Accessibility navigation


Importance subsampling: improving power system planning under climate-based uncertainty

Hilbers, A. P., Brayshaw, D. J. and Gandy, A. (2019) Importance subsampling: improving power system planning under climate-based uncertainty. Applied Energy, 251. 113114. ISSN 0306-2619

[img] Text - Accepted Version
· Restricted to Repository staff only until 17 May 2020.
· Available under License Creative Commons Attribution Non-commercial No Derivatives.

762kB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.apenergy.2019.04.110

Abstract/Summary

Recent studies indicate that the effects of inter-annual climate-based variability in power system planning are significant and that long samples of demand & weather data (spanning multiple decades) should be considered. At the same time, modelling renewable generation such as solar and wind requires high temporal resolution to capture fluctuations in output levels. In many realistic power system models, using long samples at high temporal resolution is computationally unfeasible. This paper introduces a novel subsampling approach, referred to as importance subsampling, allowing the use of multiple decades of demand & weather data in power system planning models at reduced computational cost. The methodology can be applied in a wide class of optimisation based power system simulations. A test case is performed on a model of the United Kingdom created using the open-source modelling framework Calliope and 36 years of hourly demand and wind data. Standard data reduction approaches such as using individual years or clustering into representative days lead to significant errors in estimates of optimal system design. Furthermore, the resultant power systems lead to supply capacity shortages, raising questions of generation capacity adequacy. In contrast, importance subsampling leads to accurate estimates of optimal system design at greatly reduced computational cost, with resultant power systems able to meet demand across all 36 years of demand & weather scenarios.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > NCAS
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:83771
Publisher:Elsevier

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation