Distribution Network Reconfiguration Based on NoisyNet Deep Q-Learning Network

作者：Beibei Wang, Hong Zhu, Honghua Xu, Yu‐Qing Bao, Hui‐Fang Di · 发表于：IEEE Access · 年份：2021 · DOI：10.1109/access.2021.3089625 · 被引用次数：74 · 研究领域：Optimal Power Flow Distribution、Microgrid Control and Optimization、Power Systems and Renewable Energy

The distribution network reconfiguration (DNR) aims at minimizing the power losses and improving the voltage profile. Traditional model-based methods exactly need the network parameters to derive the optimal configuration of the distribution network. This paper proposes a DNR method based on model-free reinforcement learning (RL) approach. The proposed method adopts NoisyNet deep Q-learning network (DQN), by which the exploration can be automatically realized without need of tuning the exploration parameters, in order to accelerate the training process and improve the optimization performance. The proposed method is validated by the simulation results.