our jamaica, electricity peak and energy demand forecast 2010-2030, 6-2010

Office of Utilities Regulation

Electricity Peak and Energy Demand Forecast

2010 - 2030

June, 2010

Office of Utilities Regulation Electricity Peak and Energy Demand Forecasts 2010-2030 Document No. Elec2010005_FCT001

Office of Utilities Regulation Electricity Peak and Energy Demand Forecasts 2010-2030

Document No. Elec2010005_FCT001 Page 1

Table of Contents

1. EXECUTIVE SUMMARY.…………………………………………………………………………………………………………………….2

2. ELECTRICITY DEMAND FORECAST MODEL REVIEW ............................................................................ 17

3. HISTORICAL ELECTRICITY DEMAND PATTERNS/ CHARACTERISTICS ................................................... 21

4. ECONOMIC OUTLOOK AND THE UNCERTAINTY IN ELCTRIC DEMAND FORECASTING…………………….27

4.1 General Economic Conditions………………………………………………………………………………………………………27

4.1.1 National Economic Growth……………………………………………………………………………………………………….27

5. FORECAST METHODOLOGY……………………………………………………………………………………………………………30

5.1 Overview ..................................................................................................................................... 30

5.2 Explanatory Variables for Models ............................................................................................... 30

5.3 Determination of Variables Selected .......................................................................................... 35

5.4 Regression Model Specifications and Results ............................................................................. 38

5.5 Forecast results ........................................................................................................................... 43

5.6.1 Base Forecast .......................................................................................................................... 44

5.6.2 High Forecast Scenario ............................................................................................................ 45

5.6.3 Low Forecast Scenario ............................................................................................................ 46

6. APPENDICES ........................................................................................................................................ 47

6.1 Inputs ................................................................................................................................................. 48

6.2 Econometric Model Outputs ............................................................................................................. 49



1. EXECUTIVE SUMMARY

SUMMARY OF CUSTOMER, SALES, AND DEMAND FORECAST

The projection of demand and energy requirements for Jamaica’s Electric System was

developed for the period 2010 through 2030. Over this period, system net energy requirements

and peak demand were projected to increase at a compound annual growth rate of 4.0 and 3.80

percent respectively. The present projections are lower when compared to projections of 4.6

percent for Net Energy requirements and 4.5 percent for net peak demand obtained for the 2003

and 2004 forecasts done by JPS and the OUR respectively.

There are two main reasons for the change in these overall growth patterns:

1. Assumptions about economic growth variables used in both previous base

forecast were more optimistic than that assumed for this forecast. The

assumptions for this forecast are illustrated in Table 1e.

2. The higher nominal electricity prices brought on by the higher fuel prices over

the period, coupled with the significant retrofitting of household lighting with

fluorescence Cuban Light bulbs have served to put a damper on average

electricity usage, in particular for the residential class. Analysis has shown a

slower rate of growth in the residential demand curve. Additionally, end-use

data analysis is indicating that major appliance usage for refrigerators and

microwave are near the saturation points whereas, prior to the last ten years

these two appliances were significant drivers for average residential usage.

The forecast method used by the OUR seeks to define electricity consumption as a function of

the growth in the average number of customers and growth in the level of average usage per

customer. Average use per customer is defined as a function of socio-economic variables where

different models are developed for each rate class according to their particular usage pattern.



Table 1a. Base forecast of Sales, Net Energy and Net Peak Demand, 2010 - 2030

Year R10 Sales (MWh)

R20 Sales (MWh)

R40 Sales (MWh)

R50 Sales (MWh)

R60 Sales (MWh)

Base Sales (MWh)

Net Generation (MWh)

Load Factor

Net System Peak (MW)

Peak Demand Growth Rate

2008

1,032,182

650,424

759,739

592,918

96,973

3,132,236 4,123,290 77.97% 603.7 -1.18%

2009

1,071,646

651,045

679,027

626,473

99,588

3,127,780 4,213,983 78.42% 619.9 2.69%

2010

1,133,078

640,736

705,873

649,412

102,274

3,231,374 4,253,796 77.60% 625.8 0.95%

2011

1,171,834

636,917

731,245

677,541

105,033

3,322,569 4,373,845 77.96% 640.5 2.35%

2012

1,225,444

640,269

755,770

713,232

107,795

3,442,509 4,531,735 78.28% 660.8 3.18%

2013

1,271,458

659,217

779,429

768,906

110,561

3,589,573 4,725,330 78.57% 686.5 3.89%

2014

1,322,824

686,293

802,215

836,670

113,333

3,761,334 4,951,437 78.84% 717.0 4.44%

2015

1,372,082

717,962

824,125

912,565

116,111

3,942,845 5,190,379 79.07% 749.3 4.51%

2016

1,423,534

750,137

845,166

990,900

118,896

4,128,634 5,434,953 79.28% 782.6 4.43%

2017

1,474,719

782,831

865,351

1,071,501

121,689

4,316,089 5,681,720 79.47% 816.1 4.29%

2018

1,527,064

821,142

884,696

1,162,486

124,490

4,519,879 5,949,989 79.64% 852.8 4.50%

2019

1,579,770

860,463

903,224

1,256,698

127,302

4,727,457 6,223,245 79.80% 890.3 4.39%

2020

1,633,316

900,830

920,959

1,354,055

130,125

4,939,285 6,502,098 79.93% 928.6 4.30%

2021

1,687,452

942,283

937,929

1,454,487

132,960

5,155,112 6,786,213 80.06% 967.7 4.21%

2022

1,742,350

984,867

954,165

1,557,937

135,809

5,375,128 7,075,842 80.17% 1007.6 4.12%

2023

1,797,940

1,028,628

969,696

1,664,365

138,672

5,599,301 7,370,946 80.27% 1048.3 4.04%

2024

1,854,294

1,073,615

984,557

1,773,745

141,551

5,827,762 7,671,693 80.35% 1089.9 3.97%

2025

1,911,407

1,119,881

998,778

1,886,067

144,446

6,060,580 7,978,175 80.43% 1132.3 3.89%

2026

1,969,318

1,167,480

1,012,393

2,001,337

147,360

6,297,888 8,290,569 80.51% 1175.6 3.82%

2027

2,028,043

1,216,470

1,025,435

2,119,575

150,293

6,539,816 8,609,043 80.57% 1219.8 3.76%

2028

2,087,614

1,266,911

1,037,935

2,240,815

153,246

6,786,521 8,933,808 80.63% 1264.9 3.70%

2029

2,148,055

1,318,865

1,049,925

2,365,108

156,222

7,038,175 9,265,086 80.68% 1310.9 3.64%

2030

2,209,398

1,372,398

1,061,435

2,492,517

159,220

7,294,967 9,603,128 80.72% 1358.0 3.59%

1982to 2008

4.50% 4.10% 3.05% 5.75% 4.05% 4.18% 4.42% 0.64% 3.75% ?

2009to 2030

3.51% 3.61% 2.15% 6.80% 2.26% 4.12% 4.00% 0.14% 3.80%



The model specification, diagnostic tests and elasticities established are documented to add to

the usefulness of the document to all levels of users (see appendix 1).

The base forecast is summarized in Tables 1a. The projected demand is based on independent

estimates of energy sales for each rate class. Attendant with expanding to meet growth in

electricity demand, JPS will need to expand its customer service infrastructure to meet a growth

in customer base of 3.20 percent per annum (see table 1b).

1.2 Losses:

Over the forecast period, it is assumed that JPS will reduce its overall system losses to 15.8

percent from the present level of 22.9 percent per annum. For the purpose of this forecast it is

reasonable to assume that the reduction in losses will have some effect on the system load factor

while not necessarily reducing the growth of the net generation requirement. This is likely to be

the case as the reduction in non-technical losses is expected to result in non paying customer

becoming legitimised. However, it is also likely that they will adjust their consumption pattern

of electricity downwards, once they are forced to begin paying for the commodity. The resultant

effect of the loss reduction programme is an increase in sales, an adjustment to the system load

factor but little or no impact on the net generation requirements.



1.3 Net Generation

In order to facilitate the increase in energy demand, net generation is expected to grow at an

average annual rate of 4.00% (see table 1). The growth rate is dependent on the main drivers

such as real GDP growth, population, real prices and real disposable income. The forecast

projects that growth in consumption will be strongest in the Industrial class (Rate 50) with an

average annual growth rate of 6.8 percent. This compares with General Service (Rate 20) of 3.61

percent and Residential (Rate 10) of 3.51 percent.



