Distributed deep reinforcement learning for integrated generation‐control and power‐dispatch of interconnected power grid with various renewable units-Reference-Cited by-同舟云学术

Distributed deep reinforcement learning for integrated generation‐control and power‐dispatch of interconnected power grid with various renewable units

Published:2021-11-27 Issue: Volume: Page:
ISSN:1752-1416
Container-title:IET Renewable Power Generation
language:en
Short-container-title:IET Renewable Power Generation

Author:

Li Jiawen¹,Yao Jianguo²,Yu Tao¹^ORCID,Zhang Xiaoshun³

Affiliation:

1. College of Electric Power South China University of Technology Guangzhou China

2. China Electric Power Research Institute (Nanjing) Nanjing China

3. College of Engineering Shantou University Shantou China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/rpg2.12310

Reference36 articles.

1. Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system;Li J.;Appl. Energy,2021

2. A novel data‐driven controller for solid oxide fuel cell via deep reinforcement learning;Li J.;Journal of Cleaner Production,2021

3. Dynamic energy conversion and management strategy for an integrated electricity and natural gas system with renewable energy: Deep reinforcement learning approach;Zhang B.;Energy Convers. Manage.,2020

4. Guest editorial: Applications of artificial intelligence in modern power systems: Challenges and solutions;Hu W.;J. Mod. Power Syst. Clean Energy,2020

5. A data‐driven output voltage control of solid oxide fuel cell using multi‐agent deep reinforcement learning;Li J.;Applied Energy,2021

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