International Journal of Engineering Business
and Social Science
Vol. 1 No. 06, July-Augusts 2023, pages: 654-665
e-ISSN: 2980-4108, p-ISSN: 2980-4272
https://ijebss.ph/index.php/ijebss
654
Economic Analysis of Remaining Geothermal Reserves to Increase
Geothermal Power Plant Capacity in Achieving National Energy
Mix Target
Noval Suryadi
1
, Iwa Garniwa M. K.
2
1,2
Universitas Indonesia
Email: nov[email protected]
Keywords
Abstract
Geothermal Proven
Reserves, Monte Carlo
Simulation, Net Present
Value
In order to meet the renewable energy mix target related to the installed capacity of
Geothermal Power Plants in 2025 of 7,200 MW, with the potential of geothermal
resources of 23,060 MW, only 2,360 MW has been utilized as a Geothermal Power Plant.
In the Geothermal Working Area "XYZ" there are potential geothermal reserves of 464
MW, but only 55 MW (12%) has been utilized as a Geothermal Power Plant. Increasing
the generating capacity in the "XYZ" Geothermal Working Area that has been operating
can reduce the risk level of geothermal resources, initial investment costs, and plant
construction time because the geothermal development process does not start from the
initial stage. The purpose of this study is to evaluate and analyze the investment in
geothermal power plant capacity development using Monte Carlo Simulation in
decision-making, by taking into account uncertain variables such as capacity factor,
interest rate, inflation, tax, proportion of equity financing, and construction period. The
analysis results show that the investment scheme for developing generating capacity by
maximizing geothermal reserves increases the probability of a positive Net Present
Value.
© 2023 by the authors. Submitted
for possible open access publication
under the terms and conditions of the Creative Commons Attribution (CC BY SA)
license (https://creativecommons.org/licenses/by-sa/4.0/).
1. Introduction
The continuous use of fossil energy will result in global problems, such as depletion of energy reserves,
thereby increasing the price of fossil fuels in the global market. In addition, the use of fossil fuels can cause
environmental damage that leads to global warming and climate change. Population growth can cause more severe
environmental damage, thus encouraging humans to explore and exploit fossil energy sources. The potential of
renewable energy in Indonesia is very diverse, including hydroelectricity, geothermal, wind, solar, and biomass.
However, in 2022, only 12.3% of the target of 23% of energy in 2025 will come from renewable energy sources
(Direktorat Jenderal EBTKE, 2023).
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Figure 1[1]
(Direktorat Jenderal EBTKE, 2023)
The installed renewable energy generation capacity until 2022 is 12.55 GW (Kementerian Energi dan Sumber
Daya Mineral, 2023) as shown in Figure 2. After water energy, geothermal energy is one of the largest renewable
energy sectors.
Figure 2 Installed RE Power Plant Capacity in 2022 (Direktorat Jenderal EBTKE, 2023)
Indonesia is one of the countries with the largest geothermal energy potential in the world, with a potential of
23,060 MW, but only 2,360 MW has been exploited as Geothermal Power Plants (Kementerian Energi dan Sumber
Daya Mineral, 2023). The lack of utilized geothermal capacity that has been utilized is a result of high uncertainty or
risk factors at the geothermal exploration stage (Witter et al., 2019). In addition, there are risks that arise in utilized
geothermal development projects such as completion or delay risks, risks of non-absorption of electrical energy, price
risks, operational risks, and regulations (Nur et al., 2023).
A number of studies on the development of geothermal potential, especially the economics of power
generation facilities, have been conducted in the past. Project valuation methods, such as Net Present Value (NPV)
and Discounted Cash Flow (DCF) have been developed to factor in operation and maintenance costs. Geothermal
power plant initiatives are evaluated using NPV to calculate and analyze their financial value.
This study aims to analyze geothermal development from the remaining geothermal reserves in the
Geothermal Working Area "XYZ" that have not been exploited optimally, accompanied by economic evaluation to
increase installed geothermal capacity, so as to show decisions from the investment side for geothermal companies
and the government in the use of renewable energy.
Literature Study
Geothermal Power Plant
Geothermal energy is energy produced by heat from within the earth. Under the earth's crust there is a hot,
dense layer of rock with pockets of water, which sometimes come to the surface in the form of hot springs. When water
does not reach the Earth's surface naturally, sometimes water can penetrate into it, this hot water can be used as an
essentially free energy source, either as hot, steam, or hot water, or as a power plant (Igwe, 2021).
