Abstract
Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A high-level research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of three fusing approaches, i.e., model identification and parameter tuning, supplementary signal generation, and controller substitution, with the existing control framework. Finally, the fundamental challenges associated with adopting MFRL in microgrid control and corresponding insights for addressing these concerns are fully discussed.
Recommended Citation
B. She et al., "Fusion Of Microgrid Control With Model-Free Reinforcement Learning: Review And Vision," IEEE Transactions on Smart Grid, vol. 14, no. 4, pp. 3232 - 3245, Institute of Electrical and Electronics Engineers, Jul 2023.
The definitive version is available at https://doi.org/10.1109/TSG.2022.3222323
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
data-driven control; grid-following and grid-forming inverters; Microgrid control; modelfree reinforcement learning; review and vision
International Standard Serial Number (ISSN)
1949-3061; 1949-3053
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jul 2023
