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Energy Policy 35 (2007) 2352–2359

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Scenario planning for the electricity generation in Indonesia

C. Rachmatullah, Lu Aye�, R.J. Fuller

International Technologies Centre (IDTC), Department of Civil and Environmental Engineering, The University of Melbourne, Victoria 3010, Australia

Received 17 October 2005; accepted 28 August 2006

Available online 16 October 2006

Abstract

The long-term planning of a future electricity supply system requires data about future demand. Planners who use the conventional

planning method forecast future demand by observing past trends or alternatively by developing scenarios and then selecting the scenario

considered to be the most likely to occur. This method, however, fails to include future uncertainties. To consider such uncertainties, the

scenario planning method may be used. This study uses this method to devise a long-term electricity supply plan for the

Java–Madura–Bali electricity system. It was found that the scenario planning method could save up to US$3.5 billion over a 15-year

period if the method was applied right at the beginning of the period. In the case of the Java–Madura–Bali system, which currently has

excess installed capacity, the scenario planning method does not provide such large benefits. It was also found that introducing integrated

coal gasification combined cycle and advanced gas combined cycle units would reduce greenhouse gas emissions from the

Java–Madura–Bali system by approximately 230 million tonnes or 15% compared to a business-as-usual (BAU) scenario over a15-year

planning timeframe. The abatement cost was found to be US$4 per tonne of CO2.

Crown Copyright r 2006 Published by Elsevier Ltd. All rights reserved.

Keywords: Scenario planning; Power expansion plan; Indonesia

1. Introduction

A developing country is generally characterised byinadequate essential infrastructure but with limited finan-cial resources to address the problem. Most developingcountries urgently need electricity for their economicdevelopment. Power projects, however, are capital inten-sive. Planning errors can cause either an unmet demand oran oversupply in electricity capacity and both can impactadversely on the country’s economic growth. There canalso be social consequences such as lost job opportunities,reduced public safety and increased political instability.Reducing the risk of a mismatch between electricitydemand and supply is therefore essential to optimise theuse of a limited budget and ensure orderly development.

The expansion of electricity supply in Indonesia has beenconsiderable over the last 15 years. According to the reportof PLN-JBIC (2002), the installed capacity nearly doubledin seven years, from 8500MW in 1988 to 16 000MW in

e front matter Crown Copyright r 2006 Published by Elsevie

pol.2006.08.015

ing author. Tel.: +613 8344 6879; fax: +61 3 8344 6868.

ess: lua@unimelb.edu.au (L. Aye).

1995. In the early 1990s, to meet the projections of sharplyincreased demand, PLN, the national electricity authority,began constructing additional power plants. However, theprojections failed to foresee changes in the world economy,particularly in the Southeast Asia region, and this resultedin an excess capacity of more than 7700MW at the start ofthe regional economic downturn in 1997 (EIA 1998). Thepostponement or cancellation of some power purchaseagreements has caused disputes between PLN and itspower suppliers.This paper describes the application of the scenario

planning method to a future expansion of the Java–Madura–Bali supply system, which represents 73% of totalelectricity generation in Indonesia (PLN, 2003). Theframework for the scenario planning method used followsthat proposed by Soontornrangson et al. (2003). However,those authors used only a simplified case to demonstratethat the scenario planning method can be applied toelectricity planning and can result in financial benefitscompared to the conventional planning method. Thispaper develops the concept proposed by Soontornrangsonet al. (2003) further and compares the results obtained by

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ARTICLE IN PRESSC. Rachmatullah et al. / Energy Policy 35 (2007) 2352–2359 2353

this planning method with those determined by theconventional planning method used by PLN. In addition,the increase in generation costs when greenhouse gasemission reduction options are included is also examined.It is argued that if action to minimise emissions from theelectricity sector is taken at the planning stage, emissionscan be reduced below projected levels at a much lower cost.Prior to presenting details of the model and the resultsobtained, the paper describes briefly the difference betweenconventional and scenario planning, and provides anoverview of the present Indonesian electricity system.

2. Conventional and scenario planning

The long lead times and the dependency of the economyon electricity require utilities to plan ahead in order to meetthe demand for electricity on a timely and economic basis.Long-term planning for future supply needs data aboutfuture demand. Planners usually forecast the futuredemand by observing past trends in demand and relatingthem to the parallel economic trends. However, economicperformance is uncertain and consequently introducesuncertainties into the planning procedure. Governmentinterventions in the form of subsidies, tax and monetarypolicies are examples of local factors that can affecteconomic behaviour. World economic activity is also asignificant influence on the local economy. Other uncer-tainties in electricity demand are generated by factors suchas technological change, environmental constraints, fuelprice increases and the application of demand management(Schnaars, 1989; Ringland, 1998). All these factors mayalter the relationship between economic trends andelectricity demand.

