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Eastern Network

This is an entirely synthetic power system network model that covers the geographic footprint of the Eastern Interconnection states: (Arkansas, Louisiana, Missouri, Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont, Alabama, Kentucky, Mississippi, Tennessee New York, New Jersey, Pennsylvania, Washington DC, Delaware, Maryland, North Carolina, Virginia, West Virginia, Illinois, Iowa, Minnesota, Wisconsin Indiana, Michigan, Ohio, Florida, Georgia, South Carolina, Kansas, Nebraska, North Dakota, Oklahoma, South Dakota, Texas). The full geographic area of each state is included in this model, resulting in the State of Texas being fully modeled as part of the Eastern Grid Model The case is designed with a 765, 500, 345, 230, 138 and 69 kV transmission network to serve a load that roughly mimics the actual population of its geographic footprint. The synthetic transmission system was designed by algorithms described in [1] to be statistically similar to actual transmission system models but without modeling any actual lines.

When publishing results based on this data, please cite: [1] J. M. Snodgrass, “Tractable Algorithms for Constructing Electric Power Network Models,” Ph.D. Thesis, The University of Wisconsin – Madison, 2021. This is a synthetic power system model that does not represent the actual grid. It was developed as part of the US ARPA-E GRID DATA research project and contains no CEII.

 

One-line diagrams and other data formats available at: https://github.com/WISPO-POP/SyntheticElectricNetworkModels https://electricgrids.engr.tamu.edu

Dataset Download

This power system dataset is synthetic and does not represent any actual grid. It is provided by Texas A&M University researchers free for commercial or non-commercial use. We do encourage those who use these datasets to: (1) Fill out the information in the form below, and (2) Cite the appropriate papers when using the datasets. https://electricgrids.engr.tamu.edu/references/

Your Name
We are always interested to hear how our datasets are being used and how they can be improved. Let us know if you have any interesting use cases to share, or any questions or comments.

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