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California and West Coast Power Systems model
by North Carolina State University
Authors:
Jordan Kern, Yufei Su
Contact:
Jordan Kern
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Python-based multi-zone unit commitment/economic dispatch model of CAISO and Mid-C markets coupled with "stochastic engine" for representing effects of multiple spatiotemporally correlated hydrometeorological processes on demand, hydropower and wind and solar power production.
Based on Python (Pyomo). Using for data processing.
Website / Documentation
Download
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 Open Source
MIT license (MIT)
Directly downloadable
Input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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CAISO and Mid-Columbia markets/U.S. West Coast
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| Sectors
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Electric power
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| Technologies
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Renewables, Conventional Generation
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| Decisions
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dispatch
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| Regions
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| Geographic Resolution
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Zonal
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| Time resolution
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Hour
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| Network coverage
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transmission
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| Model type
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Simulation
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Iterative mixed-integer program, with user defined operating horizon
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| Variables
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| Computation time
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minutes
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| Objective
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Cost minimization
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| Uncertainty modeling
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Short-term and long-term stochastics are available
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| Suited for many scenarios / monte-carlo
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Yes
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References
Scientific 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
https://dx.doi.org/https://doi.org/10.1016/j.envsoft.2020.104667
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