EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 1

EVS26

Los Angeles, California, May 6–9, 2012

A First Look at the Impact of Electric Vehicle Charging on

the Electric Grid in The EV Project

Stephen Schey1, Don Scoffield2, John Smart2

1

ECOtality North America, 430 S. 2nd Ave., Phoenix, AZ 85003, sschey@ecotality.com

2

Idaho National Laboratory, 2351 .N Boulevard, Idaho Falls, ID 83415, don.scoffield@inl.gov, john.smart@inl.gov

Abstract

ECOtality was awarded a grant from the U.S. Department of Energy to lead a large-scale electric vehicle 

charging infrastructure demonstration, called The EV Project. ECOtality has partnered with Nissan North 

America, General Motors, the Idaho National Laboratory, and others to deploy and collect data from over 

5,000 Nissan LEAFsTM and Chevrolet Volts and over 10,000 charging systems in 18 regions across the 

United States. This paper summarizes usage of residential charging units in The EV Project, based on data 

collected through the end of 2011. This information is provided to help analysts assess the impact on the 

electric grid of early adopter charging of grid-connected electric drive vehicles.

A method of data aggregation was developed to summarize charging unit usage by the means of two 

metrics: charging availability and charging demand. Charging availability is plotted to show the percentage 

of charging units connected to a vehicle over time. Charging demand is plotted to show charging demand 

on the electric gird over time.

Charging availability for residential charging units is similar in each EV Project region. It is low during the 

day, steadily increases in evening, and remains high at night. Charging demand, however, varies by region. 

Two EV Project regions were examined to identify regional differences. In Nashville, where EV Project 

participants do not have time-of-use electricity rates, demand increases each evening as charging 

availability increases, starting at about 16:00. Demand peaks in the 20:00 hour on weekdays. In San 

Francisco, where the majority of EV Project participants have the option of choosing a time-of-use rate 

plan from their electric utility, demand spikes at 00:00. This coincides with the beginning of the off-peak 

electricity rate period. Demand peaks at 01:00.

Keywords: BEV (battery electric vehicle), demonstration, infrastructure

1 Introduction

Concerns with global climate change, United 

States reliance on foreign oil, increasing global 

demand for petroleum-based fuels, and increasing 

gas prices are changing consumer preferences and 

industry direction toward more fuel-efficient and 

alternative energy vehicles. Nissan and General 

Motors have successfully introduced a new 

generation of plug-in electric vehicles (PEV).
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 2

Several other automotive manufacturers plan to 

launch PEVs in 2012. This illustrates a shift to 

cleaner and more efficient electric drive systems.

These vehicles, which include plug-in hybrid 

electric vehicles, extended range electric vehicles,

and battery electric vehicles draw some or all of 

their motive power from onboard batteries, which 

are charged from the electric grid. In order for 

PEVs to be commercialized, electric charging 

infrastructure must be deployed. Charging 

infrastructure must be safe, financially viable, and 

convenient. Additionally, electric utilities must be 

able to manage PEV charging demand on the 

electric grid.

For years, researchers have worked to model 

future PEV and charging infrastructure markets to 

assess the impact of charging PEVs on the electric 

grid [1-3]. Small-scale demonstrations have been 

conducted to document actual PEV charging 

behavior and grid impact using aftermarket 

conversion PEVs [4]. With the recent launch of 

high-volume PEVs by major automakers, it is 

now possible to deploy charging infrastructure 

and assess grid impact with large-scale 

demonstrations.

In 2009, ECOtality was awarded a grant from the 

U.S. Department of Energy to embark on such a 

demonstration, called The EV Project. With

matching cost share from ECOtality and its 

partners, The EV Project’s total project budget is 

approximately $230 million. ECOtality is 

partnering with Nissan North America, General 

Motors, and several other companies to deploy 

over 5,000 Nissan LEAFTM battery electric 

vehicles and Chevrolet Volts extended range 

electric vehicles and over 10,000 charging 

systems to support them. These charging systems, 

referred to as electric vehicle supply equipment 

(EVSE), are being installed in private and public 

locations in 18 strategic markets across the United 

States.

