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Smart generation control based on multi-agent reinforcement learning with the idea of the time tunnel

Author

Listed:
  • Xi, Lei
  • Chen, Jianfeng
  • Huang, Yuehua
  • Xu, Yanchun
  • Liu, Lang
  • Zhou, Yimin
  • Li, Yudan

Abstract

One of the significant solutions for hazy is to reduce carbon emission by introducing renewable energy on a large scale. However, the large-scale integration of new energy will result in stochastic disturbance to power grid. Therefore it becomes a top priority to make new energy compatible with power system. The PDWoLF-PHC(λ) based on the idea of time tunnel is to be proposed in this paper. Optimal strategy could be obtained by adopting the variable learning rate in a variety of complex operating environments, and thence it can deal with stochastic disturbance caused by massive integrations of new energy and distributed energy sources to the power grid, which is difficult for traditional centralized AGC. The proposed algorithm is simulated to be effective according to the improved IEEE standard two-area load-frequency control power system model and the Central China Power Grid model. Compared with the traditional smart ones, the proposed algorithm is characterized with faster convergence and stronger robustness, which makes it able to reduce carbon emission and enhance utilization rate of the new energy.

Suggested Citation

  • Xi, Lei & Chen, Jianfeng & Huang, Yuehua & Xu, Yanchun & Liu, Lang & Zhou, Yimin & Li, Yudan, 2018. "Smart generation control based on multi-agent reinforcement learning with the idea of the time tunnel," Energy, Elsevier, vol. 153(C), pages 977-987.
    • Handle: RePEc:eee:energy:v:153:y:2018:i:c:p:977-987
    DOI: 10.1016/j.energy.2018.04.042
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    References listed on IDEAS

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    Cited by:

    1. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
    2. Yin, Linfei & Zhang, Bin, 2021. "Time series generative adversarial network controller for long-term smart generation control of microgrids," Applied Energy, Elsevier, vol. 281(C).
    3. Huaizhi Wang & Xian Zhang & Qing Li & Guibin Wang & Hui Jiang & Jianchun Peng, 2018. "Recursive Method for Distribution System Reliability Evaluation," Energies, MDPI, vol. 11(10), pages 1-15, October.
    4. Ochoa, Tomás & Gil, Esteban & Angulo, Alejandro & Valle, Carlos, 2022. "Multi-agent deep reinforcement learning for efficient multi-timescale bidding of a hybrid power plant in day-ahead and real-time markets," Applied Energy, Elsevier, vol. 317(C).
    5. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).
    6. Yin, Linfei & Gao, Qi & Zhao, Lulin & Wang, Tao, 2020. "Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids," Energy, Elsevier, vol. 191(C).
    7. Ju, Liwei & Zhang, Qi & Tan, Zhongfu & Wang, Wei & Xin, He & Zhang, Zehao, 2018. "Multi-agent-system-based coupling control optimization model for micro-grid group intelligent scheduling considering autonomy-cooperative operation strategy," Energy, Elsevier, vol. 157(C), pages 1035-1052.
    8. 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).
    9. Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(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).

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