Home Life
Resume:
Impact Analysis of Electric Vehicle Charging on
Distribution System
Qin Yan Mladen Kezunovic
Department of Electrical and Computer Engineering Department of Electrical and Computer Engineering
Texas A&M University Texas A&M University
College Station, TX USA College Station, TX USA
**************@*****.*** *******@***.****.***
compare the impacts. Paper [3] published in 2012 developed a
Abstract With the growing penetration of various types of
probabilistic model for vehicle arriving time and charge left on
electrical vehicles (EVs) such as Plug-in Hybrid Vehicles/
Battery Electric Vehicles (PHEVs/ BEVs), EV charging has the arrival. Paper [4] in 2012 considered both loss and thermal
potential to affect the existing distribution system, especially the models of transformer and analyzed load loss due to current
service lifetime of distribution components. This paper aims at harmonics. The importance of ambient temperatures to the
analyzing the potential impact of EV charging on distribution impact on transformer aging was illustrated in [5]. In this
transformers at residential level. Based on the load consumption paper, the authors combined single residence hourly load from
in East Texas and different assumptions of EV charging RELOAD database and travel demand data from the National
scenarios, the impact is demonstrated and compared on monthly
Household Transportation Survey for EV demand, but they
basis.
applied the same daily base load repeatedly to 365 days
through a year. Paper [6] analyzed the impact over a year
Keywords EV charging scenarios, Transformer loss of life
under different charging scenarios, i.e. simultaneous charging,
staggered charging, and proportional charging. A Monte-Carlo
I. INTRODUCTION
scheme simulated each day of the year, evaluating 100 load
Power transformers are one of the most expensive scenarios, in [7]. In these papers, however, the results for four
components in a distribution network. With the increasing seasons were not separately discussed.
penetration of electric vehicles, new load peak may be created,
Our paper focuses on the impact of electric vehicle
which may exceed the transformer capacity. Therefore, in a
charging on distribution system, especially distribution
residential house, owning an electric vehicle may mean a need
transformers. The actual 15-minute load profiles around
to upgrade the utility s local transformer or lead to early
College Station area (East Texas) available from ERCOT
replacement [1]. Reduction in transformer life expectancy will
website are used as base load [8]. Various charging scenarios
result in an increase of costs to utilities and consumers. Hence,
are assumed under different charging start times, penetration
the reduced transformer life becomes a very important impact
rates and usage ratios. Several EV cases are defined based on
when extra load is taken into consideration. According to the
actual routes in College Station. A MATLAB program is
latest IEEE guide, the relationship between insulation life and
developed to simulate aging acceleration factor and loss-of-
transformer life still remains a question. However, percent loss
insulation life of distribution transformer through a year. The
of total insulation life is usually used to evaluate transformer's
simulation results for four seasons are compared and analyzed.
aging [2].
The paper is organized as follows: Section II describes and
To analyze the impact of electric vehicle (EV) penetration
summarizes base load model, EV model, and transformer
on transformers, three models are required: base load model,
thermal model, which are used for the impact analysis. Based
non-base load (electric vehicle load) model, and transformer
on the total load profiles for different EV charging scenarios
model.
provided in Section II, the aging acceleration factor and the
Non-EV loads are obtained from different sources. Since transformer loss-of-life are simulated in MATLAB. The
there are no actual data for distribution of electric vehicle simulation results for different load profiles are tabulated and
chargers, EV models are based on assumptions of charging illustrated in Section III, and compared with the original base
scenarios with different charging start times and different residential load. Also, a comparison is made between the
penetration rates, or in some cases statistical methods are used results through different seasons. Section IV concludes the
to build models. To analyze the impact on distribution impact analysis (considering potential scenarios of EV
transformers, different factors are used to evaluate and charging in East Texas).
This study is sponsored by NSF I/UCRC: Electric Vehicle
Transportation and Electricity Convergence (EV-TEC) under project titled:
The Impact of PHEV/BEV Charging on Utility Distribution System
vehicle type, or charging duration. For instance, case 1 differs
II. MODELS
from case 3 because case 1 vehicle charges only at home
In this paper, three models are built: base load model, EV
load model, and transformer s thermal model.