The purpose of this paper is to summarize early 

EV Project residential EVSE usage and demand 

on the electric grid, based on data collected 

through the end of 2011. This information is 

provided to help analysts assess the impact of 

early adopter PEV charging on the electric grid.

1.1 Project Description

The purposes of The EV Project are to 

characterize vehicle and EVSE usage in diverse 

topographic and climatic conditions, evaluate the 

effectiveness of charging infrastructure, and 

conduct trials of various revenue systems for 

commercial and public charging infrastructure. 

The ultimate goal of The EV Project is to take the 

lessons learned from the deployment of these first 

PEVs and the charging infrastructure supporting 

them to enable the streamlined deployment of the 

next five million PEVs. To accomplish these 

purposes, ECOtality has partnered with the Idaho 

National Laboratory to collect and analyze

electronic data from EV Project vehicles and 

charging units.

The EV Project uses the Blink brand of EVSE, 

which is manufactured by ECOtality. The Blink 

product line consists of AC Level 2 residential 

and commercial EVSE and a DC Level 2 

commercial fast charger. The AC Level 2 EVSEs

are 240-VAC, single-phase units, operating at 

charge rates up to 7.2 kW. Blink EVSE are 

networked, enabling data collection, user 

authentication, and additional functionality. All 

units have internal energy meters and touchscreen user interfaces and allow user-controlled 

charge scheduling. Numerous data parameters are 

collected from the Blink EVSE participating in 

The EV Project, including time when the EVSE is 

connected to a vehicle and transferring power to 

the vehicle, energy consumed from the grid, and 

15-minute rolling average power demand.

Vehicles enrolled in The EV Project include the 

Nissan LEAF and the Chevrolet Volt. Both 

vehicles connect to AC Level 2 EVSE using SAE 

J1772®-compliant connectors. Figure 1 shows a 

Volt connected to a Blink AC Level 2 commercial 

EVSE unit.

Figure 1: Blink AC Level 2 with Chevrolet Volt.
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 3

Table1: Number of EV Project EVSE and vehicles, by 

region, from which data was collected in 2011.

Additionally, both the LEAF and Volt are capable 

of charging at any standard 120-volt outlet, using 

the AC Level 1 cordset supplied by the 

manufacturer. Nissan LEAFs in The EV Project 

also accept DC fast charging. Figure 2 shows a 

Nissan LEAF connected to a Blink DC fast 

charger. Both the LEAF and Volt have user 

interfaces allowing drivers to set charge schedule 

preferences.

Figure 2: Blink DC fast charger with Nissan LEAF.

Figure 3 shows the major metropolitan areas 

where The EV Project is deploying charging 

infrastructure.

Figure 3: EV Project cities.

At the end of 2011, there were approximately 

4,000 Nissan LEAFs and 200 Chevrolet Volts 

enrolled in the project. There were equal numbers

of residential EVSE installed, because each 

participating vehicle owner had a Blink residential 

AC Level 2 EVSE installed in their residence.

Additionally, approximately 950 publicly 

available AC Level 2 EVSE and 15 DC fast 

chargers had been installed by the end of 2011. 

Table 1 shows the number of units, by project 

region, that had been deployed and transferred 

data to the Idaho National Laboratory.

The EV Project’s deployment phase will continue 

through 2012, during which time additional 

vehicles and EVSE will be enrolled in the project. 

Data collection will continue for 1 year following 

completion of the deployment phase.

While still early in the project at the time of this 

writing, the number of vehicles and EVSE 

enrolled is significant. This allows a preliminary 

assessment of the impacts of these PEVs on the 

grid. This paper focuses on the usage of 

residential EVSE in households with Nissan 

LEAFs and their aggregate electricity demand 

relative to time of day and day of the week. 

Additional studies will be conducted as The EV 

Project progresses to include the effect of nonresidential charging, including DC fast charging 

and localized distribution of EVSE on the electric 

grid.

2 Data Aggregation Approach

It is useful to understand how EVSEs are being 

utilized through time. Specifically, knowing when 

vehicles are connected to the grid and when 

power is being drawn from the grid is valuable 

information. In order to communicate this 

information, two curves were calculated from EV 

Project EVSE usage data: the charging 

availability curve and charging demand curve.

