GREENING THE ELECTRIC POWER- GENERATING SYSTEM: A HYPOTHETICAL OF SECTORAL APPROACH IN INDONESIA1

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127

OF SECTORAL APPROACH IN INDONESIA

¹

Maxensius Tri Sambodo

Economic Research Center – Indonesian Institute of Sciences Jl. Jend. Gatot Subroto 10, Jakarta, Indonesia

smaxensius@yahoo.com

Abstract

This paper depicts a ‘green’ electric power-generating system for Indonesia that takes reduction of CO₂ emissions into account. By developing an optimisation model for a future electric power-generating system, we evaluated the benefits from the sectoral approach based on CO₂ emissions intensity data. There were three main findings. First, by applying demand-side management and adopting more advanced steam-coal technology, Indonesia can directly reduce CO₂ emissions. However, in order to obtain an effective reduction in emissions intensity, renewable energy needs to be more readily available. Second, by deploying new steam-coal technology, Indonesia can participate actively in the sectoral approach. We found that the sectoral approach could allow a greater emissions reduction in the electricity sector than would the National Action Plan. Indonesia will obtain more carbon credits by selecting less stringent parameters during the early periods of negotiation. Third, advanced technology can help reduce the risk of a rapid increase in fossil-fuel prices, but if Indonesia can implement demand-side management effectively and increase the share of renewable energy in the system, there can be a ‘triple dividend’, such as reductions in generating costs, in carbon emissions, and in carbon intensity. Thus, supply and demand-side policies both need to be implemented together.

Keywords: Electric Power-Generating System, Optimisation Model, Sectoral Approach JEL Classification: Q42, Q48, Q54, Q55

INTRODUCTION

According to the International Energy Agency (IEA, 2015), direct CO₂ emissions from electricity and heat was equivalent to 42 per cent of global greenhouse gas emissions in 2013.

Further, about 72 per cent of the total greenhouse gas (GHG) emissions from

power generation in 2013 originated from coal-fired power generators. Al- most half of the energy-related CO₂ emissions in 2020 are expected to be by the major emerging economies, where the power sector and industry play an even larger role (IEA, 2009).¹

Table 1 shows that, in 2013, the share of CO₂ emissions from

1 According to World Energy Outlook 2008 (IEA, 2008), other major economies are Brazil, China, India, Indonesia, Russia and Saudi Arabia.

1An earlier version of this paper was prepared for the 4th International Conference on Applied En- ergy, 5–8 July 2012, Suzhou, China. This work is a part of a dissertation.

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Indonesia was about 1.3 per cent of the total world emissions, that is, much smaller than China and India. In terms of CO₂ emissions per capita and CO₂-GDP (gross domestic product) intensity, Indonesia has greater intensity than India. According to Sambodo and Oyama (2012a), only in China and India will the expected cumulative CO₂ emissions decrease but in Indonesia they will tend to increase. Further, regarding energy-CO₂ relations in Indonesia, China and India, there are three main findings that we have made (Sambodo and Oyama, 2012a). First, in 1960, China’s CO₂ emissions were 6.5 times larger than India’s and 36.5 times larger than Indonesia’s; but in 2007, China emitted 4.1 times more CO₂ than India and 16.5 times more than Indonesia. Further, in 1960, India emitted 5.6 times more than Indonesia, but in 2007, it was about 4.1 times more.

Thus, during the past 50 years, CO₂ emissions from India and Indonesia have shown higher growth than China, and growth of emissions from Indone- sia is the highest of the countries being compared. Second, although its share of CO₂ emissions from solid fuels is the lowest, it has increased rapidly since the mid-1980s and now it is about 38

per cent. This is because Indonesia is relatively a latecomer in coal utilisation.

Third, in China, the intensity increased from 2236 kt/Mtoe in 1971 to about 3340 kt/Mtoe in 2007; in India it increased from 1310 kt/Mtoe to about 2708 kt/Mtoe; and in Indonesia from 1076 kt/Mtoe to about 2082 kt/

Mtoe. Thus, China showed the lowest increase during this period and Indo- nesia the highest. There has been much research that supports these findings on CO₂ emissions and climate change, for instance, by Zhou et al. (2011) and Cai et al. (2011a; 2011b).

