Efficient quantification of the impact of demand and weather uncertainty in power system models

This paper introduces a novel approach to quantify the effect of forward propagated demand and weather uncertainty on power system planning and operation model outputs. 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 computational resources and additional data required for Monte Carlo-based analysis. The method introduced here quantifies uncertainty on model outputs using a bootstrap scheme 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 performed on two generation and transmission expansion planning models and one unit commitment and economic dispatch model illustrate the method's efficacy. A test is introduced allowing users to determine whether estimated uncertainty bounds are valid. The models, data and code applying the method are provided as open-source software