dieser Veranstaltung
Heimatinstitut: Institut für Kommunikationsnetze (E-4)
In Stud.IP angemeldete Teilnehmer: 29
Anzahl der Dokumente im Stud.IP-Downloadbereich: 11

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.

TU Hamburg
Institute of Electrical Power and Energy Technology (ieet) E-6
Prof. Dr.-Ing. Christian Becker
Harburger Schloßstraße 22a
21079 Hamburg
E-mail : ieet(at)tuhh.de
Phone : +49 40 42878 3213
Links
Map and Directions
Data Privacy
Imprint