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.

Contract No. DE-AC36-08GO28308

Transportation Electrification 

Load Development for a 

Renewable Future Analysis 

Preprint 

Tony Markel and Trieu Mai

National Renewable Energy Laboratory 

Michael Kintner-Meyer 

Pacific Northwest National Laboratory

Presented at the 25th World Battery, Hybrid and Fuel Cell Electric 

Vehicle Symposium & Exhibition

Shenzhen, China

November 5 – 9, 2010

Conference Paper

NREL/CP-5400-49181

December 2010 
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1

Transportation Electrification Load Development For a Renewable Future Analysis

Tony Markel1, Trieu Mai1, and Michael Kintner-Meyer2

1

National Renewable Energy Laboratory, 1617 Cole Boulevard, Golden, CO 80401, USA

2

Pacific Northwest National Laboratory, Richland, WA 99352, USA

Email: Tony.Markel@nrel.gov, Trieu.Mai@nrel.gov, Michael.Kintner-Meyer@pnl.gov

 Abstract—Electrification of the transportation sector, which accounts for 70% of U.S. petroleum consumption, offers the 

opportunity to significantly reduce petroleum consumption. The transition to electricity as a transportation fuel will create a new 

load for electricity generation. In support of a recent U.S. Department of Energy-funded activity that analyzed a future generation 

scenario with high renewable energy technology contributions, a set of regional hourly load profiles for electrified vehicles was 

developed for the 2010 to 2050 timeframe. These load profiles with their underlying assumptions will be presented in this paper. 

The transportation electrical energy was determined using regional population forecast data, historical vehicle per capita data, and 

market penetration growth functions to determine the number of plug-in electric vehicles (PEVs) in each analysis region. Market 

saturation scenarios of 30% of sales and 50% of sales of PEVs consuming on average ~6 kWh per day were considered. Results 

were generated for 3109 counties and were consolidated to 134 Power Control Areas (PCA) for use in the National Renewable 

Energy Laboratory’s (NREL’s) electric generation and transmission capacity expansion model, ReEDS. PEV aggregate load 

profiles from previous work were combined with vehicle population data to generate hourly loads on a regional basis. A transition 

from consumer-controlled charging toward utility-controlled charging was assumed such that by 2050 approximately 45% of the 

transportation energy demands could be delivered across four daily time slices under optimal control from the utility’s perspective. 

No other literature has addressed the potential flexibility in energy delivery to electric vehicles in connection with a regional power 

generation study. This electrified transportation analysis resulted in an estimate for both the flexible load and fixed load shapes on 

a regional basis that may evolve under two PEV market penetration scenarios. 

 Keywords—Electric vehicle, EV, PEV, plug-in hybrid, PHEV, load profile, electric power

1. INTRODUCTION

 Plug-in electric vehicles (PEVs), including both plug-in 

hybrid (PHEVs) and battery-only electric vehicles (EVs),

offer the opportunity for the transportation sector to 

significantly reduce petroleum consumption through 

electrification. PEVs may have a moderately sized energy 

storage system and a combustion engine to ensure most 

miles are electrified while retaining the range capability of 

today’s vehicles. Other PEVs may be entirely battery 

dependent and provide complete petroleum displacement for 

certain vehicle sectors. As of 2010, the timeline for vehicle 

introduction will start in 2011 with several manufacturers 

adding to the options over a 2–3 year period toward market 

creation. Based on past technology markets, maturity would 

likely occur within 25–30 years from introduction.

 Charging infrastructure for delivering electricity to these 

vehicles is also under development. For short-range vehicles, 

common 120V service outlets would generally suffice, while 

owners of vehicles with longer range will likely prefer 

moderate charge rates from 240V service. From a utility’s

perspective, 120V or Level I (typically 1.4 kW) charging has 

limited impact on infrastructure but has less value as a 

flexible load whereas vehicles and infrastructure delivering 

240V or Level II charging (typically 6–7 kW) offers more 

opportunity for load shaping and management as individual 

vehicle needs can be delivered and scheduled optimally to 

match generation opportunities.

