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 T he EV Project

Stephen Schey 1



### , Don Scoffield 2 

### , John Smart 2

1 ECOta l ity North Amer i ca, 430 S. 2nd Ave., Phoenix, AZ 85003

, sschey@ecotality.com
2 Idaho National Laboratory, 2351

. N B ou l e v ar d, 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 i nfrastructure 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 LEAFs TM 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 chargin g demand. Charging availability i s plotted to show the percentage

of charging units connected to a vehicle over time. Charging demand i s plotted to show charging demand on the electric gird over time.

C harging availability for residential charging units i s 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 w ere 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 par ticipants 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 vehic le), 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 en ergy 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

S everal other auto motive manufacturers plan to launch PEVs in 2012. This illustrate s a shift to cleaner and more efficient electric d rive systems. These vehicles, which include plug

- in hybrid electric vehicle s, 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.

Fo r 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 impa ct 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 fr om 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 LEAF TM 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 electr ic 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 charg ing infrastructure, and conduct trials of various revenue systems for commercial and public charg ing infrastructure. The ultimate goal of The EV Project is to ta ke 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 analyz e 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 a nd a DC Level 2 commercial fast charger. The AC Level 2 EVSE s 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 int ernal energy meters and touch

screen user interfaces and allow user

- controlled charge scheduling. Numerous data parameters are collected from the Blink EVSE participating in The EV Project , including t ime 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 LEA F 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 infras tructure. Figure 3 : EV Project cities

. At the end of 2011, there were approximately 4,000 Nissan LEAFs and 2 00 Chevrolet Volts enrolled in the project. There were equal number s of residential EVSE installed, because e ach 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 ha d been installed by the end of 2011. Table 1 shows the number of units , by project region, that ha d been deployed and transferred data to the I daho N ational L aboratory

. 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 fo llowing 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 hou seholds 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 non

residential charging, including DC fast charging

an d 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, k nowing 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 c harging availability curve and c harging d emand 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 3,785EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 4



### 2.1 Charging A vailability
C harging availability at a point in time is the percentage of EVSE in a geographical area that are connected to a vehicle. The c harging availability curve depicts the c harging availability on a 15

- minute interval versus time. The c harging 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

c harging availability curve for a 3

- week time period during October 2011 for many residential EVSE in T he EV Project.

Figure 4 : Charging availability curve for many

r esidential EVSE in The EV Project.

The c harging 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 (presum ably) leave their residences. The weekly pattern revolves around the weekends. The weekend days tend to have lower peaks and h igher 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 c harging availability curve can be displayed using a 24

- hour t ime

- of

- day plot for weekdays and another 24

- hou r t ime

- of

- day plot for weekend days. This kind of t ime

- of

- day plot is a concise way to visualize the daily behavior of many calendar days of data simultaneously. To create a t ime

- of

- day plot , the charging availability curves for each calendar day are super imposed on the same 24

- hour scale. Figure 5 show s 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 cur ves 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,
respec tively.

Figure 5 : Weekday time

- of

- day charging availability from Figure 4 plotted on a single 24

- hour scale

.

Figure 6 : Weekday t ime

- 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 curv e 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 simil ar 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 demandEVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 5

during the day is close to zero. This indicates a
stron g 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 w eekend time

- of

- day plots also will be used to visualize the charging demand.



### 2.3 T ime

- of

- Day Pl ot Variations Additional information beyond the max imum and minimum curves can be added to a time

- of

- day plot

. Two variations of the time

- of

- day p lots 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 Oct ober 1 3 , 2011

. Therefore

, the charging demand curve for the entire day of October 1 3 is shown as the peak day curve on the time

- of

- day charging demand plot. The time

- of

- day charging availability plot show s the charging availability curve for October 1 3 , 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 max imum and min imum at any time of day

. This is depicted by dividing the range between the max imum and the min imum into quartiles and displaying the median and the inner quartile ra nge (IQR)

. The median is

a measure of central tendency that corresponds to the 50 th percentile. T he IQR is the range between the 25 th to the 75 th percentiles . It is used as a measure of the spread of the data

. Time

- of

- day plots with these features are show n in Fig ure s 10 and 11

.

Figure 1 0 : 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 inEVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 6

charging availability d uring the night

- time hours than during day

- time hours. For example

, at 0 3 :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 0 6:00, 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 1 1 : 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 t ime

- of

- day c harging 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 indica tes 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

conn ected on weekend days is higher at 18%. Fewer EVSE are connected during the early mornings after midnigh t on Saturdays and Sundays than on weekday early mornings.

