.png){kind=icon}
Return
Consensus Transfer Q-Learning for Decentralized Generation Command Dispatch Based on Virtual Generation Tribe
.png){kind=icon}
PRE
AI
DOI：10.1109/TSG.2016.2607801.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper develops a consensus transfer Q-learning (CTQ) for decentralized generation command dispatch (GCD) of automatic generation control (AGC). A two-layer decentralized GCD based on virtual generation tribe (VGT) is adopted to resolve the course of dimension emerged in large-scale power systems. The leader of VGTs can calculate the generation command of each VGT through exchanging the Q-value matrices with its adjacent VGTs. In addition, a behavior transfer is employed into CTQ to exploit the prior knowledge of source tasks for a new optimization task according to their similarities, such that the algorithm convergence rate can be accelerated and the requirement of AGC period is satisfied. Case studies are carried out to evaluate the performance of CTQ for decentralized GCD on China southern power grid model.
Keywords:
Consensus transfer Q-learning
virtual generation tribe
decentralized generation command dispatch
automatic generation control
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.8
Papers:
5.7K
Citations:
4.3W
Organization
Cited Papers
ShareSaveShareSaveDecentralized cooperative aerial-surveillance using fixed-wing miniature UAVs.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
ShareSaveShareSaveShareSave
View more