Energy Mix Simulation to Reach Regional Energy Strategy:

A National Impact of East Nusa Tenggara Province Energy Mix

Adrianus Amheka

, Julius Tanesab

, Nonce Farida Tuati

, Kathleen Aviso

and Krista Danielle Yu

Department of Mechanical Engineering, State Polytechnic of Kupang, Indonesia

Department of Electrical Engineering, State Polytechnic of Kupang, Indonesia

Department of Accounting, State Polytechnic of Kupang, Indonesia

Department of Chemical Engineering, De La Salle University, Manila, Philippine

School of Economics, De La Salle University, Manila, Philippine

Keywords: NTT Province, Regional Energy Mix, GHG Emission.

Abstract: More than 70% of global energy demand growth was met by fossil fuels as a trigger for increasing GHG

emissions and the Indonesian itself has contributed around 6.678 million stock tank barrels (MMSTB) for

that. Indonesian primary energy demand in 2025 from oil is 98.7 million tonnes of oil equivalent (MTOE) or

24.7% from energy mix and expected to be increased up to 197.7 MTOE or around 19.5% from energy mix

in 2050. In fulfilling national energy needs, regional functions in achieving energy security are indispensable.

For that through national energy general plan is targeted to reach renewable energy (RE) mix in 2025 is a

minimum of 23% of total primary energy. In this study, several indicators were used such as social economy,

energy, and the environment using LEAP simulation. The results show that East Nusa Tenggara (NTT)

Province has the potential to develop RE, with the potential of primary energy resources of 23.8 GW enabling

the province to reach the RE mix target of up to 24% in 2025 and 31% in 2050 while GHG emission decreased

9% and 11% from the usual condition in 2025 and 2050 respectively. Therefore, the current energy structure

has space to restructure the energy system to be more optimal in achieving regional energy independence as

well as support for the achievement of the SDGs and global competitiveness.

1 INTRODUCTION

Indonesia is an archipelago countries with a

population of 265 million peoples spread across 35

provinces with diverse socio-economic conditions

(A. Amheka & Higano, 2015, 2018) has the potential

for primary energy which is adequate in supporting

the economy at both national and local levels and

currently as G-20 member countries which have

actually made this country potentially to become a

contributor to the achievement of world prosperity

which is currently positioned in 16th the largest GDP

level between Mexico and Turkey. At present, 70%

of global primary energy supply comes from fossil

fuels and as a consequence trend of increasing global

emissions will absolutely increase (A. Amheka &

Higano, 2015; Kumar, 2016). Taking part of that,

Indonesia government through national energy were

supplied as 6.678 million stock tank barrels

(MMSTB) to support national development (INEP,

2017).

Strengthening the national energy buffer through

regional primary energy supply, instead of costly

fossil fuel imports (Kumar, 2016). The current fossil

fuel subsidies, which make the present energy supply

affordable for the population, cost Indonesia over 100

trillion IDR/year (~USD7.04 million/year) and

despite fossil fuel reserves, the dependence on fossil

fuel imports is steadily growing. Although Indonesia

made great advances in the electrification across the

country, some provinces in East and Central Java,

East Nusa Tenggara (NTT) and Papua are proving

particularly hard to reach and 2,110 out of 2,424

villages remain without any access (International

Energy Agency (IEA), 2018). In order to achieve

global sustainable development goals (SDGs) no.7

“Affordable and clean energy” while meet

Indonesia’s national energy policy targets and

national energy general plan (INEP, 2017; Indonesia

National Energy Policy (INEP), 2014; Nusa Tenggara

Timur Government, 2019), mentioned that

Indonesian primary energy demand in 2025 from oil

Amheka, A., Tanesab, J., Tuati, N., Aviso, K. and Yu, K.

Energy Mix Simulation to Reach Regional Energy Strategy: A National Impact of East Nusa Tenggara Province Energy Mix.

DOI: 10.5220/0010950600003260

In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 657-663

ISBN: 978-989-758-615-6; ISSN: 2975-8246

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

657

is 98.7 million tonnes of oil equivalent (MTOE) or

24.7% from energy mix and expected to be increased

up to 197.7 MTOE or around 19.5% from energy mix

in 2050 and a minimum of 23% of total primary

energy must be supplied by renewable energy (RE)

by 2025 and increased to be 31% of energy mix or

around 92.3 MTOE and 315.7 MTOE respectively.

Optimization of energy balance to support the

responsibility of achieving SDGs at the local level in

Indonesia focused on NTT Province become a

motivation for this study as well as an inventory of

information and references for regional energy

policies. Current RE potential at NTT Province in

2015 as baseline shown in Table 1.

