Electrical load characteristics of transmission line

1. Module-2
Load Characteristics
A.K. Mishra
4/21/2024 AKM/distplang/Load Charachterestics 1

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Charachterestics 2
PREDICTION OF CUSTOMERS DEMAND
CHARACTERISTICS
 Distribution system design/planning from
consumers
 Necessary the electricity use pattern for each
individual consumers
 An electric utility’s customers purchase electricity
as a means to some end-uses for which electricity
is only an intermediate means.
 These end-uses span a wide range of applications
with its unique behavior.

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Charachterestics 3
 The term load in general refers to:
The demand of a device connected to and
draws power from the system for the purpose
of accomplishing some task or converting that
power to some other form of energy.
So for analysis of load characteristics:
It is very important for all planers that they
must have knowledge (through understanding)
of Who is buying their electricity and of
course for what purpose?.

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Charachterestics 4
The load almost for any consumer vary from hour to hour, from
day to day and from season to season.
The load as a function of time are called load curve.

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Charachterestics 5
 The planner is most interested in Annual peak
load & energy Sell.
– Annual peak --- the equipment capacity
requirements.
– Energy Sell --- benefit to the utility.
 The load factor gives the relation between
energy and peak demand
Kw
Peak
hrs
/
KWh
LF 
hrs
Kw
Peak
KWh
LF



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Charachterestics 6
 It depends on
– Customer end-uses
– Daily, monthly or yearly basis.
– level of power delivery system.
(usually higher at higher level)
 It gives the extent to which the
peak load is maintained during
the period under study.
– Great economic activity High
LF
– low economic conditions Low
LF.
 Lower load factor not only
requires high capital investments
but also higher system losses and
voltage drops.

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Charachterestics 7
Prediction of load curve at any unit of
equipment
 Every customer is somewhat different in his/her
electrical usage.
 There is no even two electrical customers who uses
electrical energy of identical characteristics even a very
small fraction of time.
 For simplicity often electric utilities group their
customers based on similar end-use appliances e.g.
residential, commercial, industrial, irrigation etc.
 These classes are further subdivided into sub-classes
based on their per capita income, spatial locations etc.

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Charachterestics 8
Typical load variation

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 But truly speaking:
 There is no consumer in any utility whose load curve
is such a smooth curve similar to that.
 The natural question is the smooth representation is
correct?
 If not why people use it?
 To get the answer

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Charachterestics 10
 These peaks are observable even all the equipments are
connected continuously to the supply because many
appliances are of its on ON/OFF cycles.
 What would be the case if many such devices connected
in group?

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Suppose
we were to consider one hundred homes served by
the same segment of a distribution feeder.
In this case,
 Though each household will have an individual
daily load curve similar to the erratic and choppy,
Each will be slightly different appliances, is
occupied by people with slightly different
schedules and usage preference,
 The individual peaks are not additives because
they occur at different times. Do not occur
simultaneously.
 They are non-coincident.

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Charachterestics 12

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Coincidence Factor
 The tendency of observed peak load per consumer to
drop as the size of the customer group being observed
increases is termed coincidence
 And is measured by the coincidence factor, the
fraction of its individual peak that each customer
contributes to the group’s peak.
C = Coincidence factor =
 peaks
individual
group
for the
peak
Observed

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Charachterestics 14
 Since the load curves vary depending on number of
sample consumers taken.
 Thus coincidence factor, C, can be thought of as a
function of the number of customers in a group.
 C(n) has the value between 0 and 1 and usually
decreases with increased number of customers
C(n) = Coincidence factor =
 peaks
individual
customers
n
of
group
for the
peak
Observed
Group peak load for n consumer = C(n) x n x average individual peak load

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Example
Peak load(kw)
Consumer
class
Monthly
energy
consumption
Kwh/consumer
1
Consumer
10
Cons.
50
Cons.
100
Cons.
200
Cons.
Class A 30 0.6 4 15 25 50
Class B 20 0.5 3 10 15 30
Class C 30 0.7 5 20 35 70
Determine the load factor and coincidence factor for each
consumer class as a function of number of consumer.

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Solution For Class-B consumer
Load factor for classB Lf(n)
Lf(1)=20/(.5*30*24) 0.055
Lf(10)=(20*10)/(3*30*24) 0.092
Lf(50)=(20*50)/(10*30*24) 0.139
Lf(100)=(20*100)/(15*30*24) 0.185
Lf(200)=(20*200)/(30*30*24) 0.185
Coincidence
factor for class B Cf(n)
Cf(1)=.5/.5 1
Cf(10)=3/(10*.5) 0.6
Cf(50)=10/(50*.5) 0.4
Cf(100)=15/(100*.5) 0.3
Cf(200)=30/(200*.5) 0.3

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Observation
 For the consumers average energy
consumptions is known the load
factor and coincidence factor gives
the same information's
 Hence sometimes for such consumer
class coincidence factor specified is 1
or around 1
 But this does not mean it has 1
coincidence factor in practice

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1 10 100 1000 10000
Number of Customer in Group
Peak
Load
/
C
us
tom
er
C
oinc
edanc
e
fac
tor
1
0
0.4
0.8

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Charachterestics 19
0
10
20
30
40
50
60
70
1 10 100 1000 10000
Number of Customer in Group
Peak
Load
duration
(minutes)
 The duration of the peak increases as the
number of customer in group increases.
 i.e. Low coincidence factor results longer
peak periods.

