Electricity & climate change
SEEKING FOR THE TRIPLE NEXUS OF ELECTRIFICATION,
CLIMATE CHANGE MITIGATION,
AND CLIMATE CHANGE ADAPTATIONDISSERTATION

to obtain
the degree of doctor at the University of Twente,on the authority of the rector magnificus,
Prof. dr. T.T.M. Palstra,on account of the decision of the graduation committee,
to be publicly defendedon Thursday, the 21st of November 2019 at 12:45 hrs

byKamia Handayani
born on the 27th of May 1978
In Garut, Indonesia

This dissertation has been approved by
Prof. dr. T. Filatova
Dr. Y. Krozer

Graduation committee:

Chair and secretary
Prof. dr. T.A.J. Toonen

University of Twente

Promotor
Prof. dr. T. Filatova

University of Twente

Co-Supervisor
Dr. Y. Krozer

University of Twente

Members:
Prof. dr. J.S. Clancy
Dr. M.J. Arentsen
Prof. dr. P. HerderProf. dr. K. Blok
Prof. dr. K. Lindgren

University of Twente
University of Twente
Delft University of Technology
Delft University of Technology
Chalmers University of Technology

Colophon
Copyright ©2019, Kamia Handayani, University of Twente, BMS-CSTM
All rights reserved. No parts of this dissertation may be reproduced,
stored in a retrieval system, or transmitted in any form or by any means
without prior written permission of the author.
Cover design: Edi Trihartono Nuryatno
Published by: Ipskamp printing, Enschede, the Netherlands
ISBN: 978-90-365-4890-8
DOI: 10.3990/1.9789036548908
URL: https://doi.org/10.3990/1.9789036548908

This work was funded by The Indonesian Endowment Fund for Education(LPDP) under Grant No. PRJ-2570/LPDP/2015, and supported by PT
PLN (Persero).

Acknowledgments
All praise is due to Allah, Lord of the universe.
It was January 2016 when I started my PhD journey. Here I am now, writing
the last part of my dissertation book. This book exists due to hard workand the tremendous support received from many people and organizations.
I would like to take this opportunity to express my gratitude to those whohave significantly contributed to this achievement.
I am deeply grateful to my husband and my two daughters foraccompanying me during the course of my PhD, for their full understanding,and for always being my fanatical supporters. I owe everything to myparents. Their prayers and their selfless, unwavering support throughout
my life have made me who I am today. I am always thankful to my motherin-law for giving her blessing to us living far from home these past four
years. I thank my late sister, Teh Yuniar, for her kindness – my prayers
are always with you. I appreciate my little sister, Ina, for taking care of ourparents while I am away from home.
My deepest appreciation goes to my supervisors, Prof. Tatiana Filatova
and Prof. Yoram Krozer, who consistently provided constructive feedback
and support at critical points of my research development. Tatiana, you
are indeed a great mentor. Coming from the industry, I have greatly
benefited from your valuable insights concerning the academic world. Your
expertise and wisdom helped to shape my research talent, and your carefulattention to detail and your well-structured thoughts helped to vastly
improve my academic writing skill. Thank you for giving me challenges
when I needed them, and for supporting me when I was in doubt. Your
commitment as a supervisor is remarkable. I remember you called me from
Sydney late at night to provide me with mental support, and you responded
to one of my emails while admitted to a hospital. You are an exceptionallykind and warm person. Your feedback was always encouraging and mademe felt appreciated. I am also indebted to you for your great referenceiletters, which have led to several travel grants and admissions to highquality research schools.
My intellectual debt is to Yoram. Yoram, you are the first person to
introduce me to academia. Our master thesis project roused my interestin undertaking PhD research. Your critical yet constructive views have
improved my capabilities. I remember those first weeks when you
challenged me to explore “technological change” in energy modeling. One
and a half years later, my work was published in Energy Policy. I always
appreciated your swift responses to my queries and your timely feedback.
For me, you are not just a supervisor, but also a generous friend. Thankyou for the treats during our lunch meetings. My family and I were also
grateful to you for inviting us to stay at your place during a vacation in
Amsterdam.
My sincere gratitude goes to the rest of my graduation committee fromthe University of Twente: Prof. Joy S. Clancy and Dr. Maarten J. Arentsen.
I feel honored to have you both as part of my PhD graduation committee.
Thank you very much for your professional feedback during my qualifierpresentation and for reading and assessing my dissertation book. I extend
my sincere gratitude to the external committee members: Prof. Paulien
Herder, Prof. Kristian Lindgren, and Prof. Kornelis Blok. It is a great honor
to have you all in my committee. Thank you for taking the time to readand evaluate my book.
This book would not have been possible without the support from a numberof organizations. I would like to express my gratitude to LPDP for theirfinancial support and to PLN for supporting me during my study leave.
Throughout the data collection process, I received persistent support fromIndonesia’s three electric utilities: PLN, IP and PJB. I am thankful to SEI
and to IBM for providing free access to the LEAP software and the CPLEX
software, respectively. Several research schools made contributions at
different points in my PhD journey: the CERES research school during theconstruction of my research proposal, the Research on SustainableiiDevelopment summer school, organized by the University of Basel, during
the development of Chapter 3, and the ETH-EAERE winter school during
the preparation of Chapter 5. I would also like to thank the Energy
Academy Indonesia (Ecadin) for the opportunity to share my research with
the Indonesian energy community. Finally, I am grateful for the opportunity
to share a panel with resourceful people in COP24, organized by theIndonesian pavilion, TYK Consulting and Action Research, and Ecadin.
This book has had support from many individuals. Taylor Bennington and
Emily Gosh from SEI provided answers to my queries on the LEAP
software. Faris Al Rasyid helped me during the analysis of Chapter 4. Pinto
Anugrah has run the WEAP simulation used in Chapter 5. I am also thankful
to Aji P. Perdana for his assistance in using ArcGIS software to retrieve
the CIMP5 SST data. I appreciate the help from Dr. Musa Marbun, Anindita
Nugraha, Marwah, Wuri Prasetyo, Andrey Kennedy, Muhammad Maulana
Saputra, Arief Sugiyanto, Fitria Leli, Amna Apriliani, Ricky Aldrian, Pak
Ajrun, Mbak Mekkadinah, Pak Purnomo, Pak Wisnu, Pak Hijrah, Pak Ismail,Pak Romadhony, Pak Alan, Tania, Pak Haris, Bu Esti, Ofa, Pak Kristofa,
Mbak Indah, Dian, Pak Wayan, Pak Lingga, and Eko for their support in thedata collection process. Finally, I appreciate the generous support from all
resourceful people and respondents of the three utilities who contributedwith their knowledge, experience, and time during my fieldwork andafterward.
I am blessed to have been surrounded by wonderful people throughout thispath. The Department of Government and Technology for Sustainability(CSTM) has been my second home since 2016. I would like to thank Prof.
Hans Bressers for accepting me at CSTM in the first place. I extend my
gratitude to Prof. Michiel Heldeweg and all senior staff members for thegood times we shared in the office as well as in our outbound gathering. I
am thankful to Barbera, who was always there to help me with generalaffairs throughout my PhD trajectory. I am also grateful to Annemiek, whohelped me a lot with the administrative procedures at the beginning of my
PhD research project. I enjoyed the company of Karen - who serves asiii

my first paranymph - this past three years. We have shared memorable
times during countless lunches, dinners, sports, and our girl talks. Thankyou for being a great friend! I also thank Dwi, my second paranymph, for
the wonderful times we shared at the office, Basel summer school, COP24,
and within the Indonesian community in Enschede. It is my pleasure toacknowledge my PhD colleagues at CSTM for the good times we shared:
Heksi, Amro, Brayton, Juliane, Franziska, Juli, Kristina, Kenia, andAlessandro. I also enjoyed the times spent with my previous PhD
colleagues: Dr. Monica, Dr. Helmi, Dr. Cesar, Dr. Shaheen, Dr. Houda, Dr.
Imke, Dr. Koen, Dr. Ewert, Dr. Maia, Dr. Leila, Dr. Fariba, and Dr. Narges.
Enschede felt like home, thanks to the company of its Indonesiancommunity. In particular, I would like to thank Fajar and family, Akbar andfamily, and Aji and family for taking care of my daughters while my husband
and I were away for the hajj pilgrimage. I also thank other Indonesianfriends who have been very kind to me and my family: Lulu, Pak Dhadhang
and family, Muthia and family, Pak Kunaifi and family, Mbak Dwi and family,
and other Indonesian PhD colleagues. I extend my gratitude to Yosia, Mbak
Zaki, Mbak Dewi, Mbak Elly, Bu Ells, Ceu Enung, Ancalas NL (Inna, Arief,
Ayu, Mira), Mbak Marni, PPIE board members of 2017, the IMEA family, theAnnissa family, and many others. Thank you very much for being my family
in NL.
This book ends my journey as a PhD researcher, but it is certainly not theend of my work on energy and climate change.

Enschede, October 2019
Kamia Handayani

iv

Contents
1

General Introduction .................................................................................... 1
1.1.
1.2.
1.3.
1.4.
1.5.
1.6.

