https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54162-9/MediaObjects/41467_2024_54162_MOESM1_ESM.pdf4.14 Load
The future load assumed in this analysis represents a case of high energy efficiency and building electrification, as well as increased adoption of Zero Emissions Vehicles (ZEVs), primarily from electric vehicles 13. The load forecast achieves a doubling of the rate of energy efficiency by 2030 in California, compliant with the state’s SB 350 legislative targets, aggressive building electrification starting in 2020, growing industry electrification, and high levels of electric vehicle adoption. Hourly demand profiles from 2006 (consistent with the weather-year used for calculating solar and wind capacity factors) from FERC Form 714 and a dataset procured from ITRON were used as a base from which demand projects (residential, commercial, industrial, transportation) are scaled by sector to meet states’ policy targets and reflect population growth 25. Where detailed state/province-level load forecasts with state efficiency, electrification, and population estimates are available (including California, Washington, Oregon, British Columbia, and Alberta) load zone forecasts are scaled to those projections; otherwise forecasts are scaled to the EIA’s Annual Energy Outlook projections in the prior SWITCH analysis 13,26. In the 2017 Annual Energy Outlook Electric projections used, population growth across the U.S. is on average about 1% annually, based on the U.S. Census Bureau’s mid-case projections at the time 26
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Electric vehicles are assumed to charge in an “unmanaged” way (without smart charging or time-of-use rates), based on charging profiles developed with an agent-based mobility model BEAM 27,28
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