1. Introduction

1.1. Jawa-Madura-Bali Power System (Grid Capacity and Peak Load Demand)

Figure 1. Jamali Power System [5].

Figure 1. Jamali Power System [5].

The Jawa Bali grid system plays a vital role in providing power to the islands of Java and Bali in Indonesia, supporting their sizable populations and industrial sectors. With an installed capacity of 47,647 megawatts (MW) in 2023, it ranks among the most substantial electricity grids in Southeast Asia [1]. During that year, the system faced a peak load of approximately 40,223 MW during peak hours, indicating a relatively narrow margin between capacity and demand [1].

As of 2022, the population of the combined regions of Java and Bali in Indonesia is estimated to be around 157 million [6]. The expected population growth for Jawa and Bali regions to 2029 is projected to be significant, driven by factors such as natural population increase (births exceeding deaths) and migration from other parts of Indonesia to these regions. While specific population projections can vary depending on various factors and assumptions, it is estimated that the population of Jawa and Bali regions could surpass 164 million by 2029 (around 0,8% growth [6]).

However, the region’s rapid economic expansion and urbanization are anticipated to drive an annual peak load increase of about 8% [1,7,8,9,10,11,12,13,14]. To accommodate this growth and ensure a dependable power supply, significant investments are being made in infrastructure and renewable energy sources to bolster the Jawa Bali grid system. The average capacity growth from 2014 to 2023 is 5.37% [1,7,8,9,10,11,12,13,14].

Table 1. Jamali System Supply and Demand Historical Data. [1,7,8,9,10,11,12,13,14].

Table 1. Jamali System Supply and Demand Historical Data. [1,7,8,9,10,11,12,13,14].

Year	Total Installed Capacity in MW	Annual Change in %	Peak Demand Load in MW	Annual Change in %
2014	31,062		23,908
2015	27,867	-10.29	24,269	1.51
2016	36,712	31.74	33,208	36.83
2017	36,517	-0.53	25,680	-22.67
2018	37,721	3.30	27,097	5.52
2019	40,174	6.50	26,657	-1.62
2020	40,685	1.27	24,420	-8.39
2021	41,743	2.60	25,852	5.86
2022	45,835	9.80	24,228	-6.28
2023	47,647	3.95	40,223	66.02
	Annual Change in average:	5.37	Annual Change in average:	8.53

Figure 2. Jamali System Capacity and Peak Load Demand 2014-2023. [1,7,8,9,10,11,12,13,14].

Figure 2. Jamali System Capacity and Peak Load Demand 2014-2023. [1,7,8,9,10,11,12,13,14].

Two key variables of Jamali system important for this simulation are Peak Load and Installed capacity. Peak Load represents the highest load reached by each system within a calendar year. Installed Capacity refers to the capacity of a single generating unit as indicated on the generator nameplate or prime mover, whichever is smaller [16]. Upon analyzing historical Supply-Demand data from the Jamali system, a notable increase in peak load is observed in 2023. This surge could suggest a rise in demand attributable to the growth of electric vehicles (EVs).

The peak load for the Jawa Bali grid system is usually during the day when demand is highest [17]. The peak load is driven by various factors, including the time of day, season, and economic activity. It is typically higher during the daytime as commercial and industrial activities are at their peak.

Figure 3. Jamali Hourly Peak Load. [17].

Figure 3. Jamali Hourly Peak Load. [17].

A crucial aspect of this simulation is the determination of the base peak load, which represents the electricity demand excluding the demand from electric vehicles (EVs). While obtaining this metric can be challenging, it can be estimated through extrapolation of historical data. Since the number of EVs before the introduction of subsidies and regulations in 2023 was minimal, their load demand was insignificant. Therefore, the peak load before that year can be closely regarded as the base peak load.

2. Materials and Methods

2.1. Data Gathering and Data Analysis

The analysis initiates with the acquisition of data pertaining to the present and anticipated growth of variables impacting both the supply and demand aspects of the grid. This data is imperative for the execution of the simulation.

