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A guided deep reinforcement learning method for distribution voltage regulation via battery systems

  • State University of New York Binghamton University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The rapid growth of photovoltaic penetration leads to serious voltage issues in distribution grids. While the battery energy storage system has been used for voltage regulation, its effectiveness is limited by the assumption of adequate battery sizes. In this paper, we first propose a battery scheduling model to relax the battery size assumption. Afterward, a deep reinforcement learning-based method is proposed to solve this high-dimensional scheduling model. The method includes a guided training framework, which combines reward shaping and curriculum learning techniques to guide the training. The proposed method has been implemented to the IEEE 13-bus test feeder. It shows that the guided training framework accelerates the training and improves the convergence.

Original languageEnglish
Title of host publication2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728188973
DOIs
StatePublished - Feb 16 2021
Event2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2021 - Washington, United States
Duration: Feb 16 2021Feb 18 2021

Publication series

Name2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2021

Conference

Conference2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2021
Country/TerritoryUnited States
CityWashington
Period02/16/2102/18/21

Keywords

  • Battery size
  • Deep reinforcement learning
  • Renewable energy integration
  • Voltage regulation

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