With the continuous development of smart grids, diversified power service types lead to different service demands. The 5G network slicing technology can provide virtual wireless private networks for smart grids in response to challenges in security, reliability, and time delay. Considering the differentiated service characteristics of smart grids, this study aims to use Deep Reinforcement Learning (DRL) to solve the resource allocation of the Radio Access Network (RAN) slices of smart grids. This study reviews the background of smart grids and the related research on network slicing technology, then analyzes the RAN slicing model of smart grids, and proposes a slice allocation strategy based on DRL. Simulation results show that the proposed algorithm can reduce the cost and meet the resource allocation requirements of smart grids on the RAN side to the maximum extent .