Artificial intelligence based approach to improve the frequency control in hybrid power system

Hao Wang, Guozhou Zhang, Weihao Hu*, Di Cao, Jian Li, Shuwen Xu, Dechao Xu, Zhe Chen

*Corresponding author for this work

Research output: Contribution to journalConference article in JournalResearchpeer-review

10 Citations (Scopus)
36 Downloads (Pure)

Abstract

Frequency control over networks is done using the frequency droop control technique which has the simplicity advantage although it allows that, in certain situations, frequency control is not very efficient. Artificial intelligence techniques have been increasingly used, so it is justified to explore their viability in electrical networks. The present work analyzes the use of Artificial Intelligence in networks to improve the frequency droop control. In order to realize this, a deep reinforcement learning (DRL)-based agent is proposed to tune the controller parameters for voltage source converter (VSC) in this paper. The DRL-based agent is trained by numerous hybrid grid operation conditions to lean the optimal control policy, which make it achieve a good adaptability to variety of operation conditions. For the purpose of demonstrating this method, a time-domain simulation model of hybrid power system is built with MATLAB/Simulink to act as test system. The simulation results verify the effectiveness of the proposed method.

Original languageEnglish
JournalEnergy Reports
Volume6
Issue numberSuppl. 8
Pages (from-to)174-181
Number of pages8
DOIs
Publication statusPublished - Dec 2020
Event7th International Conference on Energy and Environment Research, ICEER 2020 - Virtual, Porto, Portugal
Duration: 14 Sept 202018 Sept 2020
http://iceer.net/2020.html

Conference

Conference7th International Conference on Energy and Environment Research, ICEER 2020
LocationVirtual
Country/TerritoryPortugal
CityPorto
Period14/09/202018/09/2020
Internet address

Bibliographical note

Funding Information:
This work was supported by Open Fund of State Key Laboratory of Operation and Control of Renewable Energy & Storage System, China ( NYB51201901204 ); and the Science and Technology Project of SGCC (Research on Supporting Technology of Power System Operation Mode Calculation Platform Based on Supercomputer), China .

Publisher Copyright:
© 2020 The Authors

Keywords

  • Deep reinforcement learning
  • Droop control
  • Frequency support
  • MTDC

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