As can be seen in Figure 1, in In- donesia, the production of electricity and heating make the highest contribu- tion to CO₂ emissions from the energy sector and the share will increase in the future if the projected energy system depends on carbon-intensive sources.

There are three main policies that will determine the structure of Indonesia’s power system. First, in July 2006, the government launched its first fast- track program to increase national electricity capacity over the years 2006 to 2009 by between 7,900 MW and 11,422 MW. The program was called the 10,000 MW Fast Track Program and it was based on steam-coal power

Indicators China India Indonesia World

CO₂ emissions (million tonnes of CO₂)* 9,023 1,869 425 32,190

CO₂/TPES (tonnes CO₂/terajoule) 71.3 57.6 47.5 57.7**

CO₂/Population (tonnes CO₂/capita) 6.6 1.49 1.7 4.52

CO₂/GDP (PPP) (kg CO₂/2000 USD) 0.63 0.32 0.21 0.41**

Table 1. Comparative Indicators in 2013

*CO₂ emissions from fuel combustion only; **Non-OECD total.

Source: IEA (2015)

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plants. About 83 per cent of that additional capacity was for Java, the main beneficiary of the first fast-track electricity program. Second, in January 2010, the government launched the second fast-track program that will add to capacity by about 10,000 MW.

But now the additional capacity is not based on coal only but also on renewable energy and gas. The project has been a little delayed even though the fast-track program is still continuing.

Finally, early May 2015, government launched 35,000 MW power program.

Based on the work plan of the fast-track program for the power sector, in 2014, the share of steam power plants (coal-based) of the total installed power generation capacity will rise from about 48.8 per cent in 2006 to about 63 per cent in 2014 (Sambodo and Oyama, 2011a, 2011b).

This also indicates that coal will be the backbone of the primary energy sup-

Figure 1. Share of CO2 Emissions as Total Share of the Energy Sector (2007) Source: International Energy Agency (2015)

ply for the national electricity system in the medium term. Further, according to PT. PLN’s business plan for 2011–2020, CO₂ emissions will increase from about 110 million tonnes in 2010 to about 236 million tonnes in 2020 if there is no policy intervention to promote renewable energy and steam-coal plants continue to use conventional technology (PT. PLN, 2010).

Following information from the Indonesia Energy Balance Table, in 1995, the share of renewable energy, such as hydropower and geothermal, to fossil fuel was 36 per cent but it decreased rapidly to about 16 per cent in 2009 (MEMR, 2006; MEMR, 2010). This indicates that the power generation system will be trapped into a carbon ‘lock in’ situation if there is no well-designed inclusion of a green power system in the future.

Indonesia must pursue the best strategy to reduce CO₂ emissions

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effectively, especially after the Kyoto Protocol was agreed. The most important challenge is how to redesign the Indonesian electric power sector to avoid carbon ‘lock in’ in the future.

Our research aims answer questions of whether and how Indonesia could participate in the sectoral approach (SA), and then adopt new advanced steam-coal technology focusing on the electric power-generating system. Thus, we consider that participating in an SA and adopting new advanced steam-coal technology can help Indonesia to reduce its CO₂ emissions more effectively by applying an optimisation modelling approach to obtain an optimal power- generating system expansion plan, and evaluating three types of steam-coal technology: subcritical, supercritical and ultra-supercritical. This strategy will complement the National Action Plan (NAP) released as part of regulation 61 of 2011 and the Clean Development Mechanism (CDM) for reducing greenhouse gas emissions.

LITERATURE REVIEW

The COP 13 in December 2007 reached agreement on the Bali Action Plan, which had five major elements (Aldy and Stavins, 2009): a long-term global climate policy goal, emissions mitigation, adaptation, technology transfer, and financing. As part of the recommended mitigating action, sectoral approaches have been developed for the electricity sector. Similarly, Sawa (2008) said that the term ‘sectoral approach’ was coined in the Bali Action Plan as ‘cooperative sectoral approach.’

