load forecast ntdc

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NATIONAL TRANSMISSION AND DESPATCH

COMPANY LTD. PAKISTAN

OFFICE OF G.M. PLANNING POWERNTDC / PEPCO WAPDA HOUSE LAHORE

February 2008

ELECTRICITY DEMAND FORECASTBased On Regression Analysis

(Period 2008 to 2030)


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Table of Contents

Number Title Page No.

Glossary of Abbreviations 2

List of Tables 3

List of Figures 4

Executive Summary 5-6

1. Introduction 7

1.1 Background 7

1.2 Strategy 7-8

2. Historical Power System Supply And Demand Analysis 9

2.1 Supply Pattern 9

2.1.1 Load Shedding 9

2.1.2 Transmission and Distribution Losses 9

2.1.3 System Load Factor 11

2.1.4 Demand Side Management 12

2.2 Energy Share (Province Wise & Category Wise) 12

3. Long-Term (Regression Based) Load Forecast 14

3.1 Regression Methodology 14

3.1.1 Parameters of Regression 14

3.2 Regression Relationships 15

3.3 Elasticity Coefficients by Customer Class 15

3.3.1 Low, Normal and High Scenarios 15

3.4 Projections of Independent Variables 15

3.5 Regression Results 16

3.6 Generation Forecast 17

3.6.1 Projection of Losses 17

3.6.2 Projection of Load Factor 17

3.7 Forecast Results 18

3.8 Forecast with DSM 18

3.9 Category Wise Composition of Loads (GWh Sales) 18


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Glossary of Abbreviations

Sr. No. Abbreviations Full Word

1 AGR Agriculture

2 COM Commercial

3 DSM Demand Side Management

4 Exp. to KESC Export to Karachi Electric Supply Company

5 GDP Gross Domestic Product

6 GoP Government of Pakistan

7 GR Growth Rate

8 GWh Gega Watt Hour

9 IND Industrial

10 IPPs Independent Power Producer

11 KESC Karachi Electric Supply Company

12 MkWh Million Kilo Watt Hour13 NPP National Power Plan

14 PEPCO Pakistan Electric Power Company

15 PIDE Pakistan Institute Development Economic

16 PMS Power Market Survey

17 POP Population

18 Ps/KWh Paisa Per Kilo Watt Hour


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List of Tables

Number Title

1 Summary of Forecast Results (Country)

2-1 Historical Energy Generation, Sale and Losses

2-2 PEPCO Load Factor (Historical)

2-3 KESC Load Factor (Historical)

2-4 Historical Province-Wise Energy Sale (GWh)

3-1 Elasticity Coefficients by Customer Class

3-2 Modified Elasticity Coefficients by Customer Class-Low

3-3 Modified Elasticity Coefficients by Customer Class-Normal

3-4 Modified Elasticity Coefficients by Customer Class-High

3-5 Projected GDP Growth Rates3-6(a) Actual Population and Customers Country (1970-2007)

3-6(b) Projected Population and Customers Country (2008-2030)

3-7 Category-wise Sales (GWh) Forecast (PEPCO and KESC) Low

3-8 Category-wise Sales (GWh) Forecast (PEPCO and KESC) Normal

3-9 Category-wise Sales (GWh) Forecast (PEPCO and KESC) High

3-10 Load Forecast (PEPCO) Low

3-11 Load Forecast (PEPCO) Normal

3-12 Load Forecast (PEPCO) High

3-13 Load Forecast (KESC) Low

3-14 Load Forecast (KESC) Normal

3-15 Load Forecast (KESC) High

3-16 Load Forecast PEPCO, KESC, Self Generation and Country Low

3-17 Load Forecast PEPCO, KESC, Self Generation and Country Normal

3-18 Load Forecast PEPCO, KESC, Self Generation and Country High

3-19 Load Forecast PEPCO, KESC, Self Generation and Country including DSM


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List of Figures

Number Title

1 Summary of Forecast Results Country

2-1 Province wise share in Energy Sale

2-2 Energy Share in Sales (Category wise)

3-1 Category Wise Composition of Loads (GWh Sales)


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Executive Summary

The growth in electric energy use in Pakistan is expected to remain high for many years to come,

and, by 2030, total demand is expected to increase by factors of between 5.6 to 8.2 times over the

present year 2007 level i.e. 18,883 MW. This implies average annual growth rates of between7.8% and 9.6% of the low and high scenarios of demand respectively. Demand for electric

energy grew at an annual rate of 8.8% for the normal scenario during the period from 2007 to2030. Many factors influence the load growth, and as these factors are difficult to predict, futureload growth is potentially very variable, hence, needs review periodically. The longer-term

forecast was based on historic relationships between past consumption, economic growth and

number of customers.

The study determined that there was a very significant potential to improve the efficiency ofenergy use through programs of DSM. Experience in other countries shows that important capital

and operating cost savings for the electric sector can be obtained through DSM. This study

recommends that Pakistan commit to an aggressive DSM program immediately. A forecast,including the potential impact of DSM, was prepared and is given in Table 3-19.

The methodology employed in this report is multiple regression technique. The overall energy

demand projections are achieved by summing the forecasted individual category-wise energy

demands (i.e. Domestic, Commercial, Industrial, Agricultural, Traction, Street light and Bulk).The power demand is thereafter evaluated by adding appropriate losses and employing a suitable

load factor on yearly basis. Three scenarios of the load forecast are illustrated graphically in the

Figure below.

Figure 1

Load Forecast

FIG 1

Summary Of Forecast Results (MW) - Country

0

20000

40000

60000

80000

100000

120000

140000

160000

2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029

Years

Megawatts(MW)

Low Normal High


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In the year 2030, under Normal scenario the forecast for Peak Demand is 1,13,695 MW, nearly15.2 % higher than Low scenario (98,557 MW) and nearly 21.8 % lower than High scenario

(1,45,304 MW). The three principal energy and demand forecasts for the country including

KESC are summarized in the Table below.

Table - 1

Summary of Forecast Results

Description 2007 2010 2015 2020 2025 2030G.R.

(2007-30)

Sale (GWh)

Low Scenario 83463 112311 176178 261042 370882 500117 8.1%

Normal Scenario 83463 112955 181018 276937 409874 578560 8.8%

High Scenario 92647 113355 185239 295706 470527 735592 9.9%

Generation (GWh)

Low Scenario 111078 143910 212724 307328 436911 589460 7.5%

Normal Scenario 111078 144711 218448 325740 482080 680330 8.2%

High Scenario 111078 145233 223618 348182 554680 868434 9.4%

Peak Demand (MW)

Low Scenario 18883 24339 35271 51296 73041 98557 7.4%

Normal Scenario 18883 24474 36217 54359 80566 113695 8.1%

High Scenario 18883 24562 37075 58120 92762 145304 9.3%

In addition to the above, an overview of load, losses and system demand for the three scenarios

(Low, Normal, High) in the form of MW is depicted in tables Table-1(A), Table-1(B), Table-1(C) receptively.

The forecast can be used for generation planning, however, for transmission planning during the

medium term period (2008-2014) PMS (Power Market Survey) forecast (Grid wise demand)

would be advisable and appropriate to be referred, which is in process of development.

It is important to note that in long term planning, a range of forecast is typically developed tobracket the expectations about higher and lower scenarios for load growth. The longer the

forecast period, the greater is the uncertainty. However to reduce the uncertainty, it is

recommended that Normal scenario should be considered as base case.


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2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 2023

11365 12519 13719 15011 16346 17746 19225 20817 22678 24618 26652 28808 31108 33561 36133 38837 41700 446

Transmission 1000 1034 1061 1084 1098 1105 1105 1099 1093 1077 1049 1011 1092 1178 1268 1363 1463 156

Distribution 2242 2337 2420 2496 2557 2605 2642 2670 2705 2721 2717 2696 2911 3140 3381 3634 3902 417

Auxiliary 531 577 625 675 727 779 834 893 962 1032 1105 1181 1275 1376 1481 1592 1710 183

Total Losses 3773 3949 4105 4255 4381 4489 4581 4661 4760 4829 4871 4888 5278 5694 6130 6589 7075 757

System Demand 15138 16468 17824 19266 20728 22235 23805 25479 27437 29447 31523 33696 36386 39255 42264 45426 48774 522

Table-1 (A)

Losses

Load

YearDescription

Summary of Load Forecast PEPCO - Low

Year

Description


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2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 202

11365 12531 13752 15078 16462 17926 19483 21177 23167 25266 27481 29853 32408 35166 38094 41196 44523 4

Transmission 1000 1035 1064 1088 1106 1116 1119 1118 1117 1105 1082 1048 1137 1234 1337 1446 1562 1

Distribution 2242 2340 2425 2507 2575 2632 2677 2716 2763 2792 2802 2793 3033 3291 3565 3855 4166 4

Auxiliary 531 578 626 678 732 787 846 908 982 1059 1139 1224 1329 1442 1562 1689 1825 1

Total Losses 3773 3953 4115 4274 4412 4535 4642 4742 4862 4957 5023 5065 5498 5966 6463 6989 7554 8

System Demand 15138 16484 17868 19352 20874 22460 24126 25919 28029 30223 32504 34918 37907 41132 44557 48185 52077 5

Table-1 (B)

Losses

Load

YearDescription

Summary of Load Forecast PEPCO - Normal

Year

Description


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2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 2023-24 2

11365 12540 13777 15132 16561 18090 19738 21557 23718 26036 28535 31267 34281 37616 41252 45229 49628 54393

Transmission 1000 1036 1 065 1092 1112 1126 1134 1138 1143 1139 1123 1097 1203 1320 1448 1587 1741 1909

Distribution 2242 2341 2430 2516 2590 2656 2712 2764 2829 2877 2909 2926 3208 3520 3860 4232 4644 5090

Auxiliary 531 578 627 681 736 794 857 925 1006 1092 1183 1282 1405 1542 1691 1854 2035 2230

Total Losses 3 773 3955 4123 4289 4439 4576 4703 4827 4978 5108 5215 5305 5816 6382 6999 7674 8420 9228

System Demand 15138 16495 17899 19422 21000 22666 24441 26383 28696 31143 33750 36572 40097 43998 48251 52902 58048 63622

Table-1 C

Losses

Load

Year

Description

Summary of Load Forecast PEPCO - High

YearDescription


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1. Introduction

1.1 Background

The power system planning broadly covers three segments of an electric power network

namely Generation, Transmission and Distribution facilities. The developments in these

sub-sectors whereas depend on many diverse parameters are also governed by the growthof economic parameters in domestic, commercial, agricultural and industrial sectors. The

supply-side potential has to be assessed and developed to fulfil the demand side

requirements, and side by side the interconnected system of transmission lines has to bedeveloped to transmit the generated power to load centres.

The load forecasting has always been the basis of additions of generation plants and

transmission lines in any grid system. WAPDA was created in 1958 and at that time

Pakistan was quite under developed and the total demand of WAPDA was around 100MW. All along these years the country has been passing through different stages of

development and the peak load met during 2006-07 was 14,941 MW. However a lot of

hydro developments along upper Indus are yet to be carried out, indigenous coal

resources are to be harnessed for power generation and import of power from

neighbouring countries are options which may also be considered. This undoubtedly willinvolve huge expenditures which will necessitate least cost options to be adopted after

assessing the future loads and developing a long range power plan to meet demandtargets.

1.2 Strategy

The load forecasting is the fundamental element of power planning involving predictionof the future levels of power demand to serve as the basis for supply-side and demand

side planning. Load forecasts are prepared for different time frames and level of detail.

Generation planning requires a system level forecast of total generation requirements.

Transmission and distribution planning requires far more load-levels and geographic

details to assess the location, timing and loading of individual lines and transformationfacilities. The load forecast is also used in demand-side management, revenue estimation

and financial planning.

The long-term forecast (2008 to 2030) presented in this report is based on a regressionanalysis of past consumption trends and relationships. The long range forecast is

appropriate for generation planning. Segregation of the forecast into load centres and

grid stations is based on PMS model considering developments in sub-sectors of industry,commerce, agriculture and domestic which have been taking place in a random fashion.

In view of this an extensive data has to be collected for PMS model. Originally data used

to be collected by PMS subdivisions and forwarded to divisional office in Lahore forfinal processing.

The data collected for existing loads includes consumption by customer types defined as

domestic, commercial, small industrial, large industrial, tube wells, public lighting and

traction. Energy forecasts are computed for each consumption category at the sub-arealevel on the basis of trend analysis per customer sales including new developments in

industrial, domestic and commercial sub-sectors. Industrial, commercial and domestic

load forecasts include interviews, trend projections and a review of the applications fornew and increased service. Customer growth factors, load factors, and diversity factors

are applied separately to estimate the distribution of demand by grid station.


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A detailed regression analysis involves the review of fundamental quantitativerelationships in the electricity demand of Pakistan and the related independent variables

in the data like electricity price, sectoral GDP, population etc.

