Highly Resolved Projections ofPassenger Electric Vehicle Charging
Loads for the Contiguous United States
Results From and Methods Behind Bottom-Up
Simulations of County-Specific Household
Electric Vehicle Charging Load (Hourly 8760)
Profiles Projected Through 2050 for
Differentiated Household and Vehicle Types
Arthur Yip, Christopher Hoehne, Paige Jadun,
Catherine Ledna, Elaine Hale, and Matteo Muratori
National Renewable Energy Laboratory

NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
Contract No. DE-AC36-08GO28308

Technical Report
NREL/TP-5400-83916
June 2023

Highly Resolved Projections ofPassenger Electric Vehicle Charging
Loads for the Contiguous United States
Results From and Methods Behind Bottom-Up
Simulations of County-Specific Household
Electric Vehicle Charging Load (Hourly 8760)
Profiles Projected Through 2050 for
Differentiated Household and Vehicle Types
Arthur Yip, Christopher Hoehne, Paige Jadun,
Catherine Ledna, Elaine Hale, and Matteo Muratori
National Renewable Energy Laboratory
Suggested Citation

Yip, Arthur, Christopher Hoehne, Paige Jadun, Catherine Ledna, Elaine Hale, and Matteo
Muratori. 2023. Highly Resolved Projections of Passenger Electric Vehicle Charging
Loads for the Contiguous United States: Results From and Methods Behind Bottom-Up
Simulations of County-Specific Household Electric Vehicle Charging Load (Hourly 8760)
Profiles Projected Through 2050 for Differentiated Household and Vehicle Types. Golden,
CO: National Renewable Energy Laboratory. NREL/TP-5400-83916.
https://www.nrel.gov/docs/fy23osti/83916.pdf.

NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC

Technical Report
NREL/TP-5400-83916
June 2023

This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.

National Renewable Energy Laboratory
15013 Denver West Parkway
Golden, CO 80401
303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

NOTICEThis work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable
Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding
provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Strategic Analysis
Team. The views expressed herein do not necessarily represent the views of the DOE or the U.S. Government.
This research was performed in part using high-performance computational resources sponsored by the
Department of Energy’s Office of Energy Efficiency and Renewable Energy and located at the National Renewable
Energy Laboratory.
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Abstract

This report documents enhancements made to the Transportation Energy & Mobility Pathway
Options™ (TEMPO) model to project spatially, demographically, and temporally resolved
national-scale electric vehicle (EV) charging load profiles and describes three scenarios andcorresponding data sets created for the National Renewable Energy Laboratory’s (NREL’s)demand-side grid (dsgrid) project in support of bulk power systems modeling. In brief, TEMPO
was enhanced to disaggregate national and annual energy demand projections into householdand county-level projections of passenger EV hourly charging load profiles (8760 profiles),accounting for consumer, travel, and temperature variations that impact EV energy demand. Inalignment with NREL’s forward-looking grid modeling, three scenarios for EV adoption
covering 2020–2050 were created—Annual Energy Outlook (AEO) Reference Case,
Electrification Futures Study (EFS) High Electrification, and All EV Sales by 2035—andassociated data sets have been included in the dsgrid platform for public use.

iii
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Acknowledgments
We would like to thank our reviewers, Kara Podkaminer, Ookie Ma, Sam Baldwin, Kevin Lynn,
Patrick Brown, Wesley Cole, and Eric Wood, for their insightful comments and discussion. We
are also grateful for Michael Deneen’s copy editing. We extend special thanks to Ookie Ma, whoshared his IPF code to generate household data; Meghan Mooney, Daniel Thom, Lixi Liu, andCarlos Bianchi for their help with dsgrid; and Brian Bush for his help with TEMPO.

iv
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List of Acronyms

ACS
AEO
ATB
BEV
DCFC
EPA
dsgrid
EFS
EIA
EV

IPF
L1
L2
LDV
NHTS
NREL
PUMS
TEMPO
VMT
ZEV

American Community Survey
Annual Energy Outlook
Annual Technology Baseline
battery electric vehicle
direct current fast charging
U.S. Environmental Protection Agency
demand-side gridElectrification Futures Study
U.S. Energy Information Administrationplug-in electric vehicle, including both plug-in hybrid electric vehicles andbattery electric vehicles, and excluding hybrid electric vehicles and fuelcell electric vehicles
iterative proportional fitting
Level 1 electric vehicle charging
Level 2 electric vehicle charging
light-duty vehicle
National Household Travel Survey
National Renewable Energy Laboratory
Public Use Microdata Sample
Transportation Energy & Mobility Pathway Options model
vehicle miles traveled (typically in units of miles/year/vehicle)
zero-emission vehiclev
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Executive Summary

Vehicle electrification is increasing in speed and scale, with widespread light-duty vehicle(LDV) electrification moving from a question of “if” to “when” and “where.” Vehicle
electrification will increase electricity consumption by a substantial but manageable amount,with multiple studies projecting that full electrification of the light-duty vehicle stock would
account for 13%–29% of future aggregate U.S. load (Mai et al. 2018; Fox-Penner, Gorman, andHatch 2018; Milovanoff et al. 2020). However, for energy system planning and design, “whenand where electric vehicle (EV) charging occurs will be as critical as how much electricity isneeded” (Muratori and Mai 2020), especially in the transitional medium-term horizon.
Significant spatial and temporal heterogeneity in EV loads across the United States is expected
due to variations in travel behavior, vehicle ownership preferences (e.g., pickup trucks orcompact sedans), and technology adoption. Spatiotemporal differences in EV energy use also
arise from weather impacts. For example, the energy used by the same EV to drive a mile canchange by up to 50%–100% in very cold temperatures. Overall, these factors combine to result inmajor spatial and temporal variations in EV electricity demand, which heavily impacts charging
needs and implications for the electricity system. In addition, patterns in these factors are
expected to change in future scenarios. This report describes how the Transportation Energy &Mobility Pathway Options™ (TEMPO) model was enhanced to project spatially,
demographically, and temporally resolved passenger EV charging demand.
TEMPO is a comprehensive transportation demand model from the National Renewable Energy
Laboratory (NREL) used to explore long-term scenarios of energy use across the entire U.S.
transportation sector (Muratori, Jadun, et al. 2021). TEMPO simulates travel demand, household
vehicle ownership, mode choice, and technology choice, for both passenger travel and freighttransport. TEMPO models heterogeneous passenger travel demand, vehicle ownership, travel
mode choice, and technology preferences and choices by categorizing U.S. households according
to socio-demographics and geographic characteristics. Here we document the methodologies andassumptions used to enhance TEMPO’s resolution, which was previously limited to the national
scale and annual level, in order for TEMPO to simulate spatially, demographically, andtemporally resolved household EV charging loads. This process is organized into four steps:
1. Spatially disaggregating and harmonizing TEMPO’s demographically resolved
household and vehicle input data to enable county-level TEMPO simulations forhouseholds and their passenger light-duty EVs for the contiguous United States, projected
through 2050.
2. Capturing spatially and temporally resolved effects of temperature on EV energy use fora specific weather year.
3. Developing spatially resolved vehicle electrification scenarios that determine the extent
of EV adoption in each county.
4. Simulating week-long trip energy consumption schedules, applying charging strategies,
and generating hourly EV charging load profiles for different scenarios.
Using these enhancements, TEMPO generated annual hourly load projections for household EV
charging for all counties in the contiguous United States, projected through 2050 for a set ofviThis report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

vehicle electrification scenarios. The resulting data sets have been included in the NREL
demand-side grid (dsgrid) 1 repository. The scenarios are:
A. AEO Reference Case, aligned with passenger light-duty EV adoption in the Reference
Case in the 2018 U.S. Energy Information Administration (EIA) Annual Energy Outlook(AEO) (EIA 2019), which was used to calibrate the TEMPO model.
B. EFS High Electrification, with passenger light-duty EV adoption in line with the HighElectrification scenario in the Electrification Futures Study (EFS) (Mai et al. 2018).
C. All EV Sales by 2035, assuming U.S. average passenger light-duty EV sales reaching
50% in 2030 and 100% in 2035, in line with various announced targets.
For each scenario, annual hourly charging profiles are provided for every other year from 2020
through 2050, differentiated by county, household type, and vehicle type.
This report explains TEMPO’s county-level disaggregation methodology, incorporation of
spatiotemporally specific temperature impacts, development of disaggregate EV sales scenarios,and simulation of EV charging load profiles. It then summarizes aggregate results from all
scenarios and focuses on detailed results from the All EV Sales by 2035 scenario, demonstrating
TEMPO’s capabilities in simulating when, where, and how much household EVs will be charged
in the United States. Figures ES-1 and ES-2 highlight the EV charging load results generated by
TEMPO for this report.
2036

Peak Hourly Load from EV Charging (GWh/h)

1000

EV Load (TWh/yr)

800

600

400

200

0
2020

2030

Year

2040

2050

200

150

100

50

0
1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112

2050

Month

Scenario
All LDV Sales EV by 2035
EFS High Electrif ication
AEO Ref erence

Figure ES-1. Aggregate U.S. annual and peak hourly EV load under three scenarios
1

https://www.nrel.gov/analysis/dsgrid.html

viiThis report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

WI

VT

NH

NY

MA

ME

EV Charging Load Profile scaled for population (kWh/h/capita)

0.3
0.2
0.1
0.0
WA

ID

MT

ND

MN

IL

MI

OR

NV

WY

SD

IA

IN

OH

PA

NJ

CT

CA

UT

CO

NE

MO

KY

WV

VA

MD

DE

AZ

NM

KS

AR

TN

NC

SC

DC

OK

LA

MS

AL

GA

0.3
0.2
0.1
0.0
0.3
0.2
0.1
0.0
0.3
0.2
0.1
0.0

0

6 12 18
0.3
0.2
0.1
0.0

0

6 12 180

6 12 18
0.3
0.2
0.1
0.0

TX
0.3
0.2
0.1
0.0

0

0

6 12 18

6 12 180

6 12 180

6 12 18

Hour of Day
Month

0

6 12 18

0

RI

0

6 12 18

6 12 18FL

0

6 12 18

Mar Jun Sep Dec

Figure ES-2. State-level per-capita EV charging load profiles for an average weekday for the All EV Sales by 2035 scenario for projected
year 2035 under the immediate and ubiquitous charging strategy, for the contiguous United States, with seasonal variation shown byline color (blue for winter and orange for summer) and U.S. annual average in black dashes

viiiThis report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table of Contents

Executive Summary ................................................................................................................................... vi
1. Introduction ............................................................................................................................................. 1
2. Methods and Model Development ......................................................................................................... 4

2.1 TEMPO Overview ............................................................................................................................ 4
2.2 Disaggregating Household Counts for Each TEMPO Household Bin to the County Level ............ 5
2.3 Disaggregating Vehicle-Related Data ............................................................................................ 12
2.4 Accounting for the Effect of Temperature on EV Loads ............................................................... 17
2.5 Developing Vehicle Electrification Scenarios ................................................................................ 22
2.6 Simulating Week-long Hourly EV Charging Profiles .................................................................... 26

3. Results: Charging Load Scenarios ..................................................................................................... 29

3.1 Scenario Result Summary .............................................................................................................. 29
3.2 Spatial and Temporal Heterogeneity in EV Load .......................................................................... 30

4. Conclusions ........................................................................................................................................... 36
5. References ............................................................................................................................................. 39
Appendix A.
Disaggregation and Enhancement of TEMPO Inputs Possible in Future Work .... 43
Appendix B.
TEMPO-dsgrid Linkage ................................................................................................ 46ix
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