These curves and the associated plots are 

described below.

EV Project Region

Number of

Vehicles 

Enrolled

Number of 

EVSE 

Installed

Phoenix, AZ 210 254

Tucson, AZ 64 78

Los Angeles, CA 384 311

San Diego, CA 622 606

San Francisco, CA 1,044 869

Washington, D.C. 45 2

Oregon 365 429

Chattanooga, TN 34 58

Knoxville, TN 67 105

Memphis, TN 21 18

Nashville, TN 289 335

Dallas/Ft. Worth, TX 52 40

Houston, TX 43 22

Washington State 607 658

Total 3,847 3,785
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 4

2.1 Charging Availability

Charging availability at a point in time is the 

percentage of EVSE in a geographical area that 

are connected to a vehicle. The charging 

availability curve depicts the charging availability

on a 15-minute interval versus time. The charging 

availability curve is a good way to observe the 

collective behavior of a large sample of vehicle 

owners as they connect and disconnect their PEVs

to and from their EVSE. Figure 4 shows the 

charging availability curve for a 3-week time 

period during October 2011 for many residential 

EVSE in The EV Project.

Figure 4: Charging availability curve for many 

residential EVSE in The EV Project.

The charging availability curve for residential 

EVSE is a periodic curve with both daily and 

weekly patterns. The daily peaks and troughs of 

the curve correspond to the night time and day

time, respectively. The peaks are caused as people 

return to their residences and plug in their 

vehicles in the evening. The troughs are caused as

people unplug their vehicles and (presumably) 

leave their residences. The weekly pattern 

revolves around the weekends. The weekend days 

tend to have lower peaks and higher troughs than 

the weekdays. Higher troughs during the day 

result from fewer people unplugging their 

vehicles on weekend days. Lower peaks are due 

to the fact that fewer EVSE, which had been 

disconnected, were connected in the evening.

The daily and weekly patterns in the charging 

availability curve can be displayed using a 

24-hour time-of-day plot for weekdays and 

another 24-hour time-of-day plot for weekend 

days. This kind of time-of-day plot is a concise 

way to visualize the daily behavior of many 

calendar days of data simultaneously. To create a 

time-of-day plot, the charging availability curves 

for each calendar day are superimposed on the 

same 24-hour scale. Figure 5 shows this 

superposition of each day in the weekday 

charging availability curve depicted in Figure 4. 

To reduce the noise caused by the individual 

calendar day curves, the area between the 

maximum and minimum curves at each point in 

time are filled in. This creates an envelope of 

charging availability (as shown in Figure 6). The 

maximum and minimum charging availability at 

each point in time across all calendar days is 

highlighted with blue and green lines, 

respectively.

Figure 5: Weekday time-of-day charging 

availability from Figure 4 plotted on a single 

24-hour scale.

Figure 6: Weekday time-of-day charging 

availability envelope derived from Figure 5.

2.2 Charging Demand

Charging demand at a point in time is the total 

amount of power being drawn from the electric 

grid by a group of EVSE in a geographical area.

This is typically shown as a curve of charging 

demand versus time, which is sometimes referred 

to as a load profile. Figure 7 shows the charging 

demand curve during a 3-week time period for 

many residential EVSE in The EV Project. This 

curve is based on 15-minute rolling average 

power measurements collected from the EVSE.

The charging demand curve is a periodic curve, 

with both daily and weekly patterns similar to the 

charging availability curve. The daily peaks and 

troughs of the charging demand curve correspond 

to the night time and day time, respectively. The 

demand at night is high, whereas the demand 
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 5

during the day is close to zero. This indicates a 

strong preference among EV Project participants 

for night-time residential charging. The weekly 

pattern revolves around the weekends. The lowest 

demand occurs on the weekend days. Demand 

increases on each weekday until it reaches a peak 

on Wednesday or Thursday night. Then demand 

diminishes again as the weekend comes.

Figure 7: Charging demand curve for many 

residential EVSE in The EV Project.

