Scholarlink

<p>Distribution network reconfiguration (DNR) is one of the most widely employed methods for minimizing distribution network power losses with minimal investment. Most methods used for DNR are based on an accurate model. However, this study employs deep reinforcement learning (DRL), a model-free approach. This study proposes a loop-based method for effectively managing the large action space, which simultaneously utilizes the modified Q-learning algorithm to account for inter-loop coupling effects. Additionally, an innovative replay method is utilized to enhance convergence speed. Our approach has been tested on the IEEE 33-, 69-, and 119-bus distribution networks. The simulation results indicate that the proposed approach achieves significant superiority compared to DNR’s previous methods, such as metaheuristic and mathematical techniques, in terms of computational time, as well as final distribution network power loss and voltage deviation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Accelerating the learning process of deep reinforcement learning algorithms in distribution network reconfiguration using an innovative replay method

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月9日

查看全文
  • Amirhossein Ghaemipour,
  • Habib Rajabi Mashhadi,
  • Seyed Hossein Mostafavi

摘要

Distribution network reconfiguration (DNR) is one of the most widely employed methods for minimizing distribution network power losses with minimal investment. Most methods used for DNR are based on an accurate model. However, this study employs deep reinforcement learning (DRL), a model-free approach. This study proposes a loop-based method for effectively managing the large action space, which simultaneously utilizes the modified Q-learning algorithm to account for inter-loop coupling effects. Additionally, an innovative replay method is utilized to enhance convergence speed. Our approach has been tested on the IEEE 33-, 69-, and 119-bus distribution networks. The simulation results indicate that the proposed approach achieves significant superiority compared to DNR’s previous methods, such as metaheuristic and mathematical techniques, in terms of computational time, as well as final distribution network power loss and voltage deviation.