INDONESIA’S POWER SECTOR: SECTORAL EMISSIONS...

February 2011

INDONESIA’S POWER SECTOR: SECTORAL

EMISSIONS PROFILE AND MITIGATION

OPPORTUNITIES

CE

NT

ER

F

OR

C

LE

AN

A

IR

P

OL

IC

Y

Di

al

og

ue

.

In

si

gh

t.

So

lu

ti

on

s

DRAFT Report for Developing Country Project: Mobilizing Sectoral Mitigation Actions in

Indonesia – Power Sector

Center for Clean Air Policy 2 12/20/2011

ACKNOWLEDGMENTS

This report was prepared by Nitin Zamre and Rashika Agrawal of ICF International and Thomas

Polzin and Anmol Vanamali from the Center for Clean Air Policy. The authors also thank the

main sponsor of this report, the Center for Clean Air Policy (CCAP), and especially Matthew

Ogonowski and Ned Helme. The authors would also like to thank Pelangi Indonesia for their

valuable input throughout the process.

The authors would like to thank Jos Wheatley and Sarah Love of the UK Department for

International Development (DFID) for their generous financial support for the project. The

authors would also like to thank Agus Purnomo from DNPI for his support for this project. For

avoidance of any doubt and for the purpose of clarity, the authors would like to state that this

report is based on an independent study. The contents of the report reflect the views of the

authors and not necessarily the views of the UK or Indonesian governments.




Table of Contents

I. Electricity Sector Overview ...........................................................................................5

I.A. Summary ..........................................................................................................................9 I.A.1. Total Output/Production ................................................................................................9 I.A.2. Revenues, share of GDP ...............................................................................................10 I.A.3. Role of power sector in overall economy ....................................................................11

I.B. Quantitative and Qualitative Characterization of Sector .................................11 I.C. Ownership patterns of the sector.....................................................................12

II. Emissions overview of the sector .................................................................................14 II.A. Background and Discussion of Emissions: Sources, Drivers, and Trends ......14 II.B. Historical annual CO2 emissions from power sector ........................................15

III. Background Assumptions for Sector Analysis ...........................................................17 III.A. Fuel Prices & Emission Factors .......................................................................17 III.B. New Capacity .....................................................................................................17

III.C. Policies included ................................................................................................19 III.D. Demand ..............................................................................................................19

III.E. Reserve Margin Requirements ........................................................................19 III.F. Transmission .....................................................................................................20

IV. Description of analytical model and methodology used ....................................21

V. Baseline (Business as Usual) scenario for the electricity sector ................................22 V.A. Production/output .................................................................................................22

V.B. Annual GHG Emissions .......................................................................................25 V.C. Energy intensity and CO2 intensity .....................................................................27

VI. GHG Mitigation Options and Costs ............................................................................29 VI.A. Selection criteria for consideration of mitigation options .................................29

VI.B. Overview of each mitigation option considered .................................................29

VII. Analysis of GHG Mitigation Scenarios .......................................................................34 VII.A. Mitigation Scenario in 2020 .................................................................................34

VII.B. Mitigation Scenarios in 2030 ................................................................................40 VII.C. Demand Side Management Measures .................................................................45

VII.D. Summary of Mitigation Scenarios Analysis .......................................................46 VIII. Cost Summary .......................................................................................................48

IX. Results Comparison ..............................................................................................50 IX.A. Indonesian Climate Change Sectoral Roadmap by BAPPENAS .....................50 IX.B. Indonesia’s Greenhouse Gas Abatement Cost Curve by DNPI .......................51 X. Conclusions and Next Steps .................................................................................53

APPENDIX 1 .............................................................................................................................55 APPENDIX 2 .............................................................................................................................58 APPENDIX 3 .............................................................................................................................59

APPENDIX 4 .............................................................................................................................60 APPENDIX 5 .............................................................................................................................60 APPENDIX 6 .............................................................................................................................61 APPENDIX 7 .............................................................................................................................63 APPENDIX 8 .............................................................................................................................64 APPENDIX 9 .............................................................................................................................65




APPENDIX 10 ...........................................................................................................................66 Applications of the IPM®...................................................................................................66 IPM® Modelling Approach ...............................................................................................67




I. Executive Summary

I.A. Introduction

Of the 17,000 islands that constitute the archipelagic nation of Indonesia, only two, Java and

Sumatra, account for 90% of the electricity demand. Electricity consumption in Indonesia has

grown at an average rate of 9% from 1990 to 2005 and at 6% from 2001 onwards. As shown in

the table majority of Indonesia’s electricity is generated using fossil fuels.

Capacity Type

Capacity (GW)

Generation (TWh)

CO2 Emissions

(Million Metric Tons)

Average Efficiency (%)

Average CO2 Intensity (Metric

Ton/TWh)

Coal 8 56 51 34% 0.91

Gas 7 26 12 40% 0.46

Oil 11 29 21 31% 0.73

Hydro 4 11 - - -

Geothermal 1 3 0 - -

Total 30 125 84 - -

The Government of Indonesia has taken a series of progressive and regressive steps which have

hitherto ensured government control of the electricity sector. Two-thirds of the generating

capacity is owned by PLN, the stated-owned utility and there is an acute need of private

investment. The power sector in Indonesia faces issues such as government regulated electricity

tariffs which do not reflect the actual cost of supply, lack of investment supporting policies with

a well defined criteria for financial investments, lack of regulation on fuel costs for captive plants

and IPPs which have led to low participation of private sector in the power sector. Emissions

from power sector emissions have grown historically at an average rate of 6% per annum since

1990 reaching to a level of approximately 85 MtCO2 in 2005.

I.B. Analysis

I.B.1. Methodology

The study was conducted in three phases viz (1) Estimate emission upto 2030 under BAU

scenario (2) Estimate impact of a range of carbon prices on emissions from the electricity sector

and (3) Analyze effectiveness of other mitigation policies in comparison with various carbon

price scenarios. These analysis was performed using IPM, a proprietary cost-optimization

developed by ICF International that specifically designed to analyze effects of policy choices on

electricity networks by taking into account assumptions on growth, demand and supply patterns,

commodity price assumptions and policy choices (such as a carbon taxes). This report lays down

details of the first and second phases.

I.B.2. BAU Projections

Installed capacity is expected to grow to 243 GW by 2030 with coal-based generation accounting

for more than half of it.




Installed Capacity under BAU

Year

Annual Installed Capacity (GW)

Coal Natural Gas Oil Nuclear Hydro (>10MW) Other* Total

Sub-Critical Super-Critical

2005 8 - 7 11 0 4 1 30

2010 12 1 9 11 0 4 1 38

2015 21 5 15 13 0 5 5 64

2020 24 19 24 18 0 6 9 99

2025 29 41 39 28 0 6 10 153

2030 37 79 64 44 0 7 12 243

Installed Renewable Capacity under BAU

Year Annual Installed Capacity (GW)

Small Hydro (<10MW) Geothermal Biomass Total

2005 0.1 0.8 0.0 0.9

2010 0.1 1.1 0.0 1.2

2015 0.2 5.3 0.1 5.6

2020 0.2 8.9 0.2 9.3

2025 0.2 10.1 0.3 10.6

2030 0.2 11.3 0.4 11.9

The electricity sector of Indonesia has been growing at a rate of 9% from 1990 to 2005. GHG

emissions in the electricity sector were approximately 83 MtCO2 in 2005. As per this study’s

projections, GHG emissions under a BAU scenario reached 268 MtCO2 by 2020 with a CAGR

of 7%, while reaches 688 MtCO2 by 2030, with a CAGR of 13%. After calculating baseline

emissions, our study studied the impact on the electricity system of a carbon tax ranging from

10$/tCO2 to $90/tCO2. These scenarios were analyzed with the help of a proprietary cost

optimization model developed by our partner consultants ICF International. The The mitigation

options considered reflect options that the Indonesian government have with a new carbon price.

Different options could evolve based on different carbon prices as reflected in our analysis. For

the purposes of our analysis we focused on 2020 and 2030 as our target years to be able to

directly compare to existing analysis as well as to Indonesia’s climate targets.

I.B.3 GHG Mitigation Options

In this paper, step-by-step analysis was done by imposing a carbon cost on the business as usual

(BAU) case, starting at $10/tCO2 and increasing it by $10/ tCO2 in successive iterations. In every

stage, the changes related to capacity mix, generation mix, emissions level and investment

requirements over BAU were noted. Imposition of carbon cost on the system (in BAU scenario)

would in-effect prompt changes viz. retirement of inefficient capacity, conversion of existing

inefficient plants to more efficient plants, preference of more efficient or gas based capacity as a

resort for system expansion and increase in proportion of renewable energy in the generation

mix. However, different combinations of these changes would occur at different carbon costs.

I.C. Overview of Results




I.C.1. Analysis of GHG Mitigation Scenarios

The findings for the variouis mitigation scnarios for the years 2020 and 2030 are illustrated

though an (a) emissions profile (b) installed capacity (c) generation mix and (d) resulting

emissions reduction compared to BAU at a range of carbon prices (0-$80/tCO2)

In 2020, the abatement potential observed under a carbon price of $80/tCO2 32 MtCO2 without

DSM and 54 MtCO2 with DSM. In 2030, the abatement potential observed under a carbon price

of $80/tCO2 is 115 MtCO2 without DSM and 218 MtCO2 with DSM. The reduction in

emissions are due to changes in the system generation mix brought upon by imposing a carbon

price which simultaneiously penalizes dirtier sources (e.g. sub-critical coal) while incentivizing

cleaner sources (e.g. geothermal). The majority of the emissions reductions from the BAU are

achieved through introduction of cleaner sources such as Biomass, IGCC, Nuclear, Hydro, and

Geothermal in varying proportions that replace dirtier srouces uch as Gas, Coal (sub and super-

critical) and Oil.

I.C.2. Summary of Results

Emissions from the power sector are expected to increase to 268 MtCO2 by 2020, and under the

mitigation scenarios, Indonesia has the maximum potential to abate approximately 54MtCO2 in

2020 at a carbon price of $80/tCO2, which contributes to approximately 7% of the total emission

targets of 26% announced by the Indonesian Government. Of this total abatement, contribution

from DSM towards emission reduction can be significant: up to 20 MtCO2 in 2020. By 2030, the

maximum abatement that can be achieved by Indonesia is approximately 200 MtCO2 and of

which 50% can be achieved through DSM programs under our assumptions. Under the Dynamic

MACC analysis, assuming all else is constant, the choice of generation mix depends on two

primary external variables – carbon price and the time period under consideration. The higher the

carbon price, the more incentive to introduce cleaner technologies in order to displace dirtier

ones and the longer the time period, the higher the ability of clean projects to earn their returns

and hence prove more profitable than dirtier ones.

Indonesian policymakers should accordingly focus on implementing mitigation measures with a

longer time frame in mind and an incentive mechanism (imposing a carbon price on emissions is

one method that we consider) that financially incentivizes clean energy technology while

simultaneously penalizes polluting ones.

I.C.3. Conclusion

This analysis suggests that the power sector can contribute to emissions reductions of around 54

MtCO2 and 219 MtCO2 by 2020 and 2030 respectively. While our study suggests that there is a

large potential to reduce electricity demand by increasing the potential of energy efficient

technologies (or DSM), the supply side analysis done in this report demonstrates that the

imposition of a carbon price will also be successful in reducing emissions and making the

generation mix cleaner.




A mitigation potential of 32 MtCO2 and 115 MtCO2 exists by years 2020 and 2030

respecectively (not considering reduced demand from DSM) at a carbon price of $80/tCO2. A

high carbon price scenario may not be feasible in the short –to-medium term, but policy-makers

have a range of domestic policy options that could to lead to the replacement of dirty/inefficient

sources of energy to cleaner ones.

The mitigation analysis presented in this paper is designed for policymakers and industry alike to

evaluate possible implications of different mitigation scenarios. A broad range of carbon price

and integrated demand side management scenarios are presented so that the optimal mitigation

policies and actions can be realized. Policymakers should view this analysis as a resource to

determine whether their public policy decisions can be viewed as effective and efficient. Industry

can subsequently view the analysis to determine what effect public policy decisions will have on

the generation and capacity mix of Indonesia and invest accordingly




I. Electricity Sector Overview

I.A. Summary

I.A.1. Total Output/Production

The archipelagic geography has outlined the electricity sector in Indonesia. More than 17,000

islands constitute this country and all but two are electrically isolated. Only 5 islands are densely

populated viz. Java, Sumatra, Kalimantan, Sulawesi and Irian Jaya with a majority of electricity

demand centers residing in Java and Sumatra. Together, they account for 90% of the total

electricity demand in Indonesia.

Historically, most of the isolated and off-grid systems relied on oil-based power plants,

especially diesel plants and some oil-steam plants. Furthermore, outside of the Java-Bali system

oil plants were used extensively. The main reason for this has been the availability of oil fuels

and ease of oil fuels transportation across islands, and even into remote areas.