Because the charging demand curve follows the 

same periodic patterns as the charging availability 

curve, weekday and weekend time-of-day plots 

also will be used to visualize the charging 

demand.

2.3 Time-of-Day Plot Variations

Additional information beyond the maximum and 

minimum curves can be added to a time-of-day 

plot. Two variations of the time-of-day plots are 

described below.

Peak Day

In the time-of-day demand plot, it is sometimes 

helpful to visualize charging demand for one 

calendar day. While any calendar day could be 

chosen, it was decided to show the demand for the 

“peak day” in some reports for The EV Project 

[5]. The peak day is defined as the calendar day 

during the time period being analyzed, on which 

day the highest demand was experienced. For 

example, the highest weekday charging demand 

during the 3-week period in October analyzed 

above occurred at 23:00 on October 13, 2011.

Therefore, the charging demand curve for the 

entire day of October 13 is shown as the peak day 

curve on the time-of-day charging demand plot.

The time-of-day charging availability plot shows

the charging availability curve for October 13, as

well. Figures 8 and 9 show the peak day curves on 

these two plots.

Figure 8: Time-of-day charging availability plot 

with peak day curve.

Figure 9: Time-of-day demand plot with peak day 

curve.

Quartiles

In a time-of-day plot, it also is helpful to see how 

the data points from each calendar day are 

distributed between the maximum and minimum 

at any time of day. This is depicted by dividing 

the range between the maximum and the 

minimum into quartiles and displaying the median 

and the inner quartile range (IQR). The median is 

a measure of central tendency that corresponds to 

the 50th percentile. The IQR is the range between 

the 25th to the 75th percentiles. It is used as a 

measure of the spread of the data. Time-of-day 

plots with these features are shown in Figures 10

and 11.

Figure 10: Time-of-day charging availability plot 

with median and inner quartile range.

Figure 10 shows that during the 3-week period in 

October 2011, there tends to be more variation in 
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 6

charging availability during the night-time hours 

than during day-time hours. For example, at 

03:00, the IQR is 3% and the overall range is 8%.

At 12:00 (noon), the IQR is about 1% and the 

overall range is 3%. Further, between 22:00 and 

06:00, the upper three quartiles are grouped 

closely together. This means that wide overall 

range of charging availability during these hours 

is due to a relatively small number of days with 

lower charging availability.

Figure 11: Time-of-day demand plot with median 

and inner quartile range.

Figure 11 shows that variation in charging 

demand is similar to variation in charging 

availability, with higher variability in the 

night-time hours.

3 Results

The data aggregation approach described in the 

preceding section was applied to a set of EV 

Project EVSE usage data. These data were 

collected from 2,704 residential EVSE between 

October 1 and December 31, 2011. The EVSE are 

located in private households owning Nissan 

LEAFs in each of the project regions.

3.1 Charging Availability

Weekday and weekend time-of-day charging 

availability plots were first created for all EVSE 

in the data set. These are shown in Figures 12 and 

13.

In general, Figures 12 and 13 show that The EV 

Project participants tend to have their vehicles 

connected at home in the evening and late night 

hours. The percent of EVSE connected begins to 

increase after 16:00 and reaches a peak between 

04:00 and 05:00 on both weekdays and weekend 

days. The slow but steady increase in the number 

of EVSE connected between midnight and 04:00 

indicates that some participants plug in their 

vehicles in the early morning hours. Charging 

availability then begins to drop as vehicles are 

disconnected from EVSE in increasing numbers 

after about 05:00, presumably as individuals 

depart their homes for work or other daily 

activities. As few as 8% of residential EVSE have 

vehicles connected during the mid-day hours on 

weekdays. The minimum percent of EVSE 

connected on weekend days is higher at 18%.

Fewer EVSE are connected during the early 

mornings after midnight on Saturdays and 

Sundays than on weekday early mornings.

Figure 12: Weekday time-of-day charging 

availability for all EV Project regions.

Figure 13: Weekend time-of-day charging 

availability for all EV Project regions

The range of variation in charging availability 

from calendar day to calendar day during the 

quarter is significant. In Figure 12, the time with 

highest variation on weekdays occurs between 

0:00 and 05:00, when the percent of EVSE 

connected varies from about 30 to over 50%. The 

range of the top three quartiles from midnight and 

05:00 is about 9%, but the range of the bottom 

quartile over the same time period is about 15%.