2

Trade-offs between electrification and climate change
mitigation: An Analysis of the Java-Bali power system inIndonesia ........................................................................................................ 27
2.1.
2.2.
2.3.
2.4.
2.5.
2.6.

3

Background .............................................................................................3
Case study: The Indonesian power sector ................................5
Analysis of climate change mitigation and adaptation in
the power sector: State of the art ...............................................9
Research gaps .................................................................................... 16
Main goal, research questions, and approach........................ 19
Structure of the dissertation ....................................................... 24

Introduction ......................................................................................... 30
Paris Agreement and decarbonization of the power
sector ..................................................................................................... 32
Methodology and data ..................................................................... 35
Balancing the Paris climate target and the Java-Bali
capacity expansion: LEAP results.............................................. 49
Discussions .......................................................................................... 56
Conclusions ......................................................................................... 58

From fossil fuels to renewables: An analysis of long-term
scenarios considering technological learning ................................... 61
3.1.
3.2.
3.3.
3.4.
3.5.
3.6.

Introduction ......................................................................................... 64
Technological learning ..................................................................... 66
Methodology and data ..................................................................... 68
Results ................................................................................................... 79
Discussions .......................................................................................... 88
Conclusions ......................................................................................... 89v

4

The vulnerability of the power sector to climate variability andchange: Evidence from Indonesia ......................................................... 91
4.1.
4.2.
4.3.
4.4.
4.5.

5

Climate change mitigation and adaptation nexus: Analysis of alow-carbon electrification scenario incorporating climatechange impacts ......................................................................................... 123
5.1.
5.2.
5.3.
5.4.
5.5.
5.6.

6

Introduction ......................................................................................... 94
Methodology ........................................................................................ 97
Results: Weather and climate effects and adaptive
responses of the power sector .................................................100
Discussions ........................................................................................119
Conclusions .......................................................................................121

Introduction .......................................................................................126
Modeling climate change impacts on the power sector ..128
Methodology and data ...................................................................132
Climate change mitigation-adaptation synergies: LEAP
Results .................................................................................................141
Discussion ..........................................................................................148
Conclusions .......................................................................................150

Synthesis of the Dissertation ............................................................. 153
6.1.
6.2.
6.3.
6.4.
6.5.

Introduction .......................................................................................155
Summary of the findings ..............................................................156
Innovative contributions to science ........................................161
Policy implications ..........................................................................164
Limitations and the agenda for future research.................167

Bibliography ............................................................................................................ 169
Appendices ............................................................................................................ 197
Supplementary ...................................................................................................... 227
Summary ............................................................................................................ 231
Samenvatting ......................................................................................................... 235
Ringkasan ............................................................................................................ 239
About the author ................................................................................................. 243
List of Publications ............................................................................................. 244
vi

List of Abbreviations
BAU

Business As Usual

BMKG

Badan Meteorologi, Klimatologi dan Geofisika (the Agency for
Meteorology, Climatology, and Geophysics)

CFPP

Coal-fired Power Plant

CST

Coal Steam Turbine

IEA

International Energy Agency

ENS

Energy Not Supplied

ETL

Endogenous Technological Learning

GHG

Greenhouse Gas

GWh

Gigawatt-hour

HEPP

Hydroelectric Power Plant

IEO

Indonesia Energy Outlook

INDC

Intended Nationally Determined Contribution

IP

PT Indonesia Power

IPCC

Intergovernmental Panel on Climate Change

IPP

Independent Power Producer

IRENAInternational Renewable Energy Agency

kWh

Kilowatt-hour

LEAPLong-range Energy Alternatives Planning system

NDC

Nationally Determined Contribution

NEP

National Energy Policy

NGCC

Natural Gas Combined Cycle

vii

NGOC

Natural Gas Open Cycle

NGPP

Natural Gas Power Plant

NRE

New and Renewable Energy

P2B

Pusat Pengatur Beban (Load Control Center)

PJB

PT Pembangkitan Jawa Bali

PLN

Perusahaan Listrik Negara (State Electricity Company)

PV

Photovoltaic

RCP

Representative Concentration Pathway

RUPTL

Rencana Usaha Penyediaan Tenaga Listrik (Electricity
Business Supply Plan)

SDGs

Sustainable Development Goals

SEI

Stockholm Environment Institute

T&D

Transmission and Distribution

TWh

Terawatt-hour

TPP

Thermal Power Plant

UNFCCC

United Nations Framework Convention on Climate Change

USC

Ultra-Supercritical

WEAP

Water Evaluation And Planning system

WEO

World Energy OutlookviiiCHAPTER 1 GENERAL INTRODUCTION

1.1. Background
Electricity is a basic need in the modern world. Economic development
and social well-being are possible because of electricity (Ferguson et al.,
2000; Fouquet, 2011). Accordingly, the speed of electricity growth is thefastest among other sources of total energy demand (IEA, 2018a). In 2016,
the world’s electricity consumption doubled that of 1990. However, nearly
1 billion people worldwide today are still without access to electricity (IEA,
2018b). Looking ahead, the International Energy Agency (IEA) estimates a
90% increase in global electricity consumption in 2040 due toelectrification. Therefore, the power sector is expected to expand further
to satisfy future demand for electricity.
While the increase in electricity demand in advanced economies is
expected to be relatively modest, developing economies will share an
extensive account of the future demand increase, which will be driven by
rapid economic and population growth (IEA, 2018a). For the global South,
growth in electricity demand is a crucial prerequisite for the development
and satisfaction of the United Nations (UN) Sustainable Development
Goals (SDGs). For example, Indonesia, a developing economy with 262
million inhabitants, records an annual economic growth of 5.3% between
2012-2016 (BPS, 2018). In the same period, Indonesia experienced a yearly
growth of 6.7% in electricity demand (PLN, 2018). Thus, the country is
projected to be the world’s fourth-largest economy during the 2030s,
owing to an increase in the working-age population, which will reach 68%
of the total population by 2030 (OECD, 2018). Consequently, the demandfor electricity will continue to grow, making the power sector’s
infrastructure increasingly vital for enabling socio-economic development
and progress on SDGs.
Nonetheless, this paramount role of the power sector in driving thecountry’s development comes at a cost. Electricity generation inIndonesia contributes to 43% of the energy sector emissions and 16% tonational greenhouse gas emission (GHG) (Rizaldi Boer et al., 2018), owing
3CHAPTER 1 GENERAL INTRODUCTION

to the power sector’s reliance on fossil fuels, especially coal. Under theParis Agreement, the country is embarking on a low-carbon pathway andaims to reduce 29% of its carbon emissions by 2030 (Government of The
Republic of Indonesia, 2015a). This goal aligns with Indonesia’s ambitious
target of increasing the share of new1 and renewable energy in the nationalenergy mix up to 23% in 2025 and 31% in 2050 (Government of The Republicof Indonesia, 2014). Furthermore, while the power sector infrastructure isincreasingly crucial for meeting socio-economic and climate change
mitigation goals, at the same time, it is threatened by the adverse impactsof climate change. A United Nations Framework Convention on Climate
Change (UNFCCC) report confirms that the global South is to bare themost of the latter (UNFCCC, 2007a).
Extreme weather events and gradual changes in climate variables have
implications for the reliability, cost, and environmental impacts of theenergy supply worldwide (Schaeffer et al., 2012; Cronin et al., 2018).
Climate change impacts are expected throughout the entire power sector
value chain, including production, transmission, distribution, andconsumption. On the supply side, effects of climate change include
changes in water availability and the seasonality of hydropower, alterations
in wind speed frequency and distribution, reductions in solar cell efficiency,
generation cycle efficiency, and the cooling water availability of thermal
power plants, and failures and reductions in the capacity of transmission
and distribution lines. On the demand side, climate change alters thebalance of heating and demand patterns (Schaeffer et al., 2012; Cronin etal., 2018; Audinet et al., 2014).
Moreover, electricity infrastructures are vulnerable to extreme weather
events, which are one of the world’s leading causes of power outages. A

1New energy refers to energy sources that can be produced using newtechnology, either originating from renewable energy or non-renewable energy,
among others, including nuclear, hydrogen, coal bed methane, liquefied coal, andgasified coal (Government of The Republic of Indonesia, 2014)4

CHAPTER 1 GENERAL INTRODUCTION

World Bank report on resilient infrastructure highlights that natural shocks
caused 44% of power outages in the US between 2000 and 2017 and 37%
of outages in Europe between 2010 and 2017. Such events cost billions ofdollars per year for electric utilities, consumers, and governments.
Therefore, natural hazards and climate change are pressing problems thatin the coming decades, would involve substantial investments (Nicolas etal., 2019).
Thus, the grand challenge for the power sector worldwide and Indonesia,
in particular, is to develop in a resilient way under the nexus of threeobjectives: satisfying fast-rising electricity demand, meeting the Paris
Agreement and coping with the impact of climate change. One practicalchallenge is the substantial investment required to develop clean, reliable,
and climate-resilient power systems. Even without climate change
considerations, the power sector is already capital-intensive(Bhattacharyya, 2011; Nicolas et al., 2019) and with additional objectivesof climate change mitigation and adaptation, much higher investments are
expected.
To date, the scientific community has accumulated knowledge to address
this societal problem, which is reviewed in Section 1.3. However, first, an
overview of the case study is presented in Section 1.2. and then Chapter
1.4 discusses the research gaps in the current literature. Thereafter,
Chapter 1.5 highlights the core research goal of the dissertation, together
with the guiding research questions for attaining this goal and an overview
of the mixed methods used in the dissertation. Finally, this chapter ends
with an outline of the structure of the entire doctoral dissertation.

