Solar energy paper index
A Deep Reinforcement Learning Framework for Optimal Distribution Network Structure
One-line summary
A solar energy research paper on A Deep Reinforcement Learning Framework for Optimal Distribution Network Structure.
Engineering notes
Engineering notes will be added by the Power for Solar editorial team.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
Distribution network reconfiguration is one solution for optimal utilization of distribution networks under different conditions. With the advent of distributed generation and electric vehicles, distribution networks are facing various challenges, each of which requires the detection of an optimal distribution network structure. In this paper, one of the most popular deep reinforcement learning algorithms, the Double Deep Q Network, has been employed to reconfigure IEEE 33- and 69-bus distribution networks. This modeling has been investigated in two analyses: normal and daily. An innovative method is utilized in this paper to detect radial constraints and non-isolation among buses. The effects of uncertainty in distributed generation, such as photovoltaic panels, and variable loading, such as commercial, industrial, and residential load profiles, have been evaluated in the daily analysis of this paper. This modeling aims to minimize power losses and voltage deviation. This modeling demonstrates that the suggested approach surpasses previous approaches in reducing power losses and voltage deviation.
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