Construction Method of Power Grid Simulation Environment for Reinforcement Learning

Yupeng Huang*, Nan Yang, Yifang Jin, Lei Song, Zhaowei Ling, Kai Wang

*Corresponding author for this work

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

Abstract

In recent years, with the construction of new power systems and the gradual maturity of the application of deep reinforcement learning technology, researchers have applied reinforcement learning technology to power system optimization and control. The training and application of reinforcement learning algorithms rely on the power grid simulation environment, which can simulate the operation status of the power grid and interact with the power grid dispatching intelligent agent. The current construction method of power grid simulation environment is only designed for a single scenario and does not have universality in the field of power grid dispatching and control. This article proposes a method for constructing a reinforcement learning oriented power grid simulation environment, constructing a universal power grid simulation environment in the field of power grid dispatching and control, and supporting reinforcement learning intelligent agent training in multi scenarios.

Original languageEnglish
Title of host publicationIEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference
EditorsBing Xu, Kefen Mou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages542-546
Number of pages5
ISBN (Electronic)9798350333664
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023 - Chongqing, China
Duration: 8 Dec 202310 Dec 2023

Publication series

NameIEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
ISSN (Print)2693-2865

Conference

Conference11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023
Country/TerritoryChina
CityChongqing
Period8/12/2310/12/23

Keywords

  • power grid simulation environment
  • reinforcement learning
  • space design

Fingerprint

Dive into the research topics of 'Construction Method of Power Grid Simulation Environment for Reinforcement Learning'. Together they form a unique fingerprint.

Cite this

Huang, Y., Yang, N., Jin, Y., Song, L., Ling, Z., & Wang, K. (2023). Construction Method of Power Grid Simulation Environment for Reinforcement Learning. In B. Xu, & K. Mou (Eds.), IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (pp. 542-546). (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITAIC58329.2023.10408884