Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62383
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
Multiagent
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.
URI: http://hdl.handle.net/10397/62383
ISSN: 0733-9402 (print)
1943-7897 (online)
DOI: 10.1061/(ASCE)EY.1943-7897.0000275
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