# Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision

arXiv: https://arxiv.org/abs/2206.11398 · HTML: https://mlsanity.com/paper/2206.11398/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2022.

## Built on by (most cited first, 3 of 3)

- [A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2403.01013/) (2024)
- [Model-Free DRL Control for Power Inverters: From Policy Learning to Real-Time Implementation via Knowledge Distillation](https://mlsanity.com/paper/2603.07964/) (2026)
- [Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework](https://mlsanity.com/paper/2608.02599/) (2026)

## Abstract (arXiv)

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.