Chapter 8
Electricity Demand Forecast
8.1 Introduction
Money supply, a proxy variable for GDP, electricity prices, temperature, and
the autoregressive terms are exogenous variables that can significantly explain
variations in electricity consumption in the econometric models developed in chapters
5 through 7. These tested models will be used to forecast the electricity demand in this
chapter.
The forecasting sequence starts at the level of electricity customers’ demand,
and ends at the EGAT’s gross generation requirements. The electricity demand which
includes publid lighting will be forecasted by customer groups in the MEA and the
PEA system. The focus of forecasting at the customer level is not the actual electricity
energy consumption. Instead, the models will be used to forecast the annual ratio of
each customer group’s consumption to the total consumption in the MEA and the
PEA system.
Table 8.1
Scenarios of Real GDP Growth
Year
Growth Rate(percent)
Low
Medium
2003
6.1
6.1
2004
3.5
4.0
2005
4.0
4.6
2007
4.0
6.5
2008–2012
4.0
6.5
2013–2017
3.8
6.5
2018–2020
3.8
6.5
Source : TDRI
Table 8.2
Inflation Rate Scenario
Year
2003
2004
2005
2007
2008–2012
2013–2017
2018–2020
Target
6.1
4.5
5.6
7.5
7.5
7.5
7.5
Inflation Rate(percent)
4.1
4.3
3.9
3.5
3.0
3.0
3.0
Electricity energy demand will be forecasted under three scenarios : Low
Economic Growth (LEG), Medium Economic Growth (MEG), and target growth. The
growth rate under the three scenarios are presented in Table 8.1 and the assumed
inflation rate during the same period is presented in Table 8.2.
Initially, the total electricity energy demand at the customer level will be
forecasted. The electricity energy consumption by customer groups in the MEA
109
system and in each region of the PEA system are then forecasted. The MEA and PEA
forecast are then linked to the forecast of gross generation requirements in the EGAT
system. Finally, peak demand will be forecasted by the econometric models and by
the load profile patterns.
8.2 Forecast of Total Electricity Energy Demand
The annual forecast of electricity energy consumption may be computed from
the monthly forecast by
12
r
r
Eijk
=  Eitk
j = 1, 2, 3, ...
(8.1)
m=1
where
r
Eijk
= electricity energy consumption of the i customer group in the r system
in year j under the k growth rate scenario
r
Eitk
= electricity energy demand of the i customer group in the r system in
month t under the k growth rate scenario
t =  j  1 *12 + m
m = 1, 2, ..., 12
j = 1, 2, ...
(8.2)
The i customer group energy share under the k growth rate scenarios is the
ratio of the i customer group’s electricity energy consumption to the total electricity
energy consumption in year j in the r system under the same scenario and is computed
by
r
fijk
=
r
Eijk
(8.3)
r
 Eijk
i
The total electricity energy consumption in the r distribution system in year j
under the k growth rate scenario, E rjk , is obtained from
12
E rjk =  E rtk
j = 1, 2, ...
(8.4)
m=1
where t is defined by (8.2)
Electricity energy consumption of the i customer group in the r distribution
system in year j under the k growth rate scenario is then forecasted by
r
r
Eijk
= fijk
* E rjk
(8.5)
110
Allowing for the energy losses, Lrj , in the distribution system, the required
electricity energy for the r distribution system in year j may be forecasted by
RE rjk =
E rjk
1L
r
j
100
(8.6)

