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This paper investigates the possibility of charging battery electric vehicles at workplace in Netherlands using solar energy. Data from the Dutch Meteorological Institute is used to determine the optimal orientation of PV panels for maximum energy yield in the Netherlands. The seasonal and diurnal variation in solar insolation is analyzed to determine the energy availability for EV charging and the necessity for grid connection. Due to relatively low solar insolation in Netherlands, it has been determined that the power rating of the PV array can be oversized by 30% with respect to power rating of the converter. Various dynamic EV charging profiles are compared with an aim to minimize the grid dependency and to maximize the usage of solar power to directly charge the EV. Two scenarios are considered – one where the EVs have to be charged only on weekdays and the second case where EV have to be charged all 7 days/week. A priority mechanism is proposed to facilitate the charging of multiple EV from a single EV–PV charger. The feasibility of integrating a local storage to the EV–PV charger to make it grid independent is evaluated. The optimal storage size that reduces the grid dependency by 25% is evaluated.
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System design for a solar powered electric vehicle charging station
for workplaces
q
G.R. Chandra Mouli, P. Bauer
, M. Zeman
Department of Electrical Sustainable Energy, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands
highlights
10 kW solar powered EV charger with V2G for workplaces in Netherlands is analyzed.
Optimal tilt for PV panels to get maximum yield in Netherlands is 28°.
PV array can be 30% oversized than converter, resulting in only 3.2% energy loss.
Gaussian EV charging profile with low peak closely follows PV generation.
10 kW h local storage reduced grid energy exchange by 25%.
article info
Article history:
Received 18 September 2015
Received in revised form 19 January 2016
Accepted 28 January 2016
Keywords:
Batteries
Electric vehicles
Energy storage
Photovoltaic systems
Solar energy
abstract
This paper investigates the possibility of charging battery electric vehicles at workplace in Netherlands
using solar energy. Data from the Dutch Meteorological Institute is used to determine the optimal orien-
tation of PV panels for maximum energy yield in the Netherlands. The seasonal and diurnal variation in
solar insolation is analyzed to determine the energy availability for EV charging and the necessity for grid
connection. Due to relatively low solar insolation in Netherlands, it has been determined that the power
rating of the PV array can be oversized by 30% with respect to power rating of the converter. Various
dynamic EV charging profiles are compared with an aim to minimize the grid dependency and to max-
imize the usage of solar power to directly charge the EV. Two scenarios are considered – one where
the EVs have to be charged only on weekdays and the second case where EV have to be charged all
7 days/week. A priority mechanism is proposed to facilitate the charging of multiple EV from a single
EV–PV charger. The feasibility of integrating a local storage to the EV–PV charger to make it grid indepen-
dent is evaluated. The optimal storage size that reduces the grid dependency by 25% is evaluated.
Ó2016 Elsevier Ltd. All rights reserved.
1. Introduction
Two major trends in energy usage that are expected for future
smart grids are:
1. Large-scale decentralized renewable energy production
through photovoltaic (PV) system.
2. Emergence of battery electric vehicles (EV) as the future mode
of transport.
Firstly, the use of renewable energy sources such as solar
energy is accessible to a wider audience because of the falling cost
of PV panels [1]. Industrial sites and office buildings in the Nether-
lands harbor a great potential for photovoltaic (PV) panels with
their large surface on flat roofs. Examples include warehouses,
industrial buildings, universities, factories, etc. This potential is lar-
gely unexploited today. Secondly, EVs provide a clean, energy effi-
cient and noise-free means for commuting when compared with
gasoline vehicles. The current forecast is that in the Netherlands
there will be 200,000 EV in 2020 [2].
This paper examines the possibility of creating an electric vehi-
cle charging infrastructure using PV panels as shown in Fig. 1. The
system is designed for use in workplaces to charge electric cars of
the employees as they are parked during the day. The motive is to
maximize the use of PV energy for EV charging with minimal
energy exchange with the grid. The advantages of such an EV–PV
charger will be:
http://dx.doi.org/10.1016/j.apenergy.2016.01.110
0306-2619/Ó2016 Elsevier Ltd. All rights reserved.
q
This work was supported by TKI Switch2SmartGrids Grant, Netherlands.
Corresponding author at: Faculty of EEMCS (Building 36), Delft University of
Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 (0)15 27 84654.
E-mail addresses: G.R.Chandamouli@tudelft.nl (G.R. Chandra Mouli), P.Bauer@
tudelft.nl (P. Bauer), M.Zeman@tudelft.nl (M. Zeman).
Applied Energy 168 (2016) 434–443
Contents lists available at ScienceDirect
Applied Energy
journal homepage: www.elsevier.com/locate/apenergy
1. Reduced energy demand on the grid due to EV charging as the
charging power is locally generated in a ‘green’ manner through
solar panels.
2. EV battery doubles up as an energy storage for the PV and
reduces negative impact of large scale PV integration in distri-
bution network [3].
3. Long parking time of EV paves way for implementation of
Vehicle-to-grid (V2G) technology where the EV acts as a con-
trollable spinning reserve for the smart grid [4–7].
Several earlier works have analyzed the design of an EV charg-
ing station based on PV [8–17]. The mutual benefit of charging EV
from solar energy has been highlighted in [18,19] where the poten-
tial to charge EV from solar allows for higher penetration of both
technologies. In [20], the negative effects of excess solar generation
from PV on a national level has been shown to be mitigated by
using it for charging EVs. This is especially applicable for charging
at workplace as shown in [19].In[21,22], for the case of Columbus
and Los Angeles, USA, the economic incentive and CO
2
offsets for
PV charging have been shown to be greater than charging the EV
from grid.
A major disadvantage of charging EV from PV is the variability
in the PV production. Smart charging provides for flexibility of
EV charging in order to closely match the PV production. [23] has
shown that smart charging combined with V2G has the dual ben-
efit of increasing PV self-consumption and reducing peak demand
on grid. In [24], the EV charging profile is varied with time so that
maximum PV utilization occurs. In can be seen that the excess PV
energy reduces with higher EV penetration [25,26]. Alternately, the
total number of vehicles that are charging at a constant power can
be dynamically varied so that the net charging power follows the
PV generation, as seen in [27]. This type of sequential charging
shows great benefit than simultaneous EV charging, which is
proved in [28] by considering 9000 different cases. A time shift
scheduling is used in [29] to manage the charging of e-scooters
so that the net charging power follows the PV profile. This method
is further improved with the use of weather forecast data [30].
