Volume 3,
No. 8 August 2024 (1663-1678)
p-ISSN
2980-4868 | e-ISSN 2980-4841
https://ajesh.ph/index.php/gp
Analysis of the Financial Feasibility Study of
Investment in Electricity Infrastructure Development Using the Monte Carlo
Method Case Study of the Construction of GITET 500 kV Cikande
Ficry Haechal1*, Budi Sudiarto2
1,2SKSG Universitas
Indonesia, Depok, West Java, Indonesia
Email: ficry.haechal@gmail.com1*,
budi.sudiarto@ui.ac.id2
ABSTRACT
The development of electricity
infrastructure is an important part of the planning of the electric power
system in order to ensure that the condition of the
electricity system can meet the load growth plan while still paying attention
to safety and economic factors. The 500 kV Cikande
Extra High Voltage Substation (GITET) and its 150 kV Outlet are one of the
infrastructures that will be built by PT PLN (Persero) with an operational
target in accordance with the General Plan for the Provision of Electricity (RUPTL)
for 2021 – 2030 in 2025. To be able to carry out the infrastructure
development, PT PLN (Persero) needs to prepare a large enough investment fund
so that it is necessary to carry out a financial feasibility analysis at the
time the investment will be carried out, but the financial feasibility analysis
currently carried out still uses a deterministic method, where the input
parameters used in the calculation of financial feasibility have not considered
the element of uncertainty/risk that will affect the output of the calculation
of the financial feasibility study. In this study, the Net Present Value (NPV),
Internal Rate of Return (IRR) and Payback Period (PbP)
values will be tested against the required investment value by simulating
changes in several input parameters simultaneously using the Monte Carlo
method. This research aims to ensure that the investment issued by PT PLN
(Persero) meets the parameters of financial feasibility by considering the
element of uncertainty/risk in the future.
Keywords: Financial
Feasibility Study, Monte Carlo, Net Present Value (NPV), Internal Rate of
Return (IRR) and Payback Period (PbP).
INTRODUCTION
The need for electricity supply has become one of the main needs of
the community at this time
The need for electricity in an area is driven by several main factors,
namely economic growth, population growth and the number of electricity tariffs
as well as electrification programs and government programs, including building
Special Economic Zones (SEZs), Industrial Areas, National Tourism Strategic
Areas (KSPN), Integrated Marine and Fisheries Centers (SKPT) and the State
Border Patrol Post (PLBN) power network
To ensure the availability of sufficient quantities of electrical
energy, good quality and reasonable prices and realize sustainable development,
it is necessary to add power plants, transmissions and substations, and
distribution networks
One area with considerable potential for growth in electrical energy
needs is Banten Province, where the composition of electrical energy needs in
these areas is dominated by the Industry, Household, Business and Public
sectors. One of the electricity infrastructure development plans that will be
built in this area is the 500 kV Cikande GITET along
with SUTET and its 150 kV outlet, which aims to provide additional supply to
the Cilegon subsystem.
The DKI and Banten Load
Regulating Service Unit (UP2B) system currently consists of 11 Sub-Systems. The
loading condition of the Cilegon Sub System 1,2 &
3 Cilegon – PLTGU Cilegon –
PLTU Labuan is currently quite high, where the load on IBT 1, 2 and 3 at GITET Cilegon has reached 78%, 78% and 91%. This is due to the
increase in natural load and the need to connect high voltage consumers (KTT),
which is quite rapid in the area. Additional power supply to the Cilegon sub-system is needed to maintain N-1 contingency in
the event of a disruption to one of the IBTs or 1 trip generating unit.
The peak load on UP2B DKI & Banten in 2022 occurred on Monday,
April 18, 2022, at 13.30, amounting to 11,615 MW, where the load on the Cilegon sub-system was recorded at 1,426 MW. Currently, the
Cilegon sub-system is supplied from IBT 1, 2 and 3,
the Cilegon PLTGU and the Labuan PLTU, the majority of the load served by this sub-system is the
industrial sector summit. With the condition of the Cilegon
IBT load has > 60%, the limitation of gas primary energy for the Cilegon PLTGU and the potential outage of the Labuan PLTU, it can be seen that this sub-system no longer meets the N-1
contingency, if there is a disruption in one of the sub-systems, the load on
the other IBT will be overloaded.