Table 1: Indonesia's Electric Power Output

Installed Capacity1 (GW) Power Generation

2 (TWh)

1992 11 42

1993 14 47

1994 14 51

1995 15 59

1996 16 67

1997 19 77

1998 21 78

1999 21 85

2000 21 93

2001 21 102

2002 21 108

2003 21 113

2004 21 120

2005 23 127 1

Installed capacity of PLN Plants 2

Electricity produced from power plants of PLN and the power purchased by PLN

Source: Indonesia Energy Outlook & Statistics 2006




Figure 1: Development of Indonesia's Power System

Historically, the growth of Indonesia’s power sector has been very sluggish. As shown in Table 1

over 1992-2005, the PLN’s installed capacity has grown at a Compound Annual Growth Rate

(CAGR) of 6% with almost negligible change in installed capacity over 1998-2004. Similarly, the

total electricity supplied by PLN has grown at a CAGR of 9% over 1992-2005 with average

growth of 6% since 2001.

I.A.2. Revenues, share of GDP

While the economy has grown at an average rate of 6% per annum over the last 5 years (2005-

2009), the revenue generated from power sector has grown at an average rate of 10%1. However,

the percent share contribution of power sector to the national economy has been consistent over

the same period at an average 1% per annum. In 2009, the power sector contribution to the total

GDP of $5,613 Billion (at current price) was merely $1 Billion2.

This dismal contribution of power sector to the national economy can be attributed to the

inability of the government to reflect the real cost of electricity generation in the consumer

power tariffs in Indonesia. The Indonesian government, which decides the power tariffs in

Indonesia, has failed to revise the tariffs since 2003 due to strong public agitation against tariff

hikes.

The Presidential Decree No. 104, 2003 was the last decree passed for power tariff revision in

Indonesia. The decree created a uniform tariff structure in all regions of the country with the

tariff divided among various consumer categories.

1 Statistics from Bank of Indonesia, March 2010

2 Statistics from Bank of Indonesia, March 2010

0

5

10

15

20

25

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

(GW

)

0

20

40

60

80

100

120

140

(TW

h)

Installed Capacity of PLN Power Plants (GW)

Electricity produced by PLN's Power Plants and Power purchased by PLN (TWh)




I.A.3. Role of power sector in overall economy

The electricity consumption in Indonesia has grown at an average rate of 9% from 1990 to

20053. Although the industrial sector has dominated the electricity consumption among the

different consumer categories, its share in the total electricity consumption has dropped from

56% in 1990 to 41% in 2005. Over the same period, the share of residential consumption has

increased from 29% to 37% mainly due to increase in electrification ratio and extremely slow

pace of increasing electricity tariffs. Also, the electricity consumption of commercial consumer

has increased from 15% to 22% over 1990-2005 while the transportation sector has had a

consistent level of electricity consumption. Figure 2 illustrates the electricity consumption

pattern of different consumer categories over time.

Figure 2: Electricity Consumption by Sector

I.B. Quantitative and Qualitative Characterization of Sector

By the end of 2005, 53% of installed capacity was based on oil and gas. Despite being one of the

largest net exporters of coal in the world, coal supported only 33% capacity in Indonesia. Also,

hydro supported 12% of installed capacity while the rest was based on geothermal and biomass

resources, which are abundantly available in Indonesia.

Table 2 below provides details of Indonesia installed capacity in 2005 across various fuels, while

Figure 3 lays out the age characterization of the installed capacity. As can be observed most of

the plants in Indonesia are old and falls in the 5-20 years age category.

Table 2: Breakdown of facilities by fuel type, 2005

Capacity Type

Capacity (GW)

Generation (TWh)

CO2 Emissions




Ton/TWh)

Coal 8 56 51 34% 0.91

Gas 7 26 12 40% 0.46

Oil 11 29 21 31% 0.73

3 Indonesia Energy Outlook and Statistics, 2006




Capacity Type

Capacity (GW)

Generation (TWh)

CO2 Emissions




Ton/TWh)

Hydro 4 11 - - -

Geothermal 1 3 0 - -

Total 30 125 84 - -

Source: PLN Statistics, ICF Research on Captive Plants

Note: Data on 2005 Installed capacity in Indonesia includes 7GW of existing captive capacity

Figure 3: Age Characterization of Installed Capacity (2008)

I.C. Ownership patterns of the sector

The national government of Indonesia has been responsible for the development and

administration of electricity sector for the last half century4. The state-owned electric utility PT

PLN, through its subsidiaries, carries out the functions of generation, transmission and

distribution of electricity in Indonesia. It operates nearly two-third of country’s generating

capacity while the rest is owned by IPPs and captive power producers.

Captive capacity, which totals 6,560 MW, has been installed by industries that do not have an

easy access to the PLN’s distribution grid or that provide backup for PLN’s service5.

Approximately, 60% of the captive capacity is based on diesel while the rest comes from

cogeneration. Of the total captive capacity in Indonesia, approximately 4,000 MW capacity

provides backup to PLN services6. However, there are other reports

7 which state the total captive

capacity to be much higher than 6.5 GW. Since the details of the additional capacity are not

accessible the same has not been considered in this analysis.

4 http://pdf.wri.org/powerpolitics_chap5.pdf

5 http://pdf.wri.org/powerpolitics_chap5.pdf

6 PLN

7 Electric Power Sector in Indonesia, U.S. Commercial Service, CS Jakarta, 11/8/2005

0

1

2

3

4

5

6

7

8

9

< 5 Years 5-9 Years 10-14

Years

15-19

Years

20-24

Years

25-29

Years

30-34

Years

35-39

Years

> 40 Years

Insta

lle

d C

ap

acity (

GW

)




The Electricity Law, passed in 1985, encouraged the participation of private enterprises in power

sector without promoting competition in electricity business. It provided opportunity to the

private companies to participate in power sector by operating power plants for sale of electricity

to PLN only. However, the law came into effect when the necessary accompanying regulations

were promulgated through the Presidential Decree No. 37 in 1992, which invited the private

sector investors for operating large scale Independent Power Plants (IPPs) for sale of electricity

to PLN. This led to nearly 25 IPPs entering the power system, from 1994-1997.

In September 2002, Indonesia enacted Law No. 20 to transform the monopolistic structure of the

power sector into a limited competitive market within a five-year time frame. It emphasized the

efficient, transparent and competitive provision of electricity supply along with providing equal

treatment to all market players including PLN. It encouraged the establishment of an independent

market regulator and market operator.

However, the government later revoked the Law 20/2002 stating that electricity is a ―wealth for

the people‖ should be under full control of the State and hence, should be viewed as integrated

business rather than being unbundled. The annulment of this law drove the power sector’s re-

structuring process back to the starting point.

These drastic changes in the government policies for the sector restructuring provided an

uncertain environment for the investors which led to miniscule capacity additions during this

period. In order to re-instate confidence in electricity business, the government issued

Regulations No.3/2005 to address general planning on power requirement, role of private players

in power sector, priority for renewable energy sources and the pricing policy. The new

regulations establish the National Electricity General Plan under the auspices of the Government

of Indonesia along with the development of yearly Electrical Power Supply Plan by PLN and

approved by MEMR. It established that the electricity prices and the permits to conduct

electricity business for public use have to be issued by the government.

Despite these efforts, many problems remain. The power sector in Indonesia faces issues such as

government regulated electricity tariffs which do not reflect the actual cost of supply, lack of

investment supporting policies with a well defined criteria for financial investments, lack of

regulation on fuel costs for captive plants and IPPs which have led to low participation of private

sector in the power sector. At present, there are nearly 35 IPPs in Indonesia, operating only 12%

of the total installed capacity in Indonesia (2,588 MW).




II. Emissions overview of the sector

II.A. Background and Discussion of Emissions: Sources, Drivers, and Trends

Historically, Indonesia was one of the largest oil producers. After reaching their peak production

in 1991, oil reserves started declining and in 2005, Indonesia became a net oil importer.

However, the dependency on oil for the country’s energy needs has not reduced. The continued

dependency on oil for power generation is due to the oil price subsidies provided by the

government, which encourage economic inefficiencies. Indonesia oil subsidies are amongst the

largest in the world amounting to almost 3% of Indonesia’s GDP and 30% of the government.8

The high prices and extreme volatility in international oil prices over the last few years has

increased the subsidy burden on the Government exchequer. In order to mitigate this exposure

and reduce the dependency on imported fuel, the government initiated fast track programs to

promote other fuels (coal, gas and renewables) in the capacity mix, and is also encouraging a

switch from oil based capacity to gas or coal based capacity.

Coal is Indonesia’s largest fossil fuel resource. Indonesia’s coal reserves are fairly well

distributed along the islands of Sumatra, Kalimantan, Sulawesi and Papua. Of the total estimated

coal reserves of 90.45 billion tons, Indonesia has mineable coal reserves of around 18.71 billion

tons. The quality of coal varies and mostly consists of lignite (24.4%), sub-bituminous (61.45%),

bituminous (13.02%) and little amount of anthracite. The calorific value of Indonesian coal

ranges between 5,000-7,000 kcal/kg.

The average growth rate of coal production in Indonesia over 2000-2005 was 14% per annum

and most of this growth was export-oriented owing to the high international coal prices. The

share of coal exports from total production during this period has been in excess of 70%. Of the

total domestic consumption during 2000-2005, the coal consumption in electricity sector has

remained rather flat owing to little coal based capacity additions. However, the coal consumption

for the electricity sector is becoming increasingly more important with the decrease in oil and

gas reserves and increasing power demand. To address these issues, the Indonesian government

through Law Number 4 of 2009 concerning Mineral and Coal Mining in Indonesia, aims to curb

the excessive coal exports and make it available for domestic consumption. This would help

achieve the targeted coal capacity planned under first and second 10,000 MW crash programs.

The story with natural gas is very similar. Despite having one of the highest natural gas reserves

in the world, the domestic gas consumption is among the lowest. The main obstacle to rapid

development and to expanded utilization of gas has been Indonesia’s energy pricing policy and

gas development issues both in upstream and downstream infrastructure.

For the last two years, export of oil and gas has contributed approximately 22-25% to the

national current account. Any expansion in the domestic use of gas would reduce export

revenues and, hence, does not feature prominently in national energy plans9. However, since a

large number of liquefied natural gas export contracts will end in 2010, leaving the total gas

8 http://www.southsearepublic.org/article/268/read/indonesian_oil_subsidies, October 2005

9 Indonesia Country Report, USAID ASIA, June 2007

http://www.southsearepublic.org/article/268/read/indonesian_oil_subsidies




volume available of approximately 9.5 million ton per annum for new sales agreement, it is

expected that the domestic usage of gas will increase and the same is reflected through increased

gas generation in PLN’s plans till 2018. The prospects of gas contracts being renewed are little

as the government plans to phase out the existing oil price subsidies and rationalize energy

prices. This would encourage the promotion of domestic gas use and reduce reliance on oil fuels.

Indonesia exports almost half of its gas production to the Asia-Pacific region10

. With falling oil

production and rising international prices, Indonesia has been encouraging greater use of

domestic gas. But this requires a major investment in setting up the infrastructure for gas as more

than 70% of Indonesia’s gas fields are located off-shore.

Historically, PLN has faced problems to secure gas supply from the existing gas infrastructures.

To address this problem to some extent, PLN, in their Rencana Usaha Penyediaan Tenaga

Listrik (RUPTL), mention that they will use gas from LNG to power their 750 MW combined-

cycle in West Java. An LNG receiving terminal will be built in the province of Banten in West

Java. Using LNG will give PLN more flexibility in gas supply. Nevertheless, the source of LNG

is still unclear. Some of the expected sources of LNG are: Tangguh LNG plant in West Papua,

existing Bontang LNG plant in East Kalimantan and the newly planned Donggi LNG plant in

Central Sulawesi.

The volume of natural gas available for domestic use in Indonesia in the future is uncertain. For

the purpose of this analysis, we therefore assume the ratio of gas generation in the supply mix to

remain similar to the ratio of gas generation in PLN RUPTL’s supply mix forecast until 2018.

Beyond that year, proportion of gas based generation is assumed to remain constant till 2030.

Overall, fossil fuels have been dominating the fuel mix in Indonesia’s power sector despite

abundantly available renewable energy sources. Oil has been the mainstay of power sector.

Existence of a price subsidy for oil without any subsidy for gas makes the latter an expensive

fuel option thus causing its low consumption despite being more environmental friendly.

Although the share of coal in the generation mix has been limited historically, the 10,000 MW

Fast Track program would increase the use of coal and hence the carbon emissions in coming

years.

II.B. Historical annual CO2 emissions from power sector

Emissions from power sector emissions have grown historically at an average rate of 6% per

annum since 1990 reaching to a level of approximately 85 MtCO2 in 2005. Concerns over rising

GHG emissions and climate change have caused Indonesia to set a target of a 26% reduction to

the country’s overall GHG emissions by 2020.