2.1.1. Power Grid Capacity

Utilizing the National Electricity Company’s data (RUPTL) [3], an analysis of the Jamali electricity supply can be conducted. The supply side of the grid is simulated by projecting the current capacity and a growth factor based on the RUPTL. The variables concerning the supply side include:

• Power Plant Capacity
• Power Plant Growth Factor

2.1.2. Demand Peak Load

The demand side of the grid contains two main components, which are the base peak load which is excluding electric vehicles load and the EV load itself. A demand simulation model for the EV load was created to calculate the total increase in demand load with a combination of ICE and Electric Vehicle population, initial charging time (ICT), the ratio between slow and fast charging, and the charge load curve of each type of vehicle using the data gathered. The variables related to the demand side and their values are as follows:

• Power Plant Capacity [1,7,8,9,10,11,12,13,14]
• Base Peak Load (excluding EV Load Demand) [1,7,8,9,10,11,12,13,14]
• Jamali Population [6]
• Personal Vehicle Population [32]
• Electric Vehicle Population Probability [33]
• 2- and 4Wheelers ratio [32]

Table 2. Demand side input variables values.

Table 2. Demand side input variables values.

Variable	Label in Simulation	Value in 2024
Supply Side
Power Plant Capacity	PowPlaCap_0	50,206,000 kW
Base Peak Load	BasPeaLoa_0	28,361,000 kW
Demand Side
Java and Bali Population	JamPop_0	157,820,000
Personal Vehicle Population	TotVeh_0	89,757,134
EV Population Probability	PerEV_0	0.091%
2- and 4Wheelers Ratio	2w4wRat	120.04:16.41

2.1.3. Initial Charging Time

In the simulation of demand load, it is imperative to account for the Initial Charging Time (ICT), denoting the moment when EVs are connected to the grid for daily charging. For most EV users, ICT can be approximated as coinciding with the conclusion of their daily travel. As per the National Household Travel Survey (NHTS) findings, the distribution of end-of-travel times depicted in the graph conforms to a normal distribution [34]. Subsequently, the data for hourly Initial Charging Times is segmented into intervals of 12 minutes each (5 ICTs per hour, with a resolution of 12 minutes). The probability density function of the graph is presented as follows:

Figure 4. Generic Initial Charging Time.

Figure 4. Generic Initial Charging Time.

Figure 5. ICT with 12 minutes resultion.

Figure 5. ICT with 12 minutes resultion.

2.1.4. Electric Vehicle’s Specification Details and Charge Curve

The total load demand will be divided into two categories: two types of four-wheelers EVs (4wEV) and two types of two-wheelers EVs (2wEV). Each category represents both a luxury and a daily-use type of vehicle for both four-wheeled and two-wheeled EVs. The samples are as follows:

• 2021 Hyundai Ioniq 5 Long Range
• 2023 Wuling Air EV Long Range
• 2022 United TX1800
• 2023 Viar new Q1L, with following specifications:

Table 3. EV Type Specifications.

Table 3. EV Type Specifications.

Vehicle Type	Hyundai Ioniq 5 Long Range	Hyundai Ioniq 5 Long Range	Wuling Air EV Long Range	United TX1800	VIAR new Q1L
Label in Simulation	EVTyp1F	EVTyp1S	EVTyp2	EVTyp3	EVTyp4
Driving Range	450 km	450 km	300 km	65 km	60 km
Battery Size	72.6 kWh	72.6 kWh	26.7 kWh	1.68 kWh	1.38 kWh
DC Fast Charging Capability	Yes	Yes	No	No	No
Charging Duration	20 mins	720 mins	240 mins	90 mins	300 mins
Max Charging Load	224 kW DC	11 kW AC	6.6 kW AC	1.3 kW AC	0.24 kW AC
Daily Commute	30 km	30 km	30 km	30 km	30 km
Charge Frequency	15 days	15 days	10 days	2.17 days	2 days
Nationwide Yearly Sales in 2023	7,176 units	7,176 units	5,575 units	9,00 units	2,700 units
Production Start	2022	2022	2022	2022	2017

The charging load data for four-wheeled electric vehicles (4wEV) is based on the charge load curve of the 2021 Hyundai Ioniq5, which features a 72.6 kWh battery capacity [35]. The Hyundai Ioniq is Indonesia’s top-selling 4wEV, with 7,176 units sold in 2023 [19]. During slow charging mode, the peak load of the Ioniq5 is 7.2 kW, while the peak demand during fast charging mode reaches 225 kW [35].