The use of a ‘‘supply-following-demand’’ methodologyand the long lead times required for power plant projectsmeans that the electric power industry is significantlydriven by forecasts. The conventional planning methodrelies on forecasting the future based on the most likelytrend. Forecasts based on current trends or estimates ofgrowth based on history will be dangerous for businessesthat can have a fast changing environment such as theelectricity sector. In the presence of uncertainties such asnew technologies, a changing economic situation orenvironmental policies, the forecasting approach of con-ventional planning involves a high risk of mismatch. Infact, it is unlikely that a correct forecast of future electricitydemand can be made.

Since defining a certain future demand to design thefuture supply plan is always likely to fail, Finlay (1998),Ringland (1998) and Schnaars (1989) recommend the useof the scenario planning method. This methodologyencourages and harnesses foresight rather than forecasting.The development of a scenario evolves from where we arenow and what the future might be, having considered notonly history or current trends, but also the possibility ofnew factors such as technological change or more stringentenvironmental regulations occurring in the future. Most

importantly, as scenario planning is based on manypossible future events, planners have to consider all thosepossibilities as having an equal potential to occur. Plannersmust therefore prepare several options and a decision isthen made to select an appropriate option that provides anoutcome with minimum mismatch under any possiblescenario. Consequently, the selected option should producea plan that gives the most robust outcome no matter whatoccurs in the future.There are three main activities in scenario planning

process, namely scenario writing, scenario analysis anddecision-making. Scenario writing is the process ofidentifying any possible future events that may affect theelectricity demand. The success of the planning task usingthe scenario method greatly depends on how well theexternal factors (social, political and economic) of thecountry are identified and described in the scenarios.Scenario analysis is the process of examining the con-sequences of electricity generation by various supplyoptions designed for any possible demand scenario. Theinitial supply option would be a single supply growth rate.Only when it is apparent that a mismatch will occur wouldthe planner initiate remedial action to maintain the balancebetween demand and supply. A particular combination ofsupply options and demand scenarios will generate aparticular electricity supply construction program with aparticular average generation cost. This scenario analysisprocess examines the consequences of each combination.The decision-making activity occurs when planners have toidentify the mismatch developed by each outcome andfinally when they must select the most robust outcome.Once the decision for the most robust supply option ismade, the scenario planning process is terminated.

3. Indonesian electricity sector

The electricity business in Indonesia is run by PLN, whomonopolise the business and act as the national electricityauthority. Currently, the national electrification ratio is53% and 57% for the Java–Madura–Bali region only. Theelectricity sector in Indonesia plays an important role in theoverall development of the country. Total generationcapacity has reached 21GW. However, of the 21GW ofinstalled capacity, the actual rated capacity is just 18GW.As the peak load is around 17GW it is obvious that the30% reserve margin as planned by PLN cannot be met,leaving the system in a vulnerable condition. The largedifference between the total installed capacity and the ratedcapacity also suggests that these systems are backed up bysome ageing power plants. The largest system component isthe Java–Madura–Bali supply, representing 73% of totalgeneration capacity, this being generated by only 256 outof the 4766 generating units installed in the whole country.All the large generation units are installed on Java Islandsince past demand was concentrated in this locationbecause it is the most developed island in the country.

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Demand projection

Supply option

Load duty

Investment cost

Generation cost

CO2 emissions

Fig. 1. Model calculation sequence.