As historical load-shedding estimates are included in the data base, the resulting

equations predict the energy that will be supplied if PEPCO system has no technical orsupply constraints.

This report provides detailed load forecast for PEPCO, KESC and Country for years from

2008 to 2030. The country level forecast has not been disaggregated at grid station level.It will be undertaken subsequently in the next step.


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2. Historical Power System Supply And DemandAnalysis

2.1 SupplyPattern

Supply characteristics such as load shedding practices, auxiliary, transmission &

distribution losses and the system load factor are essential ingredients needed to forecastgeneration as well as electricity demand requirements.

2.1.1 Load Shedding

PEPCO and KESC systems are currently supply constrained in both capacity and

transmission capability necessitating selective interruption of supply seasonally at peak

and during off-peak periods as well. Load shedding will continue until sufficientadditional capacity can be brought on line to recover suppressed demand and satisfy new

growth.

In the meantime several measures, in addition to capacity addition, are proposed and

being implemented on an urgent basis to avert / mitigate the load shedding crises.Presently NPCC (National Power Control Centre) estimates the daily quantum of demand

/ energy deficit and DISCOs have the responsibility to implement the shedding as per

criteria of distribution of rural / urban and priority feeders. A maximum quantum of 2,546

MW has been shed in year 2007. Following are the main reasons of load shedding:

Reasons of Load Shedding

Sufficient generation capacity has not been added in the National Grid to meetthe load requirements.

Hydro capacity limitations due to water shortages in the reservoirs of Hydelplants during winter period

Reduced gas quotas for thermal plants in winter due to increased consumptionof gas in domestic sector.

Generation capacity de-ration of old GENCO plants in summer due to weatherconditions

The forecasts developed in this report have accounted for load shedding. It is, therefore,

assumed that the energy demand will be fully met. To that end, the sales data used in the

regression, for example, has been adjusted to include an estimate of the energy shed.Thus, the sales figures reported here will tend to be higher than the actual level of sales

reported in the official NTDC statistics publications.

2.1.2 Transmission and Distribution Losses

During the 1980s, WAPDA introduced programs to reduce power and energy lossesthroughout its system, the implementation of which proved to be quite fruitful. Present

high level of system losses particularly distribution losses suggests that loss reduction

efforts should continue to receive considerable attention in the near term in order toeffectively reduce the cost of electricity to the customers. The following table contains a

summary of energy generation, sale and auxiliary, transmission & distribution losses

since 1997 for both PEPCO and KESC.


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Table 2-1

Historical Energy Generation, Sale and Losses

Gross

Generation

Auxiliary

Consumption

Energy

sent out

Transmission

Losses

Distribution

Losses

Units

soldYear

(GWh) (GWh) (%) (GWh) (GWh) (%) (GWh) (%) (GWh)

PEPCO

1997 50782 1222 2.4 49560 4169 8.2 6862 13.5 38529

1998 53259 1071 2.0 52188 4470 8.4 8296 15.6 39422

1999 53683 935 1.7 52748 4181 7.8 9667 18.0 38900

2000 55873 1200 2.1 54673 4017 7.2 9746 17.4 40910

2001 58455 1173 2.0 57282 4594 7.9 9304 15.9 43384

2002 60860 1318 2.2 59542 4600 7.6 9738 16.0 45204

GR(1997-2002) 3.7% 1.5% 3.7% 2.0% 3.2%2003 64040 1346 2.1 62694 4908 7.7 10365 16.2 47421

2004 69094 1397 2.0 67697 5054 7.3 11151 16.1 51492

2005 73520 1464 2.0 72056 5467 7.4 11247 15.3 55342

2006 82225 1821 2.2 80404 5839 7.1 12160 14.8 62405

2007 87837 1762 2.0 86075 5709 6.50 12797 14.6 67569

GR(2002-2007) 7.6% 6.0% 7.6% 4.4% 8.4%

KESC

1997 9327 489 6.6 8715 270 2.9 2853 30.6 5640

1998 10348 494 4.8 9854 259 2.5 3125 30.2 6385

1999 10620 470 4.4 10150 266 2.5 3656 34.4 6131

2000 11446 512 4.5 10934 286 2.5 4113 35.9 6430

2001 11677 534 4.6 11143 292 2.5 3810 32.6 6923

2002 12115 568 4.7 11547 303 2.5 4444 36.7 6718

GR(1997-2002) 5.4% 3.0% 5.8% 2.3% 9.3% 3.6%

2003 12616 581 4.6 12035 315 2.5 4594 36.4 6976

2004 13392 662 4.9 12730 335 2.5 4485 33.5 7818

2005 13593 661 4.9 12932 340 2.5 4085 30.1 8416

2006 14500 685 4.7 13815 363 2.5 4393 30.3 9060

2007 14876 639 4.3 14237 372 2.5 4498 30.2 9367

GR(2002-2007) 4.2% 2.4% 4.3% 4.2% 0.2% 6.9%

Source: Power System Statistics by Planning Power NTDC (31st issue) & KESC letters.

Note: Gross Generation of PEPCO includes Export to KESC.

Auxiliary losses of IPPs are not included in Gross Generation of PEPCO.


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The figures indicate that PEPCO distribution losses dropped from 18 % to 14.6 % ofgross generation during 1999 to 2007. In KESC, distribution losses are significantly high

and range from 30.2% to 36.7% during the past 10 years.

2.1.3 System Load Factor

The annual load factor depends to a high degree on the composition of loads on the

system and how these loads vary seasonally. The load factors for PEPCO and KESC for

the period 2002-2007 are tabulated below. These figures have been calculated on the

basis of computed energy generation and computed peak demand for the two systems i.e.taking into account the load shedding and excluding import/export of electricity between

PEPCO and KESC.

Table 2-2

PEPCO Load Factor (Historical)

PeriodComputed Gross

Generation (GWh)

Computed Peak

Demand (MW)

Load Factor

(%)

2001-02 59876 10109 67.6

2002-03 62677 10484 68.2

2003-04 67738 11078 69.8

2004-05 71306 12035 67.6

2005-06 79743 13212 68.9

2006-07 85211 15138 64.3

Average Load Factor (2001-02 to 2005-06) 68.4

Note: IPPs auxiliary losses are not included.Source: Power System Statistics (31stissue) Planning Department NTDC

Table 2-3KESC Load Factor (Historical)

PeriodGross Generation

(GWh)

Peak Demand

(MW)Load Factor (%)

2001-02 12115 1885 73.4

2002-03 12617 1973 72.9

2003-04 13389 2073 73.7

2004-05 13593 2197 70.6

2005-06 14500 2223 74.5

2006-07 14876 2349 72.3

Average Load Factor (2001-02 to 2006-07) 72.9

Source: Power System Statistics (31stissue) Planning Department NTDC

Load management efforts to reduce or shift industrial loads with non-continuous

processes, and other large tube well use at peak, likely have also contributed to load

factor improvements.