Also the range of the bottom three quartiles from 

11:00 to 16:00 is about 4%, whereas the range of 

the top quartile over the same time period is about 

7%. In both of these time periods, only one 

quarter of the data is responsible for about 60% of 

the spread between the maximum and the 

minimum.

Inspection of the charging availability curve for 

all of the fourth quarter (Q4) 2011 found that the

increased size of the lower quartile between 0:00
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 7

and 05:00 and the increased size of the upper 

quartile between 11:00 and 16:00 is due to a 

change in behavior on the days surrounding 

Thanksgiving and Christmas 2011. The weekdays 

from Monday, December 26 through Friday, 

December 30 saw fewer EVSE connected during 

the night and more EVSE connected during the 

day than during other weeks in the quarter. This

trend is represented in Figure 12 by a decrease in 

the minimum charging availability (green line) 

between 20:00 and 06:00 and an increase in the 

maximum charging availability (blue line) from 

09:00 to 18:00.

The IQR can be examined to focus on common 

behavior and ignore atypical behavior, such as 

that seen around the holidays. The tight IQR 

shown in Figure 12 indicates that common 

weekday charging availability has little variation 

from day to day. The largest IQR on weekdays or 

weekend days occurs on weekends between 20:00 

and 06:00, indicating that this is the period of 

greatest variation in user “plugging-in” behavior 

from day to day.

Weekend charging availability in Figure 13

follows a pattern similar to behavior on weekdays 

around the holidays, in that the median charging 

availability is lower at night and higher during the 

day compared to most weekdays. On weekend 

days, there is very little variation across days 

between noon and 16:00 on weekends, when 18 to 

23% of EVSE are connected. 

Time-of-day charging availability plots also were

generated for individual EV Project regions. 

Figures 14 and 15 show these plots for the 

Nashville region in Q4 2011.

With the exception of less smooth lines due to a 

smaller sample size, the patterns in these figures 

are similar to the patterns in the charging 

availability plots for all EV Project EVSE in the 

data set (Figures 12 and 13).

Figure 14: Weekday time-of-day charging 

availability for Nashville.

Figure 15: Weekend time-of-day charging 

availability for Nashville

Figures 16 and 17 show time-of-day charging 

availability for the San Francisco region in the Q4

2011.

Figure 16: Weekday time-of-day charging 

availability for San Francisco.

Figure 17: Weekend time-of-day charging 

availability for San Francisco

Again, these plots show similar patterns. This 

indicates that participants in the San Francisco 

region exhibited the same behavior as their 

counterparts in the Nashville region and as the 

overall project population, with respect to when 

they connect their vehicles to their residential 

EVSE.

3.2 Charging Demand

Time-of-day charging demand plots were 

generated from the data set for all EV Project 

regions in Q4 2011. The magnitude of the demand 

was normalized per EVSE by dividing the 
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 8

magnitude of the charging demand curve by the 

number of EVSE available for use on a given day 

and time. These plots are shown in Figures 19 and 

20.

Figure 19: Weekday time-of-day charging 

demand for all EV Project regions.

Figure 20: Weekend time-of-day charging 

demand for all EV Project regions.

On first glance, it may appear that the charging 

demand magnitude in these figures is too low. 

After all, a single Nissan LEAF draws about 

3.3 kW during steady-state charging, yet the 

charging demand time-of-day plot never exceeds 

1 kW. Note, however, that the percent of EVSE 

connected to a vehicle never exceeds 60%, as 

shown in Figure 12. Thus, the normalized 

charging demand per EVSE will never exceed 

60% of the maximum possible demand for one 

vehicle. Furthermore, not all vehicles that are 

connected to EVSE are drawing power. At any 

given time, a fraction of the vehicles connected 

have full battery packs and have ceased drawing 

power from the EVSE. The charging demand 

plots show the resulting demand of EVSE with 

vehicles connected and drawing power, 

normalized with respect to all EVSE in the data 

set.

