Distribution Network Reconfiguration and Radiality Detection using Deep Reinforcement Learning

Distribution network reconfiguration (DNR) is one of the most effective methods for reducing losses and damages in distribution networks, resulting in the least investment for power companies. One of the most critical constraints of the distribution network is that it remains radial during operation. In this paper, a novel method is presented for detecting the radiality of distribution networks. Then, this novel method is combined with the Double Deep Q Network algorithm, which is used to determine the optimal structure of the IEEE ۳۳- and ۶۹-bus distribution networks. This approach is implemented to minimize power losses and voltage deviation. Additionally, distributed generation sources, such as wind turbines and photovoltaic cells under fixed and uncertain capacity with varying load conditions, are utilized to investigate their impact on this approach.

کلیدواژه ها:

Distribution network radial constraint ، optimal distribution network structure ، deep reinforcement learning