EPCN: Enhancing the Modeling, Simulation, and Visualization of Large-Scale Electric Grids Utilizing Detailed Synthetic Power Grids and Data Sets

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The high voltage electric grid plays a critical role in the day-to-day functioning and welfare of society, while supporting other major infrastructure as well. Tremendous research and development efforts are needed for improved grid design and operations to ensure reliability in the face of ever-changing load and generation characteristics, natural disasters, and evolving electricity markets. Electric grid research is heavily dependent upon access to high fidelity electric grid models and datasets. However, much of the crucial information about the design and operation of actual electric grids is considered confidential, and hence not publically available and often not even available to researchers in the field. The proposed project aims to meet this need by developing and applying high quality, synthetic electric grid models and datasets that represent the complexities of actual electric grids, but contain no information about real grids and hence can be made publicly available. The project results should be helpful to researchers and educators in the electric power area, and also to industry practitioners in training new engineers to solve problems facing the grid. Another important aspect of this project is the results will be made publicly available for the benefit of the many different groups of people interested in learning more about the electric grid including researchers and educations in a wide variety of different fields that depend upon the electric grid, and the public. The goal of this project is to advance research and development in many domains by providing modeling and computational analytics associated with simulations of large-scale synthetic electric grids. The modern power grid is continuously generating large volumes of data, such as from phasor measurement units (PMUs). With the right analytics and measurements, it is possible to monitor system health, detect disturbances, and predict events such as outages. The project has four main parts. First, to build on the network creation methodology with detailed modeling to allow for extended time dynamic simulations and generating realistic synthetic data. The detailed modeling will include sub-transmission networks, protection system elements, cyber infrastructure, and enhanced dynamics of system elements. Second, to leverage the synthetic grids from the first task to develop improved algorithms and models for a real-time simulation. Several new computational enhancements will be needed at this stage, with a goal being to innovate through adventurous, potentially transformative techniques that greatly extend conventional power system dynamic simulation. Third, to create synthetic and hence public data sets that reflect the complexity, and often errors, that would be obtained from actual devices such as from PMUs. Last, to take the outcomes of the first three parts and develop interactive, multi-user simulation scenarios. There will be a major education and research component to this. Improved visualization of the system parameters and results will also be explored to help with improved decision-making.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.