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2.1.4 Demand Side Management

Load forecasting must consider changes in both supply-side and demand-side efficiency

as a power system expands over time. Supply-side efficiency is concerned with how wellthe technical and non-technical losses on the transmission and distribution system can be

controlled, as well as the relative amount of generator station service consumption andequipment performance.

The growing attention to demand-side efficiency is motivated by the cost effectiveness of

demand-side management (DSM) in improving the system load factor, and its potential

for reducing overall load growth, while maintaining acceptable levels of service.

Benefits of DSM

Among the benefits are

The potential for deferment or reduction of bulk transmission and generationinvestment leading to reduced capital cost.

The deferment of tariff increases. The reduction of environmental impacts of generation. The decrease in the cost of power as a proportion of production cost in the

economy.

NTDC has the target to improve the load factor by employing the DSM techniques and

by promoting the Industrial share in the composition of system load. It is expected that

the load factor of the system shall improve to 71% provided the proposed DSM measuresare implemented. The estimated impact of DSM on the load forecast has been considered

in Chapter-3 in future projections.

2.2 Energy Share (Province Wise & Category Wise)

PEPCO and KESC have detailed statistics on historical electricity sales and number of

consumers for various electricity consuming sectors which capture the combined

influence of the various factors underlying growth in electricity demand. Some of thesalient features of this statistics are summarized in the following paragraphs.

Energy Share in Sales (Province wise)

The province-wise share of energy sale for the year 2006-07 is shown below in the form

of a pie chart.


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Province Wise Share in Energy Sale

Punjab

62%

KESC

13%

NWFP

12%

Baluchistan

6%

Sindh

excluding

KESC

7%

Figure 2-1Source: Power System Statistics (31stissue)

Planning Department NTDC

The province wise share in energy sales (1997-2007) is shown in the following table.

Table 2-4Historical Province-Wise Energy Sale (GWh)

Period PunjabSindh with

KESCBaluchistan NWFP Pakistan

1997 25119 9513 1666 6638 42936

1998 25639 10526 1704 6794 44663

1999 25214 10248 1687 6243 43392

2000 27033 10117 1822 6528 45500

2001 28796 10718 2138 6843 48495

2002 30565 10441 2552 7000 50558

2003 32355 10618 2864 6759 52596

2004 35399 11572 3267 7230 57468

2005 37706 12424 3500 7648 61278

2006 42024 13511 3834 8260 67629

2007 45389 14210 3968 8464 72031

AAGR(1997-2007) 6.1% 4.1% 9.1% 2.5% 5.3%

Source: Power System Statistics by Planning Power NTDC (31st issue)

Energy Share in Sales (Category wise)

Following figure shows category wise share in electricity sales for the year 2006-07.


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Indus t rial

2 5 %

o the rs

2 1%

Co m m e rc ia l

6 %

Do m e s ti c

3 8 %

Ag ric u l tu re

10%

Figure 2-2

Source: Power System Statistics (31stissue)

Planning Department NTDC


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3 Long-Term (Regression Based) Load Forecast

The long term forecast is determined through regression analysis of past energy consumption

trends using a statistical data base for WAPDA and KESC system for the period 1970 to 2006and other government publications and economic statistics. Following the Grid Code NTDC

(clause PC 4.2 of the planning code), three scenarios low, normal and high have been developed

considering economic & demographic factors and tariff etc.

3.1 Regression MethodologyThe forecasts of WAPDA and KESC sales by consumption category are determined by

regression equations relating these sales to the growth in a number of independentvariables. Regression analysis is concerned with describing and evaluating the

relationship between a given variable (often called the explained or dependent variable)

and one or more other variables (often called the explanatory or independent variables).

3.1.1 Parameters of Regression

Dependent Variables (Y)

Electricity consumption (GWh) for various consumer categories includingdomestic, commercial, industrial and agriculture.

Independent Variables (X)

The potential independent variables (demographic and economic) for regression analysis

included:

Total GDP GDP by major sector (agriculture, manufacturing, trade, services, etc.) Electricity revenue per kWh sold by customer class (real price) Number of customers by consumption categoryLag variables

In many cases, dependence of variable Y (Dependent Variable) on another Variable X

(Independent variable) is not instantaneous. Very often Y responds to X with a lapse of

time. Such a lapse of time is called Lag.

Dummy Variable

Dependence of variable Y (Dependent Variable) is not only influenced by variables

which can be quantified on some well defined scale (e.g., income, price, costs, etc.) but

also by variables which are essentially qualitative in nature (e.g., sex, race, colour,

Change in Government Policy, etc.). Such qualitative variables usually indicate presenceor absence of a quality by assuming 0 and 1 value and are referred as dummy variables.


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3.2 Regression Relationships

Within the regression analysis, each of the independent variables (e.g. population, Gross

Domestic Product, customers etc) was tested as an explanatory variable for the growthpattern in the dependent variable (Sales). For each combination of dependent and

independent variables tested, the analysis calculates the least squares coefficient relating

the variables and a number of measures (T-statistics, Minimum Mean Absolute

Percentage Error (MAPE) and minimum probability) of the goodness-of-fit of theproposed equation. One equation was selected for each major consumption category

(domestic, commercial, industrial and agriculture) for each utility (PEPCO and KESC).

In general the equations were selected on the basis of the equation displaying the best-fit between the sales and the appropriate independent variable.

3.3 Elasticity Coefficients by Customer Class

There are four elasticities corresponding to GDP, Real Price, Lag and Customers for

each customer category Domestic, Commercial, Agriculture and Industrial as shown in

Table 3-1.

3.3.1 Low, Normal and High Scenarios.

The modified long term elasticity of each sector was gradually reduced or increased

depending upon the scenario which is explained in detail in the Tables 3-2 to Table 3-4.

