Hierarchical Hybrid Multi-Agent Deep Reinforcement Learning for Peer-to-Peer Energy Trading Among Multiple Heterogeneous Microgrids-Reference-Cited by-同舟云学术

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Hierarchical Hybrid Multi-Agent Deep Reinforcement Learning for Peer-to-Peer Energy Trading Among Multiple Heterogeneous Microgrids

Published:2023-11 Issue:6 Volume:14 Page:4649-4665
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Wu Yuxin¹^ORCID,Zhao Tianyang²^ORCID,Yan Haoyuan²^ORCID,Liu Min²,Liu Nian³^ORCID

Affiliation:

1. College of Information Science and Technology, Jinan University, Guangzhou, China

2. Energy Electricity Research Center, Jinan University, Zhuhai, China

3. State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Beijing, China

Funder

National Natural Science Foundation of China

State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/10288469/10056418.pdf?arnumber=10056418

Reference65 articles.

1. A Multiagent-Based Hierarchical Energy Management Strategy for Multi-Microgrids Considering Adjustable Power and Demand Response

2. Implementation of smart grid environment in Green Campus project

3. Microgrids: Overview and guidelines for practical implementations and operation

4. Microgrid clustering architectures

5. Multi-microgrid Energy Management Systems: Architecture, Communication, and Scheduling Strategies

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