When comparing charging demand in Figures 19 

and 20 to charging availability in Figures 12 and 

13, it is immediately apparent that demand is not 

proportional to the percent of EVSE connected to 

a vehicle. This occurs for two reasons. First, 

demand begins to fall off after about 01:00, even 

though charging availability remains high until 

about 6:00. Demand falls during this period as 

battery packs reach full charge and the vehicle 

control system stops power flow, even though the 

vehicle is still connected to the EVSE. This is 

consistent with analysis of individual charging 

events. For residential EVSE in all project 

regions, the average duration of time connected 

per charging event is 11.5 hours, whereas the 

average period of time when the vehicle draws 

power per charging event is 2.2 hours [5].

Second, charging availability steadily increases in 

the evening hours, whereas demand increases 

only slightly in the evening and then increases

dramatically at midnight. This difference is due to 

user charge scheduling. The large spike in 

demand at midnight, as well as the small jumps on 

the hour between 20:00 and 01:00, is the result of 

numerous users programming their EVSE or 

vehicles to commence charging at these times.

As with charging availability, the minimum 

charging demand (green lines in Figures 19 and 

20) is reduced considerably due to different user 

behavior around Thanksgiving and Christmas 

time. Otherwise, the upper three quartiles are 

fairly tightly grouped, indicating there is 

consistent demand for electricity from day to day

in Q4, excluding the days around the holidays.

Demand on weekdays and weekend days drops to 

nearly 0 kW per EVSE between 05:00 and 06:00, 

even though charging availability does not begin 

to fall off until 06:00 or later. This indicates that 

the Nissan LEAFs being charged have sufficient 

time to fully charge during the night. Note that 

this is a function of the state of charge of the 

vehicles’ batteries prior to charging, which is a 

function of how much the vehicles have been 

driven prior to charging. This topic will be studied 

in future works.

To investigate regional differences in demand, 

charging demand plots were generated for the 

Nashville EV Project region. These plots are 

shown in Figures 21 and 22.
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 9

Figure 21: Weekday time-of-day charging 

demand for Nashville.

In Nashville, an increase in the weekday demand 

curve from 16:00 to 20:00 corresponds to the 

increase in the charging availability curve over 

the same time period shown in Figure 14. In this 

region, most users do not program their vehicles 

or EVSE to begin charging at a scheduled time. 

Instead, the vehicles begin to draw power from 

the EVSE immediately after they are plugged in. 

Because people arrive home or otherwise choose 

to plug in their vehicles at home at different times 

throughout the evening, charging demand 

increases gradually. This charging diversity leads 

to relatively low peak demand and smooth 

changes in demand.

Figure 22: Weekend time-of-day charging 

demand for Nashville.

The weekend charging demand increases in 

Nashville on weekend days between 08:00 and 

12:00 (Figure 22), despite a decrease in charging 

availability during this time (Figure 15). Demand 

increases as vehicles are connected to EVSE 

during this period and the vehicles begin to charge 

immediately. Charging availability decreases

during this time because the number of vehicles 

being disconnected from EVSE is greater than the 

number of vehicles being connected. However, 

the vehicles being disconnected had already 

completed charging prior to the time they are 

unplugged; therefore, the disconnecting of 

vehicles does not reduce charging demand.

Charging demand plots also were generated for 

the San Francisco EV Project region. These plots 

are shown in Figures 23 and 24.

Figure 23: Weekday time-of-day charging 

demand for San Francisco.

In San Francisco, a large increase in demand 

occurs at 00:00 (midnight). This is depicted in 

Figures 23 and 24 by comparing the far right-hand 

side of each plot to the far left-hand side of the 

same plot.

Figure 24: Weekend time-of-day charging 

demand for San Francisco.

Figures 25 and 26 show the same weekday and 

weekend charging demand for San Francisco, but 

with the time scale on the x axis shifted to the 

right by 16 hours to allow visualization of the 

increase in demand at midnight.
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 10

Figure 25: Weekday time-of-day charging 

demand for San Francisco with shifted time scale.

Figure 25: Weekend time-of-day charging 

demand for San Francisco with shifted time scale.