3.4 Projections of Independent Variables

Projections of the key independent variables used in the long-term regression model

are as under:

GDP Growth by Sector (Country)

Projected GDP growth rates used in preparing load forecast of energy demand are

based on forecast prepared by planning commission. This economic forecastprovides growth rates by economic sector. The regression equations which utilize

income as an independent variable (GDP as a proxy variable for income) implicitly

assume that historical income and electricity consumption ratio would be applicablein the future. Given the low relative levels of income measured in per capita terms

and the fact that the per capita income would remain in a low band which

characterizes developing countries of similar economic structure, it is assumed thatincome-to-energy consumption relationships will not change appreciable over the

forecast period. The projections of GDP obtained from Planning & Development

Division are presented in Table 3-5.

Electricity Prices

Real changes in future electricity prices will be determined by the cash flow

requirements of the utilities, and this, in turn, will be dependent on the plannedcapital investment program. Until the investment program and related revenues are

determined, it is not possible to estimate the impact on electricity sales of real price

changes. In this load forecast, real price changes have been assumed to be zero, inline with historical tariff increases.


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Domestic Customers

The projection of population, domestic and agriculture customer are presented inTable 3-6. As far as population is concerned, its growth rate for the last three years

was 1.9% and it is applied throughout the forecast period. The domestic customers

are adjusted in order to capture the number of domestic customers present in asingle bulk supply connection for a housing scheme.

Electrified Households.

In 1980 the electrified household percentage was 30% and in 1998 the electrified

households were 70% as reported in the respective census reports. However, the

percentage of electrified households is estimated to be 97% in the year 2030 asshown in Table 3-6.

Agricultural Customers

The average annual growth rate of last ten years is 3.39 % which has been used

throughout the regression period as shown in Table 3-6.

3.5 Regression Results

Once the growth rates in the independent variables (domestic & agricultural customers,category wise GDP, etc) are determined, the growth rates for the various consumption

categories are readily calculated using the general equation for the regression analysisgiven below:

YT= YT-1* (1+GR of G)b*(1+GR of R)

C* (1+GR of L)

d*(1+GR of C)

e

Where in the above equation

Y Represents electricity demand (Sales GWh)

GR Represents Growth Rate

G, R, L,C Represent independent variable (GDP, Real Price, Lag and Customers

respectively)

T Represents current year or time period,

T-1 Represents previous year or time period,

b, c, d, e Represent elasticities of independent variables. (GDP, Real Price, Lag

and Customers respectively)

The category-wise sale (GWh) forecast for the low, normal, and high scenarios of thecountry are shown in Table 3-7, Table 3-8 and Table 3-9 respectively with the category-

wise sale (GWh) break up between PEPCO and KESC on the basis of historical share of

each category for the last five years.


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3.6 Generation Forecast

In order to work out generation forecast from the energy sale, the important parameters

are power system losses i.e. distribution losses, transmission losses, auxiliaryconsumption and load factor for PEPCO and KESC system. These are discussed as

follows:

3.6.1 Projection of Losses

PEPCO System

The present level of PEPCO transmission losses is 6.6% and these losses have been

gradually reduced to 3% from year 2007-08 up to the year 2017-18. Further these are

kept constant for the rest of the forecast period. However the present level ofdistribution losses is 14.8%. These losses are reduced gradually upon 8% from the year

2007-08 to 2017-18, and then kept constant up to the year 2029-30. Auxiliary

consumption in PEPCO system at present is 3.5% and is kept constant throughout the

forecast period. These losses reduction pattern is presented in Tables 3-10 to 3-12.

KESC System

Present level of transmission losses (2.5%) is kept constant throughout the forecast

period. The distribution losses have been reduced gradually from 29.7% to 17.5% upto

the year 2014-15 and then these are kept constant for the rest of the forecast period.

Auxiliary consumption in KESC system is kept 4% constant throughout the forecastperiod. These are presented in Tables 3-13 to 3-15.

3.6.2 Projections of Load Factor

PEPCO System

The average load factor from the year 2001-02 to 2005-06 comes out to be 68.4%(Table 2-2). As per discussions in different meetings with Deputy Chief Energy Wing

Planning & Development Division, acting Chief Energy Wing Planning &

Development Division and Manager Generation Planning, the current load factor is

kept constant (65.1%) for the next one years and after that it is increased upto 68% inthe year 2013-14 and then it is kept constant for the rest of the forecast period. The

projections of load factor for PEPCO are shown in Tables 3-10 to 3-12.

KESC System

The base case load factor 73.6% is reduced gradually to 65% in the year 2020-21 andthen it is kept constant for the rest of the forecast period as per decision in the meeting

with Advisor to KESC on planning and energy. These projections are shown in Tables3-13 to 3-15.


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3.7 Forecast Results

Based on the above system parameters, forecasts of computed energy sale, generation and

peak demand have been developed for PEPCO, KESC and Country.

Three scenarios, Low, Normal and High for PEPCO system, are shown in Table 3-10,

Table 3-11 and Table 3-12respectively.

Three scenarios, Low, Normal and High for KESC system, are shown in Table 3-13,

Table 3-14 and Table 3-15 respectively.

Three scenarios, Low, Normal and High for the Country are shown in Table 3-16, Table

3-17 and Table 3-18 respectively.

3.8 Forecast with DSM

The impact of DSM on the forecast considering a load factor improvement of 71%, withthe proposed DSM measures, is presented year wise in Table 3-19.

3.9 Category Wise Composition of Loads (GWh Sales)

The constitution of loads provides vital information to predict the shape of daily, monthlyand annual load curves. The over all load factor of a system having greater percentage of

domestic load is always less than a system having greater percentage of industrial load.

In addition to this, the economic well being and financial status of a country is assessedfrom the constitution of loads i.e. what nature of industrial development is taking place in

the country. In this connection, it is worth while to highlight the composition of loads at

various intervals of the forecast period from 2008 to 2030 in the form of pie chart given

in figure 3-1.


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Table 3-1

Sales Category Variables Elasticities

Domestic GDP Per Capita 0.7226

Real-Price -0.1375

Domestic Sales lag (-2) 0.3287

Domestic Customers Lag (-1) 0.7057

Dummy 0.0000

Constant -11.242

Commercial GDP-Commercial 1.4009

Real-Price Lag (-1) -0.5453

Commercial Sales Lag (-2) 0.3826

Commercial Customers 0.0000

Dummy 0.0000

Constant -11.239

Industrial Industrial GDP Per Capita 1.30922

Real Price With Self Generation -0.44205Industrial Sales lag 0.00000

Dummy 0.24732

Constant -4.14178

Agriculture GDP-Agriculture 1.16506

Real-Price -0.28023

Agriculture Sales lag 0.00000

Agricultural Customers Lag (-2) 0.73473

Dummy -0.24805

Constant -14.4201

Figures in the brackets are corresponding Lag values

These elasticity coefficients are calculated by Regression Analysis using the software

E-Views on the basis of 1970 to 2006 data.

