TUHH Open Research
Help
  • Log In
    New user? Click here to register.Have you forgotten your password?
  • English
  • Deutsch
  • Communities & Collections
  • Publications
  • Research Data
  • People
  • Institutions
  • Projects
  • Statistics
  1. Home
  2. TUHH
  3. Publication References
  4. Physics-informed Actor-Critic for Coordination of Virtual Inertia from Power Distribution Systems
 
Options

Physics-informed Actor-Critic for Coordination of Virtual Inertia from Power Distribution Systems

Publikationstyp
Preprint
Date Issued
2024-06-06
Sprache
English
Author(s)
Stock, Simon  orcid-logo
Elektrische Energietechnik E-6  
Babazadeh, Davood  orcid-logo
Electrical Power and Energy Technology E-6  
Eid, Sari
Becker, Christian  orcid-logo
Elektrische Energietechnik E-6  
TORE-URI
https://hdl.handle.net/11420/50349
Citation
Authorea (2024-06-06)
Publisher DOI
10.22541/au.171769323.32693407/v1
Publisher
Authorea, Inc.
The vanishing inertia of synchronous generators in transmission systems requires the utilization of renewables for inertial support. These are often connected to the distribution system and their support should be coordinated to avoid violation of grid limits. To this end, this paper presents the Physics-informed Actor-Critic (PI-AC) algorithm for coordination of Virtual Inertia (VI) from renewable Inverter-based Resources (IBRs) in power distribution systems. Acquiring a model of the distribution grid can be difficult, since certain parts are often unknown or the parameters are highly uncertain. To favor model-free coordination, Reinforcement Learning (RL) methods can be employed, necessitating a substantial level of training beforehand. The PI-AC is a RL algorithm that integrates the physical behavior of the power system into the Actor-Critic (AC) approach in order to achieve faster learning. To this end, we regularize the loss function with an aggregated power system dynamics model based on the swing equation. Throughout this paper, we explore the PI-AC functionality in a case study with the CIGRE 14-bus and IEEE 37-bus power distribution system in various grid settings. The PI-AC is able to achieve better rewards and faster learning than the exclusively data-driven AC algorithm and the metaheuristic Genetic Algorithm (GA).
Subjects
MLE@TUHH
DDC Class
600: Technology
TUHH
Weiterführende Links
  • Contact
  • Send Feedback
  • Cookie settings
  • Privacy policy
  • Impress
DSpace Software

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science
Design by effective webwork GmbH

  • Deutsche NationalbibliothekDeutsche Nationalbibliothek
  • ORCiD Member OrganizationORCiD Member Organization
  • DataCiteDataCite
  • Re3DataRe3Data
  • OpenDOAROpenDOAR
  • OpenAireOpenAire
  • BASE Bielefeld Academic Search EngineBASE Bielefeld Academic Search Engine
Feedback