Table 1

Candidate power plants for Java–Madura–Bali system (Source: PLN,

2004)

Plants Type Size (MW)

Tanjung Jati B unit 1 Coal steam 660

Tanjung Jati B unit 2 Coal steam 660

Cilacap Coal steam 600

Cibuni Geothermal 10

Kamojang Geothermal 60

Wayang Windhu Geothermal 110

Dieng unit 1 Geothermal 60

Dieng unit 2 Geothermal 60

Patuha unit 1 Geothermal 60

Patuha unit 2 Geothermal 120

Bedugul Geothermal 10

Muarakarang repowering 1 Combined cycle 500

Muarakarang repowering 2 Combined cycle 220

Muara Tawar extension 1 Combined cycle 150

Muara Tawar extension 2 Combined cycle 75

Priok extension 1 Combined cycle 500

Priok extension 2 Combined cycle 220

Pemaron extension Combined cycle 80

Cilegon unit 1 Combined cycle 500

Cilegon unit 2 Combined cycle 230

Upper Cisokan unit 1 Hydropower 500

Upper Cisokan unit 2 Hydropower 500

Muara Tawar Gas Turbine 858

C. Rachmatullah et al. / Energy Policy 35 (2007) 2352–23592354

Operational expenses of PLN’s power plants are mostlybased on foreign currency, while the revenue is purely inthe local currency. Consequently, when this currencycollapsed during the economic crisis of mid-1997, PLNincurred deep financial problems. A selective marketingpolicy aimed at limiting the increase of residentialcustomers resulted in a longer waiting list. In 2003, therewere around 600 000 new applicants, representing up to2.8GW of new generation capacity, awaiting connection.

Long-term electricity system planning for Indonesia ispart of PLN’s responsibility. In the planning process, onlyfour factors namely: population growth, demand–supplystatistics, economics and private generation capacity areconsidered. External influences such as the environment,social, political and technological changes are not takeninto account. Long-term electricity planning uses theconventional method, where future demand is forecastusing historic statistical data and assumptions. Eventhough many scenarios are usually prepared with the aimof including some uncertainties, the method used can stillbe categorised as conventional, because just one scenario ofthe many scenarios, deemed to be the most likely to occur,is chosen for implementation. Currently PLN uses ascenario plan called the ‘limited’ scenario, so called becauseit is based on PLN’s limited ability to provide more supply.Some strategies such as selective marketing or demand sidemanagement programs have been implemented to slowdown demand.

The total supply of the Java–Madura–Bali system is18.4GW, generated from 256 gas and coal-fired powerplants, each of these fuels supplying about 36% of thetotal. To accommodate peak load conditions, gas turbineand hydropower plants are usually employed. Gas turbinesare suited to this type of service as they have theadvantages of a fast start-up time and a low initial cost.However, the heat rate is usually higher, resulting in lowerthermal efficiency. The low efficiency and high fuel costs ofthis type of plant preclude their use as base load units. Themain renewable sources of energy used for electricitygenerations are geothermal and hydropower. A total of724MW of geothermal are currently in operation as baseload units, while hydropower totals 2567MW (PLN P3B2002).

Kleeman (1994) has identified some typical uncertaintiesthat planners have to deal with in the Indonesian energysector. Amongst these are incomplete and unreliabletechnical and economic data, international energy pricevolatility, unknown long-term technological and politicalchanges and unexpected changes in the availability ofresources. The challenge is therefore to develop amethodology that manages to cope with these contingen-cies as far as possible.

4. Model overview

The simulation model used in this study is a modificationof the model used by Soontornrangson et al. (2003). Most

of the modifications were made to more accurately reflectthe current condition of the Indonesian electricity sector.The main calculations performed by the model are thedetermination of future demand, supply options, loadduties, costs and emissions (Fig. 1). Annual demand foreach load type (base, intermediate and peak) is firstcalculated based on the selected demand growth rates. Byadding the required installed reserve margin and thetransmission and distribution losses, total annual desiredcapacity is obtained.The next step is to determine the supply options. Table 1

shows the candidate power plants planned for constructionin the near future by PLN for the Java–Madura–Balisystem.Different types of plants are used to meet the projected

required capacity. To reflect the current practice in theJava–Madura–Bali supply system, the simulation modelused steam coal and geothermal units for the base load, gascombined cycle units for the intermediate load and oil-firedcombustion turbine and hydropower units for the peak

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Table 2

Typical EPC costs

Type Unit size (MW) EPC price (USD/kW)

Coal-steam 400–600 900

Gas turbine 100 350

Combined cycle 500 700

Hydropower 4100 2000

Geothermal 55–110 980

Table 3

Projection of electricity demand growth for Java–Madura–Bali

(2000–2020)

Source Annual demand

growth (%)

World Energy Outlook (IEA, 2002) 5.4

International Energy Outlook (EIA, 2004a) 3.7

Kleeman (1994) 7.8

PLN (2004) 6–7

PT. Data Consult (2002) 6–8

Prijono (1997) 8.5

Country Analysis Report (EIA, 2004b) 10

Historical growth between 1994 and 2002 (PLN, 2003) 11

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

3% 5% 7% 9% 11% 13%

supply growth rate

life

cycl

e co

st (

US

c/kW

h)

demand = 5%demand = 11%average

Fig. 2. Life cycle generation costs for various supply growth rates.