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In theory, geothermal power plants are identical to steam power plants, but in steam power plants, steam is
generated on the surface inside the steam boiler. If the fluid in the wellhead is already in the form of steam, it can be
piped directly to the turbine, where it will be converted from geothermal energy into the mechanical energy needed to
spin the generator and generate electricity (Saptadji, 2018). Different countries classify geothermal power resources
differently; however, it is generally categorized into three groups based on enthalpy levels, namely low (20-70°C),
medium (70-150°C), and high (greater than 150°C) (Özkaraca, 2018).
Geothermal power plants operates through a closed-cycle system, where geothermal fluid sourced from
energy generation wells undergoes processing to extract its thermal energy, before being injected back into the
reservoir through injection wells. There are 3 (three) types of geothermal power plants, including:
1. Dry Steam Plant
Dry steam geothermal power plants utilizes a dry steam reservoir that occurs naturally used directly to drive
turbines.
Figure 3 Dry Steam Plant (Özkaraca, 2018)
2. Flash Steam Plant
Flash steam geothermal power plants utilizes the separation of high-pressure fluid coming from the well into
water and steam through a low-pressure separator, followed by the use of separate steam to drive the turbine.
Figure 4 Flash Steam Plant (Özkaraca, 2018)
3. Binary Cycle Plant
Binary Cycle geothermal power plants utilizes heat from geothermal fluids, which causes the fluid to
evaporate at a lower temperature than water. This evaporation process takes place in the heat-exchanger, and
the steam generated is used to rotate the turbine.
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Figure 5 Binary Cycle Plant (Özkaraca, 2018)
Geothermal Development in Indonesia
Kamojang geothermal power plants is the first geothermal development for geothermal power plants in
Indonesia. Starting in 1926, when the Dutch East Indies Government began drilling in the Kamojang field. A long
journey to introduce geothermal energy to the Kampung Baru geothermal power plants. Only in 1983, the Kamojang
geothermal power plants unit with a capacity of 130 Mega Watt (MW) began to operate effectively (Pribadi, 2020).
The stages of geothermal exploitation activities consist of Preliminary Survey, Exploration, Feasibility Study,
Exploitation, and Utilization (Republik Indonesia, 2014).
The figure presented below illustrates the geothermal exploitation procedure, which begins with a Preliminary
Survey conducted by government and private agencies. This survey is usually run by high-ranking officials such as
Ministers, Governors, and/or Regents/Mayors. After the successful auction, the Investment Coordinating Board
(BKPM) will issue a Geothermal License (IPB) on behalf of the Minister of Energy and Mineral Resources (ESDM).
Next, the Exploration phase will begin, followed by a Feasibility Study (FS). If the results of the Feasibility Study
show economic feasibility, then the Electricity Supply Business License (IUPTL) will be issued, and the electricity
will be purchased by PLN after there is an agreement on the Electricity Sale and Purchase Agreement (PJBL). Finally,
the Exploitation and Utilization phase will be completed.
Figure 6 Process of Geothermal Power Plant Operation in Indonesia (Direktorat Jenderal EBTKE, 2021)
Geothermal Potential
In accordance with the National Standard for the Classification of Geothermal Energy Potential in Indonesia,
as outlined with SNI No. 13 - 5012 - 1998, geothermal energy potential in Indonesia is categorized into five different
classes. The classes are Speculative Resources, Hypothetical Resources, Possible Reserves, Probable Reserves, and
Proven Reserves (Badan Standardisasi Nasional, 1998).
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Table 1 Geothermal Resources and Reserves in Indonesia in 2022 (Kementerian Energi dan Sumber Daya
Mineral, 2023)
Geothermal Power Plants Construction Cost Structure
The costs associated with the construction of a power plant can be classified into four main components:
Component A, which relates to the cost of return on investment; Component B, which includes fixed operational and
maintenance costs; Component C, which relates to fuel costs; and Component D, which includes variable operational
and maintenance costs (Direktorat Jenderal EBTKE, 2021).
