the long run marginal cost analysis for deriving electricity energy mix

Yodha Yudhistra / Thesis Summary (2009)

The Long Run Marginal Cost Analysis for Deriving Electricity

Energy Mix Scene: Case Study in Southern Sumatera

Yodha Yudhistra N.

UGM – ITB Joint Masters, Natural Resources Management, Faculty of Mining and Petroleum Engineering ITB

2009

Abstract

Continuous growth of electricity demand in Indonesia is a major challenge for electric utilities trying to ensure adequate supply.

Tendency of coal for supplying electricity demand for next decade and given abundant geothermal resources expose the country to mix

energy considering lowest cost, security of supply, and environmental protection concerns. To find optimal solution to these triple

challenges, this thesis assessed two main models of energy mixes for southern Sumatera system as the case study for the period of

between 2009 and 2018. The first is base model which observed base case without and with geothermal plant candidate. Second is

policy model which examined Clean Development Mechanism (CDM), externality, and natural gas subsidy policies impact to the base

model. It is found that the lower cost of generation and environmental emissions are obtained from the scenario where geothermal

power plant is occupied to the base load accompanying coal-fired power plant. In supply security point of view, under this scene, coal

requirement is 28.75% lower than that on the base case (without geothermal) which can be stored as reserve. This coal reserve will

assist Domestic Market Obligation (DMO) of coal policy, as it can be either used for supplying existing coal-fired power plants

outside southern Sumatera or exported. This scene proposes new build energy mix comprises 55% of coal, 43.5% of geothermal, and

1.5% of natural gas. For promoting geothermal candidate, CDM policy can effectively support financing due to geothermal investment

uncertainties. In particular, the results show that externality and natural gas subsidy are not effective to mitigate the challenges.

Keywords: Long run marginal cost; electricity energy mix; southern Sumatera

1. Introduction

Electricity, an essential source of energy for many

activities, is one of the important capitals for development

in both developed and developing countries. Generally,

economic growth brings a growth of electricity demand in

the country. In Indonesia, the average annual growth rate

of peak electricity demand (MW) between 2000 and 2008

was significantly high at 11% (PLN, 2009).

Indonesia has expanded its electricity capacity to

meet its increasing demand. Although diversification was

taken into account, fossil fuel power generation ratio is

remaining high. Coal plays dominant role, approximately

70% of coal domestic productions was used for generating

electricity (Indonesia Mineral and Coal Statistics, ESDM,

2005). Moreover, oil is still hold significant share and

spend almost a half of electricity generation operational

cost due to its hike price (PLN, 2008).

This fact brings up to national sustainability problem

such fossil fuels which roles as the main electricity energy

sources are exhaustible and still being needed as national

income sources. Moreover, implementation role of these

energies is widely spread, not only for generating

electricity but also for chemical, agriculture, or

manufacture purposes. By these reasons, the use of fossil

fuels in Indonesia must be wisely considered in order to

obtain public wealth of the country.

Electricity energy mix planning should consider of

following concerns, namely lowest cost, security of

supply, and environmental protection. This thesis focuses

on the new build generation costs on how to mix primary

energy supplying the incremental electricity demand with

the lowest cost considering those. The questions that may

arise from the implementation are what appropriate new

power generation sources could be applied? How to

obtain the optimal (lowest cost) new build generation mix

related to sustainability role and climate change

protection? And then what policies needed to obtain that?

Such questions are becoming objectives in this work.

Southern Sumatera interconnected grid system has

been chosen as study case. Why southern Sumatera is

chosen? Southern Sumatera is well known by its abundant

energy resources, such as coal, geothermal, natural gas,

oil, and hydro. In addition, southern Sumatera is

connected by one interconnection system.

The thesis organized by the following. The concept of

long-run marginal cost and how it can facilitate

optimization are discussed in Section 2. Section 3 will be

then discussing electricity demand and energy resources

available to supply electricity demand on southern

Sumatera system, by dividing it into two main aspects:

demand and supply assessment. Meanwhile, Section 4

approaches the development of model for simulating

optimal mix of electricity energy. Section 5 discusses the

policy simulation by analyzing impact of sustainable

development and its policies. Finally, conclusion of the

argument as well as recommendation for further research

is presented on the final section.

2. Methodology

The analysis presented in this study employs the

principle of traditional electric capacity planning, which is


supply oriented. The basic objective of this planning is to

determine the optimal mix of generation technologies that

meet anticipated electricity demand while fulfilling all

specified constraints (Stoll, 1989).

