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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 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

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
Economics
Untertitel:
The module consists of the following courses: International Economics (VL), Main Theoretical and Political Concepts (VL) and Economics (PBL)
Semester:
SoSe 24
Veranstaltungstyp:
Vorlesung (Lehre)
Veranstaltungsnummer:
lv641, lv700, lv2714
DozentIn:
Timo Heinrich, Martin Sterner, Anika Bittner
Beschreibung:
Introduction Microeconomics - Theory of the Household - Theory of the Firm - Competitive Markets in Equilibrium - Market Failure: Monopoly and External Effects - Government Policies Macroeconomics - A Nation’s Real Income and Production - The Real Economy in the Long Run: Capital and Labor Market - Money and Prices in the Long Run - Aggregate Demand and Supply: Short-Run Economic Fluctuations - Monetary and Fiscal Policy in the Short and the Long Run International Trade Theory and Policy - Comparative Advantage, the Ricardian Model - The Heckscher-Ohlin Model - The Standard Trade Model - Intrasectoral Trade - International Trade Policy
Voraussetzungen:
- Basic Knowledge in Economics. - Relevant previous knowledge is taught and tested by an online module.
Lernorganisation:
PBL There are four parallel groups; students who would like to take part in the group poster presentation are expected to attend one of the four parallel groups. Before attending, you are required to sign up to one of the parallel groups on Stud.IP. You can join one of the four groups on Stud.IP in the Participants section of the course by clicking on Groups on the left side of the screen and selecting a group. You can join the groups between April 8 at 12:00 (noon) and April 19 at 12:00 (noon). As capacity is limited, admission is granted on a first-come-first-serve basis. Due to public holidays and the May holidays, the PBL sessions will only be held on the days specified (see syllabus).
Leistungsnachweis:
- 60 min written exam (100% of the final grade). - It is mandatory to complete and pass the online module with the possibility to earn bonus percentage (bonus of up to 5%). - It is optional to give a group poster presentation on topics worked on in the PBL to earn bonus percentage (bonus of up 15%). Further details will be announced in the lecture or PBL sessions of the course. The instructions will be made available on Stud.IP.
ECTS-Kreditpunkte:
6
Weitere Informationen aus Stud.IP zu dieser Veranstaltung
Heimatinstitut: Institut für Digital Economics (W-5)
In Stud.IP angemeldete Teilnehmer: 106
Anzahl der Dokumente im Stud.IP-Downloadbereich: 39

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 36
21079 Hamburg
E-mail : ieet(at)tuhh.de
Phone : +49 40 42878 3213
Fax : +49 40 42878 2382
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