He also said that ‘the Japanese government has led the world in making specific proposals for employing a sectoral approach as a basis for negotiations on the next framework to follow the Kyoto Protocol’. Schmidt et al. (2008) proposed that electricity and five other major industrial sectors: iron and steel; chemical and petrochemical;

aluminium; cement and limestone; and paper, pulp and printing, be candidates for the SA.

With respect to the power- generating sector, Sawa (2008) argued that there is a need to shift the power mix to low-carbon resources when considering the national circumstances.

He also argued that efficiency improve- ments in the power sector, especially improvement in thermal power plants that use fossil fuels, have significant meaning for global warming counter- measures. He also suggested that the most appropriate commitment to nu- merical targets would be to increase the average efficiency of coal-fired power plants. He proposed three channels:

transferring technology and know-how from private companies in developed countries; extending information on best practices and providing on-site diagnosis and guidance; and financial support by private companies for such activities should be a major theme or concern of government policies and measures.

Two instruments for sectoral mitigation are sectoral crediting and sectoral trading (IEA, 2009). Under both, the baseline would be set below business-as-usual so that the host

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country contributes to global mitigation (IEA, 2009). There are two ways to estimate crediting baselines (IEA, 2009). The first is to use a sectoral crediting baseline that is inspired by the CDM. The second is the dynamic baseline method. According to the CDM model, the baseline emissions factor is calculated using the weighted average of the emissions factors for all operational power plants and a cohort of newly built power plants. The CDM model assumes a crediting baseline that is fixed once and for all over the agreed crediting period.

RESEARCH METHODS

We only consider technology switching on steam-coal technology because technology is the key factor in the SA for developing power plant expansion in a desirable and optimal way. Our optimisation model contributes to the investigation of what would happen were new steam-coal plants to be in- vested with the three types of technology proposed: subcritical, supercritical and ultra-supercritical.

In preparing the optimisation model, we explore theoretical and empirical evidence from previous works such as Anderson (1972), Dong et al. (2011), Rowse (1978), Shrestha and Marpaung (1999, 2006), Oyama (1983, 1987), Rachmatullah et al.

(2007), Sarker and Newton (2008) and Sambodo et al. (2012b). The energy- planning horizon covered periods between 2010 and 2019. About 77 per cent of the national rated capacity and electricity production is based in the

Java–Bali system (PT. PLN, 2009) and our analysis concentrates on Java–Bali power plants, whose current share of installed capacity is about 72.3 per cent.

Input Data and Assumptions Load Duration Curve

There are several methods for forecast- ing the load duration curve (LDC).

Tanoto et al. (2010) applied artificial neural networks (ANN) to forecast long-term peak loads between 2010 and 2018. Suhartono and Endharta (2009) used Elman-Recurrent Neural Networks (RNN). Because we only have information on peak loads, we assume that the pattern of hourly consumption remains unchanged during the period of analysis or we assume that the pattern of electricity consumption between 2010 and 2019 follows the pattern of 2006.²

Emissions Intensity

We calculate CO₂ emissions intensity for each type of power plant by applying the power generation formula as follows (Graus and Worrell, 2011):

CO CO ⁻INTENSITY_j⁼

∑

ⁿ

(

C_iI_i

)

P_j

2 1 /

where i indicates the fuel source 1, …, n; C_i indicates CO₂ emissions factor per fuel source (tonne CO₂/TJ) (IPCC, 2006); I_i, the fuel input per fuel source (TJ); and P_j, the energy production per fuel source (GWh) for plant j.

2 We used the daily LDC on 21 November 2006 in Java-Bali system as a basis because the peak load reached the highest level on the day.