Since energy losses in the distribution system may be controlled to a certain
extent, a 3.64 percent loss is set for the MEA system and a 5.20 percent loss is set for
the PEA system. The loss figures are based on the average losses in the MEA and
PEA systems between 2002 and 2004. The underlying assumption is that the future
losses will not exceed past losses in either system.
The MEA’s electricity energy demands are purchased entirely from EGAT as
indicated by the expression
PE MEA
= RE MEA
jk
jk
(8.7)
In contrast, the PEA generated a small amount of electricity energy to
supplement its electricity energy purchase from EGAT. The amount of PEA’s
electricity energy purchase and its own generation between 2002 and 2004 is
presented below
Year
Units Purchased (GWh)
2002–2004
Proportion
580,492.378
0.999262
Own Generation and Other
Purchase (GWh)
428.640
0.000738
The ratio of electricity energy purchased from EGAT, indicated by a, to the
total electricity energy demand required in the PEA system is set at 0.999262. The
electricity energy purchased from EGAT in year j under the k growth rate scenario is
thus expressed by
PE PEA
= a*RE PEA
jk
jk
(8.8)
The electricity energy forecasting sequences for the MEA and PEA systems
are presented in Figure 8.1 and Figure 8.2, respectively. The total energy requirement
from the EGAT system to satisfy the MEA and PEA demand may be expressed by
E EGAT
= PE MEA
+ PE PEA
+ PE DC
jk
jk
jk
j
(8.9)
where PE DC
is the electricity energy sales to EGAT’s direct customers in year j
j
Allowing for station uses and losses in the EGAT system of LEGAT
percent, the
j
electricity energy requirement from EGAT in year j under the k growth rate scenario
which may be met by EGAT’s own generation, purchases from the IPP and from
foreign sources, may be expressed by
111
RE EGAT
= E EGAT
jk
jk
1L
EGAT
j
100