 Many analyses have been conducted and papers published 

that include PEV market projections. Electric Power 

Research Institute and Natural Resources Defense Council 

collaborated on a foundational study highlighting the 

nationwide greenhouse gas and pollutant emissions impacts 

of plug-in hybrid electric vehicles on the US electricity grid 

on a regional basis [1]. The fleet makeup assumed ~40% 

PEVs by 2030 and 60% by 2050. An aggregate hourly load 

profile was assumed in which 74% of the energy was 

delivered during the off-peak period and 26% during 

daytime. Both Kintner-Meyer et al. of Pacific Northwest 

National Laboratory (PNNL) and Hadley et al. of Oak Ridge 

National Laboratory (ORNL) have assessed regional plug-in 

vehicle penetrations and future load characteristics. The 

PNNL study considers the situation in which all PEV loads 

could be managed and fit into the low points of the daily 

utility load curve [2]. The ORNL study considered a variety 

of charge levels and loading scenarios to understand the 

regional capacity and emissions impacts of the various 

scenarios [3].

 This work expands upon past activities to uniquely define 

both a transitioning fixed load for the transportation sector 

and a load portion that can be dynamically managed by 

utilities for integration with high renewable energy 

integration analyses.
2

2. APPROACH

 In 2009, the U.S. Department of Energy funded a multilaboratory analysis referred to as Renewable Electricity 

Futures study to explore the electric generation mix over the 

next 40 years under a very high renewable portfolio 

constraint. As input to the Renewable Electricity Futures 

study [4], an hourly PEV load profile and the energy demand 

for the fleet of PEVs by region over time (2010 through 

2050) were developed. The approach was as follows:

1. Use population growth forecasts and historical vehicle 

ownership trends to estimate vehicle population by 

region.

2. Use a market penetration model to estimate the 

fraction of vehicles that will be PEVs throughout the 

study period.

3. Develop the vehicle fleet electrical energy demand 

profile varying over time as the fleet transitions its 

charging strategy from a fully customer-controlled to 

a partially price-based or utility-controlled charging 

scheme. 

 The customer-controlled charging profiles were based on 

past fleet studies. The price-based or utility-controlled 

profiles were based on optimal electric generation dispatch 

decision by the utility or grid operator. In developing the 

electrified transportation loads and energy requirements, the 

following simplifying assumptions were made:

1. There will be no significant change in transportation 

mode selection and miles driven. Personal vehicles 

will continue to be the mode of choice. As a result, 

loads due to mass transit are neglected.

2. Transportation electrical energy demands will not 

vary significantly in amount or timing between 

seasons of the year. 

3. PHEVs are expected to make up the majority of the 

stock, meaning that electrical energy is likely to 

provide the majority, but not all, of the energy 

needed. The hybrid combustion engine would likely 

make up any limitations of the electric drive system 

and thus take up any variability.

4. The PHEV fleet load shapes are based on historical 

consumer travel survey data and assume 120V, 

1.4kW charge rates from widespread infrastructure. 

Level II, 240V charging was only considered if the 

charging was under utility control. The following 

three charging profiles were considered:

a. No-control charging (Level I): primarily home 

charging

b. Opportunity charging (Level I): assuming 

ubiquitous charging stations and charging 

whenever vehicle is parked

c. Utility-controlled charging (Level II): based on 

optimal dispatch generation/load dispatch by the 

grid operator.

5. PEV load curves are based on PHEVs with 20 mi. of 

electric range (PHEV20) and urban power capability. 

On average, this results in ~6 kWh of energy per PEV

per day. Some vehicle designs and some vehicle 

usage profiles may use more or less energy.

6. No differentiation between car and truck vehicle 

energy needs was included. 

7. It has been assumed that there are no differences in 

regional penetration rates.

8. There is no differentiation in the growth rate among 

counties in the same state.

9. No vehicle-to-grid or grid service functions are 

considered. 

10. The utility-controlled charging strategy was defined 

by NREL’s ReEDS (Regional Energy Deployment 

Systems) model. The model dispatches loads and 

generators in certain time slices (blocks of time) to 

minimize the cost. Based on the ReEDS time slice 

definition, charging would be selected at a constant 

rate during a time slices. It should be noted that 

utility-controlled charging would be very similar if 

not identical with price-based charging where the 

customer would charge his/her vehicle based on timevarying electricity prices to minimize cost. 

