Smart electrical grids play a major role in energy transition but raise important software problems. Some of them can be efficiently solved by AI techniques. In particular, the increasing use of distributed generation based on renewable energies (wind, photovoltaic, among others) leads to the issue of its integration into the distribution network. The distribution network was not originally designed to accommodate generation units but to carry electricity from the distribution network to medium and low voltage consumers. Some methods have been used to automatically build target architectures to be reached within a given time horizon (of several decades) capable of accommodating a massive insertion of distributed generation while guaranteeing some technical constraints. However, these target networks may be quite different from the existing ones and therefore a direct mutation of the network would be too costly. It is therefore necessary to define the succession of works year after year to reach the target. We addressed it by translating it to an Automated Planning problem. We defined a transformation of the distribution network knowledge into a PDDL representation. The modelled domain representation was fed to a planner to obtain the set of lines to be built and deconstructed until the target is reached. Experimental analysis, on several networks at different scales, demonstrated the applicability of the approach and the reduction in reliance on expert knowledge. The objective of further work is to mutate an initial network towards a target network while minimizing the total cost and respecting technical constraints.