A. Base Load Model
Base load is obtained from ERCOT website [8]. Fig. 1
demonstrates 15-minute interval load consumed by an average
residential individual customer for different seasons in East
Texas. Fig. 2 is the daily peak load per residential customer
through 2011. The red points are labeled with the amount and
the exact time of the peak load in that day. In East Texas
where College Station is located, the load consumed in
summer is higher than the load consumed in winter in general.
However, different from the load profile obtained from
RELOAD Database Documentation and Evaluation and Use
in NEMS [5], for some days in February, the peak load
happens in the early morning and is pretty high (For instance,
in 2009, the peak load of the year happened in winter [8]).
Hence, in some winter days (e.g. Feb.11th), the load consumed
Figure 2. Daily peak load per residential customer in East Texas
in the early morning is higher than that in summer days. For
the purpose of fair analysis, the load consumption in Feb.11th TABLE II. EV MODEL CASES
is added to Fig. 1. The results of this phenomenon will be
presented in Section III. Energy Charging
Vehicle Charging SOC
Case Daily Route Needed Duration
Type Location (kWh) (hour)
Home-Work
(9 miles)
Chevy 2.457
1 Work-Shop- Home 59 6.552
Volt (2.5)
Home
(9.2 miles)
Home-Work
(9 miles)
Nissan 1.805
2 Work-Shop- Home 74.2 6.188
Leaf (2.0)
Home
(9.2 miles)
Home-Work
(9 miles)
Chevy Home & 1.242
3 Work-Shop- 79.3 3.312
Volt Work (1.25)
Home
(9.2 miles)
Home-Work
(9 miles)
Figure 1. 15-minute interval data of average residential individual customer Nissan Home & 0.912
4 Work-Shop- 87 3.128
in East Texas Leaf Work (1.0)
Home
(9.2 miles)
B. EV Load Model Home-Work
(22.5 miles)
The EPA estimation results of two representative auto Chevy
5 Work-Shop- Home X X X
models, Nissan Leaf and Chevy Volt, are employed in this Volt
Home
study (in Table I). (22.7 miles)
Home-Work
TABLE I. EPA ESTIMATION RESULTS (22.5 miles)
Nissan 4.482
6 Work-Shop- Home 36 15.368
Leaf (4.5)
Auto EV Battery Electricity Electric Charging Home
Model Type Size Consumption Range Time (22.7 miles)
Home-Work
Nissan 34 kWh / 7 hours
(22.5 miles) Home
EV 24 kWh 73 miles
Chevy 3.065
Leaf 100 miles (240 V)
7 Work-Shop- & 48.9 8.172
Volt (3.0)
Chevy 36 kWh / 6-6.5 hours Home Work
EREV 16 kWh 35 miles
Volt 100 miles (240 V) (22.7 miles)
Home-Work
(22.5 miles) Home
Nissan
Eight EV cases are defined in Table II. Charging duration 8 Work-Shop- & 67.8 7.718 2.25
Leaf
indicates the time required to fully charge the battery. The Home Work
driving cycle for each case is based on the actual routes in (22.7 miles)
College Station [9]. The cases differ in state of charge (SOC),
whereas case 3 vehicle charges both at work and home. Hence start time is defined based on the driving time in Table IV by
case 1 vehicle when reaching home has less state of charge assuming people start driving home from workplace at 5 pm
compared with case 3 vehicle. and charge on arriving home. EV load model for charging on
arriving home is shown in Fig. 3, which will be added to
Since the electric range of Chevy Volt is only 35 miles everyday base load data. Figures 4 and 5 show the daily total
(from Table I), the EV case 5 (from Table II) for Chevy Volt load in one typical day per season for scenarios 2 and 4 with
involving driving from home to work for 22.5 miles and from EV charging on arriving home. Figures 6 and 7 show the total
work to home via supermarket for 22.7 miles, while charging load of a specific day for charging at 1 am and charging at
only at home, is not considered. distributed timing.
