Value Quantification of Electric Vehicle
Response on Network Investment in the UK
Xiaohe Yan, Chenghong Gu, Furong Li

Abstract— Electric vehicles (EVs) are a significant element in the
electricity system to meet the environmental target and can help the
network operator to shift the peak demand in the future. Therefore, it
is important to evaluate the future economic impacts resulting from the
increasing EV penetration to the national network. However, the
contribution of EVs to network investment is difficult to quantify.
Although we can measure the reduced capacity during peak demand
for a specified low voltage network, it is difficult to quantify the impact
on a national level. This paper uses a typical LV network to analyze
the investment savings at this voltage level and then extrapolates the
results to the entire UK network by considering the coincidence factor,
peak shaving percentage etc. From the demonstration, it can be
concluded that semi-urban areas and low voltage networks enjoy the
mostly investment savings compared with other areas and voltage
levels.
Keywords— Electric vehicle, Investment savings, Present value,
Savings allocation.
I. INTRODUCTION
A
ccording to the government report in 2014, the CO2
emissions from transportation are increasing significantly
higher than previous years, especially from cars and vehicles on
the road [1]. In order to reduce CO2 emissions to meet their
environmental targets, the UK government has implemented
some policies to incentivize the use of electric vehicles (EVs).
For example, £400 million will be used to advance EV
technologies and £500 million will be used for citizens to shift
from traditional cars to EVs [2]. In National Grid’s Gone Green
scenario, the amount spends on EVs in 2030 will be £3.19
million [3]. Due to the increase in the penetration of EVs’, it is
important to know their impact on the electricity systems.
EVs, typically a transportation tool, can be treated as a
storage strategy to shift the peak demand and fill the valley,
which can lead to investment deferral and saving. Several
studies focus on this impact. Paper [4] discusses the impact of
EVs on energy usage and electricity demand profiles by
considering their using time period and location. An algorithm
has been developed to determine the impact of charging and
discharging behavior of EVs based on the statistics of driving
habits on daily trips and distance. But this paper has not
qualified the economic impact of EV penetration, no matter
from customer side or customer side. Paper [5] provides a
Vehicle to Home concept which treats EVs as an energy
All authors are with the Department of Electronic & Electrical Engineering,
University of Bath, Bath, BA2 7AY, UK. Tel: (+44) 01225 383040; E-mail:
xy442@ bath.ac.uk; C.Gu@ bath.ac.uk; F.Li@ bath.ac.uk.
provider for households. The cost reduction in local electricity
bills is evaluated due to EVs’ supplement during the peak
demand period. However, these results cannot contribute to the
network investment guidance. Paper [6] evaluates the impact on
electricity demand resulting from unscheduled charging of
EVs’. Electricity demand is simulated by Monte Carlo methods
to determine the relationship between potential demographic
characteristics and charging habits of EV drivers. Although less
than 1% of the peak load will be increased by uncontrolled
charging, this method focuses more on a specific region rather
than on national level. A spatiotemporal model is used in the
paper [7] to assess the potential of peak-shaving from EV. It
found that EVs can reduce peak loads 2~5% of the day in
specific geographical regions.
In the typical electricity load profile, the peak demand period
occurs from 6 pm to 8 pm which is 4~6 times higher than the
valley demand. The peak demand period exists only for a short
time, but this total required capacity determines the investment
needed. However, due to the network losses and the complex of
electricity distribution system, it is difficult to know the demand
capacity reduction results from EV penetration. In other words,
1 MW EV penetration in the low voltage (LV) system will lead
to different peak demand reduction in the high voltage (HV)
and extra high voltage (EHV) levels. Therefore, it is much more
difficult to quantify the peak demand capacity reduction which
can be achieved at a national level.
This paper focuses on quantifying the investment reduction
from flexible EV charging/discharging at different voltage
levels and in different areas of the distribution network. The
performance assessment is conducted by: 1) quantifying the
impact of different EV penetrations on network investment
deferral on a real UK LV network; 2) using present value to
measure the economic savings from EV penetrations; 3)
evaluating the investment savings at the national level by
considering different voltage levels and areas. Sensitivity
analysis is also conducted to mitigate the uncertainty of EVs’
unit contribution to network peak demand shift.
