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

Hilbers, A., Brayshaw, D. ORCID: https://orcid.org/0000-0002-3927-4362 and Gandy, A. (2021) Efficient quantification of the impact of demand and weather uncertainty in power system models. IEEE Transactions on Power Systems, 36 (3). pp. 1771-1779. ISSN 0885-8950 doi: 10.1109/TPWRS.2020.3031187

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/93424
Identification Number/DOI	10.1109/TPWRS.2020.3031187
Refereed	Yes
Divisions	Interdisciplinary centres and themes > Energy Research Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	IEEE
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