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No.DescriptionUnitCombine cycleGas turbineSteam subcriticalSteam su- percriticalSteam ultra- supercriticalGeother- malHydro, large 1LifeYear30303030302550 2Discount rate%12121212121212 3Recovery factor0.1240.1240.1240.1240.1240.1270.120 4Investment costUSD (million)563901,2001,4001,600369500 5Capital cost/yearUSD (million)/year69.8311.17148.97173.80198.6346.9860.21 6Capital costUSD/kW7504501,2001,4001,6003,3502,000 7CapacityMW7502001,0001,0001,000110250 8CF%85908080808750 9ProductionGWh/year5,5851,5777,0087,0087,0088381,095 10Fuel type (pure)gas/natural gasgas/natural gascoal lignitecoal lignitecoal lignite-- Before fuel cost increases Gas price (coal

price)USD/MMBTU (USD/tonne)6*6*(50*)(50*)(50*)-- 11SFC gas (SFC coal)

Mscf/kWh (Kg0.00610.0095(0.5388)(0.4875)(0.427)-- /kWh) Kcal/Mscf 12Heat content252,000252,000(4,200)(4,200)(4,200)-- (Kcal/kg) 13Efficiency (net, LHV)%5636384248-- 14Heat rateKcal/kWh1,5362,3892,2632,0481,792-- 15Fuel consumptionMscf (tonne)34,032,52614,947,619(3,776,241)(3,416,599)(2,989,524)-- Compo-USD (mil- Fuel cost204.2089.69188.81170.83149.5-- nentlion)/year ACapacity charge/kWh1.250.712.132.482.835.605.50 BO&M fixUSD/100/kWh0.200.110.340.400.464.104.26 CFuel costUSD/100/kWh3.665.692.692.442.13-- DO&M var.USD/100/kWh0.110.170.080.070.06-- Total costUSD/100/kWh5.226.685.245.395.499.709.75 Total cost in lo- IDR/MWh469,800601,200471,600485,100494,100873,000877,500 cal currency Carbon intensitytonneCO/MWh0.360.560.980.880.770.000.002

Table 2. Assumptions, Emissions Intensity and Generating Cost for New Power Plant

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Note: CF = capacity factor; Recovery factor = PMT(Discount rate/100, life, 1), where PMT is a calculation of the payment for a loan based on constant payments and a constant interest rate; Investment cost = (capital cost x capacity)/1000; Capital cost/year = Recovery factor x investment cost; Production = (CF/100) x capacity x (8.760); SFC = Heat rate/Heat content; Fuel consumption = SFC x Production x 1000; Fuel cost = Fuel consumption x (coal price/100); Heat rate = 860/(Efficiency/100); Capacity charge = capital cost per year/(production x 100); O & M Fix = 2% (or 9% for renewable energy) x (Investment cost/(production x 100)); fuel cost = fuel cost/(production x 100); O & M var. = 3% x fuel cost; plant efficiency is net (sent out basis), LHV (lower heating value). The difference between lower and higher heating values, based on IEA conven- tions, is 5% for coal and 10% for gas; we assume the exchange rate is IDR9000 to the USD; and MMBTU = million metric British thermal units. * Figures according to PT. PLN’s business plan scenario 2010 to 2019. Source: IEA, 2008

NoDescriptionUnit

Combine cySteam sub-Steam Steam ultra-Geother-Hydro, Gas turbine clecriticalsupercriticalsupercriticalmallarge After fuel cost increases

Gas price (coal

price)USD/MMBTU (USD/tonne)1212(100)(100)(100)-- Compo- nentFuel costUSD (million)/year408.39179.37377.6341.7299.0-- ACapacity chargeUSD/100/kWh1.250.712.132.482.83-- BO&M fixUSD/100/kWh0.200.110.340.400.46-- CFuel costUSD/100/kWh7.3111.385.394.884.27-- DO&M var.USD/100/kWh0.220.340.160.150.13-- Total costUSD/100/kWh8.9812.548.027.907.68-- Total cost in lo- cal currencyIDR/MWh808,2001,128,600721,800711,000691,200--

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Generating Cost and Capacity Planning Regarding generating costs for geothermal power plants, we adopted the Ministry of Energy and Mineral Resources Regulation No 2 Year 2011, which requires that PT. PLN buy electricity from geothermal power plants at

