Breakthrough Energy Model

2021-06-30T23:00:04Z

Dan Livengood: Initial Breakthrough Energy Model description upload

{{Model
|Full_Model_Name=Breakthrough Energy Model
|author_institution=Breakthrough Energy Foundation
|authors=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
|contact_persons=Yixing Xu
|contact_email=sciences@breakthroughenergy.org
|website=https://breakthrough-energy.github.io/docs/index.html
|source_download=https://github.com/Breakthrough-Energy
|text_description=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.
|Primary outputs=DCOPF, scenario studies
|Support=https://join.slack.com/t/besciencescommunity/shared_invite/zt-or95p3pi-kO1pj1b6O64THiHU9bgzkA
|Framework=https://science.breakthroughenergy.org/open-source-release
|User documentation=https://breakthrough-energy.github.io/docs/index.html
|Code documentation=https://breakthrough-energy.github.io/docs/index.html
|Number of developers=20
|Number of users=40
|open_source_licensed=Yes
|license=MIT license (MIT)
|model_source_public=Yes
|Link to source=https://github.com/Breakthrough-Energy
|data_availability=all
|open_future=No
|modelling_software=Julia/JuMP
|processing_software=Python
|External optimizer=Gurobi
|GUI=No
|model_class=Framework
|sectors=Electricity
|technologies=Renewables, Conventional Generation
|Demand sectors=Other
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Transmission
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|Market models=Assumes a single-actor cost minimization
|decisions=dispatch
|georegions=Currently U.S., but extendable to any region
|georesolution=Nodal
|timeresolution=Hour
|network_coverage=transmission, DC load flow
|Observation period=Less than one month, Less than one year, More than one year
|math_modeltype=Optimization, Simulation
|math_modeltype_shortdesc=The Breakthrough Energy Model runs DCOPF simulations
|math_objective=Minimize cost
|deterministic=Scenario Analysis (Deterministic)
|is_suited_for_many_scenarios=No
|number_of_variables=1e9
|montecarlo=No
|computation_time_minutes=1,200
|computation_time_hardware=AMD EPYC 7452 32-Core Processor
|computation_time_comments=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.
|citation_references=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.
|citation_doi=10.1109/PESGM41954.2020.9281850
|report_references=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
|Model validation=Historical comparison to annual, state-level generation levels by generation type from EIA-923 form for 2016
|Model input file format=Yes
|Model file format=No
|Model output file format=Yes
}}

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Breakthrough Energy Model