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

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.