List of Figures

Figure ES-1. Aggregate U.S. annual and peak hourly EV load under three scenarios ............................... vii
Figure ES-2. State-level per-capita EV charging load profiles for an average weekday for the All EV
Sales by 2035 scenario for projected year 2035 under the immediate and ubiquitous charging
strategy, for the contiguous United States, with seasonal variation shown by line color (bluefor winter and orange for summer) and U.S. annual average in black dashes ...................... viii
Figure 1. National household counts by TEMPO household bin as of 2017. ............................................... 6
Figure 2. Summary of Claritas urbanicity data plotted against raw population density (population per
square mile). ............................................................................................................................. 7
Figure 3. Household counts by TEMPO household bin for selected U.S. counties as of 2017. ................... 9
Figure 4. Household counts by TEMPO household bin for selected U.S. counties, ordered by population
density, as of 2017. ................................................................................................................ 10
Figure 5. Proportions of households in selected urbanity, household composition, and income bins in eachcounty in the United States as of 2017 (TEMPO baseline calibration year).......................... 11
Figure 6. Total number of U.S. households across each of TEMPO’s three household bin dimensions,with comparison between TEMPO national distributions (red, data from NHTS) andTEMPO county distributions (blue, data based on ACS/PUMS/Claritas), aggregated to thenational level. ......................................................................................................................... 12
Figure 7. Average vehicle ownership per household by U.S. county as of 2017, based on ACS/PUMS
household microdata and Polk registration data .................................................................... 15
Figure 8. Proportions of vehicle size class across U.S. counties in 2017 based on Polk registration data. 16
Figure 9. Battery-electric vehicle (BEV) (“CMU Leaf”) and internal combustion engine vehicle (“NREL
Fusion”) energy efficiency adjustment for different ambient temperatures based on NREL
2016, Yuksel and Michalek 2015, and Fleetcarma (Geotab) 2014 data ................................ 18
Figure 10. BEV energy efficiency adjustment in TEMPO and EVI-Pro (black dashes) compared to
Geotab (2020) data (colored lines for average, 90th, and 10th percentiles) ............................ 18
Figure 11. Effective DCFC charger efficiency for three different ambient temperatures. ......................... 19
Figure 12. Ambient temperatures (left) and impact on BEV energy consumption (right) across the UnitedStates for January 2012, 8 a.m., and July 2012, 5 p.m., reflective of spatial, temporal, andseasonal variation in weather conditions................................................................................ 20
Figure 13. Adjustments to EV energy consumption in TEMPO based on weather year 2012 temperatures.
................................................................................................................................................ 21
Figure 14. Projected national technology sales shares for each EV adoption scenario .............................. 22
Figure 15. Projected ZEV sales shares for all counties (each colored line) in the United States forthree TEMPO vehicle electrification scenarios...................................................................... 24
Figure 16. TEMPO’s projected ZEV sales shares in 2025 and 2030 for all counties in the contiguousUnited States in the All EV Sales by 2035 scenario. ............................................................. 25
Figure 17. EV charging envelopes characterize the charging opportunities that are consistent withvehicles’ consumption patterns, charging opportunities, and technological capabilities....... 27
Figure 18. Aggregate U.S. annual and peak hourly EV load under three scenarios ................................... 30
Figure 19. Aggregate U.S. EV charging load profile for an average weekday for the All EV Sales by 2035
scenario for projected years 2036 and 2050 under the immediate and ubiquitous charging
strategy, with seasonal variation shown by line color (blue for winter, orange for summer) 31
Figure 20. State-level per-capita EV charging load profiles for an average weekday for the All EV Salesby 2035 scenario for projected year 2036 under the immediate and ubiquitous charging
strategy, for the contiguous United States, with seasonal variation shown by line color (bluefor winter, orange for summer) and U.S. annual average in black dashes. ............................ 33
Figure 21. County-level per-capita EV charging load profiles for an average weekday for the All EV
Sales by 2035 scenario for projected year 2036 under the immediate and ubiquitous charging

xThis report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

strategy, with seasonal variation in color (blue for winter, orange for summer) and U.S.
annual average in black dashes. ............................................................................................. 34

List of Tables

Table 1. TEMPO Data Inputs Disaggregated by County.............................................................................. 3
Table 2. Charger Assumptions Used in TEMPO for dsgrid Scenarios ....................................................... 28
Table A-1. TEMPO Data Inputs That Could Be Further Explicitly Spatially Disaggregated or May
Benefit From Further Enhanced Representation .................................................................... 43
Table B-1. Structure and Format of TEMPO Intermediate and Output Data Files for dsgrid .................... 46xi
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

1. Introduction

Vehicle electrification is increasing in speed and scale, with widespread light-duty vehicle
electrification moving from a question of “if” to “when” and “where.” Vehicle electrification
will increase electricity consumption by a substantial but manageable amount, with multiple
studies projecting that full electrification of the light-duty vehicle stock would account for 13%–29% of future aggregate U.S. load (Mai et al. 2018; Fox-Penner, Gorman, and Hatch 2018;
Milovanoff et al. 2020). However, for energy system planning and design, “when and where
electric vehicle (EV) charging occurs will be as critical as how much electricity is needed”(Muratori and Mai 2020), especially in the transitional medium-term horizon.
Significant spatial and temporal heterogeneity in EV loads across the United States is expected
due to variations in travel behavior, vehicle ownership preferences (e.g., pickup trucks or cars),
and technology adoption. Spatiotemporal differences in EV energy use also arise from weather
impacts. For example, the energy used by the same EV to drive a mile can change by up to 50%–
100% in very cold temperatures. Overall, these factors combine to result in major spatial andtemporal variations in EV electricity demand, which heavily impacts charging needs andimplications for the electricity system. In addition, patterns in these factors are expected tochange as electrified transportation technologies and infrastructure mature.
Previous literature has clearly illustrated and demonstrated significant spatial and temporal
heterogeneity in EV energy use 2 (Tamayao et al. 2015; Yuksel and Michalek 2015; Wu et al.
2019; Miller, Arbabzadeh, and Gençer 2020; Desai et al. 2020; Parker et al. 2021; Yang et al.
2021; Vega-Perkins et al. 2023) due to factors such as temperature, household demographics andtravel behavior, and vehicle type. However, most only present data and analysis forrepresentative consumers, vehicles, scenarios, and/or cases—for example, for 16 regions, forfour seasons, or for intermediate or representative metrics such as energy or emissions per EV or
per mile—rather than projections for absolute values such as total electric load in each county. In
addition, most studies focus on annual energy use, typically in connection with calculating
economics of adoption or emissions benefit, while some derive hourly energy use patterns fromassumed and static timing of charging. None produce hourly charging load data comprehensively
for differentiated household types in every U.S. county from bottom-up simulation ofheterogeneous household travel demand, vehicle ownership, transportation mode choice,technology adoption, and flexible charging. Additionally, while aggregate charging load impacts
at the national level have been produced by top-down approaches, integrated assessment
modeling, and other large-scale modeling efforts (e.g., the Energy Information Administration(EIA’s) Annual Energy Outlook (AEO) (EIA 2019), National Renewable Energy Laboratory’s(NREL’s) Electrification Future Study [Mai et al. 2018]), we are unaware of corresponding
spatially, demographically, and temporally disaggregated load projections that add up to national
totals. The work described in this report enhances the Transportation Energy & Mobility
Most studies have focused on the heterogeneity of emissions and costs associated with EV energy use, because
they are also heavily spatially and temporally dependent with when, where, and how the energy is procured andconsumed. Many studies have also focused on heterogeneity in both EV and non-EV energy use, affordability, andvulnerability (Zhou, Aeschliman, and Gohlke 2021; Liu and Kontou 2022). Here, we focus on spatially,
demographically, and temporally differentiated EV energy consumption and charging loads, and defer to othermodeling tools for detailed emissions and cost modeling.

2

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Pathway Options (TEMPO) model to enable and create comprehensive bottom-up projections ofhighly resolved charging loads from simulation of representative households across the entire
United States. The data resolution has been designed to integrate with bulk power system
investment and operational models, in addition to enabling detailed analysis of EV charging
loads.
This report describes how TEMPO was enhanced to project spatially, demographically, andtemporally resolved household EV charging demand in future scenarios. Heterogeneity in many
drivers of transportation demand, options, and choice are expected to contribute to heterogeneity
in energy use and EV charging load profiles. In the TEMPO model, we disaggregated four major
categories of inputs to the county level:i.

Household and demographics data that are most relevant to transportation demand bycounty, captured by the unique combinations and proportions of TEMPO’s 60 householdbins in each county. Each bin has unique estimates of trip behavior and mode choiceaffecting passenger miles traveled and vehicle miles traveled (VMT), vehicle ownership,
and preferences such as those related to fuel economy and technology choice.

ii.

Vehicle-related data such as stock, fuel type and technology, fuel economy, and vehicle
class (e.g., midsize cars, pickup trucks) preferences by county. Each bin and county haveunique distributions of these characteristics and attributes that affect vehicle availability,
usage decisions, and the energy intensity of vehicle usage.

iii.

Vehicle energy efficiency affected by temperature, by county, month, and hour.

iv.

Vehicle electrification scenarios based on historical and existing EV adoption by county
and future goals.

Table 1 summarizes the subset of TEMPO inputs that were disaggregated in this work. Appendix
A and Table A-1 document other TEMPO inputs that were not disaggregated for this project but
could be in the future. The methodologies and assumptions used for this enhanced resolution are
documented in Sections 2.2–2.5. With these TEMPO enhancements, we are able to simulate
highly resolved household travel behavior and EV ownership and use in every county. Then,
Section 2.6 discusses how TEMPO was enhanced to simulate week-long trip energy
consumption schedules, apply charging strategies, and generate hourly household EV charging
load profiles for different scenarios. Finally, Section 3 presents summarized results from all
scenarios and then focuses on detailed results from the All EV Sales by 2035 scenario,
demonstrating TEMPO’s capabilities in simulating spatially, demographically, and temporally
resolved household electric vehicle charging load scenarios for the contiguous United States.

