Abstract
The Probabilistic Resource Adequacy Suite (PRAS) provides an open-source, research-oriented collection of tools for analysing the resource adequacy of a bulk power system. The simulation methods offered support everything from classical convolution-based analytical techniques through to high-performance sequential Monte Carlo methods supporting multi-region composite reliability assessment, including simulation of energy-limited resources such as storage.
- Developers:
-
Stephen, Gord [1]
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Release Date:
- 2019-08-05
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Julia
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Water Power Technologies Office (EE-4WP)Primary Award/Contract Number:AC36-08GO28308
- Code ID:
- 28741
- Site Accession Number:
- NREL SWR 18-61
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Stephen, Gord.
Probabilistic Resource Adequacy Suite (PRAS).
Computer Software.
https://github.com/NREL/PRAS.
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Water Power Technologies Office (EE-4WP).
05 Aug. 2019.
Web.
doi:10.11578/dc.20190814.1.
Stephen, Gord.
(2019, August 05).
Probabilistic Resource Adequacy Suite (PRAS).
[Computer software].
https://github.com/NREL/PRAS.
https://doi.org/10.11578/dc.20190814.1.
Stephen, Gord.
"Probabilistic Resource Adequacy Suite (PRAS)." Computer software.
August 05, 2019.
https://github.com/NREL/PRAS.
https://doi.org/10.11578/dc.20190814.1.
@misc{
doecode_28741,
title = {Probabilistic Resource Adequacy Suite (PRAS)},
author = {Stephen, Gord},
abstractNote = {The Probabilistic Resource Adequacy Suite (PRAS) provides an open-source, research-oriented collection of tools for analysing the resource adequacy of a bulk power system. The simulation methods offered support everything from classical convolution-based analytical techniques through to high-performance sequential Monte Carlo methods supporting multi-region composite reliability assessment, including simulation of energy-limited resources such as storage.},
doi = {10.11578/dc.20190814.1},
url = {https://doi.org/10.11578/dc.20190814.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20190814.1}},
year = {2019},
month = {aug}
}