A MATD3 -based Voltage Control Strategy for Distribution Networks Considering Active and Reactive Power Adjustment Costs

Bin Zhang, Zhe Chen, Xuewei Wu, Di Cao, Weihao Hu

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)

Abstract

The rapid development of distributed renewable resources brings challenges and opportunities to the future power systems. In the article, we focus on solving one of the most important challenges – voltage control problem in a power distributed network with high penetration of photovoltaic resources. Distinguished from traditional local control, centralized control and model-based distributed control, this paper proposes a data-driven/model-based multi-agent deep reinforcement learning (MADRL) -based voltage control method while minimizing active and reactive power adjustment costs. Without the knowledge of the network topology and fully state information, the proposed method can quickly regulate the bus voltages within proper thresholds. Comparative results with alternative methods demonstrate the effectiveness of the proposed method.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Power Systems and Electrical Technology (PSET)
Number of pages6
PublisherIEEE
Publication date15 Oct 2022
Pages189-194
Article number10100398
ISBN (Print)978-1-6654-8916-4
DOIs
Publication statusPublished - 15 Oct 2022
Event2022 IEEE International Conference on Power Systems and Electrical Technology (PSET) - Aalborg, Denmark
Duration: 13 Oct 202215 Oct 2022

Conference

Conference2022 IEEE International Conference on Power Systems and Electrical Technology (PSET)
LocationAalborg, Denmark
Period13/10/202215/10/2022

Keywords

  • Training
  • Reactive power
  • Costs
  • Network topology
  • Simulation
  • Distribution networks
  • Threshold voltage

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