C. Rachmatullah et al. / Energy Policy 35 (2007) 2352–2359 2355

load. The normal supply option is automatically calculatedfrom the input of initial supply growth rate. Only when thewarning indicator detects a mismatch between supply anddemand, does the operator take any remedial action.

In the load duty section, the balance between demandand supply for each load type is checked. When thedemand for a certain load cannot be met, other units areused to supply the shortfall. For example, if the supply forbase load is insufficient, then intermediate units areoperated to meet this excess base load demand, i.e. theplant is operated on an ‘order of merit’ basis. The loadduty arrangements also consider the spinning reserve thatis required, which in this case is 600MW, i.e. the size of thelargest unit in the system. As the operational requirementsof each plant are now known, the total energy producedcan be calculated using suitable load factors. Based on theenergy produced and appropriate emission factors, the CO2

emissions can be calculated.The capital needed to provide the electricity supply is

determined in the investment calculation section. Theengineering procurement and construction (EPC) costs(Table 2) were calculated for each plant using officialfigures (PLN Jasdik, 2003). After the costs and energyproduced are known, the life cycle generation cost can thenbe calculated. In calculating the cost of electricity genera-tion, the electricity costs were separated into four maincategories, namely: fixed capital cost, fixed operation andmaintenance cost, variable fuel cost and variable operationand maintenance cost. Taxes were not included in theanalysis because none are applied to power generation inIndonesia.

5. Model use

Six analyses in total were performed with the model. Thefirst analysis determined the cost of electricity when a widerange of supply growth rates tried to meet the maximumand minimum demand growth rates considered possible.These rates are known as the boundary levels. The supplygrowth rate that produced the least electricity cost was usedas the supply growth rate for the scenario planningexercise. The second analysis compared the selected supplygrowth rate obtained from the first analysis to the supplygrowth rate used by PLN for the Java–Madura–Balielectricity system. The third analysis used the demandgrowth rate projected by PLN to test both the supplygrowth rate determined by the scenario planning method

and the rate applied by PLN. The sensitivity of the modelto various parameters was tested in the last three analyses.Further calculations determined the magnitude of theemissions, the effect of mitigation and the increase in thegeneration cost due to the mitigation measures.

6. Results

6.1. Scenario planning supply growth rate

Prior to the first analysis, the two demand boundarieswere chosen. The demand boundaries used were 5% and11% for the lower and upper boundaries, respectively.These values were assumed after inspection of theprojections made by various other authors and authorities(Table 3). The average generation cost calculated for arange of supply growth rates (3–13%) to meet each ofthe boundary demand growth rates was then calculated(Fig. 2). The range of supply growth rates is larger than thedemand growth rate range to ensure that the (unknown)optimum supply growth rate lies within the range ofplausible values tested.Fig. 2 shows that the lowest average generation cost for a

5% demand growth rate occurs when the supply growthrate is 5%. Similarly, when the demand growth is 11%, thelowest generation cost occurs when the supply growth isequal to 11%. This result, i.e. that the lowest cost occurs

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Table 4

Projected demand by PLN over 15-year period (1998–2013)

Year Demand (GW)

1998 9.876

1999 11.032

2000 11.796

2001 12.577

2002 13.374

2003 14.187

2004 14.522

2005 15.386

2006 16.275

2007 17.165

2008 18.226

2009 19.344

2010 20.539

2011 21.798

2012 23.141

2013 24.557

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4.0

6% 9% 10.4%

supply growth rate

life

cycl

e co

st (

cen

t/kW

h) Balanced supply

Over-supply

Fig. 4. Generation costs for PLN-based demand for various supply

growth rates.

C. Rachmatullah et al. / Energy Policy 35 (2007) 2352–23592356

when supply and demand growth rates are equal is asexpected. However, such coincidence rarely occurs inpractice. Soontornrangson et al. (2003) suggest that thelowest cost for the range of supply growth rates costswithin the demand boundaries is the average of the leastgeneration costs, which is also shown in Fig. 2. The lowestaverage generation cost occurs when the supply growthrate is 9%. This supply growth rate is hereafter known asthe scenario planning supply growth rate.