Figure 1 2 : Weekday time

- of

- day charging availability for all EV Project regions

.

Figure 1 3 : Weekend time

- of

- day charging availability for all EV Project regions

T he range of variation in charging availability from calendar day to calendar day during the quarter is significant

. In Figure 12, t he time with highest variation on weekdays occurs between 0:00 and 0 5:00, when the percent of EVSE connected varies from abou t 30 to over 50%. The range of the top three quartiles from midnight and 0 5:00 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 t he 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 max imum and the min imum

. Inspection of the charging availability curve fo r all of the fourth qu arter (Q4) 2011 found that th e increased size of the lower quartile between 0:00EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 7

and 0 5:00 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

. Th is trend is represented in Figure 12 by a decrease in the minimum charging availability (green line) between 20 :00 and 0 6:00 and an increase in the maximum charging availability (blue line) from 0 9:00 to 18 :00

. The IQR can be examined to focus on common behavior and ignore atypical behavior , such as that s een around the holidays

. The tight IQR shown in Figure 12 indicates that common weekday charging availability has little variation from day to day

. T he 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 nigh t and higher during the day compared to most weekdays. On weekend days , there is very little variation acr oss days between noon and 16 :00 on weekends, when 18 to 23 % of EVSE are connected.

Time

- of

- day c harging 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 1 4: Weekday time

- of

- day charging availability for Nashville. Figure 1 5: Weekend time

- of

- day charging availability for Nashville

F igures 16 and 17 show time

- of

- day ch arging availability for the San Francisco region in the Q4 2011.

Figure 1 6 : Weekday time

- of

- day charging availability for San Francisco.

Figure 1 7 : Weekend time

- of

- day charging availability for San Francisco Again, t hese 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 C hargi ng Demand

T ime

- of

- day charging demand plots were generated from the data set for all EV Project regions in Q4 2011. T he magnitude of the demand

was normalized per EVSE by dividing theEVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 8

magnitude of the charging demand curve by the number of EVSE available f or 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 app ear 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

connec ted 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 an y 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 EV SE 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 0 1:00 , even though charging availability remains high until about 6:00. Demand falls during this period as batter y 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 h ou rs, whereas the
average period of time when t he vehicle draws power per charging event is 2.2 h ou rs [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 0 1 :00, is the result of

numerous users programming their EVSE or vehicles to commence charging at these times

. As with charging availabi lity, 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, t he upper three quartiles are fairly tightly grouped, indicating there is consist ent 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, chargi ng 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 bet ween 0 8:00 and 12:00 (Figure 22), d espite a decrease in charging availability during this time (Figure 15). Demand increas es as vehicles are connected to EVSE during this period and the vehi cles begin to charge immediately. Charging availability decreas es during this time because t he n umber 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 a re unplugged ; therefore

, th e 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.

I n 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 , th e midnight spike in demand is a result of a large number o f 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 r esidential 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 0 7:00. Weekend off

- peak hours start at 21 :00 [6]. While it is not possible to determine which customers have signed u p for these rates, it obvious from the plots that many EVSE users schedul e the start of vehicle chargi ng at midnight . It is assumed that this behavior is motivated by the desi re 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 a n equivalen t increase in the demand curve. Instead,
t he 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 a t 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 occur s 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 vehicl es begin charg ing 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 Ni ssan LEAFs, based on data collected during Q4 2011 from 2,704 EVSE . A method of data aggregation was dev eloped to summarize EVSE usage by the means of two metrics: charging availability and charging demand. C harging availability was plotted relative to tim e 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 availa bility 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 region s

. Day

- to

- day v ariation 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 theEVS26 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 weekda ys and weekend days during the 0 0:00 hour. Weekday demand is lowest between 0 6:00 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 tw o individual EV Project regions was exa mined. 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 majo rity 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 0 0:00, 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 LEAF s being charged have sufficient time t o 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 a n increase in peak system demand. However, a large number of us ers in this region schedule charging to begin immediately at midnight, which is the beginning of th e 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 provi ded 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 G rid 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 SA

53700, November 2007
[3] S. Hadley, A. Tsvetkova, “Potential Impacts of Plug

- in Hybrid Electric Vehicles on Regional Power Generation,” ORNL/TM

2007/150, 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 Sch ey

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 infrastruc ture. 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 charg ing 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 dire cts the electric utilityEVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 12

relations group and leads the EVSE Infrastructure planning consulting efforts.

Don Scoffield Mr. S coffield is a n engineer in the Energy Storage and Transportation Systems department at the Idaho National Laboratory

.

John Smart Mr. Smart is a n 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.

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