Table 1: Potential and utilization of RE in NTT Province in

2015 (base year).

No Type of

Energy

Potential

(MW)

Installed

capacity

(MW)

Utilization

(%)

1 Geothermal 629 12.5 1.99

2 Water 53

- -

3 Mini & Micro

Hydro

95 5.2 5.47

4 Bioenergy 240.5 1 0.42

5 Solar 7,272 7.43 0.1

6 Wind 10,188 3.1** 0.03

7 Tidal 5,335 - 0

Total 23,812.5 29.23 0.12

Source: (INEP, 2017; Indonesia National Energy Policy (INEP),

2014; Nusa Tenggara Timur Government, 2019).

Baseline data is used among other the total

population is 5,120,061 souls with a rate of

population growth is 1,67%; total of households are

1,108,400; electrification ratio 58.64%; the GDP is

56,821 billion IDR with its growth rate 5.05% per

year; the GDP per capita is 11 billion IDR with annual

growth rate is 3.31%; the growth elasticity is around

1.15 per year. While as baseline of GHG emission in

beginning of 2015 was expected around 2,2 Million

tons with a emission per capita is around 0,45 million

tons (A. Amheka & Higano, 2015; Nusa Tenggara

Timur Government, 2019). There are some data

trends in terms of social economy, energy and

environment was entered into the model.

2 MATERIALS AND METHODS

This study investigates the energy balance analysis of

taking into account the quantity of energy demand

and energy supply of each sectors activity for a

province which is NTT Province in Indonesia. The

indicators used for the optimization are the social-

economy, energy and environment which means

GHG emission. The Long-range Energy Alternatives

Planning (LEAP) was developed by the Stockholm

Environment Institute, which is a system

optimization software was used for the system

analysis which is allowed users input current

quantitative data and future energy demands as a

good accounting tool for energy supply and demand

model (Aized et al., 2018; Emodi et al., 2017; Ferrão,

2017; Halkos et al., 2015; Kusumadewi et al., 2017;

Ouedraogo, 2017; Pan et al., 2013; Phdungsilp & Ã,

2010; Wongsapai et al., 2016; Zhang et al., 2019).

The LEAP basically a description or plan that

describes the complex system of production,

distribution, and consumption of energy into a

mathematical formula to display a reference to

describe the energy system in a region within a period

of time. Every country has specific model customized

depends on social, economic and environmental

conditions and other parameters and indicators

(Awopone et al., 2017; Emodi et al., 2017; Huang et

al., 2011; Kemausuor et al., 2015; Mirjat et al., 2017;

Yang et al., 2017). For Indonesia the model is

customized to fully describe a comprehensive social-

energy-environment analysis in evaluate the

alternative configuration and design based on general

standard indicators (HaCohen-Kerner & Mughaz,

2010; Heaps, 2008). The customized model structure

allowed by Indonesia government as business as

usual (BAU) condition as shows in Fig. 1. (INEP,

2017; HaCohen-Kerner & Mughaz, 2010; Nusa

Tenggara Timur Government, 2019).

Figure 1: Framework and model structure.

In the household, commercial, transport and

industrial sectors, the LEAP model has helped to

assess their energy consumption and greenhouse gas

emissions. Because of LEAP only as a tool, so that

iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science

658

primary data collection is still needed as a reference

key assumption, demand sectors, transformations,

dan potential natural resources for a period 6 years

between 2010 to 2015. Lack of data availability

become a barrier. The scenario assumption is based

on current data collection on energy system

conditions including electricity in NTT Province was

obtained through FGD activities involving various

stakeholders. The output of the activity is met through

update the latest data inventory as primary data for a

period of 6 years from 2010 to 2015. Basically, we

choose the selection of the 6-year period is to

anticipate if the collection data information obtained

is not as complete as expected so that we may able to

make assumptions accurate as possible due to the

primary data range used is not too far.

The algorithm structure for a total energy

consumption as follow (Emodi et al., 2017):





∑

(    



) (1)

where, 



is total energy demand of industrial sector

(en); 



is intensity energy consumption of Industrial

sector (ex)





∑













∑(





.



)





.



; 





=



.



.



(2)

Where, 



is total energy consumption of household

sector (en); 





is final energy consumption of using

technology (en); 



activity level of energy use (en);





is number of households using

equipment/technology (ex); and 



is penetration of

equipment/technology (en).





∑





. 



(3)





is energy demand of transportation sector (en)





∑

(    



)

; 



∑

(  



) (4)





is energy demand of subsector private (en);  is

floor area; 



is energy demand of subsector

government (en).