Based on different charging start time, three charging
assumptions are defined: charging on arriving home, charging
at 1 am, and charging at distributed timing. Generally
speaking, the peak load of a day happens at early evening
(Fig.1). Thus, without any power management and charge
distribution, charging on arriving (starting to drive back at 5
pm and charging on arrival) may be the worst case of EV
penetration [6]. Usually, it is recommended to charge electric
vehicles at midnight since the load consumption is low then
and it may avoid increasing the existing load peak of the day.
Moreover, the impact of EV charging with a controlled
charging strategy (e.g. distributed charging through a day) will
be analyzed. For each charging assumption, four charging
scenarios are analyzed. In our case study, assuming that the
distribution transformer serves 14 residential households, 4
scenarios are proposed (in Table III): Figure 3. EV load for charging on arriving
Scenario 1 includes 7 electric vehicles (corresponding to
EV cases in Table II), only considering driving to and from
work via supermarket. Scenario 2 includes 14 electric vehicles
(considering each case in Table II twice). Scenario 3 includes
7 electric vehicles, with zero charge at the beginning of
charging. Scenario 4 includes 14 electric vehicles, with zero
charge before charging starts.
TABLE III. CHARGING SCENARIOS
Scenario Vehicle Quantity Usage
Limited to
1 7
Home-Work-Shop-Home
Limited to
2 14
Home-Work-Shop-Home
Charge depleted on
3 7
arriving home Figure 4. Load with EV charging on arriving home - Scenario 2
Charge depleted on
4 14
arriving home
TABLE IV. DRIVING TIME FOR EACH DRIVING CYCLE
Cycle Cycle 1 Cycle 2 Cycle 3 Cycle 4
Work-Shop- Work-Shop-
Home-Work Home-Work
Route Home Home
(9 miles) (22.5 miles)
(9.2 miles) (22.7 miles)
Driving
778-****-**** 2361
Time (s)
Driving
13 29 23 39
Time (min)
The SOC, energy needed, and charging duration in Table
II which indicate limited usage of electric vehicles are used in
scenarios 1 and 2. For charging on arriving home, the charging Figure 5. Load with EV charging on arriving home - Scenario 4
2) Aging acceleration factor and percent loss-of-life:
Aging acceleration factor is a function of ambient
temperature, transformer loading, and certain specific
transformer parameters. Loss of life (LOL) is the equivalent
aging in hours over a time period times 100 divided by the
total normal insulation life in hours at the reference hottest-
spot temperature. Normal insulation life for a transformer is
the expected lifetime when operated with a continuous hot-
spot temperature of 110, which is 180,000 hours in this
paper. Table V shows the specifications used for the
transformer s loss of life calculation [2].
Procedure for calculating the thermal factors:
a) Hottest-spot temperature
b) Aging acceleration factor for every 15 min
Figure 6. Load with EV charging at 1 am August 18th
FAA e (2)
H
c) Equivalent aging factor for each month
N FAA,
FEQA (3)
N
d) Annual average factor and the corresponding percent
loss of insulation life
FEQA
%Loss of life (4)
N
TABLE V. DISTRIBUTION TRANSFORMER PROPERTIES
Symbol Property Units
Ratio of load loss at rated load to no-
R
Figure 7. Load with EV charging at distributed timing August 18th load losses
Oil time constant hr
C. Transformer Thermal Model
Winding time constant min
1) Correlation between load and ambient temperature:
Top-oil temperature rise
Ambient temperature is an important factor in determining,
at rated load
the load capacity of a transformer since the temperature rise Winding hottest-spot rise
for the load should be added to the ambient temperature to,
at rated load
determine operating temperature [2]. n Empirically derived exponent
H A TO H (1) m Empirically derived exponent
Time variable Top-oil rise
where H is the winding hottest-spot temperature which is
Top-oil rise over
used to calculate loss-of-life factor. A is the average ambient
ambient temperature
temperature. The average temperature used in this paper is Time variable winding
the actual data obtained from Weather Channel website [10]. hottest-spot rise
TO is the top-oil rise over ambient temperature, H is the Winding hottest-spot rise
over top-oil
winding hottest-spot rise over top-oil temperature.
