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
zur Veranstaltung in Stud.IP Studip_icon
Join Mini Challenges of the ECIU University (PBL) [MA]
Untertitel:
This course is part of the module: Non-technical Courses for Master
Semester:
SoSe 24
Veranstaltungstyp:
PBL -Projekt-/problembasierte Lehrveranstaltung (Lehre)
Veranstaltungsnummer:
lv2851_s24
DozentIn:
Prof. Dr. Kerstin Kuchta, Dipl.-Ing., Sascha Diedler
Beschreibung:

Join multidisciplinary and international teams at the ECIU University and solve mini challenges linked to SDG11 - Sustainable cities and communities, provided by business and societal partners across Europe. Participation in mini challenges will allow you to make a real impact in the community, city, or region by solving real-time local, national, and global challenges with a new way of learning - challenge-based learning.

Generalprocedure of a challenge:

  1. The mini challenge is provided by a city, region or business stakeholder and is entered on the ECIU UniversityChallenge platform (engage.eciu.eu).
  2. You register for the mini challenge you find relevant on the platform.
  3. An international and interdisciplinary team is formed from registered participants from all ECIU partner universities and a team facilitator from the host university is assigned.
  4. You work with the team on the mini challenge, engage, investigate, and propose non-technical solutions using the challenge-based learning methodology (https://eciu.tuhh.de/challenge-based-learning/).
  5. During the process, you can select relevant micro-modules from ECIU member universities that help you gain additional knowledge or skills that are relevant to solve the mini challenge.
  6. Finally, teams deliver their outputs- which may include services, products, research questions, start-ups and spin-offs.

By working in multi-disciplinary and/or international teams, you will build up inter-cultural competences and increase your network of expertise by developing problem-solving and team-work skills.

TUHH is major part of the ECIU University leading institution related to the Challenge-based learning. All ECIU challenges will constantly be updated at the challenge platform: engage.eciu.eu

“ Mini challenges” are challenges in the ECIU University that are supposed to be done within 2-6 weeks. The focus is to define your actual challenge, find a suitable solution(s) and to implement them. https://eciu.tuhh.de/cbl-in-more-detail/

This course is aimed at Master students from member universities of the ECIU network (www.eciu.org). The course requires an independent approach to work, the willingness to learn independently about new non-technical topics and research methods, and the motivation to learn and actively participate in an international/disciplinary team.

Leistungsnachweis:
tm2851 - Join Mini Challenges of the ECIU University (Projekt-/problembasierte Lehrveranstaltung)<ul><li>p1717-2021 - Join Mini Challenges of the ECIU University: Subject theoretical and practical work</li></ul>
ECTS-Kreditpunkte:
3
Weitere Informationen aus Stud.IP zu dieser Veranstaltung
Heimatinstitut: NTA - Nicht-technisches Angebot (0-NTA)
beteiligte Institute: Institut für Circular Resource Engineering and Management [CREM] (V-11)
In Stud.IP angemeldete Teilnehmer: 14
Anzahl der Dokumente im Stud.IP-Downloadbereich: 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.