The frequency of charging for an electric vehicle (EV) is determined by the total travel distance covered per charge. For example, the IONIQ 5, with a range of 450 kilometers [35], divided by a norm of 30 kilometers traveled each day [36], would require charging approximately every 15 days. The graph below illustrates the charge curve of the IONIQ, measured during charging [37].

Figure 6. Hyundai Ioniq 5 Long Range Charge Curve.

Figure 6. Hyundai Ioniq 5 Long Range Charge Curve.

Another source for charging load data for budget four-wheeled electric vehicles (4wEV) was obtained from the Wuling Air EV Long Range model [38]. The Air EV is the second best-selling 4wEV unit in 2023 [19]. Unlike the Ioniq5, it does not feature fast charging capability. Based on the average daily commute, the Wuling Air EV Long Range model requires charging approximately every 10 days.

Figure 7. Wuling Air EV Long Range Charge Curve.

Figure 7. Wuling Air EV Long Range Charge Curve.

The total load from two-wheeled electric vehicles (2wEV) is calculated using charge load data obtained from the United TX1800 [39] and the budget-oriented VIAR new Q1L electric motorcycle, which was a pioneer of 2wEV in Indonesia in 2017 [40]. The United TX1800 has a 1300W charge peak [39], while the VIAR new Q1L has a 0.24 kW peak [40]. Daily distance traveled and initial charging time (ICT) are similar to the 4wEV load simulation. Generally, 2wEVs in Indonesia do not feature fast charging capabilities.

Figure 8. United TX 1800 and VIAR new Q1L Charge Curve.

Figure 8. United TX 1800 and VIAR new Q1L Charge Curve.

These charge curves are utilized as inputs in the demand simulation. The total additional electricity demand from EVs is determined by the combination of the number and type of vehicles, ICT, and the fast/slow charging curve of each vehicle. The charge curves from each EV type used in the simulation are shown below:

Figure 9. EV Charge Curves.

Figure 9. EV Charge Curves.

2.2. Supply-Demand Simulation

The collected data will be used as inputs in MATLAB and simulated in Simulink. The power plant capacity is used as the supply capacity in the simulation. The current capacity and its projected growth factor were obtained from the RUPTL PLN.

Actual Power Plant Capacity = Capacity in 2024 + (Number of Year x Capacity Growth Factor x Capacity in 2024)

(1)

The base peak load, which excludes EV load, is also simulated using its current value and growth factor.

Actual Base Peak Load = Base Load in 2024 + (Number of Year x Base Load Growth Factor x Base Load in 2024)

(2)

The demand load was calculated based on the Jamali population and its ratio to vehicle ownership. A certain percentage of these vehicles are EVs. The growth factor of EVs is influenced by their attractiveness and the impact of EV subsidies.

Actual Jawa Bali Population = Population in 2024 + (Number of Year x Population Growth Factor x Population in 2024)

(3)

Actual Total Vehicle Population = (Actual Jawa Bali Population x Vehicle to Population Ratio) + (Number of Year x Vehicle Growth Factor x Total Vehicle Population in 2024)

(4)

EV Growth Factor = EV Attractiveness Growth Factor + Subsidy Impact Growth Factor

(5)

Actual EV Population = Total Vehicle Population x Total Vehicle to EV Ratio + (Number of Year x EV Population Growth Factor + EV Population in 2024

(6)

The demand simulation then progresses in Simulink. The simulations are conducted individually for each type of EV: including EVTyp_1F, EVTyp_1S, EVTyp2, EVTyp3, and EVTyp4.

