Event

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Therefore, if you have any questions, please contact the person listed under the item Lehrende/DozentIn (Lecturers) directly.

Event

Semester: Summer term 2025

2.01.515 Intelligent Energy Systems -  


Event date(s) | room

  • Donnerstag, 10.4.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 10.4.2025 16:00 - 18:00 | W02 1-156
  • Donnerstag, 17.4.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 17.4.2025 16:00 - 18:00 | W02 1-156
  • Donnerstag, 24.4.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 24.4.2025 16:00 - 18:00 | W02 1-156
  • Donnerstag, 8.5.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 8.5.2025 16:00 - 18:00 | W02 1-156
  • Donnerstag, 15.5.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 15.5.2025 16:00 - 18:00 | W02 1-156
  • Donnerstag, 22.5.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 22.5.2025 16:00 - 18:00 | W02 1-156
  • Donnerstag, 5.6.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 5.6.2025 16:00 - 18:00 | W02 1-156
  • Donnerstag, 12.6.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 12.6.2025 16:00 - 18:00 | W02 1-156
  • Donnerstag, 19.6.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 19.6.2025 16:00 - 18:00 | W02 1-156
  • Donnerstag, 26.6.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 26.6.2025 16:00 - 18:00 | W02 1-156
  • Donnerstag, 3.7.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 3.7.2025 16:00 - 18:00 | W02 1-156
  • Donnerstag, 10.7.2025 14:00 - 16:00 | W02 1-156
  • Donnerstag, 10.7.2025 16:00 - 18:00 | W02 1-156

Description

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).

Lecturers

Tutors

Study fields

  • Studium generale / Gasthörstudium

SWS
4

Lehrsprache
deutsch und englisch

Für Gasthörende / Studium generale geöffnet:
Ja

Hinweise zum Inhalt der Veranstaltung für Gasthörende
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).

(Changed: 22 May 2025)  |  Kurz-URL:Shortlink: https://uol.de/en/students/lehrveranstaltungen/va-details?course_id=ad7f0100c71f03f013eb156f5c9ca156&cHash=b9eea6efdaec689c8940f36c5144a3ad
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