1.2. Case study: The Indonesian power sector
Indonesia is one of the world’s fast-developing economies. The
Government of Indonesia is promoting an average of 5% economic growth
per annum to reduce the poverty rate below 4% by 2025 (Government of
The Republic of Indonesia, 2016). Meanwhile, to date, more than 14 million
Indonesians do not have electricity access (IEA, 2018b). Moreover, the5CHAPTER 1 GENERAL INTRODUCTION

electricity consumption per capita of Indonesia is relatively low, i.e., 870
kilowatt-hours (kWh) in 2016, which is much lower compared to 3,110 kWhof the world's average per capita consumption in the same year (IEA,
2018c). These statistics imply that the demand for electricity in Indonesia
will continue to grow in the next decade.
The structure of Indonesia’s power sector is vertically integrated: thestate-owned electricity company (PLN) monopolizes the retail electricity
sale and is the sole purchaser of electricity produced by independent
power producers (IPPs). The PLN solely owns and operates thetransmission and distribution (T&D) networks, while the power generation
assets are divided between PLN, its subsidiaries, and IPPs.
As an archipelagic nation that has more than 16,000 islands (BIG, 2017a),Indonesia’s electricity infrastructures are spread into eight major
electricity grids and more than 600 isolated grids distributed throughout
the archipelago (PWC, 2017). Thus, it is pertinent to note that while thephysical infrastructure spreads throughout the Indonesian archipelago,
most of the power generation capacity (65%) is situated in the Java andBali islands (Table 1.1). These islands are the most populated islands,
inhabited by over 148 million people, which comprises 57% of the national
population (BPS, 2018).
Table 1.1: Capacities of power generation, transmission, and distribution: thetotal Indonesia and Java-Bali system (KESDM, 2017; PLN, 2017a)
AssetsIndonesia
59,656

JavaBali
38,690Percentage of theJava-Bali capacity
65%

Generation capacity (MW)Transmission network:
Transmission lines (kmc)
Substation transformer (MVA)
Distribution network:
Distribution lines (kmc)
Substation transformer (MVA)

44,064
98,89922,55378,69751%
80%

887,681
50,100466,686
32,82253%
65%

Note: MW=megawatt; kmc=kilometer circuit; MVA= megavolt ampere

6CHAPTER 1 GENERAL INTRODUCTION

The Indonesian power sector, including the Java-Bali interconnected
power system, is highly dependent on fossil fuels. As a locally extracted
and relatively cheap resource, coal became the primary energy source inthe country. In 2015, fossil fuels constituted 90% and 91% of the national
and Java-Bali power generation mixes, respectively (Fig. 1.1). Therefore,
the Java-Bali power system is illustrative of the national electricity mix.
Furthermore, since Java and Bali are the most populated and developed
islands in Indonesia, electricity consumption on these islands has
continued to increase, having an annual average growth of 5.9% between
2012 and 2016 (PLN, 2018). Furthermore, in 2018, the Java-Bali powersystem served 74% of national electricity consumption (PLN, 2019).
Consequently, Java-Bali contributed the highest share to the nationalbalance of power sector’s GHG emissions, compared to the outer islands.

Fig. 1.1: The national and Java-Bali power generation mixes in 2015 (PLN,
2016a). This data includes both PLN and IPP productions.

Indonesia owns abundant energy resources, including oil, coal, natural gas,hydro, geothermal, solar, biomass, and wind resources (Table 1.2 and Table
1.3). In 2015, the cumulative reserves of the three fossil fuel resources
constituted 93 billion tons of oil equivalent/toe. Nonetheless, fossil fuelresources are depleting, and unless new reserves are discovered,Indonesian oil is expected to be exhausted in 12 years, natural gas in 33
years, and coal in 82 years (DEN, 2016a).
7

CHAPTER 1 GENERAL INTRODUCTION
Table 1.2: Primary energy resources in Indonesia
PrimaryenergyReserve
Total Indonesia

Java, Madura, and Bali

Coal

126.6 billion tons (88.6 billiontoe)a19.8 million tonsa

Natural gas

151.3 TCF (3.9 billion toe)a10.6 TCFb

Oil

3.6 billion barrels (0.5 billion toe)a

1.2 billion barrelsca

MEMR (2016), bKESDM (2018a), and cKESDM (2018b)

Meanwhile, the potential for renewable energy is enormous (Table 1.3);
however, this potential is hardly utilized. In 2015, nearly 9 GW, or less than
1% of renewable energy potential, was employed (EBTKE, 2016).
Table 1.3: Renewable energy potential and current practices in Indonesia
Renewable

Potential in
Gigawatt (GW)

Renewable deployment
by 2015, total Indonesia

Total
Indonesia

JavaBaliislands

Installed
capacity(GW)

Renewableutilization
(%)

Hydro

75

4.2

5.4

7.2%

Hydro P/S

4.3

3.9

0

0.0%

Mini hydro

19.4

2.9

0.4

2.1%

Sources(DEN, 2016b;
ESDM, 2019)(ESDM, 2019;
PLN, 2017b)(DEN, 2016b;
ESDM, 2019)aa
Geothermal 17.5
6.8
1.9
11%
(DEN, 2016b;
ESDM, 2019)
Biomass
30
7.4
1.8
6.0%
(DEN, 2016b;
ESDM, 2019)
b
b
Solar
5,374
2,747
0.06
0.0%
(ESDM, 2019;
Kunaifi andReinders, 2016)
Wind
60.6
24.1
0.00040.2%(DEN, 2016b;
ESDM, 2019)a
Note: Hydro P/S = hydro pumped storage, excluding speculative and hypotheticalpotential, bin Gigawatt peak8

CHAPTER 1 GENERAL INTRODUCTION

Similar to many other countries, the Indonesian energy sector is required
to contribute to the nationally determined contribution (NDC) under theParis Agreement. This sector is required to cut 11% of national GHG
emissions from business as usual (BAU) by 2030. Meanwhile, the power
sector in Indonesia is already affected by severe weather events andchanges in climate variables. For example, heavy rainfall in March and Aprilof 2010 resulted in excessive water entering reservoirs of the Citarum
cascade hydropower on Java island, which caused flooding downstream.
In contrast, in 2011, the water level in these reservoirs fell drasticallybelow the normal level, reducing their electricity production (Syariman andHeru, 2011). Likewise, severe weather events adversely affected
electricity distribution networks, often causing widespread power cuts
(PLN Yogyakarta, 2015).
Considering its vital role for meeting the electrification and Paris
Agreement goals, as well as the fact that it is already affected by climate
variability, the Indonesian power sector is a vivid example when studying
the triple nexus of electrification, climate change mitigation, and climatechange adaptation. As such, this doctoral dissertation focuses on the mostextensive power system in Indonesia, i.e., the Java-Bali power system,
since it is representative of demand growth, energy mix, and CO2 emissions
and given the author’s access to high-quality data for this system.

1.3. Analysis of climate change mitigation and adaptation in the
power sector: State of the art
1.3.1.

Modeling low-carbon pathways in a power system expansion

Making assessments about the future of power systems is not an easytask considering the uncertainty about the future of the power sector,
economic situation, and technological progress, among other things. In thiscontext, scenario analysis offers flexibility for the actors to explore avariety of pathways to expand the power system over time, and to evaluate
the benefits and consequences of each path.