Elasticity Coefficients by Customer Class

Dummy variable minimizes the unevenness of the curve at rare places.


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Table 3-5

Total Commercial Industrial Agriculture Total Industrial

2005-06 6.6 6.7 9.3 3.3

2006-07 7.3 7.4 9.9 3.9 5.4 7.4

2007-08 7.6 7.7 10.0 4.2 5.6 7.9

2008-09 8.0 8.1 10.3 4.6 6.0 8.0

2009-10 8.0 8.1 10.2 4.6 6.0 8.3

2010-11 8.0 8.1 10.1 4.6 6.0 8.2

2011-12 7.5 7.6 9.4 4.1 5.5 8.1

2012-13 7.5 7.6 9.4 4.1 5.5 7.4

2013-14 7.5 7.6 9.3 4.1 5.5 7.4

2014-15 7.5 7.6 9.2 4.1 5.5 7.3

2015-16 7.0 7.1 8.6 3.6 5.0 7.2

2016-17 7.0 7.1 8.5 3.6 5.2 6.8

2017-18 7.0 7.1 8.4 3.6 5.2 6.7

2018-19 7.0 7.1 8.4 3.6 5.2 6.6

2019-20 7.0 7.1 8.3 3.6 5.2 6.6

2020-21 6.8 6.9 8.0 3.4 5.0 6.5

2021-22 6.8 6.9 8.0 3.4 5.0 6.2

2022-23 6.8 6.9 7.9 3.4 5.0 6.2

2023-24 6.5 6.6 7.6 3.2 4.7 6.1

2024-25 6.5 6.6 7.5 3.2 4.7 5.8

2025-26 6.5 6.6 7.5 3.2 4.7 5.7

2026-27 6.0 6.1 6.9 2.7 4.4 5.9

2027-08 6.0 6.1 6.9 2.7 4.4 5.3

2028-29 6.0 6.1 6.9 2.7 4.4 5.3

2029-30 6.0 6.1 6.8 2.7 4.4 5.3

ACGR

(2007-30) 7.0 7.1 8.4 3.6 5.2 6.7

Source:-

Projected GDP Growth Rates

Year

Planning and Development Division-Energy Wing (Ref: Fax dated 23-9-2007).

Gross Domestic Product (%) GDP Per Capita (%)


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(Millions) GR (%) No. GR(%) No. No No

2008 161.2 1.9 16200992 4.1 6.80 23707117 1.36 22033350 92

2009 164.3 1.9 16734747 3.3 6.80 24157941 1.36 22759256 94

2010 167.4 1.9 17152209 2.5 6.80 24617337 1.36 23327004 94

2011 170.6 1.9 17495253 2.0 6.80 25085470 1.36 23793544 94

2012 173.8 1.9 17845158 2.0 6.80 25562506 1.36 24269415 94

2013 177.1 1.9 18202061 2.0 6.80 26048612 1.36 24754804 95

2014 180.5 1.9 18566103 2.0 6.80 26543963 1.36 25249900 95

2015 183.9 1.9 18937425 2.0 6.80 27048733 1.36 25754898 95

2016 187.4 1.9 19316173 2.0 6.80 27563103 1.36 26269996 95

2017 191.0 1.9 19702497 2.0 6.80 28087254 1.36 26795395 95

2018 194.6 1.9 20096547 2.0 6.80 28621372 1.36 27331303 95

2019 198.3 1.9 20498477 2.0 6.80 29165647 1.36 27877929 95

2020 202.1 1.9 20908447 2.0 6.80 29720272 1.36 28435488 95

2021 205.9 1.9 21326616 2.0 6.80 30285445 1.36 29004198 95

2022 209.9 1.9 21753148 2.0 6.80 30861365 1.36 29584282 95

2023 213.8 1.9 22188211 2.0 6.80 31448237 1.36 30175967 95

2024 217.9 1.9 22631976 2.0 6.80 32046269 1.36 30779487 96

2025 222.1 1.9 23084615 2.0 6.80 32655673 1.36 31395076 96

2026 226.3 1.9 23546307 2.0 6.80 33276666 1.36 32022978 96

2027 230.6 1.9 24017233 2.0 6.80 33909468 1.36 32663438 96

2028 235.0 1.9 24497578 2.0 6.80 34554304 1.36 33316706 96

2029 239.4 1.9 24987530 2.0 6.80 35211402 1.36 33983040 96

2030 244.0 1.9 25487280 2.0 6.80 35880996 1.36 34662701 96

Projected Population and Customers-Country (2008-2

Adj. Factor

Source:Economic Survey of Pakistan 2005-06 and Census reports.

Fiscal YearPopulation Domestic Customers

House Hold

Size House Holds Domestic Customer Adjusted

Elec

Hous

An adjustment factor is used to capture the number of domestic customers present in a single bulk supply connection for a housing scheme.

The average growth rate of the last three years (1.9%) is applied to population from 2008-30.

The average growth rate of the last ten years (3.39%) is applied to agricultural customers from 2008-30.

Domestic customers are increased such that 97 % population becomes electrified in the year 2030.


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Year Transmission Distribution Auxillaries

(GWh) (%) (GWh) (%) (GWh) ( %) (GWh) (GWh) (%) (GWh) (%) (GWh) 2006-07 64854 6.61 5709 14.81 12797 21.4 18506 83360 3.5 3028 24.9 21534

2007-08 71393 6.28 5898 14.19 13330 20.5 19228 90622 3.5 3292 24.0 22520

2008-09 78356 5.95 6060 13.57 13819 19.5 19879 98235 3.5 3568 23.0 23447

2009-10 86525 5.62 6246 12.96 14387 18.6 20633 107158 3.5 3892 22.1 24525

G.R. (2007-10) 10.09% 8.73% 2010-11 95010 5.30 6381 12.34 14862 17.6 21242 116252 3.5 4223 21.1 25465

2011-12 104152 4.97 6484 11.72 15290 16.7 21773 125926 3.5 4574 20.2 26348

2012-13 113675 4.64 6532 11.10 15620 15.7 22152 135827 3.5 4934 19.2 27086

2013-14 124005 4.31 6545 10.48 15902 14.8 22447 146452 3.5 5320 18.3 27766

2014-15 135087 3.98 6512 9.86 16112 13.8 22624 157711 3.5 5729 17.3 28353

G.R. (2010-15) 9.3% 8.0%

2015-16 146643 3.66 6413 9.24 16206 12.9 22619 169262 3.5 6148 16.4 28767

2016-17 158762 3.33 6249 8.62 16185 11.9 22435 181197 3.5 6582 15.5 29017

2017-18 171603 3.00 6022 8.00 16057 11.0 22079 193682 3.5 7035 14.5 29114 22018-19 185303 3.00 6502 8.00 17339 11.0 23842 209144 3.5 7597 14.5 31439 2