10

Paper, ―On Prospects on Sustainable Energy Sources for Power Generation in Indonesia‖, Department of Energy

and Environment, Sweden




Figure 4: Historical CO2 Emissions Trend from Power Sector




III. Background Assumptions for Sector Analysis

III.A. Fuel Prices & Emission Factors

Table 3: Fuel Price Assumptions

The fuel prices considered for BAU analysis are as

per ICF’s forecast of international prices for coal and

oil, and the gas prices are based on the existing long

term gas supply contracts for domestic gas supply in

Indonesia. The domestic gas market is subsidized and

the existing gas supply contracts are priced in the

range of 3-4$/MMBTU. As the international oil and

gas prices are increasing there is pressure in the

domestic market for the increase in gas prices in the

future. The fuel price assumptions are listed in Table

3 and further detailed in Appendix 6

The emission factors considered for BAU analysis are

as per 2006 IPCC Guidelines for National

Greenhouse Gas Inventories as shown in Appendix 7.

III.B. New Capacity

To cater to the rapid increase in electricity demand,

the government designed the first crash program to

establish new coal based electricity generation with a

total capacity of 10,000 MW. Of the 10,000 MW coal

capacity 1,885 MW would use super critical

technology while the rest would use sub-critical

technology. As per PT PLN’s Business Plan for Electricity Supply, the total 10,000 MW coal

capacity is scheduled for commissioning by 2011. However, looking at the progress in achieving

the above said targets it looks highly unlikely that these targets would be realized; and hence for

our analysis, we have staggered the addition of 10,000 MW of coal build up to 2013 as shown in

Appendix 4. Also, 7,500 MW capacity under this plan is scheduled to cater Java-Bali region (Of

7,500 MW in Java-Bali, 1,885 MW would be super-critical coal plants and the rest would be

sub-critical coal plants) while 2,500 MW capacity would serve regions outside Java-Bali region 11

(the entire 2,500 MW would be sub-critical coal capacity).

The Presidential Decree No.5, 2006 identifies renewable energy as one of the primary energy

resources in Indonesia and mandates its use for electricity generation for on-grid and off-grid

applications. To achieve the targets set in National Energy Policy for renewable energy gestation

by 2025, the government designed the second crash program whose administrative process is

scheduled to be completed during the period 2009-2014. The program commits another 10,000

11

PLN Annual Report, 2008

Year HSD Coal Gas

USD/Barrel USD/tCO2 USD/MMBtu

2008 45.0 98.0 4.0

2009 48.6 89.3 4.2

2010 58.8 83.7 4.5

2011 63.9 80.2 4.7

2012 66.6 79.6 5.0

2013 68.7 78.8 5.3

2014 70.6 77.9 6.0

2015 72.7 77.2 6.0

2016 74.2 77.1 6.0

2017 75.3 77.1 6.0

2018 75.3 77.1 6.0

2019 75.3 77.1 6.0

2020 75.3 77.1 6.0

2021 75.3 77.1 6.0

2022 75.3 77.1 6.0

2023 75.3 77.1 6.0

2024 75.3 77.1 6.0

2025 75.3 77.1 6.0

2026 75.3 77.1 6.0

2027 75.3 77.1 6.0

2028 75.3 77.1 6.0

2029 75.3 77.0 6.0

2030 75.3 77.0 6.0




MW to Indonesia’s electricity system through 60% renewable (48% geothermal and 12% hydro),

26% coal (all sub critical coal capacity) and 14% gas based capacity. In our analysis, we assume

that the entire 10,000 MW under second crash program shall enter the system over 2014-2020 as

shown in Appendix 4.

The development of nuclear power plants (NPP) in Indonesia is still unclear although there has

been some previous planning. The blueprint estimates the uranium resources at 24,000 tons

mostly in Central Kalimantan. The proven reserves are estimated at 4,600 tons, with most of the

reserves categorized as low grade ores. These deposits are not sufficient even to run a 4 GW NPP

for 10 years and hence, in the long-run, Indonesia will have to depend on imported uranium,

possibly from Australia. In terms of waste management, it is still unclear where the waste will be

stored. The National Energy Management (2006-2025) blueprint outlays a plan of building 4

GW of nuclear capacity over 2016-2024 but the inconclusive political environment has caused

uncertainty about the time frame of nuclear gestation in Indonesia. ICF assumes no nuclear

capacity to enter the Indonesia’s power system under the business as usual scenario.

The total geothermal potential in Indonesia is estimated at 27 GW as shown in Appendix 5.

However, until 2005, the total installed capacity of geothermal was around 815 MW. The

significantly low investments in geothermal capacity can be attributed to the ambiguity in the

law for geothermal capacity development. The law mandates geothermal projects to be

developed as ―Total Projects‖12

but does not, by itself, facilitate the development of such

projects. As per the MEMR’s Road Map of Development of Geothermal, the target of

geothermal utilization for electricity is 9,500 MW in the year 2025. Taking this forward, ICF

analysis has assumed the total geothermal capacity of 11 GW by 2030.

Among the other renewable resources, Indonesia has a rich potential of hydro and biomass. The

National Energy Management (2006-2025) blueprint estimates the total large hydro potential at

75 GW, small hydro potential at 450MW and biomass potential at 50 GW. ICF assumes a yearly

addition of 100 MW hydro plant throughout the analysis period while 100 MW biomass

additions every 5 years starting 2015, primarily to cater to the rural demand. Biomass in

Indonesia is available mainly from solid waste, rice residue, rubber wood, sugar residues and

palm oil residues. For our analysis, we assume the biomass generation to be based on solid

waste.

Appendix 7 shows the cost and performance data for various electricity generation technologies

considered in the analysis. The parameters for different new technologies likely to enter the

power system are different for Java-Bali region and for the regions outside Java-Bali due to the

capacity size required to meet the demand in various regions.

12

Total Projects are those where the upstream (involving generation of steam, its treatment and delivery) and the

downstream (generation of electricity) of the electricity generation process are handled by the same company.




III.C. Policies included

The Indonesian energy sector is in the early stages of transformation. The reform process was

initiated by Electricity Law 20/2002 from the year 2002. Table 4 details the policies that have

been considered for the BAU analysis.

Table 4: Policies considered for BAU Analysis

Policy Date Details

Indonesia’s National Climate Change Action Plan

2007 Guideline for all sectors for an integrated effort to tackle climate change

Presidential Regulation No.5 on National Energy Policy

2006 Sets energy mix composition to be achieved in 2025

Presidential Regulation No. 71 2006 Mandates the implementation of 10,000MW Fast Track Program to build coal plants; Mandates the implementation of second 10,000MW Fast Track Program with thrust on renewable energy sources

Blueprint of National Energy Implementation Program 2005-2025

2005 Describes measures for energy supply security, road maps for various sectors and programs to phase out subsidies and improve energy efficiency

Presidential Instruction No.10 2005 Describes the energy efficiency programs in Indonesia

Ministerial Regulations No.31 2005 Guidelines for implementing Energy Efficiency Programs

MEMR Regulation 8 2005 Seeks to attract private investments in oil and gas explorations

Green Energy Policy, MEMR Decree No.2

2004 Highlights renewable energy potential in Indonesia

Law No.27 on Geothermal Power 2003 Provides certainty of law to geothermal industry; Defines rules for exploration and development under competitive bidding

National Energy Conservation Plan

2002 Details the opportunities to save energy in various sectors

Oil and gas Law No.22 2001 Stipulates transparency in the downstream oil and gas operations; bases pricing mechanism on market prices; provides investors equal regulatory and legal treatment

PROPENAS Law 2001 Reduces burden of energy subsidies on state budget; increases prices of energy

III.D. Demand

The demand forecasts by the Ministry of Energy & Mineral Resources and PLN assume

considerably different growth rates. The Ministry of Energy and Mineral Resource’s (MEMR)

RUKN projections assume the energy demand to grow at a 9.98% (2008-2050) compounded

average growth rate (CAGR) while PLN’s RUPTL assumes a CAGR of 9.3% (2008-2020). ICF

assumed the growth rates under the MEMR’s projections and applied the same to the actual

demand in 2007. The demand forecasts by MEMR, PLN and the one used in this analysis are

shown in Appendix 1.

III.E. Reserve Margin Requirements

The measure of available capacity above the requirements to meet standard peak demand levels

is called the reserve margin. This measures the ability of an oil or gas producer to generate more

energy than the usual system demand. The existing reserve margins of 15~ 25% in Indonesia are

high relative to international standards (typically 10~20%). This is due to different types (based

on total installed capacity in the system, rather than basing it on actual available capacity) of

accounting for reserves.




For the long term planning, MEMR’s assumption of 30% reserve margin requirement for West

Java while 40% reserve margin requirement for other regions are based on total installed

capacity. However, for our analysis we have based our reserve margin requirements on the

available capacity as shown in Appendix 2. West Java is the only region with a reserve margin of

less than 25% at approximately 15%. These numbers are not expected to change through 2030

for all regions.

III.F. Transmission

PLN’s existing transmission network has an inter-island link only between Java and Bali while

all other islands have isolated power systems. A 3,000 MW transmission line between West Java

and Sumatra is expected to be fully operational from 201813

.

The study does not include any inter island links except the existing Java-Bali link and the

proposed 3,000MW West Java-Sumatra link as per PLN’s inter-island transmission connectivity

plan. No other transmission capacity has been considered in the study.

The T&D losses assumed in our study are based on the losses considered by the MEMR for their

demand estimates. As listed in Appendix 3 these losses decline sufficiently over time leaving no

scope for any further improvement under mitigation scenarios.

13

RUPTL, PT PLN




IV. Description of analytical model and methodology used

The criterion of optimization that the model makes use of is the minimization of the system’s

total cost, so that the alternatives for power generation are chosen in the order of the lowest cost.

It is important to emphasize that, besides the capital and operation costs, the Model allows to

incorporate additional ―penalty‖ costs for the alternatives, such as environmental and social

costs.

Additionally, the cost and the performance characteristics (efficiency and capacity factor) of the

technological alternatives for electricity supply are also inputs for the Model. These data are used

together with the fuel price data for the economic competition of the alternatives whenever the

addition of capacity is necessary.

ICF has used Indonesia - Integrated Planning Model (I-IPM®) is a dynamic linear programming

model that generates optimal decisions using perfect foresight. It determines the least-cost

method of meeting energy demands and peak energy requirements over a specified period (e.g.

2005 to 2030). In its solution, the model considers a number of key operating or regulatory

constraints (e.g. emission limits, transmission capabilities, renewable generation requirements,

fuel market constraints) that are placed on the power and fuel markets. In particular, the model is

well-suited to consider complex treatment of emission regulations involving trading, banking,

and progressive flow control of emission allowances, as well as traditional command-and-control

emission policies. A detailed description of IPM® is given in Appendix 9.

ICF has been using Indonesia - Integrated Planning Model (I-IPM®) since 2003. Over the course

of last 5 years ICF has been engaged by various private sector utilities to prepare complete

market assessments and dispatch analyses to assess the developments on existing as well as new

power installations, with a view to determine the size of the gas-to-power markets in Indonesia.

The work has involved developing Indonesia’s power sector modeling database as well as full

short and long-term dispatch analyses including detailed forecasts on capacity additions, forward

prices, fuel consumption, generation and CO2 emissions.




V. Baseline (Business as Usual) scenario for the electricity sector

V.A. Production/output

Table 5 below shows the growth in installed capacity from 2005 to 2030 under the Business As

Usual (BAU) scenario, clearly illustrating a nearly fourfold increase in the oil based capacity in

the system over 2010-2030. This increase in oil-based capacity is to meet the super peak load of

the system which persists for very few hours, which is also reflected by very little generation

(Table 8) from this huge oil capacity addition.

Table 5: Installed Capacity under BAU

Year

Annual Installed Capacity (GW)

Coal Natural Gas Oil Nuclear Hydro (>10MW) Other* Total


2005 8 - 7 11 0 4 1 30

2010 12 1 9 11 0 4 1 38

2015 21 5 15 13 0 5 5 64

2020 24 19 24 18 0 6 9 99

2025 29 41 39 28 0 6 10 153

2030 37 79 64 44 0 7 12 243

*Other includes small hydro, geothermal and biomass

Note: Data on 2005 Installed capacity in Indonesia includes 7GW of existing captive capacity

Table 6: Installed Renewable Capacity under BAU

Year Annual Installed Capacity (GW)

Small Hydro (<10MW) Geothermal Biomass Total

2005 0.1 0.8 0.0 0.9

2010 0.1 1.1 0.0 1.2

2015 0.2 5.3 0.1 5.6

2020 0.2 8.9 0.2 9.3

2025 0.2 10.1 0.3 10.6

2030 0.2 11.3 0.4 11.9

Table 7: Power Generation under BAU

Year

Annual Power Generation (TWh)

Coal Natural Gas Oil Nuclear Hydro Other* Total


2005 56 0 26 29 0 11 3 125

2010 88 5 38 29 0 12 5 177

2015 146 38 45 2 0 14 29 273

2020 160 138 64 2 0 18 47 428

2025 188 304 102 2 0 20 62 678

2030 237 577 162 4 0 21 78 1080

*Other includes small hydro, geothermal and biomass

Figure 5 and




Figure 6 below show the change in capacity and generation mix over 2015 to 2030 under the

BAU. Figures clearly demonstrate the continued dominance of coal in the power sector. The

share of coal under the BAU in the capacity mix increases from 41% in 2015 to 48% in 2030,

while increases from 67% to 75% in the generation mix during the same time horizon.

