University of Michigan

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.