∑

( ℎ   



) (5)





is energy demand of other sector (en).

 =

∑∑



.







.



(6)

 is CO

emission (en) (Cai et al., 2008; Emodi et

al., 2017; Handayani et al., 2017); 

.

is emission

factor of primary energy  which is consumed to

produce electricity from technology  (en); 



technology efficiency (ex); dan 



is power outputs

are required  (ex).

2.1 Results and Discussion

The structure of the social, economic and

environmental conditions of NTT Province during the

period of 2012 to 2015 can be well-controlled and

conducive situations (A. Amheka & Higano, 2015;

Adrianus Amheka et al., 2016, 2014) which allow the

minimum condition to the transition of energy

structures from conservative consumption patterns

towards a better direction in supporting national

energy development. The simulation results as in Fig.

2, Table 2 and Table 3 respectively, show NTT

Province of NTT Energy mix 2015 to 2050.

Figure 2: Energy mix of the NTT Province 2015 to 2050.

Table 2: NTT Province’s Energy mix 2015 to 2050.

(Unit: percentage)

Fuels 2015 2020 2025 2030 2035 2040 2045 2050

Coal 4.% 14.% 12.% 14.% 15.% 15.% 15.% 16.%

Gas 1.% 8.% 10.% 11.% 11.% 12.% 14.% 14.%

Oil 94.% 67.% 54.% 48.% 43.% 38.% 35.% 31.%

RE 2.% 10.% 24.% 27.% 31.% 34.% 37.% 39.%

Total 100.% 100.% 100.% 100.% 100.% 100.% 100.% 100.%

At the base year 2015, primary energy use sourced

from oil is dominated this shows how massive the

supply and demand of energy from oil to support the

region where it reaches 94% or equals to 1.1 MTOE

of the total energy mix in NTT Province. In the same

year, the primary energy use from RE sources was

only 2% or equivalent to 20 TOE and followed by

coal by 4% and the rest of 1%.

Energy Mix Simulation to Reach Regional Energy Strategy: A National Impact of East Nusa Tenggara Province Energy Mix

659

Table 3: Province of NTT Energy mix 2015 to 2050.

Unit: TOE

Fuels 2015 2020 2025 2030 2035 2040 2045 2050

Coal

40 248 312 460 610 754 917 1,177

Gas

7 146 258 348 474 634 837 1,040

Oil

1,072 1,193 1,380 1,580 1,764 1,949 2,142 2,326

20 186 609 901 1,273 1,743 2,290 2,893

Total 1,139 1,774 2,560 3,289 4,121 5,079 6,186 7,436

The primary energy mix trend continues to vary

where the portion of petroleum use has drastically

reduced in the next 5 years, which is down 40% in

2025 compared to the base year of 2015, while the

portion of RE usage has increased to 24% and gas use

has increased 9% in 2025 which is to be 10%

compared to the initial year. This is in line with the

spirit of Indonesia's national energy policy (INEP,

2017; Indonesia National Energy Policy (INEP),

2014) which implies maximizing the use of the RE

from every year while minimizing energy use from

oil and optimizing the use of gas. Whereas if it is still

lacking, it will be filled by energy supply from coal,

where for the condition of NTT the use of gas has

increased sharply in the first 5 years, namely between

2015 and 2025, up 10% in 2025 compared to the base

year. This indicates that the economic structure of

NTT Province will be able to be optimized well,

while the contribution of sustainable long-term

regional energy management is maintained as seen in

2050 the share of energy use sourced from the RE

increased to be 39% increased 15% from 2025 or

equivalent to 2.9 MTOE. Sharp increase in RE

according to national energy policy targets especially

between 2040 and 2050 where it is assumed that in

those years the development of power plant

technology has been very good, especially from RE

plants while coupled by the quality of human

resources in the management and utilization of RE

technology, but still around 33,18% of electricity

supplied is still from coal. The portion of fossil

energy in providing electricity generation capacity

continues to be reduced. Based on installed electricity

generation capacity, the portion of fossil plants in

2015 was around 93.38% and will be reduced to

42.73% in 2025 and 48.58% in 2050 (Nusa Tenggara

Timur Government, 2019). But if we compared

globally, the proportion of coal and oil in global

primary energy consumption was only reduced 61.4%

in 2016 (BP Statistical Review, 2017), while

proportion of natural gas and non-fossil energy in

total primary energy consumption globally increased

by 2.7% in the last 10 years. This indicates that the

NTT province's energy system transformation will

have a major impact not only at the national level but

also have a global impact to support achieving the

SDGs through local good practice specifically in the

energy sector and secure national competitiveness (A.

