Uncertainty quantification for critical energy systems during compound weather extremes via probabilistic simulation of climate data

Music City Center 

Extreme weather and climate change pose a large risk to critical energy systems. Uncertainty quantification of these negative impacts is important for developing resilience, especially during compound extreme weather events involving multiple climate variables. We create a modeling workflow that investigates simultaneous risks of extreme weather to the interdependent electricity and natural gas network systems. Our solution relies on fitting Bayesian Generative Additive models in moving-windows, which is embarrassingly parallel. A Gaussian copula accounts for the multivariate spatio-temporal dependence. Overall, the formulation is interpretable and provides uncertainty quantification from probabilistic simulations of weather variables during extreme events. This framework is invariant to the definition of extreme, and thus can be used in other case studies with similar objectives. We illustrate our methodology using Argonne EVS' high-fidelity climate model output of temperature, wind speed, and solar irradiance to assess the impact of compound hazards on critical energy systems in ISO New England.