indonesian energy security and clean energy modeling...

Indonesian Energy Security and Clean Energy Modeling (IESCEM) Background Study RPJMN 2015-2019 Directorate of Energy, Mineral and Mining Resources Kementerian Perencanaan Pembangunan Nasional/BAPPENAS

Integrated Energy Security and Clean Energy Model (IESCEM)

Work Report of Modeling Software Development Rachmat Sugandi Hamdani 2014

EXECUTIVE SUMMARY

Integrated Energy Security and Clean Energy Model (IESCEM) is developed in order to assist

practitioners, planners, as well as policy makers – especially in energy sector – to assess

energy security level in a particular policy scenario.

This particular program developed with MS Excel platform has several parts, namely: Home

Page, Calculation engine, and Result Page. Home Page is the starting interaction part

between user and the program, which will bring the user to an interactive interface.

Calculation engine is functioned to accommodate model calculation. On this page, user can

also check and revise the inputted data. Result Page is a part to display data processing

result in the form of Energy Security Index and Clean Energy Index, as well as its indicator

graph. In addition to the three parts, user will be asked to complete an expert questionnaire

(in a separated file) with an evaluation on energy security and clean energy indicators.

In calculations which compare Indonesian condition from 2007 until 2011, the highest

energy security levels for Indonesia are achieved in:

Year 2011 with 0.580 score, using alt. 1 method.

Year 2007 with 0.708 score, using root mean square (alt. 2) method

While for clean energy, the highest level is achieved in 2008, both from the calculation

involving expert judgment (alt. 1) and calculating using root mean square (alt. 2), with

0.705 score (CEI Score alt. 1) and 0.834 (CEI Score alt. 2).

In calculations which compare Indonesia’s BAU with Counter Measure 1 (CM1) and Counter

Measure 2 (CM2), CM1 scenario achieved the top rank of energy security with score

involving internal expert judgment i.e. 0.734 and score calculated with root mean square

(RMS) method is 0.756. CM1 is also the most ―clean‖ scenario compared to BAU and CM2.

Clean Energy score for CM1 scenario which involves expert judgment is 0.532 and score

calculated with root mean square (RMS) method is 0.866.

Based on modeling result, a policy alternative was given i.e. Indonesia should reduce its

dependency on import, especially in terms of oil fuel, through oil refinery capacity

improvement, Strategic Petroleum Reserve realization, transportation sector efficiency, as

well as expanded use of biofuel.

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Another alternative is for Indonesian Government to encourage achievement of energy

diversification target through gas exploitation improvement and renewable energy

development. Also to reduce energy supply cost, by improve efficiency at supply side, using

environmental friendly technology, and carbon tax implementation. Government should

encourage the community to reduce energy intensity through energy efficiency program at

the demand side, supporting energy-saving tools industry growth, as well as building

integrated online information system for energy efficiency. As well as to continue and

accelerate national electrification program, formulate new renewable energy and energy

efficiency curriculum, implement dialogue between government with energy stakeholders,

and building an integrated Indonesian energy management system.

A recommendation on IESCEM use is to use it to make comparisons between scenarios in a

particular country. If intending to compare between countries, aggregate indicators should

be specified first into indicators which illustrates activity level.

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TABLE OF CONTENTS 1. Background ......................................................................................................... 6

2. Objectives ........................................................................................................... 6

3. Program’s Input - Output ...................................................................................... 7

4. Flow Chart ........................................................................................................... 8

5. IESCEM Program Body ......................................................................................................... 10

a. Homepage ...................................................................................................................... 10

b. Calculation engine ......................................................................................................... 10

c. Result Page ..................................................................................................................... 11

6. Software Technical Guide (Software Manual) .................................................................... 11

a. IESCEM Platform ............................................................................................................ 11

b. Data Processing Process Flow ....................................................................................... 12 7. User Guide (Training Manual) ............................................................................................. 16 8. Expert Judgment Questionnaire .......................................................................................... 23

a. Indicator Descriptions ................................................................................................... 24

b. Questionnaire Completion Guide .................................................................................. 28 9. Qualification Test ................................................................................................................ 30

a. Data ................................................................................................................................ 30

b. Running Result ............................................................................................................... 32

b.1. Energy Security Index ........................................................................................... 33

b.2. Clean Energy Index ............................................................................................... 35 10. Indonesia Modeling ............................................................................................................. 37

a. Data ................................................................................................................................ 37

b. First Method ................................................................................................................... 37

b.1. Indonesia Running Result ..................................................................................... 38

c. Second Method ............................................................................................................... 41

c.1. Indonesia Running Result ..................................................................................... 43

d. ESI and CSI Calculation with Expert Judgment from BAPPENAS .............................. 46 11. Indonesia Modeling Analysis .............................................................................................. 48 12. Conclusions and Recommendations .................................................................................. 56

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LIST OF FIGURES

Figure 4-1. IESCEM Flowchart in General .................................................................... 9 Figure 5-1. Parts in IESCEM Program Body ................................................................. 10 Figure 5-2. IESCEM Main Interface ...........................................................................11 Figure 7-1. MS Excel Notification on Active Content ....................................................17 Figure 7-2. MS Office Security Options .................................................................................... 17 Figure 7-3. Choosing Trust Center Settings in Excel .............................................................. 18 Figure 7-4. Enabling Macro through Trust Center................................................................... 18 Figure 7-5. Scenario and Simulation Year Forms ................................................................... 19 Figure 7-6. Energy Data Form .................................................................................................. 20 Figure 7-7. Form to Input Energy Consumption (Coal) .......................................................... 20 Figure 7-8. Form to Input Energy Consumption (Crude Oil) ................................................. 21 Figure 7-10. Form to Add Indicators ......................................................................................... 22 Figure 7-11. Expert Weighting Value Form .............................................................................. 22 Figure 7-12. Model Calculation Result Page............................................................................... 23 Figure 8-1. Expert Questionnaire Form ................................................................................... 28 Figure 8-2. Expert Judgment Value ......................................................................................... 29 Figure 9-1. Energy Security Rank for Indonesia, India, Japan, Malaysia, and Thailand

Relative to Each Other ......................................................................................... 33 Figure 9-2. Energy Security Rank for China, Philippine, Singapore, Korea and Vietnam

Relative to Each Other ......................................................................................... 34 Figure 9-3. Clean Energy Rank for Indonesia, India, Japan, Malaysia, and Thailand Relative

to Each Other ........................................................................................................ 35 Figure 9-4. Clean Energy Rank for China, Philippine, Singapore, Korea and Vietnam

Relative to Each Other ......................................................................................... 36 Figure 10-1. Tricks to Defining Scenario for Indonesia Modeling ........................................... 38 Figure 10-2. Indonesian ESI Score and Relative Indicator Value Graph for 2007-2011 ...... 39 Figure 10-3 Indonesian CEI Score and Relative Indicator Value Graph for 2007-2011 ...... 40 Figure 10-4. Defining Scenario with One Year Data ................................................................ 43 Figure 10-5. ESI Score for BAU, CM1 and CM2 Scenarios and Relative Indicator Value Graph

in 2011 .................................................................................................................. 44 Figure 10-6. CSI Score for BAU, CM1 and CM2 Scenarios and Relative Indicator Value

Graph in 2011 ....................................................................................................... 45

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1. Background

Energy security has become a proxy that is commonly used as a reference in a nation’s

development planning. Therefore, it is not exaggerating that energy security is correlated

with advantage and welfare of a nation’s community. To this date, experts from various

science fields and institutional backgrounds had proposed various indicators to assess

energy security level. Of course, there are differences in views in formulating factors or

elements categorized as energy security supporting indicators. Some experts categorized

several indicators that are grouped or focused on one an-sich energy security aspect. Other

experts aggregated several energy security aspects into one indicator.

It should be considered that energy security indicators are designed for particular contexts

so they can always be applied for all cases. Therefore, subjectivity in evaluating energy

security indication and level is unavoidable, because specific variables in a particular science

field perspective force an expert to give an evaluation based on his understanding,

experience, as well as a special point of view.

Integrated Energy Security and Clean Energy Model (IESCEM) is developed in order to assist

practitioners, planners, as well as policy makers – especially in energy sector – to assess

energy security level in a particular policy scenario. In addition, IESCEM will also answer the

challenge of expert evaluation subjectivity on energy security, by utilizing Consistency Ratio

formula. Next, the model will map the relationship between indicators on energy security

dimension along with its supporting elements (availability, accessibility, affordability,

acceptability and efficiency).

2. Objectives Creating an MS Excel based modeling tool to:

a. Measure Energy Security Index (ESI)

b. Measure Clean Energy Index (CEI)

c. Map the relationship between energy security indicators in each policy scenario.

Make objective evaluation on urgency level for various indicators.

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3. Program’s Input – Output

Program’s Input – Output (I/O) is done at the start of program design. In this inventory,

execution level is distinguished into 3 parts, i.e. input, data processing, and output. The

following is IESCEM I/O in general according to order of execution.

Table 1. IESCEM Input – Output Order

Order Execution Type Location in Excel

Data entry:

1 - Amount of Scenario

Input Front

- Amount of Data’s Year Sheet

- Amount of Expert Judgment

Data entry: Front

2 - Scenario Name Input

Sheet

- Data Year

3 Data entry: Primary Energy Type Input Front

Sheet

Create Excel sheet containing:

4 - Scenario Sheet

Data Processing Front

- ESI Scenario Sheet

- CSI Scenario

5 Create Energy Data Table Data Processing Scenario Sheet

6 Create Expert Questionnaire Data Processing ESI & CSI Sheet

7 Pre-definition of Base Year data Input (Built-in) Scenario Sheet

Energy Data Entry:

- Indigenous

8 - Import Input Scenario Sheet

- System Cost

- GDP

9 Expert questionnaire Input ESI & CSI Sheet

Calculation and Weighting:

a. Total energy consumption

(indigenous resources)

10 b. Consumption percentage per Pemrosesan Data Scenario Sheet

Type of energy on total energy

consumption

c. Total indigenous + import

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d. Energy import percentage on

total primary energy supply

e. Calculating Shanon's Diversity

Index (SDI)

f. Weighting indigenous

resources - import

g. SDI Weighting a

share of net import (D').

h. Normalization SDI. Point e/ln (j)

i. Normalization point g. Point g/ln (j)

j. Weighting primary energy Concentration according to share of net Import (E2). 1 – poin i.

k. Calculating Primary energy consumption per GDP

l. Calculating Shares of fossil to

the total primary energy

m. Calculating Emission

Calculation:

- Relative value of indicators (e)

11 - Pair-wise comparison matrix Data Processing ESI & CSI Sheet

- Normalization of pair-wise

comparison matrix

- ESI Score Alternative I

12 - ESI Score Alternative II Output ESI Sheet

- ESI Graph (Radar type)

- CSI Score Alternative I

13 - CSI Score Alternative II Output CSI Sheet

- CSI Graph (Radar Type)

4. Flowchart

Visualization of IESCEM execution order, in general is illustrated in the following flowchart.

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Input primary energy types

Start

Input number

of experts

Input amount of:

- Scenario

- Data year

Set Scenario Yes Amount of No Input:

Name = - Scenario name

scenario = 1?

“Base Case”

- Data year

Input data

year

(Base Year)

Create Excel Sheet:

- Scenario name

- ESI

- CSI

Create Expert Create energy

Quest. In ESI Data in

& CSI sheets Scenario sheet

Scenario sheet Move to Sheet ESI & CSI Sheet

Base Year Input

Default Data Questionnaire

Input Energy Data:

- Indigenous Data

- Import

Calculation

Year = 1?

- System Cost No

Process

- GDP

Yes

Calculation

And weighting

processes

Figure 4-1. IESCEM Flowchart in general

LEGEND:

Execution at the start of Program Program’s core execution

Execution desc.

Explanation on Parallel Execution

Finish

Output Visual:

- ESI Score - CSI Score

- ESI & CSI Graphs

Finish

5. IESCEM Program Body

The program developed with MS Excel platform has several parts which can be illustrated as

follows:

INPUT

HOME

PAGE CALCULATION

MACHINE DATA

RESULT

PAGE

OUTPUT

Figure 5-1. Parts in IESCEM Program Body

a. Homepage

This part is the initial interaction between user and the program. In this part, user will be

faced with an interactive interface. Next, user can click on interactive colored boxes (see

Figure 2) that will display a data input form as a dialog box.

More detailed description on this interface use flow will be discussed in User Guide section.

b. Calculation Engine

As an MS Excel based program, IESCEM utilizes existing excel features to execute data

processing. In order to accommodate the calculation, there are 3 (three) special worksheets

dedicated as calculation engine. These calculation engines are hidden by default and do not

displayed to IESCEM user.

In this page, user can check and revise the inputted data. In Software Technical Guide,

data processing mechanism by IESCEM calculation engine as well as the equations used will

be discussed.

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FRONT

PAGE

RESULT

PAGE

CALCULAT

ION

ENGINE

INPUT

DATA

PROCESSING

OUTPUT

Figure 5-2. IESCEM Main Interface

c. Result Page

This page is a section to display data processing result in the form of Energy Security Index

and Clean Energy Index, as well as indicators graph. However, before getting into the result,

user will be asked to complete an expert questionnaire with evaluation on energy security and

clean energy indicators. At the bottom part of the questionnaire, there is a notice for

evaluator about the evaluation consistency. If inconsistent, the evaluator will be asked to

complete the questionnaire again. However, user can still see the scenario result even though

the evaluation on indicators is inconsistent.

Details of expert questionnaire methodology will be explained in Software Technical Guide,

while questionnaire completion guide will be elaborated in User Guide.

6. Software Technical Guide (Software Manual)

a. IESCEM Platform

IESCEM is aimed to simulate energy security and clean energy level from a particular policy

scenario, and the input variable is the output of other model projection. This tool is planned to

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be one part/module of other integrated modeling program. The current development is only to

see as well as test the reliability of Energy Security Index and Clean Energy Index concepts

used in this model. Due to the temporary nature, the program was developed using MS Excel

program based macro (basic visual application). There are some things considered in deciding

to use MS Excel platform in IESCEM, namely:

1. There is no need to install IESCEM as a standalone program, because many people already

have MS Excel in their computers. 2. MS Excel has a good consistency in its data processing. 3. Visual Basic Application (VBA) is already integrated in MS Excel so it can be programmed to

automate the calculation execution simultaneously, as well as data input through a user

friendly form.

Meanwhile, the limitation that can become an issue is compatibility issue between MS Excel

versions. IESCEM was made using VBA in MS Excel 2007 and was tested using MS Excel 2010,

but not necessarily will function appropriately in other MS Excel versions (older or newer).

b. Data Processing Process Flow Data processing in IESCEM is executed with the following steps.

1. Primary energy consumption data that has been inputted is accumulated until total of

primary energy consumption is obtained. The total value is then divided by total energy

import that also been inputted. The formulation is as follow:

Total Primary Energy Import E1 = Total Primary Energy Consumption

With E1 is 1st Indicator of Energy Security, i.e. comparison between total import

and primary energy consumption.

2. Consumption of each primary energy type is compared to total primary energy

consumption (in percent), this share figure will be denoted as pi. Afterwards, the diversity

index is calculated with –pi ln(pi) formula. Diversity index for each type of energy is

totaled with the following formula:

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D = - ∑ pi ln(p1)

3. Indigenous resources is compared to total energy consumption (in percent), and denoted

with ci. This ci value can also be calculated with ci = 1 – mi, with mi is energy import

percentage on total consumption. Afterwards, the diversity level (Shanon’s Diversity

Index) is calculated with the following formula:

D’ = - ∑ cipi ln(p1)

Diversity index above is normalized with the following formula:

D’ D’ ND’ = = D’max lnT

With T is the amount (type) of primary energy used. Afterwards, normalization

value from the index is weighted as follow:

E2 = 1 – ND’

With E2 is 2nd Indicator of Energy Security, i.e. comparison weight between energy

resources and energy import.

4. Total energy supply (consumption) is divided with GDP, as follows

Total Primary Energy Supply E4 = GDP

E4 is 4th Indicator of Energy Security which states the amount of primary

energy supply per GDP or commonly known as energy intensity.

5. Other indicators of Energy Security, i.e. E3 (Overall System Cost), E5 (CO2

Emission), E6 (Energy Elasticity), and E7 (Electrification Ratio), are not

calculated because the values are obtained directly from input.

6. Relative Value of Indicator for Energy Security (e) is calculated with the following formula:

max(En,j )

en,i,j ( ) ( )

With:

en,i,j = amount of n-th relative indicator value for i scenario, in j scenario

year

En,i,j = n-th indicator, for i scenario, in j scenario year

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max(En,i) = n-th indicator maximum value, in j scenario year min(En,i) = n-th indicator minimum value, in j scenario year

7. The first alternative Energy Security Index is determined with the following formula:

With:

en = n-th relative indicator value

n = number of indicators

8. Meanwhile, the second alternative Energy Security Index is determined with the following

formula:

With:

en = n-th relative indicator value

n = = number of indicators

wn = expert judgment weight value for n-th indicator

9. Expert judgment weight value is calculated using Analytical Hierarchy Process (AHP).

10. Clean Energy Index (CEI) measure how clean is the energy used by a country. The

inputted data to determine CEI are mostly Greenhouse gas emission data. The 1st

indicator of Clean Energy (C1) is the comparison of fossil energy supply with the whole

primary energy supply, calculated with the following formula:

11. Other indicators of Clean Energy, i.e. C2, C3 and C4 are calculated based on the GHG

data inputted which include CH4, N2O, CO, NOx, SOx, and NH3.

12. C2 is the 2nd indicator of Clean Energy, namely Global Warming Potential (GWP) which is the cumulative emission from CO2, CH4, N2O, and CO which are stated in kg CO2

equivalent. Proportionality factor of those gasses on CO2 can be seen in the following

table.

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13. C3 is the 3rd indicator of Clean Energy, namely Photochemical Ozone Creation Potential

(POCP). This indicator is stated in kg C2H4 equivalent unit. POCP represents cumulative

emission from NOx, SOx, CH4, and CO. Proportionality factor of those gasses on C2H4 can

be seen in the following table.

14. Finally, C4 is the 4th indicator of Clean Energy, namely Acidification Potential (AP). This

indicator represents cumulative emission from NOx, SOx, NH3 which is stated in kg SO2

equivalent unit. Proportionality factor of those gasses on SO2 can be seen in the

following table.

15. Relative Value of Indicator for Clean Energy (c) is calculated with the following formula:

With:

cn,i,j = n-th relative indicator value, for i scenario, in j scenario year

Cn,i,j = n-th indicator, for i scenario, in j scenario year

max(Cn,i) = n-th indicator maximum value, in j scenario year

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min(Cn,i) = n-th indicator minimum value, in j scenario year

16. The first alternative Clean Energy Index is determined with the following formula:

With:

cn = n-th relative indicator value

n = amount of indicators

17. Meanwhile, the second alternative Energy Security Index is determined with the following

formula:

With:

cn = n-th relative indicator value

n = amount of indicators

wn = expert judgment weight value for n-th indicator 7. User Guide (Training Manual) 1. Prepare the required data for each scenario, which includes:

a. Total energy consumption per energy type.

b. Energy import per energy type.

c. Gross Domestic Product (GDP) value.

d. Total energy supply cost (overall system cost).

e. Energy elasticity value.

f. Electrification ratio.

g. Pollutant emission: CO2 , CH4 , N2 O, CO, NOX, SOX, NH3 .

h. Other indicators that are intended to be added, if any. For example, population size, GDP

per capita, strategic oil reserve, and so on.

It is important to note that IESCEM was designed as a finalization tool of an energy model, to

determine energy security level and environmental friendliness of a policy scenario. Therefore,

the prepared data are ideally output or projection of other models.

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2. Open IESCEM.xlsm file 3. After the file is opened, we will see a notification at the top of the formula bar. This

notification is a security warning from MS Excel which shows that the opened file contains

active content in the form of macro and visual basic application (VBA). Click ―Options…‖

button as shown in the following figure

Figure 7-1. MS Excel Notification on Active Content 4. Next, a Security Options window will appear as in Figure 4. Choose Enable this content >

OK. Next, IESCEM is ready to use.