(8.10)
MEA energy demand in year j
PE MEA
= RE MEA
j
j
MEA energy consumption in year j
E MEA
j
= 
i=1
MEA energy loss in year j
EijMEA
LMEA
j
(Econometric model)
Energy consumption by MEA customer groups
including street lighting
EijMEA
(Energy share from econometric model)
Figure 8.1 Forecasting Sequences in the MEA System
EGAT energy sales to PEA in year j
PE PEA
j
PEA energy generation
PEA energy demand in year j
RE PEA
j
PEA energy consumption in year j
E PEA
j
(Econometric model)
PEA energy loss in year j
LPEA
j
Energy consumption by PEA customer
groups including street lighting
EijPEA
(Energy share from econometric model)
Figure 8.2 Forecasting Sequences in the Total PEA System
112
Losses in the EGAT transmission system is recorded at 5.10 percent in 2003.
This figure will be used in the forecasting model under the assumption that the future
losses will not exceed this level. Figure 8.3 presents the forecasting sequences in the
EGAT system.
EGAT energy demand in year j
RE EGAT
j
EGAT energy sales in year j
EGAT energy loss in year j
E jEGAT
LEGAT
j
EGAT energy sales to MEA
in year j
PE MEA
= RE MEA
j
j
EGAT energy sales to PEA in year j
PEjPEA
EGAT energy sales to direct
customer in year j
PE DC
j
Figure 8.3 Forecasting Sequences in the EGAT System
8.3 Forecast of EGAT’s Electricity Energy Requirements by
Regions
The econometric models developed in chapter 4 to forecast EGAT’s electricity
energy sales by regions are used to estimate the ratio of each region’s electricity
energy sales to the total energy sales by
q
EGAT_f jk
=
EGAT_Eqjk
q
 EGAT_E jk
(8.11)
q
The computed ratio is then used to forecast the electricity energy sales of
region q in year j under the k k growth rate scenario by
q
EGAT_E qjk = EGAT_f jk
* E EGAT
jk
where E EGAT
is obtained from (8.9)
jk
Electricity energy losses in the region q system is estimated from losses in the
total EGAT system which may be expressed by
LEGAT
*RE EGAT
100
j
jk
(8.12)
Electricity energy losses in the region q system is then computed from
EGAT_Lqjk = EGAT_Lf q * LEGAT
*RE EGAT
100
j
jk
(8.13)
113
where EGAT_Lf q is the ratio of electricity energy losses in region q to the total
system losses between 2002 and 2004(see Table 8.3). This ratio is assumed to be
constant throughout the forecasting period.
Allowing for losses in the transmission system, the electricity energy
requirement from the EGAT system for region q in year j under the k growth rate
scenario may be estimated from
EGAT_RE qjk = EGAT_E qjk + EGAT_Lqjk
(8.14)
The forecasting sequence of electricity energy requirement from EGAT by
regions is presented in Figure 8.4
EGAT energy sales in year j
E EGAT
j
EGAT energy sales in central
region in year j
EGAT energy sales in north
region in year j
EGAT energy sales in northeast
region in year j
EGAT_E Cj
EGAT_E Nj
EGAT_E NE
j
EGAT energy loss in central
region in year j
EGAT energy loss in north
region in year j
EGAT_LCj
EGAT_LNj
EGAT energy demand in central
region in year j
EGAT energy demand in north
region in year j
EGAT_RE Nj
EGAT_RE Cj
EGAT energy sales in south
region in year j
EGAT_ESj
EGAT energy loss in northeast
region in year j
EGAT energy loss in south
region in year j
EGAT_LSj
EGAT_LNE
j
EGAT energy demand in
northeast region in year j
EGAT energy demand in south
region in year j
EGAT_RESj
EGAT_RE NE
j
Figure 8.4 Forecasting Sequences by Regions in the EGAT System
Table 8.3
Year
2543
2544
2545
Total
Proportion
Losses in EGAT System by Regions (GWH)
Central
935.9795
952.2188
1040.2572
2928.4554
0.4097
Losses in EGAT’s System
Northeast
North
352.0301
738.9166
304.2259
748.0052
340.2004
750.0269
996.4563
2236.9487
0.1394
0.3130
South
268.4409
341.2693
376.0052
985.7154
0.1379
Total
2295.3671
2345.7192
2506.4896
7147.5759
1.0000
Energy consumption in each region of the PEA system is forecasted within the
same framework by linking the sum of electricity energy consumption of the i
customer group in each regions to the group’s total consumption. The link is
established by computing the ratio of electricity energy consumption of the i customer
group in region q to the group’s total electricity energy consumption from
114
q
PEA_fijk
=
q
PEA_Eijk
q
 PEA_Eijk
(8.15)
q
q
where PEA_Eijk
is the forecast of electricity energy consumption of the i customer
group in region q in year j under the k growth rate scenario from the models
developed in chapter 5.
Electricity energy consumption of the i customer group in region q under the k
growth rate scenario is then forecasted by
q
q
PEA
PEA_Eijk
= PEA_fijk
* E ijk
(8.16)
PEA
where Eijk
is computed from (8.5)
The electricity energy consumption in region q is simply the sum of each
customer group’ consumption, i.e.,
q
PEA_Eqjk =  PEA_Eijk
(8.17)
i
Losses in region q in the PEA system in year j under the k growth rate
scenario, PEA_Lqjk , may be computed from
PEA
PEA_Lqjk = PEA_Lf q *LPEA
100
j *RE jk
(8.18)
where PEA_Lf q is the losses in region q estimated from the losses data between 2001
and 2003 in Table 8.4. Allowing for losses in its distribution system, the amount of
electricity energy that PEA must purchase and generate in order to satisfy its
customers in region q in year j under the k growth rate scenario is then
PEA_RE qjk = PEA_Eqjk + PEA_Lqjk
(8.19)
The forecasting sequence of electricity energy consumption in the PEA system
by regions is presented in Figure 8.5
Table 8.4
Year
Losses in the PEA System by Regions (GWh)
Energy Losses in Distributed System
Central
North
Northeast
2544
1,694
950
995
2545
1,824
990
1,043
2546
1,999
1,174
1,236
Total
5,516
3,115
3,274
Proportion
0.3671
0.2073
0.2179
South
932
1,000
1,188
3,120
0.2077
115
PEA energy consumption by customer groups in year j
including street lighting
EijPEA ; i = 1, 2,
PEA energy consumption by region q are by customer
group i in year j
PEA_Eijq
(Energy share from econometric model)
PEA energy consumption in region q
in year j
PEA_E qj =  PEA_Eijq
i
PEA energy demand in region q in year j
PEA energy loss in region q in year j
PEA_RE qj
PEA_Lqj
Figure 8.5 Forecasting Sequences by Regions in the PEA System
8.4 Forecast of Total Electricity Energy Consumption
The total electricity energy requirement in the kingdom is the sum of EGAT’s
gross generation, purchases from the IPPs and SPPs, from foreign sources, and from
 PE PEA
PEA’s own generation or RE PEA
. The flow of electricity energy requirements
j
j
is presented in Figure 8.6
Total electricity energy demand
EGAT’s gross generation and
purchase from SPP, IPP in year j
RE EGAT
j
PEA’s own generation
Energy purchased from sources
outside EGAT system
Table 8.6 Forecasting Sequences of Total Electricity energy Requirement
116
8.5 Forecast of Peak Demand
Peak demand will be forecasted from the load factor computed from
econometric models. For comparison purpose, the peak demand will also be
forecasted from the customers’ load profile.
8.5.1
Forecast from Econometric Model
EGAT
Forecast of the electricity energy consumption and peak demand from the
econometric models are used to compute the system load factor from the relationship
EGAT
EGAT
LFjk
=
RE jk
*100
(8.20)
EGAT
D jk *8760
where
EGAT
= EGAT’s gross energy generation and purchase from SPP and IPP in
year j under the k growth rate scenario forecasted by models developed in chapter 4
RE jk
EGAT
= Forecast of EGAT system peak demand in year j under the k growth
rate scenario from econometric models developed in chapter 6
D jk
The system peak demand is forecasted from the system load factor in (8.20),
EGAT energy demand, RE EGAT
as forecasted by (8.10) in year j under the k
jk
growth rate scenario.
EGAT
LFjk
,
DEGAT
jk
LEGAT
j
=
RE EGAT
jk
The power losses in EGAT’s system may be computed from the energy losses
and the load factor LFjkEGAT by the relationship
PDLEGAT
=
jk
where
(8.21)
EGAT
LFjk
*8760 100
EGAT EGAT
LFjk
*L j
(8.22)
LSEGAT
jk

