]> 2026-04-18T11:22:37+02:00 Breakthrough Energy Model 0 Yixing Xu, Dhileep Sivam, Kaspar Mueller, Bainan Xia, Daniel Olsen, Yifan Li, Dan Livengood, Victoria Hunt, Ben Rouillé d’Orfeuil, Merrielle Ondreicka, Anna Hurlimann, Daniel Muldrew, Jon Hagg, Kamilah Jenkins 10.1109/PESGM41954.2020.9281850 Y. Xu et al., "U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies," 2020 IEEE Power & Energy Society General Meeting (PESGM), 2020, pp. 1-5. Computation time and number of variables reflects a typical run using 8 or 16 cores for a full 82,000 node model of the continental U.S. 1200 Yixing Xu all dispatch Scenario Analysis (Deterministic) Breakthrough Energy Model Nodal false MIT license (MIT) Optimization Simulation The Breakthrough Energy Model runs DCOPF simulations Minimize cost Framework true Julia/JuMP transmission DC load flow 1000000000 false true Python Yixing Xu, Daniel Olsen, Bainan Xia, Dan Livengood, Victoria Hunt, Yifan Li, and Lane Smith. 2021. “A 2030 United States Macro Grid: Unlocking Geographical Diversity to Accomplish Clean Energy Goals.” Seattle, WA: Breakthrough Energy Sciences. https://science.breakthroughenergy.org/publications/MacroGridReport.pdf Electricity Renewables Conventional Generation The Breakthrough Energy Model is a production cost model with capacity expansion algorithms and heuristics, originally designed to explore the generation and transmission expansion needs to meet U.S. states’ clean energy goals. The data management occurs within Python, the DCOPF optimization problem is created via Julia, and the preferred solver currently being used is Gurobi, while it is flexible to choose various free or proprietary solvers. A fully integrated capacity expansion model is in development. Hour 2021-06-30T23:00:04Z 2459396.4583796 2021-06-30T23:00:04Z 2459396.4583796 true Breakthrough Energy Model