6.2. Scenario planning growth rate versus PLN supply

growth rate

This analysis compared the supply growth rate used byPLN with the supply growth rate determined by thescenario planning method to meet any possible demandwithin the boundary levels. The supply growth rate used byPLN originally was 10.4% per year, applied in 1993 over a10-year planning period. The economic crisis caused PLNto lower its supply growth rate to 6%, starting from 2003(PLN 2004). To provide a fair comparison, this study usedboth the PLN supply growth rates. Fig. 3 shows thecomparison of the average life cycle generation cost for thewhole planning period for a range of hypothetical futuredemands using the scenario planning, PLN pre-crisis(10.4%) and PLN post-crisis (6%) supply growth rates. Itis shown that for most demand rates the supply growth rateof 9% produces the lowest average generation costs. The10.4% supply growth rate only yields the lowest generationcost when the demand is 10% or 11%, i.e. close to thechosen construction rate. The 6% supply rate onlyprovides the lowest average generation costs for similardemand rates, i.e. when the demand rate is 5% or 6%.

6.3. Analysis of the PLN plan

This analysis tested the scenario planning plant con-struction growth rate, i.e. 9%, against the supply growthrates (6% and 10.4%) determined by the conventionalplanning method under a set of future demands projectedand used by PLN (Table 4). This analysis also tested theuse of the scenario planning method when the existingcapacity is similar to the desired capacity, i.e. a balanced

3.23.33.43.53.63.73.83.94.0

5% 6% 7% 8% 9% 10% 11%

demand growth rate

life

cycl

e co

st (

cen

t/kW

h)

supply = 6%supply = 9%supply =10.4%

Fig. 3. Comparison of scenario planning, PLN pre- and post-crisis growth

rates.

supply condition, and when the scenario planning has todeal with the result of a past mistake, e.g. an oversupplycondition.Therefore in this analysis, both supply growth rates used

by PLN, i.e. the pre-crisis supply growth rate (10.4%) andfor post-crisis supply growth rate (6%) were tested andcompared to the 9% initial plant construction growth rateobtained from the scenario planning analysis. Thebalanced supply scenario assumed that the installedcapacity was the same as the desired capacity, while theoversupply scenario used the real installed capacity ofJava–Madura–Bali system on the first year of the planningperiod, i.e. 1995. The actual installed capacity was greatlyin excess of the desired capacity, giving an installed reservemargin of 60% for the first year in the planning period.Fig. 4 shows the average generation costs given by eachinitial construction growth rate when used to supply thePLN-projected demand.For the balanced condition, the scenario planning rate

produced the lowest average generation cost. However,the lowest average generation cost for the oversupply

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1.830

1.840

1.850

1.860

1.870

1.880

1.890

1.900

1.910

1.920

6% 9% 10.4%

supply growth rate

ele

ctr

icit

y g

en

era

ted

(T

Wh

) Balanced supply

Over-supply

Fig. 6. Power generated by each supply growth rate.

Table 5

Generation and total costs for a 9% demand in an oversupply condition

Supply growth

rate (%)

Ave. generation

costs (cent/kWh)

Total costs

(US$ million)

6 3.6729 142 238.47

9 3.7201 142 835.18

10.4 3.7441 143 451.89

C. Rachmatullah et al. / Energy Policy 35 (2007) 2352–2359 2357

condition was produced by the 6% initial constructiongrowth rate. This is not surprising as this plan absorbed theexcess established by the previous oversupply plan. Theexcess capacity caused delays in the construction of newplants at an early stage of the planning period to reduce thereserve margin. This resulted in savings in plant operatingcosts and interest payments on the capital required. Ahigher supply growth rate would have further increased theexisting excessive reserve margin. However, the changefrom a 10.4% to a 6% supply growth rate incurred somecosts for PLN. They had to pay out significant amounts ofmoney for the fines, interest and the arbitration lawyers,none of which were included in this simulation.

Fig. 5 shows the difference in total costs for the balancedsupply condition and the oversupply condition. Bycalculating the difference between the total cost for 9%and 10.4% supply growth rates, it can be concluded that upto US$3.5 billion could potentially be saved over 15 years ifscenario planning was applied right at the beginning ofperiod.

The lowest supply growth rate (6%) certainly producesthe lowest total costs. The dominance of capital costssuggests that these significantly influence the total costs. Alow initial supply rate would inevitably decrease the totalfixed capital costs, because this growth rate requires thebuilding of fewer plants. Adopting this strategy will alsoincrease the variable costs, because some expensive unitsmust be operated to meet the load duty, but the highcapital costs still dominate the total costs.