3. RESULTS ANALYSIS

 Data on population growth projections to 2030 by state 

available from the U.S. Census Bureau provides the starting 

point for projecting the energy demands of electrified 

vehicles. Figure 1 shows the consolidated growth rates for 

the nine census regions [5]. The projections for each state 

were fit with either a linear or quadratic function, whichever 

provided the best fit, and extended to 2050. Table 1 

highlights the states with the least and greatest calculated 

rates of change in population growth between 2010 and 

2050. 

Figure 1: U.S. Census regional population projections to 2030

0

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60

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90

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Population Projections (Millions)

Year

East North Central

West North Central

New England

Middle Atlantic

South Atlantic

East South Central

West South Central

Pacific

Mountain
3

Table 1: Ten States with Least and Greatest Percent Change in 

Population between 2010 and 2050

State Name

Population Percent

Change 2050

Relative to 2010

District of Columbia 65

West Virginia 77

Iowa 87

Wyoming 88

North Dakota 94

Ohio 94

New York 95

Pennsylvania 105

South Dakota 108

Nebraska 108

State Name

Population Percent

Change 2050

Relative to 2010

Nevada 211

Arizona 209

Florida 185

Texas 166

Utah 163

Idaho 159

North Carolina 159

Washington 154

Georgia 153

Oregon 149

 Using data from an RL Polk database query, 2005 county 

population measurements were extended to estimate county 

population between 2010 and 2050 using the state-level 

population growth trends [6]. It was assumed that all 

counties within a state grow at the state rate.

Figure 2: Historical and projected motor vehicles per capita 

 The population estimates on a county basis were then 

scaled to estimate the number of motor vehicles on a county 

basis. Historical data from the Federal Highway 

Administration presents the number of motor vehicles per 

capita between 1960 and 2007 [7]. The last 20–30 years of 

data fit well to a logarithmic function. This trend suggests 

that between 2010 and 2050 the number of motor vehicles 

per capita is likely to grow from just over .8 to a little over .9 

motor vehicles per person. These analyses are summarized 

in Figure 2.

 PEV market penetration model based on a logit function 

was used with the motor vehicle estimates to determine the 

number of PEVs likely to be in use on a county level over 

the time period of the study [8]. The penetration model 

represents a slow ramp toward consistent market growth and 

a final tapering of growth to saturation. The model used is 

represented by Equation 1.

( )) ln(81) 1 exp(

( )

m t t

t

N t

− ∆+ −

= κ (1)

Where, 

κ = the maximum market share potential

∆t = the time to grow from 10% to 90% of potential 

(years)

tm = the year in which 50% of potential is reached

 Two scenarios, default and aggressive, were defined. In the 

default scenario, the sales of PEVs saturate at a level of 30% 

penetration over a ~35 year period. In the aggressive 

scenario, sales saturate at 50% market share after ~50 years. 

The parameter values for each scenario are shown in Table 

3. A comparison of the sales rates, vehicle stock, and 

historical HEV sales shifted by introduction year are shown 

in Figure 3. The rates in the aggressive scenario are 

consistent with results recently developed by Greene and Lin 

in 2010 [9].

Table 2: PEV Market Penetration Model Parameter Values

Parameter Value–Default Value–

Aggressive

κ 30% 50%

∆t 20 30

tm 17 25

Figure 3: PEV market penetration model comparison showing both

annual sales and vehicle stock data.

 Using the PEV market models, the number of vehicles per 

capita, and county population estimates, it is possible to 

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1

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Ratio of MVs to Population

Sample Year

1980-2007

1960-2007

Log Fit to Last 30 Years

y=0.2014+0.1602*log(year-1960)

Source: US Federal Highway Administration - Highway Statistics 2007 

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60

2010 2020 2030 2040 2050 2060 2070

Percent (%)

Year

Default - Stock

Aggressive - Stock

Default - Sales

Aggressive - Sales

Historical HEV - Stock (Shifted)
4

calculate the number of PEVs on a county and state basis. 

