Generating Realistic Information for the Development of Distribution and Transmission Algorithms


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Program Description:

The Generating Realistic Information for the Development of Distribution and Transmission Algorithms (GRID DATA) program will fund the development of large-scale, realistic, validated, and open-access power system network models. These models will have the detail required to allow the successful development and testing of transformational power system optimization and control algorithms, including new Optimal Power Flow (OPF) algorithms. Project teams will take one of two tracks to develop models. The first option is to partner with a utility to collect and then anonymize real data as the basis for a model that can be released publically. The second approach is to construct purely synthetic power system models. The program will also fund the creation of an open-access, self-sustaining repository for the storage, annotation, and curation of these power systems models, as well as others generated by the community.

Innovation Need:

A number of emerging trends will substantially alter the operation and control of the electric grid over the next several decades. These trends include insuring resiliency under severe weather events, increasing integration of renewable electricity generation, supporting changing electricity demand patterns and the improving cost effectiveness of distributed energy resources. The reliable operation of the more complex future power systems will have to rely more on algorithm outputs and decision support tools and less on operator intuition. This grid of the future also requires new advances in distribution system management, including near real-time estimation, optimization, and control of power flows. But the development of these new optimization and control strategies in recent years has been hindered because the research community lacks high-fidelity, public, large-scale power system models that realistically represent evolving grid characteristics. Electric power system models play an essential role in the testing and validation of new optimization and control algorithms, but current models are typically too easy to optimize, too small in scale, and lack a sufficient number of scenarios to fully test the robustness of new algorithms. The GRID DATA program will fund the development of new publicly available power system network models with the detail required to successfully support the development of powerful new algorithms.

Potential Impact:

If successful, the GRID DATA Program will accelerate the development of new power system optimization algorithms by enabling more comprehensive and transparent testing. The new electric power system network models and the open-access, self-sustaining repository for the storage, annotation, and curation of these models (as well as others generated by the community) will also enable richer and more comprehensive research collaborations. The models and repository will provide a basis for developing optimal power flow competitions, further incentivizing future progress. New grid optimization algorithms promise to enable increased grid resilience, flexibility and improved energy efficiency while helping deliver the benefits of integrating renewable generation technologies into the electric power system in the United States. Overall, these algorithms should improve grid reliability, and safety, while also significantly increasing economic and energy security.


Program Director:
Dr. Richard O'Neill;Dr. Kory Hedman;Dr. Mario Garcia-Sanz;Dr. Patrick McGrath
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Project Listing

• Georgia Tech Research Corporation - High-fidelity, Large-scale, Realistic Dataset Development
• GridBright - Power Systems Model Repository
• National Renewable Energy Laboratory (NREL) - SMARTDATA Grid Models
• Pacific Northwest National Laboratory (PNNL) - Sustainable Data Evolution Technology
• Pacific Northwest National Laboratory (PNNL) - Data Repository for Power System Models
• University of Illinois, Urbana-Champaign (UIUC) - Synthetic Data for Power Grid R&D
• University of Michigan - Transmission System Data Set
• University of Wisconsin-Madison (UW-Madison) - EPIGRIDS Transmission System Models