Indices Definitions Correspondences

i Old plant: fossil fuel i = 1 (steam); i = 2 (combined cycle); i = 3 (gas turbine); i = 4 (diesel) j Old plant: non-fossil fuel j = 1 (geothermal); j = 2 (hydro) k New plant: fossil fuel k = 1 (steam); k = 2 (combined

cycle); k = 3 (gas turbine) l New plant: non-fossil fuel l = 1 (geothermal); l = 2 (hydro) p For the LDC block p = 1 peak hours; p = 2

intermediate 1; p = 3 intermediate 2;

p = 4 intermediate 3; p = 5 base load

Category Symbol Definition

Electricity demand TD_p

PD_p Duration of load block p in hours

Maximum power demand in MWh in a load block p Generating cost

(IDR/MWh) VCF_i

VCNF_j VCFP_i VCNFP_j VCFN_k VCFNP_k VCNFNP_l

Old fossil-fuel power plant type i – PT. PLN Old non-fossil-fuel power plant type j – PT. PLN Old fossil-fuel power plant type i – IPP Old non-fossil-fuel power plant type j – IPP New fossil-fuel power plant type k – PT. PLN New fossil-fuel power plant type k – IPP New non-fossil-fuel power plant type l – IPP Capacity (MWh) CEF_i

CENF_j CEFP_i CENFP_j ADDF_k ADDNF_l ADDFP_k ADDNFP_l

Old fossil-fuel power plant type i – PT. PLN Old non-fossil-fuel power plant type j – PT. PLN Old fossil-fuel power plant type i – IPP Old non-fossil-fuel power plant type j – IPP

New capacity for fossil-fuel power plant type k – PT. PLN New capacity for non-fossil power plant type l – PT. PLN New capacity for fossil-fuel power plant type k – IPP New capacity for non-fossil power plant type l – IPP

Availability factor AFFi AFNFj AFFPi AFNFPj AFFNk AFNFNl AFFPNk AFNFPl

Old fossil-fuel power plant type I – PT. PLN Old non-fossil-fuel power plant type j – PT. PLN Old fossil-fuel power plant type i – IPP Old non-fossil-fuel power plant type j – IPP New fossil-fuel power plant type k – PT. PLN New non-fossil-fuel power plant type l – PT. PLN New fossil-fuel power plant type k – IPP New non-fossil-fuel power plant type l – IPP

a cost of USD 0.097/kWh. Regarding capacity planning, we used PLN’s business plan 2010 – 2019.

Optimisation Model Nomenclature

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Objective Function

The Indonesian government still provides electricity subsidies, thus, minimising generating costs will lead to minimising subsidies. Economically and politically, minimising generating costs is preferable than minimising CO₂ emissions.

Electricity

production(MWh) Symbol Definition

Old plants OutF_ip OutNF_jp OutFP_ip OutNFP_jp

Fossil-fuel power plant of type i in block p – PT. PLN Non-fossil-fuel power plant of type j in block p – PT. PLN Fossil-fuel power plant of type i in block p – IPP Non-fossil-fuel power plant of type j in block p – IPP New plants OutNEWF_kp

OutNEWNF_lp OutNEWFP_kp OutNEWNFP_lp

Fossil-fuel power plant of type k in block p – PT. PLN Non-fossil-fuel power plant of type l in block p – PT. PLN Fossil-fuel power plant of type k in block p – IPP Non-fossil-fuel power plant of type l in block p – IPP

After we obtain an optimal system for

electricity production, we calculate CO₂ emissions based on the following formula:

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where EI1_i indicates CO₂ emissions intensity (tonne CO₂/MWh) for old fossil-fuel power plants of type i, and EI2_k for new fossil power plants of type k.

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Constraints

(i) Capacity constraints. Output for each type of power generation unit cannot

exceed the total capacity of the current or planned units of this type, multi- plied by the corresponding availability factor (Koelzer, 2012):

Decision Variables

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(ii) Primary energy supply constraints for fossil fuels. Total output from fossil-fuel power plants cannot exceed the upper bound of the fossil-fuel consumption (fuelcons) after we calculate the possibility of energy requirements (req.fos) during the process of energy

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transformation. Parameter req.fos can be estimated using historical data from the Indonesia Energy Balance Table on primary energy supply, which is published by the Ministry of Energy and Mineral Resources.

