• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 59 Issue 2
Apr.  2024
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Article Contents
LI Qi, AI Yuxuan, SUN Cai, QIU Yibin, CHEN Weirong. Optimal Reconfiguration of Distribution Network Based on Backtracking Search Algorithm Under the Background of Non-cooperative Game Theory[J]. Journal of Southwest Jiaotong University, 2024, 59(2): 438-446. doi: 10.3969/j.issn.0258-2724.20210547
Citation: LI Qi, AI Yuxuan, SUN Cai, QIU Yibin, CHEN Weirong. Optimal Reconfiguration of Distribution Network Based on Backtracking Search Algorithm Under the Background of Non-cooperative Game Theory[J]. Journal of Southwest Jiaotong University, 2024, 59(2): 438-446. doi: 10.3969/j.issn.0258-2724.20210547

Optimal Reconfiguration of Distribution Network Based on Backtracking Search Algorithm Under the Background of Non-cooperative Game Theory

doi: 10.3969/j.issn.0258-2724.20210547
  • Received Date: 13 Jul 2021
  • Rev Recd Date: 28 Oct 2021
  • Available Online: 02 Jan 2024
  • Publish Date: 18 Nov 2021
  • To mitigate the impact of large-scale integration of distributed generation (DG) on the secure and stable operation of distribution networks, we propose an active distribution network optimal reconfiguration method that considers the uncertainty of distributed power generation output, based on non-cooperative game theory. Firstly, non-cooperative game theory is employed to analyze the game relationship between the distribution network topology and DG output, considering the uncertainty of photovoltaic units in the distribution network system as a player. Secondly, an optimal reconfiguration model with the objective functions of minimizing active network loss, balancing load and minimizing voltage deviation is established. The model is solved iteratively using the backtracking search algorithm (BSA) to obtain the optimal reconfiguration solution. Finally, simulation analysis is conducted using the IEEE33-node system to verify the correctness of the proposed model and the effectiveness of the algorithm. The results indicate that, compared to traditional reconfiguration methods, the proposed optimal reconfiguration approach in this study comprehensively addresses the uncertainty of distributed power generation output. In the most adverse scenario, the reconfiguration strategy can lead to a reduction of 0.31%, 0.59%, and 0.48% in active power loss, load balancing, and voltage deviation indices within the distribution network system.

     

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