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Transmission System Data Set

University of Michigan

High Fidelity, Year Long Power Network Data Sets for Replicable Power System Research

Program: 
ARPA-E Award: 
$1,418,845
Location: 
Ann Arbor, MI
Project Term: 
05/27/2016 to 11/26/2018
Project Status: 
ACTIVE
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 University of Michigan, with partners from Los Alamos National Laboratory, the California Institute of Technology, and Columbia University, will develop a transmission system data set with greater reliability, size, and scope compared to current models. The project combines existing power systems data with advanced obfuscation techniques to anonymize the data while still creating realistic models. In addition, the project delivers year-long test cases that capture grid network behavior over time, enabling the analysis of optimization algorithms over different time scales. These realistic datasets will be used to develop synthetic test cases to examine the scalability and robustness of optimization algorithms. The team is also developing a new format for capturing power system model data using JavaScript Object Notation and will provide open-source tools for data quality control and validation, format translation, synthetic test case generation, and obfuscation. Finally, the project aims at developing an infrastructure for ensuring replicable research and easing experimentation, using the concept of virtual machines to enable comparison of algorithms as hardware and software evolve over time.

Potential Impact: 

If successful, the University of Michigan'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.

Contacts
ARPA-E Program Director: 
Dr. Kory Hedman
Project Contact: 
Ian Hiskens
Partners
California Institute of Technology
Columbia University
Los Alamos National Laboratory
Release Date: 
1/15/2016