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Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems

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  • Zhang, Xiaoshun
  • Chen, Yixuan
  • Yu, Tao
  • Yang, Bo
  • Qu, Kaiping
  • Mao, Senmao

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.

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  • Zhang, Xiaoshun & Chen, Yixuan & Yu, Tao & Yang, Bo & Qu, Kaiping & Mao, Senmao, 2017. "Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems," Applied Energy, Elsevier, vol. 189(C), pages 157-176.
    • Handle: RePEc:eee:appene:v:189:y:2017:i:c:p:157-176
    DOI: 10.1016/j.apenergy.2016.12.080
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    3. Yin, Linfei & Wang, Tao & Zheng, Baomin, 2021. "Analytical adaptive distributed multi-objective optimization algorithm for optimal power flow problems," Energy, Elsevier, vol. 216(C).
    4. Yin, Linfei & Wang, Tao & Wang, Senlin & Zheng, Baomin, 2019. "Interchange objective value method for distributed multi-objective optimization: Theory, application, implementation," Applied Energy, Elsevier, vol. 239(C), pages 1066-1076.
    5. Chuanjia Han & Bo Yang & Tao Bao & Tao Yu & Xiaoshun Zhang, 2017. "Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer," Energies, MDPI, vol. 10(5), pages 1-24, May.
    6. Roslan, M.F. & Hannan, M.A. & Jern Ker, Pin & Begum, R.A. & Indra Mahlia, TM & Dong, Z.Y., 2021. "Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction," Applied Energy, Elsevier, vol. 292(C).
    7. Hu, Chenxi & Zhang, Jun & Yuan, Hongxia & Gao, Tianlu & Jiang, Huaiguang & Yan, Jing & Wenzhong Gao, David & Wang, Fei-Yue, 2022. "Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19," Applied Energy, Elsevier, vol. 309(C).
    8. Xiaoming Zhou & Maosheng Sang & Minglei Bao & Yi Ding, 2022. "Tracing and Evaluating Life-Cycle Carbon Emissions of Urban Multi-Energy Systems," Energies, MDPI, vol. 15(8), pages 1-19, April.
    9. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun & Li, Fusheng & Lin, Dan & Zhu, Hanxin, 2021. "Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system," Applied Energy, Elsevier, vol. 285(C).
    10. Yin, Linfei & Li, Yu, 2022. "Hybrid multi-agent emotional deep Q network for generation control of multi-area integrated energy systems," Applied Energy, Elsevier, vol. 324(C).
    11. Fan Li & Jingxi Su & Bo Sun, 2023. "An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm," Energies, MDPI, vol. 16(9), pages 1-22, April.
    12. Dong, Lei & Lin, Hao & Qiao, Ji & Zhang, Tao & Zhang, Shiming & Pu, Tianjiao, 2024. "A coordinated active and reactive power optimization approach for multi-microgrids connected to distribution networks with multi-actor-attention-critic deep reinforcement learning," Applied Energy, Elsevier, vol. 373(C).

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