A second method to overcome the PV variation is to use a local
storage in the PV powered EV charging station, like in [26,31–35].
The storage is typically charged when there is excess solar energy
and is then used to charge the EV when solar generation is insuffi-
cient [26].In[36], three different algorithms for (dis)charging the
local storage are compared and it was shown that a sigmoid func-
tion based discharging of the storage and charging during night
and solar excess was the best strategy.
Since storage is an expensive component, optimally sizing the
storage is vital. This aspect has been neglected by the papers men-
tioned above. Secondly, research works that analyzed the use of
smart charging have not considered the use of local storage and
vice versa. The two methods are investigated together in this
work for a solar powered EV charging station. Thirdly, in case of
workplace charging it is important to distinguish the effects of
weekday and weekend EV charging load. This is because rooftop
PV installed in workplace will produce energy even in the week-
ends even though the EVs of the employee are not present on
Saturday-Sunday. This paper analyses the PV system design and
EV charging in a holistic manner considering the above aspects.
The new contributions of the work compared to earlier works are
as follows:
1. Determination of the optimal orientation of PV panels for max-
imizing energy yield in Netherlands and comparing it with the
use of tracking systems.
2. Possibility of oversizing the PV array power rating with respect
to the power converter size based on metrological conditions of
the location.
3. Dynamic charging of EV using Gaussian charging profile and EV
prioritization, which is superior to constant power charging.
4. Determination of grid impact of two different types of work-
place/commercial charging scenario considering 5 days/week
and 7 days/week EV load by running round-the-year
simulation.
5. Optimal sizing of local storage considering both meteorological
data and smart charging of EV
The paper is divided into five sections. In the second section, a
model is developed to estimate the electricity output of a PV sys-
tem in the Netherlands, taking into account the meteorological
conditions. The optimal orientation of PV panels in the Netherlands
for maximum yield is determined. In the third section, different
dynamic charging strategies for EV are analyzed, such that EV
charging can closely follow the PV generation. In the fourth section,
the benefits of having local battery storage in the EV–PV charger
are investigated and the optimal storage size is determined.
2. EV charging in workplace using PV
EV charging in Europe is defined by the standards in [37,38].
The charging plug type widely used in Europe for AC charging is
the Type 2 Mennekes plug. It supports both single and three phase
AC charging at Level 2 charging power level [39].
However in the future, DC charging using Chademo and the
Combined Charging Standard (CCS) will be most preferred charging
standard for charging EV from PV at workplace due to the follow-
ing reasons:
1. Both EV and PV are inherently DC by nature.
2. Dynamic charging of EV is possible, where the EV charging
power can be varied with time.
3. DC charging facilitates vehicle-to-grid (V2G) protocol.
In this paper, a 10 kW EV–PV charger will be considered that
provides both charging and discharging of car for up to 10 kW, as
shown in Fig. 2. This is in line with the draft proposal of the Chad-
meo standard for enabling 10 kW V2G from EV. The three-port
converter connected to the 50 Hz AC grid was chosen as the most
suitable system architecture based on [12]. Since the cars are
parked for long durations of 7–9 h at the workplace, fast charging
of EV at 50 kW or more would be unnecessary. Solar power is the
primary power source of the grid connected EV–PV charging sys-
tem. The solar power is generated using a 10 kW
p
photovoltaic
(PV) array that is located at the workplace. The panels could be
located on the roof top of the buildings or installed as a solar car-
port [8].
The EV–PV charger has two bidirectional ports for the grid and
EV, and one unidirectional port for PV. The PV converter, grid
Fig. 1. Design of solar powered EV charging station.
G.R. Chandra Mouli et al. / Applied Energy 168 (2016) 434–443 435
inverter and the isolated EV charger are integrated on a central DC-
link. Direct interfacing of EV and PV on DC would be more benefi-
cial than AC interfacing due to lower conversion steps and
improved efficiency [10,12,40,41].
3. PV system design
3.1. Estimation of optimal orientation of PV array in the Netherlands
To evaluate the power and energy generated by a 10 kW
p
PV
array in the Netherlands, an accurate measurement of weather
data is required. For this purpose, the meteorological data from
the Dutch Meteorological Institute (KNMI) is used, which has a res-
olution of 1 min [42]. Global horizontal irradiance (S
GHI
), Diffuse
Horizontal Irradiance (S
DHI
), Direct Normal Irradiance (S
DNI
) and
ambient temperature (T
a
) are obtained from KNMI for the years
2011–2013. A 10 kW
p
PV array was modelled in MATLAB using
30 modules of Sun power E20-327 modules rated at 327 W [43],
whose specifications are shown in Table 1. They are connected in
5 parallel strings having 6 modules in series having a combined
installed power of 9810 W.
To estimate the solar irradiance on a module (S
m
) with a specific
azimuth (A
m
) and tilt angle (h
m
) as shown in Fig. 3, an estimation of
the position of the sun throughout the year is required. A solar
position calculator is hence built using [44,45] by which the azi-
muth (A
s
) and altitude (a
s
) of the sun throughout the year at the
location of the KNMI observatory can be determined. With the
sun’s position, the irradiance on a panel with specific orientation
(A
m,
h
m
) can be estimated using the geometric models in [46–48]
and the Isotropic sky diffused model [46,49] where S
DNIm
,S
DHIm
are the components of DNI and DHI which is incident on the panel:
S
DNI
m
¼S
DNI
ðsin h
m
cos a
s
cosðA
m
A
s
Þþcos h
m
sin a
s
Þð1Þ
S
DHI
m
¼S
DHI
1þcos h
m
2ð2Þ
S
m
¼S
DHI
m
þS
DNI
m
ð3Þ
In order estimate the output power of a PV array, it is important
to consider the ambient temperature, besides the magnitude of
incident solar insolation. The PV array is rated for 327 W at the
STC ambient temperature of 25°. For other ambient temperatures
(T
a
), the PV array output power (P
m
) can be estimated using [50–
52], where T
cell
is the temperature of the PV cells:
T
cell
¼T
a
þS
m
800 ðT
NOCT
20Þð4Þ
P
m
¼P
r
S
m
1000 1kðT
cell
25Þ½ ð5Þ
Using the above equations and meteorological data from KNMI,
the output of the 10 kW PV array can be estimated. For geograph-
ical locations in the northern hemisphere like Netherlands, the
optimal azimuth for the PV panels is A
m
=0°i.e. facing south. To
determine the optimal tilt angle h
m
, the annual energy yield of
the 10 kW PV system is determined for different tilt angles, as
shown in Fig. 4.
