tell: a Python package to model future total electricity loads in the United States

McGrath, Casey R.; Burleyson, Casey D.; Khan, Zarrar; Rahman, Aowabin; Thurber, Travis B.; Vernon, Christopher R.; Voisin, Nathalie; Rice, Jennie S.

doi:10.21105/joss.04472

DOE OSTI · 1925227

tell: a Python package to model future total electricity loads in the United States

McGrath, Casey R.·Burleyson, Casey D.·Khan, Zarrar·Rahman, Aowabin·Thurber, Travis B.·Vernon, Christopher R.·Voisin, Nathalie·Rice, Jennie S.

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Abstract

The purpose of the Total ELectricity Load (tell) model is to generate 21st century profiles of hourly electricity load (demand) across the Conterminous United States (CONUS). tell loads reflect the impact of climate and socioeconomic change at a spatial and temporal resolution adequate for input to an electricity grid operations model. tell uses machine learning to develop profiles that are driven by projections of climate/meteorology and population. tell also harmonizes its results with United States (U.S.) state-level, annual projections from a national- to global-scale energy-economy model. This model accounts for a wide range of other factors affecting electricity demand, including technology change in the building sector, energy prices, and demand elasticities, which stems from model coupling with the U.S. version of the Global Change Analysis Model (GCAM-USA). tell was developed as part of the Integrated Multisector Multiscale Modeling (IM3) project. IM3 explores the vulnerability and resilience of interacting energy, water, land, and urban systems in response to compound stressors, such as climate trends, extreme events, population, urbanization, energy system transitions, and technology change

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McGrath, Casey R., Burleyson, Casey D., Khan, Zarrar, Rahman, Aowabin, Thurber, Travis B., Vernon, Christopher R., Voisin, Nathalie, Rice, Jennie S.. 2022-11-11. tell: a Python package to model future total electricity loads in the United States. https://doi.org/10.21105/joss.04472

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