Table 3 summarizes the PEV population estimates for the 

largest growing vehicle sales markets in the United States.

The vehicles in these 14 states highlighted compose nearly 

70% of the total U.S. PEV population. The PEV population 

growth trend for these 14 states is shown in Figure 4. The 

resulting shape is a function of both population growth and 

PEV market growth and saturation.

Table 3: PEV Stock Distribution by State – Top 14 (millions)

2010 2030 2050

2050 

Percent 

of U.S.

2050 

Cumulative

Percent

United States 0.283 31.07 154.43 100

California 0.035 4.01 20.58 13.32 13.32

Texas 0.023 2.85 15.49 10.03 23.36

Florida 0.018 2.45 13.83 8.96 32.32

New York 0.018 1.67 6.88 4.45 36.77

North 

Carolina 0.009 1.04 5.55 3.59 40.36

Georgia 0.009 1.05 5.55 3.59 43.95

Illinois 0.012 1.17 5.33 3.45 47.41

Arizona 0.006 0.89 5.26 3.41 50.81

Pennsylvania 0.011 1.10 4.93 3.19 54.01

Virginia 0.007 0.85 4.36 2.83 56.83

Michigan 0.010 0.93 4.26 2.76 59.59

Ohio 0.011 0.99 4.08 2.64 62.23

New Jersey 0.008 0.84 4.01 2.60 64.83

Washington 0.006 0.73 3.87 2.51 67.33

Figure 4: Growth Trends of PEV Stock for the States with the 

Greatest 2050 PEV Population

Hourly load shapes for PEVs have been presented in several 

locations [1, 2, 3]. For this study, three profiles from 

previous work based on detailed vehicle system simulations 

using second-by-second vehicle speed and trip profile 

characteristics collected using GPS units on-board a vehicle 

were used. This data was collected under a periodic 

household travel survey from the St. Louis metropolitan area 

and is based on 227 24-hr driving profiles for unique 

vehicles. Three scenarios defined in previous work [10, 11] 

were used in this study and are shown in Figure 5.

In the first case, the consumer is allowed to plug in and 

charge as soon as the vehicle ends the last trip for the day. 

This case is called “No Utility Control” because the vehicle 

load occurs on consumer demand and charges until complete 

or the consumer starts another trip. A second scenario is 

labeled “Opportunity”: under this case, it is assumed that 

charging infrastructure is ubiquitous and the consumer will 

choose to plug-in any time the vehicle is parked regardless 

of stop duration. This scenario leads to significantly more 

fuel savings but also increases the daytime electric vehicle 

loads, total energy demands, and potential battery wear. The 

total electric energy consumed is limited by the size of the 

battery and travel behavior. “Opportunity” charging 

demands more electric energy (kWh) indicating that PEVs 

typically exceeded the range of the moderate battery 

assumed and gained value from ubiquitous infrastructure. 

Finally, a “Valley Fill/Managed” scenario is used. Although 

this scenario is shown in Figure 5 to optimally fill the lowest 

load point of a traditional hourly utility load curve, in this 

study, with high penetration of renewables the optimal 

dispatch time for this total energy is allowed to shift between 

several defined daily time periods as needed to support the 

renewables integration. Both the “No Utility Control” and 

“Opportunity” scenarios assume 120V 1.4kW charge rates 

(Level I) while the “Valley Fill/Managed” curve allowed 

3kW (Level II) charging to best match the energy demands 

with the utility valley shape.

Figure 5: Three PEV fleet charging profiles based on 227 driving 

profile vehicle simulation results

 It was assumed that initially, all consumers would charge at 

home without utility controls and as public charging 

infrastructure is created and consumers learn to optimize 

value of their investment in vehicle technology, the growth 

of opportunity charging would occur. Furthermore, we 

assumed that over the duration of this study, PEV owners 

would migrate toward the price-based or utility-controlled 

charging strategies primarily induced by lower electricity 

cost and technology advancements that support seamless 

communications to the vehicles and automating the load 

0

5

10

15

20

25

2010 2015 2020 2025 2030 2035 2040 2045 2050

PHEV stock (millions)

Year

California

Texas

Florida

New York

North Carolina

Georgia

Illinois

Arizona

Pennsylvania

Virginia

Michigan

Ohio

New Jersey

Washington

0

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1.2

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Power Demand kW/vehicle

Hour of the Day

Valley Fill/Managed

Opportunity 

No Utility Control
5

management and advanced charging strategies. The rate at 

which opportunity charging and price-based/utilitycontrolled charging would displace home charging was 

based on our judgment. The transition between the three 

scenarios over time is summarized in Figure 6.

