Comparison of temporal resolution selection approaches in energy systems models
Abstract
Capacity expansion models for the power sector are used to project future decisions over the coming decades by simulating investment and operation decisions for the use of electricity. Due to model performance constraints, these models typically do not explicitly simulate every hour within a year, but instead simulate representative time segments (groups of hours). This paper evaluates different approaches for selecting time segments across three methods: sequential, categorical, and clustering, across a wide range of time-segment quantities, for a total of 204 temporal profiles. To measure the performance of each profile's ability to accurately represent data, the root-mean-square-error of each profile's time segments are compared to the data's original hourly data. The temporal alignment across regions is also measured (i.e., how often windy days align across regions). Different spatial resolutions were applied for a subset of the temporal selection methods to investigate the impact spatial resolution has on performance. This paper provides a framework for measuring the value of different temporal selection methods and of adding more granular data to energy system models. Overall, multi-criteria clustering yields the lowest root-mean-square-error across all datasets evaluated and provides a holistic view of the intertwined relationships between renewable generation and electricity demand.
- Authors:
-
[2];
- US Environmental Protection Agency (EPA), Washington, DC (United States). Office of Air and Radiation
- Carnegie Mellon Univ., Pittsburgh, PA (United States)
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Strategic Programs
- OSTI Identifier:
- 1869693
- Report Number(s):
- NREL/JA-6A20-79959
Journal ID: ISSN 0360-5442; MainId:41164;UUID:fbec3862-71eb-4e69-a380-f92b574c81da;MainAdminID:64553
- Grant/Contract Number:
- AC36-08GO28308; 2017789
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Energy
- Additional Journal Information:
- Journal Volume: 251; Journal ID: ISSN 0360-5442
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 29 ENERGY PLANNING, POLICY, AND ECONOMY; capacity expansion planning; electrical load; energy system modeling; renewable energy; spatial resolution; temporal resolution
Citation Formats
Marcy, Cara, Goforth, Teagan, Nock, Destenie, and Brown, Maxwell. Comparison of temporal resolution selection approaches in energy systems models. United States: N. p., 2022.
Web. doi:10.1016/j.energy.2022.123969.
Marcy, Cara, Goforth, Teagan, Nock, Destenie, & Brown, Maxwell. Comparison of temporal resolution selection approaches in energy systems models. United States. https://doi.org/10.1016/j.energy.2022.123969
Marcy, Cara, Goforth, Teagan, Nock, Destenie, and Brown, Maxwell. Thu .
"Comparison of temporal resolution selection approaches in energy systems models". United States. https://doi.org/10.1016/j.energy.2022.123969. https://www.osti.gov/servlets/purl/1869693.
@article{osti_1869693,
title = {Comparison of temporal resolution selection approaches in energy systems models},
author = {Marcy, Cara and Goforth, Teagan and Nock, Destenie and Brown, Maxwell},
abstractNote = {Capacity expansion models for the power sector are used to project future decisions over the coming decades by simulating investment and operation decisions for the use of electricity. Due to model performance constraints, these models typically do not explicitly simulate every hour within a year, but instead simulate representative time segments (groups of hours). This paper evaluates different approaches for selecting time segments across three methods: sequential, categorical, and clustering, across a wide range of time-segment quantities, for a total of 204 temporal profiles. To measure the performance of each profile's ability to accurately represent data, the root-mean-square-error of each profile's time segments are compared to the data's original hourly data. The temporal alignment across regions is also measured (i.e., how often windy days align across regions). Different spatial resolutions were applied for a subset of the temporal selection methods to investigate the impact spatial resolution has on performance. This paper provides a framework for measuring the value of different temporal selection methods and of adding more granular data to energy system models. Overall, multi-criteria clustering yields the lowest root-mean-square-error across all datasets evaluated and provides a holistic view of the intertwined relationships between renewable generation and electricity demand.},
doi = {10.1016/j.energy.2022.123969},
journal = {Energy},
number = ,
volume = 251,
place = {United States},
year = {Thu Apr 14 00:00:00 EDT 2022},
month = {Thu Apr 14 00:00:00 EDT 2022}
}
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