Figure 5: Capacity Mix under BAU

2020 2030

Biomass0%

Gas15%

Coal76%

Oil0%

Nuclear0%

Hydro2%

Geothermal7%

Biomass0%

Gas26%

Coal48%

Oil18%

Nuclear0%

Hydro3%

Geothermal5%

Biomass Gas Coal Oil Nuclear Hydro Geothermal




Figure 6: Generation Mix under BAU

2020 2030

Under the BAU, approximately 18 GW of renewable power is realized of which approximately

11 GW is geothermal capacity. Figure 7 provide details of capacity additions of different

renewable capacity.

Biomass0%

Gas15%

Coal70%

Oil0%

Nuclear0%

Hydro4%

Geothermal11%

Biomass0%

Gas15%

Coal76%

Oil0%

Nuclear0%

Hydro2%

Geothermal7%

Biomass Gas Coal Oil Nuclear Hydro Geothermal




Figure 7: Renewable capacity additions under BAU by 2030

V.B. Annual GHG Emissions

CO2 emissions were approximately 83 MtCO2 in 2005. Under BAU the emissions reaches 268

MtCO2 by 2020 with a CAGR of 7%, while reaches 688 MtCO2 by 2030, with a CAGR of 13%.

Major contributor of emissions is coal followed by gas. Table 8 provides the emission details

from each capacity types while

Figure 8 pictorially shows the increasing emissions level from 2015 to 2030. Table 8: CO2 Emissions

Year

CO2 Emissions (MmtCO2) Coal

Natural Gas Oil Other Total Sub-Critical Super-Critical

2005 49 0 17 16 0 83

2010 79 3 16 23 0 122

2015 132 26 18 2 0 178

2020 146 94 26 1 1 268

2025 173 209 41 2 1 427

2030 221 396 66 3 2 688

815

3,520

57

920

4054,800

1,200

4,800

400

1,700

0

2000

4000

6000

8000

10000

12000

Geothermal Biomass Large Hydro Small Hydro

R

e

l

i

z

a

b

l

e

R

e

n

e

w

a

b

l

e

P

o

t

e

n

t

i

a

l

(

M

W)

Total Installed Capacity till 2005 Except Captive Captive Capacity till 2005Firm Capacity First Crash ProgramSecond Crash Program Additional Builds considered in BAU

11 GW

0.4 GW

6.8 GW

0.067 GW




Figure 8: CO2 Emissions under BAU

178

268

427

688

0

100

200

300

400

500

600

700

800

900

2010 2015 2020 2025 2030 2035

CO2 Emissions (MtCO2)




V.C. Energy intensity and CO2 intensity

Energy intensity of the power sector under the BAU improves from the level of 10 PJ/TWh to 8

PJ/TWh, while CO2 intensity improves from 0.66 to 0.64 MtCO2/MWh.

Figure 9: Energy and CO2 Intensity

0.6

0.61

0.62

0.63

0.64

0.65

0.66

0.67

0.68

0.69

0.7

0

2

4

6

8

10

12

2005 2010 2015 2020 2025 2030

Energy and CO2 Intensity

Energy Intensity (PJ/TWh) CO2 Intensity (MtCO2/ MWh)




Figure 10: CO2 Intensity by Fuel Type (MtCO2/MWh)

Thus concludes our BAU analysis for the electricity sector in Indonesia. We will now explore the

effects of different carbon prices on the emissions of the power sector.

0.660.69

0.65 0.63 0.63 0.64

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

2005 2010 2015 2020 2025 2030

CO2 Intensity by Fuel Type (Mt CO2 / MWh)

Sub-Critical Coal Super-CriticalCOal Natural Gas Oil Average All Fuels




VI. GHG Mitigation Options and Costs

Once the baseline emissions were projected, mitigation options were allowed in the modelling

system and the power system of Indonesia was again simulated to analyze its response to carbon

costs. The mitigation options reflect options that the Indonesian government have with a new

carbon price. Different options could evolve based on different carbon prices as reflected in our

analysis. For the purposes of our analysis we focused on 2020 and 2030 as our target years to be

able to directly compare to existing analysis as well as to Indonesia’s climate targets.

Step-by-step analysis was done by imposing a carbon cost on the business as usual (BAU) case,

starting at $10/tCO2 and increasing it by $10/ tCO2 in successive iterations. In every stage, the

changes related to capacity mix, generation mix, emissions level and investment requirements

over BAU were noted. Similarly, for modelling negative cost options, for each of the DSM

programs – viz. General Lighting (DSM GL), Street Lightning & Ballasts (DSM SLBL)14

,

Chillers (DSM CH), Air conditions (DSM AC), Refrigerators (DSM RF) and Televisions (DSM

TV) the associated costs and the demand reduction were computed and modelled on BAU

considering all the of them are implemented.

Imposition of carbon cost on the system (in BAU scenario) would in-effect prompt changes viz.

retirement of inefficient capacity, conversion of existing inefficient plants to more efficient

plants, preference of more efficient or gas based capacity as a resort for system expansion and

increase in proportion of renewable energy in the generation mix. However, different

combinations of these changes would occur at different carbon costs.

VI.A. Selection criteria for consideration of mitigation options

The GHG abatement options suggested here are important for the electricity sector, and are

consistent with the sectoral development goals and relevant policies by the government of

Indonesia. They have significant emissions reduction potential and their implementation is

feasible.

VI.B. Overview of each mitigation option considered

Demand Side Management

The National Energy Conservation Master Plan (2005)—RIKEN (Rencana Induk Konservasi

Energi Nasional) states that Indonesia’s goal is to decrease energy intensity by around 1% per

year on average until 2025. In affect various energy saving Demand Side Management (DMS)

measures are planned to be implemented in Indonesia. This analysis includes mainly energy

efficiency improvements occurring in the residential and commercial sectors including

installation of efficient lighting (viz. CFL or General Lighting (GL), Ballasts and Street

Lighting), efficient space cooling systems (viz. Chillers and Air-conditioning) and efficient

appliances (viz. Televisions15 and Refrigerators). Implementation of efficient TVs is not a

negative cost option and hence are not been considered as a DSM option. Under the BAU

14

As the individual energy savings from Street lightning and Ballasts were not significant the two measures are

implemented together to study the impact. 15

Implementation of efficient TV’s is not a negative cost option and hence not been presented in the results.




Scenario - each category assumes technology penetration in 2005 remains constant until 2030.

Table 9 provides the details on the efficient technology penetration in 2005. While in the DSM

cases i.e. the Conservation Scenario – Technology penetration for each category increases

incrementally by 1% every year from 2010 to 2030. The actual BAU may be such that the

technology penetration increases at a steady rate, if not at 1% each year. In that case our analysis

would have over-estimated the impact of energy efficiency in the residential and commercial

sectors. However, this information is not easy to ascertain and certainly beyond the scope of this

project’s mandate.

The limitations and implications of the above methodology will be explored in another report

that will form part of this project.

Appendix 9 details the yearly energy saving and the investment requirement of various DSM

activities.

Table 9: Penetration of efficient technology in 2005

Technology Penetration of efficient technology in 2005

Lighting

General lighting 10% - 30%

Ballasts 9% - 10%

Street Lighting 40%

Space Cooling

Air-conditioning 10% - 20%

Chillers 5%

Efficient Appliances

Refrigerators 10% - 20%

Televisions 5% - 15%

Solar

Being a tropical country, Indonesia is bestowed with enormous solar potential with the maximum

potential in the Sulawesi, Maluku and Nusa islands. However, due to considerable capital cost

for solar technology and lack of local manufacturing industry for technology components, solar

has not found its place in the plausible mitigation options for Indonesia in our study.

Nonetheless, modular installations of solar PV provide for meeting the off-grid energy demand

on a larg0065 scale.

Geothermal power

Geothermal is not an intermittent resource and can provide a reliable electricity production.

Typical geothermal plants in Indonesia have a capacity factor of more than 90% which

outperforms other conventional power plants.

As shown in Appendix 5, the total geothermal potential in Indonesia is estimated at 27 GW and

is predominantly available in Sumatra and Java regions. However, given the unavailability of

adequate transmission evacuation facilities available near geothermal sites and environmental

barriers associated with geothermal site gestation, only 15GW potential can be exploited for

electricity generation. The analysis assumes 11GW geothermal capacity to enter the system




under BAU (as discussed earlier) and a possibility of additional 4GW to enter under mitigation

scenario.

Hydro power

As discussed earlier, Indonesia has huge large hydro potential. Most of the potential in Java Bali

region has already been exploited. The water level of rivers in Java-Bali region has receded due

to deforestation which prevents further expansion of large hydro power in this region. However,

there is large untapped potential in regions outside Java-Bali16

.

With the proven advantages like low maintenance cost, high reliability, efficiency and

availability; hydro power has disadvantages related to deforestation and population

rehabilitation. Due to this, the entire 75GW of large hydro potential cannot be exploited for

electricity generation. This analysis assumes only 5GW of large hydro potential to enter the

system under mitigation scenario.

Micro, mini and small hydro are mature technologies in Indonesia. Even though the total

potential for these options is as small as 500 MW, small hydro plants prove beneficial for

providing electricity to rural communities.

Biomass power

The total biomass potential in Indonesia is estimated at 50GW. The majority of this potential is

scattered over Kalimantan, Sumatra, Papua and Sulawesi. However, the total potential of

biomass energy for electricity generation assumed under mitigation scenarios is around 15GW

and is mostly available outside Java.

Various technologies available in Indonesia to produce electricity from biomass energy are:

• Biomass combustion power – This technology is in a mature stage and is widely used to

produce electricity from agro-waste, forest products and municipal waste.

• Biomass combined heat and power (CHP) – Here, a boiler is used to produce both heat and

electricity which improves the overall system efficiency to as much as 85%.

• Biomass co-firing - Co-firing increases the efficiency of the energy conversion to about 30%-

38% which is much higher than that in biomass combustion plant (typically about 15% to

20%).

• Biomass integrated gasification combined cycle (BIGCC) - Gasification is the main alternative

to combustion for electricity generation. There are many examples of biomass gasification

projects in the R&D stage, although the only technologies commercially deployed are CFB

(Circulating Fluidised Bed) atmospheric pressure.

Wind

Indonesia does not have either on-shore or off-shore wind potential. Although, the National

Blueprint for Energy shows large wind potential, but due to poor wind speeds, these sites cannot

be exploited for electricity generation. For the purpose of this analysis, no wind potential (in

both, BAU and mitigation scenario) is assumed.

16

Paper, ―On Prospects of Sustainable Energy Sources for Power Generation in Indonesia”, Department of Energy

and Environment, Sweden




Nuclear

As the development of nuclear capacity in Indonesia is highly dependent on the government’s

decision, ICF assumes that under mitigation scenarios, the total nuclear potential of 4 GW (in

line with the National Energy Management blueprint) can enter the system beyond 2025.

IGCC

An IGCC plant is a combination of both combined cycle and gasification plant. The coal is

gasified into synthetic gas (syngas), which is then used as fuel for electricity generation in a

combined cycle operation. The operation of the IGCC plant can be summarized as:

1. The feedstock (coal in this case) is gasified in an air or oxygen blown gasifier at high

temperature and pressure.

2. This gasification results in the production of synthetic gas (syngas) which is made up of

carbon monoxide and hydrogen; this syngas is then combusted in a gas turbine.

3. The hot exhaust gases from the gas turbine are used to produce steam to drive a steam

turbine.

Hence power is produced both from the gas and the steam turbines. In an IGCC installation,

typically 60-70% of the power comes from the gas turbine.17

IGCC plants offer the benefits of low emissions, low water use, low carbon dioxide emissions,

high efficiency, ability to use various fuels depending on the gasifiers used (coal, refinery

residues, biomass), and also production of by-products like chemicals, synthetic fuels, fertilizers

and hydrogen. Estimated efficiency for IGCC plants is assumed to 40%.

Since the technology is not too technically advanced, there is still time for it to be commercially

viable. So in the mitigation scenario, we assume that IGCC can be a possibility for Indonesia

after 2020.

IGCC-CCS

Gasification carried out in the IGCC process is also the baseline technology for carbon capture

and sequestration (CCS). CCS consists of the separation of CO2 from energy-related sources, its

transportation to a storage location and long-term isolation from the atmosphere. To facilitate

carbon transportation and storage, CO2 is typically compressed to a high density when it is

captured.

One of the key benefits of CCS technology is that when it is used with Enhanced oil recovery

(EOR), or with coal bed methane, it leads to the maximum extraction of those resources as well.

In Indonesia, Java-Bali and Sumatra are the demand centric regions. Hence, given the size and

costs associated with IGCC-CCS plants, this technology would mainly be developed to cater to

demand in these regions. However, due to space constraints in Java-Bali, there are two

possibilities for developing IGCC-CCS for this region: first, develop the IGCC plant in Java-Bali

and lay a transportation line for carbon storage in a nearby island or second, build the IGCC-

17

http://www.worldenergy.org/documents/scenariosgeneration.pdf




CCS plant in some other island and lay transmission line for power evacuation to Java-Bali

region. Based on the storage capacity available IGCC-CCS options for the analysis are

considered viable only in Kalimantan and Sumatra with a potential of approximately 2 GW.