EGAT
EGAT
LSEGAT
= 0.3LFjk
+ 0.7 LFjk
jk

2
(8.23)
The maximum power that EGAT sells to the MEA, the PEA, and direct
customers in year j under the k growth rate scenario may be expressed by

PDEGAT
= DEGAT
* 1  PDLEGAT
100
jk
jk
j

(8.24)
117
The peak demand forecasting sequence in the EGAT system is presented in
Figure 8.7
EGAT peak demand in EGAT’s system
in year j
D EGAT
j
(Econometric model)
EGAT electricity energy demand
in year j
RE EGAT
j
(Econometric model)
EGAT load factor in year j
LFjEGAT
Peak at purchase point
IPP’s energy sales to customers
outside the EGAT system in
year j
EGAT peak demand in EGAT’s
system in year j
EGAT electricity energy
demand in year
RE EGAT
j
D EGAT
j
Peak at load point
Peak demand of customers
outside EGAT’s system in year j
Dj
Thailand peak demand in year j
EGAT peak demand power
consumption in year j
PD EGAT
j
DTHAI
j
+
PEA large general service
customers’ load factor
EGAT peak power loss in year j
Figure 8.7 Forecasting Sequences of Peak Demand in EGAT’s System by
Econometric Models
The peak power demand by regions in the EGAT system is forecasted in
similar manners to the kingdom forecast. The forecast of electricity energy and peak
demand by region in the EGAT system are used to compute the regional load factor.
The load factor and electricity energy sales in region q are then used to forecast the
region’s peak demand from
q
q
EGAT_LFjk
=
EGAT_Dqjk
=
EGAT_RE jk *100
q
EGAT_D jk *8760
EGAT_REqjk *100
q
EGAT_LFjk
*8760
(8.25)
(8.26)
where
q
EGAT_RE jk is the electricity energy consumption in region q in year j under
the k growth rate scenario forecasted by econometric models
118
q
EGAT_D jk is the peak demand in region q in year j under the k growth rate
scenario forecasted by econometric models
q
EGAT_LFjk
is the load factor at EGAT’s input point in region q in year j under
the k growth rate scenario
EGAT_RE qjk is the electricity energy demand in region q in year j under the k
growth rate scenario computed from (8.14)
EGAT_Dqjk is the forecast of peak demand at the input point in region q in year
j under the k growth rate scenario
MEA
Peak demand at the MEA purchase point is forecasted both from the
econometric models and from the customers’ load profiles. As in the case of EGAT,
the system load factor at the purchase point in year j under the k growth rate scenario
is estimated from
MEA
MEA
LFjk
RE jk
=
*100
MEA
D jk *8760
(8.27)
where
MEA
is the electricity energy required by the MEA system in year j under
the k growth rate scenario forecasted from the econometric models in chapter 4
RE jk
MEA
is the peak demand in the MEA system in year j under the k growth rate
scenario forecasted from the econometric models in chapter 6
D jk
Peak demand in the MEA system in year j under the k growth rate scenario is
then forecasted by
PDMEA
jk
=
RE MEA
jk
MEA
LFjk
*8760 100
(8.28)
where RE MEA
is the MEA peak demand at the input point computed from (8.6)
jk
Power losse in the MEA distribution system, PDLMEA
jk , may be computed by
PDLMEA
jk
where
=
MEA
LFjk
*LMEA
j
LSMEA
jk
(8.29)
119