5. If the third and fourth steps are skipped, there is another way to enable the macro, namely by

clicking Office Button at the top left of Excel window, and then choose Excel Options. After

the Excel Options are opened, click Trust Center > Trust Center Settings… as seen in

Figure 5.

Figure 7-2. MS Office Security Options

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Figure 7-3. Choosing Trust Center Settings in Excel And then in Trust Center window, see Figure 6, click Macro Settings and choose Enable all

macros > OK. Next, IESCEM is ready to use.

Figure 7-4. Enabling Macro through Trust Center

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6. The initial IESCEM v1.0 screen looks like in Figure 2. Use flow and the order of data input in

the main interface are as follows:

a. What is IESCEM. This object (button) is designated to access the descriptions about

IESCEM basic concept, calculation mechanism, as well as IESCEM user manual.

This part is only a description (some kind of ―Help‖) and input data is not done yet.

b. Define Scenarios. This button is to access Scenario and Simulation Year forms.

Scenario and simulation year can be added up to 5 (five) scenarios and 5 (five) simulation

years. This scenario form is shown in Figure 7.

c. Energy Data Input. This button is to input energy data which consists of energy

consumption and energy import. Data entry form will appear as Figure 8. To make it

easy, energy types are categorized into 4 types, i.e. fossil energy, renewable energy, final

energy, and other energy.

Figure 7-5. Scenario and Simulation Year Forms

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Figure 7-6. Energy Data Form

If the checkbox is checked, energy consumption data input form will appear according to

the energy type chosen. Figure 9a and 9b are entry forms for Coal and Crude Oil. The unit

used for energy consumption data input is Million BOE.

The same steps are applied on energy import data entry, because it has the same entry

form as energy consumption entry form.

Figure 7-7. Form to Input Energy Consumption (Coal)

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Figure 7-8. Form to Input Energy Consumption (Crude Oil) d. Key Indicators. This button functions to show indicator and province entry form if the

user has additional energy security or clean energy indicators. Figure 10 shows an

interactive menu to fill the main indicators. Those indicators are categorized based on

environmental aspect, economic aspect, and social aspect. If the checkbox is checked, a

data form will appear (Figure 9.

Figure 7-9. Indicator and Province Entry Form

In this form also exists a province button to add new indicators. If the button is clicked,

display as in Figure 11 will appear which consists of Code, Indicator Name, and Add Data

columns. In the default condition, indicator name and Add Data buttons are inactive.

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If the Code is checked, the two columns along with the checkboxes below will be activated.

To entry the data, click Add Data button, and then a data form will appear (Figure 9).

Figure 7-10. Form to Add Indicators e. Expert Judgment. This button functions to give expert judgment weight value on energy

security and clean energy indicators. The evaluation is done in separated tools which are

incorporated in IESCEM software bundle, with Form Kuesioner.xlsx file name. The tools

execute value weighting by using analytical hierarchy process (AHP) technique. Besides

using the incorporated tools, expert judgment weight calculation can also be done with

commercial software which can be easily found in the market such as Expert Choice, or

through online AHP tools that are available free-of-charge on the internet such as in the

following websites:

http://www.isc.senshu-u.ac.jp/~thc0456/EAHP/AHPweb.html

http://bpmsg.com/academic/ahp_calc.php

Figure 7-11. Expert Weighting Value Form

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f. View Results. This button directs the user directly to the model calculation result. This

part displays Relative Value of Indicators graph as well as Energy Security Index (ESI) and

Clean Energy Index (CEI). At the top right of ESI score or CEI score, there is a dropdown

menu which shows the year. And then there are also ―Update Indicators‖ and ―Show

Calculations‖ buttons. Update Indicators is used/clicked if the user uses province or adds

indicators in the data but the indicators are not shown in the graph. Meanwhile, Show

Calculations is used to view the data table and calculation executed by IESCEM.

Figure 7-12. Model Calculation Result Page 8. Expert Judgment Questionnaire

Expert judgment in IESCEM uses Analytical Hierarchy Process (AHP) technique. This technique use

is aimed to determine the importance and priority of an indicator on other indicators.

Basically, every expert or stakeholder can has different priorities on Energy Security and Clean

Energy evaluation. AHP based expert judgment accommodates expert opinions about policy

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direction priorities, whether it will put forward a particular indicator or positioning all indicators at

the same level of priority.

a. Indicator Descriptions

Energy Security

1. Import dependency (E1)

This indicator illustrates primary energy supply percentage which comes from import on

total primary energy supply. The higher Indonesia consumes primary energy that comes

from import, the higher chances for Indonesia to be exposed to energy supply disturbance

risk. The higher percentage of energy that comes from import (E1), the lower

energy security level is, and vice versa. The value of e1 relative indicator that is closer to

zero shows that a scenario is highly dependent on import, so that the scenario will have the

lowest energy security compared to other scenarios. Conversely, if e1 value is closer to

one, it shows that the scenario can fulfill its energy needs from indigenous sources

so that the scenario will have the highest energy security compared to other

scenarios.

2. Concentration of primary energy sources weighted by a share of net import (E2)

This indicator illustrates the diversification level of primary energy sources. This indicator

illustrates the concentration level of primary energy source in an energy supply

system. The value of e2 relative indicator that is closer to zero shows that the concentration

level of primary energy sources in a scenario is the highest among other scenarios; it means

that the scenario has the lowest energy security level, and vice versa.

3. Overall system cost (E3)

This indicator represents economic dimension of energy security. For scenario with Demand

Side Management (DSM), the total cost includes additional cost for the DSM. Overall system

cost index for a particular scenario is defined as E3, while the relative indicator is e3. Total of

low-cost energy supply shows that the scenario has a high energy security level,

and vice versa. The value of e3 that is closer to zero shows that energy supply cost for a

particular scenario is the most expensive among other scenarios, which means that the

scenario has the lowest energy security level, and vice versa.

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If used to compare Indonesian internal scenarios, E3 indicator can be directly used, because

lower system cost represents process efficiency in energy supply.

To make comparisons between countries, indicator with units in form of monetary value

requires adjustments. Besides due to the difference in energy supply in each country, this

indicator is has yet to indicate the efficiency of an energy supply system.

Adjustment that should be made so that the model can make comparisons between countries

is the use of value which represents specific activity level, especially USD/BOE which

illustrates energy supply cost per energy unit which is consumed by the particular country.

Ideally, for comparison between countries, E3 indicator is the result of the following formula:

with: Q = Energy consumption

P = Energy price

i = Energy type (ex: oil, gas, coal, etc.)

If the relevant data are not available, we can also use energy market price data. For

example, non-subsidized oil fuel price. The use of oil fuel market price (USD/Liter) can be

accepted because it represents activity level.

4. Primary energy supply per GDP (E4) This indicator illustrates a country’s primary energy use effectiveness to mobilize its

economy. A high E4 value shows that a country’s economy is lavishly use the energy. The

value of e4 relative indicator that is closer to zero means that a scenario is the most

spendthrift in using energy compared to other scenarios; it means that the scenario has

the lowest energy security compared to other scenarios.

5. CO2 Emission (E5) This indicator represents environmental sustainability dimension, i.e. related to acceptability.

This index is defined as E5, while the relative indicator is e5. The value of e5 that is closer

to zero shows that a particular scenario is increasingly not environmental friendly

compared to other scenarios; which means that it has the lowest energy security.

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Emission value used for a modeling which compares internal scenario of a country, can use

total CO2 emission. Meanwhile, for modeling which compares inter-countries, this emission

indicator should be adjusted into a value that represents a more specific activity level. In this

case, adjustment can be done by dividing total emission with total energy consumption, so

that the unit becomes pollutant mass per energy unit (kg/BOE).

6. Energy Elasticity (E6) This indicator represents energy consumption growth ratio on economic growth (GDP).

Ideally, energy consumption growth level is lower than economic growth level, because it can

represents a good energy efficiency. However, a low elasticity value can also represent

community limitedness condition in accessing energy. In IESCEM, a higher value of e6

shows that community’s energy consumption is more inelastic and the energy

security is higher.

7. Electrification Ratio (E7) This indicator represents social dimension related to energy (electricity) access supply.

Electrification ratio is the comparison between electrified household with total household

in a region or country. A higher electrification ratio (e7) in a scenario illustrates a

wide community access on the energy, so that the energy security level is better.

Clean Energy

1. Fossil energy percentage on total energy supply (C1) This indicator represents government reluctance to not depend on fossil energy source. This

indicator also illustrates percentage of energy supply that comes from fossil on total energy

supply. A high percentage of fossil energy reflects government’s reluctance to not depend on

fossil energy source. The value of c1 relative indicator that is closer to zero shows

that a particular scenario has a high dependency on fossil fuel, and vice versa.

2. GWP : Global Warming Potential (C2) This indicator is calculated by totaling all CO2, CH4, N2O, and CO emission that has the

potential to cause global warming. Higher C2 value means a higher potential of global

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warming. The value of c2 relative indicator that is closer to zero shows that a particular

scenario has the highest potential of global warming compared to other scenarios. It means

that the scenario has the lowest level of clean energy compared to other scenarios,

and vice versa.

For a modeling which compares a country’s internal scenario, the value that can be used is

total emission of global warming triggering gas. Meanwhile, for the modeling to compare

between countries, adjustment for C2 emission indicator is done by dividing total GWP

emission with total energy consumption, so that the unit becomes GWP pollutant mass per

energy unit.

3. POCP: Photochemical ozone creation potential (C3) This indicator illustrates the potential of smog creation due to reaction between hydrocarbon

and NOx under ultraviolet rays. This indicator is calculated by totaling all emissions that have

the potential to cause photochemical ozone creation. A higher value of c3 means a higher

potential of smog creation. The value of c3 relative indicator that is closer to zero shows

that a scenario has the highest potential of photochemical ozone creation compared to the

other scenarios. It means that the scenario has the lowest level of clean energy

compared to other scenarios.

In a comparison of a country’s internal scenario, data used is usually the total of POCP

triggering pollutant emission. However, if the modeling orientation is to compare between

countries, the data stating activity level have to be in form of total pollutant mass with total

energy consumption in each country. The unit is similar with E5 and C2 indicators, i.e. POCP

pollutant mass per energy unit (kg/BOE).