2019-20 199916 3.00 7015 8.00 18707 11.0 25722 225637 3.5 8196 14.5 33918 2

G.R. (2015-20) 8.2% 7.4%

2020-21 215239 3.00 7553 8.00 20141 11.0 27693 242933 3.5 8824 14.5 36518 2

2021-22 231342 3.00 8118 8.00 21647 11.0 29765 261107 3.5 9485 14.5 39250 2

2022-23 248396 3.00 8716 8.00 23243 11.0 31959 280356 3.5 10184 14.5 42143 2

2023-24 266039 3.00 9335 8.00 24894 11.0 34229 300268 3.5 10907 14.5 45136 3

2024-25 284441 3.00 9981 8.00 26616 11.0 36597 321038 3.5 11662 14.5 48258 3

G.R. (2020-25) 7.3% 7.3%

2025-26 303662 3.00 10655 8.00 28415 11.0 39070 342732 3.5 12450 14.5 51519 3

2026-27 323112 3.00 11338 8.00 30235 11.0 41573 364685 3.5 13247 14.5 54819 3

2027-28 342930 3.00 12033 8.00 32089 11.0 44122 387052 3.5 14059 14.5 58182 4

2028-29 363430 3.00 12753 8.00 34007 11.0 46760 410190 3.5 14900 14.5 61660 4

2029-30 384844 3.00 13504 8.00 36011 11.0 49515 434359 3.5 15778 14.5 65293 4G.R. (2025-30) 6.2% 6.2%

AVG. G. R.(2007-2030) 8.0% 7.4%

Note: The base year sale, generation and demand are computed figures excluding export to KESC and including load shedding.

Auxiliary consumption of IPPs are added proportionally.

*Excluding KESC export.

E

Ge

Losses

Total

LossesEnergy

Sent Out

Load Forecast (PEPCO)*-LowComputed

Sale T & D Losses


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Computed Energy

Year Sale Sent Out G

(GWh) (%) (GWh) ( %) (GWh) (%) ( GWh) (GWh) (%) (GWh) (%) (GWh) 2006-07 9643 29.7 4498 2.5 372 32.1 4870 14513 4.2 638 36.3 5508

2007-08 11193 28.0 4781 2.5 419 30.5 5201 16394 4.0 683 34.5 5884

2008-09 12360 26.5 4885 2.5 453 29.0 5338 17698 4.0 737 33.0 6076

2009-10 13739 25.0 5011 2.5 492 27.5 5503 19242 4.0 802 31.5 6305

G.R. (2007-10) 12.5% 9.9%

2010-11 15186 23.5 5095 2.5 532 26.0 5627 20814 4.0 867 30.0 6495

2011-12 16767 22.0 5156 2.5 575 24.5 5731 22498 4.0 937 28.5 6669

2012-13 18420 20.5 5170 2.5 619 23.0 5789 24209 4.0 1009 27.0 6797

2013-14 20225 19.0 5155 2.5 666 21.5 5821 26047 4.0 1085 25.5 6906

2014-15 22175 17.5 5103 2.5 716 20.0 5819 27994 4.0 1166 24.0 6985

G.R. (2010-15) 10.0% 7.8%

2015-16 24226 17.5 5575 2.5 782 20.0 6357 30583 4.0 1274 24.0 7632

2016-17 26377 17.5 6070 2.5 852 20.0 6922 33299 4.0 1387 24.0 8309

2017-18 28660 17.5 6595 2.5 925 20.0 7521 36181 4.0 1508 24.0 9028 2018-19 31099 17.5 7157 2.5 1004 20.0 8161 39260 4.0 1636 24.0 9797

2019-20 33705 17.5 7756 2.5 1088 20.0 8845 42550 4.0 1773 24.0 10618

G.R. (2015-20) 8.7% 8.7%

2020-21 36444 17.5 8387 2.5 1177 20.0 9564 46008 4.0 1917 24.0 11481

2021-22 39319 17.5 9048 2.5 1269 20.0 10318 49636 4.0 2068 24.0 12386

2022-23 42361 17.5 9748 2.5 1368 20.0 11116 53477 4.0 2228 24.0 13344

2023-24 45508 17.5 10473 2.5 1469 20.0 11942 57451 4.0 2394 24.0 14336

2024-25 48780 17.5 11226 2.5 1575 20.0 12800 61580 4.0 2566 24.0 15366

G.R. (2020-25) 7.7% 7.7%

2025-26 52184 17.5 12009 2.5 1685 20.0 13694 65878 4.0 2745 24.0 16439

2026-27 55639 17.5 12804 2.5 1796 20.0 14600 70239 4.0 2927 24.0 17527

2027-28 59130 17.5 13607 2.5 1909 20.0 15516 74646 4.0 3110 24.0 18627

2028-29 62722 17.5 14434 2.5 2025 20.0 16459 79181 4.0 3299 24.0 19758

2029-30 66454 17.5 15293 2.5 2146 20.0 17438 83892 4.0 3495 24.0 20934 G.R. (2025-30) 6.4% 6.4%

AVG. G. R.(2007-2030) 8.8% 7.9%

Note: Base year Energy Sale is computed sale including energy shed & excluding export to PEPCO .

Base year Energy Generated is computed energy generated by KESC.

Base year Peak Demand is computed peak including import from PEPCO.

TotalDistribution Transmission T & D Losses Auxiliary

Load Forecast (KESC)-Low

L o s s e s L o s s e s


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Computed Energy Ene

Year Sale Sent Out Gene

(GWh) (%) (GWh) (%) (GWh) (%) (GWh) (GWh) (%) (GWh) (%) (GWh) (GW2006-07 9643 29.7 4498 2.5 372 32.1 4870 14513 4.2 638 36.4 5508 15

2007-08 11207 28.0 4787 2.5 420 30.5 5207 16414 4.0 684 34.5 5891 17

2008-09 12399 26.5 4901 2.5 454 29.0 5355 17754 4.0 740 33.0 6095 18

2009-10 13818 25.0 5040 2.5 495 27.5 5535 19353 4.0 806 31.5 6341 20

G.R. (2007-10) 12.7% 10.1% 10.