MEA
MEA
LSMEA
= 0.3LFjk
+ 0.7 LFjk
jk

2
(8.30)
Allowing for power losse in the distribution system, the MEA peak demand at
the customer level is computed by

MEA
DMEA
= PDMEA
100
jk
jk * 1  PDL jk

(8.31)
PEA
Peak demand in the PEA’s distribution system is forecasted in similar manners
to the EGAT peak demand forecast. The system load factor at the purchase point in
year j under the k growth rate scenario is estimated from
PEA
PEA
LFjk
RE jk *100
=
(8.32)
PEA
D jk *8760
and the peak demand is estimated from
PDPEA
jk
=
RE PEA
jk
(8.33)
PEA
LFjk
*8760 100
where
PEA
is the electricity energy at the purchase point in year j under the k
growth rate scenario forecasted from the econometric models in chapter 4
RE jk
PEA
is the peak demand at the input point in year j under the k growth rate
scenario forecasted from the econometric models in chapter 6
RE PEA
jk is the electricity energy at the input point in year j under the k growth
rate scenario estimated from (8.6)
D jk
Peak demand at the customer level in year j under the k growth rate scenario is
then forecasted by

PEA
DPEA
= PDPEA
100
jk
jk * 1  PDL jk
PEA PEA
LFjk
*L j
where
PDLPEA
jk
and
PEA
PEA
LSPEA
= 0.3LFjk
+ 0.7 LFjk
jk
=

