  Generating representative scenarios for power system planning in which the
stochasticity of renewable generation and cross-correlations between renewables
and load are fully captured, is a challenging problem. Traditional methods for
scenario generation often fail to generate diverse scenarios that include both
seasonal (frequently occurring) and atypical (extreme) days required for
planning purposes. This paper presents a methodical approach to generate
representative scenarios. It also proposes new metrics that are more relevant
for evaluating the generated scenarios from an applications perspective. When
applied to historical data from a power utility, the proposed approach resulted
in scenarios that included a good mix of seasonal and atypical days. The
results also demonstrated pertinence of the proposed cluster validation
metrics. Finally, the paper presents a trade-off for determining optimal number
of scenarios for a given application.
