Active Power Correction Strategies Based on Deep Reinforcement Learning—Part II: A Distributed Solution for Adaptability

Siyuan Chen; Jiajun Duan; Yuyang Bai; Jun Zhang; Di Shi; Zhiwei Wang; Xuzhu Dong; Yuanzhang Sun

doi:10.17775/CSEEJPES.2020.07070

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

Active Power Correction Strategies Based on Deep Reinforcement Learning—Part II: A Distributed Solution for Adaptability

Regular Papers | 更新时间：2026-10-15

- Active Power Correction Strategies Based on Deep Reinforcement Learning—Part II: A Distributed Solution for Adaptability
- Active Power Correction Strategies Based on Deep Reinforcement Learning—Part II: A Distributed Solution for Adaptability
- 中国电机工程学会电力与能源系统学报(英文) 2022年8卷第4期页码：1134-1144
- 作者机构：
  
  1. School of Electrical Engineering and Automation Wuhan University,Wuhan,China
  2. GEIRI North America,CA,San Jose,USA
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.07070
  中图分类号：
- 纸质出版：2022-07-01
- 稿件说明：
移动端阅览
Siyuan Chen, Jiajun Duan, Yuyang Bai, 等. Active Power Correction Strategies Based on Deep Reinforcement Learning—Part II: A Distributed Solution for Adaptability[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(4):1134-1144.

Siyuan Chen, Jiajun Duan, Yuyang Bai, et al. Active Power Correction Strategies Based on Deep Reinforcement Learning—Part II: A Distributed Solution for Adaptability[J]. CSEE Journal of Power and Energy Systems, 2022, 8(4): 1134-1144.
Siyuan Chen, Jiajun Duan, Yuyang Bai, 等. Active Power Correction Strategies Based on Deep Reinforcement Learning—Part II: A Distributed Solution for Adaptability[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(4):1134-1144. DOI： 10.17775/CSEEJPES.2020.07070.

Siyuan Chen, Jiajun Duan, Yuyang Bai, et al. Active Power Correction Strategies Based on Deep Reinforcement Learning—Part II: A Distributed Solution for Adaptability[J]. CSEE Journal of Power and Energy Systems, 2022, 8(4): 1134-1144. DOI： 10.17775/CSEEJPES.2020.07070.

摘要

Abstract

This article is the second part of Active Power Correction Strategies Based on Deep Reinforcement Learning. In Part II

we consider the renewable energy scenarios plugged into the large-scale power grid and provide an adaptive algorithmic implementation to maintain power grid stability. Based on the robustness method in Part I

a distributed deep reinforcement learning method is proposed to overcome the influence of the increasing renewable energy penetration. A multi-agent system is implemented in multiple control areas of the power system

which conducts a fully cooperative stochastic game. Based on the Monte Carlo tree search mentioned in Part I

we select practical actions in each sub-control area to search the Nash equilibrium of the game. Based on the QMIX method

a structure of offline centralized training and online distributed execution is proposed to employ better practical actions in the active power correction control. Our proposed method is evaluated in the modified global competition scenario cases of “2020 Learning to Run a Power Network - Neurips Track 2”.

关键词

Keywords

references

6. O. Vinyals, T. Ewalds, S. Bartunov, P. Georgiev, A. S. Vezhnevets, M. Yeo, A. Makhzani, H. Kuttler, J. Agapiou, J. Schrittwieser, J. Quan, S. Gaffney, S. Petersen, K. Simonyan, T. Schaul, H. van Hasselt, D. Silver, T. Lillicrap, K. Calderone, P. Keet, A. Brunasso, D. Lawrence, A. Ekermo, J. Repp, and R. Tsing, “ StarCraft II: A new challenge for reinforcement learning ,” arXiv:1708.04782, Aug. 2017. [Online ] . Available: https://arxiv.org/pdf/1708.04782.pdf.

18. Universidad Nacional de Colombia. L2RPN-NEURIPS- 2020. [Online]. Available: https://github.com/unaioperator/12rpn-neurips-2020.

19. M. Hausknecht and P. Stone, “ Deep recurrent Q-learning for partially observable MDPs ,” arXiv: 1507.06527, Jan. 2017. [Online ] . Available: https://arxiv.org/pdf/1507.06527.pdf.

21. RTE-France. Grid2Op . [Online ] . Available: https://github.com/rte-france/Grid2Op.

22. RTE-France. L2RPN NEURIPS 2020 - robustness track . [Online ] . Available: https://competitions.codalab.org/competitions/25426.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

Improved Successive Branch Reduction for Stochastic Distribution Network Reconfiguration in the Presence of Renewables

Single-Phase Single-Stage Three-Port High-Frequency Isolated DC-AC Converter

Interpretable Multi-Agent Reinforcement Learning for Continuous Double Auction-Based P2P Energy Trading

High Step-up DC-DC Converter Using Voltage Lift Techniques for Renewable Applications

Novel Control Method for the MMC Connected to Wind Farms