Efficient quantification of the impact of demand and weather uncertainty in power system models
File(s)
Accepted version
Author(s)
Hilbers, Adriaan P
Brayshaw, David J
Gandy, Axel
Type
Journal Article
Abstract
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.
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.
Date Issued
2021-05-01
Date Acceptance
2020-10-01
Citation
IEEE Transactions on Power Systems, 2021, 36 (3), pp.1771-1779
ISSN
0885-8950
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1771
End Page
1779
Journal / Book Title
IEEE Transactions on Power Systems
Volume
36
Issue
3
Copyright Statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Sponsor
Engineering and Physical Sciences Research Council (EPSRC)
Identifier
http://arxiv.org/abs/1912.10326v3
Grant Number
EP/L016613/1
Subjects
stat.AP
stat.AP
Notes
12 pages for main article, 20 including appendix, references and supplementary material
Publication Status
Published
Date Publish Online
2020-10-14