2
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Table 1. TEMPO Data Inputs Disaggregated by County

VehicleHousehold

TEMPO Data Input

Data Source

Projected
Evolution

Household count per TEMPO
bin (household income,60 bins,
household composition, andnational
urbanity) [# households/bin]National
Household60 bins for each of 3,143 U.S.
Travel Survey
counties(NHTS)(FHWA 2017)

American Community
Survey (ACS)/Public Use
Microdata Sample (PUMS)(2019), Claritas (2021)

Annual Energy
Outlook (AEO)(2019)

Household vehicle ownership 60 bins,[# vehicles/households]
national

NHTS (2017), 60 bins for each of 3,143 U.S.
AEO (2019)
counties

ACS/PUMS (2019), Polk(2018)AEO (2019)

Household vehicle stock, bysize class, technology/fuel,and vintage

National

NHTS (2017), County-specific (3,143 U.S.
AEO (2019)
counties)

ACS/PUMS (2019), Polk(2018)AEO (2019)

Household vehicle size class
distributions

60 bins,
national

Bin- (60) and county- (3,143)
NHTS (2017),
specific, via county household
AEO (2019)
counts and vehicle ownership

NHTS (2017), Polk (2018)

AEO (2019)

NHTS (2017), ACS/PUMS(2019), Polk (2018), AEO(2019), EPA (2019)

ATB (2020)

National
Household vehicle energy
efficiency, by size class andtechnology/fuel [kWh/mi]

VehicleElectrification

National
County-Level Model
Model
Data Source
Resolution
Resolution

Temporal:annualaverageEV sales share, by technology
National
type [EVs/vehicles]

Bin- (60) and county- (3,143)
AEO (2019), specific, via county household
Annualcounts and vehicle ownershipTechnology
Baseline (ATB) Temporal:(2020), U.S.
Environmental Location- (geographic centroid ofcounty population), seasonProtection
Agency (EPA) (monthly average), and trip-time(hour) specific temperature,(2019)
affecting energy efficiency
Scenariospecific:
AEO (2019),
Mai et al.
(2018) (EFS),
Biden (2021)

County-specific (3,143 U.S.
counties)

Temperature:
NWS/MesoWest via
ComStock (Liu et al. 2023);
Energy efficiency: EVI-Pro Weather year 2012
(NREL 2016), Yuksel andMichalek (2015), Geotab(2020)

Polk (2021)

CARB (ACC II),
anticipatedparticipation fromstates that currently
follow CA ACC I(ZEV) via Section
177 of Clean Air Act,and Biden 2021 goal

3
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2. Methods and Model Development
2.1 TEMPO Overview

TEMPO is a comprehensive transportation demand model that generates long-term scenarios of
U.S. mobility energy use and emissions (Muratori, Jadun, et al. 2021). TEMPO fills a criticalmodeling gap by enabling national assessment of emerging transportation technologies andbehaviors and cross-sectoral integrated studies (Muratori et al. 2020). TEMPO models the entire
U.S. domestic passenger and freight mobility systems across all travel modes, including walking,biking, public transport, light-duty personal travel, mobility as a service (MaaS), and domestic
aviation passenger modes, as well as air, rail, truck, and ship freight modes. However, the scopeof this project is limited to household travel demand and household-owned passenger light-duty
vehicles.
TEMPO models heterogeneous passenger travel demand by categorizing U.S. households
according to socio-demographics and geographic characteristics. For each nationally
representative household type, TEMPO estimates household-level, activity-based travel demandand consumer preferences for travel mode, vehicle ownership, vehicle size class, and drivetraintechnology. TEMPO then characterizes available options for each trip and household by
attributes such as cost and time and system-level variables such as travel mode and infrastructure
availability. Then, for samples of representative households, TEMPO simulates decisions
regarding trip mode choice, vehicle purchase, technology adoption, and vehicle
refueling/charging. The impacts of these simulated decisions are aggregated into vehicle sales,
vehicle stock, energy use, and emissions metrics.
TEMPO was originally developed to run at the national and annual level using household, travel,
vehicle, and energy data binned by household income, urbanity, and household size, and with no
explicit spatial resolution. However, significant variations in factors affecting mobility demandand choices exist across U.S. counties, such as household and vehicle characteristics andconsumer preferences. Table 1 reviews how data for these factors were disaggregated andharmonized to enable county-level simulations for the entire United States.
At its core, TEMPO derives nationally representative household-based travel demand andattributes from the 2017 National Household Travel Survey (NHTS) (FHWA 2017). For countylevel projections of travel demand, TEMPO leverages the nationally representative householdbin-specific travel behavior derived from NHTS and applies it to every county based on eachcounty’s household mix (i.e., the distribution of households in each of the TEMPO household
types/bins). This draws from geographically specific data on households from the American
Community Survey (ACS) 2014–2018 Public Use Microdata Sample (PUMS) (U.S. Census
Bureau 2019) and proprietary Claritas data (Claritas 2021) on urbanicity to capture differences inhousehold attributes, mobility behavior, and technology adoption. The details of this process aredescribed in Section 2.2.
Spatial heterogeneity and differences across counties apply to vehicle-related data as well, withdifferent levels of vehicle stock, vehicle size class, and technology preferences across different
counties. Base year (2017) vehicle ownership and stock by household bin and county were
4This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

drawn from both ACS/PUMS (U.S. Census Bureau 2019) and Polk vehicle registration data (IHS
Markit 2018) at the county level instead of the national level. Other vehicle attributes weredisaggregated by applying the nationally representative NHTS distributions to the ACS/PUMS
household counts for each county. The details of this process are described in Section 2.3.
Temperature affects the energy efficiency of EVs; Section 2.4 addresses how vehicle energy
efficiency in TEMPO was adjusted to account for location-specific seasonal temperature.
Adoption of EVs significantly varies across the country and over projected years; Section 2.5
discusses one of TEMPO’s approaches in determining disaggregated EV adoption for thesimulated scenarios in this report. Finally, EV charging occurs at instances throughout days andweeks; Section 2.6 describes the assignment of charging amounts and times for each vehicle andhousehold in each county according to simulated trip schedules, charging constraints,
preferences, and behaviors.

2.2 Disaggregating Household Counts for Each TEMPO Household
Bin to the County Level

In TEMPO, households are classified along three dimensions: income (low, middle, high),household composition (no driver, single driver, small driver household, large driver household),and urbanity (rural, small town, second city, suburban, urban). NHTS (2017) data were used todevelop unique travel demand profiles (trip length, frequency, vehicle occupancy distributions,mode preferences) for each of these 60 bins 3 (Muratori, Jadun, et al. 2021). Figure 1 shows
national household counts in each household bin.

TEMPO’s 60 household bins arise from the combination of 3 income bins (low, household income <$50,000;
middle, $50,000–125,000; high, >$125,000) × 4 household composition bins (No Driver; Driver, Single [1-personhousehold]; Driver, Small [2-person household]; Driver, Large [3+]) × 5 urbanity bins (Urban, Suburban, Second
City, Small Town, Rural).
3

5This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure 1. National household counts by TEMPO household bin as of 2017.
Data from NHTS (2017). Each colored segment represents one of 60 TEMPO household bins.

A number of households are sampled to represent each bin in TEMPO simulations and thenweighted by the number of households in that bin divided by the number of samples to produce
nationally representative results, leveraging unique travel behavior and patterns estimated foreach household bin based on NHTS households. For example, in TEMPO, No Driver households
have very different travel behavior than other types of households and are weighted according totheir counts, reflecting their prevalence in the United States.
To estimate household counts by TEMPO household bin (three-way joint distributions) for every
county, two-way joint income and composition household counts at the census tract level wereconstructed via iterative proportional fitting (IPF) using data from ACS5 (2014–2018) 4
supplemented by PUMS data, 5 building on methodology used in the Low-Income Energy
Because the NHTS (2017) data were collected between 2016 and 2017, the time frame of the ACS5 2014–2018
data set is the closest match, though data for some census areas in this data set will be from 2014 and others will befrom 2018, creating some inherent mismatches to the NHTS data.
5
The ACS5 data are used to inform the 1D distributions of households in each income and composition bin at thetract level, while PUMS data informs the 2D distribution of households in each income and composition bin at the
PUMA level. These are combined using IPF with ACS5 data as enforced marginal totals and PUMS microdata as
seed data.
4

6This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Affordability Data (LEAD) Tool (Ma et al. 2019). The third dimension (i.e., urbanity) was thenadded based on data at the census block group level from Claritas, a firm specializing inmarketing and customer segmentation that provided the same definitions for the NHTS. Figure 2
summarizes the distribution of urbanicity across the United States.

Figure 2. Summary of Claritas urbanicity data plotted against raw population density (population
per square mile).
The black dashed lines are Claritas’ publicly stated approximate cutoffs for urbanicity bins. The proprietary Claritas
definitions include manual assignment based on other factors such as geographic contiguity, particularly to
distinguish between “suburban” and “second city,” which had the same population density cutoffs but were
determined based on neighbors and access. 6

These urbanicity data were aggregated to produce urbanity distributions within each census tract
and then combined with the tract-level income and composition data and further aggregated togenerate joint distributions 7 of households in every bin for every county.
To illustrate the variety of household counts in different U.S. counties, Figure 3 and Figure 4
show the resulting household counts for selected counties, and Figure 5 maps out theheterogeneity across the contiguous United States. For those familiar with U.S. geography, thetrends seen in the figures likely confirm expectations. The sheer amount of heterogeneity seen
across these transportation-relevant characteristics suggests that most county-level TEMPO
results are unlikely to resemble the national averages given by the national-scale TEMPO model.
Furthermore, even counties that are nominally similar (like all-urban New York and San
Francisco) have different breakdowns in terms of income and number of drivers, as well as
characteristics of the mobility systems and travel options (e.g., public transit speed andAttempts at replicating Claritas’ definitions based on ACS population density data and GIS analysis were blockedby proprietary definitions, a lack of transparent methodology, and a lack of validation data. Assignment of urbanity
based solely on publicly available data was generally valid but noticeably different in inspected areas.
7
Also known as cross-tabulation by multiple variables.
6

7This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

availability) that differentiate TEMPO’s findings in terms of vehicle adoption, trip modalities,
and energy use (with the caveat that public transit availability inputs in TEMPO have not yet
been made more geographically specific than urbanity).
This derived set of household data is used as the 2017 baseline for TEMPO and scaled to matchthe NHTS and U.S. Energy Information Administration (EIA) national totals. TEMPO then uses
national household 8 growth projections from EIA AEO and applies them uniformly to every
county and bin. This means that TEMPO does not project growth in household counts differently
for different household bins or counties and does not project changes to the distribution ofhousehold income, sizes, or urbanicity in individual counties over time. For example, TEMPO
does not currently account for population migration between counties or from/to the UnitedStates; changing demographics such as rising or falling real incomes by county; changes inhousehold sizes, drivers, or ages differing by county; or urbanization or densification.

A potential future source of population data and projections is LandScan, which may help improve the resolutionof population projections across the United States. While changes in demographics within each county may not be
captured by LandScan if the data are not demographically distinguished, changes in U.S. demographics overall may
be captured if LandScan projects higher population increases in areas of certain demographics—for example, inurban areas or richer areas, thereby representing more urban households in the future or more households being
higher income in the future. We stress that this would likely only capture part of the actual demographic trend
because the household bin distributions remain fixed at the county and national levels and we do not havecomprehensive projections how these distributions will shift in the future, particularly at the necessary geographic
resolution.
88This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure 3. Household counts by TEMPO household bin for selected U.S. counties as of 2017.
Data derived from ACS/PUMS (2018). Every county is represented by a unique mix of household counts in each of
the 60 TEMPO household bins. TEMPO samples from each bin in every county, allowing the model to consider bothhousehold-bin-specific and county-specific factors affecting trips and choices.

9This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure 4. Household counts by TEMPO household bin for selected U.S. counties, ordered bypopulation density, as of 2017.
Data derived from ACS/PUMS (2018). Every county is represented by a unique mix of household counts in each of
the 60 TEMPO household bins. TEMPO samples from each bin in every county, allowing the model to consider bothhousehold-bin-specific and county-specific factors affecting trips and choices.

10
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure 5. Proportions of households in selected urbanity, household composition, and income
bins in each county in the United States as of 2017 (TEMPO baseline calibration year).
Many counties exclusively contain rural-classified households. Most counties have very few households with nodrivers. Many counties across the United States have large proportions of households classified as low-income.

The household data derived from ACS/PUMS using IPF were validated by comparing aggregate
results to the NHTS and AEO data, checking the national totals first, then by household
dimension, and then by each bin. Figure 6 shows minor discrepancies in totals between data
sources, which we concluded to fall within a reasonable margin of error due to definitions, suchas driver status, year of survey, and inherent uncertainty from survey results.
11This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure 6. Total number of U.S. households across each of TEMPO’s three household bin
dimensions, with comparison between TEMPO national distributions (red, data from NHTS) andTEMPO county distributions (blue, data based on ACS/PUMS/Claritas), aggregated to thenational level.

