GODEEEP Light Duty Vehicle (LDV) Hourly Time Series Loads by County

Each file contains projected light duty vehicle (LDV) load by county for a particular U.S. state, GCAM-USA scenario, and climate pathway as specified in the file name. For the full discussion of the methodology, please see https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf. For the balancing authority level timeseries, please see 10.5281/zenodo.7888568. The code used to produce this data is available at https://github.com/GODEEEP/transportation_electrification. Note that fleet sizes at the state scale are derived from the GCAM-USA scenario output. Downscaling to the county scale uses the electric vehicle penetration rates found in the appendix of M. Kintner-Meyer, S. Davis, S. Sridhar, D. Bhatnagar, S. Mahserejian and M. Ghosal, "Electric vehicles at scale-phase I analysis: High EV adoption impacts on the western US power grid", Tech. Rep., 2020. To harmonize the state scale LDV energy use from the GCAM-USA scenarios with the LDV load calculated with EV-Pro Lite, a scale factor was applied to the county level loads, so note that if the reported scale factor is much different than 1.0 there is potentially some disagreement between the load and the fleet size. This scale factor has already been applied to the loads reported in these files (but has NOT been applied to the fleet sizes).
GCAM-USA scenarios
See 10.5281/zenodo.7838871 and 10.5281/zenodo.8377778 for more details

BAU_Climate - a business-as-usual scenario without IRA incentives
business_as_usual_ira_ccs_climate - a business-as-usual scenario with IRA incentives for CCS technology
NetZeroNoCCS_Climate - a scenario targeting net-zero by 2050 without IRA incentives, disallowing CCS technology
net_zero_ira_ccs_climate - scenario targeting net-zero by 2050 with IRA incentives for CCS technology
Climate pathways
See 10.1038/s41597-023-02485-5 for more details

rcp45cooler - historical weather patterns projected into the future with a warming signal applied commensurate with a cooler ensemble of RCP4.5 CMIP6 models
rcp85hotter - historical weather patterns projected into the future with a warming signal applied commensurate with a hotter ensemble of RCP4.5 CMIP6 models
Fields in the data files:

time - hourly timestamp in UTC representing the preceding hour of data
county - the county name
State - the state abbreviation for this county
FIPS - FIPS code for the county
balancing_authority - the balancing authority responsible for the load reported in this row; note that some counties span multiple balancing authorities and their load is divided between those balancing authorities proportional to the population residing within that balancing authority
load_MWh - load on the grid caused by the charging of LDVs during this hour within this county and balancing authority in megawatt hours
temperature_celsius - mean temperature within this county and balancing authority in degrees Celsius
fleet_size - number of electrified LDV cars within this county and balancing authority
daily_miles - average number of miles traveled per day per LDV within this county and balancing authority in miles/day
scale_factor - the values in the load_MWh field have been scaled by this multiplier in order to harmonize the state scale LDV loads with the GCAM-USA scenarios
Changelog

v1.0.1 - added fleet_size, daily_miles, and scale_factor to the output, and updated the README accordingly
Acknowledgements
This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).
PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.