4. AP: Acidification potential (C4) This indicator illustrates the potential of acid rain. This indicator is calculated by totaling

emission with acid rain potential. Higher value of C4 means higher potential of acid rain. The

value of C4 relative indicator that is closer to zero shows that a scenario has the highest

potential of acid rain. It means that the scenario has the lowest clean energy level

compared to other scenario.

This indicator is stated in total pollutant that is causing acid rain if this modeling is performed

to compare a country’s internal scenario. However, the data should be specified if the

modeling is used to compare conditions between countries. C4 indicator data should be in

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form of comparison of pollutant total mass per energy unit and stated in kg/BOE.

b. Questionnaire Completion Guide

1. To complete expert judgment questionnaire, open Questionnaire Form.xlsx file. The display

on your screen should be as follow:

Figure 8-1. Expert questionnaire form

2. In one line, there are three empty columns (cells). Entry cells is filled with number that

represents priority level (see Table 2) on compared indicators.

Table 2. Priority Level, Definition and Description Priority

Definition Description

Level

1 3

5

7

Both elements are equally Both elements have equally important important contribution on an objective An element is slightly more There is a sign showing that an element

important than the other is preferred than the others but not

convincing. An element is more There is a sign that logically shows that an

important than the other an element is more important than the

other. An element is far more There is a convincing sign that shows that

important than the other. an element is more important than the

other. 9 An element is more An element is proved to be preferred than the

Important than the other other element at the highest confidence level.

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2, 4, 6, 8 Middle values between Valuation is given if a compromise is

Adjacent opinions. required.

3. The center columns can only be filled with ―1‖, which means that both indicators are

perceived to be equally important. 4. Meanwhile, the right or left column is filled if an indicator is perceived to be more

important than other indicators. The figure filled in this column is adjusted to the priority. 5. In one line there should be only one evaluation figure. 6. Evaluation weights are presented in ES Weight and CE Weight Sheets.

Figure 8-2. Expert Judgment Value

7. This form is designed to be filled by ten experts simultaneously. If intended to do the

judgment by only one expert, use only one sheet. After filling one questionnaire, click

menu > Review > Unprotect Sheet. And then input the password: ―iescem‖. And then block

AM column, replace the text color into black or automatic. Evaluation weight will be

displayed.

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9. Qualification Test

To assure that IESCEM is run properly, a test is executed using 10 (ten) scenarios with 2005 and

2010 data. Scenarios in the test are based on data for Indonesia, India, Japan, Malaysia, Thailand,

China, Philippine, Singapore, South Korea and Vietnam.

a. Data Data used in the qualification test scenario are derived from various sources, i.e. World Bank, US

Energy Information Agency (US EIA), Bloomberg Network, as well as manual calculation. The main

reason in using the data from the mentioned sources is because the sources have data for all

scenarios, so that the variable consistency between scenarios is better.

Table 1 and 2 show energy consumption and import in ten Asia Pacific countries, with million BOE

(barrel of oil equivalent) in unit. This data source is US EIA.

Table 3. Energy Consumption (Million BOE) Consumption

Petroleum Coal Natural Gas Bio-Ethanol Bio-Diesel 2005 2010 2005 2010 2005 2010 2005 2010 2005 2010 Indonesia 466.89 505.29 217.66 305.50 124.00 240.93 0.00 0.02 0.07 2.90 India 917.04 1188.22 2418.76 3174.11 218.88 392.76 0.78 1.06 0.07 0.73 Japan 1944.70 1626.23 940.29 987.44 536.35 663.54 0.00 0.21 0.00 0.11 Malaysia 190.47 218.42 55.68 122.30 157.62 198.66 0.00 0.00 0.00 0.73 Thailand 339.78 368.85 156.37 188.46 198.30 274.55 0.25 1.59 0.15 3.99 China 2443.84 3405.51 11424.40 16775.26 285.33 649.89 4.38 7.82 0.29 2.18 Philippines 124.39 112.87 52.83 72.27 17.66 17.42 0.00 0.21 0.07 0.87 Singapore 295.13 503.72 0.02 0.06 40.26 51.16 0.00 0.00 0.01 0.18 South Korea 799.84 828.03 421.14 610.38 185.61 263.10 0.00 0.00 0.07 2.36 Vietnam 89.27 116.93 71.40 142.26 24.36 51.22 0.00 0.04 0.00 0.15

Table 4. Energy Import (Million BOE)

Import

Petroleum Coal

2005 2010 2005 2010

Indonesia 151.90 141.77 0.52 0.29

India 707.44 1194.24 217.59 306.71

Japan 1513.33 1267.33 952.06 990.20

Malaysia 56.30 58.58 53.57 109.50

Thailand 293.77 309.38 45.23 89.83

China 948.60 1735.08 138.47 784.78

Philippines 80.16 66.44 37.62 58.80

Singapore 381.73 415.14 0.02 0.06

South Korea 826.61 865.77 392.02 602.68

Vietnam 0.00 0.00 3.11 5.42

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For emission, data required by IESCEM are CO2, CH4, N2O, CO, NOx, SOx, NH3 emissions. From the

emission substances, the categorization is as follows:

CO2 , CH4 , N2 O, CO are categorized as pollutants with potential to trigger global warming

(Global Warming Potential).

CH4 , CO, NOX, SOX are categorized as pollutants with potential to trigger smog

(Photochemical Ozone Creation Potential).

NOX, SOX, NH3 are categorized as pollutants with potential to trigger acid rain (Acidification

Potential).

Emission data used are derived from World Bank data, as seen in Table 3. And unit used is Million

Tons for each substance. In this qualification test, data for NOx, SOx and NH3 emission can be

derived so that the Acidification Potential value cannot be calculated. So that the pollutant

emission represents activity level that can be compared apple to apple between countries, so that

emission data used is the amount of emission per energy use unit. Unit used is Million Tons of

Substance per Million BOE or simplified into Tons of Substance per BOE.

Table 5. Emission (Million Ton of substances/Million BOE) Emission

CO2 CH4 N2O CO

2005 2010 2005 2010 2005 2010 2005 2010

Indonesia 0.42293 0.41150 0.01396 0.00903 0.00065 0.00029 0.00542 0.00528

India 0.39688 0.42230 0.00715 0.00568 0.00020 0.00017 0.00509 0.00541

Japan 0.36190 0.35719 0.00054 0.00053 0.00003 0.00003 0.00464 0.00458

Malaysia 0.43929 0.40141 0.00393 0.00270 0.00013 0.00009 0.00563 0.00515

Thailand 0.36867 0.35260 0.00559 0.00542 0.00011 0.00012 0.00473 0.00452

China 0.40895 0.39763 0.00408 0.00343 0.00011 0.00009 0.00524 0.00510

Philippines 0.38385 0.40065 0.01186 0.01197 0.00021 0.00021 0.00492 0.00514

Singapore 0.09051 0.02436 0.00030 0.00018 0.00001 0.00001 0.00116 0.00031

South Korea 0.32909 0.33311 0.00099 0.00082 0.00003 0.00003 0.00422 0.00427

Vietnam 0.52932 0.48368 0.02216 0.01559 0.00042 0.00037 0.00679 0.00620

Other data are data on indicators required in IESCEM, i.e. System Cost, GDP, Energy Elasticity,

and Electrification Ratio.

Data on energy supply cost (system cost) in a while is hard to be determined, considering that

energy supply chain in each country will be different. However, because this indicator data is very

important in IESCEM is replaced with non-subsidized oil fuel data which applies in every country.

Page 31 of 57

The assumption is that market price represents production and distribution cost for oil fuel.

Table 6. Other Data Other

Gasoline Price GDP Energy Elasticity Electrification Ratio

2005 2010 2005 2010 2005 2010 2005 2010

Indonesia 0.96 0.96 285.87 377.85 0.942 0.942 0.016 0.014

India 1.25 1.25 834.22 1246.91 0.692 0.692 0.015 0.013

Japan 1.56 1.56 4571.88 4648.48 0.597 0.597 0.010 0.010

Malaysia 0.61 0.61 143.53 178.22 1.263 1.263 0.010 0.010

Thailand 1.23 1.23 176.35 210.09 0.963 0.963 0.013 0.011

China 1.25 1.25 2256.90 3838.00 0.693 0.693 0.010 0.010

Philippines 1.23 1.23 103.07 131.13 0.005 0.005 0.012 0.011

Singapore 1.67 1.67 123.51 169.47 1.762 1.762 0.010 0.010

South Korea 1.7 1.7 844.86 1019.09 0.754 0.754 0.010 0.010

Vietnam 1.03 1.03 52.92 74.27 1.461 1.461 0.010 0.010

.

Expert questionnaire is filled with moderate value 1 (one), with the assumption that all indicators

have equally important position and role in supporting energy security

b. Running Result The model is run in two steps with ten countries categorization into two groups. It is because the

IESCEM tool is designed so that it can only process five scenarios in one running. The

consideration on five scenarios maximum limitation is so that the radar graph on result page

would not be too overlapping, and make it easier to be analyzed visually. Indonesia, India, Japan,

Malaysia, and Thailand are categorized into the first group. China, Philippine, Singapore, South

Korea and Vietnam are categorized into the second group. The result is as follows.

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b.1. Energy Security Index

Year: 2010

Relative Value of Energy Security Indicators ESI Score Alt. I Alt. II

Indonesia 0.518 0.631

e1 India 0.286 0.430

1 Japan 0.595 0.749

0.8 Malaysia 0.586 0.689 e7

e2

0.6

Thailand 0.501 0.579

0.4 Indonesia

0.2 India

0 Japan

e6 e3 Malaysia

Thailand

e5 e4

Figure 9-1. Energy Security Rank for Indonesia, India, Japan, Malaysia, and Thailand Relative to

Each Other For import dependency and diversification level indicator, Indonesia is better than the four other

countries. it is because Indonesia’s energy import is lower than the others’. From energy

concentration and diversification perspective, Indonesia’s energy consumption tends to be well-

distributed for each type of energy consumed so it can be perceived that Indonesia is more secure

than the others.