Figure 6: Transition assumptions from no utility control to 

opportunity and managed scenarios

Figure 7: Average daily per-vehicle energy demands by charging 

scenario

 Figure 7 shows how the average per-vehicle energy 

demand grows slightly over time due to the increasing 

portion of the vehicles that are being opportunity charged. It 

also shows that the “Valley Fill/Managed” portion of the 

total vehicle energy demands grows to ~45% of the total 

PEV energy demands by 2050.

 The ReEDS model assesses energy delivery by 134 PCAs. 

Load profiles were generated on a county basis. A total of

3,109 counties in the contiguous United States were 

consolidated into 134 PCAs.

Figure 8: 2050 PEV daily fixed energy demand by PCA region

 Figure 8 shows a map of the regional distribution of the 

fixed portion of the daily energy demands for PEVs (no 

utility control and opportunity charging) by PCA region in 

2050. The energy shown is ~55% of the total PEV load in 

2050. PEV population growth follows general population 

growth in this analysis; therefore, highly populated areas are 

highlighted as PEV load centers.

 By combining the hourly load profile results from previous 

work and the transition assumptions over the period from 

2010 to 2050, an aggregate per vehicle load profile that 

changes shape over time was generated (Figure 9). Figure 9 

only shows the fixed portion that is not under the control of 

the utility. This includes the “No Utility Control” profile and 

the “Opportunity” profile. The transition from a large 

fraction of the vehicles in the “No Utility Control” scheme

in 2010 to more in the “Opportunity” scheme by 2050 is 

observed in Figure 9 by comparing the shape of the 2010 

and 2050 curves to those in Figure 5. Figure 9 only shows 

the fixed hourly load as the flexible portion is allowed to be 

different for each of the 134 PCAs.

Figure 9: Shape and transition of the fixed hourly aggregate load 

profile for PEVs

 The aggregate load shape in Figure 9 only represents the 

fixed load profile. From Figure 7, the dynamic portion under 

utility control (Valley Fill/Managed) grows from 0% of the 

load in 2010 to ~45% of the total load in 2050.

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Fraction of PEV Fleet

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Valley Fill/Managed

Opportunity

No Utility Control

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Average Daily Energy Per Vehicle (kWh/vehicle)

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Valley Fill - Managed

Opportunity

No Utility Control

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Aggregate Load kW/Vehicle

Hour of Day

2010

2020

2030

2040

2050

> 20

15 - 20

10 - 15

5 - 10

2.5 - 5

1.25 - 2.5

< 1.25

2050 PEV Daily Fixed

Energy Demand 

(GWh)
6

Figure 10: Projected PEV fixed and dynamic annual electricity 

consumption for aggressive scenario

 In Figure 10, the total annual energy demand for PEVs is 

shown. Both the fixed and dynamic portions are highlighted. 

In 2030, the fixed demand is ~50 TWh, accounting for ~80% 

of the total load. By 2050, the total load grows to 350 TWh,

and the fixed portion is ~180 TWh, or 55% of the total load. 

Greene and Lin predict the total annual PEV energy demand 

as ~100 TWh in 2050 in a PEV Success scenario.[9] Several 

assumptions contribute to these differences.

• Greene and Lin assumed mostly PHEV10 with a few 

PHEV40 vehicles while this study bases the energy 

demands and load curves on a PHEV20 design.

• Greene and Lin assumed a single daily charge per day 

while this study includes opportunity charging, which 

increases daily electrical energy consumption per 

vehicle.

 As a result, the total annual energy for PEVs is about 3 

times greater than suggested by Greene and Lin’s work. 

However, the estimated annual electric energy demand for 

PEVs in 2050 is only about 9% of the total electricity 

consumption in the base case of the Renewable Electricity 

Futures study scenario.