Component A depends on the initial capital investment of the plant and its rate of return. This cost component
consists of depreciation expense, interest expense and/or pre-tax income. Component B consists of costs related to
employee salaries, maintenance and repair services, insurance, research, consulting services, PNBP, and production
bonuses. Component C consists of the cost of fuel, which is determined by the factor of power generation capacity as
well as whether there is a purchase of steam. Geothermal power plants do not require fuel for their operation (Gonzalez-
García et al., 2023). The D component consists of incentives, water use, chemicals, as well as overhaul costs .
Engineering Economics
The use of economic analysis is an important component in the decision-making process (Ahmed Khan,
2014). The study of engineering economics consists of several components, including problem identification, goal
definition, cash flow estimation, financial analysis, and decision making. Adopting a method procedure is the optimal
approach to determine the most suitable solution to a given problem. There are at least 7 (seven) steps in engineering
economics (Blank & Tarquin, 2012), namely (1) the process of identifying and defining the problem, (2) the process
of formulating alternative solutions to a problem, (3) providing realistic cash flow projections, (4) determining
economic assessment criteria to facilitate decision making, (5) assessing each available option by considering non-
financial factors, and using sensitivity analysis if necessary, (6) the optimal choice is determined by evaluating the
results of the previous analysis phase, and (7) executing the proposed resolution and monitoring the results.
The cash inflows during the project operation period mainly come from the revenue generated, while the cash
outflows are related to operation and maintenance (O&M) costs and tax costs [15]. Costs associated with issuing
multiple securities, agency costs associated with different securities, and costs associated with unfavourable selection
[16]. Weighted Average Cost of Capital (WACC) is a suitable metric to determine the overall cost of financing. It is
important to note that the equity component of WACC can be calculated using the Capital Asset Pricing Model
(CAPM), which takes into account a country's sovereign risk (represented by the risk-free interest rate or government
bond yield, before and after taxes), market volatility, and market risk premium [17]. It is crucial for decision-makers
to consider the potential tax risks associated with investments. This includes the recognition that tax policies that are
currently favourable to investors may undergo modifications in the future, thus making tax protection less effective
than previously anticipated [18]. Therefore, it can be concluded that factors outside financial considerations also have
an impact on the composition of the company's capital structure. The formula to calculate WACC is as follows:
 󰇡
󰇢 󰇡
󰇛 󰇜󰇢
where:
D = Assessment of the company's outstanding debt in the market.
E = Valuation of the company's outstanding equity in the market
Tc = corporate tax rate
Re = Cost of equity
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Rd = Cost of debt
V = amount between debt and equity (D+E)
The time value of money is a financial concept concerned with fluctuations in the value of a currency over
time (Newnan et al., 2004). NPV is the result of calculations used to determine the present value of future payment
and expenditure flows (Kamel et al., 2023). To calculate NPV, it is necessary to discount future cash flows in addition
to investment costs, as shown in Equation:
 

󰇛󰇜

where:
CI = capital investment
CF = cash flow
t = period
n = length of project
i = discount rate
The table below provides an explanation for determining investment feasibility based on NPV value.
Table 2 Investment Feasibility Based on NPV
NPV
calculation
Description
Decision
NPV > 0
Investments have the potential to provide
favorable returns for the company
feasible investment
NPV < 0
Investments made by the company can
result in financial losses for the company.
investment is not feasible
NPV = 0
The investment made has a neutral impact
on the company
Evaluate the potential impact on the
company resulting from accepting or
rejecting the proposed initiative
DCF analysis is typically used to estimate the potential for discounted cash flows over the life of a project to
derive present value (Kamel et al., 2023). The notion that all investments are non-refundable is a fundamental weakness
of most discounted cash flow (DCF) techniques. The project manager has the authority to influence the outcome of the
project and may choose to terminate the project if the results are not satisfactory. Instead, they have the option to
extend the project if the results exceed expectations. Conventional NPV methods do not place a high value on
managerial flexibility. To determine the rate of return on investment, it is necessary to transform the various results
from these investments into cash inflows and cash outflows. Next, the cash flow will be analyzed to determine the
unknown value of the internal rate of return (IRR). IRR discounts all refunds in addition, resulting in NPV = zero over
the life of the investment (Colantoni et al., 2021)
2. Materials and Methods
PT XYZ is a geothermal energy company operating in the XYZ Geothermal Working Area (WKP). WKP
XYZ has a total geothermal reserve of 464 MW with details of Possible Reserve 224 MW, Proven Reserve 240 MW
and has been operating at 55 MW. Therefore, in this study, experiments were carried out to maximize the utilization
of the remaining geothermal reserves into geothermal power plants. The company was originally established as a
collaboration between PT Pertamina (Persero) and PT Perusahaan Listrik Negara (Persero) to carry out integrated
activities covering the entire geothermal energy production process, from the upstream side (steam field) to
downstream (power plant). To obtain funding, the feasibility of the project must be evaluated through a feasibility
study, which involves assessing the economic feasibility of the project and conducting a Front End Engineering
Design (FEED) to determine technical feasibility. After determination of eligibility for development, the maintainer
proceeds to seek funding. PT XYZ entered into a partnership with ADB (Asian Development Bank) on August 8,
2020, to obtain a credit facility under the Ordinary Capital Resources and Clean Technology Fund (The World Bank,
2019).