9CHAPTER 1 GENERAL INTRODUCTION

Energy system models are often utilized to aid in the detailed
quantification of scenarios (Hall and Buckley, 2016). They developed during
the 1970s and have been used worldwide (Kemfert and Truong, 2009).
Varied in purpose (Connolly et al., 2010), each model has a uniqueparadigm, technique, and solution (Hall and Buckley, 2016). Moreover,energy models can be classified based on the level of aggregation and thetheoretical approach being used. This classification divides energy systemmodels into two types: top-down and bottom-up models. While top-down
models focus on aggregate linkages between energy, economy, andenvironment from the context of the national, regional, or global economy
as a whole, bottom-up models look at the issues from the perspective ofa specific sector, such as electricity generation or transport (Kemfert andTruong, 2009). As such, top-down models focus on market processes
rather than technological details, addressing policy concerns related to
public finances, economic competitiveness, and employment (van Vuuren
et al., 2009). Meanwhile, bottom-up models include a more detailed
quantitative description of the technological structure utilized in a sector(Kemfert and Truong, 2009; van Vuuren et al., 2009), thereby modeling thedetailed technological complexity of the energy system (Pfenninger et al.,2014).
To assess the trade-offs between electrification and climate change
mitigation goals in the power sector, one needs a tool that enables
performing a technological, economic, and environmental analysis of thepower system expansion. Furthermore, since climate change mitigation in
the power sector relies heavily on a variety of energy technologies, it isessential that this tool represents technologies in detail and can
incorporate a competition between current and future energy
technologies.
Bottom-up energy models, such as the Long-range Energy Alternatives
Planning system (LEAP), offer the capability to analyze both a powersystem expansion and climate policy scenarios, taking into accountdetailed characteristics of electric power technologies. LEAP, which was
10CHAPTER 1 GENERAL INTRODUCTION

developed by the Stockholm Environment Institute (SEI), has been used in
190 countries, becoming the de facto standard tool in developing countries(Heaps, 2017). Its success is accelerated by its free access for developing
countries and by the fact that it accommodates various characteristics
essential for an energy sector analysis in developing countries, such as
electrification and flexible data requirements (Urban et al., 2007).
Numerous studies have applied LEAP when assessing the decarbonizationof the power system. For example, McPherson and Karney (2014) exploredscenarios for climate change mitigation within Panama’s power sector.
The study indicates that there is an opportunity for Panama to reduce
both greenhouse gas emissions and system generation costs on thecondition that there is sufficient private investment. Perwez et al. (2015)
included an analysis of a green future scenario for Pakistan’s power
sector, concluding that a green scenario is more economically efficient in
the long run compared to fossil-fuel based scenarios. Samsudin et al.
(2016) analyzed the paths of the Malaysian sustainable power sector,
showing that sustainable scenarios assure sufficient electricity supply
while keeping emissions below the target limit. Ouedraogo (2017) employed
LEAP to examine reference and sustainable scenarios for Africa. The
study implies that the modification of existing oil and coal plants, as wellas the promotion of local renewable resources, allow for fuel diversificationin the power sector, which will increase energy security while reducing
CO2 emissions. Similarly, Bhuvanesh et al. (2018) evaluated greenhousegas mitigation scenarios for Tamil Nadu State, India, and suggested thatthe adoption of renewable energy sources in the power sector will increase
Tamil Nadu’s energy independence and also reduce CO2 emissions.
Worldwide scholars have also applied LEAP to examine the achievementof NDCs in the power sector at a national level, including Mexico’s(Grande-Acosta and Islas-Samperio, 2017), Thailand’s (Kusumadewi et al.,2017) and Botswana’s (Baek et al., 2019) power sectors. Together, thesestudies indicate that each country’s NDC is achievable through theincreased deployment of renewable energy, and in the case of Mexico, also11

CHAPTER 1 GENERAL INTRODUCTION

through energy-efficient measures. Meanwhile, Kumar and Madlener(2018) go beyond the current NDC by exploring a plausible pathway for
the German power sector to achieve 1.5 ºC, the Paris Agreement goal.
The study reveals that GHG emission comes down to almost zero in 2040,
thereby ensuring the achievement of the temperature limit of 1.5 ºC.
However, in doing so, this will entail high GHG mitigation costs: 194.2 €/tof CO2e.
A variety of other modeling tools are used in the literature to analyze thepower sector pathways towards meeting the Paris Agreement goal. Dalla
Longa and van der Zwaan (2017) adopted the TIAM-ECN model to examine
the role of low carbon technologies in achieving Kenya’s target under theParis Agreement. Bogdanov et al. (2018) used the LUT Energy System
Transition modeling tool to explore the technical feasibility of theNortheast Asia power system for deep decarbonization mandated by theParis Agreement. Moreover, Haiges et al. (2019) applied TIMES to assess
the Malaysian power sector pathways, considering the country’s NDC as
well as 2050’s deep decarbonization. Fortes et al. (2019) employed
TIMES_PT to analyze the extension of end-use sector electrification as a
cost-effective strategy for deep decarbonization in Portugal. Meanwhile,Gómez-Calvet and Martínez-Duart (2019) proposed a mathematical model
based on linear programming to optimize the balance between variable
renewable energy sources to be extensively added in the Spanish power
sector as a response to the EU’s NDC.
Nevertheless, there is still no assessment of the implications ofIndonesia’s NDC for the power sector, let alone an assessment of the roleof technological change in attaining the NDC targets. Furthermore, the link
between the Paris Accord’s mitigation objectives with the sector’s
adaptation to climate change has not yet been investigated. Such analyses
are critical, as they could provide insights into formulating a policy
framework for the power sector to curb CO2 emissions.

12CHAPTER 1 GENERAL INTRODUCTION

1.3.2.

Endogenous technological learning in energy system models

The academic literature agrees that technological changes play a criticalrole in the historical energy transition, as well as in future scenarios ofenergy transition (Nakicenovic et al., 2000; Berglund and Söderholm, 2006;
IPCC, 2007; Wilson and Grubler, 2011). Even more, technological change
is viewed as a critical component of long-term climate change mitigation
strategy (Pizer and Popp, 2008). Accordingly, the IPCC Fifth Assessment
Report (AR5) highlights the inability of existing technologies to gain asignificant reduction needed to meet the IPCC’s mitigation scenarios(Pachauri et al., 2014). Thereby, it underlines the importance of institutions
and economic incentives to encourage technological change that would
lead to reductions in climate change mitigation costs. However,incorporating technological change in energy modeling remains a challenge(Grübler et al., 1999; Frei et al., 2003; Berglund and Söderholm, 2006).
Most energy models treat technological change as exogenous (Grübler etal., 1999; Berglund and Söderholm, 2006; Ma and Nakamori, 2009),
integrating this change through numerous assumptions about the costs
and performance of future technology. For example, in optimization
models, the adoption of low-carbon yet uneconomical technologies can betriggered by creating environmental or capacity constraints, among others(Ma and Nakamori, 2009).
Nonetheless, in reality, technological change is tightly embedded in thedevelopmental trajectory of technology and requires considerable
developmental efforts (Berglund and Söderholm, 2006). Accordingly, there
is an increasing number of studies that adopt endogenous technologicalchange in energy system models. Technological learning or a learning
effect is the most common approach to specifying and incorporating
endogenous technological change (Kahouli-Brahmi, 2008). For example,
Pratama et al. (2017) applied a multi-objective optimization method thatconsiders technological learning to minimize both the costs of power
generation and its corresponding CO2 emissions for multiple regions in
13

CHAPTER 1 GENERAL INTRODUCTION

Indonesia. The study reveals that the incorporation of the learning rate ofrenewables results in a considerable deployment of renewables to replacecoal-based power generation. Meanwhile, Heuberger et al. (2017)
integrated endogenous technological learning (ETL) into the ESO-XEL
model to simulate the UK’s power capacity expansion for the period of
2015-2050 and compared the results between with and without
technological learning. The study finds that the consideration oftechnological learning influences the competitiveness of technology andresults in earlier optimal investment. Similarly, Daggash and Mac Dowell(2019) used ESO-XEL, which embodies technological learning to evaluate
the UK’s power system expansion up to 2100 to address deep
decarbonization mandated by the Paris Agreement. The study indicatesthat the deployment of CCS technology -including bioenergy with carbon
capture and storage and direct air capture and storage- is required from2030 onward to comply with the Paris Agreement goal by the end of the
century. Finally, Liu et al. (2018) incorporated technological learning into
the TIMES model to evaluate low-carbon technology diffusion in the
decarbonization of the Tianjin power sector in China. The study implies
that the learning rate is a critical factor in optimization simulation, as it
affects the choice of technologies and total system costs.
Despite the increasing recognition of the critical role played bytechnological learning, there have been no studies incorporating ETL into
LEAP, precluding an evaluation of the effects of induced cost reduction
on the entire power system.
1.3.3.

