论文标题
与微电网控制的无模型增强学习融合：审查和视觉
Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision
论文作者
论文摘要
由于出现了大规模的分布式能源（DER）和高级控制技术，微电网的挑战和机会共存。在本文中，对微电网控制的全面综述了其无模型增强学习（MFRL）的融合。从六个不同的角度开发了微电网控制的高级研究图，其次是底层模块化控制块，说明了网格遵循（GFL）和格式形成（GFM）逆变器的配置。然后，引入了主流MFRL算法，并解释了如何将MFRL集成到现有的控制框架中。接下来，总结了MFRL的应用指南，并讨论了三种融合方法，即模型识别和参数调整，补充信号生成和控制器替换，并使用现有的控制框架进行了讨论。最后，充分讨论了与在微电网控制中采用MFRL以及解决这些问题的相应见解相关的基本挑战。
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.