Linear programming (LP) and dynamic programming

(DP) approaches are used to solve the optimization

problem in this study. There are two LP tools used which

are spreadsheet (excel) linear programming and dynamic

programming (WASP-IV package). Spreadsheet linear

programming is used for understanding the behavior of

energy mix regarding to costs variation. For simplicity,

the spreadsheet one is not presented in this thesis

summary. WASP-IV will deeply used as optimization

tool.

The Wien Automatic System Planning version IV

(WASP-IV) package is developed by International

Atomic Energy Agency (IAEA). WASP is widely used

tool that has become the standard approach to electricity

investment planning around the world (Hertzmark, 2007).

WASP utilizes dynamic programming optimization

method to find optimal solution. Figure 1 illustrates

general framework of the research.

3. Supply and demand of electricity

3.1 Electricity demand forecast

The demand forecast carried out by Pusat Pengaturan

dan Pengendalian Beban Sumatera (P3BS) (P3BS, 2009)

was retained for the period up to 2020. The data used is

2009 – 2018 data based on years of study period and

assumed to have the same load pattern. During study

period electricity demand in southern Sumatera grid

system is expected to growth on average 8% per year (see

Table 1). Load pattern is assumed to be same each year as

shown in Figure 2.

Research Results

Supply - Demand Assessment

Generation costs:

+ Capital cost

+ Fixed O&M cost

+ Variable O&M cost

Modeling

Impact of barriers

Barriers on generation

implementation

Impact by policy

Externalities, CDM,

subsidies

Base model

Research problem

Research objectives

Fuel cost

Generation resources

potential

Fossil fuel prices

forecast

- To define new appropriate generation sources

- To define optimal electrical energy mix/plant

capacity mix

- To define supporting policies needed

How to provide electricity demand growth optimally by

mixing various generation sources considering

sustainability and climate change protection.

Electricity demand

+ Peak load growth

+ Load pattern (LDC)

Policy simulation

Recommended electrical energy mix, plant

capacity mix and supporting policies

Figure 1 General framework of the research

Table 1 Electricity demand forecast from 2009 – 2018

Year Peak demand (MW) Energy (GWh) Load factor (%)

2009 1,736 9,798 64.4 2010 1,907 10,755 64.4

2011 2,084 11,804 64.7

2012 2,283 13,031 65.2 2013 2,491 14,240 65.2

2014 2,710 15,561 65.6

2015 2,944 17,004 65.9 2016 3,208 18,596 66.2

2017 3,495 20,351 66.5

2018 3,779 21,719 65.6

Source: P3BS PLN (2009)

Figure 2 Yearly load pattern

0

0.2

0.4

0.6

0.8

1

0% 20% 40% 60% 80% 100%

x P

eak

Load

[M

W]

% of a Year


3.2 Available energy resources

Southern Sumatera has almost of recent developed

energy sources. Figure 3 shows the energy candidates for

supplying the demand.

Coal Natural gas Geothermal Hydro

Figure 3 Electricity energy sources

Because of its low reliability of supply and environmental

restrictions, large hydro plant will not be priority in the

expansion-mix plan. However, small hydro is still good to

develop in decentralized system due to its small capacity.

3.3 Power generation costs

3.3.1 Weight Average Cost of Capital (WACC)

WACC is used in this study as discount rate for

discounting all costs in study period to the discounting

date.

Generally, cost of debt (kd) used in Indonesia is

defined as the same level with the credit investment

interest rate which is granted in the national banking (i

loan), while cost of equity (ke) is calculated by using

CAPM (Capital Asset Pricing Model) approach which can

be defined as

where,

ke Cost of Equity [%]

kRF Risk-Free Rate [%]

(km-kRF) Equity Market Risk Premium

β Company stock reaction to stock indices

volatility in the stock market [%]

Risk-Free Rate (kRF) in Indonesia can be derived

from Suku Bunga Bank Indonesia (SBI-rate) which has

value of 8.25 %, and Equity Market Risk Return (km)

which describes return reflected to investment in the stock

market. This value is defined based on average rate of

return in the stock market, which is 16%, (km – kRF) value

will be generated as 7.75%.

WACC is needed for analyzing generating electricity

cost of each generation option. WACC is then calculated

by the following assumptions

Market Value of Equity is assumed to be 10% based

on government obligation yield period

Pre-Tax Cost of Debt is planned to be 11% in case of

recent credit investment interest rate (2009) is around

11%

Guarantee institution cost is assumed to be 2%

Applied tax is assumed to be 30%

Beta (β), stock price volatility in such industry

(electricity generation) = 0.6

Risk-free Rate as described in the previous is defined

by SBI-rate which has value of 8.25%

Expected Equity Market Return is assumed to be 16%

The results of WACC calculation then derived with

three alternatives of capital structure (debt/equity

composition) of 65/35, 70/30, and 75/25. Thus, from

calculation WACC resulted is 12%.