For the third indicator, Malaysia is better than other countries. It is because by the use of oil fuel

price data as the third indicator as the replacement of Overall System Cost data that is not

available. Malaysia’s oil fuel price in this case is lower than the others’. Once again, it is should be

noted that oil fuel price that is used as the third indicator is market price or non-subsidized price,

with the initial assumption that non-subsidized oil fuel price represents oil fuel economic price

which consists production and distribution cost. From CO2 emission per energy consumption unit perspective, Japan and Thailand have the lowest

emission level. Low emission is an implication from the lower use fossil energy than other

countries for equal consumption amount. Meanwhile from intensity, elasticity and electrification

perspective, Japan is the best. The reason is better consumption efficiency level than other

countries. Japan generates higher GDP for each energy unit (BOE) consumed. This country’s

electrification also already reached one hundred percent.

Page 33 of 57

As a whole, Japan is at the first rank of energy security with a 0.595 score (alt. 1 method) and a

0.749 score (alt. 2 method). Meanwhile, a country with the lowest energy security is India with

0.286 (alt. 1) and 0.430 (alt. 2).

The running result on the second group is presented in the following figure.

Year: 2010

Relative Value of Energy Security Indicators

ESI Score Alt. I Alt. II

China 0.530 0.625

e1 Philippines 0.541 0.653

1 Singapore 0.391 0.577

0.8 South Korea 0.495 0.625 e7 e2

0.6

Vietnam 0.611 0.732

0.4 China

0.2 Philippines

0 Singapore

e6 e3 South Korea

Vietnam

e5 e4

Figure 9-2. Energy Security Rank for China, Philippine, Singapore, Korea and Vietnam Relative to

Each Other For the first (e1), second (e2) and third (e3) indicators, Vietnam is better than other countries.

Besides due to lower percentage of energy import, Vietnam also has a more well-distributed

energy consumption which is not concentrated on a particular type of energy. And of course,

Vietnam’s oil fuel price is lower than the other four countries, so that in these three indicators,

Vietnam is more secure than others. From energy intensity and elasticity as well as CO2 emission perspective, Philippine and South

Korea are the most secure compared to the others. Energy consumption ratios on GDP for the two

countries are smaller than the others. It is also the case with consumption growth and GDP ratio.

Lower fossil energy use compared to other countries had caused the two countries to generate

lower CO2 emission. For electrification, South Korea and Singapore already reached one hundred

percent, thus the two countries became the most secure in the seventh indicator (e7).

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As a whole, for calculation with expert judgment method (alt. 1), Vietnam obtained the first rank

for energy security with 0.611 while the lowest is Singapore with 0.391. Meanwhile, for calculation

with root-mean-square (alt. 2), a country with the highest index of energy security is Vietnam

with 0.732 and the lowest is Singapore with 0.577.

b.2. Clean Energy Index

Year: 2010

Relative Value of Clean Energy Indicators

CEI Score Alt. I Alt. II

Indonesia 0.102 0.204

1

c1 India 0.147 0.197

Japan 0.500 0.707

0.8 Malaysia 0.359 0.451

0.6 Thailand 0.547 0.653

0.4 Indonesia

0.2 India

c4 0 c2 Japan

Malaysia

Thailand

c3

Figure 9-3. Clean Energy Rank for Indonesia, India, Japan, Malaysia, and Thailand Relative to

Each Other The graph above shows relative indicators for clean energy of five countries in the first group.

From the amount of fossil energy use in total energy consumption, India has the highest

percentage of fossil energy consumption on total energy consumption. Thus, India became a

country with the lowest level of clean energy. Conversely, Thailand has the ―cleanest‖ energy due

the lowest fossil energy percentage on total energy consumption.

Meanwhile, from Global Warming Potential (GWP) perspective, Japan is the cleanest from the

threat. The cause is of course because GWP builder gas emission per energy consumption unit in

Japan is the lowest compared to the other countries. Meanwhile, if seen from smog builder

emission or Photochemical Ozone Creation Potential (POCP), Japan is also the cleanest among the

others.

As a whole, from calculation involving expert judgment (alt. 1) the highest level of clean energy

among five countries is Thailand with 0.547. Meanwhile, the worst is Indonesia with 0.102.

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For calculation with root-mean-square (alt. 2) method, the highest score is obtained for Japan

with 0707 and the lowest is for India with 0.197.

Year: 2010



China 0.231 0.317

c1 Philippines 0.358 0.523

1 Singapore 0.500 0.707

0.8 South Korea 0.353 0.438

0.6 Vietnam 0.014 0.028

0.4 China

0.2 Philippines

c4 0 c2 Singapore

South Korea

Vietnam

c3

Figure 9-4. Clean Energy Rank for China, Philippine, Singapore, Korea and Vietnam Relative

to Each Other In the second group, Singapore is the ―cleanest‖ for second and third indicators. It is of course

due to emission level per energy consumption unit that is the lowest among the other countries.

Meanwhile, Philippine has the lowest portion of fossil energy on total energy consumption.

Meanwhile, from Global Warming Potential (GWP) and Photochemical Ozone Creation Potential

(POCP) per consumption unit perspectives, South Korea and China only have a slight difference.

Thus, it can be concluded that both countries have the same ―clean‖ energy level. However, the

graph above can also be interpreted that percentage of fossil energy use in Vietnam as well as its

emission level per consumption unit is far higher than the other four countries.

As a whole, from both calculation methods used, Singapore has the highest level of clean energy.

Its score is 0.500 (alt. 1) and 0.707 (alt. 2). Meanwhile, a country with the lowest level of clean

energy is Vietnam with 0.014 (alt. 1) and 0.028 (alt. 2).

Page 36 of 57

10. Indonesia Modeling

After software qualification test, the next challenge is to model Indonesia. The objective of

Indonesia modeling is to see the energy security condition in Indonesia from an internal

perspective.

Considering that IESCEM software is designed to relatively compare between scenarios, there

are two methods in modeling energy security and clean energy indexes for Indonesia. The first

method is by directly comparing Indonesia’s condition each year, and the second method is by

creating a dummy scenario to be compared with the actual condition. The detailed explanation

along with program running result will be presented in First Method and Second Method

sections.

a. Data The data used is data for 2007-2011 which is extracted from Handbook of Energy Economic

Statistics of Indonesia 2012 published by Pusdatin ESDM. Extracted data can be accessed on Data

IESCEM Indonesia.xlsx file. In the file, there are three sheets, i.e. Data Indonesia, Counter

Measure 1, and Counter Measure 2.

―Data Indonesia‖ sheet represents the actual data based on the existing sources, in this case are

Handbook of Energy Economic Statistics for energy data and World Bank for emission data. To

replace Overall System Cost (E3) data, Electricity Price Index (EPI) is used.

―Counter Measure 1‖ and ―Counter Measure 2‖ sheets are dummies where the data values are

calculated based on ―Data Indonesia‖ which is conditioned with a particular condition. Further

detail on CM1 and CM2 scenarios can be seen in Second Method section.

b. First Method The first method to calculate energy security index and clean energy index of Indonesia can be

assumed as IESCEM’s cheat. Because, IESCEM is designed as a tool to compared energy security

level from different scenarios, and each scenario consists of several years’ data.

In qualification test section, an example of comparison between countries (scenarios) with 2005

and 2010 data has been given. The question is that can IESCEM determine ESI and CEI for

Indonesia only? By comparing the condition of each year—say, in the last five years.

From the design, IESCEM is designed to simulate minimum three policy scenarios. If there was

only one policy scenario, the index value cannot be calculated because the relative indicator

Page 37 of 57

calculation formula requires scenarios with maximum and minimum values. If there are only two

or less scenarios, it will cause an error in relative indicator value calculation.

In the first method, the trick is to make the data each year as a scenario. On ―Define Scenarios‖

menu, input year data as a scenario (see figure 1). Meanwhile, year data can be filled with value

that describes the scenario, for example ―Indonesia‖ or ―All Years‖

Figure 10-1. Tricks to Defining Scenario for Indonesia Modeling In the principle, year data entry with values except year (ex: 2007) would not affect the

calculation because IESCEM calculates per year simulation block. Therefore, if this section is filled

with one simulation, only simulation on that particular block is calculated.

b.1. Indonesia Running Result By using data on ―Data Indonesia‖ sheet (see again Data IESCEM Indonesia.xlsx file, and expert

judgment which positions all indicators as equally important, relative indicator and index value

graph is obtained:

Page 38 of 57

Year: All Years

Relative Value of Energy Security Indicators

ESI Score Alt. I Alt. II

Indonesia2007 0.520 0.708

e1 Indonesia2008 0.481 0.598

1 Indonesia2009 0.397 0.564

0.8 Indonesia2010 0.318 0.406

e7

e2

0.6 Indonesia2011 0.540 0.671

0.4 Indonesia2007

0.2 Indonesia2008

0 Indonesia2009

e6 e3 Indonesia2010

Indonesia2011

e5 e4

Figure 10-2. Indonesian ESI Score and Relative Indicator Value Graph for 2007-2011

On the graph, we can see that from 2007 to 2011 Indonesia has different level of energy security

on each indicator. Viewed from import dependency perspective, Indonesia is the most ―secure‖ in

2011. It is due to the lowest energy import percentage compared to the other years, even though

physically import in 2012 is the highest. The decline of import percentage on consumption is

caused by higher consumption growth compared to import volume. Energy consumption growth

can be triggered by many factors including energy access increase and community purchase

ability increase, which caused energy price to be more ―affordable‖.

From the perspective of concentration on one particular type pf energy or diversity level,

Indonesia is the most ―secure‖ in 2007. It is due to national energy consumption pattern in 2007

is less concentrated on one particular type of energy compared to the other years. It means

that the smallest difference on energy consumption between one type of energy and the others

occurred in 2007.

From energy supply cost perspective, which is represented by electricity price index, Indonesia

also placed at the most ―secure‖ position in 2007 because electricity price index on this year is the

lowest. In IESCEM context, lower energy supply cost means higher energy supply system

efficiency. Higher energy supply system efficiency means higher energy security level.

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From energy consumption per GDP perspective, Indonesia is the most ―secure‖ in 2009. It

indicates that in 2007-2011 period the biggest contribution of energy use on GDP is reached in

2009. CO2 emission (from energy sector) in Indonesia is the lowest in 2007. It makes the particular

year to be most ―secure‖ for Indonesia viewed from emission perspective. Meanwhile for energy

elasticity, 2008 is when Indonesia has the best (lowest) elasticity value, so that from this

perspective it is assumed that Indonesia is most ―secure‖ on that particular year.

