As a consequence of international goals to reduce greenhouse gas emissions, the power system is undergoing a major transformation process along the entire value chain. One pillar of the employed strategies is generating electricity from renewable energy sources with generators usually connected to the distribution networks. However, as these networks were not designed for such a purpose it is expected that many of them can no longer be operated within technical and legal limits. Such constraints are given by impermissible voltage levels or thermal overloading of assets due to impermissibly high currents. To prevent such constraints, the network capacity needs to be increased by reinforcing the networks. Consequently, network operators are facing new challenges when planning and operating their networks, especially at low-voltage level. To derive the optimal reinforcement strategy, a modular automation and optimisation approach to strategic network planning at low-voltage level was developed by the author and is presented in this dissertation. In the approach, a genetic algorithm is customised and traditional measures as well as novel assets and operational schemes are taken into account for network reinforcement.