2.3 Disaggregating Vehicle-Related Data

Besides household composition variability, there are also significant county-level differences invehicle ownership and vehicle class preferences. At the national level, TEMPO uses NHTS
(2017), Polk (IHS Markit 2018), AEO (EIA 2019), 9 and ATB (NREL 2020) for base year (2017)
data on light-duty vehicle ownership, stock, and attributes (fuel/technology type, size class, andenergy efficiency). Future projections for these data are also taken from the same sources to
In addition, AEO reports composite test cycle fuel economy in mpg-equivalent—i.e., the ratings in the EPA Fuel
Economy Guide, but calculates actual energy use accounting for on-road actual use with degradation factors. Details
in EPA 420-R-06-017. In this work, the energy adjustment due to temperature incorporates both a temperaturedependency and a real-world efficiency adjustment relative to the EPA rating.

912This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

create a reference scenario and scenarios with different technology costs and fuel prices.
Additional scenarios are created by exogenously changing parameters related to energy
efficiency and sales shares by technology. Analysis of these different possible futures
demonstrates the relationship between key input variables and outcomes in terms of mobility,
energy use, costs, and emissions.
To create county-level TEMPO, we estimated household-bin- and county-specific discrete
distributions of vehicle ownership from the ACS/PUMS-derived vehicle ownership data. This
allows for better county specificity and larger sample sizes than applying NHTS bin-specificdistributions. 10 While this shifts the data source for vehicle ownership from NHTS, atransportation-focused survey, to ACS, a broader demographics survey, we found that the overallmean vehicle ownership based on ACS was aligned with that from NHTS. However, in both theaggregated NHTS national total and ACS county totals (survey data), vehicle counts were on
average about 11% lower than those in AEO and Polk vehicle registration data, 11 potentiallyreflecting survey bias or some mismatch in definitions of household vehicles. TEMPO addresses
this by scaling its ACS-derived vehicle ownership discrete distributions (shifting of means) tomatch 12 AEO/Polk vehicle count totals in each county for 2017 (the TEMPO calibration year).
Finally, projections to 2050 were created by scaling these disaggregated vehicle ownership
distributions based on the EIA AEO trend in vehicle ownership. 13
Vehicle size class and energy efficiency distributions were also disaggregated into county- andhousehold-bin-specific distributions. We applied NHTS (national) size class distributions to ACS(county) household counts to maintain household bin integrity and consistency so thatappropriate travel behaviors were matched with their likely vehicle owners and vehicle
attributes. Although using detailed Polk registration data about vehicles would result in bettercounty specificity (with directly observed county-specific data providing greater fidelity), it
requires assumptions that enforce all types of households in a county having the same countyspecific distribution of vehicles, which could assign the wrong types of vehicles to many

An alternative simpler approach that would maintain household bin specificity and national model alignment
would be to apply the NHTS (national) bin-specific vehicle ownership distributions to the ACS-derived county-level
household bin counts. However, this would likely cause some loss of county-specific variation that is not explainedby TEMPO’s household bin classification.
11
Total 2017 registered non-fleet light-duty vehicles from Polk: 238 million, compared to total 2017 householdowned vehicles derived from ACS: 212 million.
12
We also find several small outlier counties with very high (up to 9) and very low (0.5) registered vehicles perhousehold based on Polk data, which likely reflect anomalies or quirks in registration behavior or data issues. We reestimate the vehicle counts and distributions for these outlier counties based on that expected from NHTS and ACS
data.
13
AEO’s projection of vehicle ownership is roughly a constant 0.8 vehicles per capita for 2017–2050.
1013This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

households. 14 Further disaggregation of size classification and energy efficiency is limited bydata availability. 15
We then created projections to 2050 for vehicle size class and fuel economy by scaling thesedisaggregated starting points using the national projected trends in size from EIA AEO (EIA2019) and projected trends in fuel economy from NREL’s Annual Technology Baseline (ATB)
Mid and Advanced trajectories (NREL 2020). Similar to the projections for household data, theprojections for vehicle data were scaled uniformly. These attributes are disaggregated andcalibrated to the heterogeneity observed in 2017, and then their values are projected forward
uniformly by the national trend from EIA AEO and NREL ATB. 16 For example, EIA projects acontinued national shift away from compact cars toward SUVs. TEMPO builds on the existing
distribution in preferences seen across individual household bins and counties and projects anation-wide shift in vehicle size preference from compact cars toward SUVs for every household
bin and county. 17 In both EFS and All EV Sales by 2035 scenarios, vehicle energy efficiency arebased on the NREL ATB 2020 Advanced trajectories. 18 In addition, vehicle energy efficiency isfurther adjusted from rated values according to temperature, as described in the next chapter.
Figure 7 and Figure 8 show the disaggregated vehicle data based on the aforementioned methods
for 2017 and reflect the heterogeneity in vehicle ownership and size classes across the UnitedStates represented in TEMPO.

Polk ZIP code/county-level data about size class distributions and make-model information that can be associatedwith EPA-based fuel economy statistics could be used for county-specific detail, but household bin information
would be lost because the vehicle characteristics would be applied uniformly to the mix of household bins in the
county. The trade-off considered is whether county disaggregation or household-bin disaggregation captures theheterogeneity in household vehicle characteristics and attributes.
15
For example, we use EIA AEO’s estimated real-world energy efficiency performance adjusted from test cycle
results, but this varies in reality according to finer heterogeneity in driving and road conditions, road grade, andcity/highway driving. These conditions can be expected to differ by urbanity and county, but is not captured in thiswork.
16
That is, we do not project that certain household bins, counties, or vehicle types will change in vehicle ownershiplevels, vehicle size preferences, or vehicle fuel economy differently than others.
17
Building on this example: Some household bins and counties already have few compact cars and many SUVs. The
national trends in vehicle size class preferences are uniformly applied as rates of change so that the aggregatedprojections of all households and counties align with the national projection. This results in maintained growth inrelative SUV share in these household bins and counties, as opposed to an alternative assumption where there ispotential saturation in these bins and counties and relatively more growth in other bins and counties.
18
However, note that the original EFS study used projections from Jadun et al. (2017) and Moawad et al. (2016),
reflecting lower EV energy efficiencies (and resulting in higher load per EV-mile) than was simulated in TEMPO,
even in the “EFS” scenario. See Scenario Results Summary section for discussion of impact on results.
1414This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Average Vehicle Ownership per Household

Average Vehicles
per Household

<0.5

1.5

2.5

>3.5

Figure 7. Average vehicle ownership per household by U.S. county as of 2017, based on
ACS/PUMS household microdata and Polk registration data

15
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure 8. Proportions of vehicle size class across U.S. counties in 2017 based on Polk registration
data.
Consumers in different areas of the United States have adopted vehicles of different sizes. TEMPO uses these data
to set the baseline for county-specific vehicle size class distributions, which enables projection of consumer
preferences based on existing heterogeneity in size class preferences. Vehicle size class mix affects county-specificvehicle electrification rates and vehicle energy use.

16
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

2.4 Accounting for the Effect of Temperature on EV Loads

Vehicle energy use depends on ambient temperature because of impacts on heating and coolingenergy use and equipment performance (Miller, Arbabzadeh, and Gençer 2020; NREL 2016;
Yuksel and Michalek 2015). EVs are disproportionately affected by temperature compared toconventional internal combustion engine vehicles, due to the sensitivity of batteries and directcurrent fast charging (DCFC) chargers to temperature, lack of engine waste heat to reuse forcabin heating, and the relative efficiency of the electric drivetrain, which magnifies the heating
and cooling load portion of total vehicle energy consumption. For example, the energy used by
the same EV to drive a mile can change by up to 50% at temperature extrema (NREL 2016;
Geotab 2020), heavily impacting vehicle range, charging needs and resulting loads, cost ofdriving, and consumer experience. This means using national annual average fuel economy andenergy efficiency metrics can obscure variation in vehicle energy use in different locations and atdifferent times of day and seasons of the year, which is of particular interest to the NREL
demand-side grid (dsgrid) project and for grid planning and operational studies more broadly.
Therefore, in addition to disaggregating TEMPO inputs spatially by county, TEMPO wasmodified to capture region- and season-specific effects on vehicle energy use and charging.
TEMPO adjusts vehicle energy efficiency and DCFC charger efficiency as a function of ambient
temperature based on vehicle test data (Yuksel and Michalek 2015; NREL 2016) 19 and DCFC
charger test data (INL 2016) reflecting energy use under different temperature conditions. These
data and relationships are consistent with those used in EVI-Pro (NREL 2016) and have beenvalidated against recent 2020 data from Geotab 20 (Geotab 2020). Summaries of these
relationships are shown in Figure 9 through Figure 11.

In the National Economic Value Assessment of Plug-In Electric Vehicles (NREL 2016), temperature-based EVenergy consumption adjustment was based on Yuksel and Michalek (2015)’s fit to FleetCarma/Geotab data shownas “CMU Leaf Fit” and on NREL-collected data on a Ford Fusion.
20
Geotab (2020) describes their data sample as “5.2 million trips taken by 4,200 EVs representing 102 different
make/model/year combinations from model years 2010–2019”. Geotab’s data disclaimer: “Temperature-range curveis derived by Geotab from >4000 connected light duty EVs. Rated range refers to EPA 5-cycle test. This data issubject to change as additional data is collected and analyzed. Please refer to Geotab.com for the most up-to-datetools and analysis. v.1.0 May 2020”
19

17This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure 9. Battery-electric vehicle (BEV) (“CMU Leaf”) and internal combustion engine vehicle(“NREL Fusion”) energy efficiency adjustment for different ambient temperatures based on NREL
2016, Yuksel and Michalek 2015, and Fleetcarma (Geotab) 2014 data

Figure 10. BEV energy efficiency adjustment in TEMPO and EVI-Pro (black dashes) compared to
Geotab (2020) data (colored lines for average, 90th, and 10th percentiles) 21
We note that the current TEMPO and EVI-Pro BEV energy efficiency adjustment (black dashes) deviates fromthe median (50th percentile) of the Geotab (2020) data (blue line), especially at lower temperatures, and therefore
underestimates energy consumption at these low temperatures. In upcoming model releases, we plan to recalibrate
21

18
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Figure 11. Effective DCFC charger efficiency for three different ambient temperatures.
Source: INL 2016

For consistency with dsgrid and other participating models (ResStock and ComStock), we use
exogenous county-specific data for a specific weather year, 2012, from MesoWest/National
Weather Service weather station aggregates collected by the ComStock team (Liu et al. 2023).
From these data, TEMPO extracts a snapshot of average ambient temperatures for each hour ofthe day for every month and county for the 2012 weather year. For each trip or charging event,
TEMPO calculates and applies an adjustment to vehicle and charger energy consumption
according to this temperature at the time of day (hour), month, and county (weather station
closest to county population centroid) of the event. This directly increases (or reduces) the
electric load from charging EVs at these locations and times. While EV adoption and use
decisions may also be indirectly affected by temperature impacts, TEMPO currently only
considers the direct impact of temperature on EV load via adjusted energy intensity. Figure 12
shows ambient temperatures across the United States at two example points in time and theirassociated adjustment to energy consumption.

these adjustments to reflect the best available information on BEV performance in low-temperature conditions.
However, recent and rapid technological changes in reconfigured and improved air conditioning/heat pump systems
in new EVs are expected to mitigate some of the additional energy use at temperature extrema.

