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Sustainable Data Evolution Technology

Pacific Northwest National Laboratory (PNNL)
Program: 
ARPA-E Award: 
$1,424,845
Location: 
Richland, WA
Project Term: 
09/07/2016 to 01/18/2019
Project Status: 
ALUMNI
Technical Categories: 
Critical Need: 

Several emerging issues, including the resiliency of electric power delivery during extreme weather events, expanding use of distributed generation, the rapid growth of renewable generation and the economic benefits of improved grid efficiency and flexibility, are challenging the way electricity is delivered from suppliers to consumers. This grid of the future requires advances in transmission and distribution system management with algorithms to control and optimize how power is transmitted and distributed on the grid. However, the development of these systems has been hindered because the research community lacks high-fidelity, public, large-scale power system models that realistically represent current and evolving grid characteristics. Due to security and privacy concerns, much of the real data needed to test and validate new tools and techniques is restricted. To help drive additional innovation in the electric power industry, there is a need for grid models that mimic the characteristics of the actual grid, but do not disclose sensitive information.

Project Innovation + Advantages: 

The Pacific Northwest National Laboratory (PNNL), along with the National Rural Electric Cooperative Association, PJM, Avista, and CAISO, will develop a sustainable data evolution technology (SDET) to create open-access transmission and distribution power grid datasets as well as data creation tools that the grid community can use to create new datasets based on user requirements and changing grid complexity. The SDET approach will derive features and metrics from many private datasets provided by PNNL's industry partners. For transmission systems, PNNL will develop advanced, graph-theory based techniques and statistical approaches to reproduce the derived features and metrics in synthetic power systems models. For distribution systems, the team will use anonymization and obfuscation techniques and apply them to datasets from utility partners.

Potential Impact: 

If successful, PNNL's project will accelerate the development of new power system optimization algorithms by enabling more comprehensive and transparent testing. New grid optimization algorithms could increase the grid's resiliency and flexibility, improving its security during extreme weather and other threats. Moreover, the team's technology could enable greater integration of renewable electricity onto the grid, which would help reduce reliance on carbon-emitting, fossil fuel generation. Finally, the project could lead to greater efficiencies for grid operators and power generators and therefore help reduce operating costs.

Security: 
Environment: 
Economy: 
Contacts
ARPA-E Program Director: 
Dr. Kory Hedman
Project Contact: 
Dr. Zhenyu (Henry) Huang
Partners
National Rural Electric Cooperative Association
Alstom Grid
Release Date: 
1/15/2016