1.4 Peak Demand

Peak demand is expected to grow at an average rate of 3.7% per annum on account of continued

improvements in the system load factor (see table 1). The forecast of Peak Demand was heavily

influenced by the class demand profiles and coincidence factors determined by load research

and assumes that these trends are likely to continue over the forecast period. An important

trend in the load profile on the system in the last fifteen years has been the development of an

evening peak higher than the daytime peak. The forecast analysis of the rate classes’s

contribution to System Peak indicate that this trend is likely to continue with the Residential

Rate class forecasted to move from 33 percent to 35 percent of total peak demand over the

forecast period. The General Service category (Rate 20) contribution to System Peak is expected

to move from 17 percent to 23 percent (an average increase of 1.2 percent) annually over the

forecast period. Rate 40 and Rate 50 contributions are expected to decline marginally (<1

percent).

There are reasons for the continued dominance of an evening peak over daytime peak for the

system. These are as follows:



1. There has been a relative shift in Residential and Commercial electricity prices over the

past fifteen years mainly due to adjustments made in various tariff updates driven by

demand responsibility allocations. The real price of Residential Electricity went from

being 10 percent higher than the General Service (Rate 20) real electricity price in 2000

to only 5% percent lower in 2008. Additionally, real price of residential electricity went

from being 26 percent higher than the General Power, (Rate 40) electricity prices in 2000

to just 12 percent higher in 2008. This relative shift in electricity prices has had quite an

impact on the growth of the evening peak that coincides with this relative shift in

electricity prices.

Year

Ratio of Real

Residential/General

Service Electricity Price

Ratio of Real

Residential/General

Power Electricity Price

2000

2001

2002

2003

2004

2005

2006

2007

2008

1.10

1.045

1.094

0.159

1.050

0.986

0.973

0.976

0.951

1.26

1.30

1.42

1.48

1.26

1.27

1.21

1.23

1.12



It is also instructive to compare the relative income and price elasticities established through

econometric modeling (regression analyses) of the average use per customer for Residential,

General Service and General Power rate classes.

Table 1c

Price

Elasticity

Income

Elasticity

Residential (R10)

General Service

(R20)

General Power

(R40)

-0.2532

-0.0657

-0.0632

0.3348

-

-

It can be seen from this table that while income elasticity is greater, price elasticity is

comparatively significant. The price elasticity for residential electricity is generally higher in

magnitude than General Service (Rate 20) and generally higher than General Power (Rate 40).

This reinforces the argument and the conclusion that the relative shift in prices over the last few

years cannot be ignored as one possible cause of the shift in the peak from daytime to one of

predominantly evening peak.



The shift in evening peak is not entirely due to growth in Residential demand, as with the

exception of 1994, 1995, 1996 and 1997, during the last ten years Residential Sales have grown

no faster than commercial sales. .

Regression analysis shows that the number of visitor arrivals is a significant factor in explaining

General Power electricity sales. Hence, increased tourism activity is a significant contribution to

the growth of the evening peak. Load demand profiles on Residential Peak consumption

coincides with that of the hotel sub-category peak demand. Hotel demand therefore also

contributes to the growth and dominance of the evening peak demand.

1.5 Number of Customers

The number of JPS customers is projected to grow by an average rate of 3.20% per annum,

driven by increases chiefly in the large power and residential service classes and therefore by

increases in hotel and residential construction.



Table 1d Forecasted average number of Customers by Rate Class

Years Residential General Service

General Power

Large Power

Total (Excluding street light)

2009 566783 62668 1550 122 631123

2010 586014 61777 1568 122 649481

2011 605898 61436 1588 123 669045

2012 626199 61713 1612 125 689649

2013 646928 63334 1645 131 712038

2014 668100 65645 1682 138 735565

2015 689727 68337 1720 147 759931

2016 711824 71060 1758 156 784798

2017 734405 73815 1795 165 810180

2018 757486 77029 1834 175 836524

2019 781081 80311 1872 186 863450

2020 805208 83666 1910 197 890981

2021 829881 87095 1947 208 919131

2022 855119 90601 1984 219 947923

2023 880938 94189 2020 231 977378

2024 907356 97862 2056 243 1007517

2025 934391 101623 2091 256 1038361

2026 962063 105476 2127 269 1069935

2027 990390 109426 2161 282 1102259

2028 1019393 113476 2196 295 1135360

2029 1049091 117631 2230 309 1169261

2030 1079505 121895 2264 323 1203987

Average Annual Growth Rates

1982- 2008 3.46% 3.87% 2.29% 7.92% 4.39%

2009- 2030 3.34% 3.05% 1.82% 4.57% 3.20%

Assumptions

The base forecast of customer sales and peak demand of electricity for the period 2010 - 2030 is

based on an average annual growth rate for Gross Domestic Product (GDP) of 2.0 percent for

2010 - 2030. The assumption is taking into account the Planning Institute of Jamaica (ESSJ)

projections and analysis of the actual GDP growth rate over the period 1982 – 2009. PIOJ is



projecting GDP growth of 3.0 percent for the medium term and the average annual historical

growth rate of real GDP for the period 1982 – 2009 is 2.0 percent. The OUR has however

adjusted these projections to reflect the current and short term economic outlook in the context

of the historical growth rate and the expected recovery from the recession brought on by the

global financial crisis. OUR analysis envisage GDP growth rate of -2.7 to 0 percent for the

medium term 2009 -2014. The forecast is also based on the following macro economic

assumptions underlying the average annual GDP growth rate as shown in table 1e.

Table 1e

1.6 Scenarios

Sensitivity Analysis

Using sensitivity analysis to determine the impact of different assumed economic growth

variables on the base forecast, the OUR have developed high (optimistic) and low (pessimistic)

Variables

Compound Annual Growth Rate

2010

2011-2030

Gross Domestic Product

Exchange Rate Depreciation

Population

Inflation

0%

0%

0.75%

8.0%

2.2%

4.0%

0.75%

6.0%



forecast scenarios based on the same econometric models as the base forecast but with different

growth scenarios. The base load forecast is based on a Gross Domestic Product (GDP) growth

rate of 2.0 percent compounded annually. The higher ( optimistic) forecast assumed a Gross

Domestic Product (GDP) growth rate of 4 percent compounded annually and the lower

(pessimistic) forecast assumed a GDP growth rate of 0.5 percent compounded annually.

Alternative scenarios of projected net generation and system net peak are compared in Tables 1f

and Table 1g respectively.



Table 1.f. Pesimistic Forecast

Net Generation

(MWh)

Net Generation

Growth Rate

Load

Factor Growth

Net System Peak

(MW)

Peak Demand

Growth Rate

2008 4,123,290 1.17% 78% 2.38% 604 -1.18%

2009 4,213,983 2.20% 78% 0.58% 620 2.69%

2010 4,200,306 -0.32% 78% 0.52% 618 -0.32%

2011 4,280,216 1.90% 78% 0.46% 627 1.43%

2012 4,394,610 2.67% 78% 0.42% 641 2.25%

2013 4,515,761 2.76% 79% 0.37% 656 2.38%

2014 4,674,215 3.51% 79% 0.33% 677 3.17%

2015 4,838,504 3.51% 79% 0.30% 699 3.21%

2016 5,001,740 3.37% 79% 0.27% 720 3.10%

2017 5,160,705 3.18% 79% 0.24% 741 2.93%

2018 5,316,970 3.03% 80% 0.21% 762 2.81%

2019 5,469,455 2.87% 80% 0.19% 782 2.67%

2020 5,618,650 2.73% 80% 0.17% 802 2.55%

2021 5,764,213 2.59% 80% 0.15% 822 2.43%

2022 5,906,330 2.47% 80% 0.14% 841 2.32%

2023 6,044,935 2.35% 80% 0.12% 860 2.22%

2024 6,180,146 2.24% 80% 0.11% 878 2.12%

2025 6,312,015 2.13% 80% 0.10% 896 2.03%

2026 6,440,659 2.04% 81% 0.09% 913 1.95%

2027 6,566,187 1.95% 81% 0.08% 930 1.87%

2028 6,688,730 1.87% 81% 0.07% 947 1.79%

2029 6,808,424 1.79% 81% 0.06% 963 1.72%

2030 6,925,414 1.72% 81% 0.06% 979 1.66%

1982- 2008 4.42% 0.64% 3.75%

2009- 2030 2.39% 0.16% 10.43% 2.20%



Table 1.g. Optimistic Forecast

Net Generation

(MWh)

Net Generation

Growth Rate

Load

Factor Growth

Net System

Peak (MW)