Figure 10. Simulink Model for EV Load Demand Simulation.

Figure 10. Simulink Model for EV Load Demand Simulation.

2.3. Scenario Permutations

In addition to the supply-demand simulations for the period 2024-2029, various scenarios are to be simulated. Each scenario involves variations in the growth speed of each supply-demand simulation’s input variable. These variations in growth factors include:

• Power Plant Capacity Growth [1,7,8,9,10,11,12,13,14]
• Base Peak Load Growth Factor: using historical data projection [1,7,8,9,10,11,12,13,14]
• Personal Vehicle Growth Factor [2,41,42,43,44]
• Electric Vehicle Attractiveness Growth Factor [45]
• Subsidy Impact on EV Attractiveness
• Vehicle Type Preferences
• Home-/Public Charging Preferences, with following values:

Table 4. Input variation’s value.

Table 4. Input variation’s value.

Variable	Label in Simulation	Scenario A	Value	Scenario B	Value	Scenario C	Value
Supply-Side
Power Plant Capacity Growth	PowPlaGF	Slow	2.5%	Predicted	5%	Fast	7.5%
Base Peak Load Demand Growth	BasPeaLoaGF	Slow	6%	Predicted	8%	Fast	10%
Demand-Side
Personal Vehicle Population Growth	TotVehGF	Slow	3.5%	Predicted	4.5%	Fast	5.5%
EV Attractiveness Growth	PerEVGF	Slow	15%	Predicted	17.5%	Fast	20%
Subsidy Impact on EV Attractiveness	SubImpGF	Less Impact	25%	Balanced	50%	High Impact	75%
Vehicle Type Preferences	PerEVTypGF	Luxury Oriented	Luxury 75% Daily 25%	Balanced	Luxury 50% Daily 50%	Daily Oriented	Luxury 25% Daily 75%
Home-/Public Charging Preferences	FasSloRatGF	Public Charging Oriented	Public 75% Home 25%	Balanced	Public 50% Home 50%	Home Charging Oriented	Public 25% Home 75%

In combinatorial mathematics, a permutation refers to the different ways of selecting and arranging objects. It represents an arrangement of objects in a specific order. When repetition is allowed and the order matters, the number of possible permutations of r elements taken from a set of n elements is given by (n^r) [46].

n^r = 3⁷ = 2187

(7)

Since combination’s repetition is allowed, each of the 7 variable inputs can be filled with any of the 3 growth factor speed variation, giving us 2187 possible scenarios.

2.4. Global Simulations

The global simulation begins with the permutation of input scenarios. Each permutation provides values for the variables. With 3 possibilities for each of the 7 different variables, this results in 2187 scenarios. Based on the values of each scenario, the supply and demand simulations commence. Each supply-demand simulation runs for five years, covering the period from 2024 to 2029.

Figure 11. Simulation’s Flowchart.

Figure 11. Simulation’s Flowchart.

The three main outputs for each scenario are the end EV percentage by 2029, cost allocation for the additional yearly electricity supply, and cost allocation for the yearly EV subsidies. The total cost allocation for the additional yearly electricity supply was simulated by considering the deficit in power plant capacity by the end of 2029. The supply cost per kilowatt-hour (kWh) of Rp1,108/kWh was derived from BPP PLN. [47]

Cost Allocation for Additional Electricity Supply = (Total Simulated Load 2029 – Power Plant Capacity 2029) x Supply Cost

(8)

Figure 12. Supply Cost per kWh (BPP PLN) 2018-2022 [47].

Figure 12. Supply Cost per kWh (BPP PLN) 2018-2022 [47].

On the contrary, the cost allocation for the yearly EV subsidies was determined by considering the surplus of the power plant capacity. To achieve a balance in the grid’s supply and demand, a specific number of EVs must be added to offset the supply. These include a 10% VAT tax deduction each 4wEV [24] and an IDR 7,000,000 deduction for each 2wEV [25].

