Model-Free Voltage Control of Active Distribution System with PVs Using Surrogate Model-Based Deep Reinforcement Learning

Di Cao, Junbo Zhao, Weihao Hu, Fei Ding, Nanpeng Yu, Qi Huang, Zhe Chen

Research output: Contribution to journalArticlepeer-review

46 Scopus Citations

Abstract

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed method can achieve similar performance as that of perfect physical-model-based approaches while being advantageous over other traditional methods.

Original languageAmerican English
Article number117982
Number of pages15
JournalApplied Energy
Volume306
Issue numberPart A
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5D00-81589

Keywords

  • Active distribution network
  • Deep reinforcement learning
  • Model-free
  • Optimization
  • Solar PVs
  • Voltage regulation

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