Peak

Demand

Growth Rate

2008 4,123,290 1.17% 0.78 2.38% 604 -1.18%

2009 4,213,983 2.20% 0.78 0.58% 620 2.69%

2010 4,321,214 2.54% 0.78 0.52% 636 2.55%

2011 4,503,073 4.21% 0.78 0.46% 659 3.73%

2012 4,728,639 5.01% 0.78 0.42% 690 4.58%

2013 4,971,610 5.14% 0.79 0.37% 722 4.75%

2014 5,254,511 5.69% 0.79 0.33% 761 5.34%

2015 5,606,396 6.70% 0.79 0.30% 809 6.38%

2016 5,979,749 6.66% 0.79 0.27% 861 6.38%

2017 6,372,128 6.56% 0.79 0.24% 915 6.31%

2018 6,786,523 6.50% 0.80 0.21% 973 6.28%

2019 7,222,889 6.43% 0.80 0.19% 1,033 6.23%

2020 7,683,046 6.37% 0.80 0.17% 1,097 6.19%

2021 8,167,861 6.31% 0.80 0.15% 1,165 6.15%

2022 8,678,883 6.26% 0.80 0.14% 1,236 6.11%

2023 9,217,413 6.21% 0.80 0.12% 1,311 6.07%

2024 9,785,047 6.16% 0.80 0.11% 1,390 6.04%

2025 10,383,376 6.11% 0.80 0.10% 1,474 6.01%

2026 11,014,170 6.08% 0.81 0.09% 1,562 5.98%

2027 11,679,284 6.04% 0.81 0.08% 1,655 5.95%

2028 12,380,722 6.01% 0.81 0.07% 1,753 5.93%

2029 13,120,615 5.98% 0.81 0.06% 1,856 5.91%

2030 13,901,243 5.95% 0.81 0.06% 1,966 5.89%

1982- 2008 4.42%

0.64%

3.75%

2009- 2030 5.85%

0.16% 10.43% 5.65%



Figure 1

Figure 2



ELECTRICITY DEMAND FORECAST MODEL REVIEW

Background

The importance of electricity is evidenced by the dependence of all sectors of the

Jamaican economy on electric power to drive economic activity. This need for electricity

exists amongst all economic agents - from households to government and businesses,

which use the energy provided for lighting and as input (both direct and indirect) into

productive output. The necessity of electricity indicates that the very basis on which it is

produced must be in tact and working efficiently in order to meet the increasing

demands of a growing economy. Analysing backwards from need to supply implies

that sufficient generation and distribution mechanisms must be in place to not only

meet demands, but to do so in a timely, cost effective manner to avoid shortages and

blackouts.

This document represents the OUR analysis and assessment of the load requirements

that will impact the Least Cost Expansion Plan (LCEP). The LCEP is used as a basis for

strategic planning for expansion of production facilities to deliver electricity to

customers.

The mechanics of demand and supply and the unwanted implications of insufficient

supply mechanisms point to the need for effective planning to ensure that electricity

demand is met with efficient delivery. The latter calls for detailed forecasting of future

electricity demand in order to outline the generating capacity and new expansion

projects needed overtime. The development of new facilities (if and where needed)

takes time and therefore this demand forecast spans the period 2010-2030.

Process

The objective of the demand forecast is to estimate the total sales of electricity,

generation and peak demand overtime as driven by the increases in the average



number of customers and average use per customer. The fact that electricity is defined

into five rate classes of service means that a model is developed for each rate class so as

to estimate customer number and usage. This is then aggregated across all rate classes

and used to estimate total sales/ demand for the electricity system. Of specific note is

that the forecast across rate classes is based on certain assumptions of both economic

and non-economic explanatory variables specified in the models. These assumptions are

based on historic, a priori, as well as macroeconomic expectations and projections.

Key Drivers

There is a wide range of potential drivers of long term electricity demand, ranging from

demand for Jamaica produced goods, population growth and long tern growth in

employment. The drivers can be split into four (4) broad areas; economic activity

(measured by GDP), demographics, electricity prices (and demand responsiveness) and

energy intensities (determined by the type of electricity end use and technology). The

availability of reliable series of historical and forecast data largely determines the

drivers that can be utilised for long term forecasting. The key drivers and contributing

factors are outlined in this document.

The models assessed in this analysis are focused at producing forecasts that reflect

changes in historical demand and its drivers. Underlying historical improvements in

energy efficiency, for example, are already reflected in the demand numbers. Changes

in demand may occur as a result of policy changes. It is outside the scope of this

analysis to consider the impact of future policy changes and if, and how, they should be

wound into the demand forecasts.

General Modelling

The scope of this forecast review is limited to forecasting Energy and Peak demand and

Customer growth. Different sources of electricity demand have different growth



characteristic. Growth in demand sourced from the manufacturing industry for

example has different drivers to growth associated with household residencies. The

ability to model specific areas of demand is reliant on the availability of relevant

historical and forecast data. Although detailed breakdowns of demand are often

available on a year by year basis, consistent data over sufficiently long periods is

generally available only at a largely consolidated level. While there may be scope for

additional analysis in the future, the demand modelling considered in this review has

been focused at five key demand groups:

• Residential Service

• General Service

• Rate 40 – Power Service LOW VOLTAGE

• Rate 50 – Power Service MEDIUM VOLTAGE

• Rate 60 - Streetlights

Peak demand forecasts are at the grid exit point and therefore include lines losses

Model type

The model used to develop the electric system long term forecast is based on an

econometric approach to forecasting. This involves assessing the relationship between

historical demand and likely key drivers of demand such as GDP and population, then

using the relationships to forecast future demand using forecast of the key drivers.

It is the view of the OUR that the forecasting model needs to be intuitive and clear to

the “non-experts”. The OUR is of the view that other forecasting methods such as

- - Trend Method

- - End Use Method

- - Time Series Method



- - Advance Modelling Method

- - Hybrid of the above

Whilst useful did not meet the criteria of being most cost effective, intuitive yet

technically robust enough to assess historical demand data and establishing

relationships between demand and the demand drivers. On the assessment of

alternative models carried out as part of the development of the demand forecast, has

resulted in the selection of an econometric approach.

Modelling Software

The Eview Quantitative Micro Software Version 6 package was selected as a platform

for analysis due to its scripting flexibility ( eg. estimation, forecasting, statistical

analysis, graphics, data management) and the relative ease of setting up multiple runs

of the demand models. Once a preferred model (based on goodness of fit) has been

selected the parameter estimates from the regression output is then rebuild in excel to

derive the forecasted demand.

Modelling Period

The OUR is of the view that for the econometric method, it is reasonable to include a

wide span of data to get the best estimates of relationships between the demand for

electricity and the drivers that influence those demands. To this end annual data

covering the period 1982 to 2009 was used based on the availability of data consistently

across the set of variables used to model electricity demand.

Modelling uncertainty

Various econometric models for each rate classes was postulated and assessed. On

assessment, models were selected based on how well they fitted the historical data

while minimising forecast uncertainty. The OUR is of the view that model accuracy

needs to be balanced against the requirement that the model be intuitive, theoretically

sound and fairly easy to explain. Additionally, scenario assumptions about economic

and demographic growth projections are made to account for the risk or uncertainty of

the forecast being too high or too low.



2. HISTORICAL ELECTRICITY DEMAND PATTERNS/CHARACTERISTICS

Organization of the Electricity Sector

The electricity sector in Jamaica has been characterized by the growing access to

electricity by the population for lighting purposes. During the early 1980s, just over 50%

of Jamaicans had electricity power. However, by the end of 2008, electricity access

increased significantly to approximately 91% according to estimates from the Planning

Institute of Jamaica. The Jamaica Public Service Company Limited (JPSCo) remains the

sole commercial distributor of electricity in Jamaica, with support in service expansion

by the government funded Rural Electrification Programme (REP).

JPSCo owns the majority of the country’s electricity generating capacity with other

sources of generation stemming from the bauxite and alumina industries, the cement

company and sugar industries. These generating capacities are mainly used for internal

industrial demand and to a limited extent to generate power for the national grid. The

level of generating capacity at the end of 2008 stood at 621.09 MW, while that provided

by independent generators totalled 196.66 MW together amounting to 817.75 MW. This

compares to 503.60 MW and 152.56 MW respectively to amount to a total of 656.16 MW

at the end of the previous decade. The contribution by independent power producers

(IPP) currently emanates from the following companies:

The Jamaica Energy Partners (JEP) (124.36 MW)

JPPC (61.30 MW)

Jamalcoa (11 MW)

Wigton (20 MW)

Total IPP supply = 196.66 MW



Figure 3:

Present and Historical Demand

With majority electricity demand emanating from the residential (Rate 10) service class

over the 1998 to 2008 period, total electricity sales amounted to 3,116.96 Gigawatt hours

(GWh) by 2008. This represents an average annual increase of 3.0% over the total sales

of 2451.94 GWh in 1998. The demand in 2008 was supported by net generation of

4123.29 Gigawatt hours (GWh), with a maximum net peak of 603.7 megawatts (MW).

The general trend of electricity sales reflects modest increases up to 2007 when demand

peaked at 3150.92 GWh and declined by 1% at end 2008. The decline was lead by a

falloff in rates 10, 20 and 40 electricity demand between 2007 and 2008. This was

attributed mainly to reduced demand in the residential rate category likely due to the

results of energy conservation initiatives bolstered by increased fuel rates and high



electricity bills during the one year period when world oil prices dramatically

increased.