It can be observed that for an optimal tilt of 28°, maximum
annual energy yield is obtained for the years 2011-13, with an
average value of 10,890 kW h. The corresponding average daily
yield for the PV system is 29.84 kW h/day. It must be kept in mind
that in practice, it might not be possible to install the PV panels
along the optimal orientation due to characteristics of the roof
[48]. Further, shading on the panels due to nearby buildings, trees
and/or other objects will reduce the yield of the PV system [52].
Since the orientation and shading will vary on a case-to-case basis,
the detailed analysis of both is beyond the scope of this research
work.
AC
Grid
PV
panels
10 kWp
PV MPPT
converter
(DC/DC)
Isolated
EV charger
(DC/DC)
Grid
Inverter
(DC/AC)
EV
DC link
Fig. 2. System architecture of the grid connected 10 kW three-port EV–PV charger.
Table 1
Parameters of Sun power E20-327 module.
Quantity Value
Area of module (A
pv
) 1.63 m
2
Nominal power (P
r
) 327 W
Avg. panel efficiency (
g
) 20.4%
Rated voltage (V
mpp
) 54.7 V
Rated current (I
mpp
) 5.98 A
Open-circuit voltage (V
oc
) 64.9 V
Short-circuit current (I
sc
) 6.46 A
Nominal operating cell temperature (T
NOCT
)45°C±2°C
Power temp coefficient (k)0.38%/°C
N
S
E
W
θM
Zenith
Celesal Sphere
AM
PV panel
Sun
Fig. 3. Orientation of the PV panel is defined by azimuth angle A
m
(measured from
the South) and module tilt angle h
m
(measured from horizontal surface).
Fig. 4. Annual energy yield of 10 kW PV system as a function module tilt for years
2011-13. The PV modules were oriented south with azimuth of 0°.
436 G.R. Chandra Mouli et al. / Applied Energy 168 (2016) 434–443
3.2. Estimation of power output of optimally orinetated PV array in the
Netherlands
Using an optimally oriented PV array with A
m
=0°and h
m
=28°,
the power production over one year is estimated using Eqs. (1)–(5)
and is shown in Figs. 5 and 6.InFig. 5., the output power of the
10 kW array for every minute can be seen over the year. The sea-
sonal variation in peak output power over one year can be per-
ceived, the highest being close to 12 kW in May. However, the
peak power output in the winter months of November to January
is only 4 kW. When the yearly data estimated in Fig.5 is averaged
over a 24 h period for each month, we get the average 24 h PV pro-
file for different months of 2013 as shown in Fig. 6. Two vital
observations are:
1. The average monthly peak power ranges between 7 kW in July
and 2 kW in November. This indicates that the PV system on an
average only produces 70% of its rated power even in the sunni-
est month of the year.
2. PV generation is restricted to only 7–8 h in the winter months
while it is 15 h in summer.
Figs. 7 and 8 show the daily yield of the PV system for each day
of the year and as a monthly average for 2013. They clearly show
the seasonal variation in PV yield. The actual yield has a variation
between 75 kW h/day and 1 kW h/day for specific days in June and
December respectively. With respect to the average daily yield for
different months, a difference of up to 5 times can be observed
between summer and winter in Fig. 8. It can also be observed that
even in summer, there are cloudy days with low daily yield of
<10 kW h and sunny days in winter with yield >20 kW h.
The daily yield values are compared with the 24 kW h battery
pack of the Nissan Leaf EV in Figs. 7 and 8. For 54% of the year,
the daily yield is greater than 24 kW h/day and for 22% of the year,
the yield is greater than 48 kW h/day which equals the combined
capacity of two Nissan Leafs. Thus there is a huge difference in
energy availability between different days of the year. This sea-
sonal difference in generation directly necessitates the need for a
grid connected PV system that can ensure reliable power supply
to the EV battery throughout the year.
Since the bottleneck in the PV system design is the low winter
yield which is of the order of 10 kW h/day, the applicability of a
sun tracking system to improve winter yield was investigated.
The simulations were performed considering the panels to be
mounted on a 2-axis tracker (A
m
=A
s
,h
m
= 90-a
s
) and a 1-axis
tracker with either tracking of the sun’s azimuth (A
m
=A
s
) or the
sun’s altitude (h
m
= 90-a
s
).
The average daily yield and the annual yield due to use of a
tracking system is shown in Fig. 9 and Table 2 respectively. Com-
pared to fixed orientation of h
m
=28°A
m
=0°, 17.3% and 13.3%
improvement in annual yield is obtained using the 2-axis and 1-
axis azimuth tracking system respectively. The 1-axis altitude
tracker however results in 7.5% reduction in yield. Average gain
in yield in the winter months of November to February due to a
2-axis tracker is 1.9 kW h/day while in summer the gain is as high
as 11.6 kW h/day for month of July. The concentrated gains in sum-
mer make the use of tracking system unattractive in improving the
winter PV yield. Further, the tracking system is economically infea-
sible as the 160or 208gain in energy cost/year as seen in Table 2
cannot offset the 4750or 8177cost of installing a single or dual
axis tracking system respectively (Based on [53], 0.57$/W and 0.98
$/W is cost for 1-axis and 2-axis tracking system and 1.2$ = 1).
