Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

Venkat Krishnan, Bryan Palmintier, Brian Hodge, Elaine Hale, Tarek Elgindy, Michael Rossol, Anthony Lopez, Dheepak Krishnamurthy, Claudio Vergara, Carlos Domingo, Fernando Postigo, Fernando Cuadra, Tomas Gomez, Pablo Duenas, Max Luke, Vivian Li, Mohan Vinoth, Sree Kadankodu, Bruce Bugbee

Research output: NRELPresentation


The National Renewable Energy Laboratory (NREL) in collaboration with Massachusetts Institute of Technology (MIT), Universidad Pontificia Comillas (Comillas-IIT, Spain) and GE Grid Solutions, is working on an ARPA-E GRID DATA project, titled Smart-DS, to create: 1) High-quality, realistic, synthetic distribution network models, and 2) Advanced tools for automated scenario generation based on high-resolution weather data and generation growth projections. Through these advancements, the Smart-DS project is envisioned to accelerate the development, testing, and adoption of advanced algorithms, approaches, and technologies for sustainable and resilient electric power systems, especially in the realm of U.S. distribution systems. This talk will present the goals and overall approach of the Smart-DS project, including the process of creating the synthetic distribution datasets using reference network model (RNM) and the comprehensive validation process to ensure network realism, feasibility, and applicability to advanced use cases. The talk will provide demonstrations of early versions of synthetic models, along with the lessons learnt from expert engagements to enhance future iterations. Finally, the scenario generation framework, its development plans, and co-ordination with GRID DATA repository teams to house these datasets for public access will also be discussed.
Original languageAmerican English
Number of pages40
StatePublished - 2017

Publication series

NamePresented at the Federal Energy Regulatory Commission (FERC) Technical Conference on Increasing Real-Time and Day-Ahead Market Efficiency through Improved Software, 26-28 June 2017, Washington, D.C.

NREL Publication Number

  • NREL/PR-5D00-68764


  • grid data
  • public data for distribution
  • reference network model
  • standard scenarios
  • synthetic distribution system datasets


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