Institute of Physics |
6 KP |
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Module components |
Semester courses Summer semester 2025 |
Examination |
Lecture
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2.01.5112 - Digitalised Energy System Modeling and Control
- Prof. Dr. Sebastian Lehnhoff
- Malin Radtke, M. Sc.
- Jörg Bremer
Tuesday: 14:00 - 16:00, weekly (from 08/04/25), Location: V04 0-033 Wednesday: 10:00 - 12:00, weekly (from 09/04/25), Location: A04 2-221
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2.01.5120 - Digitalised Energy System Co-Simulation
- Prof. Dr. Sebastian Lehnhoff
- Jörg Bremer
Monday: 12:00 - 14:00, weekly (from 07/04/25), Location: A14 1-113 Friday: 10:00 - 12:00, weekly (from 11/04/25), Location: A01 0-004
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2.01.515 - Intelligent Energy Systems
- Dr.-Ing. Eric Veith
- Jörg Bremer
Thursday: 14:00 - 16:00, weekly (from 10/04/25) Thursday: 16:00 - 18:00, weekly (from 10/04/25)
Modern power grids face a multitude of challenges: A high share of renewables means a more sophisticated management of real power demand and supply, ancillary services are provided in an ever more decentralized manner, and power grids must become resilient instead of just being robust. Agent systems have established themselves as methodology for a decentralized and resilient operation of modern power grids. Especially learning agents based on Deep Reinforcement Learnings can react to unforseen events and find good strategies even in complex situations. In this lecture, we will introduce an approach for flexibility modelling as a way to provide an agent's view of the world, and will extensively concern ourselves with the application of Deep Reinforcement Learning in power grids, including approaches to explainability and learning from domain knowledge (offline learning).
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Seminar
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2.01.5112 - Digitalised Energy System Modeling and Control
- Prof. Dr. Sebastian Lehnhoff
- Malin Radtke, M. Sc.
- Jörg Bremer
Tuesday: 14:00 - 16:00, weekly (from 08/04/25), Location: V04 0-033 Wednesday: 10:00 - 12:00, weekly (from 09/04/25), Location: A04 2-221
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2.01.5120 - Digitalised Energy System Co-Simulation
- Prof. Dr. Sebastian Lehnhoff
- Jörg Bremer
Monday: 12:00 - 14:00, weekly (from 07/04/25), Location: A14 1-113 Friday: 10:00 - 12:00, weekly (from 11/04/25), Location: A01 0-004
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2.01.515 - Intelligent Energy Systems
- Dr.-Ing. Eric Veith
- Jörg Bremer
Thursday: 14:00 - 16:00, weekly (from 10/04/25) Thursday: 16:00 - 18:00, weekly (from 10/04/25)
Modern power grids face a multitude of challenges: A high share of renewables means a more sophisticated management of real power demand and supply, ancillary services are provided in an ever more decentralized manner, and power grids must become resilient instead of just being robust. Agent systems have established themselves as methodology for a decentralized and resilient operation of modern power grids. Especially learning agents based on Deep Reinforcement Learnings can react to unforseen events and find good strategies even in complex situations. In this lecture, we will introduce an approach for flexibility modelling as a way to provide an agent's view of the world, and will extensively concern ourselves with the application of Deep Reinforcement Learning in power grids, including approaches to explainability and learning from domain knowledge (offline learning).
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2.12.042 - Ecological Economics
- Prof. Dr. Bernd Siebenhüner
- Dr. Hendrik Wolter
Monday: 10:00 - 14:00, fortnightly (from 07/04/25)
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Exercises
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2.01.5112 - Digitalised Energy System Modeling and Control
- Prof. Dr. Sebastian Lehnhoff
- Malin Radtke, M. Sc.
- Jörg Bremer
Tuesday: 14:00 - 16:00, weekly (from 08/04/25), Location: V04 0-033 Wednesday: 10:00 - 12:00, weekly (from 09/04/25), Location: A04 2-221
-
2.01.5120 - Digitalised Energy System Co-Simulation
- Prof. Dr. Sebastian Lehnhoff
- Jörg Bremer
Monday: 12:00 - 14:00, weekly (from 07/04/25), Location: A14 1-113 Friday: 10:00 - 12:00, weekly (from 11/04/25), Location: A01 0-004
-
2.01.515 - Intelligent Energy Systems
- Dr.-Ing. Eric Veith
- Jörg Bremer
Thursday: 14:00 - 16:00, weekly (from 10/04/25) Thursday: 16:00 - 18:00, weekly (from 10/04/25)
Modern power grids face a multitude of challenges: A high share of renewables means a more sophisticated management of real power demand and supply, ancillary services are provided in an ever more decentralized manner, and power grids must become resilient instead of just being robust. Agent systems have established themselves as methodology for a decentralized and resilient operation of modern power grids. Especially learning agents based on Deep Reinforcement Learnings can react to unforseen events and find good strategies even in complex situations. In this lecture, we will introduce an approach for flexibility modelling as a way to provide an agent's view of the world, and will extensively concern ourselves with the application of Deep Reinforcement Learning in power grids, including approaches to explainability and learning from domain knowledge (offline learning).
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Notes on the module |
Module examination |
2 exams, depending on the selected courses. |
Skills to be acquired in this module |
After completing the module students will be able to:
- describe basic knowledge in two of a wide field of disciplines (technical, scientific, social, political, transferrable, language) as required for the implementation of renewable energy
- critically discuss basic principles of the implementation of renewable energy
- justify their personal decision on educational fields for their career development
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