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Charachterestics 20
 Sometimes engineers use diversity factor instead
of coincidence factor.
 Diversity is a term used to cover the fact that
individual loads occur at different times.
 This means that if the maximum load of two
or more loads are added, their sum will
generally be greater than the true sum
because these peaks occur at different times.
D = Diversity factor=1/Coincidence factor

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Charachterestics 21
Contribution Factor
 Contribution factor at a instant of time of a
particular class Consumer is the ratio of
class demand at the instant of time to class
peak demand
 That is contribution factor at instant of class
peak is ………
 The time variation of contribution factor is
known as load pattern of that consumer
class
 Peak demand together with load pattern
gives load curve of the consumer class
 Numerical Example:

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Charachterestics 22
 To determine the actual load at any equipment level we
need to consider the coincidence among the classes.
 This is because the load pattern for each class is
different and need not necessarily coincide the peak
value.
Equipment peak load = Rr(n) x group peak load for residential +
Rc(n) x group peak load for commercial +……+
Rnc(n) x group peak load for non commercial

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Charachterestics 23
Responsibility Factor
 Responsibility Factor of perticular
class consumer is the contribution
factor of that class consumer at the
time of system Peak
 It is thus a measure of how much
that consumer class contributes to
the system peak load.
 From utility point’s of view consumer
class with……………Responsibilty factor
is highly advantageous.

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Charachterestics 24
0.00
5.00
10.00
15.00
20.00
25.00
30.00
1 3 5 7 9 11 13 15 17 19 21 23
Time (hr)
Load
(kW) Residential
comercial
Non comercial
commulative

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Charachterestics 25
Load pattern
 For the determination of Class wise load
pattern following method may be
applicable
1. By measurement
 Use the TOD meter/ energy meter for
varying the consumer numbers
simultaneously to get the load pattern
– E.g. TOD meter may be connect to record the
load curve for 1 residential consumer,10
residential consumer ( one separate lateral of
a LT, 50 consumers connected in a LT feeder
or 200 consumers connected in a load center
– While doing so selection must consider that
almost similar types of consumers connected
in a selected group

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Charachterestics 26
 If one or a few other types of consumer
are connected in the group which can not
be separated,
– The measurement may be carried out including
those and separate observations may be done
for that
– Later subtracting from the group we can get
the load curve for the desired class.
 The observations may be repeated for;
– Different consumer class
– Different geographical location Hill, Tarai
– Urban, semi urban, rural
– Other possible variation

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Charachterestics 27
Sampling Time Selection

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Charachterestics 28
What would be the sampling time for
accurate representation of load curve
coincidence?
 Fig. in last page shows load curve for 5
residential consumers group on a 15, 30,
60 and 120 minute basis.
 It is evident from the figure that high
sampling rate is needed when studying
the non-coincident load behavior of small
groups of customers.
 But this may not be needed for large
consumers group.

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Charachterestics 29
Simulation method
 The load on a perticular distribution
transformer(Load center) at a particular
instant could be expressed as;
P(t)= D1N1+D2N2+D3N4+……+DkNk
 Where
– Dk average demand per consumer for kth
Class
– Nk Number of consumer for kth Class
 Supposes we have the load curve of many
such distribution transformers

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Charachterestics 30
1
1
31
3
21
2
11
1 .....
.......... TD
N
D
N
D
N
D
N
D k
k 




2
2
32
3
22
2
12
1 .....
.......... TD
N
D
N
D
N
D
N
D k
k 




3
3
33
3
23
2
13
1 .....
.......... TD
N
D
N
D
N
D
N
D k
k 




………………………………………………..
n
kn
k
n
n
n TD
N
D
N
D
N
D
N
D 



 .....
..........
3
3
2
2
1
1
In matrix Form





































n
k
kn
n
n
k
k
TD
TD
TD
D
D
D
N
N
N
N
N
N
N
N
N
2
1
2
1
2
1
2
22
12
1
21
11
....
...
....
...

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In Simple form
N D = TD
N' N D= N' TD
D = [N'N]-1
N' TD
 Hence vector D can be obtained for a
particular instant
 Repeating the same for all samples we can
get the average load curve per consumer
for all the classes
 Still the problem of consumer number
effect consideration etc remains which can
be tackle with appropriate logic.

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Charachterestics 32
 Dividing the load curve by their peak
results the load pattern
 Load pattern itself carry the
information regarding Load Factor
 Hence for consumers energy data is
available this method may be more
suitable
 The measurement method gives clear
idea about coincidence

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Charachterestics 33
Load Growth Factor
 Aalready described: in a power delivery
system there is a continuous load growth at
every level.
 But this growth pattaren is not same in
differnt levels
 E.g. consider the load growth for
– the Kathmandu valley for last few years
– Then same for the Kathmandu district
– then at a substation
– and fi ally at the distribution transformer
 The growth curve becomes more and more
non-linear from large area to smaller areas.
 The S shape load growth pattern is a typical
load growth at the distribution transformer
(service area) level.

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Charachterestics 34
Fig. 9.a
Fig. 9.b
Fig. 9.c
Fig. 9.d
Years
Load
Years
Load
Years
Load
Years
Load