Like IGCC, the IGCC-CCS technology is also in pilot stage and is yet to be commercially

available. Hence, IGCC-CCS also is considered to be an option after 2020, under mitigation

scenario.

Super-critical plants

Supercritical units operate at higher temperatures and pressures than sub-critical units; the higher

pressure increases turbine efficiency and power output, thereby leading to lesser coal usage for

production of same amount of electricity. Hence, building super-critical coal plants in lieu of

sub-critical coal plants can reduce the coal usage and therefore, abate carbon emissions.




VII. Analysis of GHG Mitigation Scenarios

Based on the assumptions listed in Section VI, the emissions from the electricity sector in

Indonesia were projected for the years 2020 and 2030. A significant result from these projections

analysis is the representation of the abatement potential in the electricity sector using Marginal

Abatement Cost Curve (MACC) methodology. Simplistically, the MACC relates the cost per

additional unit of emissions reductions to the total quantity of reductions.

One of the more widely recognized and used MACC models is the step curve or ―Static MACC‖

which provides information regarding the abatement potential of various technologies/mitigation

options at a particular cost step. However, in the real world setting, the abatement potential of a

mitigation option/technology depends not only on abatement cost, but also on the system

dynamics and limitations at that particular cost step. This multi-dimensional relationship is

captured through the use of ICF International’s proprietary cost optimization model IPM (refer

Appendix 10) and we refer to the resulting MACC as the Dynamic MACC. The Dynamic

MACCs for Indonesia were compiled by recording the changes in system emissions level for

every $10/tCO2 increase in the carbon cost over the BAU for the positive cost curve, while

recorded for reduction in demand level over BAU for the negative cost curve. For each carbon

cost-point, the Dynamic MACC also shows the changes in capacity and changes in generation

mix over BAU.

In the sections below we discuss our findings for the years 2020 and 2030 by discussing, in the

said order, the (a) the emissions profile (b) installed capacity (c) generation mix and (d) resulting

emissions reduction compared to BAU at a range of carbon prices (0 - $80/tCO2). Although the

results of this analysis are in the form of Dynamic MACCs, we have also represented them in the

form of Static MACCs in order to help gauge the value added by this analysis and to help in

comparisons with other studies as well. As one will observe, the implications from the Dynamic

MACC are far greater than the Static MACC. For instance while the Static MACC gives us an

idea of the abatement potential of various technologies at a certain carbon price, the Dynamic

MACCs show us the system changes over a range of carbon prices, thereby enabling the reader

to visualize the implications of a range of carbon price levels on the electricity system and the

emissions resulting from it. Dynamic MACCs guide the power sector planners in developing

long term investment strategies to select among the variety of efficiency and generation options.

Considering that the aim of this study is to enable Indonesian stakeholders in making public

policy choices in a real-world setting, we chose to focus our analysis on the implications of the

Dynamic MACCs. We will further use this analysis to compare effectiveness of other policy

mechanisms such as clean coal incentives in another report under this project.

VII.A. Mitigation Scenario in 2020

In 2020, the abatement potential observed under a carbon price of $80/tCO2 is 32 MtCO2 and 54

MtCO2 without DSM and with DSM respectively. The difference in emissions under the various

carbon price scenarios compared to BAU can be seen in Table 14.

At $80/tCO2 cost total abatement potential is approximately 50 MT, Figure 13




Table 10: Emissions by Fuel type in 2020 Source of

Emissions (MtCO2) BAU CP30 CP50 CP80 DSM30ALL DSM50ALL DSM80ALL

Oil 1 1 1 1 1 0 1

Coal 241 231 223 201 209 198 180

Gas 27 27 30 34 27 31 34

Hydro 0 0 0 0 0 0 0

Geothermal 0 0 0 0 0 0 0

Total 268 260 254 236 237 230 214

The reduction in emissions observed in Table 10 are due to changes in the system generation mix

that are brought upon by imposing a carbon price which simultaneously penalizes dirtier sources

(e.g. sub-critical coal) while incentivizing cleaner sources (E.g. geothermal).

The system changes in installed capacity mix and generation mix under various carbon prices

scenarios can be observed in Tables 11 and 12 respectively.

Table 11: Changes in Capacity Proportion by different technology types in 2020

Changes in the System

Capacity Mix (GW) CP30 CP50 CP80 DSM30ALL DSM50ALL DSM80ALL

Abatement Potential (MtCO2) 9 14 32 31 38 54

Biomass 0 1 5 0 2 5

Gas -4 -4 -4 -5 -4 -5

Coal Sub Critical -1 -3 -4 -2 -4 -4

Coal Super Critical 6 8 11 2 4 7

IGCC 0 0 0 0 0 0

Oil 0 -1 -5 -2 -4 -7

Nuclear 0 0 0 0 0 0

Hydro 0 0 2 0 0 2

Geothermal 0 0 0 0 0 0

Total 0 1 5 -7 -5 -2




Table 12: Changes in Generation Proportion by different technology types in 2020


Generation Mix (TWh) CP30 CP50 CP80 DSM30ALL DSM50ALL DSM80ALL


Biomass 0 4 17 0 8 17

Gas 0 0 0 0 0 0


Coal Super Critical 39 56 75 11 29 47

IGCC 0 0 0 0 0 0

Oil 0 -1 1 -1 -1 0

Nuclear 0 0 0 0 0 0

Hydro 1 1 7 1 1 7

Geothermal 0 0 0 -1 -1 -1

Total 0 0 0 -32 -32 -32

The Dynamic MACCs representing the abatement potential associated with resulting changes in

capacity and generation mix can be seen in Figures 11 and 12 respectively. As can be observed

from the Dynamic MACCs the majority of emissions reductions from BAU are achieved through

Supercritical, Biomass-CHP and Large hydro displacing sub-critical coal and contributes

towards total abatement of approximately 38 MtCO2 at a carbon price of $80/tCO2. DSM

measures are excluded from this particular analysis because the model assumes an optimum mix

of generation sources to meet a particular level of energy demand. Hence, energy efficiency

measures which inherently mean reduction of demand are calculated separately and the Dynamic

MACC is run for the resulting reduced demand. This issue will be further explored in a separate

paper on energy efficiency which forms part of the project. However as it can be seen from

Tables 10-12, we have calculated the impact of DSM on three carbon price scenarios and the

resulting abatement potential reflect the increase in emissions reduction from energy demand

reduction.




Figure 11: MAC Curve 2020 - Capacity Changes (GW)

4

6

910

14

23

30

32 33

-20

-10

0

10

20

30

40

CP10 CP20 CP30 CP40 CP50 CP60 CP70 CP80 CP90

Ch

ange

s in

Cap

acit

y M

ix (

GW

)

Hydro

Nuclear

Oil

IGCC

Coal Super Critical

Coal Sub Critical

Gas

Biomass

Geothermal

Abatement Potential (MtCO2)




Figure 12: MAC Curve 2020 - Generation Changes (TWh)

-150

-100

-50

0

50

100

150


Ch

ange

s in

Ge

ne

rati

on

Mix

(TW

h)

Geothermal

Hydro

Nuclear

Oil

IGCC

Coal Super Critical

Coal Sub Critical

Gas

Biomass





As mentioned in the introduction to this section, the Dynamic MACC results were converted into

Static MACC representation for ease of comparison. The methodology used to create this Static

MACC was to average out the cost levels at which a particular technology enters into the system

and the resulting emission reduction from BAU in order to represent total abatement potential at

a single cost.

Figure 13: 2020 MAC Curve at 80$/ tCO2Abatement Cost

-45

-2

39

64

-60

-40

-20

0

20

40

60

80

0 10 20 30 40 50

Ab

ate

me

nt

Co

st (

$/t

on

)

Abatement Potential (MT CO2)

DSM Coal Super Critical Small Hydro Biomass-CHP Large Hydro

81

DSM Super Critical

Coal

Small

HydroBiomass -

CHP

Large

Hydro




VII.B. Mitigation Scenarios in 2030

In 2030, the abatement potential observed under a carbon price of $80/tCO2 is 115MtCO2 and

218 MtCO2 without DSM and with DSM respectively. The difference in emissions under the

various carbon price scenarios compared to BAU can be seen in Table 13.

Table 13: Emissions by Fuel type in 2030

Emissions

(MtCO2) BAU CP30 CP50 CP80 DSM30ALL DSM50ALL DSM80ALL

Oil 3 3 3 3 3 3 3

Coal 617 587 511 488 484 410 386

Gas 68 73 83 83 71 81 81

Hydro 0 0 0 0 0 0 0

Geothermal 0 0 0 0 0 0 0

Total 688 664 598 573 558 494 470

The reduction in emissions observed in Table 13 are due to changes in the system generation mix

that are brought upon by imposing a carbon price which simultaneously penalizes dirtier sources

(e.g. sub-critical coal) while incentivizing cleaner sources (E.g. geothermal).

Table 14: Changes in Capacity Proportion by different technology types in 2030


Capacity Mix (GW) CP30 CP50 CP80 DSM30ALL DSM50ALL DSM80ALL


Biomass 5 16 16 4 16 16

Gas -4 -4 -6 -11 -11 -13


Coal Super Critical 6 7 6 -10 -13 -12

IGCC 0 2 5 0 1 2

Oil 0 -3 -6 -5 -8 -11

Nuclear 0 4 4 0 4 4

Hydro 1 1 6 1 1 6

Geothermal 0 4 4 0 4 4

Total 4 9 12 -31 -24 -22




Table 15: Changes in Generation Proportion by different technology types in 2030


Generation Mix (TWh) CP30 CP50 CP80 DSM30ALL DSM50ALL DSM80ALL


Biomass 18 52 51 13 50 49

Gas 0 0 0 0 0 0


Coal Super Critical 36 34 33 -77 -98 -91

IGCC 0 13 30 0 4 13

Oil 0 0 -1 -1 -1 -1

Nuclear 0 32 32 0 32 32

Hydro 3 3 18 3 3 18

Geothermal 0 15 15 -3 12 12

Total 0 0 0 -150 -150 -150

As one can observe from the Dynamic MACCs shown in Figures 14 and 15, the emissions

reductions vis-à-vis BAU is achieved through introduction of cleaner sources such as Biomass,

IGCC, Nuclear, Hydro and Geothermal in varying proportions that replace dirtier sources such as

Gas, coal (sub and super-critical) and Oil. Please note that one of the significant results under the

2030 $80/tCO2 carbon price scenario is that over the long run cleaner sources such as Biomass

and Geothermal become much more viable than relatively clean sources such as super-critical

coal.




Figure 14: MAC Curve 2030 – Capacity Changes (GW)

-110

-90

-70

-50

-30

-10

10

30

50

70

90

110

130

-40

-30

-20

-10

0

10

20

30

40

50


Ab

ate

me

nt

Po

ten

tial

(M

tCO

2)

Ch

ange

s in

Cap

acit

y M

ix (

GW

)

Biomass Gas Coal Sub Critical

Coal Super Critical IGCC Oil

Nuclear Hydro Geothermal





Figure 15: MAC Curve 2030 - Generation Changes (TWh)

-250

-200

-150

-100

-50

0

50

100

150

200

250


Ch

ange

s in

Ge

ne

rati

on

Mix

(TW

h)

Geothermal

Hydro

Nuclear

Oil

IGCC

Coal Super Critical

Coal Sub Critical

Gas

Biomass





As with the earlier section, the Dynamic MACC results for 2030 were converted into Static

MACCs for ease of comparison. The methodology used to create this Static MACC was to

average out the cost levels at which a particular technology enters into the system and the

resulting emission reduction from BAU in order to represent total abatement potential at a single

cost. On comparison with the Static MACC for 2020, one can observe that by 2030 a larger

proportion of clean technologies is feasible because of the ability to earn returns over a longer

period of time.

Figure 16: 2030 MAC Curve at 80$/tCO2 Abatement Cost

Table 16: 2030 - Abatement potential and Cost for Different Mitigation Options

Mitigation Options Abatement Cost ($ /tCO2) Abatement Potential (MtCO2)

DSM -44 104

Coal Super Critical -6.9 5

Geothermal 16.3 14

Nuclear 24.7 29

IGCC 33.4 7

Small Hydro 37.7 2

Biomass-Gasification 52.4 25

IGCC-CCS 64.4 9

Biomass-CHP 76.3 9

Large Hydro 79.2 14

-44

0 0

-70

16

0

25

0

33

38

0

0

52

0

79

-60

-40

-20

0

20

40

60

80

0 20 40 60 80 100 120 140 160 180 200

Ab

ate

me

nt

Co

st (

$/t

on

)

Abatement Potential (MT CO2)

DSM Coal Super Critical Geothermal Nuclear IGCC Small Hydro Biomass-Gasification Biomass-CHP IGCC-CCS Large Hydro

DSMSuper Critical

Coal

Geothermal

Nuclear

IGCC

Small Hydro

Biomass -

Gasification

Biomass -

CHP

Large

Hydro

58

64

IGCC-CCS

219




VII.C. Demand Side Management Measures

If demand reduction measures are implemented along with the 30$/tCO2 marginal carbon cost,

additional reduction in emissions can be achieved. Under such scenarios Indonesian power sector

can contribute towards 3.9% to 4.6% of the total emission reduction targets of 2020.