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Cash flow projections are made for the next 30 years. This is based on the period of exploitation and the term
of the electricity purchase agreement contract with PLN. In cash flow projections, there are several assumptions used,
namely inflation which is calculated to project the magnitude of cost increases in future years. The cash flow that
occurs during the construction phase is cash outflow, which is the initial investment in the project (Yang et al., 2023).
Cash flow in the base scenario (first scenario) uses PLTP investment guideline parameters (Direktorat
Jenderal EBTKE, 2021) without taking into account the uncertainty of sales volume and operation and maintenance
(O&M) costs. Figure 7 below shows cash flow in Unit-2 starting operations in 2024 without adding Unit-3 and Unit-
4
Figure 7 Free Cash Flow of Unit 2 Power Plant
Figure 8 shows cash flow in Unit-3 starting operations in 2029 without adding Unit-2 and Unit-4.
Figure 8 Free Cash Flow of Unit 3 Power Plant
Figure 9 shows cash flow in Unit-4 starting operations in 2034 without adding Unit-2 and Unit-3.
($300)
($200)
($100)
$0
$100
$200
$300
$400
$500
FCFP Unit-2
FCFP FCFP Cumulative
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Figure 9 Free Cash Flow of Unit 4 Power Plant
The next cash flow if the geothermal development company decides to build a unit 4 plant in 2029, combined
into the total cash flow of unit 2-3-4 plants, it will only get accumulated positive cash flow in 2037 as shown in the
figure below.
Figure 10 Free Cash Flow of Unit 2-3-4 Power Plant
Monte Carlo simulation is a quantitative risk assessment methodology that can be used by various
organizations in their risk management procedures, especially in the risk analysis and/or risk evaluation stages
involving stochastic variables. The use of risk analysis and evaluation techniques involving stochastic variables, such
as market risk, credit risk, and operational risk events, is widely used in the banking and oil and gas sectors (Guedes
& Santos, 2016). Monte Carlo simulation methodology involves mathematical models in multiple simulations using
computer programs (Nur et al., 2023). This study uses the Palisade Decision Tools @Risk program for the
implementation of Monte Carlo simulation in simulating project investment risk analysis.
Inputs have been classified into two categories: 'known' variables and 'uncertain' variables. The variables
considered are predetermined values, which include power generation capacity, capacity factor, corporate tax, interest
rate, and discount rate. Uncertain variables include risk variables associated with resource drilling/exploration (such
as number of wells and cost per well), as well as the base price of electricity tariffs. The accuracy of risk analysis
($500)
$0
$500
$1.000
$1.500
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2041
2043
2045
2047
2049
2051
2053
2055
2057
2059
2061
2063
FCFP Unit 2-3-4
FCFP FCFP Cumulative
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using Monte Carlo simulations depends largely on the use of precise distribution functions to represent uncertainty
and variability, as shown in the table below.
Table 3 Probability Distribution Functions
Distribution
Function
Criteria
Type of Use
Discrete
A value that is discrete and comes with an
assigned probability.
For variables that only contain
discrete values
Uniform
Minimum and maximum potential values.
where the mode does not exist and/or
the shape of the distribution is
uncertain or debatable, with respect
to the variables in question.
Triangle
Minimum possible value, most likely
value, and maximum possible value.
It is used in situations where the
central tendency is obvious, but the
slope of the distribution is
unnoticeable.