As mentioned previously, the midnight spike in 

demand is a result of a large number of users 

programming their EVSE or vehicles to begin 

charging at midnight.

Ninety percent of EVSE in the San Francisco 

region are located in the Pacific Gas & Electric 

service territory. This electric utility offers its 

EV-owning customers an experimental residential 

time-of-use rates for “low emission vehicle 

refueling.” In this rate structure, the cost per 

kilowatt-hour of electricity is reduced during 

off-peak hours. On weekdays, the off-peak period 

is from midnight to 07:00. Weekend off-peak 

hours start at 21:00 [6]. While it is not possible to 

determine which customers have signed up for 

these rates, it obvious from the plots that many 

EVSE users schedule the start of vehicle charging 

at midnight. It is assumed that this behavior is 

motivated by the desire to take advantage of the 

reduced electricity price.

The tendency for many EVSE users in the region 

to begin charging at midnight has consequences.

First, the strong increase in charging availability

from 16:00 to 22:00 is not accompanied by an

equivalent increase in the demand curve. Instead, 

the demand curve has only a slight increase over 

this time period. This serves to reduce the demand 

on the electric grid during the afternoon and 

evening hours, which is typically the period of 

peak demand. Second, because many people 

schedule to begin charging immediately at 00:00, 

a nearly instantaneous spike in demand occurs.

This large spike in demand may pose problems 

for low-voltage distribution systems.

Peak demand in San Francisco occurs at 01:00. 

This occurs because a relatively small number of 

EVSE users schedule charging to begin at this 

time. The demand from these EVSE augments 

the demand from EVSE whose vehicles began 

charging at 00:00 and are still charging. 

Because there is less diversity when vehicles 

begin charging in San Francisco than in Nashville, 

the absolute peak in San Francisco is greater. San 

Francisco’s weekday peak median demand is 

1.05 kW per EVSE, compared to 0.65 kW in 

Nashville. It should be noted that the energy 

consumed per EVSE per day in these two regions 

is the same, making this comparison possible.

These differences in peak demand occur because 

EV Project participants in San Francisco tend to 

program their EVSE or vehicles to start charging 

at a specific time, whereas participants in 

Nashville do not. It is assumed that this behavior 

is driven by the availability (or lack thereof) of 

reduced electricity price through time-of-use 

rates.

Conclusion

This paper summarizes early usage of EV Project 

residential EVSE in households with Nissan 

LEAFs, based on data collected during Q4 2011

from 2,704 EVSE. A method of data aggregation 

was developed to summarize EVSE usage by the 

means of two metrics: charging availability and 

charging demand. Charging availability was 

plotted relative to time of day and day of the week 

to show the range of percentage of EVSE 

connected to a vehicle over time. Charging 

demand was plotted to show the range of charging 

demand of the EVSE on the electric gird over 

time.

In general, residential EVSE charging availability 

is low during the day, steadily increases in the 

evening, and remains high at night. Charging 

availability, which is a function of when 

individuals connect their vehicles to their EVSE, 

is consistent across EV Project regions.

Day-to-day variation in charging availability and 

charging demand on weekdays is high during Q4 

2011, because user behavior on the weekdays 

surrounding Thanksgiving and Christmas varies 

from the other weekdays in the quarter. On the 
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 11

weekdays surrounding the holidays, charging 

availability was low at night and high during the 

day, similar to weekend charging availability. To 

ignore the effect of the holidays and compare 

common weekday and weekend user behavior, the 

charging availability IQR was used. The IQR is 

highest on weekends between 20:00 and 06:00, 

indicating that this is the period of greatest 

variation in user “plugging-in” behavior from day 

to day, excluding days close to or on holidays.

When EVSE in all regions are examined in 

aggregate, demand peaks on weekdays and 

weekend days during the 00:00 hour. Weekday 

demand is lowest between 06:00 and 12:00, 

during which time it is nearly 0 kW per EVSE.

In order to identify regional differences in 

charging demand, EVSE usage in two individual 

EV Project regions was examined.

In Nashville, where EV Project participants do not 

have time-of-use electricity rates, demand 

increases each evening as charging availability 

increases, starting at about 16:00. Demand peaks 

in the 20:00 hour on weekdays. 

