SMART-DS Synthetic Electrical Network Data OpenDSS Models for SFO, GSO, and AUS
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.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
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[
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| contactPoint |
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"fn": "Tarek Elgindy",
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"hasEmail": "mailto:tarek.elgindy@nrel.gov"
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|
| dataQuality |
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| description | 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. |
| distribution |
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|
| identifier | https://data.openei.org/submissions/2981 |
| issued | 2020-12-18T07:00:00Z |
| keyword |
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|
| landingPage | https://data.openei.org/submissions/2981 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2024-01-02T20:06:08Z |
| programCode |
[
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|
| projectNumber | CJ0000703 |
| projectTitle | SMART-DS |
| publisher |
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| title | SMART-DS Synthetic Electrical Network Data OpenDSS Models for SFO, GSO, and AUS |
