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Smart-DS: synthetic models for advanced, realistic testing: distribution systems and scenarios

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Autor
Krishnan, Venkat
Palmintier, Bryan
Hodge, Bri-Mathias
Hale, Elaine T.
Elgindy, Tarek
Bugbee, Bruce
Rossol, Michael N.
Lopez, Anthony J.
Krishnamurthy, Dheepak
Vergara Ramírez, Claudio Ricardo
Mateo Domingo, Carlos
Postigo Marcos, Fernando Emilio
Cuadra García, Fernando
Gómez San Román, Tomás
Dueñas Martínez, Pablo
Estado
info:eu-repo/semantics/draft
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Resumen
 
 
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.
 
URI
http://hdl.handle.net/11531/27419
Smart-DS: synthetic models for advanced, realistic testing: distribution systems and scenarios
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24 POWER TRANSMISSION AND DISTRIBUTION; synthetic distribution system datasets; standard scenarios; public data for distribution; grid data; reference network model
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