A Priority Scheduling Strategy of a Microgrid Using a Deep Reinforcement Learning Method
Author:
Affiliation:
1. School of Electric Engineering, Sichuan University,Chengdu,China
2. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China,Chengdu,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10294329/10294332/10294977.pdf?arnumber=10294977
Reference20 articles.
1. Dynamic energy conversion and management strategy for an integrated electricity and natural gas system with renewable energy: Deep reinforcement learning approach
2. A novel rolling optimization strategy considering grid-connected power fluctuations smoothing for renewable energy microgrids
3. Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning
4. Microgrids energy management systems: A critical review on methods, solutions, and prospects
5. Near-optimal energy management for plug-in hybrid fuel cell and battery propulsion using deep reinforcement learning
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1. Research on priority scheduling strategy for smoothing power fluctuations of microgrid tie‐lines based on PER‐DDPG algorithm;IET Generation, Transmission & Distribution;2024-09-10
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