19This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure 12. Ambient temperatures (left) and impact on BEV energy consumption (right) across theUnited States for January 2012, 8 a.m., and July 2012, 5 p.m., reflective of spatial, temporal, andseasonal variation in weather conditions.
Data from Liu et al. 2023.

To further demonstrate the range and variation in energy efficiency adjustment due totemperature, Figure 13 shows this simulated adjustment factor for each month and weather
station location in the United States. The national median trend (black line in Figure 13) has EVenergy consumption per mile at its highest in the winter, lowest in the spring and fall shoulder
seasons, and around or just higher than the EPA rating during the summer months. However,each U.S. county has its own weather patterns, and “average” or “median” weather can therefore
be misleading. Following the colored lines for four selected locations in the United States, we
see very different EV energy consumption patterns. A location such as Minneapolis-St. Paul
(blue line) can be considered to have similar temperature impacts on EV energy consumption as
predicted by the U.S. median (black line), with higher energy use in winter months and slightly
higher than rated energy use in the summer too. However, this is only one type of weather
pattern in the United States; in a location such as Sacramento (yellow line), we see larger
20
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

summer and smaller winter peaks, while in a location such as Phoenix (orange line), we see verylarge increases in energy consumption during summer months and no increases in the winter. In
a location such as Los Angeles (green line), we see a year-round EV energy consumption below
the EPA-rated value due to milder weather conditions compared to those in the EPA test cycles.
These distinct variations in weather patterns and their impact on energy use are incorporated into
TEMPO’s county-level simulation results.

Figure 13. Adjustments to EV energy consumption in TEMPO based on weather year 2012
temperatures.
The data for all 975 U.S. weather stations used in this study (from Liu et al. 2023) are shown in grey. The national
median pattern is shown in black. Lines for four selected locations (colored lines) demonstrate distinct seasonalpatterns, which are used to adjust EV energy consumption with location specificity.

21This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

2.5 Developing Vehicle Electrification Scenarios
The future is already here; it’s just not very evenly distributed.
– William Gibson
TEMPO has the capability to project vehicle electrification endogenously using a utility-based
technology choice logit algorithm (Muratori, Jadun, et al. 2021), or to apply sales shares basedon exogenously defined scenarios. Exogenous sales shares are uniformly applied to all vehicle
size classes. 22 For this report, we simulated three exogenous passenger light-duty EV adoption
scenarios, shown in Figure 14:
A. AEO Reference Case, with EV adoption aligned with the Reference Case in the 2018 EIA
Annual Energy Outlook (AEO) (EIA 2019), which was used to calibrate the TEMPO
model.
B. EFS High Electrification, with EV adoption in line with the High Electrification scenario
in the NREL Electrification Futures Studies (EFS) (Mai et al. 2018).
C. All EV Sales by 2035, assuming U.S.-average EV sales reaching 50% in 2030 and 100%in 2035, in line with various announced targets.

Figure 14. Projected national technology sales shares for each EV adoption scenario

In the AEO Reference Case scenario, mobility evolution and technology adoption were
calibrated to align with the EIA AEO 2018 Reference Case. The EIA AEO Reference Case is abaseline projection that assumes no new policies and static technology, reflecting “business-asusual” conditions that should not be mistaken for a scenario representative of what is likely tohappen in the future (EIA 2019). In the EFS High Electrification scenario, EV adoption resultsfrom “a combination of technology advancements, policy support, and consumer enthusiasm that
We note that as of 2023, EVs still do not yet exist in every vehicle size class and segment, but also note rapid
growth in EV model options in recent years, globally and in the United States.

22

22This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

enables transformational change in electrification” in line with that assumed in the NREL EFS(Mai et al. 2018) High Electrification scenario. This is characterized by advanced EV cost andperformance improvements and strong EV policies. The High Electrification county-specificprojections represent pathways and outcomes consistent with the national EFS scenario rather
than precise projections of the future for each county. In the All EV Sales by 2035 scenario,
national EV adoption reaches 50% in 2030, aligned with the national goal announced in a 2021
Biden Administration Executive Order, 23 and 100% by 2035, a target roughly aligned withannounced commitments of several major automakers and “ICE Phase-Out” targets of subnational governments and global coalitions, including the 2035 timeline set in the zero-emission
vehicle (ZEV) state mandates regulating manufacturer fleet composition in California andseveral other U.S. states that have announced they will follow with the same ambition (NewYork, Washington, Massachusetts, Oregon, and Vermont). 24 In this scenario, plug-in hybrid
electric vehicles (PHEVs) are assumed to be 20%–30% of the total EV sales share in the 2025–
2035 transition time frame before tapering off to 0% by 2045, based on expert opinion and latest
trends in BEV technology, infrastructure, and policies.
While exogenous scenarios and projections are defined at the national level, EV sales and stock
data disaggregated to the county level are only available for historical data. In other words, thenational-level scenarios do not project where EV sales may be lower or higher than the national
average in future years. We disaggregate projected EV adoption by county by anchoring eachcounty’s EV sales share for the base year to county-level vehicle registration data from Polk inthe base year 25 and then applying the exogenously defined national-level growth rate trends
uniformly across all counties. This approach captures the current heterogeneity in EV adoption
observed across U.S. counties and extends this into the future, assuming similar positioning ofcounties in terms of EV adoption either leading or lagging the national average growth trend.
Counties in states with ZEV regulations aligned with California 26 are also assumed to lead thenational average growth trend (i.e., grow faster and earlier than the national average).

“50% clean car sales by 2030” https://www.whitehouse.gov/briefing-room/statements-releases/2021/08/05/factsheet-president-biden-announces-steps-to-drive-american-leadership-forward-on-clean-cars-and-trucks/
24
Section 177 of the Clean Air Act allows other states to adopt California’s vehicle emissions standards. As of 2023,
five states (Washington, Massachusetts, New York, Oregon, and Vermont) have adopted standards equivalent to themost recent (finalized in 2022) California Air Resources Board Advanced Clean Cars II (ACC II) program, which
will require manufacturers to sell 100% ZEV by 2035. https://cleantechnica.com/2022/12/23/canada-joinscalifornia-oregon-washington-vermont-in-clean-transportation-push/. In addition, internationally, countries such as
Canada (2035), the UK (2035), Norway (2025), France (2040), Netherlands (2030), and Austria (2030) haveannounced 100% ZEV targets, either via fleet composition regulation and credit compliance or GHG standards.
https://theicct.org/phase-out-map-ldv/.
25
While TEMPO’s base simulation year is 2017, it is possible and useful for exogenous EV sales share projections
to be anchored and calibrated to the most recent EV sales data (e.g., 2022). We expect to regularly update thesescenarios of EV sales, given the rapid pace of change in EV adoption across the United States.
26 As of 2023, states that have joined California in its Low-Emission Vehicle (LEV) and ACC I ZEV program were
Connecticut, Maine, Maryland, Massachusetts, New Jersey, New York, Oregon, Rhode Island, and Vermont.
Several other states (Colorado, Minnesota, Nevada, Virginia, and New Mexico) have legislated or previously
indicated political intentions to join in future years. Delaware, Pennsylvania, and Washington, D.C., have so far
followed the California LEV standards but not the ZEV program. Roughly, these states account for 20%–40% of the
U.S. new vehicle sales market. https://ww2.arb.ca.gov/sites/default/files/202205/%C2%A7177_states_05132022_NADA_sales_r2_ac.pdf, https://www.c2es.org/document/us-state-clean-vehicle23

23
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Figure 15 show the result of the disaggregation of exogenously specified national sales shares
into county-level sales shares in each of the three scenarios. In every scenario, by 2022, many
counties, particularly in states with ZEV regulations, had EV sales shares well over the national
average of 7%. Although in the AEO scenario, electrification is slow on average (2% to 15% by2050), some counties reach 70% EV sales share by 2050, while others stay near zero, reflecting
high heterogeneity in counties. Similarly, in the EFS and All EV Sales by 2035 scenarios, somecounties reach 90%+ EV sales shares in 2030, though the national average is only at about 50%–
60%. Figure 16 focuses on the disaggregated exogenous sales shares for the All EV Sales by2035 scenario, showing them on a choropleth of the United States. We can see that current andnear-term (2025) EV sales shares are highly heterogeneous across the United States, with muchhigher sales in ZEV states, and that future 2030 EV sales shares, when the national average hits
the 50% goal in the Biden administration executive order, would be temporarily concentrated incounties with projected leads in sales share while other counties stay lagged in EV sales,
potentially due to infrastructure, behavioral, cultural, and/or political reasons. However, because
this scenario follows a path of national sales being 100% EV by 2035, every county is specified
to reach 100% by 2035 in this scenario, thereby projecting no heterogeneity in sales shares by
2035.

Rest of USZEV State
National
Average

Figure 15. Projected ZEV sales shares for all counties (each colored line) in the United States forthree TEMPO vehicle electrification scenarios.
Line colors indicate whether the county is in a state with ZEV mandates that regulate fleet composition. Dashed blacklines represent the exogenously specified national projections to which the county-level sales shares average.

policies-and-incentives/. O'Dea, J., State adoption of clean vehicle standards (updated May 6, 2022). Online at:https://docs.google.com/spreadsheets/d/1ihjIzOGLb7cfrvblaNwC74sfFC-LBC26HWDWovgFGuw/edit#gid=024This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

All LDV Sales EV by 2035 Scenario, projected year 2025
National average in projected year 2025: 8%

EV Sales
Share

100%

75%

All LDV Sales EV by 2035 Scenario, projected year 2030
National average in projected year 2030: 50%

50%

25%

0%

Figure 16. TEMPO’s projected ZEV sales shares in 2025 and 2030 for all counties in thecontiguous United States in the All EV Sales by 2035 scenario.

