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An open source model for quantifying risks in bulk electric power systems from spatially and temporally correlated hydrometeorological processes
Environmental Modelling & Software ( IF 4.552 ) Pub Date : 2020-02-15 , DOI: 10.1016/j.envsoft.2020.104667
Yufei Su; Jordan D. Kern; Simona Denaro; Joy Hill; Patrick Reed; Yina Sun; Jon Cohen; Gregory W. Characklis

Variability (and extremes) in streamflow, wind speeds, temperatures, and solar irradiance influence supply and demand for electricity. However, previous research falls short in addressing the risks that joint uncertainties in these processes pose in power systems and wholesale electricity markets. Limiting challenges have included the large areal extents of power systems, high temporal resolutions (hourly or sub-hourly), and the data volumes and computational intensities required. This paper introduces an open source modeling framework for evaluating risks from correlated hydrometeorological processes in electricity markets at decision relevant scales. The framework is able to reproduce historical price dynamics in high profile systems, while also offering unique capabilities for stochastic simulation. Synthetic generation of weather and hydrologic variables is coupled with simulation models of relevant infrastructure (dams, power plants). Our model will allow the role of hydrometeorological uncertainty (including compound extreme events) on electricity market outcomes to be explored using publicly available models.
更新日期:2020-02-20

 

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