.png){kind=icon}
Return
Distributed adaptive optimization for microgrids
.png){kind=icon}
OA
AI
DOI：10.1002/rnc.7036.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article, a one-critic Q-learning algorithm for the adaptive optimization of the distributed microgrids is proposed to improve the charging and discharging management and optimization of batteries in microgrids. Constrained by the random power generation of sustainable energy and the high uncertainty of power load, this article presents a dynamic balance point of the remaining power of the battery pack, and the dual battery pack constitutes a dynamic power game augmented system in the charge-discharge process. The structure of the one-critic neural network can effectively simplify the computational complexity, and the q$$ q $$ value can converge to the optimal solution in finite iterations. Simulation experiments also prove that the Q-learning can effectively curb the overcharge and over-discharge behavior of the battery pack, and realize the tracking of the dynamic SOC balance point.
Keywords:
adaptive dynamic programming
adaptive optimization
battery energy management
distributed microgrids
Q-learning
Journal
IF:
3.2
Papers:
7.0K
Citations:
1.4W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveReinforcement Q-Learning Algorithm for H∞ Tracking Control of Unknown Discrete-Time Linear Systems
PREAI
ShareSave
View more