Load Characteristics

Figure 4:

Load Research Analysis Results

The system peak for 2009 was 647.8 MW gross (619.9 MW net), and this occurred in

September 2009. This is the continuation of the pattern that has emerged since the last

fifteen years, whereby the system peak is occurring in the evening as opposed to mid

day in earlier years. The system peak frequently occurred towards the end of the year

around December, and the lowest peak for each year is frequently occurring in the

month of February.



Table 5 shows the contribution of each rate class to the system peak. The commercial

and industrial classes (rates 20, 40 and 50) accounted for a total of 58% of the on peak

and the residential class 38%. and streetlights (4%) account the remaining net

generation. Losses is excluded.

Table 5.Maximum Demand Contribution by Rate Class

Max Demand Contribution

Rates Partial Peak On Peak

RT10 22% 38%

RT20 31% 17%

RT40 28% 23%

RT50 19% 18%

RT60 0% 4%

Key Findings

The other findings from the Load Research Study and Survey for the period 2008 were

as follows:

• The system peak occurred in the evening and this represents a continuation in the

pattern from the mid-1990s’, where the system peaked in the mid-day period.

• The gap between evening and day peak is narrowing and is relatively lower than in

previous years. This represents increased commercial consumption relative to

Residential consumption.

• Based on the results of the residential survey1 of homes supplied with electricity, the

major electrical appliances found in households were as follows:

1 Survey was done in 2000 by JPS Load Researh Unit. In the absence of more recent data, the results of the

customer appliance survey 2000 is reproduce.



Fraction of households Typical for

Appliance having appliance developed countries

I) Refrigerators 87% 98 %

- Manual (65%)

- Frost free (22%)

II) Television 81% 99 %

III) Radio 74% 99 %

IV) Clothes iron 73% 95 %

V) House fan 49% 30 %

VI) Blender 48% 50 %

VII) VCR/DVD 31% 60 %

VIII) Washing machine 20% 70 % IX) Electric water heaters 10 % 90%(w/o gas)

35% (w gas)

X) Air conditioning 6% 80 %

Refrigerators, air conditioners and electric water heaters are the high energy devices to

be found in households. The refrigerator accounts for up to half of the energy

consumed in households; while the saturation level of this appliance is high, market

research will have to be done to assess the impact of this device, as well as air

conditioners and electric water heaters on future residential energy sales.



3. Economic Outlook and the Uncertainty in Electric Demand

Forecasting

3.1 General Economic Conditions

The preparation of a load forecast for strategic planning requires estimates of energy

and loads at the rate class level. For this reason consideration must be given not just to

national demographic and economic conditions but to sector growth patterns. This

section outlines general economic and demographic trends in Jamaica and discusses the

implications for power demand.

4.1.1 National Economic Growth

Structural adjustment programs were initiated in Jamaica in 1981 and although it can be

argued that tangible gains resulted over the years, economic growth as measured by the

Gross Domestic Product is at best anaemic. The last five years have seen marginal

growth. In the four years from 2005 to 2008, real economic growth was 1.0 percent, 2.8

percent, -0.1 percent, and -2.7 percent respectively. Between 1983 and 1988, real GDP

growth ranged from 1.47 percent to 6.23 percent, with the lowest rate of growth

occurring in 1988 (influenced by Hurricane Gilbert) and the highest occurring in 1987.

Real growth slowed thereafter averaging 3.5 percent for the period 1989 - 1992 and 1.0

percent for the period 1993-95.

Over the 1988 – 2009 periods, GDP of 1.0 percent average growth rate have been

achieved (see figure 5). Nonetheless, based on the views of the relevant planning

agencies, there is general agreement that positive real economic growth is possible over

the next decade. The Government of Jamaica through the Vision 2030 plan envisages a

3 - 5 percent long term GDP growth, with low inflation. This is the average growth rate



adopted for the load forecast, albeit adjusted based on the GDP outlook for the short to

medium term. Historic and forecast GDP (at constant prices) are shown in Figure 5.

Inflation has dropped from peak levels of 72.1 percent in 1991/1992 to average 10

percent in 2008.

Long term growth of 3% to 5% per annum is predicated upon continued adjustment of

the exchange rate to maintain competitiveness. The economy is highly vulnerable to

external shocks, with tourism, bauxite, alumina and other commodity sales dependent

on international prices as well as natural events. While alumina and bauxite prices

remain depressed (but expected to increase) and the likelihood of a major double dip

recession in the U.S. is not great, changes in these variables may have a strong impact

on Jamaica.



In terms of sectoral contributions to growth, non-traditional manufacturing exports rose

from 4 percent to 5 percent per annum during the period 1986 - 1988. The causes of

manufacturing growth are varied but key factors were the currency devaluations in

1984 and 1985 and the success of export zones in Kingston and Montego Bay, especially

with garments, which were helped by preferential U.S. tariff treatment. Tourism

construction activity increased in real terms by 2.0 percent and 1.0 percent in 1990 and

1991 respectively reflecting slower expansion of hotel facilities.

Unemployment fell to 15.3 percent in 1994 from a peak of 29 percent in 1983, with all

employment increases taking place in the non-public sectors. In 1994 Public sector

employment was 32 percent below 1980 levels, partly due to divestitures.

Population growth is projected to grow no faster than 0.75 percent per annum over the

next decade due largely to continued high migration levels, which itself puts a drag on

economic growth because of skilled workers are among the migrants.

Disposable income figures in Jamaica are generally thought to be understated. One

prime reason given is overseas remittances from relatives which do not show up in

official statistics. Therefore real per capita disposable income growth is likely to be

understated. However, adjusting power demand forecasts for income figures for is

difficult. An argument can be made that this other income translates into increased

power demands, especially from the residential category. We have seen significant

increase in saturation of end use appliances.

Economic variability carries a particular risk for generating capacity addition. The

Electric Utility Industry must therefore plan for expansion based on long term growth

prospects and this carries the risk of facilitating more capacity than needed during

times of economic slowdown. Without reliable power, industries such as tourism

would suffer since there is substantial competition in this market and tourist can shift

destinations rapidly as was shown in the early 1980s. JPS cannot attempt to time its



expansion to the economic swings because it must precede expansion in other sectors.

Thus the load forecast focuses on long term average growth with the exception of

known activity over the next few years. The government appears determined to

support export earning project which implies increased support for tourism, mining

and manufacturing. The tourism sector is currently strong and any government

incentives will likely accelerate this condition. Substantial increase in hotel capacity is

expected within the next decade in Montego Bay, South West Coast, St. Mary and Ocho

Rios.



4. FORECAST METHODOLOGY

4.1 Overview

The method of forecasting is based on economic modelling techniques, in particular

econometric analysis, which determines electricity demand based on growth in the

number of customers and usage per customer. This form of analysis establishes

electricity demand as dependent on a number of determinants that explain the growth/

changes in the number of customers and use per customer using least squares

regression models.

Models for each service class are developed against the background of economic theory,

seeking to estimate economic and other variables which influence electricity demand.

The selected variables for each model are determined by the characteristics and usage

pattern of each service/ rate class.

Based on the model specification, regression analysis is applied to determine whether

the selected variables provide statistically significant explanations for electricity

demand at the 95% confidence level. The results are then assessed for goodness of fit

and other standardized features of robust variables with the intention to specify the

strength and relationship between electricity demand and the variables on which it

depends.

4.2 Explanatory Variables for Models

Residential Service Class (Rate 10)

The residential service class demand forecast is a projection based on the estimates and

gross values from the models of both the average number of rate 10 customers and



average usage per rate 10 customer. For the former, the following variables were used

as determinants:

1. Mean population

2. Household size

The determinants arise from the implication that the population indicates the total

number of persons using electricity as its source of residential lighting. Meanwhile, the

household size (average number of persons in each household) is indicative of the

number of households which could be JPSCo residential customers. Both explanatory

variables were projected to have a potentially significant effect on changes in the

average number of residential customers.

The average use per residential customer is given by the annual megawatt hour (MWh)

electricity consumption per rate 10 customer. This is derived by dividing the total

annual rate 10 electricity sales by the average annual number of residential customers.

The determinants used for the average usage per customer model were:

1. Real per capita disposable income

2. Real rate 10 electricity price ($/kwh)

3. Real LPG price (J$/litre)

These determinants were selected based on economic theory which suggests that

residential consumption pattern is a result of household choices given disposable

income and the price of a commodity demanded. The implication is that households

will only demand and use the levels of a commodity that can be afforded given the

price of the commodity and the price of substitute commodities (in this case liquid

petroleum gas - LPG). The price of electricity is derived by dividing the total annual

residential sales revenue by the total residential electricity consumption ( KWh).