Adverse impacts of climate change on the power sector

The climate is changing. Even if global GHG emissions are stabilized at
1.5ºC above the pre-industrial level, we will still see changes in climate
and their impacts, including higher air and seawater temperatures,increased frequency and intensity of heavy precipitation and droughts,
sea-level rise, ecosystem damages, and land and forest degradations(IPCC, 2018).
14

CHAPTER 1 GENERAL INTRODUCTION

Climate change already affects the entire global economy, including theenergy sector. In 2005 alone, extreme climate events accounted for a 13%
variation in energy productivity in developing countries (World Bank, 2010).
Moreover, future climate change is expected to increase the vulnerabilityof the power sector, which has attracted the attention of both researchersand practitioners. Financial institutions now include an assessment ofclimate change impacts on their loan portfolios (Connell et al., 2018;
International Finance Corporation, 2012). Moreover, internationalguidelines for such an assessment are emerging. For example, the WorldBank produced a guideline for climate-resilient hydropower, which involved
a broad stakeholder engagement during the preparation process (World
Bank, 2017). Similarly, the International Standard Organization issued a
new international standard, i.e., ISO 14090, to assist companies inassessing climate change impacts and developing effective adaptationplans (Naden, 2019).
To support this societal need of assessing infrastructure resilience,researchers often use simulation models that integrate the projected
effects of climate change on future power systems. Naturally, suchmodeling links to climate change scenarios and their expected impacts
resulting from global climate change models. However, climate change may
affect various elements of the electricity supply chain and electricity
sources differently. Previous studies on this topic have established thatclimate change reduces the reliability of thermoelectric power plants
mainly due to increasing air and cooling water temperatures, anddecreasing streamflow (Förster and Lilliestam, 2010; van Vliet et al., 2012;
Zheng et al., 2016; Tobin et al., 2018).
Meanwhile, impacts on hydropower are uneven between regions. For
example, Shafiei et al. (2015) indicate that Iceland would benefit from anincrease in hydropower production under future climates while SpaldingFecher et al. (2017) imply that the Southern African power pool would
adversely be affected under a drying climate. Similarly, impacts onelectricity demand vary between regions, indicating a net decrease in15

CHAPTER 1 GENERAL INTRODUCTION

demand in colder regions while contrarily, implying a net increase indemand in warmer regions.
Furthermore, several studies indicate limited adverse impacts of gradualchanges in climate variables on wind and solar energy production (Tobin
et al., 2018; Jerez et al., 2015). Additionally, few studies estimate anincrease in wind power generation in some parts of Brazil (Pereira de
Lucena et al., 2010) and Croatia (Pašičko et al., 2012). Nevertheless, offshore wind power plants may need to invest in adaptation measuresagainst sea-level rise (Lise and van der Laan, 2015).
Studies presented so far broadly indicate the adverse impacts of climatechange on electricity supply and demand, calling for the power sector to
improve its resilience to climate change. Furthermore, although climatechange is a global phenomenon, its geography is uneven, showing diverse
impacts across the globe. Hence, assessments of these impacts at local
levels are needed to facilitate appropriate local adaptation actions.

1.4. Research gaps
While the literature on the subject is extensive, several research gaps
remain. Building upon previous studies, this doctoral dissertation aims toaddress gaps in the literature on the analysis of climate change mitigation
and adaptation in the expanding power sector. In particular:(i).

A bottom-up assessment regarding the trade-offs between
electrification and the Paris climate target of the Indonesian power
sector is still missing.
Previous studies have analyzed the decarbonization of theIndonesian power system (Marpaung et al. (2007); Dasuki et al.;(2001); Purwanto et al. (2015); Stich and Hamacher (2016); Kumar(2016)). Nonetheless, a thorough search of the relevant literatureyielded only one related article, i.e., Siagian et al. (2017), which
specifically addressed Indonesia’s intended nationally determined
contribution. However, this study was carried out based on a draft16

CHAPTER 1 GENERAL INTRODUCTION

policy document before the COP21 in Paris. Meanwhile, a study thatexamines the implications of the energy sector’s target in the final
NDC submitted under the Paris Agreement is still missing.
Moreover, the previous research by Siagian et al. employs the AsiaPacific Integrated Model/Computable General Equilibrium(AIM/CGE) model for their analysis and mentions the model’slimitations in producing technology-rich outcomes, which can beprovided by bottom-up-type engineering models. Therefore, abottom-up analysis using a technology-detailed dataset of theIndonesian power sector in achieving Indonesia’s NDC will add tothe existing literature on modeling low-carbon energy systems.

(ii).

ETL is not represented in the LEAP modeling literature.
Heuberger et al. (2017) listed state-of-the-art energy andelectricity system models, incorporating ETL, which includes
MESSAGE-MACRO, MARKAL-TIMES, NEMS, POLES, ERIS,GALLM, and ESO-XEL. The LEAP modeling literature, however, has
not included ETL thus far.
Since LEAP is claimed to be the de facto standard for developing
countries undertaking assessments of low-emission development
strategies (Heaps, 2017), it is essential to incorporate ETL into
LEAP. Such an effort enables the consideration of cost reductionsof low-carbon technologies over time for robust analysis of lowcarbon energy transition. ETL often indicates declining costs ofrelatively new technologies –such as wind or solar– in comparison
to more established fossil fuel-based ones. Neglecting thissignificant change in market forces when developing policy decisions
for the rapidly expanding energy sector in developing countries maylead to unnecessary technological lock-ins.

17

CHAPTER 1 GENERAL INTRODUCTION(iii).

Empirical evidence regarding the impacts of weather and climate on
the power sector is scarce.
As discussed in Section 1.3.3 above, previous research has provided
projections regarding climate change impacts on the power sector,
mostly by employing simulation models. Nevertheless, there havebeen few empirical investigations about the historical effects ofweather and climate on the power sector. Such an investigation on
the entire value chain of the power sector will add to theunderstanding of the risks posed by severe weather events andgradual climate change on electricity supply and demand.
Moreover, existing literature on climate change impacts on the
power sector focuses mainly on electricity infrastructure indeveloped countries, making potential impacts on the power sector
in developing countries unexplored (Audinet et al., 2014).
Accordingly, the IPCC AR5 acknowledges the scarcity of
publications regarding climate change impacts, adaptation, andvulnerability in developing countries (Field C.B. et al., 2014).
Finally, since electric utilities are responsible for ensuring thesecurity and reliability of electricity supply, the impacts of climate
variability and change directly affect the utilities’ business activities(Audinet et al., 2014). As such, this calls for investigations on direct
damage costs suffered by utilities, as well as their responses to
disruptive weather and climate, which could be further used toestimate the costs of climate change and the benefits of adaptation.

(iv).

Integrated analysis of climate change mitigation and adaptation of
the power sector remains a challenge.
The IPCC AR5 identifies several challenges in managing trade-offs
and synergies between mitigation and adaptation. These include
suitable tools for an integrative assessment, stable governancestructures, as well as an adequate capacity to design and deploy
integrated responses (Pachauri et al. 2014). The report also18

CHAPTER 1 GENERAL INTRODUCTION

highlights climate change impacts and the adaptation responses ofthe energy system as a research gap, urging their integration into
the assessment of the climate stabilization path (Clarke et al., 2014).
Section 1.3.1 and 1.3.3 have presented studies that are concernedwith climate change mitigation in the power sector and the impactsof climate change on the power sector, respectively. Nevertheless,these studies do not explicitly link climate mitigation efforts of thepower sector with the projected impacts of climate change to thesector, let alone asses their multiple effects on the future power
sector.
A separate analysis of climate change mitigation, impacts, andadaptations not only jeopardizes the power sector’s security andreliability, but it also underestimates the investment needed for
expanding the power sector, which is inevitable, especially for the
global South. Furthermore, climate change impacts could influence
the effectiveness of mitigation options, among other things.
Therefore, it is crucial to consider both climate change mitigation as
well as adverse impacts and adaptations when analyzing a powersystem expansion. This requires a clear set of modeling scenariosthat consider technological changes when assessing climate change
mitigation targets, in addition to the consideration of future climatic
regimes when examining climate change adaptation goals.

1.5. Main goal, research questions, and approach
1.5.1.

The main goal and research questions

The main goal of this doctoral research is to assess the triple nexus ofelectrification, climate change mitigation, and the climate change
adaptation objectives of the power sector. To understand how the power
sector may balance these triple objectives, this dissertation addresses thefollowing four core research questions (RQ):

19CHAPTER 1 GENERAL INTRODUCTION

RQ1: How could the power sector align electrification and the Paris
Agreement goals?
RQ2: How could technological change affect the deployment of low-carbon
technologies?
RQ3: How do severe weather and gradual changes in climate variables
affect the power system? What are the current adaptation practices?
RQ4: How can one integrate climate change impacts into power system
expansion modeling? How might this affect electrification and climatechange mitigation goals?
1.5.2.

Research approachI address these research questions by employing a set of methodologicalsteps, as depicted in Fig. 1.2. Modeling is pursued with LEAP and WEAP
(Water Evaluation and Planning system) software tools, while extensive
data collection is conducted through interviews and focus group
discussions (FGDs) with stakeholders from different elements of thepower sector supply chain. Thereafter, I gradually advance the current
modeling practice by sequentially taking into consideration endogenous
technological learning as well as climate change impacts and adaptations(the blue arrows of Fig. 1.2).
The LEAP simulation model:
I use LEAP to assist in answering RQ1, RQ2, and RQ4. LEAP is a tool toevaluate the entire energy system. The essential features of LEAP for
addressing the primary goal of this dissertation includes its support foralternative scenario projections, its least-cost optimization modeling of
power system expansion, and the calculation of CO2 emissions.
LEAP consists of three modules: I. Demand, II. Transformation and III.
Resources modules (the black boxes of Fig. 1.2). The power systemsimulation, called the electricity generation module in LEAP, belongs to
the transformation module. The electricity generation module simulates
electricity supply to satisfy the given demand, based on various input
20

CHAPTER 1 GENERAL INTRODUCTION

parameters. Accordingly, the resources module calculates the required
fuel to generate electricity simulated in the transformation module. The
model outputs, which are of most interest given the goal of thisdissertation, is the added capacity and electricity generation for eachtechnology, CO2 emissions, and costs.
Fieldwork andliterature reviews