Electrification ratio for the latest year is the greatest. It indicates access improvement on

electricity that is wider over the years. Of course from this indicator perspective, Indonesia is

most ―secure‖ in 2011.

As a whole, from calculation involving expert judgment, the highest energy security level for

Indonesia is obtained in 2011 with 0.540 (ESI Score alt. 1). Meanwhile, from calculation using

root-mean-square method, Indonesia’s highest energy security level is obtained in 2007 with

0.708 (ESI Score alt. 2).

Year: All Years



Indonesia2007 0.664 0.779

c1 Indonesia2008 0.722 0.834

1 Indonesia2009 0.374 0.537

0.8 Indonesia2010 0.286 0.333

0.6 Indonesia2011 0.205 0.409

0.4 Indonesia2007

0.2 Indonesia2008

c4 0 c2 Indonesia2009

Indonesia2010

Indonesia2011

c3

Figure 10-3. Indonesian CEI Score and Relative Indicator Value Graph for 2007-2011

The graph in Figure 10-3 shows relative indicators for clean energy in 2007 to 2011. From fossil

energy portion in total energy consumption perspective, 2008 has the lowest fossil percentage on

consumption. Therefore, it can be concluded that Indonesia is most ―clean‖ in 2008.

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Meanwhile, from Global Warming Potential (GWP) perspective, Indonesia is the cleanest from the

threat in 2007. It is of course due to GWP triggering gas emission in 2007 is the lowest compared

to other years. Meanwhile, if viewed from smog builder emission or Photochemical Ozone Creation

Potential (PCOP), Indonesia is the cleanest in 2008.

As a whole, both from calculation involving expert judgment (alt. 1) and calculation using root-

mean-square (alt. 2), Indonesia’s highest clean energy level is obtained in 2008. The score is

0.722 (CEI Score alt. 1) and 0.834 (CEI Score alt. 2).

c. Second Method On this section, the modeling for Indonesia’s condition in 2011 is executed with three scenarios.

The first scenario is Business as Usual (BAU) which illustrates the reality of Indonesia condition in

that particular year. The other scenarios are Counter Measure 1 (CM1) and Counter Measure 2

(CM2) which illustrates a ―more ideal‖ situation in energy security perspective.

CM1 illustrates the condition where in 2011 exploitation of natural gas and its derivatives already

wider and replaced most of petroleum oil products. In addition, geothermal exploitation as

electricity energy source is also greater than BAU scenario. The use of renewable energy and gas

product is assumed to replace oil product consumption by 150 million barrels. In this condition, it

is expected that pollutant emission will be 3.40% lower than BAU.

Meanwhile, CM2 scenario illustrates the use of renewable energy use in 2011 that is very wide

compared to BAU scenario. Renewable based energy supply distribution consists of electricity

supply increase by 20 million BOE and hydropower, wind power and solar power. In addition,

biofuel consumption is also illustrated to be 20 million BOE greater compared to BAU. The decline

of oil product consumption is assumed to be 210 million barrels, and this condition triggers

pollutant decline as much as 7.48%.

Total energy consumption, GDP, energy elasticity, as well as electrification ratio for the three

scenarios are assumed to be constant. The difference between BAU, CM1 and CM2 in figures can

be seen in the following tables.

Page 41 of 57

Table 7. Indonesia Energy Supply 2011

Type of Energy Unit BAU *)

CM1 CM2

Oil & Products 592,456,411 442,456,411 362,456,411

Coal 334,142,760 334,142,760 334,142,760

Gas & Products 261,708,332 361,708,332 361,708,332

Hydropower 31,268,976 31,268,976 51,268,976

Geothermal BOE

16,493,771 66,493,771 66,493,771

Biofuel 2,348,533 2,348,533 22,348,533

Biomass 280,171,358 280,171,358 280,171,358

Wind 2,875 2,875 20,002,875

Solar 472 472 20,000,472

Total 1,518,593,488 1,518,593,488 1,518,593,488

*) Derived from Handbook of Energy & Economic Statistics of Indonesia 2012, Pusdatin ESDM.

Table 8. Indonesia Energy Import 2011

Energy Import Unit BAU *) CM1 CM2 Oil & Products 275,206,538 125,206,538 65,206,538 Coal BOE 213,955 213,955 213,955 Total Import 275,420,493 125,420,493 65,420,493

*) Derived from Handbook of Energy & Economic Statistics of Indonesia 2012, Pusdatin ESDM.

Table 9. Indonesia Pollutant Emission 2011

Substance Unit BAU *) CM1 CM2 CO 414873.379 400767.6841 383840.8503

2

CH Ribu Ton 9693.780435 9364.1919 8968.685658 4

N O Substansi 298.3967905 288.2512997 276.0767106 2

CO 5799.858836 5602.663635 5366.029395 *) process ed from World Bank

Table 10. Other Indicators

Unit BAU CM1 CM2

Overall System Cost *) Juta US$ 151859.3488 151859.3488 151859.349 GDP at Constant Price 2000 Triliun Rupiah 2463 2463 2463 Energy Elasticity - 1.08 1.08 1.08 Electrification Ratio (ER) - 72.90% 75% 80% 1/ER - 1.371742112 1.333333333 1.25

*) 100 US$/BOE assumption

The year data used in this simulation is 2011, thus the scenario definition in IESCEM software is

as shown in Figure 10-4.

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Figure 10-4. Defining Scenario with One Year Data In Simulation Year, the year column is only filled in one column, while the rest are left empty. On

the next data input, the data is only filled in columns with 2011 label. The main consideration to

make the scenario year to be only one year is because CM1 and CM2 scenarios are built based on

BAU scenario data. If change patterns of these inter-scenarios data are the same for several

years’ data, it will generate a same relative indicator value and index for each year. Therefore,

data year used in this simulation is only one, and the most recent year is taken from the available

data.

c.1. Indonesia Running Result Running of BAU, CM1 and CM2 scenarios are executed by using the data above. Expert judgment

for energy security positions energy import reduction (E1) and consumption concentration

distribution as well as diversification (E2) as more important that other indicators.

Table 11. Expert Judgment Weighting Result for Energy Security

w1 w2 w3 w4 w5 w6 w7

0.210 0.156 0.115 0.126 0.140 0.126 0.126

Meanwhile for clean energy, expert judgment positions all indicators as equally important. It

means fossil energy consumption reduction and all pollutant gases are considered equally

important.

Table 12. Expert Judgment Weighting Result for Clean Energy

w1 w2 w3 w4

0.250 0.250 0.250 0.250

Based on the data, the simulation result is as follows:

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Year: 2011

Relative Value of Energy Security Indicators ESI Score Alt. I Alt. II

BAU 0.000 0.000

e1 CM1 0.321 0.379

1 CM2 0.633 0.756

0.8

e7 0.6 e2

0.4

0.2 BAU

0 CM1

e6 e3 CM2

e5 e4

Figure 10-5. ESI Score for BAU, CM1 and CM2 Scenarios and Relative Indicator Value Graph in

2011

The graph in Figure 10-5 shows the position of Indonesia BAU scenario in 2011, relative to CM1

and CM2. From import dependency perspective (e1) and electrification (e7), CM2 is the best

because in that scenario exploitation of renewable energy is greater than in BAU and CM1

scenarios. Exploitation of renewable energy itself is mostly contributed for electrification of un-

electrified regions.

From the perspective of energy consumption concentration on one type of energy (e2), CM2 is

also the most ―secure‖. Even though the type of energy used is equal to the one in BBAU and

CM1, in CM2 the consumption deviation is not that high. Difference of the highest and lowest

energy type consumption in CM2 is lower than in BAU and CM1.

From perspectives of overall system cost (e3), intensity (e4), and energy elasticity (e6), there is

no scenario that is better or worse than the others. It is because energy consumption and supply

in the three scenarios are assumed to be equal. Because in this simulation the efficiency of each

scenario is not considered, equal amount of consumption will generate equal GDP. Energy

elasticity price in the three scenarios will also equal, with the assumption that consumption growth

level and national income are the same. Equal absolute indicator value from each scenario, would

not be processed by IESCEM in relative indicator value calculation because it will cause an error.

Relative indicator calculation that causes an error by IESCEM will be automatically be given zero

point. Page 44 of 57

CO2 emission in CM2 scenario is lower than the other two scenarios so that it produces the most

―secure‖ relative indicator (e5). It is because consumption of petroleum oil product is mostly

shifted to products of natural gas and renewable energy-based power plant. Aggregately, the use

of gas and renewable energy encourages reduction of emission level.

As a whole, Indonesia CM2 in 2011 has the highest level of energy security compared to BAU and

CM1. Energy security score involving expert judgment is 0.633 and score calculated with root-

mean-square (RMS) method is 0.756.

It should be considered that graph and index score mentioned before is the relative position of

BAU scenario on CM1 and CM2 scenarios. In ESI, both for expert judgment or RMS methods, BAU

scenario has zero point. It is because the value of all absolute indicators of BAU scenario has the

lowest value (minimum) compared to other scenarios. In relative indicator value processing, the

lowest absolute indicator value will be transformed into zero.

Now we will see Indonesia’s position in its clean energy perspective. The main driver of clean

energy is the use of fossil energy by a scenario. From the portion of fossil energy use in total

consumption, CM2 is the cleanest because the total percentage is lower than BAU and CM1

scenarios.

Year: 2011



BAU 0.000 0.000

1

c1 CM1 0.323 0.375

CM2 0.750 0.866

0.8

0.6

0.4

0.2 BAU

c4 0 c2 CM1

CM2

c3

Figure 10-6. CSI Score for BAU, CM1 and CM2 Scenarios and Relative Indicator Value Graph in

2011

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From global warming potential (GWP) and smog (PCOP) perspectives, CM2 is also cleaner because

total emission of CO2, CH4, N2O, and CO as the triggers of global warming and smog is lower than

in BAU and CM1 scenarios. In this case, fossil energy consumption linearly correlated on pollutant

emission. Greater portion of fossil energy means higher potential of global warming and smog.

Acid rain potential (Acidification Potential) in this exercise is not calculated due to data availability.

