Generating capacity adequacy studies play a significant
role in long term capacity planning. Risks of capacity
deficits are usually reported in the form of one or more average
quantities, which cannot fully convey the nature of the risks
being faced. Chronological Monte Carlo simulations may be used
to construct comprehensive multi-dimensional risk profiles, but
such profiles tend to be difficult to interpret. This paper proposes
the use of a clustering method to partition the risk profile into
clusters of similar outcomes with associated probabilities. The
results are presented in accessible tabular form, and prototypical
scenarios can be analysed in detail to provide further insight.