Amheka & Higano, 2018). The achievement of

national energy policy targets will have a significant

impact on GHG reduction, as shown in the simulation

when compared to BAU condition of Province of

NTT. The GHG emissions projection in 2025 is 37.1

MtCO

e equivalent to 4% of total national emissions

and the amount of GHG emissions per capita is 6.20

tCO

e. For 2050 GHG emissions are 172 MtCO

e is

equivalent to 9% of total national and the amount of

GHG emissions per capita is 20.78 tCO

e as shows its

trends on Fig.3 ad Table 4.

Figure 3: Trend of GHG emission reduction BAU scenario

v.s current scenario.

Table 4: Trend of GHG emission reduction BAU scenario

v.s current scenario.

YEAR 2015 2020 2025 2030 2035 2040 2045 2050

BAU

scenario

3 26 58 99 149 203 263 329

Current

scenario

3 25 52 86 126 173 228 291

Optimal

scenario

0 1 5 14 23 30 36 38

GHG

reduction

(%)

0 4 9 14 15 15 13 11

The simulation results also show, the controlling

of GHG emissions in 2025 able to reduce up to 9%

while in 2050 it is only reached by 11% compared to

BAU condition where both scenarios have taken into

account the contribution of GHG emissions from

power plants.

iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science

660

Figure 4: Trend of GHG emission in NTT Province 2015-

2050.

In Fig. 4 shows, the power plant sector is

projected to be the largest contributor to GHG

emissions, followed by sectors of transportation,

industrial, household, other sectors and commercial.

Power plant, transportation and household sectors

make a substantial contribution to the base year,

which is more than 30% of each. However, the

household sector since 2020 shows a flat increase

until 2050, which is in the range of 5% to 6%, this is

because after 2020 the policy of using non-fossil

energy has been well massively implemented through

the utilization of RE potential, for example, to support

local and urban and rural gas networks which are

getting better, even though infrastructure

development to support these two things is still

needed. The opposite is happening in the power plant

sector, where the emissions released continue to

increase dramatically, in 2015 the amount is less than

33% but since 2020 until 2050 the increase is in the

range of 50% to 60% of the total emissions of NTT

Province. This shows that although national and

regional energy policies have been implemented well,

they still have not been able to reduce the contribution

of emissions from this sector, due to the slow

innovations in clean technology management that are

implemented in all power plants (Adrianus Amheka

et al., 2016). Even though the technology is already

available, maintenance from the technical side of

these plants has not been optimally carried out due to

various constraints both financially and the readiness

of established human resources in the operation of

clean technology-based plants. Similar to the

transportation sector, the upward trend is quite large

after the power plant sector, which is due to the

economic conditions of the people who have not been

able to realize emissions-free vehicles such as electric

vehicles, gas vehicles, and maintenance and spare

parts due to far from the automotive industry which

are all centralized on the island of Java.

For the industrial sector, there is a significant

increase in 2020 and after that the trend of flat

emission runs in the range of 10% to 11% until 2050,

this gives a good meaning where the governance in

regional regulations and their implementation is

already well done which is able to provide conducive

for investors to invest in various

industrial fields

cleaner production-based such as the use of

environmentally friendly industrial technology.

3 CONCLUSIONS

The contribution of sustainable long-term regional

energy management will be able to improve national

competitiveness and become one of the concrete

forms of support for achieving the SDGs through

local good practice in the energy sector by reach

proportion of natural gas and non-fossil energy in

total primary energy consumption globally increased

by 2.7% within 10 years. Participation of government

and community are very necessary so as the

development of an integrated energy system model

can be achieved optimally by considering social,

economic and environmental factors. Further the

discussion give meaning that the current economy

structure will able to provide space for optimization

in order to achieve local energy security and

management target in secure the RE mix target of up

to 24% in 2025 and 31% in 2050 while GHG

emission decreased 9% and 11% from the usual

condition in 2025 and 2050 respectively. Further,

controlling to the growth of energy demand and

energy consumption is necessary, where it can be

probably done through enhance coordination among

stakeholders such as government from any aspects,

community, business, NGOs locally and the

development partners from foreign governments to

achieve smart energy systems (Lund et al., 2017). The

preparation and availability of qualified human

resources in various fields are very necessary in order

to maintain the energy and environmental system

links, especially in environment-based energy

management in the NTT Province and Indonesia in

national level and the global world generally.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the support from

State Polytechnic of Kupang, Ministry of Education,

Culture, Research and Technology, Republic of

Indonesia.

Energy Mix Simulation to Reach Regional Energy Strategy: A National Impact of East Nusa Tenggara Province Energy Mix

661

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