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SMART-DS Synthetic Electrical Network Data OpenDSS Models for SFO, GSO, and AUS

Publicly accessible License 

The SMART-DS datasets (Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scenarios) are realistic large-scale U.S. electrical distribution models for testing advanced grid algorithms and technology analysis. This document provides a user guide for the datasets.

This dataset contains synthetic detailed electrical distribution network models, and connected timeseries loads for the greater San Francisco (SFO), Greensboro, and Austin areas. It is intended to provide researchers with very realistic and complete models that can be used for extensive powerflow simulations under a variety of scenarios. The data is synthetic, but has been validated against thousands of utility feeders to ensure statistical and operational similarity to electrical distribution networks in the US.

The OpenDSS data is partitioned into several regions (each zipped separately). After unzipping these files, each region has a folder for each substation, and subsequent folders for each feeder within the substation. This allows users to simulate smaller sections of the full dataset. Each of these folders (region, substation and feeder) has a folder titled "analysis" which contains CSV files listing voltages and overloads throughout the network for the peak loading time in the year. It also contains .png files showing the loading of residential and commercial loads on the network for every day of the year, and daily breakdowns of loads for commercial building categories. Time series data is provided in the "profiles" folder including real and reactive power at 15 minute resolution along with parquet files in the "endues" folder with breakdowns of building end-uses.

Citation Formats

TY - DATA AB - The SMART-DS datasets (Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scenarios) are realistic large-scale U.S. electrical distribution models for testing advanced grid algorithms and technology analysis. This document provides a user guide for the datasets. This dataset contains synthetic detailed electrical distribution network models, and connected timeseries loads for the greater San Francisco (SFO), Greensboro, and Austin areas. It is intended to provide researchers with very realistic and complete models that can be used for extensive powerflow simulations under a variety of scenarios. The data is synthetic, but has been validated against thousands of utility feeders to ensure statistical and operational similarity to electrical distribution networks in the US. The OpenDSS data is partitioned into several regions (each zipped separately). After unzipping these files, each region has a folder for each substation, and subsequent folders for each feeder within the substation. This allows users to simulate smaller sections of the full dataset. Each of these folders (region, substation and feeder) has a folder titled "analysis" which contains CSV files listing voltages and overloads throughout the network for the peak loading time in the year. It also contains .png files showing the loading of residential and commercial loads on the network for every day of the year, and daily breakdowns of loads for commercial building categories. Time series data is provided in the "profiles" folder including real and reactive power at 15 minute resolution along with parquet files in the "endues" folder with breakdowns of building end-uses. AU - Palmintier, Bryan A2 - Mateo Domingo, Carlos A3 - Postigo Marcos, Fernando Emilio A4 - Gomez San Roman, Tomas A5 - de Cuadra, Fernando A6 - Gensollen, Nicolas A7 - Elgindy, Tarek A8 - Duenas, Pablo DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - KW - energy KW - power KW - distribution system KW - big data KW - load timeseries KW - synthetic KW - electrical network KW - energy systems integration KW - grid modernization KW - solar power KW - solar KW - grid KW - SMART-DS KW - OpenDSS KW - powerflow KW - distribution KW - parquet KW - SFO KW - GSO KW - AUS KW - electrical KW - realistic KW - scenario KW - system KW - testing LA - English DA - 2020/12/18 PY - 2020 PB - National Renewable Energy Laboratory (NREL) T1 - SMART-DS Synthetic Electrical Network Data OpenDSS Models for SFO, GSO, and AUS UR - https://data.openei.org/submissions/2981 ER -
Export Citation to RIS
Palmintier, Bryan, et al. SMART-DS Synthetic Electrical Network Data OpenDSS Models for SFO, GSO, and AUS. National Renewable Energy Laboratory (NREL), 18 December, 2020, Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/2981.
Palmintier, B., Mateo Domingo, C., Postigo Marcos, F., Gomez San Roman, T., de Cuadra, F., Gensollen, N., Elgindy, T., & Duenas, P. (2020). SMART-DS Synthetic Electrical Network Data OpenDSS Models for SFO, GSO, and AUS. [Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Laboratory (NREL). https://data.openei.org/submissions/2981
Palmintier, Bryan, Carlos Mateo Domingo, Fernando Emilio Postigo Marcos, Tomas Gomez San Roman, Fernando de Cuadra, Nicolas Gensollen, Tarek Elgindy, and Pablo Duenas. SMART-DS Synthetic Electrical Network Data OpenDSS Models for SFO, GSO, and AUS. National Renewable Energy Laboratory (NREL), December, 18, 2020. Distributed by Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/2981
@misc{OEDI_Dataset_2981, title = {SMART-DS Synthetic Electrical Network Data OpenDSS Models for SFO, GSO, and AUS}, author = {Palmintier, Bryan and Mateo Domingo, Carlos and Postigo Marcos, Fernando Emilio and Gomez San Roman, Tomas and de Cuadra, Fernando and Gensollen, Nicolas and Elgindy, Tarek and Duenas, Pablo}, abstractNote = {The SMART-DS datasets (Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scenarios) are realistic large-scale U.S. electrical distribution models for testing advanced grid algorithms and technology analysis. This document provides a user guide for the datasets.

This dataset contains synthetic detailed electrical distribution network models, and connected timeseries loads for the greater San Francisco (SFO), Greensboro, and Austin areas. It is intended to provide researchers with very realistic and complete models that can be used for extensive powerflow simulations under a variety of scenarios. The data is synthetic, but has been validated against thousands of utility feeders to ensure statistical and operational similarity to electrical distribution networks in the US.

The OpenDSS data is partitioned into several regions (each zipped separately). After unzipping these files, each region has a folder for each substation, and subsequent folders for each feeder within the substation. This allows users to simulate smaller sections of the full dataset. Each of these folders (region, substation and feeder) has a folder titled "analysis" which contains CSV files listing voltages and overloads throughout the network for the peak loading time in the year. It also contains .png files showing the loading of residential and commercial loads on the network for every day of the year, and daily breakdowns of loads for commercial building categories. Time series data is provided in the "profiles" folder including real and reactive power at 15 minute resolution along with parquet files in the "endues" folder with breakdowns of building end-uses. }, url = {https://data.openei.org/submissions/2981}, year = {2020}, howpublished = {Open Energy Data Initiative (OEDI), National Renewable Energy Laboratory (NREL), https://data.openei.org/submissions/2981}, note = {Accessed: 2025-05-08} }

Details

Data from Dec 18, 2020

Last updated Jan 2, 2024

Submitted Apr 12, 2023

Organization

National Renewable Energy Laboratory (NREL)

Contact

Tarek Elgindy

Authors

Bryan Palmintier

National Renewable Energy Laboratory NREL

Carlos Mateo Domingo

Universidad Pontificia Comillas

Fernando Emilio Postigo Marcos

Universidad Pontificia Comillas

Tomas Gomez San Roman

Universidad Pontificia Comillas

Fernando de Cuadra

Universidad Pontificia Comillas

Nicolas Gensollen

National Renewable Energy Laboratory NREL

Tarek Elgindy

National Renewable Energy Laboratory NREL

Pablo Duenas

Massachusetts Institute of Technology MIT

DOE Project Details

Project Name SMART-DS

Project Number CJ0000703

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