4. CONCLUSION

 The introduction of PEVs creates opportunities for the 

reduction of petroleum and the creation of new flexible load 

that can be integrated in utility operations with a high 

penetration of renewables to achieve a long-term strategy of 

creating a more sustainable transportation system. This work 

developed energy system load characteristic forecasts on a 

regional basis from 2010 to 2050 for two PEV market 

penetration scenarios to be used in a Renewable Electricity 

Futures study. The work builds upon past travel survey data 

analysis, regional population forecasts, and assumptions 

regarding incentives of charge management scenarios. An 

aggressive market scenario achieving a vehicle stock of 

~40% PEVs by 2050 (or 50% in 2060) and a default 

scenario achieving ~30% PEVs in the fleet by 2050 were 

used. Both trends mirror historical HEV market stock thus 

far. 

 Three PEV charge scenarios were considered, including 

“No Utility Control,” “Opportunity,” and “Valley 

Fill/Managed.” The energy needed in the “Valley 

Fill/Managed” scenario was assumed to be flexible in terms 

of when it needed to be delivered throughout the day and 

thus provides the utility with an interesting flexible load that 

can be managed to improve renewable generation asset 

utilization. By 2050, 45% of the total vehicle energy demand 

of 350 TWh was under managed control while the remaining 

55% was a fixed load to be planned for and met by utility 

assets. The hourly load profile of the fixed transportation 

energy demand also shifted over the time period from 

mainly “No Utility Control” towards “Opportunity”

charging. This is the first study to assume that a variety of 

vehicle load shapes will exist and may transition overtime 

resulting in a unique fixed load and flexible load for 

integration into utility operational planning tools.

5. References

[1] “Environmental Assessment of Plug-In Hybrid Electric 

Vehicles” Volume 1: Nationwide Greenhouse Gas Emissions.

EPRI Report 1015325. July 2007.

[2] Kintner-Meyer, M., Pratt, R., Schneider, K. “Impacts 

Assessment of Plug-In Hybrid Vehicles on Electric Utilities and 

Regional U.S. Power Grids: Part 1: Technical Assessment.” Online 

Journal of EUEC 1:paper # 04. Nov. 2007.

[3] Hadley S.W., Tsvetkova, A. Potential Impacts of Plug-in 

Hybrid Electric Vehicles on Regional Power Generation. Oak 

Ridge National Laboratory, ORNL/TM-2007/150. Jan. 2008.

[4] U.S. Department of Energy. Renewable Electricity Futures: 

Assessment of Technical Feasibility of Achieving Electricity 

Generation Levels of Up to 80% Renewables by 2050. Washington, 

DC: U.S. Department of Energy. In Process.

[5] U.S. Census Bureau. “Total Population for Regions, Divisions, 

and States: 2000 to 2030.” http://www.census.gov/. Accessed 

10/6/2009.

[6] Database query of RL Polk 2007 Vehicle Registrations.

[7] Federal Highway Administration. Licensed Drivers, Vehicle 

Registrations and Population. 

http://www.fhwa.dot.gov/policyinformation/statistics/2007/dlchrt.c

fm Accessed Oct 14, 2009.

[8] Balducci, P. Plug-In Hybrid Electric Vehicle Scenarios. PNNL17441. Pacific Northwest National Laboratory, Richland, WA. 

September 2008. 

[9] Greene, D., Lin, Z. “A Plug-in Hybrid Consumer Choice Model 

with Detailed Market Segmentation” Transportation Research 

Board Annual Meeting. Jan 2010.

[10] Parks, K., Denholm, P., Markel, T. Costs and Emissions 

Associated with Plug-In Hybrid Electric Vehicle Charging in the

Xcel Energy Colorado Service Territory. NREL Report No. TP640-41410. Golden, CO: National Renewable Energy Laboratory, 

2009.

[11] Markel, T., Bennion, K., Kramer, W., Bryan, J., Giedd, J. 

Field Testing Plug-in Hybrid Electric Vehicles with Charge 

Control Technology in the Xcel Energy Territory. NREL/TP-550-

46345. Golden, CO: National Renewable Energy Laboratory, 

August 2009. 