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(iii) Primary energy supply constraints for non-fossil fuels. Similar to the previous constraint (ii), this constraint implies that the total output from non-fossil- fuel power plants cannot exceed the primary energy supply (primary) that is devoted to producing electricity

for each type of power plant after we adjust for the possibility of energy re- quirement (req.nonfos) during the transformation. The parameter req.nonfos can also be estimated using historical data from the Indonesia Energy Balance Table on primary energy supply.

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(iv) Demand satisfaction constraints. Elec- tricity production at each load block needs to satisfy the demand. We implement demand-side management (DSM)

policy by reducing each load block area (PD) by 5 per cent and 10 per cent respectively.

(v) Contract agreement constraints. PT. PLN needs to purchase an amount of power from the IPP. We introduce a purchase

parameter (purchase) that shows the minimum share of electricity that can be purchased by PT. PLN from IPP.

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(vi) Promoting renewable energy constraints.

We assume that there are flexibilities in setting the share of renewable energy

in the power system. We use pref to indicate parameter preference for renewable energy.

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RESULTS AND DISCUSSIONS On average, between 2010 and 2019, generating costs will increase margin- ally by about 1.3 per cent per annum (see Table 3). Decreasing generating costs are because of new investment in steam-coal power plants that have lower generating costs compared to other types of power plant. Between 2014 and 2015, there will be generating cost increases of about 11 per cent.

This is because additional capacities for those years are the lowest compared with other years. Thus, an increase in power demand needs to be supplied mostly by older power plants that have more expensive generating costs.

We also observe that the technology switching from subcritical to ultra- supercritical has a minor effect only on power generating cost.

We prepared a baseline scenario for an ultrasupercritical scenario and set different values for parameter ‘A’

(see Table 4). If the parameter value for A is zero, this indicates emissions

intensity from all new plants, but if the value of A is one, then this indicates emissions intensity from current plants only. Indonesia can fully benefit from carbon credits, and the government needs to choose the less stringent option between the baseline or A = 0.6 (for example) because it has the highest cumulative emissions reduction, which is about 67 million tonnes or about a 27.3 per cent reduction from the business-as-usual (BAU) scenario (see Table 4). In developing a DSM policy, we observed that the policy could obtain the total emissions credit corresponding to the representative parameter values for A for each DSM strategy as shown in Tables 5. Thus, we know that besides negotiating on the parameter ‘A’, it is also important to investigate the effects of DSM policy because this can significantly affect the amount of carbon credits. If Indonesia commits to implementing the DSM policy, she can demand carbon credit to implement this policy.

Table 3. Generating Cost before Fuel Cost Increases (IDR/kWh)

Note: A: without demand-side management; B: with 5 per cent demand-side management; C: with 10 per cent demand-side management.

Year

Subcritical Supercritical Ultra-supercritical

A B C A B C A B C

2010 719 679 640 722 681 643 723 683 645

2011 652 620 595 656 625 600 659 628 603

2012 670 635 626 675 640 624 678 644 628

2013 722 678 612 728 684 618 731 687 622

2014 678 648 623 684 655 630 688 659 634

2015 773 718 678 779 724 684 783 728 689

2016 767 710 665 773 717 672 778 721 677

2017 763 705 657 769 712 664 773 716 669

2018 809 705 640 815 711 647 819 716 652

2019 794 713 651 800 720 658 805 724 663

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Year

Emissionsintensity(tonneCO₂/MWh)

