Imperial College London
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Professor Axel Gandy

Faculty of Natural Sciences, Department of Mathematics

Head of Department of Mathematics & Chair in Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8518a.gandy Website

 
 
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Location

 

644Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Hilbers:2019:10.1016/j.apenergy.2019.04.110,
author = {Hilbers, A and Brayshaw, D and Gandy, A},
doi = {10.1016/j.apenergy.2019.04.110},
journal = {Applied Energy},
pages = {1--12},
title = {Importance subsampling: Improving power system planning under climate-based uncertainty},
url = {http://dx.doi.org/10.1016/j.apenergy.2019.04.110},
volume = {251},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Hilbers,A
AU  - Brayshaw,D
AU  - Gandy,A
DO  - 10.1016/j.apenergy.2019.04.110
EP  - 12
PY  - 2019///
SN  - 0306-2619
SP  - 1
TI  - Importance subsampling: Improving power system planning under climate-based uncertainty
T2  - Applied Energy
UR  - http://dx.doi.org/10.1016/j.apenergy.2019.04.110
UR  - https://www.sciencedirect.com/science/article/pii/S0306261919307639?via%3Dihub
UR  - http://hdl.handle.net/10044/1/74479
VL  - 251
ER  -
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