Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2025 , Vol. 59 >Issue 6: 732 - 745

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.394

New Type Power System and the Integrated Energy

Optimal Scheduling Strategy of Newly-Built Microgrid in Small Sample Data-Driven Mode

  • CHEN Shi ,
  • YANG Linsen ,
  • LIU Yihong ,
  • LUO Huan ,
  • ZANG Tianlei ,
  • ZHOU Buxiang
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  • College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Received date: 2023-08-14

  Accepted date: 2023-09-28

  Online published: 2024-03-21

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Abstract

Newly built microgrids lack historical operation data, making it challenging to predict renewable power output accurately using conventional data-driven methods, which in turn affects the accuracy of scheduling plans. To address this problem, an optimal scheduling method for newly built microgrids in scenarios with limited sample data is proposed. First, an improved network structure integrating a domain adversarial neural network with a long-short-term memory network is designed. The domain adversarial approach and gradient inversion mechanism are incorporated into transfer learning to improve the generalization ability of the model. This reduces the domain distribution discrepancy in the data, and uses the rich operation data of power stations with similar output characteristics to predict the output of the target station, which overcomes the challenge of poor accuracy under the conditions of small samples. Additionally, the optimal scheduling model is transformed into a Markov decision process and solved using double-delay deep deterministic policy gradient algorithm. Finally, the effectiveness of the proposed method is validated through a case study involving an improved CIGRE 14-node microgrid.

Key words： small sample; renewable energy contribution; adversarial transfer learning; deep reinforcement learning; optimal scheduling of microgrid

Cite this article

CHEN Shi , YANG Linsen , LIU Yihong , LUO Huan , ZANG Tianlei , ZHOU Buxiang . Optimal Scheduling Strategy of Newly-Built Microgrid in Small Sample Data-Driven Mode[J]. Journal of Shanghai Jiaotong University, 2025 , 59(6) : 732 -745 . DOI: 10.16183/j.cnki.jsjtu.2023.394

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