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PEV Demand (TWh)

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6. Authors

Senior Engineer, Tony Markel

National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, CO 80401

Phone: (303) 275-4478

Email: Tony.Markel@nrel.gov

Tony Markel is a Senior Engineer working on 

systems analysis of advanced vehicles for 14 

years. Tony contributes to both the Vehicle 

Systems Analysis and Energy Storage teams in the Center for 

Transportation Technologies and Systems at NREL and provides 

support to the DOE Vehicle Technologies Program focusing on 

Electric Vehicle Grid Integration technology development. Tony 

earned a B.S. degree in Mechanical Engineering from Oakland 

University and a M.S. degree in Mechanical Engineering from the 

University of Colorado. His current role is to understand and 

resolve grid integration challenges facing plug-in vehicles.

Senior Analyst, Dr. Trieu Mai

National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, CO 80401

Phone: (303) 384-7566 

Email: Trieu.Mai@nrel.gov

Dr. Trieu Mai is a member of the Energy 

Forecasting and Modeling Group in the 

Strategic Energy Analysis Center at NREL. His 

analysis expertise includes linear programming with General 

Algebraic Modeling System (GAMS) and C programming for 

simulations of physical systems. His primary research interests are 

in capacity expansion and dispatch modeling of the electricity 

sector using the Regional Energy Deployment Systems (ReEDS) 

model. Dr. Mai earned a Ph.D. in physics from the University of 

California, Santa Cruz, CA. He is a member of the American 

Physical Society.

Staff Scientist, Dr. Michael Kintner-Meyer

Pacific Northwest National Laboratory

P.O. Box 999, Richland, WA 99352

Phone: 509.375.4306

Email: Michael.Kintner-Meyer@pnl.gov

Dr. Michael Kintner-Meyer is Staff Scientist at 

the Pacific Northwest National Laboratory. He 

leads the Laboratory’s electrification of 

transportation research and manages the grid analytics for energy 

storage. He holds a patent on grid-friendly control strategies of 

appliances.

Dr. Kintner-Meyer is associate editor of the American Institute of 

Physics’ (AIP’s) “Journal of Renewable and Sustainable Energy” 

and a member of IEEE, ASME, and SAE, as well as the German 

Engineers Association, VDI. 

He received a Ph.D. in Mechanical Engineering from the 

University of Washington and a MS in Engineering from the 

University of Aachen, Germany.
F1147-E(10/2008) 

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1. REPORT DATE (DD-MM-YYYY)

December 2010

2. REPORT TYPE

Conference Paper

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

Transportation Electrification Load Development For a Renewable 

Future Analysis: Preprint

5a. CONTRACT NUMBER

DE-AC36-08GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

T. Markel, T. Mai, and M. Kintner-Meyer

5d. PROJECT NUMBER

NREL/CP-5400-49181

5e. TASK NUMBER

FC10.2400

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

8. PERFORMING ORGANIZATION

REPORT NUMBER

NREL/CP-5400-49181

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NREL

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National Technical Information Service

U.S. Department of Commerce

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13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

The transition to electricity as a transportation fuel will create a new load for electricity generation. A set of regional 

hourly load profiles for electrified vehicles was developed for the 2010 to 2050 timeframe. The transportation 

electrical energy was determined using regional population forecast data, historical vehicle per capita data, and 

market penetration growth functions to determine the number of plug-in electric vehicles (PEVs) in each analysis 

region. Market saturation scenarios of 30% and 50% of sales of PEVs consuming on average ~6 kWh per day were 

considered. PEV aggregate load profiles from previous work were combined with vehicle population data to generate 

hourly loads on a regional basis. A transition from consumer-controlled charging toward utility-controlled charging

was assumed such that by 2050 approximately 45% of the transportation energy demands could be delivered across 

four daily time slices under optimal control from the utility’s perspective. This electrified transportation analysis 

resulted in an estimate for both the flexible load and fixed load shapes on a regional basis that may evolve under two 

PEV market penetration scenarios.

15. SUBJECT TERMS

Electric vehicle; EV; PEV; plug-in hybrid; PHEV; load profile; electric power

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OF ABSTRACT

UL

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