3.3.2 Fuel price It has been considered three different fuel price

assumptions for each case: low, reference (ref.), and high.

Table 2 Fuel price assumptions

Fuel Initial price Gain

per year

Levelized

price

[US$/MBtu] [US$/MBtu]

Coal 2.1 Low 1.9% 2.5

Reference 2.1% 2.7

High 2.5% 2.8

Natural

gas

5.0 Low 2.4% 6.6

Reference 2.9% 7.0

High 3.2% 7.3

Figure 4 Fuel price assumptions from 2009 – 2050 (Net Calorific Value)

0

5

10

15

20

25

2007 '12 '17 '22 '27 '32 '37 '42 2047

[$/M

Btu

]

High Reference Low

Natural gas

Coal


3.3.3 Candidate power plants cost The economic parameters for each of the candidate

power generation technology are shown in Table 3 and 4.

Geothermal projects are assessed as 55 and 110 MW

power plant units. Thus, the make-up well cost is assumed

on average of US$ 24/kW and O&M cost of US$

18.2/MWh (Sanyal, S. K., 2005).

Table 3 Candidate thermal plants economic parameters

Fuel Technology Size Heat rate at

maximum load

Capital

cost

Fuel cost (base case) Fixed O&M cost Variable

O&M cost

[Mwe] [kcal/kWh] [US$/kW] US$/t US$/MBtu [US$/kW-month] [US$/MWh]

Coal Steam Turbine 150 2263 925 45.3 2.7 4.2 0.8

Coal Steam Turbine 65 2457 1180 45.3 2.7 4.2 0.8

Natural gas OCGT 60 2388 520 401.2 7.0 1.7 1.2

Natural gas OCGT 30 2606 610 401.2 7.0 1.7 1.2

Natural gas CCGT 150 1153 750 401.2 7.0 3.1 0.9

Source: Indonesian Electrical Power Society (2009), P3BS PLN (2009), World Gas Turbine (2008)

Table 4 Candidate geothermal plants economic parameters (Fixed O&M 12.00 $/kW-month)

Field name No.

of

units

Size Available

year

Capital

cost

Field name No.

of

units

Size Available

year

Capital

cost

[Mwe] [20..] [US$/kW]

[Mwe] [20..] [US$/kW]

Ulubelu 2 110; 110 '12; '15 1798

S. Antatai 1 110 '15 1906

Lumut Balai 3 55; 110; 110 '12; '15; '18 1820

Rajabasa 1 110 '12 2042

Sungai Penuh 3 55; 55; 55 '12; '15; '18 1834

Wai Ratai 2 55; 55 '15; '18 2042

Hululais 3 110; 55; 55 '12; '15; '18 1954

G. Sekincau 1 55 '18 2261

Source: JICA (2007) and our assumptions

In order to adapt with the model, make-up well cost is

added to the investment cost and by assuming capacity

factor of 0.9, O&M is converted to US$ 12/kW-month.

4. Development of the model

4.1 Model structure

There are five steps in generating the model, that are

problem defining, parameters determination, base model

simulation, model verification, and policy simulation.

First stage is problem defining. As mentioned before,

the problem is how to address optimal mix of generation-

energies to meet the incremental demand. Dynamic

programming approach is used to model the problem.

Each possible sequence of power units added to the

system (expansion-mix plan) meeting the constraints is

evaluated by means of a cost function (the objective

function), which is composed of (a) capital investment

costs, I, (b) salvage value of investment costs, S, (c) fuel

costs, F, (d) Operation and maintenance costs, M, and (e)

cost of energy not served, Φ. The cost of energy not

served, Φ, reflects the expected damages to the economy

of the country or region under study when a certain

amount of electric energy is not supplied.

Thus,

∑[ ]

where Bj is the objective function attached to the

expansion-mix plan j. t is the time in years (1,2,…, T) and

T is the length of the study (10 years, 2009 – 2018). All

costs are discounted to the reference date (2009) at a

given discount rate (WACC, 12%). The optimum

expansion-mix plan is the minimum Bj among all j. These

costs calculation is illustrated in Figure 5.

Figure 5 Economic evaluation scheme of the model

Next stages that are parameters determination, base

model simulation, and model verification will be derived

on subsection 4.2, 4.3, and 4.4. Afterward, policy

simulation will be presented on section 5.