Based on the Feasibility Study of the GITET
500 kV Cikande Construction Project prepared by PT
PLN (Persero) Main Unit of the Development of the Java-Bali Load Control Center (UIP2B), a power flow simulation was conducted using
several cases considered to have a significant impact on the system. These
cases include N-1 contingency of the 500/150 kV GITET Cilegon,
N-1 contingency of the 150 kV transmission line in the Cilegon
Sub-System, non-operation of Cilegon PLTGU, and the
operation of one unit of Labuan PLTU. In the first case, the IBT loading
simulation results showed that if there is a disturbance in one of the IBTs, it
was found that under normal conditions, all IBTs operated above 100%, and in
the N-1 condition with one of the IBTs, the load on the other IBTs reached 163%
to 171%.
Furthermore, for the second case, a power flow
simulation was carried out if there was a disturbance in one of the
transmission sections connected to GITET Cilegon. The
results of this power flow simulation indicated that the N-1 condition was not
met on the transmission line sections. The 150 kV Serang
– Ciruas SUTT transmission line section would be
loaded at 133.8% if one of the circuits is not operating. The 150 kV Serang – Kramatwatu SUTT
transmission line would be loaded at 113.1% if one of the circuits is not
operational. Meanwhile, the 150 kV Cilegon Baru – Kramatwatu SUTT transmission line would be loaded at 127.7%
if the 150 kV Cilegon Baru – Wilmar transmission line
is not operational.
In addition, it is also a concern for transmission lines with a single
phi SUTT configuration of 150 kV Cikande—Ciruas | Indah Kiat, where if one
of the sections is not operating, the other section will be burdened by more
than 80%. In accordance with the power flow analysis carried out above, it is
necessary to build new infrastructure that can improve the reliability of
electricity supply in the Cilegon subsystem.
In accordance with the RUPTL for 2021 – 2030, the construction plan of
GITET 500 kV Cikande and its 150 kV Outlet is one of
the planned infrastructures that can help the reliability of the Cilegon sub-system with a target of completion in 2025.
RESEARCH METHODS
This research uses quantitative research methods, which are also known
as traditional, positivistic, scientific, and discovery methods. This method
emphasizes using numerical or non-numerical data converted into numbers.
Quantitative research is known for its rigorous application of the principle of
objectivity, including using instruments tested for validity and reliability
The object of this study is a study of the financial feasibility of
the GITET 500 kV Cikande construction project. The
data sources used in this study consist of primary data and secondary data.
Primary data is obtained through verbal statements from informants or
respondents that are trustworthy and relevant to the research variables.
Secondary data comes from documents, photos, films, video recordings, and other
sources that can enrich the information obtained from primary data.
The population in this study includes all data relevant to the GITET
500 kV Cikande construction project, while the
research sample is taken from the most representative and relevant data to be
analyzed in the financial feasibility study. The research techniques used
include data collection through literature and document studies and data
analysis using Monte Carlo simulations. The main tool used in this study is the
Crystal Ball software, which allows to perform recalculations or iterations of
input variables as many as 10,000 times based on a predetermined range of
values and probability distribution types.
The analysis technique in this study involves several important steps.
First, the structure of the financial feasibility calculation model is made in
a spreadsheet, including the collection and organization of input data, such as
the annual investment disburse plan, the projected revenue from the sale of
electrical energy, the projection of the purchase of electrical energy, and the
estimate of operational and maintenance costs. Next, basic assumptions are
prepared, including projected burden growth, changes in interest rates, the
life of the infrastructure, and the depreciation value of assets. After that,
cash flow projections are made for all benefits and costs during the operation
of the infrastructure, and output variables are calculated.
After the calculation model is completed, Monte Carlo simulations are
carried out to generate data on the probability of the value of the output
variables, considering the elements of uncertainty in each input variable at
the same time. The results of this simulation are expected to help the
management of PT PLN (Persero) in understanding the level of risk from
investment and improving accuracy in decision-making. In addition, sensitivity
analysis was carried out to obtain the percentage of confidence level of output
variables by making adjustments to several input
variables that have a high risk of change. The final stage of this research is
to summarize the overall research results and provide suggestions that can be
used as material for further research development.