General Service Class (Rate 20)

The general service class consists of non-residential, small commercial/ industrial

businesses which demand under 25 kilovolt-amperes (kVA). This class span a diverse

and heterogeneous set of business which ranges from inter alia, service entities such as

banks, hairdressing/ barber shops, small hotels and restaurants to non-profit and

government entities such as hospitals, schools and churches as well as general stores,

which includes pharmacies and gas stations.

The demand forecast for this rate class is a projection also based on the estimates and

gross values from the models of both the average number of rate 20 customers and

average usage per rate 20 customers. The primary input into their growth and viability

is electricity and the nature of this service class implies that several variables may

possibly explain both components from which electricity demand is determined. To

explain the average number of customers, the following variables were used:

1. Urban population*

2. Real GDP per capita

3. Net interest rate (loans)

4. Exchange rate

Economic theory implies that these variables should affect the level of establishment

and growth in businesses overtime. The stipulated variables are believed to either

facilitate or inhibit the local business environment and therefore their projections

should explain changes in the number of general service customers.

The average use per general service rate class customer is given by the annual

megawatt hour (MWh) consumption per rate 20 customer. This is obtained by dividing

the total annual rate 20 electricity sales by the average annual number of general service

customers. The explanatory variables are selected chiefly from the premise that

businesses are established and sustainable based on economic conditions.



Therefore the determinants for the average usage per rate 20 customers included:

1. Urban population2*


3. Average exchange rate

*

Large Commercial/ Industrial Class (Rate 40)

This service class consist of medium to large commercial and industrial customers with

demand of over 25 kilovolt-amperes (kVA). The number of rate 40 customers has been

influenced by class reclassification, particularly between this and the rate 50 service

class. As such both the projection and estimates of the changes in the average number

and usage per customer are subject to errors which in the case of the latter have been

corrected for using the econometric autoregressive process at order one (1).

The determinants for the average number of rate 40 customers included:

1. Real GDP per capita3*

2. Net interest rate (loans)


4. Average annual inflation rate4*

The projected average usage per rate 40 customers were derived based on similar

economic theory as that assumed in selecting the determinants of rate 20 usage. The

2 Dropped from models due to lack of explanatory power

3 3 Dropped from models due to lack of explanatory power

4 4 Dropped from models due to lack of explanatory power



dependent variable was derived by dividing the total annual rate 40 electricity sales by

the average annual number of large commercial/ industrial service class customers.

Proposed independent variables including urban population were assessed but only the

following were selected for a determination of the average usage per rate 40 customers:

1. Tourist stopover arrivals


3. Average length of stay in hotels

Large Industrial Class (Rate 50)

The large industrial service class represents large industrial customers as well as big

hotels with demand of over 25 kilovolt-amperes (kVA). The rate 50 demand forecast is

projected based on a model specification which considers variables influencing the

growth in number of customers and their average use. The determinants of the average

number of rate 50 customers also depend on economic conditions, which included:

1. Real GDP per capita

2. Net interest rate (loans)*


To explain the average use per rate 50 customers, both urban population and the real

price of electricity were selected as likely determinants which may predict electricity

demand for the service class. The dependent variable was obtained by dividing the total

annual rate 50 electricity sales by the average annual number of large industrial class

customers.

Street Lighting and Municipalities (Rate 60)

The demand forecast for the rate 60 service class is based solely on average usage.

Therefore, the projection was executed somewhat differently, whereby an overall sales



model was developed with urban population and household size used as explanatory

variables.

4.3 Determination of Variables Selected

Several economic and other factors were used to build models of electricity demand

determination. Of these, some major explanatory variables in the specified models are

based on certain assumptions and projections, which will be explained in this section.

The critical variables include real GDP, real per capita disposable income, population

growth, real electricity price, LPG price, and the inflation rate (used to deflate nominal

prices).

Real GDP

Real GDP is a core economic indicator which is nominal GDP adjusted for inflation. As

in the 2006 Forecast, data for real GDP at 1996 prices were used. However, whereas the

historical data from 1982 to 2006 was obtained from the Statistical Institute of Jamaica

then, for this forecast, that for the period 2002 – 2007 were obtained from the Planning

Institute of Jamaica in order to maintain consistency in the flow of the data (which

otherwise would be unachievable).

Real GDP is expected to grow at an average annual rate of -2.5% in 2009, with economic

turn-around projected in 2013 at a 1% growth rate to level off at 3.0% for the final 13

years of the forecast period.

The per capita real GDP (GDP/ population) was used in the rates 20, 40 and 50 service

class models as a more precise predictor of changes in the average number of customers

in those rate classes over time.



Real per capita disposable income

Real per capita disposable income is nominal disposable income adjusted by both

inflation and population. The historical data for this variable continues to be obtained

from the Statistical institute of Jamaica. Real per capita disposable income was included

in the analysis as an indicator of the level of demand that can be expected for residential

electricity use because disposable income is a clear determinant of a household’s

affordability of consumer goods.

As such, the variable was used in the rate 10/ residential service class model to

determine the average use of electricity and therefore the level of electricity expected to

be in demand for the residential customers over the forecast period. Real disposable

income is expected to grow at an average rate of 4.07% annually.

Population/ Urban Population

Both population and urban population data were used as explanatory variables. The

population data was obtained from the Statistical Institute of Jamaica, whereas urban

population data were given by the Population Census Country Reports as a percentage

of the total population for the Census years 1982, 1991 and 2001.

The remaining data points were extrapolated based on trending methodology.

Population data was used in the residential service class model, while urban population

was included in the rates 20, 40 and 50 service class regression models but was found to

be statistically insignificant in all cases.

The average annual growth rate for the population is expected to be 0.48% in 2009 and

is forecasted to decline to 0.15% by 2030. The forecasted average annual growth rate for

the urban population ranges from 0.95% in 2009 to 0.62% by the end of the forecast

period.



Real average electricity price

Real average electricity price is the average price of electricity per kilowatt hour

adjusted for inflation. The average electricity price is the average operating sales

revenue divided by average energy sales in kilowatt hours. This was included as an

explanatory variable for the average customer usage models for each service class

excepting the street lighting service category.

Real LPG price

Liquid petroleum gas prices were used to capture the effects of non-fuel prices on

electricity usage patterns. This was included in the average customer usage models for

each service class with the exception of street lighting and municipalities. The LPG

prices were obtained from the Ministry of Energy expressed in LPG per litre and

adjusted for inflation to reflect real prices.

Inflation

The average annual point to point inflation was used to create an inflation factor which

deflates nominal variables such as GDP, disposable income, electricity prices. The

inflation factor was derived from the average annual point to point inflation rate

obtained from the Statistical Institute of Jamaica. Using 1980 prices as the base year, the

inflation factor was calculated from 1981 as follows:

Inflation factor = previous factor * (1+ current year point to point inflation rate)

Where the inflation factor for 1980 (the base year) = 1

The annual inflation factor was then used to convert nominal explanatory variables to

real variables, which were then incorporated in the various models as more precise

measure having taken account of inflation.



4.4 Regression Model Specifications and Results

The model specifications and general form of the econometric models for each service

class is outlined below

Log(Yt) = a +β1log(X1t) + β2log(X1t)+ β3log(X1t) +…….εt ; t = 1,2….27 years

The model seeks to establish parameter estimates (β1, β2, β3…...) for the explanatory

variables (Xit), driving both the number of customers and usage per customer for each

rate class. In order to form the basis for forecasting electricity demand, regression

analysis was applied to each model which yielded the following results:



Table 6: Number of Customer Models Explanatory variable Rate 10 Rate 20 Rate 40 Rate 50 Rate 60

Mean population 4.8744 - - - -

0.002*

Household size -0.4464 - - - -

0.3974** Real disposable income

- - - -

per capita

Real electricity price - - - - -

LPG price - - - - -

Urban population - - - -

Net interest rate - -0.2226 -0.2937 - -

0.0004* 0.0022*

Exchange rate - 0.1993 0.1975 0.4926 -

0.0000* 0.0000* 0.0000*

Real GDP per capita - 1.2759 - 1.4698 -

0.0048* - 0.0338*

Inflation rate - - - -

Tourist stopover - - - - -

Average length of stay - - - - -

Constant -58.4603 3.9927 5.9774 -3.9831 -

0.0113* 0.0359 0.0000* 0.1558 AR(1)

0.5541

0.0056*

0.5521 0.0107*

0.6160

0.0011*

0.5216

0.0117*

-

Probability values denoted in green = statistical significance; orange = statistical insignificance at the 95% confidence level; * denotes statistically significant probability values; **Urban population becomes statistically significant when lagged one period. AR(1) was used as a means of accounting for serial correlation. Including an autoregressive (AR)

and/or moving average (MA) term in the requisite models is a common econometric tool applied when tests have indicated that

serial correlation exists in data.