4.2 Model building

4.2.1 Input parameters Input of the model is divided into two main

parameters which are demand and supply side. The

demand side is fulfilled by assumed load pattern each year

and peak load growth during years of study (2009 –

2018). Meanwhile, the supply side is satisfied by each

energy generation technologies and they costs.

4.2.2 Population of the model Generally, WASP-IV package has 6 modules to run,

namely LOADSYS, FIXSYS, VARSYS, CONGEN,

MERSIM, and DYNPRO. The two additional modules,

REMERSIM and REPROBAT are only used when

detailed reporting needed. For generating the model, input

parameters should be written on the first three modules:

LOADSYS for demand side, FIXSYS and VARSYS for

supply side (see Figure 6).

Figure 6 WASP-IV modules

FIXSYS facilitates fix or existing power plants have

been established before the study period and new fixed

power plants. Meanwhile, VARSYS processes

information describing the various generating plants

which are to be considered as candidates for expanding

the generation system.

4.2.3 Implementation of the model The decision of the optimal expansion plan is made

by the use of forward dynamic programming optimization

in DYNPRO. The numbers of units for each candidate

plant type that may be selected each year, in addition to

other practical factors that may constrain the solution are

specified. If the solution is limited by any such constrain,

the input parameters can be adjusted (CONGEN) and the

model re-run (MERSIM-DYNPRO). The dynamic

programming optimization is repeated until the

unconstrained optimal solution is found.

4.3 Base model

4.3.1 Base case (ref.) without geothermal and CCGT The base case portrays an electricity energy mix

following the current trend of electricity planning in

Indonesia where generating system mainly relies on fossil

fuels without any policy. Therefore, the first scene

includes only coal and natural gas as energy sources.

Simulation results suggest that southern Sumatera

system would require addition of 3310 MW until 2018.

The optimal mix of these new capacities generation

comprises 3280 MW of coal-fired power plant and 30

MW of open-cycle gas power plant (OCGT) which will

produce energy mix as on Figure 7. Figure 8 shows that

total energy mix produced from 2009 to 2018 would

reveal 4.53% of hydro, 73.03% of coal, 18.17% of natural

gas, and 4.27% of oil.

Figure 7 New build energy mix (without geothermal)

Figure 8 Overall energy mix (without geothermal)

4.3.2 Base case (ref.) with geothermal and CCGT Generally, Indonesia has been given largest

geothermal resources worldwide (Sjafra, 2005). Southern

Sumatera has these resources respectively. Since the

environmental emissions, such as CO2, NOx, SO2, and

particulates are a major concern of conventional coal-fired

plants (The World Bank, 1999), these geothermal

potentials are available to reduce the environmental

consequences. It has been considered that geothermal and

combined-cycle gas turbine as the recent possible clean

technology to be applied. Therefore, the main objective of

base case (with geothermal) scenario is to examine how

electricity energy mix scene changes if geothermal and

CCGT plant is introduced.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[GW

h]

Coal Natural gas

0

5,000

10,000

15,000

20,000

25,000

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[GW

h]

Hydro Coal Gas Oil


The results indicate that under this scene, the new

required additional capacities until 2018 will be 3010 MW

comprising 1650 MW of coal-fired power plant, 1210

MW of geothermal power plant, 150 MW of OCGT

power plant and no CCGT added. Figure 9 described

energy mix produced from the new capacities added

.Total energy mix produced from 2009 to 2018 would

carry out 4.53% of hydro, 51.71% of coal, 22.18% of

geothermal, 18.58% of natural gas, and 3.01% of oil (see

Figure 10).

Figure 9 New build energy mix (with geothermal)

Figure 10 Overall energy mix (with geothermal)

4.3.3 Base model’s sensitivity analysis Volatility on fuel prices brings to fuel cost

uncertainties. In addition, unfixed WACC gives capital

cost uncertainties. Therefore, sensitivity analysis should

be done by changing fuel prices and discount rate used.

Coal price is changed to low (-6%) and high (+5%) cases

whether gas prices is changed to low (-6%) and high

(+4%) cases as assumed on previous section (3.3.2).

Upper and lower bound of 10% change from the reference

fuel prices is also carried out. Meanwhile, discount rate

(WACC) is simulated to vary between 8% and 10%, and

12% (base case), respectively.

From Figure 11 and 12, it is obvious that the optimal

energy mix will remain same. Slightly volatile fuel prices

do not significantly affect the energy mix portfolio.

Figure 13 shows that there is small increase on

geothermal employment at 8% discount rate scenario.

However, the energy mix portfolio is not significantly

change. It can be concluded from this sensitivity analysis

that energy mix portfolio remains same under assumed

fuel prices and it does not affected by discount rate

changes.

