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Title: On representation of temporal variability in electricity capacity planning models

Abstract

This study systematically investigates how to represent intra-annual temporal variability in models of optimum electricity capacity investment. Inappropriate aggregation of temporal resolution can introduce substantial error into model outputs and associated economic insight. The mechanisms underlying the introduction of this error are shown. How many representative periods are needed to fully capture the variability is then investigated. For a sample dataset, a scenario-robust aggregation of hourly (8760) resolution is possible in the order of 10 representative hours when electricity demand is the only source of variability. The inclusion of wind and solar supply variability increases the resolution of the robust aggregation to the order of 1000. A similar scale of expansion is shown for representative days and weeks. These concepts can be applied to any such temporal dataset, providing, at the least, a benchmark that any other aggregation method can aim to emulate. Finally, how prior information about peak pricing hours can potentially reduce resolution further is also discussed.

Authors:
ORCiD logo [1]
  1. Stanford Univ., CA (United States). Dept. of Management Science and Engineering
Publication Date:
Research Org.:
Stanford Univ., CA (United States). Stanford University Energy Modeling Forum
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1324468
Alternate Identifier(s):
OSTI ID: 1430578
Grant/Contract Number:
SC0005171; SC005171
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Energy Economics
Additional Journal Information:
Journal Volume: 59; Journal ID: ISSN 0140-9883
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; Electricity; Investment; Optimisation; Variability; Renewables

Citation Formats

Merrick, James H. On representation of temporal variability in electricity capacity planning models. United States: N. p., 2016. Web. doi:10.1016/j.eneco.2016.08.001.
Merrick, James H. On representation of temporal variability in electricity capacity planning models. United States. doi:10.1016/j.eneco.2016.08.001.
Merrick, James H. Tue . "On representation of temporal variability in electricity capacity planning models". United States. doi:10.1016/j.eneco.2016.08.001. https://www.osti.gov/servlets/purl/1324468.
@article{osti_1324468,
title = {On representation of temporal variability in electricity capacity planning models},
author = {Merrick, James H.},
abstractNote = {This study systematically investigates how to represent intra-annual temporal variability in models of optimum electricity capacity investment. Inappropriate aggregation of temporal resolution can introduce substantial error into model outputs and associated economic insight. The mechanisms underlying the introduction of this error are shown. How many representative periods are needed to fully capture the variability is then investigated. For a sample dataset, a scenario-robust aggregation of hourly (8760) resolution is possible in the order of 10 representative hours when electricity demand is the only source of variability. The inclusion of wind and solar supply variability increases the resolution of the robust aggregation to the order of 1000. A similar scale of expansion is shown for representative days and weeks. These concepts can be applied to any such temporal dataset, providing, at the least, a benchmark that any other aggregation method can aim to emulate. Finally, how prior information about peak pricing hours can potentially reduce resolution further is also discussed.},
doi = {10.1016/j.eneco.2016.08.001},
journal = {Energy Economics},
number = ,
volume = 59,
place = {United States},
year = {Tue Aug 23 00:00:00 EDT 2016},
month = {Tue Aug 23 00:00:00 EDT 2016}
}

Journal Article:
Free Publicly Available Full Text
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