050 100 150 200 250 300 350
0
2000
4000
6000
8000
10000
12000
Day of the year
Power ouput of 10kW system (W)
Fig. 5. Power output of 10 kW PV system as a function of time for 2013. The PV
modules were oriented south with a tile angle of 28°.
Fig. 6. Average power output of 10 kW PV system as a function of time of the day
for different months of 2013.
Fig. 7. Daily energy yield of 10 kW PV system for different days of 2013.
1 2 3 4 5 6 7 8 9 10 11 12
0
10
20
30
40
50
60
Month of the year
Avergae daily y ield (k Wh)
24kWh
2 x (24kW h)
2011
2012
2013
Fig. 8. Average daily yield for 10 kW PV system for different months of 2013.
G.R. Chandra Mouli et al. / Applied Energy 168 (2016) 434–443 437
3.3. Oversizing the PV array power rating with respect to PV converter
power rating
Fig. 10 and Table 3 show the frequency distribution of the PV
output power as a percentage of the daylight time of the year
and the corresponding energy distribution. The daylight time cor-
responds to the total sum of hours in the year when the PV output
power is non-zero, which is 4614.5 h in 2013. While the occur-
rence of high output power from PV panels is low, the energy deliv-
ered by the panels at times of high output power is very high. PV
Power >5 kW occur only 16% of the daylight time but delivers
about 50% of annual PV energy.
Similarly PV power >7 kW and >8 kW deliver 26% and 14% of
annual energy respectively as elaborated in Table 3. From the table,
we can infer that by under-sizing the PV power converter by a fac-
tor of 0.9, we will lose only 0.16% of the annual energy yield. This is
because, during times the PV panels can produce >9 kW, the inver-
ter will not shut down, it will just produce 9 kW. Similarly, using a
converter of 70% or 50% of the PV rated power results in only loss of
3.2% or 13.8% of annual yield as shown in Table 4. This observation
opens up the opportunity for the PV panel to be oversized com-
pared to the power converter rating in a country like Netherlands.
4. Dynamic charging of EV
Dynamic charging refers to charging the EV at variable charging
power instead of a fixed power. The motive of dynamic smart
charging of the EV is to vary the EV charging power to closely fol-
low the PV generation, so that minimum power is fed/drawn from
the grid.
The power drawn or fed to the grid can be expressed as given
below where P
PV
,P
EV
are the PV generation and the EV charging
power respectively:
P
grid
¼P
EV
P
PV
ð6Þ
When P
grid
> 0, power is drawn from the grid while power is fed to
the grid when P
grid
< 0. It is assumed that all the EVs arrive at the
workplace at 0830 h and are parked till 1700 h, for a total duration
of 8.5 h. 8 different EV charging profiles are compared and they are
shown in Fig. 11 along with the average PV generation profile for
different months. The charging profiles here are categorized into
three types – Gaussian (G1, G2, G3, and G4), fixed (F1, F2) and rect-
angular profiles (R1, R2) based on the shape of the 24-h EV power-
time curve, as shown in Fig. 11 and explained in Table 5. The fixed
and rectangular charging profiles are chosen as they correspond to
current EV chargers available in the market than can charge the car
with a fixed time in-varying charging power. The Gaussian charging
profiles were chosen due to their ability to closely match solar irra-
diance data [54,55] and they have their peaks at 1200 h when the
sun is at its peak.
The energy delivered by each charging profile E
PV
can be deter-
mined by integrating the power-time curve to obtain the area
under the curve:
Fig. 9. Variation of the average monthly yield for 2013 for fixed orientation and
when single/dual axis tracking system is used.
Table 2
Annual energy yield of PV system with 28°tilt and 2-axis tracker.
Annual energy yield (kW h) Gain/loss in energy yield (%) Economic gain/loss
a
()
2011 2012 2013 Average
28°tilt 11039.7 10753.5 10876.2 10,890
2 axis tracker 13,114 12,483 12,732 12,776 17.3 207.5
1 axis tracker (Azimuth) 12,573 12,116 12,329 12,339 13.3 159.4
1 axis tracker (Tilt) 10,255 9946 10,022 10,074 7.5 89.7
a
Based on industrial electricity price of 0.11 /kW h.
0 >1 >2 >3 >4 >5 >6 >7 >8 >9 >10
0
10
20
30
40
50
60
70
80
90
100
PV output power (kW)
Percentage of daylight time in year (%)
10
20
30
40
50
60
70
80
90
100
Percenta
g
e of annual yield (%)
2011 - Power
2012 - Power
2013 - Power
2011 - Energy
2012 - Energy
2013 - Energy
Fig. 10. Frequency distribution of output power of PV system shown as a
percentage of daylight time (when PV power output is non-zero) and distribution
of annual yield shown as a function of output power.
Table 3
Energy delivered and occurrence of different PV output power.
PV power output (kW)
>2 kW >5 kW >7 kW >9 kW
% Daylight time 41.3 16.5 7.7 0.95
% Annual energy 82.7 48.8 26 3.8
Table 4
Reduction in annual PV yield due to oversizing of PV array compared to PV converter.
Inverter size for 10 kW PV array
2kW 5kW 7kW 9kW
% Energy lost in year 47.5 13.84 3.2 0.16
438 G.R. Chandra Mouli et al. / Applied Energy 168 (2016) 434–443
E
PV
¼Z
t¼1700 h
t¼0830 h
P
EV
ðtÞdt ð7Þ
All charging profiles deliver 30 kW h/day to the EV battery
except profile F2 which delivers 85 kW h. If a daily commuting dis-
tance of 50 km/day is considered based on [56], 10 kW h/day
charging energy is required by a Nissan Leaf (121 km range as
per EPA driving cycle) assuming 95% charging efficiency. 30 kW h/-
day thus corresponds to the commuting energy needs of three EVs.
It also equals the average daily energy yield of the 10 kW PV sys-
tem as per Table 2.
4.1. Matching the dynamic charging of EV to PV generation
Due to seasonal and diurnal variation in solar generation, there
will always be a mismatch between EV demand and PV generation.