Normal
Continuous,
Mean, Standard deviation
is used to denote a value that
indicates a balanced distribution.
There are various empirical bases for
establishing the parameters
associated with this concept.
Log-normal
Mean, Standarddeviation dari variabel
naturallogarithm
is used to denote a value that
indicates a lack of symmetry, and
there are various empirical methods
to estimate the associated parameters
PERT or Beta
PERT
Minimum possible value, most likely
value, and maximum possible value
It is used in situations where the most
likely value is obvious, and the
distribution shows a significant
degree of asymmetry.
Binomial
Binary systems are characterised by two
distinct values, namely zero and one, and
the possible value is one.
is used to indicate the likelihood of a
potential hazard occurring, regardless
of the outcome.
Cumulative
Generates a replica of an existing
distribution by entering its lower and
upper bounds, along with a set of discrete
value/probability pairs.
It can be used in conjunction with
historical data or in cases where the
results of probabilistic analyses serve
as input for subsequent analyses.
3. Results and Discussions
Cash flow projections will use Monte Carlo simulation analysis, which involves obtaining the mean or
average of data from 1,000 iterations of random numbers. These simulations have been run to produce significant
amounts of data, which are considered reliable representations of results close to the normal distribution.
In this study, the feasibility analysis of investment in the geothermal power plants capacity development
project of PT. "XYZ", using project feasibility analysis using Monte Carlo simulation. Where takes into account the
uncertainty variable by giving the probability risk distribution function in Monte Carlo simulation, with the value of
the uncertainty variable as follows:
Table 4 Monte Carlo Simulation Uncertainty Variable Values
Variabel
Value
Probability Distribution Function
Inflation
4,16%
Distribusi Triangle
Capacity Factor
90,00%
Distribusi Normal
Period of construction
5 Years
Distribusi Triangle
Financing Structure
70 % Debt 30 % Equity
Distribusi Normal
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Interest Rate
10,47%
Distribusi Triangle
Corporate Tax
34%
Distribusi Triangle
WACC
9,06%
Distribusi Triangle
This study uses Monte Carlo Simulation with 2 (two) investment scenarios, namely:
• Investment Scenario First, where the development of PLTP Unit 2 capacity of 1x55 MW was built first
in 2019, after the generation of unit 2 commercial date in 2024, continued with the development of unit
3 generating capacity of 1x55 MW in 2024 until the commercial date in 2029, and continued with the
development of unit 4 development capacity of 1x55 MW in 2029 until the commercial date year 2034.
Figure 11 NPV Value Simulation with First Ivestment Scenario
Figure 11 is the result of a Monte Carlo simulation with the First Investment Scenario showing that the average
NPV value is $16.67 million and the probability of NPV value is ≥ 0 or positive at 75.2%. Figure 12 is a tornado
diagram showing the sensitivity of variables to NPV values. Variable capacity factor is the variable that most
sensitively affects changes in NPV value.
Figure 12 Sensitivity of Uncertainty Variable to NPV in First Investment Scenario
• Investment Scenario Second, the development of PLTP Unit 2 capacity of 1x55 MW was built in 2019
until the commercial date stage in 2024, then in 2024 continued with the development of unit 3 generating
capacity of 2x55 MW until the commercial date in 2029.
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Figure 63 NPV Value Simulation with Second Investment Scenario
Figure 13 is the result of a Monte Carlo simulation with the Second Investment Scenario showing that the
average NPV value is $30.02 million and the probability of NPV value is ≥ 0 or positive of 77.11%. Figure 14 is a
tornado diagram showing the sensitivity of variables to NPV values. Variable capacity factor is the variable that most
sensitively affects changes in NPV value.
Figure 14 Sensitivity of Uncertainty Variable to NPV in the Second Investment Scenario
For capacity development, PLTP XYZ with the second investment scenario provides better return on
investment than the first investment scenario. This is because the greater the generating capacity and the smaller the
"lead time" of a project will provide large and earlier revenues for the company.
4. Conclusion
When evaluating investment in the development of "XYZ" PLTP plant capacity, there are several factors that
influence decision making, so as to reduce investment risk, namely uncertainty variables such as generation capacity,
WACC, inflation, and implementation period. The probability value of NPV describes the distribution of possible NPV
to be obtained, so it can be used for analysis of the decision-making process to invest even though the resulting NPV
is positive.
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