Figure 17-19 below illustrates the changes over BAU in generation mix (TWh) with DSM under

different carbon cost for 2020 & 2030. The graph represents the penetration of super critical

technology, hydro and Biomass under each of the scenarios in 2020 while penetration of

Nuclear, Geothermal and IGCC by 2030.

Figure 17: 2020 Changes in Generation Mix with DSM under various Carbon Cost

Figure 18: 2030 Changes in Generation Mix with DSM under various Carbon Cost

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

DSM+30$ DSM+50$ DSM+80$

Ch

ange

in G

en

era

tio

n M

ix (

TWh

)

Geothermal

Hydro

Nuclear

Oil

IGCC

Coal Super Critical

Coal Sub Critical

Gas

Biomass

-300

-250

-200

-150

-100

-50

0

50

100

150

DSM+30$ DSM+50$ DSM+80$

Ch

ange

in G

en

era

tio

n M

ix (

TWh

)

Geothermal

Hydro

Nuclear

Oil

IGCC

Coal Super Critical

Coal Sub Critical

Gas

Biomass




As would be expected, implementation of demand side management measures are the most cost

effective options for reducing the emissions from the power sector (Figure 19).

Figure 19: Total Abatement Potential with and without DSM

VII.D. Summary of Mitigation Scenarios Analysis

Emissions from the power sector are expected to increase to 268 MtCO2 by 2020, and under the

mitigation scenarios, Indonesia has the maximum potential to abate approximately 54MtCO2 in

2020 at a carbon price of $80/tCO2, which contributes to approximately 7% of the total emission

targets of 26% announced by the Indonesian Government. Of this total abatement, contribution

from DSM towards emission reduction can be significant: up to 20 MtCO2 in 2020.

Further by 2030, the maximum abatement that can be achieved by Indonesia is approximately

200 MtCO2 and of which 50% can be achieved through DSM programs under our assumptions.

Thus the important takeaways from this section of the report are the fact that the results under the

Dynamic MACC analysis assuming all else is constant, the choice of generation mix depends on

two primary external variables – carbon price and the time period under consideration. The

higher the carbon price, the more incentive to introduce cleaner technologies in order to displace

dirtier ones and the longer the time period, the higher the ability of clean projects to earn their

returns and hence prove more profitable than dirtier ones.

Indonesian policymakers should accordingly focus on implementing mitigation measures with a

longer time frame in mind and an incentive mechanism (imposing a carbon price on emissions is

9

31 25

131

14

38

91

194

3254

115

219

0

50

100

150

200

250

Without DSM With DSM Without DSM With DSM

2020 2030

Ab

ate

me

nt

Po

ten

tia

l (M

T C

o2

)

CP30 CP50 CP80




one method that we consider) that financially incentivizes clean energy technology while

simultaneously penalizes polluting ones.

This analysis will be helpful in informing policymakers the expected emissions from imposing a

range of carbon prices. Policymakers may also choose to increase the impact of carbon pricing

by changing some of the BAU conditions such as removing oil subsidies, creating subsidies for

clean coal technology, removing implementation barriers to geothermal (thereby creating a larger

pool of exploitable resource). Such changes in domestic policy will change the equation in favor

of cleaner technologies, and hence the overall effect of carbon prices will be significantly larger.




VIII. Cost Summary

The Total Annual Resource Cost shows total System Cost under BAU and at specific carbon

prices ($30, $50, $80) as well as those carbon prices with all DSM options enacted. System Cost

includes fixed and variable cost of operations, fuel cost, capital cost, and any DSM investment

necessary. With DSM options enacted, all carbon price scenarios are less expensive when

compared to BAU. Tables 17-18 below show the Total Annual Resource cost in 2020 and 2030.

Figures 20-21 show incremental system costs as well as abatement potential without DSM

options. The optimal incremental savings is carbon price $30/tCO2 with energy efficiency

measures enacted in 2020 and 2030. With energy efficiency measures and investment cost

accounted for, these options would mitigate 31 MtCO2 and 131 MtCO2 in 2020 and 2030,

respectively. This would also save $1297 Mm and $5252 Mm respectively.

Table 17 Total Annual Resource Cost 2020

Total Annual Resource Cost

(MmUS$) BAU CP30 CP50 CP80 DSM30ALL DSM50ALL DSM80ALL

Variable O&M 1,089 1,066 1,065 1,033 994 990 958

Fixed O&M 3,132 3,184 3,226 3,440 2,999 3,067 3,255

Fuel 12,465 12,163 11,996 11,257 11,261 10,974 10,341

Capital 6,300 6,690 7,069 8,777 5,644 6,211 7,745

Total 22985.2 23103.8 23355.7 24507 20897.7 21242.2 22299

Table 18 Total Annual Resource Cost 2030

Total Annual Resource Cost

(MmUS$) BAU CP30 CP50 CP80 DSM30ALL DSM50ALL DSM80ALL

Variable O&M 2,812 2,761 2,819 2,776 2,376 2,415 2,361

Fixed O&M 6,923 7,118 7,680 7,915 6,203 6,787 7,020

Fuel 29,682 28,906 26,729 25,994 25054.5 22905.4 22264.9

Capital 27,484 28,726 33,078 35,699 23,324 27,607 30,101

Total 66,901 67,510 70,306 72,382 56,958 59,714 61,747




Figure 20 Incremental System Cost 2020

Figure 21 Incremental System Cost 2030




IX. Results Comparison

IX.A. Indonesian Climate Change Sectoral Roadmap by BAPPENAS

In December 2007 Bappenas (National Development Planning Agency) published a document

titled "National Development Planning: Indonesia Responses to Climate Change", that has been

revised in July 200818

. The document is intended to strengthen and reinforce the RPJMN

(National Medium-Term Development Plan) 2004-2009 as well as to include inputs for the

preparation of RPJMN 2010-2014 in the context of integrating climate change. The Indonesian

Climate Change Sectoral Roadmap (ICCSR) is meant to provide inputs for the 5 year RPJMN

(2010-2014), laying particular emphasis on the challenges emerging in the forestry, energy,

industry, agriculture, transportation, coastal area, water, waste and health sectors. In this, several

goals are set out with regards to both adaptation and mitigation of climate change. The most

important goal is that emissions from greenhouse gases will decrease by 26% from total

projected BAU emissions in 2020.

Within the power sector in Indonesia, the BAU scenario projects future level of emissions

against which reduction by project activity might be determined. Without policy or regulatory

intervention, the government of Indonesia predicts that emissions would increase 189% from 83

MtCO2 in 2009 to 236 MtCO2 in 2020. This compares quite closely with ICF’s result of

emissions rising under BAU up to 268 MtCO2 in 2020.

According to the analysis, using the current trend of introduction of renewable technologies

(mainly geothermal) 18 MtCO2were reduced by 2020. Constraints for some technologies was set

according to the resource limit and geographical limit. New technologies, including speculative

technologies improving emissions from coal and gas were added to the base case scenario. These

technologies include both retrofitting as well as carbon capture technology.

Four likely and speculative new technologies using coal and gas were added to the base- case

scenario for application within the life time considered in the model. Retrofitting is also included

as well as CCS and other variants of carbon capture technology. Carbon reductions from these

technologies are estimated at 40.2 MtCO2, of 17% or total emission by 2020, with an abatement

cost of $23.44/tCO2. These new technologies when combined with a 4 GW Nuclear Plant and

introduction of carbon capture technology achieve reductions or 62.4 MtCO2, or 26.4% of total

emissions by 2020, with abatement costs of $33.74/tCO2.

Finally, carbon prices were imposed in the model. Values of 25 USD and $50/tCO2were imposed

for both the base case scenario and total carbon emission cap with new technologies. In the year

2020, at $25/tCO2 emissions reduced by 88.4 MtCO2 (37.4%), while at $50/tCO2 will result in

reduction by 129.7 MtCO2 (53.8%).

The BAPPENAS analysis does not include a wide range of policy options. This analysis also

uses technology as the base for achieving reductions instead of the effects of a carbon price.

Unfortunately, this methodology does not consider the effect or benefit of energy efficiency

measures. Reduction in projected demand would also decrease the efficacy of certain

technologies that are introduced to the system.

18

Bappenas, 2009 ―Indonesia Climate Change Sectoral Roadmap‖




IX.B. Indonesia’s Greenhouse Gas Abatement Cost Curve by DNPI

To coordinate climate change-related activities within Indonesia, in July 2008 President

Yudhoyono established the Dewan Nasional Perubahan Iklim (DNPI) or National Council on

Climate Change. The Council is specifically tasked with the role of convening different

stakeholders in Indonesia to create consensus around the opportunities and challenges related to

climate change. To that effect, the DN PI has commissioned a study on GHG Abatement Cost

Curve analysis to provide a quantitative basis for a national discussion on the opportunities for

reducing GHG emissions in Indonesia consistent with national development goals. The global

greenhouse gas abatement ―cost curve‖ developed by global consultancy McKinsey & Company

summarizes the technical potential to reduce emissions of greenhouse gases at a cost of up to 80

USD per tCO2e of avoided emissions. The cost curve shows the range of emission reduction

actions that are possible with technologies that either are available today or are highly likely to

be available by 2030.19

As stated before in the comparison between static and dynamic cost curves, step curves such as

those shown in the DNPI study tells the reader that what abatement potential is achievable at a

particular carbon price, while our analysis goes a step further and demonstrates emissions

reductions resulting from the installed capacity and generation mix at a carbon price range given

the system dynamics of the Indonesian electricity sector. From a policymaker’s perspective the

Static MACC presented in the DNPI study, though helpful, lays out the mitigation potential

simplistically without considering the systemic limitations and dynamics of the Indonesian

electricity sector. Hence, comparison with the DNPI study would mean extrapolating results

from our Dynamic MACC analysis and representing it in the form of a static MACC as shown in

figures 13 and 16 in an earlier section.

The cost curve analysis in the DNPI study presents abatement opportunities that would be

available at an abatement cost ceiling of $80/tCO2 in 2030. The DNPI study estimates installing

an additional 27 GW of hydro capacity by 2030 which represents an abatement potential of 65

MtCO2e at a negative abatement cost of -7/tCO2e. This is the most significant difference from

our study as we project a maximum of 18 GW additional capacity added at a carbon price of

$80/tCO2, which represents an abatement potential of 15 MtCO2. Similarly, the DNPI study

projects Biomass power plants as the second largest abatement opportunity at 64 MtCO2e

reductions. Our analysis projects that up to 16 GW of biomass capacity could be added to the

installed capacity mix by 2030 at a carbon price of $80/tCO2 which represents an abatement

potential of 34 MtCO2. The DNPI study assumes an additional 6 GW of Geothermal capacity

into the electricity mix, while CCAP assumes a maximum of 4 GW added Geothermal capacity

by 2030.

The DNPI study projects that DSM options have an abatement potential of 47 MtCO2e, while our

projections measure it to be 104 MtCO2. The limitations of our DSM figures have been

explained in an earlier section and should be viewed in such light while making comparisons. At

19

DNPI 2010, ―Indonesia’s Greenhouse Gas Abatement Curve‖




the point of writing this report, the authors were not able to obtain the assumptions behind the

DNPI study’s methodology of estimating the DSM potential in Indonesia.




X. Conclusions and Next Steps

Emissions from the power sector in Indonesia are set to increase to approximately 268 MtCO2 in

2020 and 688 MtCO2 by 2030 in a BAU context. Our analysis suggests that the power sector can

contribute to emissions reduction of around 54 MtCO2 and 219 MtCO2 by 2020 and 2030

respectively. Considering that the Indonesian Govt. has a self-imposed emissions reduction

target of 26% by 2020, which amounts to around 658 MtCO2 (assuming that total emissions are

2.530 GtCO2 as per the DNPI report), the power sector could contribute up to 8.2% of necessary

emissions reduction.

Broadly, emissions from the electricity sector can be reduced by reducing demand and reducing

carbon content of the electricity generated. Our study suggests that there is a large potential to

reduce electricity demand by increasing the potential of energy efficient technologies (or DSM).

DSM measures can provide emission reductions of approximately 20 MtCO2 in 2020 and 100

MtCO2 in 2030. However, distinguishing the rate of adoption of these energy efficient

technologies under BAU and mitigation scenarios is a highly complex exercise that is influenced

by many unquantifiable variables such as social trends and pricing of green products. Hence,

while we recommend increasing the rate of adoption of energy efficient appliances and

technology, we would like to focus on the results relating to making the supply side options

cleaner.

Regarding the supply side analysis, it is obvious from the modeling exercise carried out in this

report that the imposition of carbon price will be successful in making the generation mix

cleaner. A mitigation potential of 32 MtCO2 and 115 MtCO2 exists by years 2020 and 2030

respectively (not considering reduced demand from DSM) at a carbon price of $80/ tCO2.

However, one should note that a high carbon price scenario such as this is not feasible either

domestically or internationally in the short-to-medium term. Hence, in order to achieve any

significant reduction, policy makers need to do more in order to shift the economics towards

cleaner sources and away from dirty/inefficient ones. While this paper only delves into carbon

pricing as a market-based policy mechanism, there is a whole range of domestic policies that the

Indonesian Govt. could enact in order to tilt the balance.