This difference in power is fed/drawn from the grid. The total
energy fed to the grid E
fed
grid
and drawn from the grid E
draw
grid
over
one year (8760 h) can be estimated as:
If P
grid
ðtÞ<0;E
grid
fed
¼Z
t¼8760 h
t¼0h
P
grid
ðtÞdt ð8Þ
If P
grid
ðtÞ>0;E
grid
draw
¼Z
t¼8760 h
t¼0h
P
grid
ðtÞdt ð9Þ
E
grid
ex
¼E
grid
draw
þjE
grid
fed
10Þ
To ensure maximum utilization of PV energy for EV charging,
the total energy exchanged with the grid E
ex
grid
must be minimum,
assuming there is no PV power curtailment. E
ex
grid
is estimated for
two cases one considering that EV is present on all 7 days of
the week and the second considering that EV is present only on
weekdays i.e. 5 days/week. The first case is applicable for shopping
malls, theatres etc. while the second case is suitable for offices,
universities and factories.
4.1.1. Scenario 1 – EV load for 7days/week
The annual PV yield for 2013 is 10,876 kW h while the annual
EV demand is 10,950 kW h (30 kW h 365 days) for all profiles
except F2. Table 6 shows the annual energy exchanged with the
grid for different charging profiles, ranked in the order of increas-
ing magnitude of grid energy exchange. It can be seen that annual
grid energy exchange of G3, G4 is the lowest while the F2 profile
results in the maximum energy exchange with the grid.
It can be observed that there exists a minimum energy that is
always drawn from the grid irrespective of the charging profile.
This is because while the EV demand is constant at 30 kW h
throughout the year, the PV yield in winter and on cloudy days
throughout the year is much less than 30 kW h, forcing the system
to draw energy from the grid.
Further there is always a minimum surplus energy fed to the
grid and this due to two reasons. Firstly, the peak PV array power
in summer is more than the peak power of all the load profiles
except G2 and F2. Secondly, the sun shines in summer months
for over 16 h (0400–2000 h approx.) which is much more than
the 8.5 h for which the EV is charging. This results in power being
fed back to grid in the early morning and late evening.
EV charging profiles with high peak charging profiles namely
G1, G2 and F2 have the lowest rank in Table 6. G3, G4, R1, R2 exhi-
bit the better matching with PV and have a peak charging power
which is the range of 40–50% of the installed watt peak of the PV
array. Since lower charging power means lower component ratings
in converter, it can be concluded that profile G4 with a peak EV
charging power of 40% of nominal PV power, is most ideal for
Netherlands.
4.1.2. Scenario 2 EV load for 5 days/week
Simulations from scenario 1 are repeated considering the EV
load to present for only 5 days/week on weekdays and no EV loads
for the weekend. Only the charging profiles with rank 1–5 are con-
sidered here namely G1, G2, G3, F1, and F2. Table 7 shows the
annual energy exchange with grid for different charging profiles
and it can be seen that the Gaussian profiles G3, G4 exhibit mini-
mum energy exchange. An obvious difference between the values
in Tables 6 and 7 is that the energy fed to the grid has increased
4 6 8 10 12 14 16 18 20
0
2
4
6
8
10
11
Hour of the day (h)
Power of PV/EV (kW)
Jan
Jul
Sep
March
G1
G2
G3
G4
R1
R2
F1
F2
Fig. 11. Various EV charging profiles compared with the average daily PV array
output for different months of 2013.
Table 5
Maximum power and energy of the 8 EV charging profiles.
EV Charging profile Max. charging
power (kW)
Energy delivered
to EV (kW h)
G1 – Gaussian profile 10 30
G2 – Gaussian profile 7 30
G3 – Gaussian profile 5 30
G4 – Gaussian profile 4 30
R1 – Rectangular (4.5 kW, 2.44 kW) 4.5 30
R2 – Rectangular (4 kW, 2.67 kW) 4 30
F1 – Constant power (2.58 kW) 2.58 30
F2 – Constant power (10 kW) 10 85
Table 6
Energy exchanged with grid for 7 days/week EV load.
EV charging profile Annual energy exchange with grid (kW h) Rank
Fed to grid
|E
fed
grid
|
Draw from
grid E
draw
grid
Total E
grid
ex
G1 5248 5350 10,598 7
G2 4455 4544 8999 6
G3 4113 4213 8326 1
G4 4119 4214 8333 2
R1 4297 4402 8699 5
R2 4180 4282 8462 3
F1 4198 4295 8493 4
F2 1336 21,546 22,882 8
Table 7
Energy exchanged with grid for 5 days/week EV load.
EV charging profile Annual energy exchange with grid (kW h) Rank
Fed to grid |E
fed
grid
| Draw from
grid E
draw
grid
Total E
grid
ex
G3 6053 3024 9077 1
G4 6059 3027 9086 2
R1 6165 3141 9306 5
R2 6094 3067 9161 3
F1 6117 3088 9205 4
G.R. Chandra Mouli et al. / Applied Energy 168 (2016) 434–443 439
and the energy drawn from the grid has reduced, effectively result-
ing in the total energy exchanged with the grid to increase.
Fig. 13 shows the cumulated daily PV energy yield and energy
fed/drawn from the grid for the year 2013 for EV load profile G4.
Red circles indicate examples when the PV energy is fully fed to
the grid on weekends as there is no EV load. In spite of optimal
matching of the EV charging with the PV generation, surplus
energy can be observed in summer months being fed to the grid
and energy drawn from the grid in the winter months.
4.2. Charging of multiple EV
The Gaussian load profile G4 can deliver 30 kW h energy to EV.
This energy could be distributed amongst multiple EV if each car
requires less than 30 kW h of energy. Fig. 12 shows an example
of the charging of three cars A, B, C with respect to the average irra-
diance for the month of July 2013.
Multiple cars can be arranged within the charging region and
charging can be started according to priority Pwhere Bis the
capacity of EV battery pack (kW h); t
a
,t
d
,t
p
are the EV arrival,
departure and parking time at workplace(h); SOC
a
,B
a
are the state
of charge and energy stored in EV at arrival to work:
B
a
¼SOC
a
100 Bt
p
¼t
d
t
a
ð11a;bÞ
P¼1000
B
a
t
p
ð12Þ
The car with the highest priority is charged first. This method
will give preference to EV with low energy and less parking time,
to charge first. Thus 30 kW h of energy is delivered in total to the
three cars and the excess PV is fed to the grid. If any of the cars
require additional energy or if a fourth car Dhas to be charged,
then charging region Dis utilized, where the EV is charged partly
from PV and partly from the grid.