Figure 11 Coal price sensitivity chart (base case)

Figure 12 Gas price sensitivity chart (base case)

Figure 13 WACC sensitivity chart (base case)

4.4 Model verification

Verification of base model is through by comparing it

to new build Indonesian and non-JAMALI system’s

energy mixes 2009 – 2018 (RUPTL 2009 – 2018). Figure

15 shows new build energy mix portfolio of Indonesia [a]

and non-JAMALI system [b], respectively. By observing

those two figures, it can be seen that each study case with

its condition and assumption leads to different new build

energy mix. Thus, comparing between portfolios will

yield no matching criteria. However, for this study

purposes, what should be verified is the trend of energy

share in the mix scene, not the exact.

Figure 14 presents base model’s new build energy

mix. By comparing this to PLN’s new build energy mix, it

is found that each energy trend from both base model and

official data are quite identical, especially compared to the

non-JAMALI ones. Coal dominantly takes share,

followed by geothermal and hydro, whether natural gas

serves. Geothermal and hydro are assumed to be one type

of energy (renewable) which represented only by

geothermal on the base model. The reason is that they

have same cost characteristic, though hydro is less

reliability of supply.

Another parameter that should be verified is yearly

amount of energy produced. Energy production forecast

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[GW

h]

Coal Geothermal Natural gas

0

5,000

10,000

15,000

20,000

25,000

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[GW

h]

Hydro Coal Geothermal Gas Oil

0.0 0.2 0.4 0.6 0.8 1.0

-10%

-6%

Reference

+5%

+10%

Percentage to total production [1], 2009 - 2018

0.0 0.2 0.4 0.6 0.8 1.0

-10%

-6%

Reference

+4%

+10%


0.0 0.2 0.4 0.6 0.8 1.0

8%

10%

(Default) 12%



data was retained from P3BS, PLN. It yields that in 2009,

10,206.3 GWh will be produced from southern Sumatera

system (P3BS, 2009). From base model result, energy

produced in 2009 is 10,535.8 GWh, only 3.23% higher

than official forecast data. Thus, it can be derived that

initial year energy production of the base model is suitable

with PLN’s requirement.

From this verification process, therefore, base model

is considered to be proper and can be used to forecast

electricity energy mix scene in southern Sumatera

generation system.

Figure 14 St. Sumatera new build energy mix (base case)

[a]

[b]

Figure 15 New build energy mix 2009 - 2018 [a] Indonesia; [b] Non-JAMALI system (PLN, 2008)

5. Policy simulation and comparison analysis

5.1 Introduction

From the Earth Summit (Rio, 1992) to the

Johannesburg conference (2002), a large step has been

taken towards the implementation of sustainable

development. Sustainable energy supply and utilization

system as a consequence has been important agenda to

address. To obtain these, optimization of energy

utilization should be done so called Green Energy

implemented in National Energy and Mineral Resources

Minister Decree No. 0002/2004. This section focuses on

how those policies will affect the mix of electricity energy

scene.

5.2 Externality model

5.2.1 Externality cost of electricity generation An externality, also known as an external cost, arises

when the social or economic activities of one group of

persons have an impact on another group and when that

impact is not fully accounted, or compensated for, by the

first group. In this case of study, fossil-fuels combustion

causes human health risk, risk of climate change, imposes

an external cost.

There are several ways of taking account of the cost

to the environment and health, as economist says to

monetize or internalize the externalities (Friedrich and

Voss, 1993). One possibility would be via carbon tax by

taxing damaging fuels and technologies according to the

external costs caused. For example, weighing electricity

cost of fossil-fuel power plant by additional cost per kWh.

Table gives external cost as estimated by European

Community in 1991 and Public Service Commission of

Nevada, United States of America. EC proposed a tax of

US $ 0.1 per kg of carbon, equivalent to US $ 0.027 per

kg of CO2. Using average marginal emission of regardless

fuel by New Zealand Authority (2001) EC’s proposed

external cost then can be derived. The proposed external

costs of candidate plants are tabulated on Table 5 below

Table 5 Proposed external cost of candidate plants

Fuel Technology Size Efficiency CO2 emissions External cost

[Mwe] [kg/MWh] [US$/MWh]

Coal Steam Turbine 150 38% 843 22.8

Coal Steam Turbine 65 35% 915 24.7

Natural gas OCGT 60 36% 527 14.2

Natural gas OCGT 30 33% 575 15.5

Natural gas CCGT 150 56% 339 9.1

Source: Murphy, H. and Niitsuma H., 1999 and our assumptions

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[GW

h]

Geothermal Coal Natural gas


Meant for fairness, although it is small, external cost

of geothermal power plant should be included in the

model. It is derived from EC proposed tax US $ 0.027 per

kg CO2 eq. and using IGA (2001) average geothermal

emission of 0.1 kg CO2e per kWh would yield of US$

2.7/MWh.