Table 6 summarizes the regression output for the number of customer models. This

includes select explanatory variables, the estimated coefficients (elasticities), the



probability values (denoted in green and orange). The R-squared for each service class

model is reflected in appendix 6.2 (below) which provide the econometric outputs. Each

model regresses the natural log of select independent variables on the logged

dependent variable (number of customers) for each service class.

Besides the reported estimates, most models were inclusive of a number of explanatory

variables some of which were dropped because of poor goodness of fit, incorrect

coefficient signs and or other modelling and data problems. The combination of

variables in the final service class model (see table above) improved goodness of fit (see

R-squares), showed mainly correct coefficient signs and reflected better explanatory

power for the number of customers per service category.

The number of observations used in each assessment was consistently 27, ranging from

the years 1982 – 2008.



Table 7: Sales Per Customer Models

Explanatory variable Rate 10 Rate 20 Rate 40 Rate 50 Rate 60

Mean population - - - - -

Household size - - - - -0.1871

0.6708

Real disposable income 0.3348 - - - -

per capita 0.0000*

Real electricity price -0.2532 -0.0657 -0.0632 -0.1469 -

0.0019* 0.0265* 0.2971 0.0069*

LPG price 0.0188 - - - -

0.6711

Urban population - - -** 0.6715 2.5169

-

0.5138 0.0000*

Net interest rate - - - - -

Exchange rate - -0.0222 - - -

0.3709

Real GDP per capita - - - - -

Inflation rate - - - - -

Tourist stopover - - 0.4640 - -

0.0400*

Average length of stay - - 0.5862 - -

0.3923

Constant -2.3611 -2.4263 -1.8751 -0.7233 -24.2315

0.0000* 0.0000 0.6554 0.9599* 0.0023*

AR(1)

0.5114 0.0002*

0.8918 0.0000*

0.7723 0.0000*

Probability values denoted in green = statistical significance; orange = statistical insignificance at the 95% confidence

level; * denotes statistically significant probability values; **Urban population becomes statistically significant when lagged one period. AR(1) was used as a means of accounting for serial correlation. Including an autoregressive (AR)

and/or moving average (MA) term in the requisite models is a common econometric tool applied when tests have indicated that

serial correlation exists in data.

Table 7 summarizes the regression output for the sales per customer models. This

includes select variables, the estimated coefficients (elasticities), the probability values



(denoted in red and orange). As in the case of the number of customer model, the R-

squared for each service class in this model is reflected in appendix 6.2 (below). Each

model regresses the natural log of select independent variables on the logged

dependent variable (usage per customer) for each service class.

Likewise, most models were inclusive of a number of explanatory variables some of

which were dropped for similar reasons. The combination of variables in each service

class model improved goodness of fit (see R-squares), showed mainly correct coefficient

signs and reflected better explanatory power for the number of customers per service

category. The number of observations used in each assessment was also consistently 27,

ranging from the years 1982 – 2008.



4.5 FORECAST RESULTS



5.5.1 Base Forecast


R20 Sales (MWh)

R40 Sales (MWh)

R50 Sales (MWh)

R60 Sales (MWh)

Base Sales (MWh)


Load Factor

Net System

Peak (MW)

Peak Demand Growth Rate

2010 1,133,078 640,736 705,873 649,412 102,274 3,231,374 4,253,796 77.60% 625.8 0.95%

2011 1,171,834 636,917 731,245 677,541 105,033 3,322,569 4,373,845 77.96% 640.5 2.35%

2012 1,225,444 640,269 755,770 713,232 107,795 3,442,509 4,531,735 78.28% 660.8 3.18%

2013 1,271,458 659,217 779,429 768,906 110,561 3,589,573 4,725,330 78.57% 686.5 3.89%

2014 1,322,824 686,293 802,215 836,670 113,333 3,761,334 4,951,437 78.84% 717.0 4.44%

2015 1,372,082 717,962 824,125 912,565 116,111 3,942,845 5,190,379 79.07% 749.3 4.51%

2016 1,423,534 750,137 845,166 990,900 118,896 4,128,634 5,434,953 79.28% 782.6 4.43%

2017 1,474,719 782,831 865,351 1,071,501 121,689 4,316,089 5,681,720 79.47% 816.1 4.29%

2018 1,527,064 821,142 884,696 1,162,486 124,490 4,519,879 5,949,989 79.64% 852.8 4.50%

2019 1,579,770 860,463 903,224 1,256,698 127,302 4,727,457 6,223,245 79.80% 890.3 4.39%

2020 1,633,316 900,830 920,959 1,354,055 130,125 4,939,285 6,502,098 79.93% 928.6 4.30%

2021 1,687,452 942,283 937,929 1,454,487 132,960 5,155,112 6,786,213 80.06% 967.7 4.21%

2022 1,742,350 984,867 954,165 1,557,937 135,809 5,375,128 7,075,842 80.17% 1007.6 4.12%

2023 1,797,940 1,028,628 969,696 1,664,365 138,672 5,599,301 7,370,946 80.27% 1048.3 4.04%

2024 1,854,294 1,073,615 984,557 1,773,745 141,551 5,827,762 7,671,693 80.35% 1089.9 3.97%

2025 1,911,407 1,119,881 998,778 1,886,067 144,446 6,060,580 7,978,175 80.43% 1132.3 3.89%

2026 1,969,318 1,167,480 1,012,393 2,001,337 147,360 6,297,888 8,290,569 80.51% 1175.6 3.82%

2027 2,028,043 1,216,470 1,025,435 2,119,575 150,293 6,539,816 8,609,043 80.57% 1219.8 3.76%

2028 2,087,614 1,266,911 1,037,935 2,240,815 153,246 6,786,521 8,933,808 80.63% 1264.9 3.70%

2029 2,148,055 1,318,865 1,049,925 2,365,108 156,222 7,038,175 9,265,086 80.68% 1310.9 3.64%

2030 2,209,398 1,372,398 1,061,435 2,492,517 159,220 7,294,967 9,603,128 80.72% 1358.0 3.59%

1982- 2008 4.50% 4.10% 3.05% 5.75% 4.05% 4.18% 4.42% 0.64% 3.75%

2009- 2030 3.51% 3.61% 2.15% 6.80% 2.26% 4.12% 4.00% 0.14% 3.80%



5.5.2 High Forecast Scenario


R20 Sales (MWh)

R40 Sales (MWh)

R50 Sales (MWh)

R60 Sales (MWh)

Base Sales (MWh)


Net Generation Growth Rate

Load Factor

Growth


Peak Demand Growth Rate 2010 1,147,613 657,640 705,873 669,188 102,274 3,282,588 4,321,214 2.54% 77.60% 0.52% 635.7 2.55%

2011 1,194,454 670,789 731,245 719,215 105,033 3,420,736 4,503,073 4.21% 77.96% 0.46% 659.4 3.73%

2012 1,257,084 691,749 755,770 779,689 107,795 3,592,087 4,728,639 5.01% 78.28% 0.42% 689.5 4.58%

2013 1,312,626 721,231 779,429 852,811 110,561 3,776,658 4,971,610 5.14% 78.57% 0.37% 722.3 4.75%

2014 1,374,385 760,258 802,215 941,371 113,333 3,991,562 5,254,511 5.69% 78.84% 0.33% 760.9 5.34%

2015 1,434,678 820,174 824,125 1,063,782 116,111 4,258,870 5,606,396 6.70% 79.07% 0.30% 809.4 6.38%

2016 1,497,994 883,686 845,166 1,196,744 118,896 4,542,486 5,979,749 6.66% 79.28% 0.27% 861.0 6.38%

2017 1,561,777 950,994 865,351 1,340,744 121,689 4,840,554 6,372,128 6.56% 79.47% 0.24% 915.3 6.31%

2018 1,627,553 1,022,315 884,696 1,496,293 124,490 5,155,348 6,786,523 6.50% 79.64% 0.21% 972.7 6.28%

2019 1,694,491 1,097,880 903,224 1,663,934 127,302 5,486,831 7,222,889 6.43% 79.80% 0.19% 1033.3 6.23%

2020 1,763,126 1,177,937 920,959 1,844,239 130,125 5,836,387 7,683,046 6.37% 79.93% 0.17% 1097.2 6.19%

2021 1,833,211 1,262,750 937,929 2,037,824 132,960 6,204,674 8,167,861 6.31% 80.06% 0.15% 1164.7 6.15%

2022 1,904,952 1,352,602 954,165 2,245,342 135,809 6,592,869 8,678,883 6.26% 80.17% 0.14% 1235.8 6.11%

2023 1,978,297 1,447,795 969,696 2,467,500 138,672 7,001,960 9,217,413 6.21% 80.27% 0.12% 1310.9 6.07%