Normal insulation life hr
Positive correlation: The electric cooling load is
dominant in summer. Higher temperature will lead to
III. SIMULATION RESULTS
more power consumption, and thus will result in
higher temperature rise. Results of transformer aging acceleration factor and loss of
life according to different EV charging assumptions are
Negative correlation: The temperature is lower in
tabulated in Table VI-VIII. The actual load data for every 15
winter. If the temperature decreases, the electric
minutes through a year are used. The original column in these
heating will increase accordingly.
tables refers to the scenario with no EV charging.
Therefore, generally, the positive correlation provides
more severe transformer duty than the negative one.
TABLE VI. FEQA AND LOL (CHARGING ON ARRIVING HOME)
Original Charging on arriving home
Base
Scenario 1 Scenario 2 Scenario 3 Scenario 4
Load
FEQA 0.0021 0.0757 7.1464 0.1631 20.2431
LOL 0.0104 0.3685 34.779 0.7935 98.5163
TABLE VII. FEQA AND LOL (SCENARIO 2)
Original Scenario 2
Base Charging on Charging at Distributed
Load arriving at 5:00 pm 1:00 am Charging
FEQA 0.0021 7.1464 0.1001 0.0039
LOL 0.0104 34.779 0.4871 0.0192
Figure 8. Monthly FEQ results for Charging on arriving
TABLE VIII. FEQA AND LOL (SCENARIO 4)
Original Scenario 4
Charging
Base Charging Distributed
Month on arriving
Load at 1:00 am Charging
at 5:00 pm
FEQ_
Jan 0.00012 0.785033 0.002730
1.357234
1
FEQ_
Feb 0.00057 2.377044 0.015482
8.422275
2
FEQ_
Mar 1.87E-05 0.028636 0.000109
0.008801
3
FEQ_
Apr 5.62E-05 0.219824 0.000446
0.013729
4
FEQ_
May 0.00021 1.180658 0.002139
0.039392
5
FEQ_
Jun 0.00271 26.33804 0.040787
0.320498
6
FEQ_
Jul 0.00468 42.80368 0.069685
0.557542
7
FEQ_
Aug 0.01597 158.4035 0.263961
1.090655
8
FEQ_
Figure 9. Monthly FEQ results for Scenario 2
Sep 0.00121 10.38140 0.016797
0.122107
9
FEQ_
Figures 8 and 9 indicate that under the same time-of-
Oct 5.21E-05 0.103050 0.000313
0.008600
10
charge, higher penetration rate of electric vehicles will
FEQ_
Nov 2.29E-05 0.046522 0.000176
0.037412
significantly increase transformers loss-of-life factor. With
11
higher penetration rate, the rise of usage ratio will exert even
FEQ_
Dec 3.99E-05 0.249521 0.000646
0.242704
12 more influence on transformers; under the same penetration
rate of electric vehicle, charging at midnight will considerably
decrease the loss-of-life factor, while distributed charging
Year FEQA 0.0021 20.2431 1.0184 0.0344
through the day will help to almost eliminate the impact of EV
charging on distribution transformers.
Year LOL 0.0104 98.5163 4.9563 0.1676
The monthly data in Table VIII are the equivalent aging
factors per month, which are the average value of all the 15
Table VI and Fig. 8 indicate that when charging on min load data of the month (as given in (3)). The fifth column
arriving, scenario 4, which has the highest penetration ratio of in Table VIII shows the results for charging scenario 4 and
EV and requires the most amount of electricity, has much charging at 1 am. It has been mentioned that some days in
higher aging acceleration factor and loss of life. And for February have very high daily peak load and the peak load
scenario 2, as shown in Table VII and Fig. 9, charging on occurs in the early morning. As shown in this column, it is
arriving home tremendously increases loss-of-life factor, obvious that the aging acceleration factor in February is higher
while controlled EV charging will help reduce transformer s than that in summer. The reason may be that the charging
loss of life. event, which begins at 1am with zero charge left in the battery
before charging, will last till early morning, when the peak
load of the day happens. Even though the correlation between
ambient temperature and load in winter is negative, charging months, winter months with peak load (e.g. February in 2011)
at 1 am in February will have more severe impact on should also be considered.
transformer life.
