  This paper introduces a new approach to quantify the impact of forward
propagated demand and weather uncertainty on power system planning and
operation models. Recent studies indicate that such sampling uncertainty,
originating from demand and weather time series inputs, should not be ignored.
However, established uncertainty quantification approaches fail in this context
due to the data and computing resources required for standard Monte Carlo
analysis with disjoint samples. The method introduced here uses an m out of n
bootstrap with shorter time series than the original, enhancing computational
efficiency and avoiding the need for any additional data. It both quantifies
output uncertainty and determines the sample length required for desired
confidence levels. Simulations and validation exercises are performed on two
capacity expansion planning models and one unit commitment and economic
dispatch model. A diagnostic for the validity of estimated uncertainty bounds
is discussed. The models, data and code are made available.