Electricity pro- duction(GWh)

w/o DSM

Emissions credit (thousandtonnesCO₂) Baseline–ultra-

supercritical A = 0 Total Emis-

sions(Tonne

CO₂)BAU

A = 0.6 A = 0.4 A = 0.2 A = 0.2 A = 0.4 A = 0.6

2010 0.714 0.718 0.722 0.726 146,577 0.000 0.000 0.000 109,515,762

2011 0.715 0.712 0.710 0.708 161,707 356 711 1,067 126,646,026

2012 0.697 0.697 0.696 0.696 178,720 71 143 214 139,003,422

2013 0.680 0.677 0.673 0.669 197,849 752 1,504 2,255 150,614,102

2014 0.656 0.641 0.627 0.613 217,400 3,121 6,243 9,364 158,944,493

2015 0.654 0.643 0.631 0.620 241,461 2,775 5,549 8,324 176,899,483

2016 0.652 0.642 0.633 0.623 266,939 2,551 5,102 7,653 197,065,209

2017 0.642 0.628 0.613 0.599 288,787 4,192 8,384 12,577 207,204,045

2018 0.597 0.588 0.578 0.568 311,839 3,042 6,083 9,125 209,688,861

2019 0.623 0.608 0.592 0.577 355,512 5,506 11,012 16,518 245,763,933

Average 0.663 0.655 0.648 0.640

Cumulative emissions credit 22,366 44,732 67,098

Share of emissions reduction from total emissions in 2019 (%) 9.10 18.20 27.30

Table 4. Emissions Credit from Sectoral Approaches (Without Demand-Side Management)

Note: BAU = emissions intensity without demand-side management under subcritical scenario; emissions credit = (emissions intensity at corresponding A – emissions intensity when A = 0) x electricity production; we do not obtain carbon credit in 2010 because emissions intensity at corresponding A is lower than BAU; w/o DSM = without demand-side management.

Table 5. Emissions Credit from Sectoral Approaches (With 5% Demand-Side Management)

Year

Emissionsintensity(tonneCO₂/MWh)

Electricity pro- duction(GWh)

w 5% DSM

Emissions credit (thousandtonnesCO₂)

Total Emissions (TonneCO₂)BAU Baseline-ultra-supercritical

A = 0

A = 0.6 A = 0.4 A = 0.2 A = 0.2 A = 0.4 A = 0.6

2010 0.716 0.719 0.723 0.726 139,248 0.000 0.000 0.000 104,725,897

2011 0.718 0.715 0.712 0.708 153,622 505 1,011 1,516 121,347,113

2012 0.702 0.700 0.698 0.696 169,784 378 755 1,133 133,557,787

2013 0.684 0.679 0.674 0.669 187,957 926 1,852 2,778 144,308,239

2014 0.646 0.635 0.624 0.613 206,529 2,264 4,527 6,791 151,735,602 2015 0.656 0.644 0.632 0.620 229,388 2,762 5,523 8,285 170,156,546 2016 0.650 0.641 0.632 0.623 253,592 2,282 4,563 6,845 189,078,502 2017 0.636 0.624 0.611 0.599 274,347 3,427 6,853 10,280 197,906,594 2018 0.595 0.586 0.577 0.568 296,247 2,611 5,222 7,832 201,004,155 2019 0.614 0.602 0.589 0.577 337,736 4,191 8,381 12,571 234,230,261 Average 0.662 0.654 0.647 0.640

Cumulative emis-

sions credit 19.344 38,689 58,033

Share of emissions reduction from total emissions in 2019 (%) 8.26 16.52 24.78

Note: BAU = emissions intensity without demand-side management under subcritical scenario; emissions credit = (emissions intensity at corresponding A – emissions intensity when A = 0) x electricity production; we do not obtain carbon credit in 2010 because emissions intensity at corresponding A is lower than BAU; w/o DSM = without demand-side management.

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Now, we assume that the fuel cost for gas and for coal increases 100 per cent. We compare generating costs before the cost increase (see Table 3) and after the cost increase (see Table 6), and then we can make the following two major findings. First, the average generating cost using subcritical, supercritical, and ultra-supercritical technology increases by about 18 per cent, 16 per cent and 14 per cent, respectively.

Furthermore, ultra-supercritical has the lowest percentage change in price.

Second, the DSM policy can help to ease rising fuel costs. The average price difference among three types of technology is between 1 and 2 per cent,

but the benefit obtained from adopting advanced technology will increase when more new investment can be realised. Furthermore, because new steam technology allows lower generating costs when fuel costs increase, new technology can help to minimise electricity subsidies when there is an unexpected increase in fuel costs.

