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202291 https://hdl.handle.net/2286/R.2.N.202291 <p><span>Ryan Sparks, Mikhail Chester, and Nathan Johnson, Estimating Realistic Hybrid-Synthetic Linear Power Flow Transmission Models, 2025, Arizona State University Metis Center Report No. ASU-METIS-25-TRS-002.</span></p> http://rightsstatements.org/vocab/InC/1.0/ open access http://creativecommons.org/licenses/by/4.0 2025-07 2025-07 16 pages eng Sparks, Ryan M. Chester, Mikhail Vin Johnson, Nathan Ira A. Fulton School of Engineering Arizona State University At head of title: "Technical report" "ASU-METIS-25-TRS-002" "July 2025" Power flow models of the bulk electric transmission system are widely used to reveal insights on system behavior. These insights are useful not only for grid planning but increasingly for looking at complex interactions between different infrastructures. However, many existing models are either inaccessible to researchers or lack geospatial significance due to the use of entirely synthetic data. The objective of this work is to develop a geospatially relevant linear power flow model of the bulk electric transmission system - first for a sample region and then generalized for the United States. The power flow model incorporates real data where it is available and fills in data gaps with synthetic network generation methods from literature. This approach produces geospatially representative models which are statistically similar to synthetic models, and which provide realistic insight as to the behavior of the underlying infrastructure. This is particularly useful in understanding infrastructure behavior that may be tied to the electric power system. It can also provide a simplified yet realistic framework for transmission planning and interconnection, a well-known bottleneck for many energy projects throughout the United States. The resultant model creates a foundation for study of the electric power transmission system where geospatial relevance is important and reactive power planning can be neglected. Text Electric power transmission Electric power systems—Mathematical models Geospatial data Estimating realistic hybrid-synthetic linear power flow transmission models