(8.34)
(8.35)
LSPEA
jk


2
(8.36)
The load factor and peak demand at the purchase point in region q in year j
under the k growth rate scenario may be estimated in similar manners by
120
q
q
PEA_LFjk
=
PEA_RE jk *100
q
(8.37)
PEA_D jk *8760
PEA_PDqjk
and
=
PEA_REqjk *100
q
PEA_LFjk
*8760
(8.38)
where
q
PEA_RE jk is the electricity energy consumption at the input point for region q
in year j under the k growth rate scenario as forecasted from the econometric models
in chapter 4
q
PEA_D jk is the peak demand at the customer level in region q in year j under
the k growth rate scenario as forecasted by the econometric models in chapter 6
PEA_RE qjk is the electricity energy at the input point in region q in year j under
the k growth rate scenario as estimated from (8.19)
8.5.2
Peak Demand Forecast from Customers’ Load Profile
An accurate picture of the customers’ load profile is dependent on the
sampling method. A power consumption recorder must be installed at selected
customers’ purchase point to record his or her consumption pattern for a minimum
period of 1 week. The recorder output will yield information on variations of
consumption during a given day and also variations between days.
However, the sampling of customers for recorder installation is not in the
framework of this study. The load patterns used to forecast the peak demand are
based on the available data collected by the two distribution utilities. The forecast of
peak demand from the load profile may thus be considered as a demonstration of the
forecasting technique.
The MEA customers’ electricity energy consumption patterns are recorded at a
15 minute interval for 24 hours on a given day. In order to have a complete coverage
of the electricity energy consumption patterns, the selected customers’ consumption
are recorded on week days, Saturday, Sunday, and on national holidays. The average
electricity energy consumption per customer during the 15 minute interval on a given
day is then computed for all customer groups from the recorded consumption.
m
Let e itd
= daily average of electricity energy of i th customer group recorded
during the 15 minute duration at time t on d day in quarter m.
N dm = number d days in quarter m
The average electricity energy consumption of i th customer group in quarter
m may be computed from
m
eim =  Ndm  eitd
d
t
(8.39)
121
The estimated eim is then used as a normalization factor so that i th customer group
daily average electricity energy consumption in quarter m is equal to 1. The
normalized daily average electrical consumption of customer i at time t on d day in
quarter m is equal to
m
m
neitd
= eitd
eim
(8.40)
The load factor of i th customer group in quarter m is computed by
LFim =
100
 24 * 60  730
m
neimax
*
*3
*
 t  24
(8.41)
where
m
m
neimax
= max neitd
(8.42)
t,d
Since  t equals 15 minutes, the load factor in (8.41) becomes
LFim =
100
m
neimax
730
* 96 *
*3
24
(8.43)
Peak demand of i th customer group in quarter m is then computed by
Dim =
E im
LFiq *730*3
(8.44)
where E im is the electricity energy consumption of i th customer group in quarter m.
The annual load factor of i th customer group is computed from
m
 Ei *100
LFi =
m
Dimax *8760
(8.45)
where
Dimax = max Dim
m
(8.46)
In this study, E im used in the load factor estimation is the electricity energy
consumption of i th customer group in quarter m of 2003, the year that MEA installed
the recorders. Peak demand of i th customer group in year j under the k growth rate
scenario is thus
122
MEA
Dijk
=
MEA
Eijk
(8.47)
LFi *8760 100
The MEA load profile may be constructed from the normalized electricity
energy consumption of each customer group weighted by the customer’s ratio of
electricity energy consumption in the total consumption, i.e.
m
MEA
m
MEA_neidjk
=  Efijk
*neitd
(8.48)
i
MEA
where Efijk
is the energy share of MEA i th customer group in year j under
the k growth rate scenario.
The MEA load factor at the customer purchase point in year j under the k
growth rate scenario is computed by
MEA_LFjk =
where MEA_ne jmax
100
8760
MEA_ne jmax *
*96
24
= max MEA_neidjk
(8.49)
(8.50)
q,t,d,
Peak demand in the MEA system at the customer purchase point in year j
under the k growth rate scenario is then computed from the customers’ load profile by
LP_DMEA
jk
=
EMEA
jk
MEA_LFij *8760 100
