SMART-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios
Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios, or SMART-DS, offers high-quality, "realistic but not real" distribution network models and advanced tools to generate automated scenarios on those models.
The SMART-DS platform allows users to test distributed automation algorithms, advanced distribution management system capabilities, and other emerging distribution technologies on full-scale, synthetic distribution networks. The networks are generated by the Reference Network Model developed by Universidad Pontificia Comillas and include high-, medium-, and low-voltage networks and geographical constraints such as topography and street maps.
Automated scenarios can be explored on top of the synthetic distribution networks, including weather patterns, control schemes, outages, and adoption of distributed energy resources. Test cases include volt/VAR optimization, distribution system reconfiguration, and single period distribution-optimal power flow. Additional scenario creation capabilities are provided for modifying transmission system generation mixes and obtaining wind and solar resource and power data. The SMART-DS platform is in partnership with Comillas-IIT, MIT, CYME International, and Electrical Distribution Design.
- Arbitrary combinations of distributed energy resources
- Detailed statistical summary of the U.S. distribution systems
- World-class, high spatial/temporal resolution of solar and wind resource data and forecasts
- Programmatic access to the NREL Standard Scenario results
- Time series data for algorithm performance evaluation over full range of operating conditions
- The largest system is 100+ substations, 500+ feeders, 1 million+ customers
- The SMART-DS datasets are available through the GRID DATA program data repositories: DR POWER and GridBright.
- SMART-DS contributed to the development of the Distribution Transformation Tool (DiTTo), which enables programmatic development of distribution models as well as translations between data formats.
- SMART-DS produced three additional open source software tools:
- layerstack: Python package for assembling workflows, especially those associated with modifying, running, and analyzing simulation models
- R2PD: Power system modeler-friendly tool for downloading wind and solar power and forecast data
- sssmatch: Apply NREL Standard Scenario generation mixes to arbitrary transmission systems.
- SMART-DS produced two repositories that demonstrate those tools: