Spring 2015 JP Dolphin Final Capstone Project Submission
Thesis458
1.
The
Impacts
of
Electric
Vehicle
Charging
on
Residential
Distribution
Systems
Matthew
Wardhaugh
Bachelor
of
Engineering
(Electrical)
October,
2012
Supervisor:
Dr
Phil
Ciufo
2. i
i
Abstract
A
significant
increase
in
the
number
of
electric
vehicles
is
expected
over
the
coming
years,
and
this
is
expected
to
create
issues
for
distribution
networks
when
charging
coincides
with
peak
demand
periods.
This
thesis
investigates
the
effects
of
uncoordinated
charging
on
the
residential
distribution
network,
and
looks
at
the
viability
of
coordinated
charging
to
mitigate
these
effects.
A
graphical
user
interface
was
created
to
aid
this
study
and
provide
a
tool
for
network
planners
to
easily
run
electric
vehicle
loading
scenarios.
This
thesis
finds
that
uncoordinated
charging
would
have
an
impact
on
low
voltage
networks,
particularly
for
overhead
networks
where
voltage
unbalance
is
a
greater
issue.
Simple
staggered
off-‐peak
charging
was
investigated
and
found
to
mitigate
loading
effects
completely,
allowing
up
to
100%
electric
vehicle
penetration
for
the
highest
charger
rating
scenario.
The
impact
of
charging
was
found
to
be
significant
at
the
zone
substation
level
during
uncoordinated
charging
scenarios,
possibly
requiring
upgrades
within
the
next
decade
if
coordinated
charging
strategies
are
not
adopted.
3. ii
ii
Acknowledgements
I
would
like
to
thank
my
supervisors
Dr.
Phil
Ciufo
and
Prof.
Danny
Soetanto
for
their
guidance,
and
Endeavour
Energy
for
providing
network
models
and
data.
4. iii
iii
Statement
of
Originality
I,
Matthew
Wardhaugh,
declare
that
this
thesis,
submitted
as
part
of
the
requirements
for
the
award
of
Bachelor
of
Engineering,
in
the
School
of
Electrical,
Computer
and
Telecommunications
Engineering,
University
of
Wollongong,
is
wholly
my
own
work
unless
otherwise
referenced
or
acknowledged.
The
document
has
not
been
submitted
for
qualifications
or
assessment
at
any
other
academic
institution.
Signature:
Print
Name:
Student
ID
Number:
3667315
Date:
5. iv
iv
Contents
Abstract
....................................................................................................................................................................
i
Acknowledgements
...........................................................................................................................................
ii
Statement
of
Originality
.................................................................................................................................
iii
Contents
................................................................................................................................................................
iv
List
of
Figures
......................................................................................................................................................
vi
List
of
Tables
......................................................................................................................................................
vii
List
of
Equations
.............................................................................................................................................
viii
Abbreviations
and
Symbols
..........................................................................................................................
ix
List
of
Changes
......................................................................................................................................................
x
1
Introduction
................................................................................................................................................
1
2
Literature
Review
.....................................................................................................................................
3
2.1
Power
system
and
network
configuration
............................................................................
3
2.1.1
Layout
of
grid
............................................................................................................................
3
2.1.2
Feeder
Voltages
........................................................................................................................
3
2.1.3
Voltage
Correction
..................................................................................................................
4
2.2
Electric
Vehicles
................................................................................................................................
5
2.2.1
EV,
PHEV,
Extended
Range
EV
...........................................................................................
5
2.2.2
Configuration
...........................................................................................................................
6
2.2.3
Battery
system
.........................................................................................................................
6
2.2.4
Charging
......................................................................................................................................
7
2.2.5
Growth
..........................................................................................................................................
8
2.3
Impacts
of
Charging
.........................................................................................................................
9
2.3.1
Uncoordinated
Charging
......................................................................................................
9
2.3.2
Coordinated
Charging
.........................................................................................................
10
2.4
Summary
............................................................................................................................................
12
3
Methodology
.............................................................................................................................................
13
3.1
Load
Flow
..........................................................................................................................................
13
3.1.1
Load-‐Flow
Solutions
.............................................................................................................
13
3.1.2
Load
Types
...............................................................................................................................
13
3.2
Modelling
...........................................................................................................................................
15
3.2.1
DIgSILENT
PowerFactory
Models
..................................................................................
15
3.2.2
DIgSILENT
Programming
Language
(DPL)
Script
...................................................
18
7. vi
vi
List
of
Figures
Figure
2.1:
Radial
Feeder
Distribution
......................................................................................................
3
Figure
2.2:
Feeder
Voltage
Profiles
............................................................................................................
4
Figure
2.3:
Electric
Vehicle
Configuration
...............................................................................................
6
Figure
2.4:
Lithium-‐Ion
Charge
Curve
[26]
.............................................................................................
8
Figure
3.1:
Load
Flow
Analysis
[4]
...........................................................................................................
13
Figure
3.2:
400
V
Overhead/Underground
DIgSILENT
Model
.....................................................
16
Figure
3.3:
11
kV
Overhead
DIgSILENT
Model
....................................................................................
17
Figure
3.4:
Average
number
of
travellers
in
NSW
on
weekdays
in
2010/11
........................
19
Figure
3.5:
Scaled
driver
arrival
times
....................................................................................................
20
Figure
3.6:
Feeder
voltage
profile,
moving
from
last
premise
to
transformer
from
right
to
left
...........................................................................................................................................................................
23
Figure
3.7:
MATLAB
GUI
...............................................................................................................................
26
Figure
3.8:
Flowchart
displaying
the
interaction
of
programs
required
for
GUI
simulations
..........................................................................................................................................................
27
Figure
4.1:
Woodlands
Drive
substation
loading
for
38.7
and
19.9
degrees
celsius
days33
Figure
4.2:
Woodlands
Drive
substation
total
load
compared
to
scaled
sample
loads
.....
34
Figure
4.3:
Woodlands
Drive
substation
load
for
overhead
and
underground
networks34
Figure
4.4:
Impact
of
increasing
charger
rating
on
undergroudn
network
at
100%
EV
penetration
.........................................................................................................................................................
40
Figure
4.5:
4
kW
three-‐group
coordinated
charging
for
different
transformer
base
levels
..................................................................................................................................................................................
43
Figure
4.6:
Six-‐group
coordinated
charging
for
a
95%
loaded
transformer
..........................
45
8. vii
vii
List
of
Tables
Table
2.1:
Current
EV
Battery
Capacities
[11][13-‐16]
.......................................................................
7
Table
2.2:
International
EV
Charging
Standards
..................................................................................
7
Table
3.1:
Network
Equipment
Parameters
.........................................................................................
17
Table
3.2:
Variable
Options
Structure
.....................................................................................................
27
Table
4.1:
Woodlands
Drive
substation
transformer
loading
and
voltage
regulation
for
varying
EV
penetrations
................................................................................................................................
36
Table
4.2:
Maximum
EV
penetration
for
4
kW
LV
uncoordinated
charging
...........................
38
Table
4.3:
Maximum
EV
penetration
for
7
kW
LV
uncoordinated
charging
...........................
39
Table
4.4:
Maximum
EV
penetration
for
10
kW
LV
uncoordinated
charging
........................
40
Table
4.5:
Maximum
EV
penetration
at
zone
substation
assuming
worst
loading
day
in
2010/11
...............................................................................................................................................................
42
Table
4.6:
Maximum
EV
penetration
for
7kW
LV
coordinated
charging
.................................
44
Table
4.7:
Maximum
EV
penetration
for
10
kW
LV
coordinated
charging
.............................
44
10. ix
ix
Abbreviations
and
Symbols
EV
Electric
Vehicle
BEV
Battery
Electric
Vehicle
PHEV
Plug-‐In
Hybrid
Electric
Vehicle
IC
Internal
Combustion
V2G
Vehicle
to
Grid
OLTC
On-‐load
tap
changer
SC
Switched
capacitor
SoC
State
of
Charge
Li-‐ion
Lithium
ion
NiMH
Nickel-‐metal
hydride
PV
Photovoltaic
DC
Direct
current
AC
Alternating
current
pu
per
unit
𝑗𝑋
Reactance,
Ohms
𝑅
Resistance,
Ohms
𝑍
Impedance,
Ohms
𝑃
Power,
Watts
𝑉
Voltage,
Volts
11. x
x
List
of
Changes
Section
Statement
of
Changes
Page
Number
1
Removed
references
to
solar
and
V2G,
added
description
of
new
work
1,2
2.2
Removed
sentence
relating
to
V2G
5
2
Removed
Solar
section
-‐
2.3.2
Removed
Solar
sub-‐subsection
11
2
Removed
‘V2G
Benefits’
section
-‐
2.3.1
Added
analysis
of
loading
assumptions
in
literature
10
3
Replaced
Methodology
section
32
4
Replaced
Results
section
13
12. 1
1
1 Introduction
The
world
is
currently
experiencing
a
major
shift
in
the
way
energy
is
generated
and
consumed.
Pressing
issues
such
as
climate
change
and
declining
fossil
fuel
reserves
are
changing
the
way
people
think
about
the
environment.
Also,
technological
advances
are
allowing
renewable
generation
and
energy
storage
to
become
technically
and
economically
viable,
paving
the
way
for
an
emissions
free
future.
Electric
vehicles
(EV)
and
plug
in
hybrid
electric
vehicles
(PHEV)
(used
interchangeably
in
this
text)
are
becoming
increasingly
popular
due
to
the
impetus
of
these
factors.
Significant
advances
in
battery
storage
capabilities
are
allowing
EVs
to
become
a
viable
alternative
to
internal
combustion
(IC)
vehicles.
Their
storage
of
electricity
allows
energy
to
be
sourced
from
renewable
sources
such
as
wind
and
solar,
allowing
for
zero
emission
driving.
This
is
significant,
as
it
would
play
a
large
role
in
reducing
CO₂
emissions
and
localised
air
pollution
levels
[1].
Without
proper
planning,
however,
EVs
are
expected
to
produce
undesired
impacts
on
the
low
voltage
distribution
network
when
charged
in
an
uncoordinated
manner.
Charging
will
occur
whenever
convenient
for
the
driver,
such
as
on
arrival
home
from
work,
increasing
the
evening
peak
load
and
causing
stress
to
network
equipment,
particularly
at
distribution
levels.
Due
to
the
large
amount
of
energy
drawn
during
charging
periods,
it
is
expected
that
at
high
penetration
levels
this
will
present
serious
power
quality
issues
for
the
grid,
including
potential
transformer
overloading
and
voltage
sags,
resulting
in
outages,
equipment
damage
and
energy
loss
[2][3].
This
outcome
may
be
avoided
if
electric
vehicle
charging
can
be
coordinated
in
such
a
way
to
avoid
the
evening
load,
and
instead
be
automated
for
charging
during
low-‐
demand
periods,
such
as
late
at
night.
Smart
infrastructure
currently
being
contemplated
will
allow
charging
times
to
be
staggered
between
different
households
to
allow
a
more
evenly
distributed
feeder
load.
The
proposed
focus
of
this
thesis
is
to
investigate
the
impact
of
introducing
a
significant
number
of
EVs
on
the
residential
distribution
system,
particularly
during
uncoordinated
charging
periods
that
coincide
with
peak
load.
The
load
flow
simulation
package
DIgSILENT
PowerFactory
will
be
used
to
carry
out
the
investigations.
Means
of
avoiding
the
undesirable
impacts
of
EV
charging
will
be
investigated,
using
several
13. 2
2
scenarios
to
determine
the
viability
of
load
levelling.
This
study
will
determine
the
effects
of
charging
on
residential
feeder
voltage
levels,
consequently
discerning
the
associated
impacts
on
transformer
loading
and
energy
loss.
In
order
to
study
the
impacts
of
charging
on
the
residential
distribution
network,
typical
400V
and
11
kV
radial
residential
feeders
have
been
modelled
in
PowerFactory,
using
smart
metering
data
from
premises
in
the
Endeavour
Energy
network
area
of
Glenmore
Park.
Associated
variables
have
been
accounted
for,
including
battery
capacities,
charging
power,
base
load
demand,
load
power
factor
and
phase
unbalance.
To
aid
network
planners
in
making
decisions
based
on
future
electric
vehicle
loading,
a
graphical
user
interface
has
been
developed
using
MATLAB
GUIDE.
This
allows
DIgSILENT
PowerFactory
to
be
controlled
remotely
to
run
various
EV
loading
scenarios,
displaying
transformer
loading
and
voltage
regulation
results
both
numerically
and
graphically
for
analysis.
14. 3
3
2 Literature
Review
2.1 Power
system
and
network
configuration
2.1.1
Layout
of
grid
The
electricity
grid
is
a
complex
network
that
acts
as
a
path
for
electricity
from
generators
to
consumers.
The
layout
of
the
grid
is
an
important
concept
that
must
be
understand
to
grasp
an
idea
of
how
electric
vehicles
will
be
connected
and
the
effects
that
they
will
have
on
the
network.
The
traditional
grid
can
be
divided
into
generation,
transmission
and
distribution
levels.
The
transmission
network
steps
generator
voltages
up
in
order
to
reduce
the
losses
associated
with
high
currents
over
long
distances,
usually
at
230
kV
to
765
kV
[4].
As
these
high
voltage
feeders
branch
towards
large
populations,
they
are
stepped
down
in
to
the
distribution
network.
Zone
substations
convert
voltages
to
11
kV
for
residential
feeders,
which
then
connect
to
pole
top
or
pad
mount
transformers
that
finally
supply
400
V,
or
230
V
line-‐to-‐neutral,
for
use
in
homes
and
businesses
[5].
The
distribution
network
is
the
most
important
section
of
the
grid
to
understand
when
conducting
load
flow
analysis
on
residential
loads,
as
EVs
and
distributed
generation,
such
as
solar
PV,
are
both
connected
at
the
low
voltage
level.
From
zone
substations,
feeders
are
typically
connected
radially
[6][4]
as
they
branch
out
through
streets,
shown
in
Fig
2.1.
This
radial
layout
will
be
used
for
modelling
residential
feeders.
Figure
2.1:
Radial
Feeder
Distribution
2.1.2
Feeder
Voltages
Basic
circuit
theory
states
that
a
voltage
drop
will
result
as
current
flows
through
an
impedance.
Therefore,
as
transformer
loading
is
increased,
the
voltage
drop
along
a
feeder
becomes
greater.
Conversely,
during
periods
of
high
generation,
net
feeder
15. 4
4
current
is
reduced,
raising
voltage
levels
closer
to
that
of
the
transformer.
During
heavy
loading
or
generation
periods,
voltage
levels
may
surpass
utility
limits.
The
AS/NZS
3000:2007
states
that
in
Australia,
voltage
limits
must
not
move
beyond
+10%
or
-‐6%
of
nominal
value
to
avoid
damage
to
connected
equipment,
corresponding
to
253
V
and
216
V
line-‐to-‐neutral
[5].
Fig.
2.2
shows
the
effects
of
different
load
scenarios
on
feeder
voltage
levels.
Realistically,
these
voltages
would
not
have
a
linear
profile,
even
for
uniform
loading
across
the
feeder,
as
currents,
and
hence
the
rate
of
voltage
drop,
is
greater
closer
to
the
transformer.
Figure
2.2:
Feeder
Voltage
Profiles
Another
consequence
of
voltage
deviations
along
feeders
is
power
loss.
Feeder
power
loss
is
proportional
to
the
square
of
a
voltage
change,
therefore
it
is
important
to
reduce
this
change
in
voltage
along
a
feeder
as
much
as
possible.
2.1.3
Voltage
Correction
Voltage
control
is
important
for
addressing
changes
in
line
voltages.
Network
equipment,
such
as
transformers
and
lines
are
designed
to
operate
within
certain
voltage
limits.
Most
importantly,
however,
are
the
loads
connected
to
LV
feeders,
which
may
become
damaged
while
drawing
power
at
excessive
or
limited
voltage
levels.
In
order
to
maintain
voltage
levels
within
a
specified
range
such
as
this,
a
range
of
network
equipment
is
utilised.
In
distribution
networks,
voltage
control
is
typically
achieved
using
on-‐load
tap
changers
(OLTC),
step
voltage
regulators
(SVR)
and
switched
capacitors
(SC)
[7].
OLTCs
and
SVRs
are
both
autotransformers
with
automatic
tap
changing.
Normally
the
voltage
regulator
in
a
substation
is
an
OLTC,
while
an
SVR
would
be
located
along
a
feeder,
down
to
LV
levels
[7].
SCs
are
used
for
reactive
power
compensation
in
distribution
networks.
An
SC
reduces
the
displacement
between
real
and
reactive
power
components
to
reduce
voltage
drop
across
lines
that
are
primarily
16. 5
5
inductive.
In
low
voltage
networks,
the
most
common
voltage
regulators
are
off-‐load
tap-‐changers,
located
within
distribution
transformers
[8].
The
transformer
ratio
must
be
changed
manually,
generally
over
a
multiple
year
span
as
network
loading
increases.
Although
SVRs
and
switched
capacitors
can
exist
in
LV
areas,
this
is
uncommon
due
to
the
large
number
of
feeders,
and
the
associated
costs.
Therefore,
on
residential
feeders,
voltage
control
is
limited
to
off-‐load
tap
changers
on
pole-‐top
and
pad
mount
transformers.
The
manual
nature
of
this
tap
changing
is
uncoordinated,
therefore
this
is
far
from
being
an
optimal
solution
to
addressing
the
large
scale
integration
of
EVs.
Taking
the
characteristics
of
common
network
equipment
into
account,
the
coordinated
charging
of
EVs
can
be
seen
as
a
worthwhile
solution
to
this
problem
as
the
load
factor
of
a
feeder
may
be
reduced.
2.2
Electric
Vehicles
Electric
vehicles
are
vehicles
that
contain
a
rechargeable
battery
pack,
requiring
charging
by
a
grid
connected
battery
charger.
EVs
are
becoming
popular
as
environmental
awareness
is
increasing
across
the
world,
as
they
produce
little
to
no
emissions.
Improvements
in
battery
technology
are
seeing
prices
fall
rapidly,
allowing
EVs
to
become
a
viable
alternative
to
internal
combustion
(IC)
vehicles.
Penetration
of
EVs
is
beginning
to
increase,
with
over
20
models
due
to
reach
the
markets
in
2012
[9].
2.2.1
EV,
PHEV,
Extended
Range
EV
There
are
four
main
types
of
electric
vehicles
that
currently
exist:
Hybrid,
Plug-‐in
Hybrid
(PHEV),
Extended-‐Range
and
Battery
EVs
(BEV)
[10].
Hybrid
and
PHEVs
contain
both
combustion
engines
and
electric
motors
with
battery
storage.
Unlike
hybrids,
however,
PHEVs
can
also
be
charged
through
an
external
battery
charger,
further
reducing
reliance
on
the
combustion
engine
[10]
Extended-‐Range
EVs
are
similar
to
PHEVs
and
include
vehicles
such
as
the
Holden
Volt
[11].
The
electric
engine
is
used
for
all
driving
speeds
until
the
battery
is
discharged,
and
is
then
replaced
by
the
combustion
engine.
Lastly,
BEVs
are
all
electric
with
no
combustion
engine.
They
contain
large
battery
packs
that
must
be
charged
by
the
grid.
17. 6
6
In
relation
to
the
topic
of
this
thesis,
hybrid
vehicles
are
considered
irrelevant,
as
they
are
not
charged
by
the
grid.
Therefore,
the
vehicles
of
focus
will
be
PHEVs,
Extended-‐Range
EVs
and
BEVs,
referred
to
collectively
throughout
this
text
as
‘EVs’.
2.2.2
Configuration
The
basic
configuration
of
an
EV,
including
an
IC
engine,
which
is
only
applicable
to
PHEVs
and
EREVs,
is
shown
by
the
simplified
block
diagram
in
Fig.
2.3.
Figure
2.3:
Electric
Vehicle
Configuration
Charging
requires
communication
with
the
battery-‐monitoring
unit
that
measures
the
batteries
state
of
charge
(SoC).
The
inverter
is
used
after
a
DC-‐DC
converter
to
convert
direct
current
(DC)
into
alternating
current
(AC)
to
power
the
electric
motor.
2.2.3
Battery
system
For
electric
vehicles
to
be
a
viable
alternative
to
IC
vehicles,
their
battery
storage
must
contain
enough
energy
to
ensure
suitable
range
for
drivers.
The
most
important
factor
affecting
this
is
the
energy
to
weight
ratio
of
a
battery
pack,
or
its
energy
density.
This
allows
vehicles
to
be
as
light
as
possible
for
a
given
amount
of
energy
storage,
ensuring
the
greatest
range
possible.
There
exist
three
main
battery
types
for
electric
vehicles:
lead-‐acid,
nickel-‐metal
hydride
(NiMH)
and
lithium-‐ion
(li-‐ion)
[12].
In
the
past,
EVs
such
as
the
General
Motors
EV1
used
lead-‐acid
and
nickel-‐metal
hydride
batteries.
In
recent
years,
however,
the
demand
for
batteries
in
laptops
and
other
portable
devices
has
driven
R&D
in
the
area
of
lithium-‐ion
batteries,
improving
energy
density
and
charge
time
beyond
other
battery
types.
Due
to
these
improvements,
major
EV
manufacturers
now
use
lithium
ion
battery
packs
[11][13-‐16].
18. 7
7
Table
2.1
provides
a
list
of
current
vehicles
and
their
battery
capacities,
showing
a
significant
range
of
battery
capacities
that
will
form
the
basis
for
modelling.
Electric
Vehicle
Battery
Capacity
Tesla
Model
S
40,
60,
85
kWh
Nissan
Leaf
24
kWh
Ford
Focus
Electric
23
kWh
Holden
Volt
8
kWh
Toyota
Prius
Plug-‐In
4.4
kWh
Table
2.1:
Current
EV
Battery
Capacities
[11][13-‐16]
2.2.4
Charging
Based
on
standards
by
the
International
Electrotechnical
Commission
(IEC)
[17]
and
the
Society
of
Automotive
Engineers
J1772
[18],
there
exists
three
charging
levels:
Level
Voltage
Current
Power
1
120
V
AC
16
A
1.92
kW
2
208-‐240
V
AC
12
–
80
A
2.5
–
19.2
kW
3
500
V
DC
125
A
50
kW
Table
2.2:
International
EV
Charging
Standards
The
residential
charger
rating
of
EV
manufacturers
vary
substantially
within
the
Level
2
range.
Nissan
and
Holden’s
chargers
are
rated
3.3
kW
[16][11],
Ford’s
at
7.7
kW
[14],
while
Tesla
manufactures
10
kW
or
20
kW
chargers
[13].
These
ratings
are
significant
in
comparison
to
other
appliances
found
in
the
home.
Fig.
2.4
shows
the
power
demand
and
battery
SoC
profiles
of
a
lithium
ion
battery.
19. 8
8
Figure
2.4:
Lithium-‐Ion
Charge
Curve
[26]
Figure
2.4
shows
a
predominantly
constant
charging
power
for
the
duration
of
the
charging
period.
Therefore,
for
modelling
purposes,
a
constant
charge
rate
can
be
considered
accurate
to
assume.
2.2.5
Growth
Due
to
economic
and
technological
factors
surrounding
the
viability
of
electric
vehicles,
their
penetration
levels
are
expected
to
soar
this
decade
[19-‐21].
Current
estimates
expect
the
price
of
oil
to
rise
by
85%
into
2020
[19],
and
this
rise
is
forecast
to
continue.
By
the
same
time,
lithium
ion
battery
technology
is
expected
to
dramatically
fall
as
economies
of
scale
reduces
manufacturing
costs,
and
technological
improvements
allow
energy
density
to
continually
increase.
Lithium
ion
battery
prices
have
fallen
considerably
from
US$650/kWh
in
2009
to
the
current
price
of
around
US$450/kWh.
Analysts
have
forecasted
prices
to
fall
at
a
7.5%
annual
compound
rate
from
2012
through
2020
to
approximately
US$250/kWh
[19].
EV
manufacturer
Tesla
Motors
is
already
producing
battery
packs
with
480
km
of
range
[13].
Taking
these
factors
into
consideration,
analysts
from
Deutsche
Bank
[19]
have
predicted
that
in
the
US,
around
10%
of
all
vehicles
will
be
hybrid/electric
by
2021,
increasing
to
20%
by
2026,
and
35%
by
2030.
In
terms
of
purchased
vehicles,
EVs
are
expected
to
make
up
3-‐10%
of
new
car
sales
as
early
as
2015
[20]
and
35%
in
2025,
comprised
of
25%
PHEVs
and
10%
EVs,
according
to
IDtechX
analysts
[21].
These
projections
show
that
a
major
shift
is
about
to
occur,
resulting
in
a
significant
percentage
of
vehicles
becoming
at
least
partially
electric.
This
analysis
raises
questions
about
the
effects
of
a
large
percentage
of
EVs
on
the
distribution
network,
as
well
as
the
20. 9
9
potential
problems
this
extra
energy
storage
may
solve.
2.3
Impacts
of
Charging
2.3.1
Uncoordinated
Charging
The
introduction
of
EVs
is
expected
to
have
a
significant
effect
on
customer
load
profiles
during
charging
periods.
Studies
in
[2],
[3]
and
[22]
have
concluded
that,
for
high
penetration
levels,
uncoordinated
domestic
charging
will
increase
peak
load
demand
significantly,
resulting
in
transformer
overloading,
poor
feeder
voltage
profiles
and
power
loss.
The
authors
of
[2]
and
[22]
have
conducted
studies
on
uncoordinated
charging
on
residential
radial
feeders,
focusing
on
evening
peaks.
The
modelled
charger
rating
was
4
kW
[2],
and
1.8
kW
in
[22],
both
showing
dramatic
rises
in
peak
load,
clearly
overloading
the
transformer
limitations
for
penetrations
above
20%
in
[22]
and
exceeding
voltage
limits
in
[2]
at
17%.
The
effects
of
peak-‐time
charging
on
summer
and
winter
load
profiles
are
explored
in
[23]
and
[3].
The
UK
winter
load
profile
in
[23]
showed
a
distinct
evening
peak
compared
to
summer
due
to
electric
heating.
This
caused
the
peak
demand
to
be
increased
by
13.6%
compared
to
10.06%
for
summer
at
10%
EV
penetration.
Although
this
paper
conducts
a
load
study
for
the
entire
UK,
it
is
probable
that
this
would
reflect
the
demand
of
residential
feeders,
as
most
vehicles
would
be
at
home
during
this
period.
A
study
is
conducted
in
[3]
to
determine
the
effects
of
peak
charging
on
power
loss
and
voltage
deviation.
The
voltage
limit
of
0.9
pu
was
found
to
be
exceeded
at
30%
EV
penetration
with
a
4
kW
charger,
with
total
power
loss
at
6%
in
winter
compared
to
5%
in
summer.
These
papers
clearly
show
that
uncoordinated
charging
would
have
a
large
impact,
even
at
low
penetration
levels.
However,
an
analysis
of
these
papers
show
the
large
number
of
variables
associated
with
such
studies.
For
example,
the
voltage
limit
of
0.9pu
in
[3]
differs
to
0.94
used
in
Australia,
as
well
as
the
UK
load
profiles
in
[23].
Another
assumption
made
in
these
studies
is
a
relatively
low
powered
charger,
particularly
in
[22].
A
higher-‐powered
charger
more
commonly
used
today
would
have
a
significantly
increase
the
peak
demand
determined
by
these
papers.
Of
all
the
assumptions
made,
however,
the
most
important
variable
used
to
determine
the
impacts
21. 10
10
of
uncoordinated
charging
is
the
time
the
vehicles
arrive
home
to
begin
charging.
In
the
related
papers
[2-‐3]
[24-‐26],
and
number
of
assumptions
in
relation
to
charging
times
have
been
made,
while
there
exists
a
significant
degree
of
ambiguity
when
these
assumptions,
such
as
the
data
used,
is
explained.
Papers
[2]
and
[22]
fail
to
explain
how
their
vehicle
arrival
times
are
modelled,
while
[23]
simply
divides
charging
into
three
groups
during
the
evening
peak,
assuming
that
all
vehicles
commence
charging
within
90
minutes
of
one
another.
Papers
[24]
and
[27]
assume
a
more
accurate
normal
distribution,
however
still
disregard
actual
driving
statistics,
such
as
those
provided
by
the
UK
Time
of
Use
survey
noted
in
[23]
and
[3].
Paper
[3]
takes
into
account
the
statistics
from
this
survey
by
dividing
charging
times
according
to
the
morning,
midday
and
late
afternoon
periods,
and
making
assumptions
about
the
percentage
of
cars
that
charge
during
these
times.
Paper
[3]
applies
the
most
accurate
data
regarding
charging
times
as
it
incorporates
the
irregular
and
skewed
peak
provided
by
a
traffic
authority.
Considering
this,
the
majority
of
research
has
been
conducted
with
inaccurate
assumptions,
possibly
causing
significant
variations
in
results
as
the
charging
times,
along
with
the
assumed
charger
rating,
are
the
factors
that
most
influence
the
results
of
loading
simulations.
Charging
times
for
the
uncoordinated
charging
simulations
in
this
thesis
will
be
based
on
local
driving
data
to
ensure
the
most
accurate
modelling
possible.
Therefore,
to
more
accurately
determine
the
effects
of
uncoordinated
charging,
it
is
important
to
use
local
load
profiles,
standards
and
driving
statistics,
with
assumptions
that
are
up
to
date,
or
reflect
expected
future
trends.
These
variables
will
be
taken
in
to
account
in
this
thesis,
to
more
accurately
determine
possible
effects
on
typical
Australian
residential
feeders.
2.3.2
Coordinated
Charging
The
effects
of
uncoordinated
charging
show
the
importance
of
coordinated
or
‘smart’
charging
in
the
future.
This
would
be
achieved
through
communication
infrastructure
in
a
smart
grid,
by
sending
signals
to
begin
charging
at
times
corresponding
to
uniform
loading
[24].
Coordinated
charging
employs
heuristic
algorithms
and
optimization
techniques
with
the
aim
to
improve
load
factor
and
reduce
network
costs
and
power
losses
by
charging
during
off
peak
periods
[2][24].
As
cars
are
available
for
94.8%
of
the
day
on
average
[23],
coordinated
charging
can
be
considered
viable,
as
a
large
amount
of
flexibility
exists
in
charging
times.
22. 11
11
A
large
number
of
studies
have
been
conducted
on
novel
approaches
to
coordinating
vehicles,
with
the
aim
to
reduce
evening
peak
demand.
These
range
from
complicated
algorithms
based
on
real-‐time
market
prices
in
[27]
to
prioritizing
charging
periods
in
[2],
to
simple
delayed
off-‐peak
charging
in
[23].
Throughout
the
majority
of
coordinated
charging
studies,
the
uncertainties
of
variables,
such
as
load
profiles
and
charging
time,
are
expressed
in
terms
of
probability
density
functions,
allowing
predictions
to
be
made
without
relying
on
fixed-‐input
variables,
such
as
an
average
past
load
profile
[27].
The
authors
in
[27]
determined
that
coordinated
charging
reduced
load
factor
and
power
losses
by
6-‐28%
for
penetration
levels
from
10%
to
100%.
In
[27],
a
control
algorithm
was
implemented
for
coordinated
charging
on
an
LV
feeder
in
Belgium,
based
on
a
typical
local
load
profile.
The
results
showed
a
peak
demand
reduction
of
29%
for
a
combination
of
3.6
kW
and
7.4
kW
chargers
at
15%
penetration.
Papers
[2]
and
[27]
take
different
real-‐time
approaches,
dividing
charging
times
into
red,
blue
and
green
zones,
based
on
the
priority
of
charging.
In
[27],
charging
priority
is
determined
based
on
the
time
vehicles
arrive
home,
as
a
vehicle
that
arrives
late
would
have
a
low
chance
of
being
used
for
the
remainder
of
the
night.
This
paper
found
that
load
demand
could
remain
below
the
evening
peak
for
penetration
levels
of
at
least
63%,
as
low
priority
vehicles
could
be
spread
further
into
the
morning
hours.
Above
this
penetration,
however,
this
paper
found
that
high
and
medium
priority
vehicles
raised
the
peak
demand
above
the
evening
peak,
therefore
stating
there
will
inevitably
be
a
rise
in
peak
demand
as
EV
penetration
reaches
high
levels.
The
study
in
[27]
assumes
a
2
kW
peak,
which
is
relatively
low,
especially
as
this
aims
to
determine
loading
decades
in
to
the
future,
which
is
expected
to
rise
irrespective
of
EVs.
Another
assumption
is
that
low
priority
charging
is
timed
to
finish
at
4
am,
however
this
could
realistically
be
increased
to
6
am,
for
example,
for
the
majority
of
people
who
leave
for
work
after
this
time.
This
would
allow
a
higher
penetration
before
peak
demand
is
raised.
The
authors
in
[23]
have
included
a
study
on
fixed
off-‐peak
charging,
which
is
implemented
by
simply
charging
in
three
groups,
at
9
pm,
9:30
pm
and
10
pm.
This
avoids
the
evening
peak,
while
allowing
sufficient
time
to
charge
through
to
early
morning.
This
paper
finds
that
the
charging
peak
is
less
than
the
evening
peak
for
low
penetration,
but
states
that
this
may
not
be
the
case
for
penetration
greater
than
10%.
This
is
compared
to
a
study
on
‘smart’
market
based
charging,
which
shows
a
noticeable
23. 12
12
reduction
in
charging
peak
load.
From
analysis
of
the
fixed
off-‐peak
charging
graph,
it
shows
charging
is
finished
by
2
am.
This
shows
a
large
percentage
of
early
morning
hours
with
lower
base
demand
that
are
not
utilized,
therefore
it
could
be
argued
that
this
method
could
support
penetration
much
higher
than
the
10%
stated.
The
simplicity
of
the
fixed
off-‐peak
method,
and
the
lack
of
research
associated
with
it,
presents
an
opportunity
for
study
in
this
thesis.
This
would
eliminate
the
need
for
complicated
algorithms
at
residential
feeders,
and
may
not
require
smart
infrastructure,
as
signalling
could
be
sent
via
high
frequency
pulses,
as
they
are
today
to
control
off-‐peak
hot
water
systems.
Lower
electricity
rates
would
provide
the
incentive
for
the
majority
of
owners
to
use
this
method,
while
allowing
a
simple
manual
over-‐ride
when
required.
However,
in
terms
of
load
levelling,
coordinated
charging
would
be
a
valuable
approach
to
further
reduce
energy
losses.
Initially,
this
method
will
be
tested
by
simulating
a
simple
fixed-‐
start
delay,
with
preliminary
work
on
staggered
charging
to
focus
on
further
reducing
power
loss.
2.4
Summary
The
results
of
various
studies
related
to
charging
produce
a
wide
range
of
results
due
to
the
number
of
variables
associated
with
distribution
networks
and
electric
vehicles.
From
this
analysis,
a
noticeable
gap
exists
in
research
of
the
impact
of
EVs
applicable
to
Australian
residential
feeders.
Particularly,
there
is
a
lack
of
study
that
incorporates
realistic
driving
pattern
data,
through
either
the
use
of
information
from
traffic
authorities
or
by
conducting
surveys.
24. 13
13
3 Methodology
The
study
of
literature
in
Chapter
2
presents
a
number
of
areas
that
can
be
further
studied
to
determine
the
impacts
of
EV
charging.
Further
study
would
gain
valuable
information
for
electricity
distribution
network
service
providers
in
planning
for
future
development,
as
well
determine
the
benefits
for
residents.
3.1 Load
Flow
3.1.1 Load-‐Flow
Solutions
To
determine
loading
effects
in
the
context
of
an
Australian
residential
feeder,
load
flow
analysis
must
be
conducted.
A
simple
single-‐line
diagram
can
be
realized
in
Fig.
3.1.
Figure
3.1:
Load
Flow
Analysis
[4]
Figure
3.1
represents
a
simple
power-‐flow
scenario.
Power-‐flow
problems
such
as
this
are
separated
in
to
the
following
components:
1. Slack
bus
–
a
reference
bus
for
which
V∠δ°
=
1.0∠0°
2. Load
(PQ)
bus
–
𝑃!
and
𝑄!
are
input
loads,
used
to
compute
𝑉!
and
δ!
3. Voltage
controlled
(PV)
bus
–
𝑃!
and
𝑉!
are
inputs,
includes
voltage
control
devices
such
as
OLTC,
switched
capacitors
The
power
flow
data
listed
is
used
to
calculate
power-‐flow
solutions
using
methods
such
as
Guass-‐Seidell
and
Newton-‐Raphson,
which
solve
nodal
equations
iteratively
[7].
3.1.2 Load
Types
Another
important
consideration
that
must
be
made
during
load
flow
analysis
is
the
type
of
load
connected
to
each
load
bus.
Load
behaviour
is
determined
by
the
25. 14
14
combination
of
R,
L
and
C
elements
and
power
electronic
circuitry
of
a
load,
and
can
be
divided
into
three
types:
1. Constant
Power
(eg.
LED
TV,
computer)
2. Constant
Current
(eg.
CFL
globe)
3. Constant
Impedance
(eg.
Toaster,
oven)
Therefore,
for
any
given
voltage
a
load
will
conform
to
one
of
these
load
behaviours.
An
appliance
with
a
power
electronics
interface,
for
example,
with
exhibit
constant
power
characteristics
as
the
voltage
is
stepped
down
and
held
at
a
constant
DC
value,
as
this
voltage
will
be
constant
for
all
AC
source
voltage
levels.
A
resistive
load,
on
the
other
hand,
is
regarded
as
constant
impedance
and
will
draw
less
power
as
voltage
levels
drop,
according
to
Ohm’s
law.
The
voltage
dependency
of
loads
can
be
modelled
by
Eqs.
(3.1)
and
(3.2):
𝑃 = 𝑃!(𝑎𝑃 ∙
𝑣
𝑣!
!_!"
+ 𝑏𝑃 ∙
𝑣
𝑣!
!_!"
+ (1 − 𝑎𝑃 − 𝑏𝑃) ∙
𝑣
𝑣!
!_!"
)
(3.1)
Where
1 − 𝑎𝑃 − 𝑏𝑃 = 𝑐𝑃
𝑄 = 𝑄!(𝑎𝑄 ∙
𝑣
𝑣!
!_!"
+ 𝑏𝑄 ∙
𝑣
𝑣!
!_!"
+ (1 − 𝑎𝑄 − 𝑏𝑄) ∙
𝑣
𝑣!
!_!"
)
(3.2)
Where
1 − 𝑎𝑄 − 𝑏𝑄 = 𝑐𝑄
When
modelling
a
house
load,
a
number
of
assumptions
have
to
be
made.
For
the
purpose
of
this
simulation,
a
house
will
be
considered
as
a
constant
power
load,
as
each
house
will
be
associated
with
load
profiles
recorded
on
a
hot
day
where
the
predominant
load
type
is
a
constant
power
air
conditioner.
EVs
will
also
be
regarded
as
constant
power
loads,
as
the
charging
profile
of
a
lithium
ion
battery
charger
is
a
constant
power
curve.
From
Eq.
(3.1),
we
simply
require 𝑃 = 𝑃!,
therefore
all
coefficients
and
exponents
have
been
set
to
zero
in
the
voltage
dependence
settings
of
each
load.
26. 15
15
3.2 Modelling
A
realistic
network
model
is
imperative
for
determining
the
effects
of
EV
charging.
DIgSILENT
PowerFactory
was
chosen
for
this
purpose
due
to
its
flexibility
in
analysis,
incorporating
functions
such
as
unbalanced
power
flow,
and
remote
control
ability
through
DIgSILENT
Engine.
To
ensure
that
loading
results
were
as
accurate
as
possible,
emphasis
was
placed
on
applying
accurate
network
modelling
parameters,
load
profiles
and
vehicle
driving
statistics.
3.2.1 DIgSILENT
PowerFactory
Models
In
order
to
accurately
model
a
typical
low
voltage
network,
data
from
smart
meter-‐
connected
premises
has
been
accumulated.
The
premises
of
interest
are
connected
to
a
500
kVA
pad-‐mount
distribution
substation
in
Woodlands
Drive,
Glenmore
Park
(located
in
Western
Sydney),
which
supplies
92
premises
on
four
low
voltage
underground
feeders.
The
network
models
used
for
simulation
are
based
off
sample
DIgSILENT
feeder
models
provided
by
Endeavour
Energy.
Three
models
–
400
V
overhead,
400
V
underground
and
11
kV
overhead
–
were
modified
to
supply
the
same
number
of
loads
as
the
substations
in
Glenmore
Park.
When
implementing
the
LV
models,
each
premise
is
represented
by
a
single-‐
phase
house
and
EV
load,
with
a
CSV
file
associated
with
each
load
containing
the
load
profile
information
for
a
single
day.
Due
to
limitations
with
the
number
of
possible
nodes
in
a
PowerFactory
student
license,
the
number
of
premises
has
been
halved
to
46
premises
split
across
two
feeders,
supplied
by
a
250
kVA
transformer.
Halving
transformer
ratings
and
feeder
numbers
ensures
an
accurately
scaled
model
for
determining
feeder
voltage
levels
and
transformer
loading.
Modelling
loads
as
single
phase
loads
allows
for
voltage
unbalance
to
be
accounted
for,
which
is
a
primary
cause
of
poor
voltage
regulation.
The
low
voltage
overhead
model
is
shown
in
Fig.
3.2.
27. 16
16
Figure
3.2:
400
V
Overhead/Underground
DIgSILENT
Model
To
model
the
impacts
of
electric
vehicle
charging
on
a
zone
substation
at
the
11
kV
level,
the
resulting
distribution
transformer
load
profiles
have
been
lumped
and
applied
to
each
of
the
transformer
loads
on
a
single
11
kV
feeder.
The
loading
magnitude
is
doubled
to
account
for
the
halved
number
of
premises
on
the
low
voltage
side,
so
that
each
transformer
is
represented
accurately
at
500
kVA.
There
are
10
11
kV
feeders
supplied
by
Glenmore
Park
Zone
Substation,
which
supplies
a
total
of
7596
premises.
Glenmore
Park
Zone
Substation
has
2
x
45
MVA
transformers
installed,
and
hence
has
an
N-‐1
capacity
of
45
MVA.
With
an
average
of
760
premises
per
11
kV
feeder,
assuming
92
premises
per
500
kVA
of
installed
capacity,
there
would
be
an
average
of
8
LV
substations
connected
to
each
11
kV
feeder.
Therefore,
8
LV
substation
loads
have
been
modelled
on
the
11
kV
feeder,
and
the
total
zone
substation
load
is
determined
by
multiplying
the
total
feeder
loading
by
10
feeders.
Figure
3.3
shows
the
11
kV
feeder
model.
28. 17
17
Figure
3.3:
11
kV
Overhead
DIgSILENT
Model
Parameters
such
as
line
and
transformer
impedances,
shown
in
Table
3.1,
were
left
constant
as
they
represent
the
most
common
ratings
used
within
the
Endeavour
Energy
network.
400
V
Overhead
400
V
Underground
11
kV
Overhead
Feeder
Impedance
0.707
+
j0.284
Ω/km
0.162
+
j0.065
Ω/km
0.224
+
j0.224
Ω/km
Feeder
Section
Length
35
m
35
m
570
m
Service
Line
Impedance
1.49
+
j0.097
Ω/km
0.927
+
j0.081
Ω/km
N/A
Service
Line
Length
20
m
20
m
N/A
Transformer
Rating
250
kVA
250
kVA
N/A
Transformer
Impedance
4%
4%
N/A
Voltage
Source
Series
Impedance
0.5
+
j5
Ω
0.5
+
j5
Ω
0.021
+
j0.635
Ω
Table
3.1:
Network
Equipment
Parameters
The
11
kV
model
assumed
a
voltage
source
at
1
pu
voltage,
as
opposed
to
a
transformer,
as
the
transformer’s
OLTC
would
act
to
maintain
this
voltage
in
reality.
The
low
voltage
transformers
modelled
are
equipped
with
offline-‐tap
changers
with
6
asymmetrical
tap
settings,
ranging
from
-‐4
to
+1.
At
typical
tap
setting
for
LV
transformers
is
-‐3,
or
-‐7.5%,
corresponding
with
a
LV
bus
voltage
of
430
V.
An
increase
in
each
tap
setting
will
raise
the
voltage
by
2.5%,
allowing
for
a
12.5%
voltage
range
(-‐10%
to
+2.5%).
As
LV
29. 18
18
transformer
taps
are
offline,
they
must
be
changed
manually
and
hence
would
only
be
changed
over
the
long
term
as
total
loading
increases,
not
in
response
to
a
permanent
increase
in
the
afternoon
peak
caused
by
EV
charging,
for
example,
as
this
would
cause
voltages
to
exceed
their
upper
limits
during
lower
loading
periods.
Instead,
this
regulation
must
be
controlled
using
zone
substation
OLTC’s
which
allow
for
real-‐time
tap
changing.
As
EV
loading
is
expected
to
only
increase
the
afternoon/evening
peak,
the
tap
setting
is
expected
to
remain
constant
in
the
future.
Although
there
may
be
future
base
load
growth
as
the
penetration
of
air
conditioners
and
other
electrical
appliances
increases,
the
relative
difference
between
low
loading
periods
and
afternoon
EV
loading
will
likely
remain
constant,
therefore
the
actual
future
LV
substation
tap
setting
can
be
disregarded.
3.2.2 DIgSILENT
Programming
Language
(DPL)
Script
A
DIgSILENT
Programming
Language
(DPL)
script
allows
the
automation
of
load
flows
to
extract
specific
data
from
a
network
model.
A
DPL
script
was
provided
by
Endeavour
Energy
which
conducts
time-‐step
simulation
load
flows
for
house
loads,
saving
power,
losses
and
voltage
data
into
result
objects
at
half
hour
intervals.
This
script
was
modified
to
read
EV
loads,
as
well
as
execute
‘export
result
objects’
so
that
result
data
would
be
exported
to
text
files
each
time
the
script
was
run.
The
DPL
script
was
associated
with
each
network
model,
and
allowed
load
flow
simulations
to
be
conducted
via
engine
control
of
PowerFactory.
3.2.3 Load
Profiles
3.2.3.1 House
Load
Profiles
Loads
in
PowerFactory
can
be
associated
with
CSV
files
containing
multiple
time
points
for
conducting
time-‐step
simulations.
Each
of
the
42
house
loads
has
an
associated
CSV
file
containing
the
smart
metering
data
of
a
premise
in
the
Glenmore
Park
trial
area,
chosen
at
random
from
the
92
metered
premises.
The
smart
metering
data
contains
the
power
usage
of
the
premises
over
a
24
hour
period
at
half
hour
intervals.
Each
premise
has
been
assigned
the
same
power
factor,
determined
as
the
average
of
the
premises
power
factor
during
the
evening
hours,
found
to
be
0.9
inductive.
The
selected
load
profiles
correspond
with
the
hottest
day
of
2011,
occurring
on
November
14
at
a
30. 19
19
maximum
temperature
of
38.7°C.
The
hottest
day
of
2011
was
chosen
as
network
planning
must
take
into
account
the
worst-‐case
loading
scenarios
that
occur
on
hot
days,
caused
primarily
by
air
conditioners.
3.2.3.2 EV
Charging
Profiles
The
spread
of
EV
charging
start
times
were
determined
by
analysing
driving
statistics
from
the
NSW
Bureau
of
Transport
Statistics
[28],
shown
in
Fig
3.4.
Figure
3.4:
Average
number
of
travellers
in
NSW
on
weekdays
in
2010/11
This
graph
shows
the
average
number
of
travellers
in
NSW
on
weekdays
by
transport
type
in
2010/11.
For
determining
vehicle
arrival
times,
only
the
‘Vehicle
Driver’
curve
was
considered.
The
time
of
arrival
was
determined
by
shifting
the
afternoon/night
peak,
between
2
pm
and
12
am,
by
20
minutes
-‐
the
average
vehicle
one-‐way
trip
time.
This
curve
was
then
normalised
between
2
pm
and
12
am,
and
multiplied
by
46
to
determine
the
number
of
premises
that
would
begin
charging
at
each
half
hour
interval
within
this
period.
The
resulting
scaled
driving
arrival
curve
is
shown
in
Fig
3.5,
shown
to
follow
the
afternoon
driving
trend
displayed
in
Fig
3.4.
31. 20
20
Figure
3.5:
Scaled
driver
arrival
times
The
number
of
vehicles
arriving
at
each
half
hour
interval
was
recorded,
and
the
vehicles,
having
been
assigned
their
specific
starting
time,
were
allocated
to
premises
using
a
random
function,
so
that
the
feeder
models
were
assigned
a
realistic
variation
in
vehicle
arrival
times.
3.2.4 Loading
Assumptions
To
model
the
effects
of
charging,
the
level
two
residential
chargers
from
Chapter
2
were
considered.
Considering
the
expected
combination
of
chargers
based
on
EV
costs,
an
average
charge
rating
of
4
kW
was
determined
to
provide
a
realistic
charging
power
that
could
be
used
to
model
a
load
of
EV
charging
homes.
The
average
battery
capacity
was
chosen
to
be
25
kWh,
a
mid-‐range
capacity
in
Table
2.1.
Assuming
a
return
trip
driving
distance
of
18.8
km
[28]
and
a
battery
consumption
of
0.168
kWh/km
[16],
the
average
charging
time
was
found
to
be
approximately
47
minutes.
Due
to
the
time-‐step
resolution
of
half
an
hour,
however,
this
charging
duration
had
to
be
modelled
as
1
hour.
This
analysis
assumes
that
each
EV
is
charged
only
once
per
day
in
the
afternoon/evening,
and
that
driving
is
split
into
a
morning
and
afternoon
peak.
Vehicles
arriving
home
during
the
late
night
hours
are
probably
drivers
that
have
travelled
previously
during
the
day,
so
charging
has
been
assumed
to
occur
after
the
second
trip.
Vehicle
driving
patterns
have
been
based
on
weekday
statistics,
and
the
vehicles
are
assumed
to
charge
on
a
daily
basis.
In
terms
of
vehicle
penetration,
a
substation
EV
penetration
of
100%
corresponds
to
all
vehicles
being
EVs,
not
100%
of
premises
containing
an
EV.
As
there
is
an
average
of
1.7
motor
vehicles
per
household
in
Australia
[29],
a
penetration
of
59%
would
represent
an
average
of
1
vehicle
per
household.
Another
consideration
made
was
the
percentage
of
travellers
that
drive
vehicles,
as
opposed
to
using
public
transport
or
travelling
as
a
passenger.
Although
we
know
32. 21
21
that
there
is
an
average
of
1.7
vehicles
per
household,
and
that
47%
of
travellers
drive
a
vehicle
[28],
it
is
impossible
to
discern
the
percentage
of
vehicle
owners
that
drive
a
vehicle
for
the
majority
of
their
travel
during
weekdays.
This
is
because
the
number
of
travellers
includes
school
students,
for
example,
who
may
travel
as
a
passenger
or
on
public
transport,
as
well
as
those
who
own
a
vehicle
but
may
cycle
or
also
use
public
transport
to
travel
to
work.
To
further
complicate
any
assumptions
made,
there
is
no
information
relating
to
the
percentage
of
people
that
actually
travel
significant
distances
during
the
week,
including
the
considerable
proportion
of
vehicle
owners
that
fall
into
this
category
such
as
pensioners
and
those
who
work
or
care
for
children
at
home.
Therefore,
with
the
data
available,
the
most
realistic
assumptions
decided
were
that
every
vehicle
owner
travels
the
average
distance
of
20
km
return-‐trip
on
weekdays
and
does
the
majority
of
this
travel
in
their
vehicle.
Although
analysis
may
seem
more
accurate
to
apply
a
statistical
spread
of
charger
ratings
across
each
household,
this
would
be
equivalent
to
assuming
an
average
charger
rating
for
each
household,
as
the
total
transformer
loading
would
be
the
same.
A
statistical
variation
in
charger
ratings
would
provide
a
more
accurate
model
of
voltage
regulation,
however
the
limited
number
of
premises
in
the
DIgSILENT
models
prevents
any
statistical
analysis
from
yielding
meaningful
results.
Therefore,
Eqn.
(3.3)
has
been
used
to
determine
the
charging
power
per
premise.
P = Charger Rating (kW) ∗ (Penetration/100%) ∗ 1.7 vehicles per premise
(3.3)
The
assumptions
made
in
this
analysis
present
an
ambiguity
issue
with
the
number
of
drivers
arriving
home
during
the
middle
of
the
day,
and
those
that
may
travel
after
arriving
home
from
work.
The
actual
number
of
drivers,
however,
is
impossible
to
predict
without
conducting
a
large-‐scale
survey
focusing
on
the
actual
arrival
times
and
driving
patterns
of
vehicle
drivers,
therefore
the
assumptions
made
can
be
considered
as
accurate
as
possible.
3.2.5 Load
Scaling
The
load
profiles
of
premises
supplied
by
the
Woodlands
Drive
substation
represent
the
energy
use
of
premises
in
a
sample
area
of
Glenmore
Park.
These
profiles
provide
an
accurate
load
shape,
however
their
combined
substation
profile
may
not
match
the
magnitude
of
those
substations
located
in
areas
of
lower
or
higher
socio-‐economic
33. 22
22
status,
such
as
a
wealthier
area
which
is
more
likely
to
contain
a
greater
number
of
air
conditioners
and
pool
pumps,
for
example.
To
account
for
the
diversity
between
areas
within
suburbs,
it
is
important
that
the
Woodlands
Drive
substation
load
profile
can
be
scaled
before
EV
loading
is
added,
however
non-‐linear
line
losses
must
also
be
accounted,
therefore
this
scaling
is
not
a
straight
forward
calculation.
As
base
loading
power
increases
linearly,
represented
by
∆ 𝑃!!"#
in
per
unit,
line
losses
increase
by
the
square
of
this
rate,
or
(∆𝑃!"#$)!
.
Therefore,
if
Woodlands
Drive
substation
is
80%
loaded
under
maximum
load,
this
load
profile
cannot
be
scaled
to
represent
substation
that
is
90%
loaded,
for
example,
without
first
separating
the
combined
house
power
and
the
line
losses.
This
would
require
a
scaling
model
in
the
following
form:
𝑃!" = 𝑃! 𝑥 + 𝑃! 𝑥!
(3.4)
Where
𝑃!"
is
the
new
total
power
drawn
by
the
transformer
after
scaling,
𝑃!is
the
combined
house
power
before
scaling,
𝑃!
is
the
line
losses
before
scaling.
For
example,
if
the
total
transformer
loading
was
required
to
be
increased
from
110
kW,
where
𝑃!