Thus, government supports is needed to boost new investment for more advanced technology. Following the use of ultra-supercritical technology in central Java, the government must provide support, such as facilitating power purchase agreements, providing guarantees and recourse agreements.

Table 6. Generating Costs after Fuel Cost Increases (IDR/kWh)

Note: A: without demand-side management; B: with 5 per cent demand-side management; C: with 10 per cent demand-side management.

Subcritical Supercritical Ultra-supercritical

Year A B C A B C A B C

2010 771 733 698 769 731 696 766 728 692

2011 752 726 707 749 722 703 742 715 696

2012 789 761 751 785 756 747 778 748 738

2013 846 809 750 848 804 746 835 796 737

2014 815 795 778 810 789 773 801 780 762

2015 900 855 824 895 850 819 886 840 809

2016 908 862 827 902 856 821 893 846 811

2017 917 870 835 912 864 829 903 854 818

2018 975 887 837 970 882 832 961 873 822

2019 968 897 852 963 892 846 954 882 836

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CONCLUSION

Because of the fast-track program, coal-steam power plants will be the backbone of the electric power system in the future. We applied a two-step approach. First, we developed an optimisation model to obtain an optimal power system expansion plan for Indonesia that minimises the power generating costs in the Java–Bali system. Second, using the results from the modelling approach in the first part, we calculate an emissions crediting baseline using the sectoral approach. These results can be summarised as follows.

Reducing the total emissions of CO₂ will depend on the following major factors: the value of DSM parameters; the type of technology; and the share of renewable energy. Output production using renewable energy tends to increase because of the decreasing trend of production from old, private power plants. This is because those old power plants have higher generating costs than those using renewable energy.

Thus, an increase in fossil-fuel price will provide more opportunities for an increase the production of renewable energy.

Emissions intensity tends to decrease for the following reasons. First, the share of natural gas consumption tends to rise. Second, the new power generating plants are more efficient in their energy use. Third, the share of renewable energy also tends to increase. However, if emissions intensity increases while we implement the DSM policy, this indicates a carbon ‘lock in’

situation. The DSM policy can effec-

tively reduce emissions intensity if the supply can be increased of renewable energy that has a lower cost than fossil fuels.

We observe that switching technology from subcritical to ultra-supercritical has minor effects on power generating costs. New steam technology has lower generating costs when fuel costs increase. The sectoral approach (SA) implies two major findings: first, by adopting ultra-supercritical technology, carbon emissions reduction from the SA scheme is larger than the National Action Plan and the Clean Development Mechanism scheme.

Second, as coal becomes the backbone of primary energy sources, Indonesia needs to be more flexible in the early stages of SA negotiations. Finally, In- donesia still has huge carbon savings possible by locking the new steam- coal investment to ultra-supercritical technology. This study supports policy recommendations as follows:

1. Instead of constructing subcritical and supercritical coal plants, PT. PLN needs to increase investment in ultra-supercritical plants. There are benefits to utilis- ing ultra-supercritical technology, such as creating low exposure to rapid increases in fuel costs, reducing CO₂ emissions and emissions intensity, and helping to deal with the carbon credit negotiation under the sectoral agreement mechanism.

2. Policies that promote renewable energy will have effects on the output mix in the future if generating costs from using renewable

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energy are much lower than for fossil fuels. Thus, there is a need to improve specific policies to reduce investment risk and to provide more attractive long-term contracts for renewable energy producers.

3. We show that demand-side management could help reduce generating costs and CO₂ emissions. Campaigns to encourage power saving need to be improved.

The government at central and lo- cal level also needs to develop its capacity to monitor, evaluate and enforce power saving. However, considering pricing mechanism in improving energy used needs to be developed.

4. Promoting natural gas use can help the Java–Bali system not only to reduce dependence on oil, but also to reduce CO₂ emissions. However, infrastructure for distributing and storing natural gas needs to be improved. The infrastructure trap for natural gas means that Java–Bali cannot obtain the full economic and environmental benefits of natural gas.

ACKNOWLEDGEMENTS

We would like to express our sincere thanks to our anonymous referees for their invaluable comments and sugges- tions.

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