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Multi-Agent Cooperation Based Reduced-Dimension Q(λ) Learning for Optimal Carbon-Energy Combined-Flow

Author

Listed:
  • Huazhen Cao

    (Power Grid Planning Center of Guangdong Power Grid Co., Ltd., Guangzhou 510640, China)

  • Chong Gao

    (Power Grid Planning Center of Guangdong Power Grid Co., Ltd., Guangzhou 510640, China)

  • Xuan He

    (Power Grid Planning Center of Guangdong Power Grid Co., Ltd., Guangzhou 510640, China)

  • Yang Li

    (Power Grid Planning Center of Guangdong Power Grid Co., Ltd., Guangzhou 510640, China)

  • Tao Yu

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

Abstract

This paper builds an optimal carbon-energy combined-flow (OCECF) model to optimize the carbon emission and energy losses of power grids simultaneously. A novel multi-agent cooperative reduced-dimension Q(λ) (MCR-Q(λ)) is proposed for solving the model. Firstly, on the basis of the traditional single-objective Q(λ) algorithm, the solution space is reduced effectively to shrink the size of Q -value matrices. Then, based on the concept of ant cooperative cooperation, multi-agents are used to update the Q -value matrices iteratively, which can significantly improve the updating rate. The simulation in the IEEE 118-bus system indicates that the proposed technique can decrease the convergence speed by hundreds of times as compared with conventional Q(λ), keeping high global stability, which is very suitable for dynamic OCECF in a large and complex power grid compared with other algorithms.

Suggested Citation

  • Huazhen Cao & Chong Gao & Xuan He & Yang Li & Tao Yu, 2020. "Multi-Agent Cooperation Based Reduced-Dimension Q(λ) Learning for Optimal Carbon-Energy Combined-Flow," Energies, MDPI, vol. 13(18), pages 1-22, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4778-:d:413055
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    References listed on IDEAS

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