=
100
kW
and
𝑃!=
10
kW,
to
240
kW,
the
combined
house
power
would
only
have
to
be
increased
by
a
factor
of
x
=
2,
to
produce
a
transformer
power
increase
of
!"#
!!"
=
2.18
pu.
This
formula,
however,
does
not
take
into
account
the
line-‐loss
increase
as
a
result
of
the
voltage
drop
that
occurs
when
constant
power
loads
are
scaled.
That
is,
when
the
power
consumption
of
a
feeder
with
constant
power
loading
increases,
so
too
does
the
voltage
drop
along
the
feeder,
causing
the
line
current,
and
hence
line
losses,
to
rise
further.
This
is
a
cyclical
response
that
converges
rapidly
due
to
the
large
difference
between
the
percentage
change
in
voltage
and
the
initial
load
power
change,
therefore
any
further
voltage
correction
can
be
considered
negligible.
Figure
3.5
shows
the
voltage
profile
of
a
typical
feeder
with
6
premises
per
phase
per
feeder
in
the
upper
graph,
approximately
the
same
as
the
Woodlands
Drive
feeders,
and
the
profile
of
the
last
4
premises
on
a
feeder
in
the
lower
graph,
with
the
voltage
levels
scaled
for
an
easier
interpretation
of
the
voltage
drop
in
each
feeder
section.
34. 23
23
Figure
3.6:
Feeder
voltage
profile,
moving
from
last
premise
to
transformer
from
right
to
left
This
feeder
shows,
when
moving
towards
the
transformer
from
right
to
left,
the
voltage
drop
increases
according
to
the
series
𝑉!"(1,
3,
6,
10)
etc.,
where
𝑉!" is
the
voltage
drop
along
the
last
section
of
feeder,
between
the
second
last
and
last
premises.
This
series
can
be
represented
by
Eq.
(3.5).
𝑖
!
!!!
=
𝑛(𝑛 + 1)
2
(3.5)
Voltage
drop
in
the
last
section
of
feeder
can
be
found
using
Eq.
(3.6).
𝑉!" =
2𝑉!"
𝑛(𝑛 + 1)
(3.6)
Where
𝑉!" is
the
voltage
drop
along
the
entire
feeder.
For
the
feeder
in
the
top
subplot
with
6
premises,
the
total
voltage
drop
is
equal
to
!(!!!)
!
𝑉!" = 21 0.004 = 0.084 pu,
which
is
reflected
on
this
plot.
This
modelling
assumes
that
each
load
draws
the
same
current.
When
a
load
is
increased
by
a
factor
𝑥,
the
line
current
supplying
a
constant
power
load
increases
by
the
same
factor.
As
voltage
drop
is
proportional
to
current,
the
voltage
drop
also
increases
by
this
factor.
When
considering
the
last
section
of
feeder,
𝑉!"#$
is
equal
to
𝑉!"# − 𝑉!"#,
where
𝑉!"#
is
the
voltage
at
the
second
last
premise
and
35. 24
24
𝑉!"#
is
the
voltage
at
the
last
premise.
𝑉!"#$
can
be
represented
as
a
percentage
by
Eq.
(3.7).
𝑥𝑉!"#$ − 𝑉!"#$
𝑉!"#
=
𝑉!"#$(𝑥 − 1)
𝑉!"#
(3.7)
As
the
load
current
increase
is
directly
proportional
to
the
voltage
drop,
the
line
current
is
increased
by
the
factor
that
is
Eq.
(3.8).
∆𝐼!! = 1 +
𝑉!"#$(𝑥 − 1)
𝑉!"#
(3.8)
We
now
know
the
factor
that
can
be
squared
to
scale
the
power
losses
in
the
last
section
of
feeder.
As
power
losses
increase
with
the
square
of
the
line
current,
the
𝑛!
series
can
be
used
to
represent
the
increase
in
power
moving
from
the
last
section
of
feeder
to
the
first,
i.e.
𝑃! = 𝑃!(1 + 2 + 4 + 9 + 16 + 25)
for
a
feeder
with
6
premises
per
phase.
The
sum
of
the
𝑛!
series
is
given
by
Eq.
(3.9):
𝑖!
!
!!!
=
𝑛(𝑛 + 1)(2𝑛 + 1)
6
(3.9)
Therefore,
if
the
total
line
losses
of
a
feeder
are
known,
the
line
losses
can
be
given
by
dividing
the
total
power
by
the
sum
of
the
𝑛!
series,
equal
to
91
for
6
premises.
Once
we
know
the
losses
and
voltage
drop
in
the
last
section
of
feeder,
and
the
scaling
factor
𝑥
by
which
the
house
loads
increase
by,
the
increase
in
line
losses
due
to
the
load
scaling
and
increased
voltage
drop
can
be
determined.
The
power
increase
∆ 𝑃!!in
the
last
section
of
feeder
therefore
becomes
∆ 𝑃!!∆𝐼!!
!
,
where
∆ 𝐼!!
!
is
the
increase
in
current
due
to
the
voltage
drop.
The
total
increase
in
power
due
to
voltage
drop
is
shown
in
Eq.
(3.10).
∆𝑃! = ∆𝑃!!∆𝐼!!
!
+ 4∆𝑃!!2∆𝐼!!
!
9∆𝑃!!3∆𝐼!!
!
+ 16∆𝑃!!4∆𝐼!!
!
+ 25∆𝑃!!5∆𝐼!!
!
+ 36∆𝑃!!6∆𝐼!!
!
= ∆𝑃!!∆𝐼!!
!
(1 + 8 + 27 + 64 + 125 + 216)
(3.10)
36. 25
25
This
series
represents
the
sum
of
cubes,
which
can
be
expressed
in
the
following
general
equation
form
of
Eq.
(3.11).
𝑖!
!
!!!
=
𝑛!
(𝑛 + 1)!
4
(3.11)
Combining
Eqs.
(3.8),
(3.9)
and
(3.11),
a
general
solution
of
Eq.
(3.12)
can
be
formed
for
line
losses.
𝑃!.!"# =
6𝑃! 𝑥!
𝑛 𝑛 + 1 2𝑛 + 1
∙ 1 +
𝑉!" 𝑥 − 1
𝑉!"#
!
∙
𝑛!
𝑛 + 1 !
4
=
6𝑃! 𝑥!
𝑛 𝑛 + 1 2𝑛 + 1
∙ 1 +
2𝑉!" 𝑥 − 1
𝑛(𝑛 + 1)𝑉!"#
!
∙
𝑛!
𝑛 + 1 !
4
(3.12)
Replacing
the
𝑃! 𝑥!
term
in
Eq.
(3.4)
with
Eq.
(3.12),
the
complete
transformer
power
formula
Eq.
(3.13)
is
formed.
𝑆!" = 𝑃! 𝑥 +
6𝑃! 𝑥!
𝑛 𝑛 + 1 2𝑛 + 1
∙ 1 +
2𝑉!" 𝑥 − 1
𝑛(𝑛 + 1)𝑉!"#
!
∙
𝑛!
𝑛 + 1 !
4
+ 𝑗𝑄! 𝑥
(3.13)
Equation
(3.13)
allows
the
apparent
transformer
power
to
be
determined
when
constant
power
house
loads
are
scaled
by
a
value 𝑥,
taking
into
account
the
non-‐linear
nature
of
line
losses
caused
by
load
scaling
and
the
subsequent
voltage
drop.
Equation
(3.13)
assumes
that
all
houses
are
loaded
equally,
and
reactive
power
is
constant.
In
reality,
reactive
power
will
increase
slightly
in
response
to
voltage
drop,
depending
on
the
characteristics
of
the
load.
This
equation,
however,
represents
a
relatively
accurate
model
and
provides
an
insight
into
the
complexity
of
load
behaviour,
and
hence
line
losses,
in
response
to
a
change
in
load
magnitude.
Due
to
the
complexity
of
this
4th
degree
polynomial,
solving
for
𝑥
is
difficult,
therefore
loads
will
be
scaled
through
trial
and
error
for
scenarios
where
the
transformer
is
at
a
higher
capacity
than
the
Woodlands
Drive
substation.