(8.51)
The PEA customers’ load profile were recorded within a different framework.
The recorded electricity energy consumption is the daily average consumption of a
customer in each consumer group in a year, whereas the recorded consumption of the
MEA is the daily average of a customer in each consumer group in a quarter.
Moreover, the days in the PEA framework are classified into weekdays, Saturday, and
Sunday. Public holidays are grouped in the “Sunday” classification.
Let N d equals the number of days in the d classification of day in a year. The
average daily electricity energy consumption of consumer in group i in region q is
computed from
q
eiq =  Nd  eitd
d
(8.52)
t
The average normalized electricity energy consumption during the 15 minute
interval on d day of the i customer group in region q is computed from
q
q
neitd
= eitd
eiq
(8.53)
The load factor of the i customer group in region q is thus
LFiq =
100
8760
q
neimax
*
*96
24
(8.54)
123
q
q
where neimax
= max neitd
(8.55)
t,d
Peak demand of the i th customer group in region q under the k growth rate
scenario is then computed from
q
E ijk
q
Dijk
=
(8.56)
LFi *8760 100
Load pattern of the i th customer group in year j under the k growth rate
scenario may be constructed from the load pattern of the i th customer group in region
q
q, eitd
, weighted by the energy shares in region q in year k under the k growth rate
scenario. From the relationship
q
q
(8.57)
neitdjk =  Efijk
*neitd
i
The load factor of customer group i in year j under the k growth rate scenario
is estimated by
100
8760
neijkmax *
*96
24
where neijkmax = max neitdjk
LFijk =
(8.58)
(8.59)
t,d
Peak demand of the i th customer group in year j under the k growth rate
scenario is then computed by
PEA
Dijk
=
PEA
Eijk
LFijk *8760 100
(8.60)
The load pattern of all PEA customers in year j under the k growth rate
scenario may be constructed from the load patterns of each customer group, neitdjk ,
PEA
weighted by the PEA customer energy shares, Efijk
, i.e.
PEA
PEA_netdjk =  Efijk
*neitdjk
(8.61)
i
Load factor of PEA at the customer purchase point in year j under the k
growth rate scenario is computed by
100
PEA_LFjk =
8760
*96
24
= max PEA_netdjk
(8.62)
PEA_ne jkmax *
where PEA_ne jkmax
(8.63)
t,d
Peak demand of the PEA system at the customer purchase point in year j under
the k growth rate scenario is then computed by
LP_DPEA
=
jk
E PEA
jk
PEA_LFjk *8760 100
(8.64)
The EGAT load profile i year j under the k growth rate scenario may be
constructed from the MEA, PEA and EGAT direct customer load profile weighted by
the corresponding energy demand share.
124
MEA
PEA
DC
EGAT_ne tdjk = EFjk
*MEA_ne tdjk + EFjk
*PEA_ne tdjk + EFjk
*DC_ne tdjk
(8.65)
The load profile on public holidays in MEA case is to be included with the
profile on Sunday before combining with PEA load profile to fit the classification of
days in PEA load survey.
The EGAT load factor at the consumer purchase point in year j under the k
growth rate scenario is given by
EGAT_LFjk =
100
8760
EGAT_ne jkmax *
*96
24
(8.66)
where
EGAT_ne jkmax = max EGAT_netdjk
(8.67)
t, d
The EGAT peak power consumption at the consumer purchase point in year j
under the k growth rate scenario can be written as
E EGAT
jk
LP_DEGAT
=
jk
EGAT_LFjk *8760 100
(8.68)
The load factor estimated from the load profile is the load factor at the
customer purchase point. The peak power load cannot be calculated by using (8.29)
and (8.30) directly since the load factor in (8.29) and (8.30) is the load factor at the
input point. The relationship between the peak power demand and the peak power
consumption may be written as

 0.7RE EGAT

jk
EGAT 2

 D EGAT
0.3  LEGAT
100
D
+
 0.3LP_D EGAT
j
jk
jk

 jk
8760





8760

MEA
0.7RE MEA
jk LP_D jk
8760
0.3  LPEA
j

100

2
D PEA
jk
8760
(8.70)
=0
 0.7RE PEA

jk
PEA
+
 0.3LP_D PEA
jk  D jk
 8760



PEA
0.7RE PEA
jk LP_D jk
(8.69)
=0
 0.7RE MEA

jk
MEA 2
MEA

 D MEA
0.3  LMEA
100
D
+

0.3LP_D
j
jk
jk
 8760
 jk




0.7RE EGAT
LP_D EGAT
jk
jk
(8.71)
=0
LMEA
where LEGAT
and LPEA
are the percentage energy losse in year j in EGAT,
j
j
j
MEA and PEA inputs respectively
DEGAT
DMEA
and DPEA
are the peak power demand in year j under the k
jk
jk
jk
growth rate scenario in EGAT, MEA and PEA system respectively.
Details of forecasts are shares in Appendix I.