RESULTS AND DISCUSSION
Input Variable
Statistical Parameters
Investment Costs
The investment cost used as an input
variable in the calculation of the financial feasibility analysis is not
limited to the cost of electricity infrastructure development but includes
supporting costs such as land acquisition costs, ROW compensation, licensing
management to environmental permit fees (UKL/UPL/AMDAL). The value of this
investment cost is influenced by various risks that can potentially increase
costs such as price changes raw materials, changes in government policies,
changes in technical standards, to social risks related to the land acquisition
process / ROW compensation to communities affected by utility development,
therefore it is necessary for this variable to be included in one of the
variables used as a reference in the analysis of financial feasibility using
the Monte Carlo method. The value of the investment cost used as a base case in
the calculation of investment feasibility is Rp. 1,041,177,359,-
with the type of log-normal probability distribution, so that by setting the
certainty level at a value of 90%, statistical parameter data for investment
costs is obtained as follows.
Figure 1. Statistical Parameters of Input
Variables Investment Costs
Cost of Purchasing Electricity from Plants
For the input variable of the cost of
purchasing electricity from the plant/transfer price of the plant, the type of
distribution used is the Beta-PERT probability distribution with the
determination of the Base Case value according to the Power Purchase Agreement
(PPA) / Power Purchase Agreement (PJBTL) document and the determination of the
Optimistic and Pessimistic value in accordance with the sensitivity analysis
data contained in the KKP document as follows.
Figure 2. Probability Distribution of
Input Variables for the Purchase of Electricity Costs from Plants
Growth of Electrical Energy
Growth/growth of electrical energy is
one of the input variables used in financial feasibility analysis using the
Monte Carlo simulation because it has a fairly high
element of uncertainty/risk. One of the main risks is regulatory uncertainty
and government policies that may change in line with changes in politics and
national priorities, economic risks, such as fluctuations in fuel prices, the
cost of new technologies, technical risks related to the reliability and safety
of the power grid and the integration of renewable energy sources, as well as
social risks, including public resistance to new energy projects and inequality
of access to electricity, It can also
affect the overall growth of electrical energy. The probability distribution
used for this variable is a normal distribution, so by setting the certainty
level of 90%, the following statistical value parameters are obtained.
Figure 3. Statistical Parameters of Input
Growth Variables
Channeled Electrical Energy
The value of the channelled
electrical energy used in the calculation of financial feasibility is adjusted
to the operating pattern contained in the calculation of the Operational
Feasibility Study contained in the KKP, where the IBT 1 and IBT 2 transformers
are burdened by 60%. In this analysis, the data on electrical energy channelled to customers is used to estimate revenue from
the sale of electrical energy so that it can provide a more accurate picture of
the strategic decision-making process. The value of electrical energy channelled to customers is also influenced by risks that
can disrupt the stability and continuity of electricity supply, such as the
risk of disruption to transmission/distribution network infrastructure, extreme
weather or natural disasters as well as operational risks related to network
management and management that can cause the value of electrical energy
distributed to customers not in accordance with the assumptions that have been
set at the beginning. The type of probability distribution used in this variable
is Log-Normal, so by setting a certainty level of 90%, the following
statistical value parameters are obtained.
Figure 4. Statistical Parameters of
Variable Input of Distributed Electrical Energy
Interest Rate
Interest rate is one of the key variables
in the calculation of the financial feasibility analysis of a project as an
important component in the calculation of Net Present Value (NPV) and Internal
Rate of Return (IRR). The base case value used is adjusted to PLN's Weighted
Average Cost of Capital (WACC) value in 2022 of 9.28%. Fluctuations in interest
rates in the calculation of financial feasibility analysis can also affect the
feasibility of an investment proposal therefore in this study, the value of
optimistic and pessimistic The results used were
10.208% and 8.35%, respectively, according to the sensitivity analysis data
contained in the MPA with a triangular probability distribution.
