On representation of temporal variability in electricity capacity planning models

Merrick, James H.

doi:10.1016/j.eneco.2016.08.001

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

Journal Article · Tue Aug 23 00:00:00 EDT 2016 · Energy Economics

DOI:https://doi.org/10.1016/j.eneco.2016.08.001· OSTI ID:1324468

^[1]

Stanford Univ., CA (United States). Dept. of Management Science and Engineering

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.

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Research Organization:: Stanford Univ., CA (United States). Stanford University Energy Modeling Forum

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0005171; SC005171

OSTI ID:: 1324468

Alternate ID(s):: OSTI ID: 1430578

Journal Information:: Energy Economics, Vol. 59; ISSN 0140-9883

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Classification and Evaluation of Concepts for Improving the Performance of Applied Energy System Optimization Models Cao, Karl-Kiên; von Krbek, Kai; Wetzel, Manuel Energies, Vol. 12, Issue 24 https://doi.org/10.3390/en12244656	journal	December 2019
Impact of myopic decision-making and disruptive events in power systems planning Heuberger, Clara F.; Staffell, Iain; Shah, Nilay Nature Energy, Vol. 3, Issue 8 https://doi.org/10.1038/s41560-018-0159-3	journal	May 2018
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A Review on Time Series Aggregation Methods for Energy System Models Hoffmann, Maximilian; Kotzur, Leander; Stolten, Detlef Energies, Vol. 13, Issue 3 https://doi.org/10.3390/en13030641	journal	February 2020
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