The Dutch electricity grid is facing growing congestion due to rapid electrification and decentralization of energy production, which threatens supply reliability and slows progress on the energy transition. While grid expansion is essential, long-term planning is complicated by deep uncertainties related to future electricity demand, renewable energy adoption, infrastructure delivery timelines, and policy developments. Existing planning approaches typically rely on deterministic forecasts or stochastic models, which are ill-suited to address deep uncertainty. This thesis addresses a critical gap in grid planning by introducing a structured method to incorporate deep uncertainty into long-term investment strategies for medium-voltage distribution networks. While Robust Decision Making (RDM) has been applied to transmission expansion and national-level energy planning, this study presents the first academic application of RDM to distribution network expansion planning.
The research focuses on Stedin’s investment plan for an area with significant expected demand growth and decentralized generation. Key uncertainties were identified using the XLRM framework, informed by a literature review and a focus group with Stedin professionals. A simulation model was developed and implemented in the EMA Workbench, incorporating Stedin’s infrastructure baseline, projected demand profiles, and investment levers. Through scenario-based stress-testing across thousands of plausible futures, the study evaluated capacity risk and cost performance. The analysis applied the Patient Rule Induction Method (PRIM) for scenario discovery and used the MoSCoW prioritization method to develop actionable advice for improving the robustness and adaptability of Stedin’s investment plan.