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  4. Reinforcement learning based coordination of virtual inertia provision from inverter-dominated distribution grids
 
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Reinforcement learning based coordination of virtual inertia provision from inverter-dominated distribution grids

Publikationstyp
Conference Paper
Date Issued
2023-09
Sprache
English
Author(s)
Stock, Simon  orcid-logo
Elektrische Energietechnik E-6  
Babazadeh, Davood  orcid-logo
Electrical Power and Energy Technology E-6  
Hund, Philipp  
Becker, Christian  orcid-logo
Elektrische Energietechnik E-6  
TORE-URI
https://hdl.handle.net/11420/43608
Volume
2023-June
Citation
32nd IEEE International Symposium on Industrial Electronics (ISIE 2023)
Contribution to Conference
32nd IEEE International Symposium on Industrial Electronics, ISIE 2023  
Publisher DOI
10.1109/ISIE51358.2023.10228065
Scopus ID
2-s2.0-85172117467
Publisher
IEEE
ISBN
9798350399714
To achieve the goals of carbon neutrality in the energy sector, inverter-coupled renewable energy sources play an important role. In contrast to conventional generation, they are mostly connected on the distribution system level and do not provide inherent inertia when the system frequency changes. To address this problem, renewable energies in distribution systems are assumed to reserve and provide additional energy. This paper aims to develop a function that coordinates this provision to avoid congestion and optimize grid operation. Therefore, we propose a Reinforcement Learning (RL) based optimization utilizing operational parameters and energy costs. The functionality and performance are explored in a case study on a part of the Australian transmission and the CIGRE Benchmark MV grid.
Subjects
Distribution Grid Coordination
Reinforcement Learning
Virtual Inertia
MLE@TUHH
DDC Class
621: Applied Physics
TUHH
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