2024 2,053,349 1,548,652 984,557 2,705,052 141,551 7,433,161 9,785,047 6.16% 80.35% 0.11% 1390.1 6.04%

2025 2,130,125 1,655,516 998,778 2,958,813 144,446 7,887,678 10,383,376 6.11% 80.43% 0.10% 1473.6 6.01%

2026 2,208,694 1,768,754 1,012,393 3,229,657 147,360 8,366,858 11,014,170 6.08% 80.51% 0.09% 1561.8 5.98%

2027 2,289,099 1,888,755 1,025,435 3,518,526 150,293 8,872,109 11,679,284 6.04% 80.57% 0.08% 1654.8 5.95%

2028 2,371,404 2,015,936 1,037,935 3,826,432 153,246 9,404,953 12,380,722 6.01% 80.63% 0.07% 1752.9 5.93%

2029 2,455,662 2,150,738 1,049,925 4,154,462 156,222 9,967,008 13,120,615 5.98% 80.68% 0.06% 1856.5 5.91%

2030 2,541,936 2,293,631 1,061,435 4,503,786 159,220 10,560,008 13,901,243 5.95% 80.72% 0.06% 1965.8 5.89%

1982- 2008 4.50% 4.10% 3.05% 5.75% 4.05% 4.18% 4.42% 0.64% 3.75%

2009- 2030 4.17% 6.18% 2.15% 9.85% 2.28% 5.68% 5.85% 0.16% 10.4% 5.65%



5.5.3 Low Forecast Scenario


R20 Sales (MWh)

R40 Sales (MWh)

R50 Sales (MWh)

R60 Sales (MWh)

Base Sales (MWh)

Base Sales Growth Rate


Net Generation Growth Rate

Load Factor

Growth


Peak Demand Growth Rate 2010 1,111,102 632,320 705,873 639,330 102,115 3,190,741 2.35% 4,200,306 -0.32% 77.60% 0.52% 617.9 -0.32%

2011 1,137,908 620,379 731,245 657,042 104,869 3,251,444 1.90% 4,280,216 1.90% 77.96% 0.46% 626.7 1.43%

2012 1,177,911 615,692 755,770 681,366 107,603 3,338,343 2.67% 4,394,610 2.67% 78.28% 0.42% 640.8 2.25%

2013 1,209,355 618,081 779,429 713,189 110,319 3,430,375 2.76% 4,515,761 2.76% 78.57% 0.37% 656.1 2.38%

2014 1,244,692 631,617 802,215 759,200 113,020 3,550,744 3.51% 4,674,215 3.51% 78.84% 0.33% 676.8 3.17%

2015 1,276,856 648,705 824,125 810,151 115,708 3,675,545 3.51% 4,838,504 3.51% 79.07% 0.30% 698.5 3.21%

2016 1,309,913 665,449 845,166 860,633 118,384 3,799,546 3.37% 5,001,740 3.37% 79.28% 0.27% 720.2 3.10%

2017 1,341,597 681,858 865,351 910,445 121,053 3,920,303 3.18% 5,160,705 3.18% 79.47% 0.24% 741.3 2.93%

2018 1,373,242 697,942 884,696 959,414 123,715 4,039,009 3.03% 5,316,970 3.03% 79.64% 0.21% 762.1 2.81%

2019 1,404,143 713,712 903,224 1,007,392 126,374 4,154,844 2.87% 5,469,455 2.87% 79.80% 0.19% 782.5 2.67%

2020 1,434,751 729,181 920,959 1,054,257 129,031 4,268,179 2.73% 5,618,650 2.73% 79.93% 0.17% 802.4 2.55%

2021 1,464,861 744,365 937,929 1,099,911 131,689 4,378,755 2.59% 5,764,213 2.59% 80.06% 0.15% 821.9 2.43%

2022 1,494,643 759,277 954,165 1,144,279 134,350 4,486,713 2.47% 5,906,330 2.47% 80.17% 0.14% 841.0 2.32%

2023 1,524,053 773,934 969,696 1,187,305 137,016 4,592,004 2.35% 6,044,935 2.35% 80.27% 0.12% 859.7 2.22%

2024 1,553,166 788,351 984,557 1,228,953 139,690 4,694,717 2.24% 6,180,146 2.24% 80.35% 0.11% 878.0 2.12%

2025 1,581,992 802,544 998,778 1,269,204 142,372 4,794,890 2.13% 6,312,015 2.13% 80.43% 0.10% 895.8 2.03%

2026 1,610,574 816,530 1,012,393 1,308,051 145,067 4,892,614 2.04% 6,440,659 2.04% 80.51% 0.09% 913.3 1.95%

2027 1,638,935 830,324 1,025,435 1,345,502 147,774 4,987,970 1.95% 6,566,187 1.95% 80.57% 0.08% 930.3 1.87%

2028 1,667,110 843,942 1,037,935 1,381,576 150,497 5,081,060 1.87% 6,688,730 1.87% 80.63% 0.07% 947.0 1.79%

2029 1,695,124 857,400 1,049,925 1,416,298 153,237 5,171,984 1.79% 6,808,424 1.79% 80.68% 0.06% 963.3 1.72%

2030 1,723,008 870,714 1,061,435 1,449,703 155,996 5,260,855 1.72% 6,925,414 1.72% 80.72% 0.06% 979.3 1.66%

1982- 2008 4.50% 4.10% 3.05% 5.75% 4.05% 4.18% 4.42% 0.64% 3.75%

2009- 2030 2.36% 1.33% 1.53% 4.15% 2.18% 2.39% 2.39% 0.16% 10.4% 2.20%



5. Appendices



5.1 DATA INPUTS

Year Mean

Population

Household

Size

Real Per

Capita

Disposable

Income

R10 Real

1980 Price

($/kWh)

Real

LPG

1980

Price

Average no.

of Rate 10

Customers

R10 Avg Use

(MWh/Cust)

Urban

Population

('000)

Net

Interest

Rate (%)

Real GDP

Per Capita

(1996 $)

Average

Annual

Exchange

Rate J$/US$

1982 2,200,100 4.2531 2,125 0.2648 0.1726 216403 1.5185 1051648 0.1574 76.08 1.78

1983 2,241,000 4.2706 2,180 0.2733 0.1627 220669 1.6562 1075607 0.1637 76.41 1.92

1984 2,280,000 4.2833 2,000 0.4187 0.1819 222895 1.6515 1098829 0.1819 74.46 3.94

1985 2,311,300 4.2804 2,033 0.3757 0.1726 226123 1.5051 1118499 0.2484 70.04 5.58

1986 2,336,100 4.2649 2,176 0.2713 0.1595 231871 1.5857 1135153 0.2441 70.47 5.50

1987 2,350,900 4.2310 2,344 0.2660 0.1443 241958 1.7239 1147046 0.2521 75.60 5.51

1988 2,356,600 4.1811 2,855 0.2063 0.1639 230366 1.7665 1154559 0.2488 77.07 5.51

1989 2,375,100 4.1541 2,749 0.2373 0.1343 260946 1.7933 1168412 0.2637 81.85 5.77

1990 2,403,000 4.1432 2,609 0.2361 0.2047 274692 1.9115 1187003 0.3417 86.00 7.24

1991 2,425,500 4.1226 2,116 0.1900 0.2280 287739 1.8529 1203048 0.3561 85.91 12.22

1992 2,428,150 4.0290 2,747 0.3309 0.1854 299544 1.7881 1210067 0.4604 87.24 22.99

1993 2,430,800 3.9376 3,013 0.2606 0.1844 312087 1.8192 1217125 0.4960 89.29 25.11

1994 2,454,800 3.8819 3,682 0.2621 0.1869 325669 1.8964 1234964 0.4579 89.29 33.29

1995 2,483,000 3.8332 3,658 0.2172 0.1779 342926 1.9950 1255067 0.4856 90.44 35.35

1996 2,509,900 3.7826 3,783 0.1951 0.2034 357310 2.1379 1274673 0.3781 89.70 37.25

1997 2,534,300 3.7286 3,751 0.1764 0.1896 376140 2.2047 1293161 0.3193 87.88 35.51

1998 2,556,800 3.6723 4,046 0.1661 0.1669 393944 2.3513 1310822 0.3008 86.11 36.65

1999 2,574,300 3.6096 4,020 0.1631 0.1896 409120 2.4625 1326045 0.2464 86.27 39.20

2000 2,589,400 3.5445 4,289 0.2057 0.2958 423799 2.4701 1340140 0.2212 86.20 43.08

2001 2,604,100 3.4799 4,275 0.2052 0.2531 439647 2.4234 1354132 0.1950 87.00 46.08

2002 2,615,200 3.4275 4,434 0.2115 0.2233 451219 2.4448 1366345 0.1826 87.50 48.54