Climate change
impacts on thermal
power plants andsolar PV

Characteristics
of electric power
technologies

WEAP results:
Impacts ofclimate change
on hydropower

LEAP demand
module (I):Electricity demand
projection

Climate change
impacts onelectricity
demandLEAPtransformation
module (II): Powersystem simulation

Results:• Capacity
addition• Electricity mix• Total costs• CO2 emissions

LEAP resources
module (III): Fuelrequirement
calculation

Endogenous
technological learningchangeFig. 1.2: Major analytical components and methodological approach
The simulation of electricity generation consists of three steps. First,
LEAP calculates the capacity expansion required to satisfy the demand
and the capacity reserve of the power system. The outputs of thiscalculation are the capacity added each year and the composition of21

CHAPTER 1 GENERAL INTRODUCTION

technology (capacity mix). Second, LEAP dispatches electricity from eachprocess in accordance with the annual demand and the load curve. The
output of the second step is the annual electricity production from eachprocess. Three, the resource module calculates the primary energy
required to generate electricity based on the fuel efficiency of eachtechnology. Additionally, LEAP calculates the power system’s total costs
based on the costs’ input data. Moreover, LEAP includes a technology
and environmental database that allows the calculation of CO2 emissions
from the electricity production based on the IPCC Tier 1 emission factor.
In LEAP, the optimal solution is defined as the power system with thelowest total net present value of the total costs over the entire period of
calculation (from the base year to the end year). Thus, the optimization
setting works through integration with the Open Source Energy Modelling
System (OSeMOSYS). LEAP automatically writes the data files requiredby OSeMOSYS, making use of the same data that were input into LEAP.
The results of the optimization are also read back into LEAP so that all
relevant results can be viewed in LEAP. In turn, OSeMOSYS depends ona solver software tool for developing decision optimization models. Due tothe complexity of the simulations performed in this doctoral research, amore powerful solver, namely the CPLEX optimizer, which is a software
toolkit developed by IBM, was used instead of the LEAP built-in GNU
Linear Programming Kit.
The WEAP simulation model:This dissertation employs WEAP for evaluating the impacts of climatechange on water availability for hydropower; thus, it supports addressing
RQ4. WEAP is a software tool that was also developed by SEI and as such,
WEAP and LEAP are sister tools that share many of the same design
features and approaches. Furthermore, WEAP has a built-in link with
LEAP, allowing the integration of WEAP outputs into the system-wide
LEAP model. WEAP can simulate water demand, flows, and storage, as wellas pollution generation, treatment, and discharge (Sieber, 2019). The
22

CHAPTER 1 GENERAL INTRODUCTION

essential features of WEAP employed for the goal of this dissertation are
its capability to simulate the water demand and supply of a river basin,
taking into account climate variables and competing uses of water.
A WEAP application is initialized by defining the scope of the study, suchas the timeframe, spatial boundary, and system components. The present
situation regarding water demand and supply is entered into the “Current
Accounts” tab. As such, alternative sets of scenarios are developed basedon various assumptions; for example, future climates. These scenarios canbe evaluated with regard to water sufficiency, cost, and environmental
impacts.
WEAP simulation results indicate water availability for hydropower under
future climates, and further determines the availability2 of the hydropower.
This data becomes the input for the power system expansion model,
allowing for an assessment of the impacts of climate-induced hydropower
availability on the power system as a whole.
Fieldwork for data collection:
To address RQ3, I carried out fieldwork from February to March 2018,
collecting data regarding climate change impacts on the Indonesian power
sector and the sector’s adaptation responses. The fieldwork involved indepth semi-structured interviews and FGDs with representatives of threeelectric utility companies, covering three head offices, ten power
generation plants, the Java-Bali grid operator (load control center), twotransmission offices, and two distribution offices. Thus, I interviewed
stakeholders that were representative of the various stages of theelectricity supply to acquire primary data on the current adverse impacts
and adaptation practices in these power sector facilities.

2

Availability in LEAP is defined as the ratio of the energy produced for a given period
to what would have been produced if the process ran at full capacity for a given
period (expressed as a percentage)

23

CHAPTER 1 GENERAL INTRODUCTION

The power plants being chosen are those major ones in the Java-Bali
power system, which account for 35% of the Java-Bali power generation
capacity. Meanwhile, the T&D offices where I conducted interviews and
FGDs are responsible for 65% and 44% of Java-Bali T&D assets,
respectively. The interviews and FGDs are supplemented with secondary
data, consisting of utilities’ internal reports and published energy sector
information that was used to validate and triangulate the results derived
from the interviews and FGDs. The secondary data for T&D networks
include all T&D assets of the Java-Bali power system. Hence, thisdissertation covers the entire Java-Bali T&D networks.
A set of questionnaires, which were structured differently for each target
group, were used as guidance for conducting interviews and FGDs. The
questionnaires were developed based on the results of a literature review
on climate change impacts and adaptations in the energy sector.

1.6. Structure of the dissertation
This doctoral dissertation consists of six chapters. Apart from the General
Introduction and the Synthesis chapters, there are four research chapters,
each dealing with one specific research question (Fig. 1.3). While Chapter
2 and Chapter 3 give attention to climate change mitigation of the power
sector, Chapter 4 focuses on climate change impacts and adaptations.
Finally, Chapter 5 brings the two parts together within an integrated
analysis framework. The remaining parts of this dissertation proceed asfollows:
Chapter 2 analyzes the trade-offs between satisfying the Java-Balielectrification goal with achieving the power sector’s emission reduction
target under the Paris Agreement. Before discussing the method andanalysis results of the electrification and climate change mitigation nexus,
the chapter elaborates on various energy models and presents thevalidation of the Indonesian LEAP model.

24

CHAPTER 1 GENERAL INTRODUCTION

Chapter 3 explores various scenarios of the Java-Bali power system
expansion to achieve the targets of renewable energy share in 2025 and2050. This chapter elaborates on how endogenous technological learning
could be integrated into the LEAP cost function and shows its effect on
the deployment of renewable energy, and subsequently, on CO2 emissions
and costs.
Chapter 4 reports the empirical evidence concerning the effects ofweather and climate on the entire elements of the power sector. The
chapter also provides estimates of direct losses suffered by electric power
utilities as well as adaptation measures taken by the utilities to deal withweather- and climate-related disruptions.

Dissertation main goal: to assess the triple
nexus of electrification, climate change
mitigation, and climate change adaptation
objectives of the power sector

Chapter 1: General Introduction
Chapter 2: Analysis of the trade-offs between electrificationand climate change mitigation objectives using LEAP
Chapter 3: An advancement of LEAP modelling methodology byintegrating ETL into the LEAP cost function to assess theimplementation of renewable energy policy targets
Chapter 4: An investigation of empirical evidence of weather
and climate effects on the power sector through extensive
fieldwork
Chapter 5: A further extension of LEAP practice through an
integration of climate change impacts on electricity demandand supply, into a power system expansion modeling to assess
the triple nexus of electrification, climate change mitigation,and climate change adaptation in the power sector

Chapter 6: Synthesis of the dissertation

Fig. 1.3: Outlines of the doctoral dissertation

Chapter 5 examines an interplay between electrification, climate change
mitigation, and climate change impacts and adaptation in the power sector.
Accordingly, adverse climate change impacts on various types of power
25

CHAPTER 1 GENERAL INTRODUCTION

generation and on electricity demand are incorporated into the simulationsof the power system expansion, using LEAP. Hence, the chapter indicates
the triple nexus of electrification, climate change mitigation, and climatechange adaptation in expanding the Java-Bali power system.
Chapter 6 provides the synthesis of the findings of this dissertation anddiscusses its innovative contributions as well as its policy implications.
The chapter ends with outlining limitations and perspectives for future
research.

26

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

Abstract
The power sector in many developing countries face challenges of fastrising electricity demand in urban areas and the urgency of improved
electricity access in rural areas. These development needs are challenged
by the vital goal of climate change mitigation. This chapter investigatesplausible trade-offs between electrification and CO2 mitigation in adeveloping country context, taking Indonesia as an example. By employing
LEAP, this chapter incorporates Indonesia’s NDC pledge into the modelingof capacity expansion of the Java-Bali power system in Indonesia. Firstly,
the LEAP model is validated using historical data of Indonesia’s powersystem. Secondly, four scenarios of the Java-Bali power system
expansion from the base year 2015 through to 2030 are developed andanalyzed. Results indicate that the shift to natural gas (NGS scenario)
decreases future CO2 emissions by 65 million tons, helping to achieve theCO2 mitigation target committed to. Likewise, an escalation of renewableenergy development (REN scenario) cuts the same amount of the
projected CO2 emissions and, thus, assures meeting the target. The cost
optimization simulation (OPT scenario) attains the targeted emission
reduction, but at 18% and 12%, lower additional costs compared to NGS
and REN, respectively. The cost-effectiveness of CO2 mitigation scenarios
ranges from 36.5 to 44.7 US$/tCO2e.

