the long run marginal cost analysis for deriving electricity energy mix
TRANSCRIPT
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
Yodha Yudhistra / Thesis Summary (2009)
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
Yodha Yudhistra / Thesis Summary (2009)
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
[$/M
Btu
]
High Reference Low
Natural gas
Coal
Yodha Yudhistra / Thesis Summary (2009)
3.3.3 Candidate power plants cost The economic parameters for each of the candidate
power generation technology are shown in Table 3 and 4.
Geothermal projects are assessed as 55 and 110 MW
power plant units. Thus, the make-up well cost is assumed
on average of US$ 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.
Yodha Yudhistra / Thesis Summary (2009)
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
Yodha Yudhistra / Thesis Summary (2009)
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%
Percentage to total production [1], 2009 - 2018
0.0 0.2 0.4 0.6 0.8 1.0
8%
10%
(Default) 12%
Percentage to total production [1], 2009 - 2018
Yodha Yudhistra / Thesis Summary (2009)
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
Yodha Yudhistra / Thesis Summary (2009)
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]
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
Projected CER price
Yodha Yudhistra / Thesis Summary (2009)
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]
Coal Geothermal Natural gas
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]
Coal Geothermal Natural gas
CDM impact
Yodha Yudhistra / Thesis Summary (2009)
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)
Yodha Yudhistra / Thesis Summary (2009)
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]
Base case w/o geothermal Base case w/ geothermal
Subsidized gas w/o geothermal Subsidized gas w/ geothermal
Externality
Yodha Yudhistra / Thesis Summary (2009)
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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