Deep Reinforcement Learning Based Approach for Optimal Power Flow of Distribution Networks Embedded with Renewable Energy and Storage Devices-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning Based Approach for Optimal Power Flow of Distribution Networks Embedded with Renewable Energy and Storage Devices

Published:2021 Issue:5 Volume:9 Page:1101-1110
ISSN:2196-5625
Container-title:Journal of Modern Power Systems and Clean Energy
language:en
Short-container-title:

Author:

Cao Di,Hu Weihao,Xu Xiao,Wu Qiuwei,Huang Qi,Chen Zhe,Blaabjerg Frede

Publisher

Journal of Modern Power Systems and Clean Energy

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Cited by 58 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. Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges;Energies;2024-09-02

2. A data-driven mixed integer programming approach for joint chance-constrained optimal power flow under uncertainty;International Journal of Machine Learning and Cybernetics;2024-08-31

3. Empowering the Grid: A Comprehensive Review of Artificial Intelligence Techniques in Smart Grids;2024 International Telecommunications Conference (ITC-Egypt);2024-07-22

4. Deep Learning-Based Optimal Scheduling Scheme for Distributed Wind Power Systems;Journal of Circuits, Systems and Computers;2024-05-25

5. Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies;Energies;2024-05-01

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