University of Wisconsin-Madison (UW-Madison)

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. 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.

The University of Wisconsin-Madison (UW-Madison) and its partners will develop realistic transmission system models and scenarios that will serve as test cases to reduce barriers to the development and adoption of new technologies in grid optimization and control. The EPIGRIDS project aims to construct realistic grid models by using software to emulate the transmission and generation expansion decision processes used by utility planners. This synthetic model development will utilize Geographic Information Systems (GIS) data on population density, industrial and commercial energy consumption patterns, and land use, over sizes ranging from the city-level to continental-scale. In order to test the robustness of the system’s solutions, it will allow users to tailor specific data sets and scenarios to challenge particular aspects of optimization and control algorithm development. Flexible methodologies for data set construction and connecting features of these data sets to geographically described energy use and land use constraints will enable collaborative development of new models, far beyond those directly delivered by this project.