Cost Allocation for the EV Subsidies = -(Total Simulated Load 2029 – Power Plant Capacity 2029) x Subsidy Cost

(8)

The outputs from each of the 2187 scenarios are sorted based on three criteria. Given that EV growth is the primary goal of the government, the initial sorting criterion for the best scenarios is the highest EV percentage result in 2029. Subsequently, the scenarios are sorted by the minimum yearly cost allocation. Conversely, to identify the worst possible scenario, the simulation sorts by the least EV percentage with the highest yearly cost allocation.

Table 5. Output’s Priority Order.

Table 5. Output’s Priority Order.

Output’s Objective	Priority Order
High EV Growth	1st
and
Low-Cost Allocation for Supply	2nd
Low-Cost Allocation for Subsidy

The second sortation criterion (cost allocation for the additional yearly electricity supply and cost allocation for the yearly EV subsidies) will offset each other depending on the power plant capacity deficit by the year 2029. Apart from presenting the best and worst possible outputs given the scenarios available, the simulation should be able to execute the inputted scenarios.

3. Results

Table 6. Scenario Outputs.

Figure 13. Global Simulations Output 1.

Figure 13. Global Simulations Output 1.

Figure 13. Global Simulations Output 2.

Figure 13. Global Simulations Output 2.

3.1. Best Case Scenario

Based on the output simulation of 2187 scenarios for 2024-2029, the best-case scenario achieves the highest EV growth without disturbing the grid’s supply and demand balance. In the global scenario, all results were sorted, and the scenario with the highest EV percentage by 2029 was selected. Subsequently, all scenarios with the best EV percentage were sorted based on the lowest cost allocation for supply adjustment or subsidy allocation. This resulted in scenario label 1776. The output for this scenario permutation was 3213313, which translates to the following variable inputs (highlighted in blue):

Figure 14. Best Scenario Input Permutation.

Figure 14. Best Scenario Input Permutation.

Table 7. Best Scenario Inputs.

Table 7. Best Scenario Inputs.

Power Plant Capacity Growth	Base Peak Load Demand Growth	Personal Vehicle Population Growth	EV Attractiveness Growth	Subsidy Impact on EV Attractiveness	Vehicle Type Preferences	Home-/Public Charging Preferences
Slow Growth	Slow Growth	Slow Growth	Slow Growth	Less Impact	Luxury Oriented	Public Charging Oriented
Predicted Growth	Predicted Growth	Predicted Growth	Predicted Growth	Balanced	Balanced	Balanced
Fast Growth	Fast Growth	Fast Growth	Fast Growth	High Impact	Daily Oriented	Home Charging Oriented

This scenario resulted in supply and demand details below:

Table 8. Best Scenario Outputs.

Table 8. Best Scenario Outputs.

Output	Label in Simulation	Value	Units
Power Plan Capacity	PowPlaCap	69,033,250	kW
Base Peak Load Demand	BasPeaLoa	39,705,400	kW
Personal Vehicle Population	TotVeh_n	105,460,000	units
Total EV Percentage	EndEVPer	45.38	%
EV Type 1 Percentage	EndEVPerTyp1	4	%
EV Type 2 Percentage	EndEVPerTyp2	1.3	%
EV Type 3 Percentage	EndEVPerTyp3	30	%
EV Type 4 Percentage	EndEVPerTyp4	10	%
EV Peak Load Demand	TotEVLoa	30,544,000	kW
Total Peak Load Demand	TotalLoad	70,249,000	kW
Plant Capacity Surplus/Deficit	PowPlaDef	121,620	kW
Cost Allocation for Supply	RpYearlySup	492,218,007,498	Rupiah
Cost Allocation for Subsidy	RpYearlySub	0	Rupiah

The hourly Demand for each EV type and the total power plant surplus/deficit are shown below:

Figure 15. Best Scenario Output Graphs.

Figure 15. Best Scenario Output Graphs.