This paper is organized as follows: Section II explains the
methodology to extrapolate results from the LV network to the
national level and Section III gives an introduction of key
components. In Section IV, a typical LV network is analyzed to
demonstrate how EV penetration affects the investment savings
in LV level. The investment savings in 2020 and 2030 in the
UK will be demonstrated in Section V and conclusions are in
Section VI.
II. METHODOLOGY
This section introduces the methodology to determine the
investment saving in LV system from a typical LV network and
then extrapolate this to the whole UK networks.
Firstly, a typical LV network is analyzed to demonstrate how
EVs reducing the peak demands in local load profiles and also
to find out EV’s unit contribution to the peak reduction. By
calculating the change of present value, investment savings can
be determined for the LV system. The reduced capacity caused
by EV penetration can be calculated on a national level by
multiplying the unit contribution and the predicted number.
Then, this reduced capacity can be allocated to EHV, HV and
LV levels by considering peak shaving percentage. Due to the
time difference of peak demand in different voltage levels,
coincidence factor is involved. Because the capacity of this
peak demand determines the network investment, the
investment savings on three voltage levels can be obtained by
multiplying the unit cost with the capacity reduction. Capacity
share can allocate these savings to three different areas which
are urban, semi-urban and rural areas. These elements will be
described in Section III. The flowchart in Fig. 1 shows the
proposed approach and the main steps in national level:
Peak Electricity
demand
EV Number &
unit
contribution
EV contribution
capacity
Reduced
capacity in
EHV
Reduced
capacity in
HV
Peak
shaving% &
Coincidence
factor
Reduced
capacity in
LV
Unit cost
Investment
saving EHV
Investment
saving HV
Investment
saving LV
Capacity
Share
Urban/Semi/
Rural
Urban/Semi/
Rural
Urban/
Semi/Rural
National Level
III. KEY COMPONENT
A. Peak Shaving Percentage
Peak shaving percentage is considered to show the allocation
of the EV penetration to different voltage level based on
distribution characteristics such as network loss. This factor is
listed in TABLE I [8]. For example, the peak shaving
percentage for EHV, 55%, means that 1 MW capacity change
in lower voltage level (HV) leads extra 55% change in EHV
networks.
TABLE I
PEAK SHAVING PERCENTAGE
EHV
HV
Peak shaving
55%
37%
LV
58%
B. Coincidence Factor
Since the peak demand time in different voltage levels occurs
differently, coincidence factor is considered to integrate the
differences. The coincidences factors networks in different
voltage levels are listed in TABLE II [8]. This factor can gather
the peak period together in different years. The coincidence
factor for HV networks is the largest one.
TABLE II
COINCIDENCE FACTOR IN DIFFERENT VOLTAGE LEVELS
EHV
HV
LV
2020
1.03
1.40
1.32
2030
1.03
1.40
1.23
C. Unit Cost
Unit cost is the investment for 1 MW reinforcement at
different voltage levels. The unit cost for three voltage levels is
listed in TABLE III [8]. For LV and EHV networks, the unit
cost in 2030 is higher than that in 2020 but the unit cost for HV
network is the highest in 2020.
TABLE III
UNIT COST FOR NETWORK INVESTMENT IN DIFFERENT VOLTAGE LEVELS
£k/ MW
EHV
HV
LV
2020
192
235
197
2030
199
153
303
D.Capacity Share
The capacity share among urban/semi/rural areas shows the
network capacity allocation in different areas in Fig. 2 [8].
Urban areas have the largest capacity share which is more than
half of the capacity of the whole distribution network and the
capacity is doubled in rural areas from 2020 to 2030.
Fig. 1 Flow chart of proposed approach
1) Convert the predicted number and unit contribution of
EV’s to the peak demand reduction;
2) Allocate this demand reduction at different voltage levels
by using the peak shaving percentage and the coincidence
factor;
3) Determine the investment savings by multiplying the unit
cost;
4) Allocate investment savings in different areas at different
voltage levels by involving the capacity share.
Fig. 2 Capacity share among urban/semi/rural areas
E. Pay Back Period and Present Value
The network cost from Long-Run Incremental Cost Pricing
(LRIC) can be determined from [9]. Equation (1) shows the
relationship between rated network capacity (C) and maximum
power flows (D). The number of years (n) it takes to grow from
D to C for a given load growth rate (r) can be presented in (2).
𝐶 = 𝐷 × (1 + 𝑟)𝑛
(1)
𝑛=
𝑙𝑜𝑔 𝐶−𝑙𝑜𝑔 𝐷
(2)
𝑙𝑜𝑔(1+𝑟)
The present value (PV) is used to quantify the current worth
with a specific discount rate (d) in the future investment in n
years.
