Multiagent Stochastic Dynamic Game for Smart Generation Control

Tao Yu, Lei Xi, Bo Yang, Zhao Xu, Lin Jiang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)


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(λ)-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(λ)-learning, R(λ)-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.
Original languageEnglish
Article number04015012
JournalJournal of Energy Engineering
Issue number1
Publication statusPublished - 1 Mar 2016


  • Automatic generation control (AGC)
  • Multiagent
  • Reinforcement learning
  • Smart generation control

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Energy Engineering and Power Technology
  • Waste Management and Disposal


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