ACKNOWLEDGMENT
IV. CONCLUSION
We would like to express our greatest gratitude to the
In this paper the potential impacts of different EV persons and organizations that supported this Project. Funding
penetration scenarios with different usage and charging start for this project is provided by National Science Foundation
time are discussed and compared. The actual load data, the Industry/ University Cooperative Research Center: Electric
driving routes in College Station, and the real ambient Vehicle Transportation & Electricity Convergence (EV-TEC).
temperature data are employed to simulate the transformer
factors. The result data demonstrate the effect of possible EV REFERENCES
penetration on the existing distribution transformer lifespan [1] L. Dickerman, and J. Harrison, A New Car, a New Grid, IEEE Power
and provide suggestions to eliminate or reduce the negative & Energy Magazones, vol. 8, pp. 55 61, March/ April 2010.
impact regarding the situation in Texas. According to the [2] IEEE Guide for Loading Mineral-Oil-Immersed Transformers and
discussions presented in this paper, the following conclusions Step-Voltage Regulators, IEEE Std C57.91TM-2011 (Revision of
IEEE Std C57.91-1995), March 2012.
can be drawn:
[3] S. Argade, V. Aravinthan and W. Jewell, Probabilistic Modeling of
a) Higher penetration rate of electric vehicle will EV Charging and Its Impact on Distribution Transformer Loss of Life,
significantly increase transformers loss-of-life factor by up to 2012 IEEE International Electric Vehicle Conference (IEVC),
Greenville, SC, Mar.4-8, 2012.
10,000 times. With the rise of penetration rate, the increase of
[4] M. Kazerooni, N.C. Kar, Impact Analysis of EV Battery Charging on
usage will exert much more influence on transformer life.
the Power System Distribution Transformers, 2012 IEEE International
b) Charging at midnight will help to considerably Electric Vehicle Conference (IEVC), Greenville, SC, Mar.4-8, 2012.
decrease the loss-of-life factor for all the scenarios defined in [5] A. D. Hilshey, P. D. H. Hines and J. R. Dowds, Estimating the
this paper. While with distributed charging, the impact can be Acceleration of Transformer Aging Due to Electric Vehicle Charging,
Power and Energy Society General Meeting, San Diego, CA, Jul. 24-
almost eliminated. Though charging at distributed timing may 29, 2011.
lead to charging at peak hour, it is still a better way to alleviate [6] M. J. Rutherford, V. Yousefzadeh, The Impact of Electric Vehicle
the impact on transformers. However, it remains a challenge Battery Charging on Distribution Transformers, Proc. Applied Power
to realize this charging strategy, as the utilities need to Electronics Conference and Exposition (APEC), Twenty-Sixth Annual
IEEE, Mar. 6-11, 2011, pp. 396-400.
coordinate with customers and charging stations.
[7] M. Kuss, T. Markel and W. Kramer, Application of Distribution
c) Generally, the impact of extra load on transformers in Transformer Thermal Life Models to Electrified Vehicle Charging
summer is much greater than that in winter. However, as Loads Using Monte-Carlo Method, 25th World Battery, Hybrid and
mentioned above, in Texas, some winter mornings with peak Fuel Cell Electric Vehicle Symposium & Exhibition, Shenzhen, China,
Nov. 5 - 9, 2010.
load may be an exception. Charging from midnight through
[8] Electric Reliability Council of Texas (ERCOT). 2011 Actual Load
early morning in those days may exert strong impact on
Profiles. [online] http://ercot.com
transformers. Therefore, it is not always appropriate to charge
[9] C. Pang, M. Kezunovic, Demand Side Management by using Electric
electric vehicles at 1 am in those days. How to develop a Vehicles as Distributed Energy Resources, 2012 IEEE International
control strategy may depend on the actual load profile in a Electric Vehicle Conference (IEVC), Greenville, SC, Mar.4-8, 2012.
particular area for a particular time period. [10] The Weather Channel. [online]
d) For charging on arriving home, the most severe http://www.weather.com/weather/wxclimatology/daily/USTX0270?cli
moMonth=1
impact occurs in June, July, August and September. Thus,
steps should be taken to reduce the impact in these months.
For charging at 1:00 am, besides the abovementioned summer
Contact this candidate