5. Integrating local storage in EV–PV charger
Due to seasonal and diurnal variation in solar insolation, grid
connection becomes pivotal and acts an energy buffer. Besides
the grid, a local storage in the form of a battery bank can be used
as well. In this section, the possibilities of using a local battery stor-
age to eradicate the grid dependence of the EV–PV charger will be
investigated.
At first, a 10 kW h lithium ion battery bank is integrated in the
EV–PV charger. The battery is charged and discharged at a maxi-
mum C-rate of 1 C corresponding to a maximum charging/dis-
charging power of P
bmax
= 10 kW. The maximum depth of
discharge is restricted to 80% (between state of charge (SOC) of
10–90%) to ensure long lifetime of the storage. Efficiency of
charging/discharging of the battery including power converter is
assumed to be 93% [57,58] and the efficiency of power exchange
with the grid is considered as 95% [59].
Fig. 14 shows the state diagram for the operation of the EV–PV
charger with local storage. Power is exchanged with the grid only
when the storage is full/empty or if the maximum power limit of
the storage is reached due to C-rate limitations. If there is a surplus
of PV power above the EV demand, it is first used to charge the
local storage, while a power deficit is first extracted from the local
storage.
If the EV demand P
EV
is more than the maximum charging/dis-
charging power of the storage P
bmax
due to C-rate limitations, then
P
bmax
is supplied to the EV from the storage and |P
EV
P
bmax
|is
drawn from the grid to supply EV. The local storage never feeds/-
draws power from the grid; it interacts only with EV and PV.
Fig. 15 shows the power exchanged with the grid and the stored
energy in local battery bank for 2013 (1 min resolution), consider-
ing EV loads for both 7 days/week and only on weekdays using pro-
file G4. For 7 days/week load, it can be clearly observed that the
battery is eternally empty in the winter months due to lack of
excess PV power for charging it. Similarly the battery is full in
the summer months (Day 80 to Day 270) due to high PV
generation.
However, the local storage has a positive effect in the case of
5 days/week EV load. As seen in Fig. 15, the local storage gets peri-
odically charged during the weekends even in winter (days 0–50
and days 300–365) as there is no EV and this helps supply the
EV energy demands on Mondays and Tuesdays. However for the
rest of the week, the storage is depleted of energy in winter and
remains full in summer.
Since 10 kW h storage is insufficient for making the EV–PV
charger grid independent, the storage size was varied from
5 kW h to 75 kW h to study its impact on the grid energy exchange,
as shown in Figs. 16 and 17. It can be observed in Fig. 16 that the
energy exchanged with the grid reduces with increasing storage
size up to a certain point and then saturates henceforth. This
means that even with large storage of up to 75 kW h, there is still
a minimum amount of energy drawn/fed to the grid and it is not
possible to make the EV–PV charger grid independent. This is espe-
cially true for a country like Netherlands which shows five times
difference in summer and winter sunshine.
Storage SOC remaining >95% or <5% are both not good for the
system as it leaves the battery in an unutilized state; it is either
nearly empty or fully charged. Since the battery is used with DoD
of 80%, SoC of 95% and 5% are scaled according to the 80% used
capacity of the battery. Fig. 17 shows that increasing the storage
size has minimal impact in improving the utilization of the battery.
For a 5-day load profile, the battery is nearly full or empty (SOC
>95% or <5%) for 70% of the time with 30 kW h storage and for
468101214161820
8
Hour of the day (h)
Power of PV/EV (kW)
Charging
Region A
Charging
Region B
Charging
Region C
Feed
grid
Feed grid
Feed
grid
Draw
from grid
Charging
Region D
6
4
2
0
Gaussian EV charging profile
EV Charging from grid
PV generation profile
Fig. 12. Charging multiple EV using Gaussian charging profile.
0 50 100 150 200 250 300 350
-20
0
20
40
60
80
Day of the year
Energy from/to PV/ EV/ grid [kWh]
30kWh EV demand
Grid Energy
PV yield
Fig. 13. Daily energy yield of PV and energy fed/drawn from grid for 30 kW h EV
load profile G4 on weekdays.
440 G.R. Chandra Mouli et al. / Applied Energy 168 (2016) 434–443
65% of the time with 75 kW h storage. This proves that with
increasing the storage size by 2.5 times, the utilization of the bat-
tery is not proportional. Further the percentage of time in a year for
which the EV–PV charger feeds/draws power from the grid does
not reduce much with increasing storage size as seen in Fig. 17.
In case of 5 day/week load, percentage of time for which energy
is fed to grid is relatively much higher than for 7 day/week load
and the percentage of time when energy is drawn from grid is
lower.
From Fig. 16, it can be noted that small storage in the range of
5–15 kW h exhibits a drastic reduction in grid dependency. This is
because 75% of variation in solar insolation between consecutive
ΔP > 0 ?
ΔP =PPV -P
EV
START
TRUE
FALSE
END
No exchange
with grid
ΔP =0
Is storage
full ?
Feed power
to grid
TRUE
Charge
storage
FALSE
Draw power
from grid Is
ΔP > Pbmax
?
FALSE
Charge storage
& feed grid
TRUE
Is storage
empty ?
TRUE
Discharge
storage
FALSE
Is
|ΔP|>Pbmax
?
FALSE
Discharge storage
& draw from grid
TRUE
Fig. 14. State diagram for operation of EV–PV charger with local storage.
0 50 100 150 200 250 300 350
-5
-2.5
0
5
10
Day of the year
Grid power (kW)
Energy storaged in battery (kWh)
Energy stored in battery (kWh)
Grid power (kW)
050 100 150 200 250 300 350
-5
-2.5
0
5
10
Day of the year
Grid power (kW)
Energy storaged in battery (kWh)
Fig. 15. Power exchanged with the grid (kW) and the stored energy in local storage (kW h) for the EV–PV charger for the year 2013 considering EV loads for 7 days/week (left)
and only on weekdays (right).