This chapter is based on a journal article:
Handayani, K., Krozer, Y., & Filatova, T. (2017). Trade-offs between
electrification and climate change mitigation: An analysis of the JavaBali power system in Indonesia. Applied Energy, 208.
29

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

2.1. Introduction
Electricity is vital to society today. Global electricity demand in the periodof 2002-2012 increased by 3.6% annually, exceeding the annual population
growth for the same period (IEA, 2015a). However, nearly 1 billion people
worldwide today do not have access to electricity (IEA, 2018b), making theprovision of universal access to electricity a vital development objective.
Yet, fossil fuel-based electricity production causes GHG emissions
measured in CO2 equivalents (CO2e). Since 2000, GHG emissions haveincreased 2.4% a year, reaching 49 GtCO2e in 2010 (IEA, 2015b), out of
which 25% came from electricity and heat production (IPCC, 2014).
The Paris Agreement requires all parties to communicate their Intended
Nationally Determined Contributions (INDC), later converted into
Nationally Determined Contributions (NDC). Around 99% of thecommunicated INDCs cover the energy sector (UNFCCC, 2016).
Accordingly, they need to incorporate their Paris target into nationalenergy planning. Developing countries, in particular, need to align the Paris
Agreement target with their vital national goals of nationwide
electrification. This research chapter addresses the question of if and how
Indonesia may satisfy the growing electricity demand while still meetingclimate mitigation targets. Aligned with the 2015 Paris Agreement,Indonesia aims to reduce its GHGs by up to 29% against business as usual,by 2030. Over and above this, an additional 12% reduction is intended withinternational cooperation3. In the meantime, more than 14 million
Indonesians still do not have access to electricity (IEA, 2018b).
This chapter considers both objectives of electrification and climatechange mitigation in the simulation of capacity expansion of the mostextensive power system in Indonesia, namely the Java-Baliinterconnected power system. This chapter analyzes various scenarios of

3

The Indonesian voluntary pledge is written in its first NDC document submitted to
the United Nations Framework Convention on Climate Change (UNFCCC) in 2016.

30

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

future power generation in the Java-Bali power system between 2016 to2030, employing LEAP and a unique dataset from PLN. LEAP is selected
over other software tools to suit the modeling needs of this dissertation,
through a systematic screening process. Despite the fact that LEAP is
actively used –in 85 UNFCCC country reports (Khan et al., 2011) and in
more than 70 peer-reviewed journal papers (Connolly et al., 2010)–,
publications explicitly discussing the LEAP model validation are limited.
First, I set up the Indonesian LEAP model and run it from the base year
2005 through to 2015. Then, model results are validated against thehistorical data of the national capacity addition, electricity production, andCO2 emissions throughout 2006 through to 2015. Secondly, I develop
scenarios for future power generation in the Java-Bali power system andanalyze the changes in resource utilization and technology deploymentthat respond to the Paris pledge with 10 power generation alternatives,
namely: ultra-supercritical (USC) coal, natural gas combined cycle(NGCC), natural gas open cycle (NGOC), large and small hydroelectric
power plant (HEPP), hydro pumped storage (hydro P/S), geothermal, solar
photovoltaic (PV), wind power, and biomass.
The chapter adds a number of innovative contributions to the body of theenergy modeling literature. Firstly, to the best of the author’s knowledge,
this chapter is the first to analyze scenarios of power system expansion,
which take into account the energy sector’s actual CO2 mitigation targets
associated with the Indonesian pledge to the Paris Agreement. This
chapter assesses the consequences of the climate mitigation policy on
the Indonesian power sector using a validated model and zoom into thelevel of individual technologies (a bottom-up approach), rather thanemploying a macro-economic approach. Secondly, this chapter uses aunique dataset from PLN that represents the historical technicalperformances of every individual power plant in the Java-Bali powersystem of 64 plants. Such a dataset enables more accurate settings forsome of the crucial LEAP input parameters, including each plant’s net

31

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

capacity and capacity factor4. Thirdly, this chapter is transparent on the
LEAP validation procedure by using ten years of Indonesian electricity
supply and demand data. As such, the study lays out an easy-to-replicate
method for assessing power sector pathways with regard to the Paris
Agreement in other developing countries.
The remainder of this chapter is organized as follows: Section 2.2 is theliterature review on the decarbonization of the power sector; Section 2.3
presents the methodology and data, including validation of the LEAP modeland scenarios development of the Java-Bali power system expansion;
Section 2.4 provides the results of LEAP simulations; Section 2.5
discusses the main findings; Section 2.6 concludes the chapter.

2.2. Paris Agreement and decarbonization of the power sector
A number of studies discuss decarbonization of the power sector indeveloping countries and the implications of their commitments to theParis Agreement. Grande-Acosta and Islas-Samperio (2017) present analternative scenario for the Mexican power sector by assessing various
mitigation options both on the demand and supply sides. Their study
concluded that the alternative scenario assures Mexican compliance with
the Paris Agreement. However, the scenario entails an additional
investment of 2 billion US$/year over the analysis period. Likewise, Dalla
Longa and van der Zwaan (2017) analyzed the role of low carbon
technologies in achieving Kenya’s CO2 mitigation target under the Paris
Agreement. One conclusion of this study is that the deployment of thesetechnologies raises the energy system costs in 2050 ranging from 0.5% to
2% of the country’s GDP. Kim and Park (2017) investigated the impact ofSouth Korea’s INDC on the power system and electricity market in Korea.
The study revealed that the implementation of INDC causes an increasein the electricity price by as much as 8.6 won/kWh. Another study of the4

Capacity factor is defined as the ratio of an actual electricity generation over a given
period of time to the maximum possible electricity generation over the same periodof time (U.S. NRC, 2017)32

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATIONSouth Korean power sector (Lee and Huh, 2017) assessed the impact ofnational policies triggered by the Paris Agreement –i.e., renewable portfolio
standard and feed-in-tariff– on the diffusion of renewable electricity. The
study confirmed that the policies indeed influence renewable electricity
diffusion. Meanwhile, in response to the Paris Agreement, China needs toradically decarbonize its power sector, which will create unintended
consequences, such as the disturbance in stability and integrity due tointermittent renewable energy generation (Guo et al., 2017). Thus, Guo etal. (2017) present an analysis of the decarbonization of the Chinese power
sector, taking into consideration these temporal variations. This study
found that the inclusion of temporal variations resulted in a significant
difference in terms of installed capacity and load factors when comparedto the standard model. Wan et al. (2016) assess the impacts of Parisclimate targets on water consumption of the power sector in major
emitting economies, which include Brazil, China, India, US, EU, and Russia.
The study discovered that the fulfillment of long-term climate targets
would increase water consumption of power sector, when compared to thebusiness as usual pathways, particularly in the case of China and India.
Studies on the decarbonization of the Indonesian power sector areavailable in the literature. Marpaung et al. (2005) used a decomposition
model to examine two factors (i.e., the technological substitution effectand the demand side effect) that affect CO2 emissions by considering an
influence of external costs on the development of the Indonesian power
sector for the period of 2006 to 2025. This study concluded that increasing
external cost at a high level allowed for technological substitution, which
led to CO2 emissions reduction by up to 82.5%. Meanwhile, at the low to
medium external costs, CO2 emission reduction was mainly due to thedemand-side effect. Shrestha et al. (2009) used an input-output
decomposition approach to analyze factors that affect economy-widechange in CO2, SO2, and NOx emissions in the case when the Indonesian
power sector employs integrated resource planning (IRP) approach, rather
than traditional supply-based electricity planning (TEP). The IRP approach33

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

resulted in CO2, SO2, and NOx emission reductions of 431, 1.6, and 1.3
million tons, respectively, during 2006-2025, as compared to that under
the TEP approach. Rachmatullah et al. (2007) used the scenario-planning
method to devise a long-term electricity supply plan (1998-2013) of theJava-Bali power system, which included analysis of CO2 emissions. This
study showed that 15% of CO2 emission could be reduced at an abatement
cost of around US$2.8–4.0 per ton. Wijaya and Limmeechokchai (2010)
introduced low carbon society actions into the long-term Indonesian
power system expansion planning (2007-2025), using the LEAP model.
This study concluded that the low carbon society actions reduced external
cost by 2 billion US$ as compared to the conventional electricity
expansion planning. Purwanto et al. (2015) developed a multi-objective
optimization model of long-term electricity generation planning (20112050) to assess the economic, environmental, and energy resources
adequacy. This study revealed that the power system scenario with highorientation on environmental protection became the most sustainable
scenario, yet lacked in terms of Reserve to Production Ratio (R/P) andcost-related indicators. Kumar (2016) investigated the effects of different
renewable energy policy scenarios on CO2 emissions reduction, employing
LEAP. The results showed that the utilization of the Indonesian renewableenergy potentials reduced up to 81% of CO2 emissions when compared tothe baseline scenario. All of these studies analyzed the long-term powersystem expansions and their associated CO2 emissions. However, they did
not set specific targets on CO2 mitigation as a constraint in the simulationsof future power generation.
A few studies set a specific target for CO2 emission in the Indonesian
power sector, and simulated patterns of power supply to meet the targets
set. Marpaung et al., 2007 developed a general equilibrium model, i.e., AsiaPacific Integrated Model (AIM)/Enduse for Indonesia, and analyzed theeffects of introducing CO2 emission targets on the technology mix of theIndonesian power sector during 2013-2035. The study concluded that the
deployment of carbon capture and storage (CCS), biomass, and wind34