𝐴𝑠𝑠𝑒𝑡
𝑃𝑉 =
(3)
𝑛
(1+𝑑)
Where: Asset is the modern equivalent asset cost.
IV.
CASE STUDY
A. Information for Selected LV Network
The proposed approach is demonstrated on a typical LV
system network in Fig. 3 which has one substation and two
feeders. Feeder 1 contains nodes from 1 to 28, which has 136
customers. Feeder 2 contains nodes from 29 to 48, which has
121 customers. The total customers who have EVs are 11 (3 in
Feeder 1 and 8 in Feeder 2). The locations of these EV
customers are listed in TABLE IV [10] and each node has one
EV unit.
0011& 0012
1
2
111715
7
6
3
4
9
11
10
12
8
5
28
26
24
13
27
25
23
14
15
16
32 37
29
0021
30
40
31
33
38
42
39
43
41
44
21
22
17
18
19
20
36
34
35
46
45
Fig. 4 The peak demand profile of the year
B. Investment savings for LV Networks
This part shows how the demand profile be influenced by EV
penetration. Fig. 5~7 depict that the peak demand is shaved and
the valley demand is filled resulting from EV penetration in
Feeder 1, Feeder 2 and substation. Normally, the EVs are
charging during the evening which is the valley of demand
profile and discharging during the peak period which is the time
that people back to their home after 5 pm. Two different EV
penetration levels are considered which are 30% and 50% of the
customers have EVs and each EV unit contributes 0.3kW
demand reduction during the peak demand period. With 30%
EV penetration, the peak demand reduces 11.4 kW, 8.4 kW and
19.8 kW in Feeder 1, Feeder2 and substation respectively. With
50% EV penetration, the peak demand reduces 19.5kW,
15.9kW and 35.4kW in Feeder 1, Feeder2 and substation
respectively. EVs are discharging over the peak demand period
from 16:00 to 20:00 and charging over the valley period from
2:00 to 6:00. From the results figures below, higher EV
penetration level leads to a more flat profile curves.
48
47
Fig. 3 The topology structure of the case system
TABLE IV
CUSTOMER CONNECTION INFORMATION
Feeder No.
House No.
Connection point
1
2
13
15
16
6
7
20
21
22
23
24
26
Node 3
Node 6
Node 6
Node 46
Node 46
Node 38
Node 38
Node39
Node 30
Node 33
Node 31
Acceding to the historical data during 2014~2015, the profile
of the peak demand day can be drawn in Fig. 4. The peak
demand in Feeder 1, 2 and substation is 141.7 kW (at 19:15),
171.5 kW (at 17:30) and 303.8 kW (at 17:30) respectively.
Fig. 5 The results for Feeder 1
Fig. 6 The results for Feeder 2
Fig. 7 The results for substation
Typically, the load growth is 2% and the discount rate is
5.6%. The typical unit cost of the feeder is £67200/km and the
unit cost of transformers is £26400 [11, 12]. Based on (3), the
investment savings are listed in TABLE V:
£
Feeder 1
Feeder 2
Substation
TABLE V
NETWORK INVESTMENT SAVINGS
Base case
30% Penetration
50% Penetration
14.7
445.1
714.0
182.3
816.1
1335.1
75.9
464.1
736.6
From the table above, the savings of the transformer is just
£75.9 because the current utility of transformer is only 42%.
The investment for Feeder 2 saves the most and Feeder 1is the
least which means Feeder 1 has lower capacity utilization.
C. Sensitivity Analysis on Investment savings
Since the unit contribution of EV is different, it is significant
to do the sensitivity analysis to mitigate this uncertainty. Fig.
8~10 show the variation of the investment deferral when the EV
unit contribution changes from 0.2 kW to 1.7 kW.
Fig. 10 Sensitivity analysis on substation
From these three figures, the trend of each feeder is similar
to the trend of the substation. At the beginning of EV
contribution, investment deferral increases dramatically. But
when the EVs’ contribution exceeds a certain level, the
investment savings are saturated because the peak demand is
shifted to another time period.
V. INVESTMENT SAVINGS FOR THE UK DISTRIBUTION
NETWORKS
The demand capacity is 60.2 GW in 2020 and increases to
62.2 GW in 2030 [3] and the number of EVs will increase from
0.57 million to 3.19 million over this 10 year period [13].