Fig. 16. Annual Energy exchanged with the grid for 2013 as a function of storage size, considering EV loads for 7 days/week (left) and only on weekdays (right) using Gaussian
EV profile G4.
G.R. Chandra Mouli et al. / Applied Energy 168 (2016) 434–443 441
days is less than 15 kW h. A small storage hence helps in balancing
out diurnal and day–day solar variations. For 5 day/week and
7 days/week EV loads, the size of storage to achieve 25% reduction
in energy exchanged with the grid is 10 kW h. A 10 kW h storage
using Li-ion batteries will cost 8000–13,000 euros [60]. If a smaller
storage is preferred, a 5 kW h storage can result in 17% and 20%
reduction in grid energy exchange for 5 day/week and 7 day/week
EV load respectively.
6. Conclusions
Workplace charging of EV from solar energy provides a sustain-
able gateway for transportation in the future. It provides a direct
utilization of the PV power during the day and exploits the solar
potential rooftops of buildings. In this paper, the PV system design
and dynamic charging for a solar energy powered EV charging sta-
tion for Netherlands is investigated.
Using data from KNMI, it was seen that the optimal tilt for PV
panels in the Netherlands to get maximum yield is 28°. The annual
yield of a 10 kW PV system using Sunpower modules was
10,890 kW h. Using a 2-axis solar tracker increases the yield by
17%, but this gain is concentrated in summer. Solar tracking was
thus found to be ineffective in increasing the winter yield, which
is the bottleneck of the system. The average daily PV energy pro-
duction exhibits a difference of five times between summer and
winter. This necessitates a grid connection for the EV–PV charger
to supply power in winter and to absorb the excess PV power in
summer.
Since high intensity insolation occurs rarely in the Netherlands,
the PV power converter can be undersized with respect to the PV
array by 30%, resulting in a loss of only 3.2% of the energy. Such
a technique can be used for different metrological conditions in
the world for optimally sizing the power converter with respect
to the peak power array for the array.
Dynamic charging of EV facilitates the variation of EV charging
power so as to closely follow the solar generation. Since solar gen-
eration exhibits a Gaussian variation with time over a 24 h period,
Gaussian EV charging profile with a peak at 1200 h and a peak les-
ser than the installed peak power of the solar panels would be
most ideal. The exact value of the Gaussian peak and width are
location dependent. EV charging using Gaussian charging profile
G3 and G4 with peak power of 5 kW and 4 kW were found to clo-
sely follow the PV generation curve of Netherlands. They delivered
30 kW h energy to the EV for both 5 days/week and 7 days/week
EV load and resulted in minimum energy exchange with the grid.
For charging multiple EV at workplace, a priority mechanism was
proposed that will decide the order of precedence for EV charging,
based on stored energy and parking time of EV.
It was proved that a local battery storage does not eliminate the
grid dependence of the EV–PV charger in Netherlands, especially
due to seasonal variations in insolation. However small sized stor-
age in the order of 10 kW h helped in mitigating the day–day solar
variations and reduced the grid energy exchange by 25%. The stor-
age remains empty in winter for 7 days/week load and gets period-
ically full in weekends for 5 days/week load. The storage sizing is
site specific and methodology presented here can be used for dif-
ferent locations to determine the optimal storage size.
Acknowledgements
The authors would like to sincerely thank and acknowledge the
guidance and support of Assistant professor O.Isabella, PhD stu-
dent V.Prasanth, V.Garita, N.Narayanan and researchers G.Nair,
M.Leendertse from the Department of Electrical Sustainable
Energy, Delft University of Technology; employees of Power
Research Electronics B.V, Breda and ABB Product Group EV Charg-
ing Infrastructure, Rijswijk and the reviewers of the journal. This
work was supported by TKI Switch2SmartGrids grant, Netherlands.
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G.R. Chandra Mouli et al. / Applied Energy 168 (2016) 434–443 443
... ( ) is the generation forecast for a 1kW p PV system installed at the workplace. The use of solar forecast data in the EMS will help in reducing the uncertainties due to variability in PV generation on diurnal and seasonal basis [14]. ...
... The 'EV-PV charger' as the term used here is an integrated power converter that consists of three ports to connect to the EVs, PV and the AC grid, as shown in Fig. 3 [14], [22]. Each EV-PV charger is connected to a PV array of rated power via a maximum power point tracking (MPPT) DC/DC converter [24]. ...
... Since an EV cannot simultaneously charge and discharge, a second binary variable , ℎ_ 2 is used to ensure that only one of the two variables , − , , + has a non-zero value for a given t. , ℎ_ 2 is set to 1 for charging and to 0 for V2G. , − , , + have to be within the power limits of the power converter and the charging and discharging power limits , as set by the EV respectively, as shown in equations (9)- (14). ...
Preprint
Workplace charging of electric vehicles (EV) from photovoltaic (PV) panels installed on an office building can provide several benefits. This includes the local production and use of PV energy for charging the EV and making use of dynamic tariffs from the grid to schedule the energy exchange with the grid. The long parking time of EV at the workplace provide the chance for the EV to support the grid via vehicle-to-grid technology, the use of a single EV charger for charging several EV by multiplexing and the offer of ancillary services to the grid for up and down regulation. Further, distribution network constraints can be considered to limit the power and prevent the overloading of the grid. A single MILP formulation that considers all the above applications has been proposed in this paper for a charging a fleet of EVs from PV. The MILP is implemented as a receding-horizon model predictive energy management system. Numerical simulation based on market and PV data in Austin, Texas have shown 31% to 650% reduction in the cost of EV charging when compared to immediate and average rate charging policies.
... To estimate the output power of PV panels, two key environmental factors are considered: solar irradiance, represented by (S m ), and ambient temperature, represented by (T a ). The output power (P m ) of a specific PV panel, given its known parameters, can be calculated as follows 48 : ...
