  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.
