Scenario Space Exploration for Robust Energy Planning

Energy and climate assessments often contrast a few narrative scenarios, limiting insight into interacting uncertainties. We map a scenario space for a whole energy system model using more than 4,500 cost optimal runs, then analyse the ensemble with global sensitivity analysis, scatter plot diagnostics and scenario discovery. This workflow identifies influential drivers, reveals thresholds and regime switching, and distinguishes robust from contingent technology portfolios under demand and weather variability. It also answers the reverse policy question, which combinations of assumptions are sufficient to reach, or avoid, target outcomes. By shifting emphasis from point comparisons to distributions, interactions and condition sets, the approach supports interactive exploration of trade-offs and risks, and prioritises where higher fidelity follow up analysis is most valuable. Compared with conventional scenario studies, scenario space substantially increases robustness and exposes boundary conditions that are typically hidden by narrative comparisons, turning energy models into stress tests that delineate where policy performs reliably, and where it becomes brittle.