This paper is one in a series of papers from CCAP regarding the Indonesian power sector. A

paper on the Demand Side Measurement analysis will focus on the limitations and implications

of relying on these measures to achieve national emission reduction targets. A paper on Clean

Coal Policy analysis will focus on possible outcomes if the Indonesian government focuses

primarily on reducing the country’s emissions from coal based power generation. Following

these CCAP will release an analysis of international policy and financing implications of

Indonesian climate goals. This will be the final paper, tying together international processes and

Indonesian climate action.

Finally, the mitigation analysis presented in this paper is designed for policymakers and industry

alike to evaluate possible implications of different mitigation scenarios. A broad range of carbon

price and integrated demand side management scenarios are presented so that the optimal

mitigation policies and actions can be realized. Policymakers should view this analysis as a

resource to determine whether their public policy decisions can be viewed as effective and




efficient. Industry can subsequently view the analysis to determine what effect public policy

decisions will have on the generation and capacity mix of Indonesia and invest accordingly.



55 12/20/2011

APPENDIX 1

MEMR’s Energy and Peak Demand Forecast

Region Energy Demand (GWh)

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Java-Bali 122,894 133,996 146,305 159,968 175,074 191,794 210,317 230,855 253,382 278,267

Sumatra 17,694 19,017 20,447 22,051 23,797 25,693 27,754 29,994 32,487 35,219

Kalimantan 5,188 5,628 6,079 6,563 7,074 7,622 8,211 8,846 9,559 10,336

Sulawesi 4,581 4,873 5,196 5,553 5,943 6,365 6,823 7,318 7,862 8,453

Nusa 960 1,029 1,104 1,188 1,281 1,381 1,493 1,609 1,742 1,882

Maluku 456 491 527 571 615 662 713 763 820 880

Papua 667 739 807 873 938 1,002 1,066 1,130 1,195 1,261

Region

Energy Demand (GWh)

2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Java-Bali 305,775 336,215 369,930 407,305 448,766 494,792 546,423 603,853 667,767 738,933

Sumatra 38,216 41,507 45,123 49,103 53,490 58,333 63,747 69,751 76,418 84,281

Kalimantan 11,185 12,116 13,137 14,259 15,494 16,863 18,384 20,072 21,951 24,046

Sulawesi 9,099 9,795 10,553 11,370 12,257 13,213 14,250 15,371 16,583 17,897

Nusa 2,037 2,205 2,385 2,581 2,791 3,020 3,265 3,532 3,818 4,128

Maluku 942 1,007 1,074 1,145 1,218 1,295 1,375 1,459 1,546 1,640

Papua 1,329 1,400 1,473 155 1,625 1,706 1,789 1,876 1,966 206

Region Peak Demand (MW)

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Java-Bali 19,389 21,141 23,084 2,524 27,624 30,264 33,188 3,643 39,986 43,914

Sumatra 3,316 3,564 3,832 4,133 4,460 4,816 5,202 5,622 6,090 6,602

Kalimantan 1,001 1,085 1,170 1,263 1,362 1,468 1,582 1,704 1,841 1,993

Sulawesi 977 1,039 1,108 1,184 1,267 1,353 1,455 1,561 1,677 1,803

Nusa 220 237 254 274 294 317 343 370 400 432

Maluku 93 100 108 116 126 135 145 156 167 180

Papua 137 151 165 179 192 205 218 231 245 258

Region

Peak Demand (MW)

2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Java-Bali 48,257 52,364 57,617 63,441 69,901 77,073 85,115 94,061 104,016 115,102

Sumatra 7,164 7,657 8,325 9,059 9,868 10,762 11,761 12,869 14,099 15,549

Kalimantan 2,157 2,299 1,488 1,591 686 734 787 842 901 964

Sulawesi 1,941 2,053 2,211 2,383 2,568 2,770 2,987 2,176 2,340 2,517

Nusa 468 497 538 581 628 679 735 795 859 928

Maluku 192 202 215 229 244 260 276 292 310 329

Papua 272 282 296 311 327 343 360 377 396 414



56 12/20/2011

PLN’s Energy and Peak Demand Forecast

Region

Energy Demand (GWh)

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Java-Bali 112,659 119,706 131,821 145,139 159,840 176,006 192,980 211,512 231,527 251,967 275,179

Sumatra 17,966 19,472 21,190 23,050 25,093 27,366 29,851 32,578 35,514 38,741 42,257

Kalimantan 4,908 5,400 5,980 6,607 7,278 8,022 8,825 9,713 10,701 11,771 12,902

Sulawesi 4,628 5,146 5,720 6,385 7,124 7,946 8,861 9,877 11,009 12,266 13,665

Nusa 1,055 1,168 1,360 1,557 1,719 1,887 2,072 2,274 2,495 2,732 2,972

Maluku 463 512 564 618 674 735 799 868 941 1,018 1,101

Papua 619 674 728 787 848 917 991 1,073 1,161 1,257 1,368

Region

Peak Demand (MW)

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Java-Bali 17,627 18,854 20,900 23,012 25,343 27,906 30,597 33,535 36,708 39,949 43,629

Sumatra 3,262 3,521 3,817 4,133 4,480 4,863 5,283 5,741 6,232 6,773 7,358

Kalimantan 914 999 1,101 1,210 1,327 1,455 1,592 1,743 1,911 2,094 2,291

Sulawesi 883 982 1,089 1,212 1,347 1,497 1,663 1,847 2,050 2,275 2,525

Nusa 228 256 298 332 357 385 416 448 484 523 566

Maluku 106 117 129 141 153 166 180 194 209 225 243

Papua 132 143 154 166 178 192 207 223 241 260 282



57 12/20/2011

ICF’s Energy and Peak Demand Forecast

Region

Energy Demand (GWh)

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Java-Bali 107,420 117,803 129,222 141,781 155,593 170,783 187,490 205,864 226,071 248,295 272,737

Sumatra 26,304 27,916 29,690 31,640 33,785 36,144 38,738 41,591 44,729 48,180 51,976

Kalimantan 9,068 9,498 9,972 10,492 11,065 11,694 12,387 13,148 13,986 14,907 15,920

Sulawesi 4,367 4,803 5,282 5,809 6,389 7,027 7,728 8,499 9,348 10,281 11,307

Nusa 1,222 1,319 1,426 1,544 1,673 1,815 1,972 2,144 2,333 2,541 2,770

Maluku 427 470 516 568 625 687 756 831 914 1,005 1,105

Papua 585 644 708 778 856 942 1,036 1,139 1,253 1,378 1,515

Region

Energy Demand (GWh)

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Java-Bali 299,619 329,183 361,698 397,458 436,786 480,040 527,610 579,928 637,467 700,748 770,345 846,888

Sumatra 56,150 60,741 65,790 71,343 77,451 84,167 91,555 99,679 108,614 118,441 129,249 141,135

Kalimantan 17,034 18,259 19,607 21,089 22,719 24,512 26,483 28,652 31,037 33,659 36,544 39,716

Sulawesi 12,435 13,676 15,041 16,542 18,193 20,008 22,005 24,201 26,617 29,273 32,194 35,407

Nusa 3,021 3,298 3,602 3,937 4,305 4,710 5,156 5,645 6,184 6,776 7,428 8,144

Maluku 1,216 1,337 1,471 1,617 1,779 1,956 2,151 2,366 2,602 2,862 3,148 3,462

Papua 1,666 1,832 2,015 2,216 2,438 2,681 2,949 3,243 3,566 3,922 4,314 4,744

Region

Peak Demand (MW)

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Java-Bali 18,262 20,046 22,009 24,168 26,541 29,152 32,024 35,182 38,655 42,475 46,676

Sumatra 4,353 4,687 5,055 5,460 5,905 6,395 6,933 7,525 8,177 8,893 9,681

Kalimantan 1,374 1,461 1,557 1,662 1,778 1,906 2,046 2,200 2,370 2,556 2,762

Sulawesi 899 989 1,088 1,197 1,316 1,447 1,592 1,751 1,925 2,117 2,329

Nusa 217 236 258 281 306 334 365 399 437 478 523

Maluku 94 104 114 125 138 152 167 183 202 222 244

Papua 138 152 167 184 202 222 244 269 296 325 358

Region

Peak Demand (MW)

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Java-Bali 51,296 56,379 61,967 68,113 74,873 82,307 90,484 99,477 109,366 120,243 132,205 145,362

Sumatra 10,547 11,500 12,548 13,700 14,968 16,362 17,895 19,581 21,435 23,474 25,717 28,184

Kalimantan 2,987 3,236 3,509 3,809 4,139 4,502 4,902 5,341 5,824 6,355 6,940 7,582

Sulawesi 2,561 2,817 3,098 3,407 3,747 4,121 4,532 4,985 5,482 6,029 6,631 7,293

Nusa 572 627 687 753 826 906 994 1,091 1,197 1,314 1,442 1,584

Maluku 268 295 325 357 393 432 475 522 575 632 695 764

Papua 393 433 476 523 575 633 696 765 842 926 1,018 1,120



58 12/20/2011

APPENDIX 2

MEMR’s Reserve Margin

Region Reserve Margin (%)

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

West Java 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15%

Central Java 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

East Java + Bali 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Sumatera 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Kalimantan 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Sulawesi 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Nusa 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Papua 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Maluku 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Region Reserve Margin (%)

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

West Java 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15%

Central Java 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

East Java + Bali 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Sumatera 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Kalimantan 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Sulawesi 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Nusa 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Papua 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Maluku 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%



59 12/20/2011

APPENDIX 3

T&D Losses

Region

T&D Losses (%)

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Java-Bali 9.5 9.4 9.3 9.2 9.1 9.0 8.9 8.8 8.7 8.6 8.5

Sumatra 11.5 11.4 11.3 11.2 11.1 11.0 10.9 10.8 10.7 10.6 10.5

Kalimantan 11.8 11.7 11.6 11.5 11.4 11.3 11.2 11.1 11.0 10.9 10.8

Sulawesi 11.5 11.4 11.3 11.2 11.1 11.0 10.9 10.8 10.7 10.6 10.5

Nusa 7.5 7.4 7.3 7.2 7.1 7.0 7.0 7.0 7.0 7.0 7.0

Maluku 8.0 7.9 7.8 7.7 7.6 7.5 7.5 7.5 7.5 7.5 7.5

Papua 10.0 9.9 9.8 9.7 9.6 9.5 9.4 9.3 9.2 9.1 9.0

Region

T&D Losses (%)

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Java-Bali 8.4 8.3 8.2 8.1 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0

Sumatra 10.4 10.3 10.2 10.1 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0

Kalimantan 10.7 10.6 10.5 10.4 10.3 10.3 10.3 10.2 10.2 10.2 10.2 10.2

Sulawesi 10.5 10.4 10.4 10.3 10.3 10.2 10.2 10.1 10.1 10.1 10.1 10.1

Nusa 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0

Maluku 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5

Papua 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0



60 12/20/2011

APPENDIX 4

Capacity under First 10,000MW Crash Program as assumed in ICF Modeling

Year Capacity in Java (MW) Capacity Outside Java (MW)

2009 1,500 400

2010 1,500 400

2011 1,500 400

2012 1,500 400

2013 1,500 400

Total 7,500 2,000

Progress of Plants planned under First 10,000MW Crash Program

Power Plant Capacity (MW) Progress till 2009

PLTU Labuan 2 x 315 COD

PLTU Rembang 2 x 315 COD

PLTU Indramayu 3 x 330 COD in 2010

PLTU Suralaya 1 x 625 80%

PLTU Paiton 1 x 660 73%

PLTU Pacitan 2 x 315 72%

PLTU Pelabuhan Ratu 3 x 350 42%

PLTU Teluk Naga 3 x 315 50%

PLTU Tanjung Awar-awar 2 X 350 8%

PLTU Kendari 2 x 10 52%

PLTU Ende 2 x 7 64%

Capacity under Second 10,000MW Crash Program as assumed in ICF Modeling Capacity

Type Region 2014 2015 2016 2017 2018 2019

Coal Java - - - - - -

Outside Java 200 200 200 200 200 200

Gas Java 300 300 300 - - -

Outside Java 600 600 500 - - -

Geothermal Java - - 500 - 400 -

Outside Java - - 250 250 - -

Hydro Java 400 400 400 400 - -

Outside Java 800 800 800 800 - -

APPENDIX 5

Geothermal Resource Potential in Indonesia

Location Resources (MWe)