25
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

2.6 Simulating Week-long Hourly EV Charging Profiles

TEMPO was previously limited to simulating estimates of annual energy consumption. We
enhanced TEMPO to generate household EV charging load profiles for each county, based on an
aggregation of individually simulated households, vehicles, and trips. Appendix B shows further
detail on how simulated data are processed, from TEMPO’s individual household-level trips to
EV consumption profiles, EV charging schedules, and the county-level load profile data for
dsgrid. 27
TEMPO first simulates a household’s mobility needs, then their intra-household and travel mode
choice given their available options (vehicle and mode availability), and then the associatedenergy use from each trip. For the simulation of temporally resolved charging load profiles, we
extract the trips that use household EVs and compile week-long hourly trip schedules for each
EV by building from weekday/weekend and time-specific travel distributions derived from
NHTS. We start each week on a Monday and simulate 12 week-long travel profiles for eachyear, one representative week per month of the specified weather year, to represent seasonal
impacts. 28
Then, TEMPO converts these week-long EV trip schedules into envelopes of minimum andmaximum battery energy required to fulfill that demand. The envelope of each representative
vehicle is a feasible space for charging behavior, given the state of charge that resulted fromprior consumption and charging, plus energy needs for upcoming trips. This is illustrated in
Figure 17, where the top two charts represent TEMPO’s simulated vehicle and energy use, andthe bottom chart shows the resulting feasible charging envelope.
TEMPO generates an “immediate” charging profile (also called “as soon as possible”), which is
the upper bound of the charging envelope, outlined in red in Figure 17. Each vehicle charges assoon as plugged in until fully recharged or taken on another trip. Under “immediate” charging
scenarios, the charging profile closely follows the vehicles’ energy consumption profile as EV
batteries are charged at the first available charging opportunity. This means drivers in TEMPO
do not consider any factors, such as electricity prices or charger convenience, that could
influence their charge timing. In this report, drivers and vehicles in TEMPO are also assumed tohave ubiquitous access to charging whenever a trip is not in progress, though TEMPO can
incorporate spatially and temporally disaggregated charging availability to better reflect realistic
charging access. The current “immediate + ubiquitous” charging strategy provides a simple
approximation reflecting an easily imagined behavioral pattern in which drivers plug in their EV
whenever they have ended a trip and there is an EV charger at all parking locations. It is a worstcase scenario from a demand-side flexibility perspective: EVs do not provide any flexibility, anddrivers, only considering mobility needs, aim to maintain as high as possible battery state ofcharge at all times (thus minimizing any possible range anxiety). This also represents a minimal
assumption on charging behavior, one that may be loosely supported by anecdotal information
on BEV “charging anxiety” and doesn’t require any supply-side information on electricity prices
or other demand response programs that are used to influence consumer behavior and choices.
The lower bound of the charging envelope, outlined in green in the bottom chart of Figure 17,Footnote 37 and the dsgrid documentation https://dsgrid.github.io/dsgrid/data_format.html discuss the data formatof representative weeks for each season, which are then extrapolated to 8760 profiles either in TEMPO or dsgrid.
28
As opposed to following the actual date-specific hourly weather conditions of the weather year.
27

26This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

corresponds to a “delayed” or “as late as possible” charging strategy where the EV is plugged inas late and rarely as possible. While there is no behavioral evidence for the realism of such a
strategy, pairing it with the “immediate” profile can provide bounds on flexibility that EV
charging can provide to the power system while still meeting all mobility needs.

VehicleState of Charge

Battery Energy
Consumption

driving

Figure 17. EV charging envelopes characterize the charging opportunities that are consistent withvehicles’ consumption patterns, charging opportunities, and technological capabilities.
The charging envelopes are bounded in red and green, red being the “immediate” or “as soon as possible” charging
strategy and green being the hypothetical “delayed” or “as late as possible” charging strategy. These strategies also
reflect EV batteries being charged to their maximum and minimum state of charge, respectively (maximum due tophysical and technological constraints, and minimum due to energy needed to support travel demand).

TEMPO has the ability to generate any charging pattern within this feasible envelope for eachsampled EV. Other patterns worth exploring in further studies include those that are optimal fortime-of-use electricity rates and those with centralized or semi-centralized managed charging
that provide grid services by scheduling the charging (and potentially discharging) of many EVs.
Determining actual or optimal charging loads within this envelope requires co-simulatingtransportation and power systems, as was done in Hale et al. (2022), where TEMPO was further
developed to estimate EV charging patterns within the feasible envelope for the purposes ofmaximizing value to the bulk power system.
27
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Under the immediate charging strategy, charging rates and sessions are bounded by trip energy
demands, vehicle battery capacities, and charger capacities. TEMPO currently distinguishes two
types of charging according to charging location: regular charging, which includes
residential/workplace/commercial, 29 and public DCFC. Both types of charging are further
differentiated by power level: Level 1 (L1) or Level 2 (L2) for regular charging, and 50 kW or
150 kW for public DCFC (Table 2).
While assumed ubiquitous here, limited charging infrastructure availability and charging power
levels specific to locations and times would impact the feasible envelope by limiting the abilityof EVs to charge where and when there are fewer chargers available than EVs that could plug in.
Future charging patterns could reflect trade-offs between overnight charging at home, if
available, and opportunity charging at workplaces or public locations. Other considerations
related to charging include range anxiety, responsiveness to prices and participation in demand
response programs, and ability and willingness to change parking behavior to access charging.
Finally, one significant limitation of the current TEMPO modeling is that all charging loads are
assumed to be associated with the household’s home county. While residential charging can besafely associated with the household’s home county, some workplaces and/or charging during
long road trips may be associated with neighboring or farther counties, particularly thosecontaining major travel corridors. However, in the immediate and ubiquitous charging scenarios
considered in this report, most of the charging load is done at local residential, workplace, anddestination chargers, and all opportunities for charging are used immediately (so even for roadtrips, EVs typically start off fully charged from the previous charging session). Therefore, theamount of misallocated charging load here in this current work is limited. This issue should be
revisited in future work and scenarios where there is more public charging away from home.
Table 2. Charger Assumptions Used in TEMPO for dsgrid Scenarios
Charger Type

DetailMaximum Charge Rate

Regular charging

Includes residential, workplace, andpublic commercial/community

95% at 7.2 kW (L2)
5% at 1.4 kW (L1)

Public DCFC

Used when trips exceed vehicle
range

75% at 50 kW
25% 150 kW

TEMPO currently does not geolocate driving or resting events and therefore does not distinguish between
residential, workplace, or public charging, with the exception of trips that exceed the electric range of a vehicle,
which necessitate a charging stop that is assumed to be at a public DCFC station. Simulating trip purpose or
origin/destination types, possibly informed by NHTS, would allow TEMPO to model more specific charging
availability and infrastructure needs.

2928This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

3. Results: Charging Load Scenarios

In this section we provide a high-level summary of the three projections created for this report(Section 3.1) and then demonstrate the spatial and temporal heterogeneity in those data byfocusing on the All EV Sales by 2035 scenario (Section 3.2).
In general, projecting future EV charging loads is a complex task fraught with many
uncertainties. A real-world charging profile depends on vehicle characteristics (e.g., energy
efficiency), travel behavior (e.g., amount of travel, timing, and duration of trips), usage details(e.g., outdoor temperature, driving styles), infrastructure availability (e.g., location and types ofchargers), policy (e.g., electricity tariffs, charger-specific rates, demand response programs), andcharging behavior (e.g., preferences and decisions made around when and where to charge).
Because the EV market is currently very small, undergoing rapid technological change, andheavily concentrated in limited consumer segments, empirical data are scarce, and in any caseare not necessarily appropriate for long-term extrapolation. As such, we have simulated feasible,
rather than likely, charging loads as part of possible scenarios to illustrate plausible outcomes,
but these should not be viewed as definitive assessments of future loads associated with EV
charging.

3.1 Scenario Result Summary

Because TEMPO is nationally comprehensive and simulates representative households and theirtrips and EVs for every county over representative days, weeks, seasons, and future years,
TEMPO can produce projections of aggregate U.S. load by summing up all the results. This isshown in Figure 18 with total annual energy (TWh/yr) over the projected years and scenarios,and peak hourly demand (GWh/h) 30 for each month, selected year, and scenario. In the All LDVSales EV by 2035 scenario, the electric load from household-owned EVs reaches 930 TWh/yr.
This is somewhat higher than the 750 TWh/yr in the EFS scenario here, primarily due to a fasteracceleration and higher proportion of EV sales over the medium term, reaching 100% by 2035
(compared to EFS, which projects EVs reaching approximately 65% of light-duty vehicles by2035 and reaching 95% only by 2050). In addition, projections of EV energy efficiency havechanged significantly since the EFS project, with 20-40% less energy used per mile in 2050 thanin the original EFS modeling inputs, resulting in lower load due to LDV electrification, in bothscenarios here 31, compared to the original EFS scenario results. The 930 TWh/yr load in 2050 in
the All LDV Sales EV by 2035 scenario would be approximately 19% of future load 32, which
remains within the range of previous estimates (13-29%) of electric load from full LDV
electrification, as discussed in the Introduction.

Hourly load (e.g., in GWh/h) is the finest temporal resolution predicted by TEMPO. The power draw during times
within an hour could be higher in terms of instantaneous demand—e.g., in instantaneous gigawatts (GW) measured
over seconds or minutes—but the hourly load is the total electricity demanded in the specified hour. Peak hourly
load refers to the highest hourly load over all hours of the specified months and years.
31
Results here reflect a TEMPO scenario with EV adoption in line with the EFS High Electrification scenario, butwith EV energy efficiency inputs updated since EFS.
32
Assuming 930 TWh/yr is the only addition to current load
3029This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

2036

Peak Hourly Load from EV Charging (GWh/h)

1000

EV Load (TWh/yr)

800

600

400

200

0
2020

2030

Year

2040

2050

200

150

100

50

2050

0
1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112

Month

Scenario
All LDV Sales EV by 2035
EFS High Electrif ication
AEO Ref erence

Figure 18. Aggregate U.S. annual and peak hourly EV load under three scenarios

3.2 Spatial and Temporal Heterogeneity in EV Load

We now focus on showing the simulated results of the All EV Sales by 2035 scenario. First, weshow the aggregate U.S. charging load profile for average weekdays for every month of the year
in Figure 19. Weekday EV charging load under ubiquitous and immediate charging behavior
assumptions has a diurnal pattern with a morning and afternoon peak when substantial numbersof vehicles end their trips and start charging. We find aggregate U.S. EV charging load reaches
peak hourly loads 33 of 82 GWh/h in 2036 and 181 GWh/h in 2050 during a weekday evening
hour 34 in the coldest winter and hottest summer months (dark purple and bright orange lines,
respectively, in Figure 19). Aggregate U.S. EV charging load is generally larger in wintermonths because vehicular heating loads add to vehicle energy consumption; however, vehicularcooling loads in the peak afternoon hours in the summer 35 appear to be comparable in magnitude
to heating loads in those hours. Morning peaks are higher in the winter than in the summer. Loadgrows from 2036 to 2050 primarily due to large increases in EV adoption and continued growthin vehicle size, vehicle ownership, and population, but the growth is mitigated by improved
energy efficiency. In this specified scenario and charging strategy, other factors such as
Hourly load (e.g., in GWh/h) is the finest temporal resolution predicted by TEMPO. The power draw during times
within an hour could be higher in terms of instantaneous demand (e.g., in GW), but the hourly load is the total
electricity demanded in the specified hour. Peak hourly load refers to the highest hourly load over all hours of thespecified months and years.
34
TEMPO develops trip and charging schedules in local time, which means the timing of loads across the UnitedStates depends on location. The aggregate U.S. loads presented here have been aggregated with time offset toCentral Time. Similarly, in dsgrid, loads are spatiotemporally specified so that time zone can be considered.
35
Note that TEMPO does not currently consider any seasonal variation in travel or trip behavior.
33

30This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

evolution in charging power, vehicle ownership, travel demand, and mode choice contribute
relatively little to changes in load.