3.2. Worst Case Scenario

Based on the output simulation of 2187 scenarios for 2024-2029, the worst-case scenario was selected by sorting the scenarios with the least EV percentage in 2029. These scenarios were then re-sorted based on the highest cost allocation for supply adjustment or subsidy allocation. The worst-performing scenario was labeled 163. Its scenario permutation was 1131111 with the following input details (highlighted in blue):

Figure 16. Worst Scenario Input Permutation.

Figure 16. Worst Scenario Input Permutation.

Table 9. Worst Scenario Inputs.

Table 9. Worst Scenario Inputs.

Power Plant Capacity Growth	Base Peak Load Demand Growth	Personal Vehicle Population Growth	EV Attractiveness Growth	Subsidy Impact on EV Attractiveness	Vehicle Type Preferences	Home-/Public Charging Preferences
Slow Growth	Slow Growth	Slow Growth	Slow Growth	Less Impact	Luxury Oriented	Public Charging Oriented
Predicted Growth	Predicted Growth	Predicted Growth	Predicted Growth	Balanced	Balanced	Balanced
Fast Growth	Fast Growth	Fast Growth	Fast Growth	High Impact	Daily Oriented	Home Charging Oriented

This scenario resulted in supply and demand details below:

Table 10. Worst Scenario Outputs.

Table 10. Worst Scenario Outputs.

Output	Label in Simulation	Value	Units
Power Plan Capacity	PowPlaCap	56,481,750	kW
Base Peak Load Demand	BasPeaLoa	36,869,300	kW
Personal Vehicle Population	TotVeh_n	114,440,000	units
Total EV Percentage	EndEVPer	23.12	%
EV Type 1 Percentage	EndEVPerTyp1	2	%
EV Type 2 Percentage	EndEVPerTyp2	0.6	%
EV Type 3 Percentage	EndEVPerTyp3	15	%
EV Type 4 Percentage	EndEVPerTyp4	5	%
EV Peak Load Demand	TotEVLoa	17,222,000	kW
Total Peak Load Demand	TotalLoad	54,092,000	kW
Plant Capacity Surplus/Deficit	PowPlaDef	-2,390,100	kW
Cost Allocation for Supply	RpYearlySup	0	Rupiah
Cost Allocation for Subsidy	RpYearlySub	47,556,001,136,600	Rupiah

The hourly Demand for each EV type and the total power plant surplus/deficit are shown below:

Figure 17. Worst Scenario Output Graphs.

Figure 17. Worst Scenario Output Graphs.

3.3. Most Probable Scenario

Another simulation result worth analyzing is the most probable scenario. This scenario was determined based on information collected during literature research. The growth of power plant capacity is expected to be slow due to PLN’s current oversupply issue [48]. Normal predictions are made for base peak load demand, personal vehicle population, and EV attractiveness growth [49]. According to a survey conducted by Indonesia Cerah in January 2024, subsidies are expected to have a high impact on EV attractiveness in Indonesia [50]. In the last year, there has been a strong preference for budget daily cars, reflecting the direction of the Indonesian market [51]. Home charging is strongly preferred over public charging [52].

Table 11. Most Probable Scenario Inputs.

Table 11. Most Probable Scenario Inputs.

Power Plant Capacity Growth	Base Peak Load Demand Growth	Personal Vehicle Population Growth	EV Attractiveness Growth	Subsidy Impact on EV Attractiveness	Vehicle Type Preferences	Home-/Public Charging Preferences
Slow Growth	Slow Growth	Slow Growth	Slow Growth	Less Impact	Luxury Oriented	Public Charging Oriented
Predicted Growth	Predicted Growth	Predicted Growth	Predicted Growth	Balanced	Balanced	Balanced
Fast Growth	Fast Growth	Fast Growth	Fast Growth	High Impact	Daily Oriented	Home Charging Oriented

The scenario permutation of 1222333 (highlighted in blue) translates to Scenario label 378. This scenario resulted in a 42.98% EV percentage in 2029. In that year, there is a power plant capacity deficit of 2,397 MW. To achieve supply-demand balance, a cost allocation of Rp970,330,692,972 needs to be prepared.