Figure 5. Probability Distribution of
Interest Rate Input Variables
Monte Carlo Simulation Output Variable
In this
study, the calculation of the financial feasibility analysis of the
construction of GITET 500 kV Cikande and its 150 kV
Outlet will be carried out using the Monte Carlo method to obtain output
parameters in the form of IRR, NPV and Payback Period values using the
parameter assumptions contained in table 2. The simulation was carried out
10,000 times using crystal ball software integrated with Microsoft Excel. Before
the simulation using the Monte Carlo method, the author made a calculation
model for the financial feasibility study based on predetermined parameters;
this calculation model represents the values of the input parameters into an estimate of the cash-in
and cash-out value of this investment plan
Analysis of the results of the Net Present Value (NPV)
simulation
The NPV
value is calculated by changing the values of several input variables in
accordance with the probability distribution range of each variable due to the
element of uncertainty/risk. The results of the NPV calculation simulation are
as follows.
Figure 6. Monte Carlo Simulation Results
on NPV Parameters
In Figure
6, there is a green and red chart bar; the green chart bar shows the
probability of NPV value > 0, while the red chart bar shows the probability
of NPV value <0. Based on the results of the simulation, it
can be seen that the probability of an NPV value of > 0 is 91.45%,
with the optimum NPV value shown at the mean value of Rp. 599,936,063,-
so from the results of the simulation, it can be concluded that the value of
this investment is still fairly feasible. Using the Monte Carlo method, it is
also possible to quantify the level of probability of NPV values.
Analysis of the Results of the Internal Rate of Return
(IRR) Simulation
The calculation of IRR is also
carried out with the same assumption as the calculation of the NPV value; the
Monte Carlo simulation is carried out to obtain the probability value of the
IRR > from the WACC VALUE OF PT PLN (Persero) in accordance with the KKP
document, which is 9.28%. The results of the IRR value calculation simulation
are as follows.
Figure 7. Monte Carlo Simulation Results
on IRR Parameters
Based on
the simulation results in Figure 7. The results of the probability of the IRR
value were obtained after 10,000 simulations. The optimum value of IRR is
14.43%; the simulation results show that, after including the element of
uncertainty/risk in several input variables, the level of confidence in the IRR
> WACC (9.28%) is 91.45%. Thus, it can be interpreted that from 10,000
calculation attempts with a combination of changes in several input variables,
the level of investment confidence that is profitable is more than 90%.
Payback Period (PbP) Result
Analysis
The
calculation of the payback period results is also carried out to get the most
optimal return time by considering every potential risk that will occur in implementing
the investment. This data is needed to be used as one of the considerations in
the decision-making process for investment implementation. The results of the
simulation of the payback period calculation are as follows.
Figure 8. Monte Carlo Simulation Results
on Payback Period Parameters
Based on
the results of the Payback Period calculation simulation of 10,000 times, the
probability value divided into percentages is obtained as follows:
1. The probability of the Payback Period value for 3.29
years is 0%
2. The probability of the Payback Period value for 5.01
years is 20%.
3. The probability of the Payback Period value for 5.74
years is 40%.
4. The probability of the Payback Period value for 6.59
years is 60%.
5. The probability of the Payback Period value for 7.98
years is 80%.
6. The probability of the value of the Payback Period for
26.77 years is 100%.
From
these results, the average return on investment is 6.68 years, which is
included in the 80% percentile. This value already represents most simulation
results, so if infrastructure development projects can be completed according
to the COD schedule 2025, then the Break Event Point (BEP) can be projected in
mid-2032. The average return on investment from the results of the 10,000 times
Monte Carlo simulation is shown in Figure 9.
Figure 9. Average Return on Investment
Comparison of Monte Carlo Method Results with
Deterministic Method
Table 1
compares the results of the financial feasibility analysis using the
deterministic method and the Monte Carlo method after the simulation.
