https://wiki.openmod-initiative.org/index.php?action=history&feed=atom&title=CAPOW 2026-05-27T00:55:15Z Revision history for this page on the wiki MediaWiki 1.19.7 https://wiki.openmod-initiative.org/index.php?title=CAPOW&diff=14800&oldid=prev 2020-03-19T16:55:36Z

<p>Created page with &quot;{{Model |Full_Model_Name=California and West Coast Power Systems model |Acronym=CAPOW |author_institution=North Carolina State University |authors=Jordan Kern, Yufei Su |conta...&quot;</p> <p><b>New page</b></p><div>{{Model<br /> |Full_Model_Name=California and West Coast Power Systems model<br /> |Acronym=CAPOW<br /> |author_institution=North Carolina State University<br /> |authors=Jordan Kern, Yufei Su<br /> |contact_persons=Jordan Kern<br /> |contact_email=jkern@ncsu.edu<br /> |website=https://kern.wordpress.ncsu.edu/<br /> |source_download=https://github.com/romulus97/CAPOW_PY36<br /> |text_description=Python-based multi-zone unit commitment/economic dispatch model of CAISO and Mid-C markets coupled with &quot;stochastic engine&quot; for representing effects of multiple spatiotemporally correlated hydrometeorological processes on demand, hydropower and wind and solar power production.<br /> |Primary outputs=Generator level electricity production and emissions, zonal market prices, and total system costs.<br /> |Source of funding=NSF INFEWS program<br /> |open_source_licensed=Yes<br /> |license=MIT license (MIT)<br /> |model_source_public=Yes<br /> |Link to source=https://github.com/romulus97/CAPOW_PY36<br /> |data_availability=all<br /> |open_future=No<br /> |modelling_software=Python (Pyomo)<br /> |GUI=No<br /> |model_class=CAISO and Mid-Columbia markets/U.S. West Coast<br /> |sectors=Electric power<br /> |technologies=Renewables, Conventional Generation<br /> |Demand sectors=Households, Industry, Transport, Commercial sector<br /> |Energy carrier (Gas)=Natural gas<br /> |Energy carriers (Solid)=Coal<br /> |Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind<br /> |Transfer (Electricity)=Transmission<br /> |Storage (Electricity)=Battery, PHS<br /> |Storage (Gas)=No<br /> |Storage (Heat)=No<br /> |User behaviour=Centrally controlled<br /> |Market models=Day-ahead<br /> |decisions=dispatch<br /> |georesolution=Zonal<br /> |timeresolution=Hour<br /> |network_coverage=transmission<br /> |Observation period=More than one year<br /> |Additional dimensions (Ecological)=Environmental flow constraints at dams<br /> |Additional dimensions (Economical)=Marginal cost based pricing<br /> |math_modeltype=Simulation<br /> |math_modeltype_shortdesc=Iterative mixed-integer program, with user defined operating horizon<br /> |math_objective=Cost minimization<br /> |deterministic=Short-term and long-term stochastics are available<br /> |is_suited_for_many_scenarios=Yes<br /> |montecarlo=Yes<br /> |citation_references=Su, Y., Kern, J., Denaro, S., Hill, J., Reed, P., Sun, Y., Cohen, J., Characklis, G. (2020). “An open source model for quantifying risks in bulk electric power systems from spatially and temporally correlated hydrometeorological processes” Environmental Modelling and Software. Vol. 126<br /> |citation_doi=https://doi.org/10.1016/j.envsoft.2020.104667 <br /> |Model input file format=No<br /> |Model file format=No<br /> |Model output file format=No<br /> }}</div>

Jordan Kern