To satisfy the diverse demands of network slicing in smart grid, a slicing resource allocation model based on cloud-edge collaboration in smart grid was proposed. Furthermore, a two-phase cooperative slice allocation model was developed to optimize the allocation of the network slices. In the first phase, an optimization model for the resource allocation in local edge network was established to optimize the user experience, and the optimization problem was solved with the Lagrange multiplier method. In the second phase, the system was modeled as a Markov decision process, and then the deep reinforcement learning was adopted to adaptively allocate the resources to the slices of the core cloud. Experimental results show that the proposed two-phrase slice resource allocation model can effectively reduce the network delay and improve the user satisfaction.