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
Innovation Management (EN) (VL)
Untertitel:
This course is part of the module: Business & Management
Semester:
SoSe 24
Veranstaltungstyp:
Vorlesung (Lehre)
Veranstaltungsnummer:
lv3093_s24
DozentIn:
Dr. Vytaute Dlugoborskyte
Beschreibung:

The course aims to provide students with an understanding of key issues in the management of innovation and development of the relevant skills needed to manage innovation at both strategic and operational levels. It provides evidence of different approaches based on leading research, real world examples and experiences of firms and organizations from around the world. The management of innovation is one of the most important and challenging aspects of modern organization. Innovation is a fundamental driver of competitiveness and it plays a large part in improving quality of life. Innovation, and particularly technological innovation, is inherently difficult, uncertain and risky, and most new technologies fail to be translated into successful products and services. Given this, it is essential that students understand the strategies, tools and techniques for managing innovation, which often requires a different set of management knowledge and skills from those employed in everyday business administration. The course itself draws upon research activities of the Innovation Management Group within TUHH, the Institute for Technology and Innovation Management (TIM, W-7, www.tuhh.de/tim)

Knowledge Objectives:
1. Understand definitions and concepts of innovation,
2. Explore major models and theories of innovation,
3. Use and apply tools for innovation management.

Skill Objectives:
1. Diagnostic and analytical skills,
2. Enhance verbal skills through class and syndicate discussions,
3. Build up critical and interpretation skills,
4. Learn how to evaluate different options,
5. Formulate and develop strategy,
6. Assess and resolve managerial challenges.

Learning Outcomes
At the end of the course students will be able to demonstrate understanding, and make critical assessments of the following:
1. Assess and interpret innovation processes,
2. Develop and formulate managerial strategies to shape innovative performance,
3. Utilize tools of innovation management to map and measure innovative activities,
4. Diagnose different innovation challenges and make recommendations for resolving them.

Course Outline - Lecture Topics:
1. The Management of (Technological) Innovation,
2. Strategy and Organization for Innovation,
3. Innovation of Products, Services and Business Models,
4. Managing the Innovation Process,
5. Networks, Communities of Innovators and Lead User-Innovation,
6. Innovation in the Age of Circular Economy (C2C),
7. Market-Research for Innovation and Design-thinking,
8. Capturing value from R&D, Open Innovation and IP,
9. Creativity and mindfulness in Innovation,
10. Conclusions and Future Challenges.

Leistungsnachweis:
tm3093 - Innovation Management (EN) (lecture)<ul><li>p1886-2023 - Innovation Management (EN): Presentation</li></ul>
ECTS-Kreditpunkte:
2
Weitere Informationen aus Stud.IP zu dieser Veranstaltung
Heimatinstitut: Institut für Technologie u. Innovationsmanagement (W-7)
In Stud.IP angemeldete Teilnehmer: 36
Anzahl der Dokumente im Stud.IP-Downloadbereich: 20

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.