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

TL;DR

This paper presents a computationally efficient bootstrap-based method to quantify the impact of demand and weather uncertainty on power system models, improving planning and operational decision-making.

Contribution

It introduces a novel m out of n bootstrap approach that reduces data and computational requirements for uncertainty quantification in power system models.

Findings

The method accurately estimates uncertainty bounds in power system planning models.

It determines the necessary sample length for specified confidence levels.

Validated on multiple power system models with open data and code.

Abstract

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.