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Integrating solar photovoltaic (PV) and battery energy storage (BES) into bus charging infrastructure offers a feasible solution to the challenge of carbon emissions and grid burdens. The deployment costs and uncertain power outputs of solar PV and BES need to be considered by public transportation agencies. This study presents a data-driven approach to optimize bus charging infrastructure and incorporates sharing charging and uncertain solar PV generation using the Latin Hypercube Sampling method. A case study in Yinchuan, China, reveals that integrating solar PV and BES at a single bus depot reduces total costs by 37.35%, carbon emissions by 41.46%, and grid loads by 49.35% over the 10-year lifetime of BES. Sharing this infrastructure with private cars offers further economic benefits. Global analysis shows this approach’s varying economic and environmental advantages. The proposed model offers practical implications for developing cost-effective and environmentally friendly electric bus charging infrastructure to advance sustainable transport.
... The idea of charging EVs at work using solar energy was looked at by Mouli et al. [31] in the Netherlands. They examined the viability of standalone battery-operated PV EV chargers. ...
... Charging electric vehicles with solar energy provides a sustainable means of transportation. Research in [13], shows the design of a solar powered e-bike charging station that provides AC, DC, and contactless e-bike charging. DC chargers allow DC charging directly on the e-bike from photovoltaic (PV) panel DC power without the need for an external AC charger adapter. ...
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Solar energy converted by the use of photovoltaic modules, requires testing the performance of the conversion results, to determine the efficiency and performance ratio, when unloaded and loaded. The implementation of this photovoltaic energy source, the researcher aims to use the load of the e-bike at the e-bike charging station with a peak power capacity of 200 Wp PV and a battery storage capacity of 200 Ah 12 V. The test results obtained that PV provides 69% of the load power needs with a peak power of 98.67W at 12.25 WIB, while the battery supplies 31%, with a total PV energy of 0.68 kWh, and the Performance Ratio (PR) of solar panels was obtained at 85.7%. The load of the e-bike itself absorbs 437.6 Wh of energy from SoC charging (62%-100%) with a charging duration of 3.42 hours, where the charging of the e-bike in planning requires 576 Wh of energy for 4.5 hours from the SoC (50%-100%).
... Mouli et al. [70] investigates the design, computation, modelling, and controller design of a solar-powered EA RF. Renewable-based RFs can be integrated into a power grid with an improved voltage profile, reduced energy dissipation, and reduced cost, as suggested by Duan et al. [71]. ...
... The optimal configuration and investment efficiency of PV-powered EV charging stations in each urban area are significantly influenced by the solar irradiation value and the feed-in tariff (FIT) of rooftop solar energy. In [8], Chandra Mouli and the research team compared various EV charging configurations to reduce grid dependency and maximize the use of solar energy. However, due to seasonal variations in sunlight, the local storage system still cannot completely eliminate grid dependency, with the average daily PV energy production differing fivefold between summer and winter. ...
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This paper presents an overview of the ideas, general design, and preliminary cost estimation for electric vehicle charging stations in planned areas. The selection of a charging station model that integrates solar panels connected to the national grid, having energy storage unit is discussed. This model is suitable for certain urban areas with potential for solar energy usage in electricity generation, contributing to efficient energy use, emission reduction, and decreased electricity consumption from the national grid, aiming towards zero-emission cities and environmental protection. The calculation process is based on the technical specifications of the Vinfast electric vehicle, with a battery capacity of 42kWh. The preliminary estimates of the research indicate that the initial investment cost is very high. However, the potential for capital recovery from the system is quite rapid and feasible upon implementation. The research calculations show that an integrated solar energy electric vehicle charging station system is feasible for the Ba Tơ town area in Quang Ngai province. The research results provide a planning orientation for technical infrastructure systems in sustainable and modern urban development in the coming years.
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This paper investigates the integration of bi-directional electric vehicle (EV) charging stations that allows for an increase in the integration capacity of the dispersed photovoltaic (PV) power plants. The study was provided with data of real network parameters of components of analyzed low voltage feeder and real-time data of daily energy from the control meter. The calculations used for this analysis are the network load flow and quasi-dynamic load flow for a whole day. The analysis is based on defined scenarios for analysis of the baseline scenarios and complementary operation of electric vehicles and photovoltaic power plants with the maximum PV penetration with 0%, 10%, 20%, and 50% of EVs. The software program used to analyze the data was DIgSILENT PowerFactory (Base package and Quasi-dynamic simulation toolbox). The results investigated the integration impact of the charging stations on a real low-voltage network. It showed that with additional electric vehicle integration, new capacity opens for additional photovoltaic power plants. The analyzes also display that the location of the connection of additional electric cars and photovoltaics have a significant impact on PV plant hosting capacity.
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Electric vehicles (EV) can be charged in a sustainable way by charging them from photovoltaic (PV) panels. Workplace charging of EV from PV results in use of the solar potential of office buildings and the long parking time at workplace paves way for implementation of vehicle-to-grid (V2G) technology. In this paper, different possible system architecture for an EV-PV charger are investigated and compared. A review of power converters that integrate EV and PV is made and the systems are compared based on system architecture, converter topology, isolation and bidirectional power capability for V2G operation. Based on the study, two optimal designs for the EV-PV charger are proposed that uses a multi-port converter. Different methods to implement modularity in the converter design for charging multiple EVs from a single EV-PV charger are presented.
Book
The two volume set LNCS 4984 and LNCS 4985 constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Neural Information Processing, ICONIP 2007, held in Kitakyushu, Japan, in November 2007, jointly with BRAINIT 2007, the 4th International Conference on Brain-Inspired Information Technology. The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models, supervised/unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.
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This paper presents the comparative study of the different models that used to predict the solar photovoltaic module temperature, which is one of the most important factors responsible for lowering the performance of photovoltaic modules. The approach of the different models was examined in order to evaluate the estimated behavior of module temperature increase with respect to ambient temperature and solar radiation. A total of 16 models have been reviewed by employing monthly mean daily meteorological data of Kuching, Sarawak. The most models showed similar trend of increase or decrease of solar photovoltaic module temperature due variation of solar radiation intensity. However, the results of reviewed models were quite different under constant solar radiation and ambient temperature conditions. It was found that the variation in the results was due to the use of different variables, climatic conditions, configuration of photovoltaic modules and the approach used by various researchers in their models.
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