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ReEDS Performance Improvement

Conference ·
OSTI ID:2491420
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The Regional Energy Deployment System (ReEDS) is an open-source, spatially explicit, long-term capacity expansion model for the bulk electric power system of the contiguous United States, encompassing multiple scenarios with technological and political assumptions (see https://github.com/NREL/ReEDS-2.0). With the increased needs for capabilities, higher temporal and spatial resolutions to model the evolution of the power system with modern technologies and low-carbon pathways, ReEDS' model solution times have increased significantly from 4-6 hours in 2018 to 18-48+ hours in 2023 . Also, the model size for commonly-run ReEDS scenarios reached 22 and 28 million equations and variables, respectively. These runtimes can be especially challenging under certain scenario settings (e.g., very high temporal or spatial resolution) or with limited computational power. In this presentation, we will discuss several methods we used to improve model runtime, including data preparation, model modification, and solver tuning. The implementation of these methods shrank the model size to 7.2 and 7.3 million equations and variables, respectively. Furthermore, this led to a 77% reduction in the model's run time for commonly-run ReEDS scenarios. We will discuss the process of identifying areas for solve time improvements and how the specific enhancements for the ReEDS model might be applied to other similar large-scale models.
Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Strategic Analysis Team
DOE Contract Number:
AC36-08GO28308;
OSTI ID:
2491420
Report Number(s):
NREL/PR-6A40-91641; MainId:93419; UUID:b14fe413-6a23-4602-a970-786443578dee; MainAdminId:75578
Conference Information:
Presented at the 2024 INFORMS Annual Meeting, 20-23 October 2024, Seattle, Washington
Country of Publication:
United States
Language:
English

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Related Subjects

ENERGY PLANNING, POLICY, AND ECONOMY,MATHEMATICS AND COMPUTING
linear program
model efficiency
ReEDS