Table 1. Comparison of the Results of
Financial Feasibility Analysis of Deterministic Methods
By Monte Carlo Method
|
Output
Variables |
Unit |
Deterministic
Method (KKP Document) |
Monte Carlo
Method |
Difference |
|
|
Value |
Confidence
Level |
||||
|
Net Present Value (NPV) |
IDR |
730,165,180 |
599,936,063 |
91.45% |
130.229.117 |
|
Internal Rate of Return (IRR) |
% |
15.57 |
14.43 |
91.45% |
1,14 |
|
Payback Period |
Tahun |
5.70 |
6.68 |
62.05% |
0,98 |
From the
table above, the difference in the value of the output parameters produced by
the deterministic and Monte Carlo methods is obtained. The difference is
because the deterministic method only uses one point of assumption on the input
variable, while in the Monte Carlo method, the input variable processed by the
financial model is a range of values according to the type of probability
distribution of each variable so that the output produced is not only the
optimal value of the output variable but also the percentage of confidence in
these results.
Sensitivity Analysis Calculation
Based on the simulation results shown
in table 4.1, with the condition of the input variable used using the base case
value according to table 3.4, it can be concluded that the investment in the
development of electricity infrastructure of GITET 500 kV Cikande
and its 150 kV outlet is very feasible with a confidence level of >90%.
However, in this study, sensitivity analysis is still carried out by widening
the range of possible uncertainty for several input parameters. The sensitivity
analysis was carried out by simulating the increase in investment costs, the
decrease in the value of the growth percentage, and the gradual decrease in the
value of energy channelled to customers, up to 30%.
This sensitivity analysis aims to obtain the percentage of confidence level of
positive NPV value and IRR value greater than WACC. The results of the
sensitivity analysis are shown in Table 2.
Table 2. Results of Sensitivity Analysis
to Changes in Investment Costs, Growth and Channeled
Energy
|
Confidence Level |
||||||
|
Output Variables |
Investment Costs, Channeled Energy
Growth |
|||||
|
|
Base Case |
Investment Fee +10% Growth & Energy -10% |
Investment Fee +15% Growth & Energy -15% |
Investment Fee +20% Growth & Energy -20% |
Investment Fee +25% Growth & Energy -25% |
Investment Fee +30% Growth & Energy -30% |
|
Internal Rate of Return (IRR) > WACC |
91.45% |
84.51% |
79.84% |
71.64% |
61.27% |
48.35% |
|
Net Present Value (NPV) Positif |
91.45% |
84.25% |
79.34% |
71.35% |
61.22% |
48.51% |
In the sensitivity analysis
simulation, the value of investment costs was increased, the growth value
decreased, and the estimated energy channelled to
customers was gradually reduced from 10% to 30% to get a sense of what
percentage of confidence in IRR and NPV is still acceptable. From these
results, it can be seen that the investment in the development of electricity
infrastructure at GITET 500 kV Cikande and 150 kV
outlets is still safe if there is an increase in investment costs, a decrease
in growth and a decrease in energy channelled to
customers of up to 25%, this is shown by the results of the probability that
the positive IRR > WACC and NPV values are still at > 60%, but if there
is an increase in investment costs, The
decrease in growth and the decrease in energy channelled
to customers >25% to 30%, this investment will be very risky financially
with a positive level of confidence in the value of IRR > WACC and NPV <
50%, so the implementation of the investment is recommended not to continue.
CONCLUSION
This study
calculates the financial feasibility analysis of the proposed investment in
electricity infrastructure development using a different method from the one
used in the project feasibility study document. Based on the results of the
analysis and discussion in the previous chapter, the following conclusions can
be drawn: First, by using the Monte Carlo simulation in analyzing the financial
feasibility of an investment plan, it is possible to quantify the confidence
level in the output variables' results against their respective eligibility
criteria. Second, Monte Carlo simulations of several input variables
simultaneously can describe more realistic conditions in sensitivity analysis
calculations compared to using deterministic methods. Third, according to the
results of the sensitivity analysis, the safe threshold for an increase in
investment costs, a decrease in growth, and a decrease in channeled energy that
is still acceptable is 25% of the base case value (initial assumption). This
research can be further developed by paying attention to several points: First,
conducting a comprehensive risk analysis is necessary to obtain the variables
with the most uncertainty/risk impacting the results of the financial
feasibility calculation. Second, this method can be used to calculate the
financial feasibility of PT PLN (Persero)'s electricity infrastructure
investment plan.
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