The Dutch electricity grid is currently facing unprecedented net congestion, with a backlog of industrial connections and estimated social costs ranging from €10 to €40 billion per year. Distribution System Operators (DSOs) like Stedin must make substantial infrastructure investments to mitigate net congestion. However, traditional Electricity Distribution Expansion Planning (EDEP) methods are often too deterministic and static, failing to account for deep uncertainty regarding changing energy policies, technological adaptations and different stakeholder perspectives. This thesis develops and applies a Robust Decision Making (RDM) based simulation model to stress-test a candidate investment plan at a masterplan scale, using Latin Hypercube Sampling to generate 10,000 plausible future scenarios. Through the Patient Rule Induction Method (PRIM), five key storylines driving capacity risks are identified: medium-to-fast residential heating transition, medium-to-fast electric vehicle growth, emergence of large datacenters, residential housing expansion combined with residential heating electrification, and high industrial electrification combined with electric vehicle growth. Based on these vulnerabilities, an adaptive investment plan is developed incorporating scenario-dependent mitigation measures, which substantially reduces capacity risks across all substations. The adaptive pathways are visualised using Dynamic Adaptive Policy Pathways (DAPP) through a MetroMap, supporting stakeholder dialogue. The results demonstrate that RDM based methods provide the analytical foundation necessary to develop robust and adaptive investment strategies for electricity distribution networks under deep uncertainty.