The transition toward sustainable and reliable energy systems requires long-term investment and operational planning. However, accurately capturing the variability of renewable energy sources and the growing complexity of power systems requires large-scale energy system optimization models (ESOMs) with high temporal resolution, which are computationally demanding and often intractable to solve. Clustering-based representative periods (RPs) are widely used alongside ESOMs to improve tractability of the problem. Yet, as clustering optimizes for typical conditions, extreme periods are often underrepresented, giving reason to include extreme representatives so that feasibility is achieved. In this paper, we investigate the combination of typical and worst-case representative periods, with a particular focus on the effect of dynamically assigning weights to worstcase representatives. We consider two worst-case inclusion strategies: a Global approach, which adds a single worst-case representative constructed from the full dataset, and a proposed Local approach, which constructs a worst-case representative within each cluster. These strategies are combined with standard Dirac weighting and blended weight variants that allow original periods to be represented as weighted combinations of representatives. The Local approach consistently outperforms the Global approach by producing less conservative worst-case RPs. However, blended weights provide only some improvements over standard Dirac weighting, which do not hold consistently across configurations in the studied case, suggesting they should not be applied blindly. Overall, the findings support the use of cluster-level worst-case representatives while showing the limited practical benefit of more complex blended weight approaches.
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