Please use this identifier to cite or link to this item:
Title: Multiagent stochastic dynamic game for smart generation control
Authors: Yu, T
Xi, L
Yang, B
Xu, Z 
Jiang, L
Keywords: Automatic generation control (AGC)
Smart generation control
Reinforcement learning
Issue Date: 2016
Publisher: American Society of Civil Engineers
Source: Journal of energy engineering, 2016, v. 142, no. 1, 4015012 How to cite?
Journal: Journal of energy engineering 
Abstract: This paper proposes a multiagent (MA) smart generation control (SGC) scheme for the coordination of automatic generation control (AGC) in power grids with system uncertainties. Under the control performance standards, SGC will undergo a non-Markov random process, of which the optimal solution can be resolved online by the reinforcement learning. Therefore, an MA decentralized correlated equilibrium Q()-learning algorithm, and an MA stochastic dynamic game-based SGC simulation platform (SGC-SP) have been proposed for its implementation, which can achieve AGC coordination in a highly uncertain environment resulting from the increasing penetration of renewable energy. Single-agent Q-learning, Q()-learning, R()-learning, and proportional integral control are implemented and embedded in SGC-SP for the control performance analysis. Two case studies on both a two-area power system and the China Southern Power Grid model have been done, which verify its effectiveness and scalability.
ISSN: 0733-9402 (print)
1943-7897 (online)
DOI: 10.1061/(ASCE)EY.1943-7897.0000275
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Aug 21, 2017

Page view(s)

Last Week
Last month
Checked on Aug 20, 2017

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.