However, a low supply rate will also fail to produce theelectricity required, causing the system to operate in acritical condition. Low production eventually results inhighest generation costs and the likelihood of an unmetreserve margin. Fig. 6 shows the electricity production ofeach supply growth rate.

Fig. 6 shows that the quantity of electricity produceddiffers significantly between the 6% and the 9% supplygrowth rate under a balanced supply condition for thePLN-based demand indicating that all the plants wouldoperate efficiently. In contrast, in the oversupply condition,

64.000

65.000

66.000

67.000

68.000

69.000

70.000

6% 9% 10.4%

supply growth rate

tota

l co

sts

(MU

SD

)

Balanced supply

Over-supply

Fig. 5. Total costs for PLN-based demand under various supply growth

rates.

electricity production does not differ to the same degree,indicating that the plants in the 9% scenario would not berun efficiently, with many plants mothballed. It can beconcluded that if scenario planning has to cope with amismatch condition in the first year of the planning period,then it would not always produce the best result. Table 5shows the results of simulating a 9% demand, i.e. equal tothe scenario planning growth rate, with various supplygrowth rates and an oversupply condition with a reservemargin of 60% in year one of the planning period.It was found that for the oversupply condition the lowest

average generation cost still resulted from a 6% supplygrowth rate even if the demand increased at a rate of 9% orexactly the same as the scenario planning’s growth rate. Incontrast, for a balanced condition the lowest generationcost is given by the 9% supply growth rate (Fig. 4). Thisevidence suggests that scenario planning will only producean optimum result when the method does not have to bearthe penalty arising from past planning mistakes.

6.4. Sensitivity analyses

The effect of changing the value of the key variables onthe performance indicator used in this study, i.e. electricitygeneration cost was tested. Sensitivity to uncertainties incapital cost, interest rate and discount rate were tested tounderstand their impact on generation costs. The simula-tions using the balanced-supply condition indicate thatscenario planning still provides the lowest average genera-tion cost (Table 6). It was found that the results are onlysensitive to the discount rate factor. In all simulations, the

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Table 6

Results of sensitivity tests

6% supply 9% supply 10.4% supply

Test 1: Capital cost

Average generation cost (cent/kWh) 3.7498 3.7029 3.7327

Total costs (MUSD) 68 666 68 722 69 283

Test 2: 3% interest rate

Average generation cost (cent/kWh) 2.3965 2.3469 2.3479

Total costs (MUSD) 43 954 43 725 43 835

Test 3: 7% interest rate

Average generation cost (cent/kWh) 2.8535 2.8102 2.8195

Total costs (MUSD) 52 261 52 216 52 442

Test 4: 6% discount rate

Average generation cost (cent/kWh) 2.3639 2.3485 2.3751

Total costs (MUSD) 40 383 40 688 41 152

Test 5: 12% discount rate

Average generation cost (cent/kWh) 1.7299 1.7251 1.7522

Total costs (MUSD) 27 787 28 122 28 571

Table 7

CO2 emissions for BAU and mitigation scenarios

6% supply 9% supply 10.4% supply

Business as usual

CO2 emissions (Mtonne) 1 548 1 560 1 562

Average generation cost (cent/kWh) 3.5117 3.4782 3.4997

Total costs (US$ million) 64 239 64 474 64 878

Mitigation in supply measures

CO2 emissions (Mtonne) 1 320 1 328 1 330

Average generation cost (cent/kWh) 3.5487 3.5273 3.5572

Total costs (US$ million) 64 879 65 295 65 799

Difference :

CO2 emissions (Mtonne) 228 232 232

Total costs (US$ million) 640 821 921

Value of CO2 reduction (US$/tonne) 2.8 3.5 4.0

C. Rachmatullah et al. / Energy Policy 35 (2007) 2352–23592358

lowest generation cost is always given by the 9% supplygrowth rate with the 6% supply rate generally the mostexpensive supply option. Changes in discount rate, how-ever, cause the 10.4% supply rate to produce the mostexpensive electricity.

