DOI:10.35833/MPCE.2020.000705 |
| |
| |
Distributed Secondary Control Strategy Based on Q-learning and Pinning Control for Droop-controlled Microgrids |
| |
|
| |
Page view: 0
Net amount: 353 |
| |
Author:
Liu Wei1, Shen Jun1, Zhang Sicong1, Li Na1, Zhu Ze1, Liang Liang2, Wen Zhen2
|
Author Affiliation:
1.Department of Electrical Engineering, School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China 2.State Grid Zhejiang Electric Power Co., Ltd., Jiaxing Power Supply Company, Jiaxing 230022, China
|
Foundation: |
This work was supported by the National Natural Science Foundation of China (No. 52077103). |
|
|
Abstract: |
A distributed secondary control (DSC) strategy that combines Q-learning and pinning control is originally proposed to achieve a fully optimal DSC for droop-controlled microgrids (MGs). It takes advantages of cross-fusion of the two algorithms to realize the high efficiency and self-adaptive control in MGs. It has the following advantages. Firstly, it adopts the advantages of reinforcement learning in autonomous learning control and intelligent decision-making, driving the action value of pinning control for feedback adaptive correction. Secondly, only a small part of points selected as pinned points needs to be controlled and pre-learned, hence the actual control problem is transformed into a synchronous tracking problem and the installation number of controllers is further reduced. Thirdly, the pinning matrix can be modified to adapt to plug-and-play operation under the distributed control architecture. Finally, the effectiveness and versatility of the proposed strategy are demonstrated with a typical droop-controlled MG model. |
Keywords: |
Microgrid ; distributed secondary control ; pinning control ; Q-learning |
| |
Received:September 25, 2020
Online Time:2022/09/24 |
| |
|
|
View Full Text
Download reader
|
|
|