As a whole, in 2011, CM2 is the ―cleanest‖ scenario compared to two other scenarios, i.e. BAU and

CM1. Clean energy score for CM2 involving expert judgment is 0.750 and score calculated with

root-mean-square method is 0.866.

d. ESI and CSI Calculation with Expert Judgment from BAPPENAS Energy security and clean energy modeling executed previously used dummy expert judgment

which positions energy import reduction (e1) and consumption concentration distribution and

diversification (E2) as more important than other indicators, for energy security. Meanwhile for

clean energy, all indicators are positioned to be equally important.

In this section, ESI and CSI calculations are executed again by using expert judgment from

BAPPENAS. Evaluators are coming internally from Directorate of Energy, Mineral and Mining

Resources (SDEMP), as well as externally from Directorate of Transportation, Directorate of

Energy, Telecommunication and Informatics (ETI), and Directorate of Environment (LH).

Evaluation result from internal group is that the main priority of development in energy security

perspective is electrification improvement. It is then followed respectively by energy elasticity and

intensity improvement, as well as national energy consumption equal distribution. Meanwhile,

carbon emission level reduction and energy import reduction are on the sixth and seventh

priorities.

Table 13. Internal Expert Judgment Weighting Result for Energy Security

w1 w2 w3 w4 w5 w6 w7

0.093 0.152 0.115 0.157 0.094 0.173 0.217

.

Meanwhile, the priority in clean energy perspective is reduction of global warming potential and

acidification potential. It is then followed by efforts of smog countermeasure and reduction of fossil

energy portion in energy distribution

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Table 14. Internal Expert Judgment Weighting Result for Clean Energy w1 w2 w3 w4

0.200 0.312 0.220 0.268

External group which consists of Directorate of Transportation, ETI and LH has a similar priority

direction. Aggregately, this external group highlights on electrification improvement, followed by

improvement on energy intensity, elasticity and diversification. Contrary to internal group with

seven priorities that are almost equal, the external group highly prioritizes electrification and less

prioritizes reduction on energy import.

Table 15. External Expert Judgment Weighting Result for Energy Security

w1 w2 w3 w4 w5 w6 w7

0.037 0.123 0.109 0.167 0.076 0.154 0.334

In clean energy perspective, the external group highly prioritizes reduction of global warming

potential and smog potential. Meanwhile, reduction of fossil energy consumption and acidification

potential are on the third and fourth priorities.

Table 16. External Expert Judgment Weighting Result for Clean Energy

w1 w2 w3 w4

0.211 0.438 0.217 0.134

Based on the judgment, energy security index and clean energy index as shown in Table 17 and

18 are obtained. Table 17 is the comparison between BAU, CM1 and CM2 as in the previous 1.c

section. Meanwhile, Table 18 is the comparison between conditions in Indonesia in 2007, 2008,

2009, 2010, and 2011 as in the previous 1.b section.

Table 17. ESI and CSI Comparison Between BAU, CM1 and CM2 Scenarios in 2011

Internal Eksternal ESI CSI ESI CSI

Indonesia 0.370 0.466 0.283 0.538

CM1 0.734 0.532 0.809 0.655

CM2 0.451 0.200 0.498 0.211

Table 18. ESI and CSI for Indonesia in 2007 to 2011

Internal Eksternal

ESI CSI ESI CSI

Indonesia2007 0.484 0.663 0.417 0.794

Indonesia2008 0.480 0.705 0.451 0.836

Indonesia2009 0.441 0.311 0.449 0.319

Indonesia2010 0.317 0.281 0.316 0.336

Indonesia2011 0.580 0.256 0.627 0.359

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Both tables show that the position of energy security index and clean energy index from each

modeled scenario is not far different. It is because the directions of policy priority according to

internal and external experts are similar. In Table 17, Counter Measure 1 (CM1) scenario has the

highest ESI and Indonesia scenario is the lowest. This index position is equally good both by

internal expert judgment and external expert judgment. For CM1, CM2 is on the last place.

In table 18, Indonesia situation in 2011 is the most ―secure‖ from energy security perspective,

both by internal expert judgment and external expert judgment. It is also the case from clean

energy perspective, clean energy indexes weighted by internal and external expert judgments

indicate that Indonesia has the cleanest environmental impact in 2008.

11. Indonesia Modeling Analysis

Based on running result described previously, it is known that the scenario that is in line with

expert judgment weighting will strengthen scores of energy security and clean energy. Therefore,

it is important for policy makers to design a policy scenario that is in line with development

priority. For example, if we value electrification ratio as the main priority, the policy focuses on

reduction of energy import will reduce the level of energy security.

In principle, to increase Indonesia energy security and clean energy, a scenario should improve its

indicators simultaneously. Other alternative is to improve one or a part of indicators with

significant improvements. The following are policy steps that could be implemented in improving

Indonesia energy security, while also improving clean energy index.

1. Reducing import dependency, especially for oil fuel. Indonesia’s trading balance is negative

due to import portion that is mostly filled with oil import. Indonesia has the highest third GDP

growth after China and India, as well as 1.2% population growth. With this condition, it is

expected that consumption level growth will increase by 8%-10% per year. For that, we

should increase domestic population and reduce import. There are several alternatives that

can be done to do this.

a. Oil refinery capacity improvement. It is aimed to support the production performance

of old refineries with declining efficiency. By building new refineries, domestic

consumption is expected to be fulfilled by domestic refineries. An ideal example is Japan.

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Even though Japan does not have oil resource, the country has the fourth highest capacity

of refinery in the world with process capacity of 4.25 million barrels per day. Most of oil

fuel demand in Japan is fulfilled from domestic refinery production. Improvement of

refinery capacity also means cost efficiency, because import cost for crude oil is lower

than import cost for final product such as oil fuel.

b. Realization of strategic petroleum reserve. Besides the challenge of oil lifting

improvement, issue on strategic reserve is not less important. Currently, Indonesia does

not have energy strategic reserve, both for primary and final energy. In 2013, Indonesia’s

petroleum oil reserve is 3700 million barrels with reserve-production ratio of 11.1 year1. It

means that if Indonesia does not immediately realize the strategic reserve and there is no

additional proven petroleum oil reserve, petroleum oil in this country will exhaust in the

next 11 years. For petroleum fuel, Pertamina only has operational reserve for 20 days2,

while the strategic reserve is zero. From state security perspective, the lack of strategic

petroleum reserve makes Indonesia vulnerable to natural disturbance such as natural

disaster and non-natural threats such as war and physical colonization.

c. Efficiency in transportation sector. In 2011, Indonesia consumed approximately 317

million BOE of petroleum fuel, and 72.7% of them are used by transportation sector3. The

majority of transportation fuel demand is fulfilled by import. Therefore, there is a need for

mass rapid transport in major cities. Of course, public transportation should be

comfortable with affordable fare, so that urban community can minimize the use of

personal vehicle for their mobility. In addition, the author also encourages government to

create a national standard for efficient and environmental-friendly transportation modes.

The mentioned vehicles are truck, bus, diesel train, as well as river and ferry transport.

Hence, it is expected that petroleum fuel consumption in this sector will decline. Efficiency

efforts in transportation sector will also directly influence greenhouse gas (GHG) emission

decline.

c. Expanded use of biofuel. Subsidized oil fuel control program on government operational

vehicle that was launched last year is still haven’t succeed, if not failed. At the end of

August last year, government released a mandatory use of biofuel through ESDM Minister

Regulation No. 25/2013. This regulation revised the previous obligation of biofuel use, i.e

1 BP Statistical Review of World Energy 2013

2 http://finance.detik.com/read/2014/02/11/081642/2492838/1034/bahaya-indonesia-tak-punya-cadangan-bbm

3 Handbook of Energy and Economic Statistics of Indonesia 2012

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ESDM Minister Regulation No. 32/2008. In the most recent regulation, minimum of 10%

biofuel use is required for transportation and industrial sectors, as well as 20% for power

plant sector. The main challenge in biofuel development is to discover raw material that

does not collide with food supply; from feedstock, land use and workforce perspectives.

Currently, biofuel in the form of bioethanol is less developed because in addition to the

feedstock that is also food plant, ethanol itself is still being absorbed by non-energy

industry such as pharmacy and cosmetic with higher sell price compared to if sold as fuel.

Therefore, the biggest expectation of biofuel use is in the form of biodiesel. Through the

mentioned minister regulation, state revenue that can be saved by the reduction of diesel

fuel import in 2014 only is expected to be 4 billion USD.

2. Encouraging target achievement on energy diversification, as well as reducing dependency on

only one particular type of energy. It’s undeniable that consumption pattern of Indonesian

community is still concentrated on oil and its derivative products. Among the causes is oil

consumption infrastructure that already very mature, while for other types of oil it is still

lacking. Recommendations related to diversification are:

a. Increased use of gas. With more availability and lower access cost, it is not without a

reason that government encourages the development of gas infrastructure. Acceleration

of gas infrastructure development will encourages diversification as well as efficiency in

manufacture industry and energy supply industry such as power plant. In addition, to

support and continue the conversion plan from oil fuel into gas fuel in transportation

sector, the government should immediately manage the problems in conversion kit

division. Related stakeholders, in this case are Pertamina and gas supplier companies, are

expected to increase gas fuel filling station.

b. Development of renewable energy. As listed in the Blueprint of National Energy

Management, renewable energy potential in Indonesia (see the table below) is mostly

unexploited.

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To support renewable energy exploitation, a clear supporting mechanism is needed.

Examples of governmental support mechanism on renewable energy development are

China and India. In China and India, renewable energy system is easy to obtain

domestically with relatively low cost. In addition, supporting industries are also available

domestically. Meanwhile, in Indonesia, the existence of supporting industries is still not

comparable to the target set by the government.

The consequence of the lack of domestic industries is that we have to buy from overseas.

Hence, the product cost becomes expensive. For example, to make turbine and generator

of renewable energy, magnet and bearing are needed. Because those components are not

made by local industries, we buy the components by importing. Another example, for 1

KW of turbine, the components are mostly imported, even though they can be obtained in

local market. Meanwhile, bearing for > 1 KW turbine is hard to obtain. The dilemma from

this case is that our renewable energy development actually supports the growth of

renewable energy industry in other countries instead.

Besides supporting mechanism, capacity building is also required. Considering that

renewable energy resource is very big, human resource that is capable of executing direct

exploitation is practically needed. If classified, there are at least two types of players in

Indonesian renewable energy development. The first players are major players. Portion of

the projects taken by this type of players are major projects.