6.5. CO2 emissions analysis

The electricity sector is one of the main sources of CO2

emissions, especially in industrialised countries, where 25%of total world’s population consume two-thirds of theworld’s electricity (EIA 2004a). The EIA projects thatworld electricity consumption will increase by 2.3% peryear, so that the current total demand will nearly double inthe next 30 years. A significant contributor to this increasewill be the developing countries of the Asian region wherethe need for electricity is growing sharply due to rapideconomic growth and/or to government policies to increaseaccess to electricity for their citizens. Birdsall and Dixon(1991) conclude that even though the developing countriescurrently contribute only a relatively small proportion oftotal global emissions, they should be considered as sources

of future growth in emissions. Since Indonesia is one ofmore than 180 signatories to the Kyoto protocol, thenation has at this stage at least a moral obligation to limitCO2 emissions from its energy sector where possible. Sincepower generation activity is a significant contributor toglobal emissions, an alternative expansion plan that couldemit lower emissions would be environmentally beneficial.The scenario plan described above was therefore used toinvestigate the impact of CO2 mitigation strategies onelectricity generation costs.Although fuel switching (coal to gas) is a more cost-

effective strategy, Indonesia does not have an extensivenatural gas network and therefore an integrated coalgasification combined cycle plant was chosen to replace thecoal fuelled steam power plant. The intermediate load unitwas replaced by a gas combined cycle with advancedtechnology. Both technologies have higher efficiency levelsthan the BAU technologies, described in Section 4. Table 7summarises the results of the analysis.The analysis compares total emissions released by the

BAU scenario and the mitigation scenario over the 15-yearperiod. It was found that the application of higher

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efficiency technology reduced the CO2 emissions in theBAU scenario by up to 232 million tonnes (15%) in 15years at a cost of US$4 per tonne.

The value of the CO2 reductions calculated in this study issmaller than that obtained by other authors. Shrestha andMarpaung (2002) calculated the cost of CO2 abatement forthe whole energy sector in Indonesia and found that theCO2 mitigation cost would be US$7.8 per tonne for a 10%reduction target. Adi et al. (1997) found the mitigationwould cost as much as US$167 per tonne of CO2 for 20%reduction target. Khatib (1997) stated that the estimates ofthe cost of CO2 emission reduction may vary between US$5and 125 per tonne, and depend greatly on the choice ofdiscount rate and variations in the scenario assumptions.

7. Conclusions

Scenarios have already been used in Indonesia to prepareexpansion plans for future electricity supplies. However,their use was not as part of the scenario planning method,which requires planners to consider all possible futurescenarios equally. In their past application, scenarioanalysis was used as an alternative method of forecastingbecause eventually the scenario considered most likely tooccur was chosen and then acted upon.

This study used lower (5%) and upper (11%) boundaryscenarios, rather than writing scenarios with their ownlabels such as low, medium, high and limited scenarios asused by PLN. Using these demand boundaries, thesimulation model was used to obtain the most robustinitial construction growth rate. This study then testedwhether this scenario planning construction growth ratewas superior to the rates used by PLN (based onconventional planning) when dealing with any demandrates within the demand boundaries or even whencompared to the PLN-based demand.

The study showed that in most situations, scenarioplanning was superior to conventional planning in produ-cing an electricity supply construction plan under variouspossible future electricity demands. In addition, scenarioplanning was able to cope with various demands within theselected demand boundaries and demonstrated that the 9%initial construction growth rate produced the least costelectricity. It was found, however, that scenario planningcould give its optimum benefit when the system had aninitial installed capacity close to the desired capacity in thefirst year of the planning period.

The simulation model was also used to calculate the CO2

emitted from the plants in the expansion plan, developedusing the scenario planning method. Under the mitigationscenario, it was found that the total CO2 emissions fromthe electricity plan could be reduced by up to 15% from1560 Mtonne to 1328 Mtonne. The application ofmitigation measures on the supply side increased the totalcosts by US$921 million compared to the US$64.9 billionin the BAU scenario. The mitigation cost of aroundUS$2.8–4.0 per tonne carbon obtained in this study is

lower than that calculated by other studies due tovariations in the assumptions. This cost is also lower thanthe marginal CO2 abatement costs of US$24.1 per tonneCO2 for OECD countries (IEA 1998). This low cost mayindicate that power projects in the Java–Madura–Balisystem may be attractive for funding by industrialisedcountries under the Clean Development Mechanism, asspecified in the Kyoto Protocol. Accordingly, this findingsupports the proposition that if actions to minimiseemissions from the electricity sector are considered fromthe start, then they can be reduced from projected levels ata reasonable cost.

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