An open source model for quantifying risks in bulk electric power systems from spatially and temporally correlated hydrometeorological processes. (April 2020)

• Record Type: Journal Article
• Authors: Su, Yufei; Kern, Jordan D.; Denaro, Simona; Hill, Joy; Reed, Patrick; Sun, Yina; Cohen, Jon; Characklis, Gregory W.
• Is Part Of: Environmental modelling & software. Volume 126(2020)
• Publication Date: 2020-04
• Subjects: Stochastic hydrology -- Weather -- Electricity markets -- Prices; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104667
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: Open source model links hydrometeorological processes to electricity supply, demand and prices in wholesale markets. Stochastic weather and streamflow generation captures statistical dependences across fields, time and space. Model reproduces historical dynamics and can be used to explore realistic, compound extreme events.