5.2.2 Externality case (reference fuel prices) In addition to conventional technologies for power

generation technologies, as described on previous section,

externality cost is internalized to the generation cost. The

main objective is to reduce environmental emission.

Figure 16 and 17 show electricity energy simulation result

under this scenario.

Figure 16 New build energy mix (externality)

Figure 17 Overall energy mix (externality)

5.3 Clean Development Mechanism (CDM) model

5.3.1 Carbon Emission Credit (CER) revenue In this section, impact of CDM policy on unattractive

geothermal investment is examined. Thus, additional

revenue from CER (Carbon Emission Credit) should be

assessed in the preliminary.

Market price for CER as shown in Figure is

fluctuating. Based on 28 of February 2008 data CER price

has valued at € 19.4 per tonne CO2 equivalent emissions.

Nowadays, in the Figure can be carried out CER price is

valued at € 13.4 per tonne CO2 equivalent emissions

which 1.0 ton CO2 is equal with 1.0 CER.

Average CO2 emission of a geothermal power plant

worldwide is estimated less than 100 gram CO2/kWh

(IGA, 2001). In Indonesia, this average value is lower,

69.2 gram CO2/kWh (IGA, 2001). For purpose of this

study, the more general value was used that is 100 gram

CO2/kWh. Thus, CO2 emission factor of a geothermal

projected to be approximately 0.1 t CO2e /MWh.

In Sumatera electricity system, baseline emission

factor of 0.743 tCO2e /MWh (Directorate General of

Electricity and Energy Utilization, 2008). Therefore CO2

emission reduction generated can be calculated as follows

( ) t CO2e/MWh

The CO2 emission reduction, as explained before, is

equal with CER produced. In the model, the values of the

CERs are projected to range between € 7.5 and € 10 per

tonne CO2

equivalent emissions based on

Carbonpositive.net prediction on next recent CER prices

(2009-2012 contracts) (see Figure). These projected CER

values are conservatively assumed by uniform distribution

with € 7.5 as the lowest value and € 10 as the highest.

Crediting period length is at max 7 years for

renewable period that can be renewed twice at the same

period length (14 and 21 years), and max 10 years for

fixed period. These crediting periods are conservatively

assumed by Weibull distribution with 3 years (P95), 10

years (P50), and 21 years (P5).

Figure 18 [b] presents the marginal CER revenue

distribution resulted, respectively. For this study purposes,

mean value of US$ 5.1/MWh is taken as geothermal cost

reduction into the model.

[a]

[b]

Figure 18 Fluctuated CER price [a]; levelized CER revenue for geothermal energy [b]

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[GW

h]


0

5,000

10,000

15,000

20,000

25,000

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[GW

h]

Hydro Coal Geothermal Gas Oil

Projected CER price


5.3.2 Sensitivity analysis on geothermal investment Before CDM policy is applied to the model,

geothermal investment is raised in order to unable some of

geothermal potential to compete with coal. In the reality,

this may be happened due to high reservoir risk on

geothermal investment. Figure 19 presents results of

geothermal investment increase of 10%, 30%, and 50%.

Figure 19 Geothermal investment sensitivity charts

5.3.3 CDM case (reference fuel prices) The worst case of geothermal investment is assumed

to be occurred on 50% increase from the base case. Thus,

under this scene how CDM affect share of geothermal

energy is examined. From the result, CDM increase

11.12% of geothermal share (see Figure 20). This is

equivalent to 3,708 GWh of energy produced.

Figure 20 CDM impact on 150% geothermal investment

5.4 Subsidized-gas model

5.4.1 Natural gas subsidy Natural gas should be an attractive power generation

source in the coming time. The reason is that gas fired

generation technologies has environmental appeal, low

capital intensiveness, shorter gestation period, and higher

efficiency. The main obstacle for the development is its

price. Indonesian natural gas, as assumed before, remains

higher over years achieving US$ 9/MBtu in 2030.

This model is built to observe how stable natural gas

price will affect the mix of generation scene. Under this

scene, it is assumed that gas prices will be kept constant at

US$ 5/MBtu over the study period.

5.4.2 Subsidized-gas case without geothermal Subsidized gas impact to generation scene without

geothermal is examined as follows (see Figure 21)

Figure 21New build energy mix (subs. gas w/o geo.)

5.4.3 Subsidized-gas case with geothermal Subsidized gas impact to generation scene without

geothermal is examined on Figure 22 and 23.