EV Load (GWh/h)

2036

2050

150
100
50
0
0

6

12

18

Month

0

Hour of Day

6

12

18

Mar Jun Sep Dec

Figure 19. Aggregate U.S. EV charging load profile for an average weekday for the All EV Sales by2035 scenario for projected years 2036 and 2050 under the immediate and ubiquitous charging
strategy, with seasonal variation shown by line color (blue for winter, orange for summer)

The spatial and temporal resolution enabled by county-level TEMPO provides data for countylevel hourly load profiles. To illustrate the importance of modeling the heterogeneity of charging
load across the United States, we present the projected 2036 EV charging load profiles at thestate level for the contiguous United States in Figure 20 and for eight example counties in Figure
21.
The spatial heterogeneity in EV load profiles shown in Figure 20 and Figure 21 arises from a
combination of many factors in TEMPO. Setting aside the inherent differences in population orhousehold count between counties, the county-specific charging load on a total, per-capita, or
per-household basis first depends on factors such as vehicle ownership [vehicles/household] andEV adoption levels [% EV], and these factors vary based on the disaggregated household andcounty characteristics and available travel options simulated in TEMPO. These determine thenumber of EVs or EV/capita in a county. Then, the scaled charging load per EV depends onvehicle use [VMT/EV], 36 which varies by household urbanity/income/size and travel options;
TEMPO does not make explicit assumptions about VMT per vehicle or household; rather, TEMPO preserves
travel needs and demand (as estimated from NHTS) and simulates the adoption and use of new options, assuming
new options do not induce behavioral changes. TEMPO does simulate households with different VMT, thereby
allowing for self-selection of households with lower or higher VMT into EV adoption. TEMPO also simulates
households with a portfolio of vehicles, allowing for shifts in VMT within the household set. TEMPO projections so
far have not resulted in significant differences in “eVMT” compared to VMT of conventional vehicles. While recent
studies have found low eVMT based on early (2014–2017) California EV adopters and their residential meter data
(Burlig et al. 2021) and from limited EV data from the 2017 NHTS, more recent data reflecting modern EV
capabilities and ranges show that future eVMT is not expected to necessarily differ significantly from typical or
average VMT. We also note that projections of possible futures to 2050 require extrapolation beyond inputs basedon existing adopters and behaviors.
36

31This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

vehicle and charger energy efficiency [kWh/mi], which varies by vehicle size and type(dependent on county and household); weather (varies by county, time, and season); and charger
type. These factors and their spatial heterogeneity combine to form the spatial patterns of EV
load shown in Figure 20 and Figure 21. Mathematically, we can decompose EV load into thesefactors with a modified Kaya-style identity:
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐸𝐸𝐸𝐸 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 �= 𝑉𝑉𝑉𝑉ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜ℎ𝑖𝑖𝑖𝑖 �𝑘𝑘𝑘𝑘ℎ�𝑦𝑦𝑦𝑦 ∗ 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐

𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
𝐸𝐸𝐸𝐸𝐸𝐸
𝑚𝑚𝑚𝑚/𝑦𝑦𝑦𝑦
𝑘𝑘𝑘𝑘ℎ� ∗ 𝐸𝐸𝐸𝐸 𝑠𝑠ℎ𝑎𝑎𝑎𝑎𝑎𝑎 �� ∗ 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑉𝑉𝑉𝑉𝑉𝑉 �� ∗ 𝐸𝐸𝐸𝐸 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ��𝐸𝐸𝐸𝐸
𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐
𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
𝑚𝑚𝑚𝑚

This formulation can reveal how the variation in factors dependent on geography and scenario
impacts total EV load and it is being used in ongoing TEMPO work to analyze the inherent
heterogeneity and uncertainty in EV load across the United States and in future scenarios.

32
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WI

VT

NH

NY

MA

ME

EV Charging Load Profile scaled for population (kWh/h/capita)

0.3
0.2
0.1
0.0
WA

ID

MT

ND

MN

IL

MI

OR

NV

WY

SD

IA

IN

OH

PA

NJ

CT

CA

UT

CO

NE

MO

KY

WV

VA

MD

DE

AZ

NM

KS

AR

TN

NC

SC

DC

OK

LA

MS

AL

GA

0.3
0.2
0.1
0.0
0.3
0.2
0.1
0.0
0.3
0.2
0.1
0.0

0

6 12 18
0.3
0.2
0.1
0.0

0

6 12 180

6 12 18
0.3
0.2
0.1
0.0

TX
0.3
0.2
0.1
0.0

0

0

6 12 18

6 12 180

6 12 180

6 12 180

6 12 18Month

Mar Jun Sep Dec

0

6 12 18

6 12 18FL

0

Hour of Day

0

RI

6 12 18

National
AnnualAverage

Figure 20. State-level per-capita EV charging load profiles for an average weekday for the All EV Sales by 2035 scenario for projected
year 2036 under the immediate and ubiquitous charging strategy, for the contiguous United States, with seasonal variation shown byline color (blue for winter, orange for summer) and U.S. annual average in black dashes.
The EV load profile variation simulated in TEMPO for this report and the dsgrid project arises from a combination of differences in EV adoption, VMT, vehicle size,and climatic impacts on EV efficiency. Northern states with higher rurality and states with higher EV adoption tend to have larger loads.

33
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Autauga County , AL

Boulder County , CO

Denv er County , COEagle County , COHarris County , TX

Jef f erson County , CO

Maricopa County , AZ

New York County , NY

San Francisco County , CA

Santa Clara County , CA

0.4
0.3
0.2
0.1
0.0

EV Charging Load scaled per capita (kWh/h/capita)

0.4
0.3
0.2
0.1
0.0
0.4
0.3
0.2
0.1
0.0
0.4
0.3
0.2
0.1
0.0
0.4
0.3
0.2
0.1
0.0

0

6

12

18

Month

0

Hour of Day

6

Mar Jun Sep Dec

1218

National
AnnualAverage

Figure 21. County-level per-capita EV charging load profiles for an average weekday for the All EV
Sales by 2035 scenario for projected year 2036 under the immediate and ubiquitous charging
strategy, with seasonal variation in color (blue for winter, orange for summer) and U.S. annual
average in black dashes.
The load profile variation simulated in TEMPO for this report and the dsgrid project arise from a combination ofdifferences in EV adoption, VMT, vehicle size, and climatic impacts on EV efficiency. The urbanity and density of acounty heavily impacts vehicle ownership and use, with New York (containing Manhattan) being a special case.
Certain counties such as Santa Clara (containing San Jose and Silicon Valley) also have particularly high EV
adoption.

34
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Of note in Figure 21, lower vehicle ownership and use in highly urban counties such as New
York County, New York (Manhattan), and San Francisco County, California, reduce theirvehicles per capita and VMT per EV, explaining their relatively lower charging load per capita.
Higher EV adoption levels in certain states such as California increase their EV share and EV per
capita. VMT per mi tends to be larger in rural areas, correlated with larger vehicles, and areas
with more temperature extrema.
While differences in household mixes and demographics modeled in TEMPO can explain part ofthe difference in EV load, county-specific factors enabled by this work can help produce load
simulations with better locational specificity. For example, Jefferson County, Colorado, andHarris County, Texas, have relatively similar household mixes, as shown from the demographics
example in Figure 5, resulting in similarities in simulated transportation demand and vehicle
preferences. However, county-specific factors such as EV adoption, vehicle size mix, andtemperature effects further differentiate the EV energy usage and load profile in these twocounties in TEMPO’s county-level simulations beyond household bin mix, as can be seen in the
middle panels of Figure 21.

35
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4. Conclusions

This report documents enhancements made to the TEMPO model to project spatially andtemporally resolved national-scale passenger EV charging load profiles and summarizes three
scenarios and corresponding data sets created for the NREL dsgrid project. In brief, TEMPO wasenhanced from a national and annual model focused on aggregate energy demand to include
county-level projections of household EV hourly charging load profiles, accounting forheterogeneity in consumers, travel, and temperature affecting EV energy demand. In alignment
with NREL’s forward-looking grid modeling, three scenarios for EV adoption covering 2020–
2050 were created: AEO Reference Case, EFS High Electrification, and All EV Sales by 2035,
and associated data sets have been included in dsgrid for public use.
Overall, TEMPO was already a projection model that coupled known relationships between
demographics, urbanity, mobility demand, transportation mode selection, and vehicle types totravel patterns and population projections to create realistic scenarios of transportation energy
use for the United States. However, geographically resolved hourly charging profiles are
required to support large-scale capacity expansion (investment) and production cost (operational)
modeling of bulk power systems. In this work, we enhanced TEMPO to capture the distributionof each county’s population across TEMPO’s native 60 household bin resolution, which capturesurbanity and sociodemographic variables; determined vehicle ownership, attribute, and typepreferences by county; and used 2012 weather data for 975 locations to capture the impact of
outdoor temperature on vehicle energy efficiency. We also developed county-disaggregated EV
adoption scenarios and simple algorithms to simulate representative week-long EV trip andcharging schedules based on heuristics and assumed charging behavior and infrastructure
availability.
Future EV charging load profiles are highly uncertain. TEMPO attempts to model realistic
mobility demands and transportation mode and technology choices given configurable sets of
inputs; brackets large uncertainties through the creation of scenarios; and conducts sensitivity,
benchmarking, and validation analyses so its results can be understood in context, used for crosssectoral analyses, and continuously improved. Extending TEMPO to produce county-level,
hourly charging profiles introduces even more uncertainties—for example, future demographic
composition and urbanity levels, charging infrastructure build-out, and charging behavior, all ofwhich could develop and change differently in different counties over time. In this work wemake basic assumptions along all of these dimensions: Urbanity of counties is held constant, anddemographic distributions are held constant while population grows according to national trends.
In these scenarios, we make the simplifying assumption that all EVs plug in as soon as they parkand charge (immediate and ubiquitous charging), until their batteries are full or they depart on
their next trip, whichever comes first (uncoordinated charging). We also assume all charging is
associated with the household’s home county.
As described in Section 2.6, TEMPO can simulate EV charging flexibility with bounded
envelopes in which EV charging can be managed to meet all mobility requirements. However,determining actual or optimal charging loads within this envelope requires co-simulatingtransportation and power systems, as was done in Hale et al. (2022), where TEMPO was further
developed to estimate EV charging patterns within the feasible envelope for the purposes ofmaximizing value to the bulk power system.
36
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There is significant work ongoing and more work possible (especially coupled with realistic
charging infrastructure scenarios) to explore different EV charging behaviors and the nexus
between transportation electrification and electricity systems transformation, including
coordination with the grid to provide value to the power system via demand response; scheduled,managed, or opportunistic charging; vehicle-to-grid (V2G) services; or direct use of vehicle
batteries to power loads (V2X) (Hoehne and Chester 2016; Anwar et al. 2021).
Given this project’s limited scope and simplifying assumptions, future work could include:
•
•

•
•
•
•
•
•
•
•
•

Incorporation of detailed baseline and projected variation in residential and public parking
and EV charger infrastructure availability, by county and household bin.
Consideration of trip purpose and expected origins/destinations, by hour, to improve
representation of charging availability, options, behavior, and appropriate charging load
assignment (i.e., for counties with significant public charging of vehicles from othercounties).
Consideration of short- and long-term travel flexibility and changes in EV usage enabled by
new technologies, behaviors, business models, and policies such as telecommuting, ridehailing, micromobility, e-commerce, and vehicle automation.
Developing advanced charging strategies and decisions informed by spatiotemporal-specificelectricity supply and prices.
Improved resolution of charging efficiency, dependent on charging location, type,power/speed, and behavior.
Enhancement of the representation of county-level vehicle stock and sales, withconsideration of vehicle age, used vehicles, and county-specific sales, survival/retirement,
and vehicle transfers.
Research to determine the appropriate temporal resolution for capturing weather and seasonal
impacts and longer-term impacts of climate change on both energy efficiency and travel
behavior.
Subsequent support for multiple weather year and typical meteorological year scenarios.
Development and validation of spatially and temporally resolved policies, regulations,infrastructure, and prices affecting EV adoption and use.
Incorporation of county-level population projections and/or more detailed demographic andurbanity projections.
Extension to charging loads from electrification of:o Non-household light-duty vehicles (e.g., taxi, ride-hailing, mobility-as-a-service fleets).
o Light-, medium-, and heavy-duty vehicles used for non-household/passenger purposes,
e.g., freight, service, vocational, work.
o Other vehicles and travel modes such as buses, transit, micromobility, rail, maritime,
aviation, and off-road vehicles (e.g., construction, agriculture, mining).