2003 2,625,700 3.3750 4,564 0.2372 0.3330 462107 2.3949 1378329 0.1932 88.90 57.93

2004 2,638,100 3.3257 4,667 0.2264 0.3857 482299 2.2412 1391397 0.1772 89.60 61.34

2005 2,650,400 3.2769 4,596 0.2571 0.4067 493696 2.2426 1404505 0.1732 90.70 62.50

2006 2,663,100 3.2292 4,975 0.2793 0.4173 505260 2.1657 1417920 0.1759 91.50 65.88

2007 2,675,800 3.1822 4,742 0.2655 0.4160 515886 2.0840 1431430 0.1711 93.30 69.06

2008 2,687,200 3.1342 4,911 0.2903 0.4546 523728 1.9708 1445068 0.1694 93.67 72.68



Year R20

Customers

R20 Real Price of Electricity

R20 Avg. Energy Use

(MWh/

Inflation Rate

(Annual Average)

Avg no of rate 40

customers

Tourist Stopover

Average length of stay in hotels

R40 Real 1980 Price

($/kWh)

R40 Avg. Energy Use

(MWh/

GDP (1996 J$ M)

Avg no of rate 50

customers

Real price of

electricity rate 50 (J$/kWh)

R50 Avg. Energy Use

(MWh/

Rate 60 + Other Sales (MWh)

1982 25496 0.1991 8.9744 0.0650 923 467763 9.70 0.1991 377 167393 22 0.2218 6301.77 34509

1983 25605 0.1921 10.4779 0.1130 914 566151 9.20 0.1921 394 171228 22 0.2498 6633.86 32867

1984 23224 0.2895 10.9284 0.2780 888 603436 9.00 0.2895 402 169765 22 0.4937 6471.73 35976

1985 22981 0.3749 10.7923 0.2600 868 571713 9.80 0.2691 422 161874 22 0.5515 7000.32 38640

1986 25149 0.3350 10.4292 0.1480 844 663593 10.20 0.2334 452 164616 24 0.5295 7149.00 43331

1987 26030 0.3137 11.5844 0.0670 827 738827 10.10 0.2250 514 177737 24 0.5397 6871.96 35906

1988 24783 0.3390 11.7481 0.0830 812 648873 10.30 0.2008 507 181622 23 0.5645 6342.96 39058

1989 28259 0.2117 12.1127 0.1430 813 829288 10.60 0.1806 566 194411 23 0.5405 6889.83 47029

1990 30018 0.2442 12.9900 0.2200 798 989275 10.90 0.1849 599 206652 31 0.6938 6766.58 47330

1991 31558 0.1783 12.0768 0.5100 787 1006804 10.90 0.1450 542 208380 58 1.0265 4987.66 47816

1992 33137 0.3291 11.7040 0.7730 777 1057182 11.20 0.2813 510 211831 69 2.6528 4662.48 47644

1993 35138 0.2598 11.4803 0.2210 820 1105382 11.00 0.2164 541 217050 74 2.7527 4541.74 47292

1994 39611 0.2538 11.0144 0.3510 1155 1098287 10.70 0.2187 374 219187 76 3.4963 4372.33 47823

1995 41673 0.2122 11.2910 0.1990 1109 1147001 10.90 0.1823 407 224565 75 3.6623 4532.21 48217

1996 43229 0.1873 11.5856 0.2640 1323 1162449 11.10 0.1609 356 225129 77 3.7478 4646.60 55002

1997 46129 0.1688 11.8354 0.0970 1280 1192194 10.80 0.1457 387 222707 80 3.6501 4458.36 60912

1998 48453 0.1597 12.1576 0.0870 1174 1225287 10.90 0.1306 440 220172 79 3.7695 4542.30 60657

1999 50282 0.1585 12.0518 0.0600 1192 1248397 10.30 0.1387 437 222083 86 4.0486 4460.53 60346

2000 51925 0.1871 11.5227 0.0820 1269 1322690 10.10 0.1627 452 223770 93 5.0091 4030.16 59166

2001 53358 0.1964 11.3342 0.0700 1308 1276516 10.20 0.1574 446 227070 97 5.2588 3969.31 62397

2002 53526 0.1934 11.5471 0.0700 1356 1266366 10.20 0.1487 440 229536 97 5.3158 4344.23 62841

2003 52885 0.2047 12.3354 0.1010 1391 1350285 10.20 0.1605 450 235200 102 6.5374 5300.24 65028

2004 54543 0.2156 11.7634 0.1350 1434 1414786 9.90 0.1790 473 237400 97 7.8715 5098.52 67353

2005 55752 0.2608 11.2732 0.1510 1469 1478663 9.80 0.2020 500 240900 91 10.2959 5026.03 70067

2006 57397 0.2871 11.2272 0.0850 1502 1678905 9.80 0.2303 507 246900 102 12.4737 4960.41 74083

2007 59221 0.2721 11.1366 0.0930 1531 1700785 9.60 0.2159 504 249600 113 13.8829 5021.55 77501

2008 60530 0.3055 10.7455 0.1687 1554 1722950 9.60 0.2582 489 251846 126 19.4909 4687.06 84044



6.2 ECONOMETRIC MODEL OUTPUT

Dependent Variable: LOGAVGCUSTRATE10 Method: Least Squares Date: 10/09/09 Time: 18:33 Sample (adjusted): 1983 2008 Included observations: 26 after adjustments Convergence achieved after 10 iterations White Heteroskedasticity-Consistent Standard Errors & Covariance

Variable Coefficient Std. Error t-Statistic Prob. C -58.46026 21.14392 -2.764873 0.0113

LOGPOPULATION 4.874430 1.388355 3.510940 0.0020 LOGHHSIZE -0.446372 0.517152 -0.863136 0.3974

AR(1) 0.554095 0.180538 3.069135 0.0056 R-squared 0.994642 Mean dependent var 12.74638

Adjusted R-squared 0.993911 S.D. dependent var 0.299849 S.E. of regression 0.023398 Akaike info criterion -4.531727 Sum squared resid 0.012044 Schwarz criterion -4.338174 Log likelihood 62.91245 Hannan-Quinn criter. -4.475991 F-statistic 1361.284 Durbin-Watson stat 2.195352 Prob(F-statistic) 0.000000

Inverted AR Roots .55

Dependent Variable: LOGR10AVGUSE Method: Least Squares Date: 10/12/09 Time: 10:52 Sample: 1982 2008 Included observations: 27 White Heteroskedasticity-Consistent Standard Errors & Covariance


LOGREALPERCAPDISINC 0.334806 0.054692 6.121669 0.0000 LOGR10REALPRICE -0.253170 0.072203 -3.506360 0.0019 LOGREALLPGPRICE 0.018785 0.043668 0.430175 0.6711

R-squared 0.855217 Mean dependent var 0.682582





Variable Coefficient Std. Error t-Statistic Prob. C 3.992690 1.780699 2.242204 0.0359

LOGINTERESTRATE -0.222641 0.052767 -4.219292 0.0004 LOGREALGDPPERCAP 1.275908 0.405055 3.149963 0.0048 LOGAVGEXCHRATE 0.199316 0.027033 7.373110 0.0000




Dependent Variable: LOGR20AVGUSE Method: Least Squares Date: 10/12/09 Time: 12:01 Sample (adjusted): 1983 2008 Included observations: 26 after adjustments Convergence achieved after 8 iterations White Heteroskedasticity-Consistent Standard Errors & Covariance


LOGR20REALPRICE -0.065743 0.027636 -2.378884 0.0265 LOGAVGEXCHRATE -0.022254 0.024363 -0.913432 0.3709








LOGINTERESTRATE -0.293685 0.084678 -3.468269 0.0022 LOGAVGEXCHRATE 0.197499 0.027154 7.273343 0.0000






LOGTOURISTSTOP 0.464024 0.211996 2.188838 0.0400 LOGR40REALPRICE -0.063171 0.059077 -1.069306 0.2971

LOGAVG_LENGTH_STAY 0.586209 0.671208 0.873364 0.3923








LOGREALGDPPERCAP 1.469838 0.649276 2.263809 0.0338 LOGAVGEXCHRATE 0.492633 0.068874 7.152702 0.0000






LOGURBANPOP 0.671548 1.011898 0.663652 0.5138 LOGR50REALPRICE -0.146924 0.049284 -2.981202 0.0069






Dependent Variable: LOGR60SALES Method: Least Squares Date: 10/12/09 Time: 16:13 Sample: 1982 2008 Included observations: 27 White Heteroskedasticity-Consistent Standard Errors & Covariance


LOGURBANPOP 2.516871 0.465711 5.404366 0.0000 LOGHHSIZE -0.187070 0.434703 -0.430339 0.6708

R-squared 0.955822 Mean dependent var 10.86042


our jamaica, electricity peak and energy demand forecast 2010-2030, 6-2010

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