It is interesting to observe that under this scenario

CCGT power plant candidate is added into the generation

system. What can be inferred from these simulations is

that under stable gas prices scene, natural gas portion will

extremely increase at 5 times of that on the base case.

Figure 22 New build capacity mix (subs. gas w/ geo.)

Figure 23 New build energy mix (subs. gas w/ geo.)

5.5 Comparison of alternative scenarios

5.5.1 Cost of electricity generation As this study focused on the capacity addition

problem, we shall emphasize on the cost of electricity

generation only as obtained from the average incremental

cost (AIC) (Shrestha et al., 1998; Shresta and Marpuang,

1999, 2005). In this research, it is assumed that average

0.0 0.2 0.4 0.6 0.8 1.0

Base case

+10%

+30%

+50%

Percentage to total-production [1], 2009 - 2018

0.0 0.2 0.4 0.6 0.8 1.0

+ 50% w/ CDM

+ 50% w/o CDM

Base case

Percentage to total-production [1], 2009 - 2018

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[GW

h]


0

500

1,000

1,500

2,000

2,500

3,000

3,500

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[MW

]

Coal plant Geothermal plant Open-cycle gas plant Combined-Cycle GT

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[GW

h]


CDM impact


incremental cost (AIC) represents the long run marginal

cost (LRMC) of electricity generation. Average

incremental cost (AIC) of electricity generation is

calculated as follows:

( ∑ ( ) ⁄

) (∑( ) ( ) ⁄

)⁄

where TC is present value of total cost of power

generation during the planning horizon, C1 is the present

value of capital cost in year 1, VC1 is total costs of fuel

and operation and maintenance in year 1, Ei is electricity

generation in year i, E1 is electricity generation in year 1, r

the discount rate (WACC, 12%), and T is the length of the

study (10 years, 2009 – 2018). Table 6 presents cost of

electricity generation from five scenarios.

Table 6

Cost of electricity generation from five scenarios, (in US cent/kWh)

Base case w/o

geothermal

Subsidized gas w/o

geothermal

Base case w/

geothermal

Subsidized gas w/

geothermal Externality

5.49 5.37 4.56 4.33 5.98

Under base case (with geothermal) scene, the cost of

electricity generation is 17.03% lower than that one in the

base case (without geothermal) scene. The gas subsidy

reduces the cost of electricity generation by 2.17% and

4.97% from that of the base cases (without and with

geothermal).

In case of externality, the cost of electricity

generation in the base case (with geothermal) scene will

increase from that under no-externality by 31.34%,

respectively.

Thus, it can be concluded that geothermal power

plants have reached its economical price and tend to

mitigate the security of supply issues.

5.5.2 Fuel consumptions The results of energy mix of each models show that

coal will continue to be main fuel for power generation in

southern Sumatera system. Backed by abundant reserve of

low rank coal in southern Sumatera, this fact would not be

a problem for next one decade. However, volatile price of

coal commodity still brings to security of supply problem.

Therefore, these fuel requirements are further

examined in this subsection. It is also noted that all coal

plant’s (existing and candidate) coal requirements are

equivalent to low rank coal (lignite), assuming its Gross

Calorific Value (GCV) as 4,200 kcal/kg (Indonesian Coal

Index (ICI), 2008). This study has reported the results for

the five scenarios. Figure 24, 25, and 26 present gas and

coal requirement, respectively.

The coal requirement in the base case (without

geothermal) increases from 3.7 million tonnes (Mt) in

2009 to 11.6 Mt in 2018. Meanwhile, in the base case

(with geothermal) coal requirement only slightly increases

from 3.7 million tonnes in 2009 to 6.0 Mt in 2018 or on

average 28.75% lower than that on the base case (without

geothermal) which can be stored as reserve. This coal

reserve will assist Domestic Market Obligation (DMO) of

coal, as it can be used for supplying existing coal-fired

power plants outside southern Sumatera or can be

exported. Under externality scene, coal requirement is

lower than that on the base case (with geothermal) but it is

not significant.

It can be concluded that subsidized gas scene does

not primarily affect the coal requirement over the study

period. On average, coal requirement is only 5.15% and

6.86% lower than that on the base cases (without and with

geothermal).

Figure 26 shows that the requirement of gas in both

base cases (with and without geothermal) are slightly

fluctuates on average of (25 × 106) MBtu per year until

the end of study period. Meanwhile, under subsidized gas

scene, gas requirement increases from (24 × 106) MBtu in

2009 to (40 × 106) MBtu in 2018. This number is larger

under externality scene, (45 × 106) MBtu in 2018, but the

average is approximately remain same.

