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Applied energy v.189, 2017년, pp.157 - 176   SCI SCIE
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Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems

Zhang, Xiaoshun (College of Electric Power, South China University of Technology, 510640 Guangzhou, China ); Chen, Yixuan (College of Electric Power, South China University of Technology, 510640 Guangzhou, China ); Yu, Tao (College of Electric Power, South China University of Technology, 510640 Guangzhou, China ); Yang, Bo (Faculty of Electric Power Engineering, Kunming University of Science and Technology, 650504 Kunming, China ); Qu, Kaiping (College of Electric Power, South China University of Technology, 510640 Guangzhou, China ); Mao, Senmao (Shenzhen Power Supply Bureau Co., Ltd., 518000 Shenzhen, China );
  • 초록  

    Abstract This paper proposes a novel equilibrium-inspired multiagent optimizer (EMO) with extreme transfer learning for decentralized optimal carbon-energy combined-flow (OCECF) of large-scale power systems. The original large-scale power system is firstly divided into several small-scale subsystems, in which each subsystem is regarded as an agent, such that a decentralized OCECF can be achieved via a Nash game among all the agents. Then, a knowledge matrix associated with a state-action chain is presented for knowledge storing of the previous optimization tasks, which can be updated by a continuous interaction with the external environment. Furthermore, an extreme learning machine is adopted for an efficient transfer learning, such that the convergence rate of a new task can be dramatically accelerated by properly exploiting the prior knowledge of the source tasks. EMO has been thoroughly evaluated for the decentralized OCECF on IEEE 57-bus system, IEEE 300-bus system, and a practical Shenzhen power grid of southern China. Case studies and engineering application verify that EMO can effectively handle the decentralized OCECF of large-scale power systems. Highlights A shared responsibility of carbon emission is introduced in decentralized OCECF. An equilibrium-inspired multiagent optimizer is proposed for decentralized OCECF. The Nash game can ensure a self-organizing optimal operation of each agent. The convergence rate can be dramatically accelerated by extreme transfer learning. The carbon emission and power loss of power network can be significantly reduced.


  • 주제어

    Equilibrium-inspired multiagent optimizer .   Extreme transfer learning .   Nash equilibrium .   State-action chain .   Decentralized optimal carbon-energy combined-flow.  

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