Assuming 1) the load growth is 2% and the discount rate is
5.6%; 2) The typical unit cost of feeder is £67200/km and the
unit cost of transformers is £26400; 3) the 30% of the customer
have EVs and the unit contribution of EV is 0.3 kW to the
reduction of the peak demand.
A. Investment savings across Voltage Levels and Areas
By considering the factors in section II and following the
steps in the flowchart (Fig. 1), the investment savings at
different voltage levels and different areas are drawn below:
£m
LV
HV
EHV
Total
TABLE VI
INVESTMENT SAVINGS IN 2020
Urban
Semi
Rural
10.2
16.2
0.9
8.7
13.8
0.8
8.1
12.9
0.7
26.9
43.0
2.4
Total
27.3
23.2
21.8
72.3
£m
LV
HV
EHV
Total
TABLE VII
INVESTMENT SAVINGS IN 2030
Urban
Semi
Rural
49.2
80.0
9.2
44.9
73.1
8.4
42.2
68.8
7.9
136.2
221.9
25.4
Total
138.3
126.3
118.9
383.5
Fig. 8 Sensitivity analysis on Feeder 1
Fig. 9 Sensitivity analysis on Feeder 2
From TABLE VI~VII, it can be found that: first, LV network
enjoys the largest investment savings which are £27.3m in 2020
and £138.3m in 2030. EHV network enjoys the least investment
savings which are £21.8m in 2020 and £118.9m in 2030;
second, the investment savings in the semi-urban areas are
much higher than other areas, especially the rural areas; third,
the total savings are increasing dramatically. It climbing from
£72.3m in 2020 to £383.5m in 2030 which climbs more than
five times.
B. Sensitivity Analysis of Investment savings for 2020 and
2030 Scenarios
Considering the uncertainty of the contribution of EV unit,
sensitivity analysis need to be involved to show how the
investment savings vary with a different unit contribution.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
Fig. 11 Sensitivity analysis on investment savings for 2020
[8]
[9]
[10]
[11]
Fig. 12 Sensitivity analysis on investment savings for 2030
From Fig. 11~12 above, it is seen that with the increasing of
EV unit contribution, the investment savings are climbing in
different voltage levels. With the unit contribution increasing
from 0.3 kW to 0.9 kW, the investment savings for LV networks
climbs from £27.3m to £81.9m in 2020 and from £138.3m to
£415.0m in 2030.
VI. CONCLUSION
This paper quantifies the investment savings in EHV, HV
and LV networks in the 2020 and 2030. There are some key
findings from the qualification analysis:
•
EVs in semi-urban areas contribute more to distribution
network investment savings;
•
The EV penetration effects are different at different
voltage levels. LV network enjoys the largest savings
and EHV has the least savings;
•
With the EV penetration increasing in national level,
investment savings are climbing notably;
•
In local distribution networks, the peak of the networks
will shift after a certain level of EV penetration, and
thus, new EV response scheme is needed;
•
In order to encourage EV response, new business
models/tariffs are essential.
EV penetration is a significant factor in the whole electricity
system. If EVs can response appropriately, the power
companies and consumers can gain the more benefits.
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Xiaohe Yan was born in Shaanxi, China in Aug/1991. He received Master’s
degree in the electrical power system from the University of Bath in 2015. He
is currently pursuing the Ph.D. degree in the Department of Electronic &
Electrical Engineering at the University of Bath. His current research activities
are in the area of low voltage network pricing.
Chenghong Gu was born in Anhui province, China. He received his, Bachelor
degree and Master degree in electrical engineering from Shanghai University
of Electric Power and Shanghai Jiao Tong University, Shanghai, China, in 2003
and 2007 respectively. In 2010, he obtained his PhD from University of Bath,
U.K. Now, he is a Lecturer and EPSRC Research Fellow at the University of
Bath. His major research is in the area of power system planning, economics
and multi-vector energy systems.
Furong Li was born in Shanxi, China. She received her B.Eng. degree in
electrical engineering from Hohai University, China, in 1990, and her Ph.D.
degree from Liverpool John Moores University, U.K., in 1997. She took up a
lectureship in 1997 in the Power and Energy Systems Group at the University
of Bath, U.K, where she is now Professor. Her major research interests are in
the areas of power system planning, operation, automation and power system
economics.