Multi-Agent Reinforcement Learning With Privacy Preservation for Continuous Double Auction-Based P2P Energy Trading-Reference-Cited by-同舟云学术

Multi-Agent Reinforcement Learning With Privacy Preservation for Continuous Double Auction-Based P2P Energy Trading

Published:2024-04 Issue:4 Volume:20 Page:6582-6590
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Zheng Jiehui¹^ORCID,Liang Ze-Ting¹^ORCID,Li Yuanzheng²^ORCID,Li Zhigang¹^ORCID,Wu Qing-Hua¹^ORCID

Affiliation:

1. School of Electric Power Engineering, South China University of Technology, Guangzhou, China

2. School of Artificial Intelligence and Automation, Key Laboratory of lmage Information Processing and Intelligent, Control of Ministry of Education of China, Huazhong University of Science and Technology, Wuhan, China

Funder

Natural Science Foundation of China

Natural Science Foundation of Guangdong Province, China

Young Elite Scientists Sponsorship Program By CSEE

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10492621/10403539.pdf?arnumber=10403539

Reference29 articles.

1. Mean-Field Multi-Agent Reinforcement Learning for Peer-to-Peer Multi-Energy Trading

2. Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges

3. Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading

4. Peer-to-Peer energy trading in a Microgrid

5. A Decentralized Bilateral Energy Trading System for Peer-to-Peer Electricity Markets

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