The second players are minor players, who take smaller project portions. This type of

player is the one who needs governmental assistance and support. Because even though

the energy exploitation is executed in small scale, if done by many parties, it will

accumulate into something big. For example, great potential of hydro-energy which is

distributed in villages and mountain areas. With exploitation potential approximately

Page 51 of 57

1 KW pico scale to 500 KW micro scale, it will significantly influence electrification if

executed by, say, one thousands developers. The problem is that most of minor

stakeholders lack the capacity to:

1. Create a technically feasible and bankable proposal. 2. Implement project management to manage organization and financial. 3. Exploit and manage the energy resource itself. Even in isolated areas, knowledge

and ability of the people are very lacking.

The government should transfer these capacities to the community in the simplest form of

communication. And, if possible, communicated using local language. Government should

also help encouraging people to optimize economic potential of their respective regions,

so that the continuity of installed renewable energy installation can be assured.

The government should also be a bridge in encouraging minor stakeholders or giving

subsidy for minor-scale renewable energy developer. Minor stakeholders can be formed in

cooperative under a bigger (main) cooperative. The main cooperative coordinates and

―gathers‖ energy products to be sold to State Electricity Company (PLN). Here, the

government plays a role as a bridge for the main cooperative to connect with PLN.

In order to ensure the quality of renewable energy system developed by small

developers, the government should make prototype and demonstrator of mature-

technology equipments. Government should also provide an information system which

exposes the technology to wider community, e.g. in terms of price, component types,

maintenance cost, etc.

As a tropical country, plants in Indonesia will produce biomass many times greater than

plants planted in subtropical climate. Indonesia’s biomass waste reserve equals to 49 GW.

Meanwhile according to PLN data, all power plants’ installed capacity in 2013 is 39 GW.

Biomass is still not optimally exploited considering that the resource is widely distributed

and difficult to collect.

As a comparison, Denmark that does not have resource like Indonesia, depends on

biomass as the second highest supply of energy. Denmark import feedstock from Siberia

(Russia), Greenland (Canada), and Southeast Asia.

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They can bring the feedstock into their country with an economic price. Indonesia with

closer distance between islands, should have it easier than Denmark. It can be done if the

government facilitates people, especially small stakeholders, to actively mobilize biomass;

where biomass is converted into pellet and briquette. The government helps briquette

production by providing other renewable energy powered (ex: wind) simple briquette

machine, which the payment is made by installment. In other words, the community is

assisted to exploit local energy to pack other local energy to be transferred to other

islands.

With wide land and high amount of idle lands, Indonesia has the chance to build what is

called energy forest, currently, forestry department of IPB (Bogor Institute of Agriculture)

already able to produce plants that generates great amount of biomass. In principle,

energy forest can also help government program in reducing greenhouse gas emission.

For wind energy, installed power plant in Indonesia is only 1.4 MW. Even though the wind

resource condition in Indonesia is less than Europe, many regions in Indonesia have wind

resources that can be optimized such as Java south cost, Nusa Tenggara and Sulawesi.

With the development of magnetic bearing technology, we can now exploit the lowest

wind speed to be converted into electrical power. In Indonesia, there are currently power

plants with 10 MW capacity per tower. If we duplicate this project by buying technology

from overseas, a problem to be faced is that the design point will be different with the

condition in Indonesia. Hence, what the government should do is encouraging partnership

between domestic and foreign stakeholders to open wind turbine industry with

technological design customized to Indonesian condition. By the existence of domestic

industries, it is expected that the spare parts will also available and easily accessed.

Indonesia also has solar energy potential with average intensity of 4.8 kWh/m2 as shown

in the table. Along with the technological development, solar cell efficiency is increasingly

better and the cost is getting lower. Therefore, stakeholders such as PLN who developed

PLTS with 100 islands program will increase. PLN itself continues with 1000 islands

program expected to be achieved in 2015. The issue with solar energy is the energy

supply stability, due to seasonal change and cloud amount, so that PLTS system cannot

stand alone. There should be hybrid system with other energy resource in the location. To

maintain the stability, small battery system and diesel generator system should be added

to fill the emptiness.

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Ideally, management from solar energy and other energy should be managed with smart

grid management system. Currently, smart grid is only applied by BPPT in Sumba Island

as a prototype. With smart grid system, renewable energy-based electricity system in

isolated areas will be more reliable and well-monitored. In addition, the author also

recommends the government to encourage the realization of domestic solar cell industry

with local raw materials. 3. Reducing energy supply cost, by improving efficiency at the supply side (upstream), efficiency

at transformation side, efficiency at transportation side (ex: mine power plant) and use of

environmental-friendly technology. 4. Government encourages the people to reduce energy intensity. Alternatives that can be done

among others are through:

a. Energy efficiency program at demand side, especially in industrial sector and

commercial sector. Free-of-charge energy audit program for industry is a program that

should be supported. In addition, reward & punishment program for business entities

which succeed/failed to do efficiency to be massively socialized.

b. Encouraging the growth of energy-saving equipment industry, or work together

with companies which already experienced in their fields to develop energy-saving

equipment fabrication in Indonesia. This policy should also be supported by socialization

of national standardization on energy-saving equipment.

c. Developing integrated online information system, about energy-saving equipment

and its standardization. For example: Energy Star in United States. 5. Continuing and accelerating national electrification program. The objective is so that

electrification target can be achieved on time and Indonesian community can gain maximum

benefit. To support this program, we recommends

a. Interconnection between regions with electricity surplus and electricity minus,

as well as electricity import for border areas which experience electricity shortage, such as

north area of West Kalimantan.

b. Small-scale renewable energy-based electricity installation as described in the

recommendation no. 2.b. 6. Creating renewable energy and energy efficiency curriculums. The target of renewable energy

is 23% of total consumption and efficiency target is 15%. With the assumption that oil price

in 2025 is 200 billion USD/barrel, renewable energy economic price is 224 billion USD/year.

To manage this great amount of energy supply, human resource is needed. Therefore, there

should be curriculum for renewable energy and energy efficiency from introduction level

(Elementary School, Middle School, High School) to higher level in university. Currently,

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discussion of renewable energy only takes place in university. Universities with curriculum of

renewable energy and energy efficiency are still limited to particular campuses such as Surya

University, ITB, UI, etc. If there was no specific major, there should be at least a course

about renewable energy and energy efficiency incorporated into majors such as civil

technique, architecture, planology, electro, machinery, chemistry, physics, economy,

agriculture, husbandry, etc. With the existence of human resources with mature capacity and

capability, it is expected that optimization target for renewable energy and efforts on energy

efficiency can be achieved. 7. Dialogue between government and stakeholder. In formulating policy and regulation,

government should improve dialogue between government institutions, industries, research

institutions, energy professionals, academicians and universities, non-profit institutions,

religion leaders, community leaders, and the community itself; so that the policy will be more

mature and acceptable. For example: Energy stakeholders are involved in policy formulation

e.g through I2E3M publication on the Internet. As what is done by UK with Energy Calculator

2050 publication. The objective is so that the stakeholders can have active roles in testing

the policy and regulation. 8. Integrated Indonesian energy management. Currently, energy policy and regulation in

Indonesia are still minus inter-institutions coordination in the implementation. Where to

achieve 23% of renewable energy in 2025, there is still no nationally ingetrated energy

management system. Energy stakeholders in Indonesia such as ESDM, PLN, KPDT, Ministry of

Industry, Ministry of Finance, NGO, Local Government, Pertamina, and so on, each has non-

integrated renewable energy program. Thus, if we want to know the target achieved so far, it

is not easy to access the data.

The government should encourage the development of integrated energy management

system, with the objective of task division among energy stakeholders. How much the

targeted percentage? Who is responsible (task owner)? What are the deliverables? How is the

schedule? As well as, how is the target achievement progress status? So that in the end of

the year, target and achievement can be evaluated. Hence, correction can be done if there

was any problem in one task group, and can be a lesson learned for the other task groups not

to repeat the same mistake

One alternative is to use Planisware, a software that has been used in various government

institutions and major industries in all over the world, as well as EADS (European Defense

Space Company). EADS uses Planisware to develop passenger airplane A350 by managing

6000 internal stakeholders and 12000 external stakeholders. Planisware helped the

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development project of A350 to be completed in accordance with the expected objective

target.

12. Conclusions and Recommendations a. Conclusions 1. IESCEM program is already running well and producing accurate calculation. Next, refinements

are needed in terms of visualization and user interface to make the user comfortable in

operating it. Complete data provision is also not less important so that the accuracy of

IESCEM model result will be maintained and can represent policy scenario projection. 2. In a calculation comparing Indonesia’s condition from the year of 2007 to 2011, by using

expert judgment from internal parties of Directorate of SDEMP, the highest level of energy

security in Indonesia is obtained in:

- Year of 2011, with score of 0.580, using alt. 1 method

- Year of 2007, with score of 0.708, using root mean square (alt. 2) method

Meanwhile for clean energy, the highest level is obtained in 2008, both from calculation

involving expert judgment (alt. 1) and calculation using root mean square (alt. 2( method.

The scores are 0.705 (CEI Score alt. 1) and 0.834 (CEI Score alt. 2), respectively. 3. In a calculation comparing Indonesia’s BAU with Counter Measure 1 (CM1) and Counter

Measure 2 (CM2), CM1 scenario achieved the highest rank of energy security with score

involving internal expert judgment of 0.734 and score calculated with root mean square

(RMS) of 0.756. CM1 is also the cleanest scenario compared to BAU and CM2. Clean energy

score for CM1 scenario involving expert judgment is 0.532 and the score calculated with root

mean square (RMS) method is 0.866.

b. Recommendations 1. To improve the accuracy of clean energy index and energy security index calculations, there

should be other important indicators in the model.

2. IESCEM tool should be used to make comparison between scenarios in a country. It is

because the model is initially designed for that purpose, with aggregate indicators of energy

security and clean energy. 3. IESCEM use to make comparison between countries is possible if the aggregate indicators are

specified into indicators that illustrate activity level. For example, emission data should be

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specified as emission per energy consumption unit. Therefore, the comparison between

countries is an apple-to-apple comparison.

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indonesian energy security and clean energy modeling...

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