Figure 24 Coal and gas requirements with / without geothermal

0

20

40

60

80

100

120

140

160

180

200

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[10

^6

MB

tu]

Base case w/o geothermal (coal) Base case w/o geothermal (gas)

Base case w/ geothermal (coal) Base case w/ geothermal (gas)


Figure 25 Coal requirements from five scenarios (eq. 4200 kcal/kg, GAR)

Figure 26 Natural gas requirements from five scenarios

5.5.3 Environmental emissions As described before, of the major concerns of

electricity generation is its environmental emissions. This

issue has become of wide interest to the public and it is

likely to remain an influential hurdle for electricity energy

mix in the future.

Based on the simulation results, the research therefore

primarily evaluates environmental emissions from all of

five scenarios. Generally, there are three pollutants which

are considered as externality producer, namely CO2, NOx,

and SO2. However, only emission of the main pollutant,

that is CO2, is examined in the study as the others (NOx,

SO2) will proportionally include. Calculation is carried

out based on emission factor of each fossil fuel as listed

on Table 7.

The yearly amount of CO2 emission is presented in

Figure 27, respectively. From this figure, an average

emission of CO2 is further calculated. Table 8 summarizes

the average emission of CO2 per energy unit.

Table 7

Emission factors for different sources of energy used, (in kg/MWh)

Energy source Technology Efficiency CO2

Coal Steam turbine 35% 915

Oil Diesel engine 35% 760

Natural gas OCGT 33% 575

CCGT 56% 345

Geothermal Geo. plant - 100

Source: New Zealand Energy Conservation Authority (2001) and our

assumptions

It is interesting to observe that the CO2 emissions

trend is quite similar with the coal requirements trend.

This can be described from Table 8 that conventional

coal-fired candidate power plant is obviously the main

source of environmental emissions. If conventional coal-

fired power plants are partially replaced by geothermal

candidate power plant as on base case with geothermal

scene, an average emission per unit will be reduced by

22.8%.

0

2

4

6

8

10

12

14

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[mil

lion

ton

nes

]

Base case w/o geothermal Base case w/ geothermal

Subsidized gas w/o geothermal Subsidized gas w/ geothermal

Externality

0

5

10

15

20

25

30

35

40

45

50

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

[10

^6

MB

tu]



Externality


Figure 27 CO2 emissions from five scenarios

Table 8

Average environmental emission per unit of electricity, (in kg/MWh)

Base case w/o

geothermal

Subsidized gas w/o

geothermal

Base case w/

geothermal

Subsidized gas w/

geothermal Externality

815 802 629 617 602

6. Conclusion and recommendation

New build power generation energies has each

playing roles on the mix scene. Plant with higher capital

costs and lower O&M cost will play role as base loader.

In the other hand, plant with lower capital cost and higher

O&M cost will serve as peaker. For next decade, in

southern Sumatera system, coal still has important role to

hold base load and geothermal following at the second

place. In that order, though geothermal has reached its

economic price it could not be further developed because

of its limited proven reserve. This makes coal stays

dominate due to its abundant resource. At peak load,

natural gas respectively, into peaking plant (open-cycle)

can achieve premium over base load.

Addition of geothermal power plant tends to offer the

lower cost of power generation, reduce environmental

emission, and a number of additional capacities based on

these sources would be able to secure the supply. Under

this scene, new build energy mix comprises 55% of coal,

43.5% of geothermal, and 1.5% of natural gas. However,

uncertainties of geothermal regarding its exploration cost

and reservoir risk tends to bring for higher investment

cost. Therefore, Clean Development Mechanism is needed

for supporting the finance. From simulation resulted,

CDM policy effectively increase geothermal employment.

It is also observed from the simulations that

externality and subsidized gas policies are not effective to

mitigate both environmental emission and supply security

issues. Externality policy increases the cost of power

generation but only slightly decreases the emissions,

whether subsidized gas gives equal cost to the country and

the impact does not improve the base case despite of

significant increase on natural gas share. It is considered

to be more effective if those policies are substituted by

geothermal subsidy and Domestic Market Obligation

(DMO) of natural gas which tend to give similar impact.

Finally, this thesis only focuses at the theoretical

aspect on how primary energies can supply electricity

demand optimally through implementing assumptions.

Deeper findings of coal and gas prices in Indonesia,

sustainability issues on geothermal energy utilization,

Domestic Market Obligation (DMO), and Clean Coal

Technology (CCT) employment should be conducted in

order to provide more comprehensive analysis to this

research.

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kil

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the long run marginal cost analysis for deriving electricity energy mix

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