In San Francisco, the majority of EV Project 

participants have the option of choosing a special 

time-of-use rate plan from their electric utility. In 

this region, demand spikes at 00:00, which is the 

beginning of the off-peak electricity rate period. 

Demand peaks at 01:00.

In both regions, demand on weekdays and 

weekend days drops to nearly 0 kW per EVSE 

between 05:00 and 06:00, even though charging 

availability does not begin to fall off until 06:00 

or later. This suggests that the Nissan LEAFs 

being charged have sufficient time to fully charge 

during the night. Note that this is a function of 

how much the vehicles are driven prior to 

charging. This topic will be addressed in future 

works.

In San Francisco, financial incentive provided by 

time-of-use rates appears to successfully shift 

charging demand to off-peak hours. This may 

benefit the electric utility by preventing an

increase in peak system demand. However, a 

large number of users in this region schedule 

charging to begin immediately at midnight, which 

is the beginning of the off-peak period. This low 

diversity in charging start time creates an 

unintended demand spike at the beginning of the 

off-peak period. This may pose a different set of 

problems to the electric utility. 

Acknowledgments

Funding for The EV Project is provided in part by 

the U.S. Department of Energy’s Vehicle 

Technologies Program through a grant from the 

American Reinvestment and Recovery Act.

References

[1] R. Liu, L. Dow, E. Liu, “A Survey of PEV 

Impacts on Electric Utilities,” IEEE PES 

Innovative Smart Grid Technologies 

Conference, Anaheim, CA, January 2011

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

“Impacts Assessment of PHEVs on Electric 

Utilities and Regional U.S. Power Grids, 

Part 1: Technical Analysis,” PNNL SA53700, November 2007

[3] S. Hadley, A. Tsvetkova, “Potential Impacts 

of Plug-in Hybrid Electric Vehicles on 

Regional Power Generation,” ORNL/TM2007/150, January 2008

[4] J. Smart, J. Davies, M. Shirk, C. Quinn, K. 

Kurani, “Electricity Demand of PHEVs 

Operated by Private Households and

Commercial Fleets: Effects of Driving and 

Charging Behavior,” EVS25, Shenzhen, 

China, Nov. 5-9, 2010

[5] “EV Project Electric Vehicle Charging 

Infrastructure Summary Report,” October

through December 2011, http://avt.inl.gov/

pdf/EVProj/EVProjInfrastructureQ42011.pdf

[6] http://www.pge.com/tariffs/tm2/pdf/ 

ELEC_SCHEDS_E-9, 

accessed January 2012.

Authors

Stephen Schey 

Director Stakeholder Services, 

ECOtality North America

Mr. Schey has a B.S. in Mathematics 

from the U.S. Naval Academy and a 

M.S. in Mathematics from the U.S. 

Naval Postgraduate School. He has 

over 12 years of experience with EVs 

and related infrastructure. Formerly 

the GM EV1 Infrastructure Manager for Edison EV (a 

subsidiary of Southern California Edison), Schey was 

involved in the deployment of EV infrastructure in 

Phoenix and Tucson. Schey was also involved in the 

development and sales of fast charging equipment for 

industrial material handling equipment and ground 

support equipment for eTec under the Minit-Charger 

brand. In his current role, he is responsible for the 

planning phases of The EV Project, OEM integration, 

and project evaluation. He directs the electric utility 
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 12

relations group and leads the EVSE Infrastructure 

planning consulting efforts.

Don Scoffield

Mr. Scoffield is an engineer in the 

Energy Storage and Transportation 

Systems department at the Idaho 

National Laboratory.

John Smart

Mr. Smart is an engineer in the Energy 

Storage and Transportation Systems 

department at the Idaho National 

Laboratory.

Disclaimer

References herein to any specific commercial product, 

process, or service by trade name, trademark, 

manufacturer, or otherwise, does not necessarily 

constitute or imply its endorsement, recommendation, 

or favoring by the U.S. Government, any agency 

thereof, or any company affiliated with the Idaho 

National Laboratory.

INL/CON-11- 22696