2010-11 15324 23.5 5141 2.5 537 26.0 5678 21002 4.0 875 30.0 6553 21

2011-12 16983 22.0 5222 2.5 583 24.5 5805 22789 4.0 950 28.5 6755 23

2012-13 18733 20.5 5257 2.5 630 23.0 5887 24620 4.0 1026 27.0 6913 25

2013-14 20664 19.0 5267 2.5 681 21.5 5947 26611 4.0 1109 25.5 7056 27

2014-15 22771 17.5 5240 2.5 735 20.0 5975 28746 4.0 1198 24.0 7173 29

G.R. (2010-15) 10.5% 8.2% 8.2

2015-16 25017 17.5 5757 2.5 808 20.0 6565 31581 4.0 1316 24.0 7881 32

2016-17 27388 17.5 6303 2.5 884 20.0 7187 34575 4.0 1441 24.0 8628 36

2017-18 29934 17.5 6889 2.5 966 20.0 7855 37790 4.0 1575 24.0 9430 392018-19 32685 17.5 7522 2.5 1055 20.0 8577 41263 4.0 1719 24.0 10296 42

2019-20 35662 17.5 8207 2.5 1151 20.0 9358 45021 4.0 1876 24.0 11234 46

G.R. (2015-20) 9.4% 9.4% 9.4

2020-21 38835 17.5 8937 2.5 1254 20.0 10191 49026 4.0 2043 24.0 12234 51

2021-22 42195 17.5 9710 2.5 1362 20.0 11073 53268 4.0 2219 24.0 13292 55

2022-23 45804 17.5 10541 2.5 1479 20.0 12020 57823 4.0 2409 24.0 14429 60

2023-24 49599 17.5 11414 2.5 1601 20.0 13016 62615 4.0 2609 24.0 15624 65

2024-25 53581 17.5 12330 2.5 1730 20.0 14060 67641 4.0 2818 24.0 16879 70

G.R. (2020-25) 8.5% 8.5% 8.5

2025-26 57786 17.5 13298 2.5 1866 20.0 15164 72949 4.0 3040 24.0 18203 75

2026-27 62154 17.5 14303 2.5 2007 20.0 16310 78465 4.0 3269 24.0 19580 81

2027-28 66574 17.5 15320 2.5 2149 20.0 17470 84043 4.0 3502 24.0 20972 87

2028-29 71209 17.5 16387 2.5 2299 20.0 18686 89895 4.0 3746 24.0 22432 93

2029-30 76113 17.5 17516 2.5 2457 20.0 19973 96086 4.0 4004 24.0 23977 100G.R. (2025-30) 7.3% 7.3% 7.3

AVG. G. R.(2007-2030) 9.4% 8. % 8.




Load Forecast (KESC)-Normal




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Computed Energy

Year Sale Sent Out

(GWh) (%) (GWh) (%) (GWh) (%) (GWh) (GWh) (%) (GWh) (%) (GWh) 2006-07 9643 29.7 4498 2.5 372 32.1 4870 14513 4.2 638 36.4 5508

2007-08 11218 28.0 4792 2.5 420 30.5 5212 16430 4.0 685 34.5 5897

2008-09 12430 26.5 4913 2.5 455 29.0 5368 17799 4.0 742 33.0 6110

2009-10 13888 25.0 5065 2.5 497 27.5 5563 19451 4.0 810 31.5 6373

G.R. (2007-10) 12.9% 10.3%

2010-11 15456 23.5 5186 2.5 542 26.0 5727 21183 4.0 883 30.0 6610

2011-12 17208 22.0 5291 2.5 591 24.5 5882 23090 4.0 962 28.5 6844

2012-13 19090 20.5 5358 2.5 642 23.0 5999 25090 4.0 1045 27.0 7045

2013-14 21207 19.0 5405 2.5 698 21.5 6104 27310 4.0 1138 25.5 7242

2014-15 23568 17.5 5424 2.5 761 20.0 6185 29753 4.0 1240 24.0 7424

G.R. (2010-15) 11.2% 8.9%

2015-16 26142 17.5 6016 2.5 844 20.0 6860 33002 4.0 1375 24.0 8235

2016-17 28943 17.5 6661 2.5 934 20.0 7595 36539 4.0 1522 24.0 9118

2017-18 32040 17.5 7373 2.5 1034 20.0 8408 40447 4.0 1685 24.0 10093 2018-19 35496 17.5 8169 2.5 1146 20.0 9315 44811 4.0 1867 24.0 11182

2019-20 39364 17.5 9059 2.5 1271 20.0 10330 49694 4.0 2071 24.0 12400

G.R. (2015-20) 10.8% 10.8%

2020-21 43638 17.5 10042 2.5 1409 20.0 11451 55090 4.0 2295 24.0 13747

2021-22 48366 17.5 11130 2.5 1562 20.0 12692 61058 4.0 2544 24.0 15236

2022-23 53656 17.5 12348 2.5 1732 20.0 14080 67736 4.0 2822 24.0 16902

2023-24 59457 17.5 13683 2.5 1920 20.0 15602 75059 4.0 3127 24.0 18730

2024-25 65868 17.5 15158 2.5 2127 20.0 17285 83153 4.0 3465 24.0 20750

G.R. (2020-25) 10.8% 10.8%

2025-26 72982 17.5 16795 2.5 2356 20.0 19151 92133 4.0 3839 24.0 22990

2026-27 80678 17.5 18566 2.5 2605 20.0 21171 101849 4.0 4244 24.0 25415

2027-28 89026 17.5 20487 2.5 2874 20.0 23362 112388 4.0 4683 24.0 28045

2028-29 98228 17.5 22605 2.5 3171 20.0 25776 124005 4.0 5167 24.0 30943

2029-30 108504 17.5 24970 2.5 3503 20.0 28473 136977 4.0 5707 24.0 34180

G.R. (2025-30) 10.5% 10.5%

AVG. G. R.

(2007-2030) 11.1% 10.3%




Load Forecast (KESC)-High




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Note Industrial energy includes self generation.

FIG 3-1

Category Wise Composition Of Loads (GWh Sales)

Domestic

41%

Commercial

8%

Agriculture

9%

others

5%

Industrial

37%

Year - 2010

Domestic

35%

Agriculture

7%

others

6%

Industrial

40%

Commercial

12%

Year - 2020

Domestic

33%

Commercial

13%

Industrial

42%

others

6%

Agriculture

6%

Year - 2030

load forecast ntdc

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