Physics-Shielded Multi-Agent Deep Reinforcement Learning for Safe Active Voltage Control with Photovoltaic/Battery Energy Storage Systems-Reference-Cited by-同舟云学术

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Physics-Shielded Multi-Agent Deep Reinforcement Learning for Safe Active Voltage Control with Photovoltaic/Battery Energy Storage Systems

Published:2022 Issue: Volume: Page:1-1
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Chen Pengcheng¹^ORCID,Liu Shichao¹^ORCID,Wang Xiaozhe²^ORCID,Kamwa Innocent³^ORCID

Affiliation:

1. Department of Electronics, Carleton University, Ottawa, ON, Canada

2. Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada

3. Electrical and Computer Science Engineering Department, Laval University, Quebec, QC, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/5446437/09983850.pdf?arnumber=9983850

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Safety-Enhanced Self-Learning for Optimal Power Converter Control;IEEE Transactions on Industrial Electronics;2024-11

2. Multiagent Soft Actor–Critic Learning for Distributed ESS Enabled Robust Voltage Regulation of Active Distribution Grids;IEEE Transactions on Industrial Informatics;2024-09

3. Phased optimization of active distribution networks incorporating distributed photovoltaic storage system: A multi-objective coati optimization algorithm;Journal of Energy Storage;2024-06

4. Rule-based shields embedded safe reinforcement learning approach for electric vehicle charging control;International Journal of Electrical Power & Energy Systems;2024-06

5. Application and progress of artificial intelligence technology in the field of distribution network voltage Control：A review;Renewable and Sustainable Energy Reviews;2024-03

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