Digital twin‐based online resilience scheduling for microgrids: An approach combining imitative learning and deep reinforcement learning

Digital twin‐based online resilience scheduling for microgrids: An approach combining imitative learning and deep reinforcement learning

Abstract Strong uncertainty of renewables puts high demands on the fast response of flexibility resources and resilience‐oriented optimal scheduling for microgrids (MGs). Digital twins (DT) technology based on data‐driven methods is a potential solution to this problem. A DT‐based online resilience...

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Bibliographic Details
Main Authors: Haonan Sun, Nian Liu, Lu Tan, Peng Du, Bocheng Zhang
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:IET Renewable Power Generation
Subjects:
artificial intelligence
energy management systems
microgrids
microgrid
online resilience scheduling
digital twins
Online Access:https://doi.org/10.1049/rpg2.12887

Internet

https://doi.org/10.1049/rpg2.12887

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