Figure 18. Most Probable Scenario Input Permutation.

Figure 18. Most Probable Scenario Input Permutation.

Table 12. Most Probable Scenario Outputs.

Table 12. Most Probable Scenario Outputs.

Output	Label in Simulation	Value	Units
Power Plan Capacity	PowPlaCap	56,481,750	kW
Base Peak Load Demand	BasPeaLoa	39,705,400	kW
Personal Vehicle Population	TotVeh_n	109,950,000	units
Total EV Percentage	EndEVPer	42.98	%
EV Type 1 Percentage	EndEVPerTyp1	1.3	%
EV Type 2 Percentage	EndEVPerTyp2	3.8	%
EV Type 3 Percentage	EndEVPerTyp3	9.5	%
EV Type 4 Percentage	EndEVPerTyp4	28.4	%
EV Peak Load Demand	TotEVLoa	19,174,000	kW
Total Peak Load Demand	TotalLoad	58,879,000	kW
Plant Capacity Surplus/Deficit	PowPlaDef	2,397,600	kW
Cost Allocation for Supply	RpYearlySup	970,330,692,972	Rupiah
Cost Allocation for Subsidy	RpYearlySub	0	Rupiah

The hourly Demand for each EV type and the total power plant surplus/deficit are shown below:

Figure 19. Most Probable Scenario Output Graphs.

Figure 19. Most Probable Scenario Output Graphs.

3.4. Least Probable Scenario

On the other hand, the least probable scenario, based on the literature review, features inputs that are the opposite of the most probable scenario. The inputs for scenario 1621 of 3111111 (highlighted in blue) are shown in the table below:

Table 13. Least Scenario Inputs.

Table 13. Least Scenario Inputs.

Power Plant Capacity Growth	Base Peak Load Demand Growth	Personal Vehicle Population Growth	EV Attractiveness Growth	Subsidy Impact on EV Attractiveness	Vehicle Type Preferences	Home-/Public Charging Preferences
Slow Growth	Slow Growth	Slow Growth	Slow Growth	Less Impact	Luxury Oriented	Public Charging Oriented
Predicted Growth	Predicted Growth	Predicted Growth	Predicted Growth	Balanced	Balanced	Balanced
Fast Growth	Fast Growth	Fast Growth	Fast Growth	High Impact	Daily Oriented	Home Charging Oriented

Figure 20. Least Probable Scenario Input Permutation.

Figure 20. Least Probable Scenario Input Permutation.

This scenario resulted in supply and demand details below:

Table 14. Least Probable Scenario Outputs.

Table 14. Least Probable Scenario Outputs.

Output	Label in Simulation	Value	Units
Power Plan Capacity	PowPlaCap	69,033,250	kW
Base Peak Load Demand	BasPeaLoa	36,869,300	kW
Personal Vehicle Population	TotVeh_n	105,460,000	units
Total EV Percentage	EndEVPer	24.32	%
EV Type 1 Percentage	EndEVPerTyp1	2.1	%
EV Type 2 Percentage	EndEVPerTyp2	0.7	%
EV Type 3 Percentage	EndEVPerTyp3	16	%
EV Type 4 Percentage	EndEVPerTyp4	5.3	%
EV Peak Load Demand	TotEVLoa	16,696,000	kW
Total Peak Load Demand	TotalLoad	53,566,000	kW
Plant Capacity Surplus/Deficit	PowPlaDef	-15,467,600	kW
Cost Allocation for Supply	RpYearlySup	0	Rupiah
Cost Allocation for Subsidy	RpYearlySub	45,475,187,899,827	Rupiah

The hourly Demand for each EV type and the total power plant surplus/deficit are shown below:

Figure 21. Least Scenario Output Graphs.

Figure 21. Least Scenario Output Graphs.

4. Discussion and Conclusions

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