Speculative Hypothetic Probable Possible Proven

Sumatera 5,275 2,194 5,555 15 380

Jawa 2,235 1,446 3,175 885 1,815

Bali-Nusa Tenggara 360 359 943 - 14

Sulawesi 925 12 865 150 78

Maluku 400 37 297 - -

Kalimatan 45 - - - -

Papua 50 - - - -

Total 9,290 4,048 10,835 1,050 2,287



61 12/20/2011

APPENDIX 6

ICF's Fuel Price Assumptions

Year HSD Coal Gas

USD/Barrel USD/Ton USD/MMBtu

2008 45.0 98.0 4.0

2009 48.6 89.3 4.2

2010 58.8 83.7 4.5

2011 63.9 80.2 4.7

2012 66.6 79.6 5.0

2013 68.7 78.8 5.3

2014 70.6 77.9 6.0

2015 72.7 77.2 6.0

2016 74.2 77.1 6.0

2017 75.3 77.1 6.0

2018 75.3 77.1 6.0

2019 75.3 77.1 6.0

2020 75.3 77.1 6.0

2021 75.3 77.1 6.0

2022 75.3 77.1 6.0

2023 75.3 77.1 6.0

2024 75.3 77.1 6.0

2025 75.3 77.1 6.0

2026 75.3 77.1 6.0

2027 75.3 77.1 6.0

2028 75.3 77.1 6.0

2029 75.3 77.0 6.0

2030 75.3 77.0 6.0 Prices in Real2008$



62 12/20/2011

PLN’s Fuel Price Assumptions

Year HSD Coal Gas

USD/Barrel USD/Ton USD/MMBtu

2008 140.0 90.0 6.0

2009 140.0 90.0 6.0

2010 140.0 90.0 6.0

2011 140.0 90.0 6.0

2012 140.0 90.0 6.0

2013 140.0 90.0 6.0

2014 140.0 90.0 6.0

2015 140.0 90.0 6.0

2016 140.0 90.0 6.0

2017 140.0 90.0 6.0

2018 140.0 90.0 6.0

2019 140.0 90.0 6.0

2020 140.0 90.0 6.0

2021 140.0 90.0 6.0

2022 140.0 90.0 6.0

2023 140.0 90.0 6.0

2024 140.0 90.0 6.0

2025 140.0 90.0 6.0

2026 140.0 90.0 6.0

2027 140.0 90.0 6.0

2028 140.0 90.0 6.0

2029 140.0 90.0 6.0

2030 140.0 90.0 6.0

Prices in Real2008$



63 12/20/2011

APPENDIX 7 Cost and Performance Data for NEW Electricity Generation Technologies

Technology Fuel Unit Size (MW)

Efficiency1

(%)

Maximum Capacity Factor

2

(%)

FOM Cost

3

(US $/kW-year)*

VOM Cost

4

(US $/MWh)*

Investment Cost

5 (US

$/kW)*

Construction Time

6 (Year)

Java Bali Region

Super Critical Coal

Coal 660 45 85 29.16 1.60 1,400 4

Sub Critical Coal

Coal 250 36 85 29.16 1.60 1,190 4

Combined Cycle

Natural gas 330 49 90 22.00 1.96 930 3

Combustion Turbine

Oil 50 25 100 22.00 1.96 550 3

IGCC Coal 600 40 95 31.77 1.60 1,800 4

IGCC-CCS Coal 600 32 95 31.77 1.60 3,100

Nuclear Uranium 1000 - 90 76.27 0.94 4,000 7

Geothermal Geothermal 50 - 95 50.00 8.00 2,800 3

Biomass (Combustion Power)

Biomass 20 15 50 38.22 0.71 $1,700 in 2020

2

Biomass (CHP)

Biomass 20 45 50 38.22 0.71 $1,550 in 2020

2

Biomass (Gasification)

Biomass 20 75 50 38.22 0.71 $2,100 in 2020

2

Large Hydro Hydro >10 - Monthly Profile

53.33 - 3,000 5

Small Hydro Hydro <10 - Monthly Profile

53.33 - 2,000 4

Outside Java Bali Region

Super Critical Coal

Coal 660 45 85 29.16 1.60 1,600 4

Sub Critical Coal – Sumatra

Coal 250 32 85 29.16 1.60 1,300 4

Sub Critical Coal – Kalimantan

Coal 65 30 85 29.16 1.60 1,300 4

Sub Critical Coal – Nusa

Coal 50 30 85 29.16 1.60 1,300 4

IGCC Coal 600 40 95 31.77 1.60 1,800 4

IGCC - CCS Coal 600 32 95 31.77 1.60 3,100 4

Combined Cycle

Natural gas 150 36 90 22 1.96 1,000 3

Combustion Turbine

Oil 50 24 100 22 1.96 600 3

Geothermal Geothermal 10-55

- 95 50 1.00 2,00 3

Biomass (Combustion Power)

Biomass 20 15 50 38.22 0.71 $1,700 in 2020

2

Biomass (CHP)

Biomass 20 45 50 38.22 0.71 $1,550 in 2020

2

Biomass (Gasification)

Biomass 20 75 50 38.22 0.71 $2,100 in 2020

2

Large Hydro Hydro >10 - Monthly Profile

53.33 - 3,000 5

Small Hydro Hydro <10 - Monthly Profile

53.33 - 2,000 4

Source: 1 and 5 – RUPTL, PLN except for Biomass and Geothermal

2, 3,4 and 6 – ICF Assumptions

Note: All cost numbers are US $2008



64 12/20/2011

APPENDIX 8

Emission Factors from IPCC tables for stationary combustion in the energy sector

Fuel

CO2 Content N2O Content

kg/TJ on a Net Calorific Basis

Anthracite 98,300 1.5

Lignite 101,000 1.5

Sub-Bituminous 94,600 1.5

Bituminous 94,600 1.5

Natural Gas 56,100 0.1

Oil 77,400 0.6

Distillate 77,400 0.6

Landfill Gas 54,600 0.1

Biomass 54,600 0.1



65 12/20/2011

APPENDIX 9

Energy savings and investment cost for various Demand Side Management Activities

GWH Mn USD GWH Mn USD GWH Mn USD GWH Mn USD GWH Mn USD GWH Mn USD

Energy Savings Investment Cost Energy Savings Investment Cost Energy Savings Investment Cost Energy Savings Investment Cost Energy Savings Investment Cost Energy Savings Investment Cost

2010 0 0 0 0 0 0 0 0 0 0 0 0

2011 295 11 25 7 43 3 92 28 822 85 182 66

2012 644 9 54 9 94 3 200 33 1791 100 396 78

2013 1053 7 88 10 154 4 327 39 2928 117 647 91

2014 1530 5 128 10 224 4 476 45 4256 137 941 107

2015 2085 3 175 10 305 5 648 53 5799 159 1282 124

2016 2727 14 229 9 399 8 848 61 7585 184 1677 143

2017 3468 26 291 9 508 12 1078 71 9645 213 2132 165

2018 4320 41 362 8 633 16 1343 81 12015 245 2656 190

2019 5297 59 444 7 776 21 1647 93 14734 281 3257 218

2020 6415 79 538 6 940 27 1995 107 17844 321 3945 250

2021 7692 103 645 5 1127 34 2392 122 21395 367 4730 285

2022 9146 130 766 3 1340 42 2844 139 25441 418 5624 325

2023 10800 162 905 1 1582 51 3358 158 30041 475 6641 369

2024 12678 197 1062 -1 1857 61 3942 179 35264 539 7795 419

2025 14806 238 1241 5 2168 73 4604 203 41183 611 9104 475

2026 17215 285 1443 13 2521 87 5353 258 47882 776 10585 625

2027 19937 338 1671 22 2920 102 6199 321 55453 966 12259 827

2028 23009 399 1928 32 3370 119 7155 393 64000 1184 14148 1061

2029 26474 468 2219 44 3877 138 8232 476 73635 1434 16278 13292030 30375 545 2545 58 4448 160 9445 570 84486 1719 18677 1639

RefrigeratorsYears General Lighting Ballasts Street Lighting Air-Conditioning Chillers



66 12/20/2011

APPENDIX 10

Integrated Planning Model (IPM®) is a linear programming formulation that selects investment

options and dispatches generating and load management resources to meet overall electric

demand today and over the chosen planning horizon. System dispatch - determining the proper

and most efficient use of the existing and new resources available to utilities and their customers

- is optimized given the security requirements, resource mix, unit operating characteristics, fuel

and other costs including environmental costs, and transmission possibilities. Although the

solution is arrived at simultaneously, conceptually it is easier to think of the model carrying out a

series of tasks.

As a forward-looking model, the IPM® can easily tackle the complex task of determining the

most efficient capacity adjustment path. Because the model solves for all years simultaneously, it

will select the most appropriate solution to ensure that system security is not compromised (e.g.

build new base-load or peaking units, retrofit or repower existing units), select units that should

be retired or mothballed, and identify the timing of such events. Capacity prices are one of the

results from this optimization process. Investment decisions are selected by the model by taking

into account system security requirements, forecasts of customer demand for electricity,

realization of electricity prices across the year, the cost and performance characteristics of

available options, technical characteristics of existing power plant units and a host of other

factors. By using this degree of foresight, the model replicates the approach used by power plant

developers, regulatory personnel, and energy users when reviewing investment options.

Applications of the IPM®

Its linear programming structure makes the IPM®

particularly well suited for a variety of

applications such as assessing planning strategies or regulatory policy options. Among the types

of analyses that can be conducted with the IPM® are:

Power price forecasts. The IPM® can be used to predict wholesale power, renewables

obligation certificates prices or the value of emission permits using scenarios developed

through the IPM® database interface.

Strategic planning. The IPM® can be used to assess the costs and risks associated with

alternative utility and consumer resource planning strategies as characterized by the

portfolio of options included in the input data base.

Analysis of uncertainty. The efficiency of the model's computational algorithms allows

it to be used with various techniques for analyzing the potential impacts of uncertain

future conditions (e.g. load growth, fuel prices, environmental regulations, costs and

performance of resource options) and the risks associated with alternative planning

strategies. Alternative approaches that have been used for analyzing uncertainty with the

IPM® include sensitivity analysis, decision analysis, and modelling uncertainty

endogenously by incorporating specific factors that are uncertain and the associated

probabilities for different values or expectations for these factors directly into the linear

programming structure.



67 12/20/2011

Optimization of operations under system-wide constraints. Various approaches can be

evaluated for meeting environmental constraints (e.g. limits on hourly, daily, or annual

emissions), fuel use constraints (e.g. optimum allocation of limited fuel supplies to

alternative plants), load management constraints (e.g. dispatch of directly controlled

loads given limits on the availability and scheduling of service interruptions), and other

operational constraints (e.g. "must-run" considerations and "area-protection" concerns).

The model can also address optimum usage of pumped storage facilities and economic

or long-term contracted power purchases from neighboring regions.

Assessing the effect of multi-pollutant policies. The environmental modelling

capabilities are extremely sophisticated, developed as they have been with a view to

support the US EPA’s federal emission control programmes since the 1980s. Whether

estimating the marginal abatement costs of NOx, SO2, or CO2, or establishing the

optimal operating and investment regime in the face of multi-pollutant regulations, the

IPM allows the user to compare alternative investment programmes thus minimizing the

possibility of stranding investments and preparing the client to respond pro-actively to

evolving environmental regulation.

Options assessment. The IPM® can be used to "screen" alternative resource options and

option combinations based upon their relative costs and potential earnings. By defining

all plausible alternatives, the model will suggest the optimal timing for different actions

on the basis that all market participants are seeking to maximize profits.

Estimation of avoided costs. Shadow prices from the linear programming solution can

be used to determine avoided costs by season or time-of-day for pricing purchases from

qualifying facilities, independent power producers, or economy and/or firm power

purchases from other utilities. Shadow prices also can be used to assess the economic

value of relaxing a constraint (e.g. what is the marginal cost of emissions reductions for

the utility?), to conduct marginal cost studies, and to determine the cost reductions of

alternative options in order for these options to be competitive with those options

selected by the model or the "preferred" options. This greatly enhances the capability to

use the model and its outputs as a screening tool.

Integrated resource planning. The IPM® can be used to perform least-cost planning

studies that simultaneously optimise demand-side options (load management and

conservation), fuel supply, non-utility supply, renewable options and traditional utility

supply-side options. The model has been licensed to Red Eléctrica de España (REE) and

Polskie Sieci Elektroenergetyczne (PSE), system operators in Spain and Poland

respectively, who use it to prepare inputs into their national energy plans. We have also

licensed the software to a number of private sector clients.

Detailed modelling of dispatch. The IPM®’s dispatch algorithms are very accurate and

have been benchmarked against detailed utility dispatch models. This includes the

ability to optimise the allocation of capacity across energy, reserve and capacity markets.

We are also adept at using this in combination with other industry standard models such

as GE MAPS and PowerWorld.

IPM® Modelling Approach



68 12/20/2011

The IPM® uses a linear optimization to simultaneously solve for power plant dispatch and fuel

use, capacity expansion, environmental retrofitting, modernization/re-powering, inter-regional

transmission, electric energy and capacity prices, and emissions costs. The model accurately

captures the unique performance characteristics and limitations of conventional and

unconventional generation technologies including gas and steam turbines, combined cycle, co-

generation, nuclear, hydro, wind, solar, and other renewables. Energy efficiency and demand

side management programs are properly evaluated in an integrated framework with other

resource options recognizing their limited capacity value and non-dispatchable characteristics.

The IPM® is also a dynamic model that optimizes capacity decisions over the entire planning

period simultaneously.

Date post:	11-Jun-2020
Category:	Documents
Upload:	others
View:	1 times
Download:	0 times

INDONESIA’S POWER SECTOR: SECTORAL EMISSIONS...

Documents