••

Extension to quantify indirect transport electrification electricity needs (i.e., electricity
consumed to produce hydrogen and other transportation fuels) and their temporal flexibility.
Inclusion of other metrics such as activity/service, vehicle sales/stock, costs, emissions, andenergy demand (not only electricity) projections in the data transferred to and published in

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•••dsgrid, which would be helpful in producing normalized metrics and/or in the evaluation ofremaining electrification potential.
Research to compare simulated results against real-world observed EV loads—e.g., Burlig etal. (2021), Qiu et al. (2022)—and validate behavioral assumptions and load impacts affecting
spatial and temporal patterns, such as rebound and response to pricing.
Research to compare simulated results against results from other models—e.g., NREL’s EVIPro (NREL 2016); Miller, Arbabzadeh, and Gençer (2020).
Validation via comparison of intermediate results such as heterogeneous VMT and fueleconomy (mpg)—e.g., BTS (2021), Zhou et al. (2021).

Table A-1 also discusses some of the limitations and potential enhancements of TEMPO’s
current representation of households, vehicles, infrastructure, economics, and travel behavior.
Limitations stated, this work developed three scenarios of annual hourly load projections forhousehold EV charging for all counties in the contiguous United States projected through 2050.
We believe this is a first-of-its-kind data set, the value of which is evident when we observe just
how heterogenous EV adoption, use, and charging is in the simulation results. The spatial andtemporal heterogeneity in the projections emerges from coupling the on-the-groundheterogeneity of today in urbanity, household composition, vehicle preferences, and weather,with the TEMPO projections of mobility need, transportation mode choice, and vehicle choice.
The result is data and supporting modeling tools that can be used today to inform locationspecific grid planning and operational models, as well as comprehensive, scenario-spanning, andinternally consistent projections of EV adoption, use, and charging load.

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Appendix A. Disaggregation and Enhancement of
TEMPO Inputs Possible in Future WorkTEMPO Data
Input

Model Resolution

Potential Enhancements, Disaggregation, andData Sources

Household counts

60 bins for each of
3,143 U.S.
counties

Enhancements of projections of migration,
urbanization, household size, and driver licensing are
possible. Potential data from LandScan.

Vehicle stock by
age/vintage, andsurvival/retirement
schedule

National

Endogenous vehicle ownership and household-levelvehicle dropping is a work in progress.
County-level preferences for “age” (i.e., used vehicles)
can be inferred from Polk/Experian vehicle registration
data.
County-level vehicle survival may vary due to regional
factors such as salt, heat on battery degradation,
VMT; vehicle factors such as ownership,
maintenance, vehicle quality; and demographic
factors such as income-based preferences.
County-level stock and regional survival, paired with arepresentation of used vehicle transfers, could better
represent vehicle stock and sales, but remains a workin progress.

Vehicle energy
efficiency

Differentiated bysize class andweather

Further disaggregation of vehicle energy use possible
to distinguish preferences for vehicle
luxury/sport/efficiency, vintage specificity, and driving
type by urbanity.
More detailed and alternative weather data could
further inform vehicle energy use, such as locationand season-specific road conditions (Moniot et al.
2021; Borlaug et al. 2020)

EV charging
availability (bycharger type/location type andby time)

Urbanity-specific(5)

In current scenarios and data for dsgrid, ubiquitous L1and L2 charging and ubiquitous DCFC availability(only used for trips that exceed range) were assumed.
County-specific residential charging availability data
available from Ge et al. (2021).
County-specific estimates of public charging areavailable from the Alternative Fuels Data Center(AFDC), EVI-X, early National Electric Vehicle
Infrastructure (NEVI) plans, and are in development.
Time-specific trip purpose and locational data
available from NHTS (2017).
To inform how much charging load should be
assigned within or to neighboring or farther counties,
the U.S. Department of Transportation Highway
Performance Monitoring System could be used toInfrastructure

VehicleHousehold

Table A-1. TEMPO Data Inputs That Could Be Further Explicitly Spatially Disaggregated or May
Benefit From Further Enhanced Representation

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TEMPO Data
Input

Model Resolution

Potential Enhancements, Disaggregation, andData Sources
approximate travel activity within/across counties andthe likely location of public charging. EVI-X/EVIRoadTrip may also be helpful.
In addition to charging availability, further detail on
charging efficiencies by charging speed/type, location,and behavior may become important with morevariations on charging scenarios.

Urbanity-specific(5)

In current scenarios and data for dsgrid, calibrated to
NHTS and/or assumed. Data available from BTS and
NTD, but difficult to disaggregate comprehensively for
3,143 counties.

Mobility as aservice (MaaS)and micromobility
availability (bymode)

Urbanity-specific(5)

In current scenarios and data for dsgrid, calibrated to
NHTS and/or assumed.

Energy prices

National, by
scenario

Although most demand (e.g., trip frequency andlength) is configured to be price-inelastic in TEMPO,
structural differences in regional prices could drive
differences in behavior and choices.
Spatially differentiated gasoline and diesel prices(e.g., with impact of differences in fuel taxes andrefining capacity) available from EIA, GasBuddy,
OPIS (Gohlke 2021).
Spatially differentiated and temporally specific (e.g.,time-of-use or hourly prices) electricity rates available
by utility region in URDB (NREL 2022) or fromsamples and/or historical wholesale market prices.
General projections available from ATB (NREL 2020)
and/or NREL Standard Scenarios.
Vehicle charging costs, especially for future medium/heavy-duty vehicle charging, are dependent onequipment/infrastructure costs, charging
situation/type, charge management, capacity
utilization, and public investment.

Technology(vehicle) costs

National, by
scenario

AEO, ATB, U.S. Department of Energy Office ofEnergy Efficiency and Renewable Energy Vehicle
Technologies Office targets.
Dealer incentives, marketing, specialized offerings
can differ by location, but data would be difficult tocollect and integrate.

Prices affected bytaxes, subsidies,
incentives, andregulations

National, by
scenario

Can be modeled as adjustments to prices.
Scenarios do not explicitly include ZEV and
CAFE/GHG regulations requiring manufacturer fleetlevel credit compliance, state and local subsidies, andInflation Reduction Act provisions for clean vehicles,
clean commercial vehicles, used clean vehicles, clean
electricity, clean hydrogen, clean fuels, advancedenergy, and advanced manufacturing. Data available

Economic

Public transit
availability (bymode and by
time)

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TEMPO Data
Input

Model Resolution

Potential Enhancements, Disaggregation, andData Sourcesfrom sources such as AFDC, Dsire, Atlas Public
Policy EV Hub.
However, the exogenous vehicle electrification
scenarios in this report are qualitatively informed bythese programs and their incentives and ambition.

Travel Behavior

Cost and timeelasticities andpreferences fortechnologies/fuels
and modes

Bin- (60) andcounty- (3,143)
specific, via
county household
countsCurrently based on literature and/or fitted andcalibrated with NHTS and AEO and/or with scenariobased assumptions.

Temporal: Nontime-varying
Trip/travel
patterns: trip
counts, trip
lengths, vehicle
occupancies

Bin- (60) andcounty- (3,143)
specific, via
county household
countsCurrently, distributions fitted to NHTS (2017) trip data,with scaling to match aggregate AEO/BTS estimates.
Adjustments for seasonal/holiday travel could bebased on Highway Performance Monitoring Systemdata, if warranted.

SystemTemporal: 10
periods/day,weekday/weekend
VMT by age andposition inhousehold

N/A

Data available from NHTS (2017). Model
representation in development.

Travel timeintensities (bymode)

Urbanity

NHTS, BTS. Localized estimates of congestion could
be incorporated, if warranted. (could affect energyuse, mode choice)

Emission
intensities (byfuel)

National

GREET, EPA (could affect energy taxes/prices,
regulations, affecting vehicle technology or mode
choice)Non-household
passenger lightduty vehicle
attributes (cost/time intensities,
stock/fueleconomy,occupancies)

National

Calibrated to NHTS/assumed

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Appendix B. TEMPO-dsgrid Linkage

Table B-1 shows the structure and format of TEMPO’s intermediate data files generated in theprocess of simulating charging profiles, and the final data set compiled and submitted to dsgrid.
While the provided load data are aggregates of individual households and vehicle loads, themetadata provided allows for a disaggregation of vehicle size class (compact car, midsize/largecar, SUV, pickup), vehicle technology type (PHEV25, PHEV50, BEV100, or BEV300), andhousehold type (vehicle energy use associated with households in each of TEMPO’s 60
household bins). This results in 2,016 data points 37 per charger type (2) for each simulatedhousehold type (up to 60), vehicle technology type (4), vehicle size class (4), county (3,143),
model year (30), and scenario (3). Thus, the total number of simulated data points is 1.095
trillion, although many values are efficiently stored zeros.
Table B-1. Structure and Format of TEMPO Intermediate and Output Data Files for dsgrid
TEMPO

dsgridFile namehouseholdtrips.csv ahousehold-evprofile.csv bhousehold-evcharging.csv clookup.parquet ddata.parquet eContent

Trips

Consumption

Charging

Metadata

Load data√√

Parquet Data ID
Scenario

√

√

√

√

Region (county)

√

√

√

√

Model Year

√

√

√

√

Weather Year2012 only

Month
TEMPO Time
Period/Weekday

√

√

√

Hour of Week√√

Hour [of Day]√Day [of Week]√

HouseholdComposition Bin

√

√

√

Household Income
Bin

√

√

√

Urbanity Bin

√

√

√

VehicleTechnology

√

√

√

Vehicle Class

√

√

√

Compiled in new“Subsector”
variable2016 data points, from 12 representative weeks of a year × 7 days of week × 24 hours of day, as opposed to 8,760
or 8,783 data points for every hour in a year. Because TEMPO only simulates representative weeks and seasonal
impacts (rather than following every hour in a specific weather year), this data format minimizes unnecessary data
duplication and avoids false precision in the TEMPO data submitted to dsgrid.

3746This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

TEMPO
File namehouseholdtrips.csv a

dsgridhousehold-evprofile.csv bhousehold-evcharging.csv clookup.parquet d

Fuel

√

Electricity only

Mode

√

Household passenger (“personal”) light-duty vehicle
onlyHousehold ID

√

√

√

Vehicle ID

√

√

√

Weighting Factor

√

√

√

Trip Distance√√

Trip/Charge
Duration√√√

Trip Energy
Consumed√√√

Charger Energy
Delivered√

Charging
Type/Power√data.parquet e

Load: L1&L2 [kWh]√Load: DCFC [kWh]√a Trip schedules for individual representative EVs from TEMPO simulations.
b Week-long energy consumption profiles of when and how much energy each EV consumes when driving.
c Week-long charging profiles of when and how much each EV accumulates energy from charging and whatcharger type was used.
d Aggregate charging load metadata “lookup” file identifying the household and vehicle type (subsector) andspecific sample.

e Aggregate charging load data, reflecting the hourly electricity demand of each household type and vehicle

type (subsector) for each county (based on weighted sums of week-long profiles and with seasonal
adjustment based on month and temperature).

At this time, only the charging load data and metadata are being directly submitted to dsgrid.
However, dsgrid has been designed to eventually enable submission of other metrics, which
could include data describing activity, service, stock, costs, energy demand, and emissions, suchas trips or miles traveled time series, vehicle counts, energy consumption (other than electricity)
time series, and/or emissions data, which—along with the differentiation across household types,
vehicle types, and geographies—would support output of a variety of normalized metrics at avariety of geographic, temporal, and sub-sectoral resolutions. This would also be important forevaluation of remaining electrification potential.

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