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

power technologies contributed significantly to achieving the targets. Das
and Ahlgren (2010) analyzed the CO2 reduction targets scenario for the
long-term Indonesian power system expansion planning (2004-2030) using
MARKAL. The results showed that constraints on CO2 emission invokedchanges in technology mixes. Similarly, Stich and Hamacher (2016) applieddifferent levels of CO2 emission reduction targets to the optimized power
supply in Indonesia. Their results demonstrated that the CO2 emission
constraints boosted geothermal power expansion to replace the coal-fired
power generation. Finally, Siagian et al. (2017) used AIM to simulate energy
sector development over the period 2005-2030 under CO2 constraints
stipulated in a draft policy document. Their study indicated carbon prices
of US$16 and US$63 (2005)/tCO2 under 17.5% and 32.7% CO2 reduction
scenarios, respectively.
Neither of these studies is associated with the prevailing Indonesian policy
as stipulated in the Nationally Determined Contribution (NDC) as
submitted to UNFCCC after the Paris Agreement came into force.
Furthermore, an analysis using a bottom-up model that allows
considerations of technological complexity in the power sector is still
missing. This chapter fills this gap by incorporating the actual energy
sector’s mitigation targets - i.e., 11% CO2 reduction on its own effort and
14% CO2 with international support - into power capacity expansion using
a technology-rich energy system model. Hence, the outcomes of thischapter can be used as a reference when formulating the legal framework
for curbing CO2 emissions in the power sector.

2.3. Methodology and data
2.3.1.

Review of available models and selection of an appropriate model

Rigorous models for analyzing long-term planning are necessary to support
any optimal allocation of investments. While numerous energy systemmodels are developed and used worldwide (Evans and Hunt, 2009), not all
are suitable for performing a technological, economic, and environmental
analysis of the national power sector. Consequently, this calls for the35

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

careful selection of a model. Urban et al. (2007) provided a comparison of
12 models in terms of their suitability for applications in developing
countries. The study argued that the characteristics of an energy system
in developing countries differed from those of developed countries. They
found that the energy-related characteristics of developing countries,
such as supply shortages, urbanization, and low electrification rate, were
neglected in many models. Bhattacharyya (2010) provided an assessmentof 12 energy system models suitable for analyzing energy, environment,
and climate change policies in developing countries. This study suggested
that the accounting-type models were more relevant for developing
countries because of their ability to capture rural-urban differences,traditional and modern energy technologies as well as non-monetarytransactions. Connolly et al. (2010) reviewed energy system models to
identify ones suitable to analyze the integration of 100% renewable energy
into various energy systems. The paper presented a brief summary of 37
models, most of them included the power sector in their analysis. However,they varied significantly in their purpose. Based on these three review
papers, this chapter filters and identifies energy system models that areopen access and suitable for analyzing the technical, economic, andenvironmental parameters of a long-term power system expansion in adeveloping country. The detailed inventory of models and screening
criteria are presented in Appendix 2.A (Table 2A.1).
This screening produced a shortlist of 30 models as being potentiallysuitable for the analysis in this chapter (Appendix 2.A, Table 2A.2). The
screening results in the LEAP model being chosen for several reasons.
Firstly, it can accommodate the technological complexity of the powersystem. Secondly, LEAP is freely accessible to students, as well as nonprofit and academic institutions in developing countries. This increases
the chances of reproducing and further developing this analysis beyond
the efforts in the current study, as and when new data and policy
considerations become available. In addition, LEAP is very user-friendly
and provides open-access online support for its users. Finally, LEAP is36

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

considered as a popular energy model, as it has been used in 190 countries
and hosts more than 42,000 members in its online platform (Heaps, 2017).
It makes results comparable across countries, which is especially relevant
when major international agreements such as the Paris accord areconsidered.
Indeed, LEAP has been used in many studies analyzing CO2 mitigation in
the power sector worldwide. The LEAP model is employed to explore
various scenarios of the energy system development in Taiwan (Yophy etal., 2011), China (Tao et al., 2011), Iran (Amirnekooei et al., 2012), Panama(McPherson and Karney, 2014), Maharashtra, India (Kale and Pohekar,2014), Pakistan (Perwez et al., 2015), and Africa (Ouedraogo, 2017). LEAP
has also been actively applied to study the energy and CO2 impacts of apower system expansion with a special focus on a particular energy sourcesuch as landfill gas in Korea (Shin et al., 2005), nuclear in Korea and Japan(Kim et al., 2011; Takase and Suzuki, 2011) and renewable in Southeast
Asia (Kumar, 2016). None of these studies combined the accounting andoptimization settings in LEAP for the analysis. Yet, the features provide
for different types of analysis, which is essential for a choice of a robust
policy. On the one hand, the accounting method in LEAP can be used torepresent future power systems based on various policy scenarios,
enabling the comparison of energy, environmental and economic impactsof various energy policies. This method provides answers for a “what if”
type of analysis. On the other hand, the optimization method in LEAP
optimizes investment decisions and, thus, provides a picture of what could
be the optimal pathway of the power system development. This chapter
employs both accounting and optimization methods from LEAP to analyse
scenarios of CO2 mitigation in the Java-Bali power system.

37

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

2.3.2.

Validation of the Indonesian LEAP model

2.3.2.1. Model setup
I started by setting up a LEAP model of the Indonesian power system fromthe base year of 2005. After which, I simulated the expansion of PLN’s
power generation capacity from 2006 to 2015. According to the PLN
statistics, coal steam turbine (CST), NGCC, NGOC, diesel generator,hydropower, geothermal, and solar power were the leading technologies
employed during this time. CST and NGCC expanded significantly during
this period, i.e., 72% and 18% of the total capacity addition respectively,
leaving the remaining 10% of the capacity addition shared between therest. Thus, the Indonesian LEAP model endogenously simulates CST and
NGCC for capacity expansion, while the other technologies are
exogenously added.
Table 2.1 lists the type of model outputs, as well as the employed input
data and the data used for validation of the Indonesian LEAP model. The
input data for electricity demand is the actual values of the national
electricity consumption from 2005 to 2015. The power systemcharacteristics such as transmission losses and a load shape as well as
technological characteristics are also based on the actual values.
Meanwhile, the data used for validation of the model results consists ofhistorical electricity generation, past capacity expansion of eachtechnology, and actual fuel consumptions. Appendix 2.B presents detailed
information regarding parameters for the Indonesian LEAP model
validation.
As mentioned in Section 2.3.1, LEAP has two different settings for
simulating electricity generation: accounting settings and optimization
settings. Table 2.2 explains the two alternative settings with regard to thecapacity addition and electricity dispatch. I compared the simulation
results from both of these settings with the actual data of the year 20052015 to validate the model performance.

38

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION
Table 2.1: Input-output parameters in the Indonesian LEAP model and data
used for validation
Input parameter
Model outputs
Data used for validation- Electricity demand- Electricity- Historical(2005-2015)a
generation (2005electricity
2015)
generation (2005- Transmission &2015)adistribution losses- Capacity added(2005-2015)a(2006-2015)- Historical capacity
addition (2006- Reserve margin*- Technology mix of2015)a(2005-2015)b
the capacity added- Actual technology- Load shapec- Fuel requirementmix of the capacity- Power generation- CO2 emissions
addedacapacitiesa- Fuel consumptiona- Fuel efficiencyd- CO2 emissionsf- Investment cost ofeach technologye- Operation cost of eachtechnologya*
Reserve margin is the percentage of reserve capacity relative to the capacity
needed to meet the normal peak demand. Source: aPLN Statistics 2005-2015;b
Calculated based on peak load and capacity data in PLN Statistics 2005-2015;c
Drawn based on hourly load data of 2015 (P2B, 2016a); dCalculated based on
actual electricity production and fuel consumption in 2005 (PLN, 2006); ePLN
(2005b); fCalculated using IPCC Tier 1 emission factor.
Table 2.2: Two alternative simulation settings in LEAP
Accounting Setting

Optimization Setting

Step 1: Capacity
addition

LEAP controls when to add
new supply capacity basedonannualelectricity
demand.

LEAP controls the type of
new supply capacity to be
added and when it will be
added based on annual
demand and cost optimization.

Step 2: Electricity
dispatch fromeach type of
power supply

LEAP controls the dispatchof electricity from eachprocess based on a userdefined merit order.

Driven by cost optimization,
LEAP controls the dispatch ofelectricity from each process.

39

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

2.3.2.2. Validation Results
The validation results show that LEAP calculated the total capacity added(Step 1 in LEAP) over the period of 2006 through to 2015 accurately(Table 2.3). LEAP slightly underestimated the added capacity, whencompared to the empirical data under both the accounting andoptimization settings: by just 0.7% and 1.8%, respectively. As a result, in
this case, the accounting setting was found to be more reliable, i.e., 99.3%
accuracy when compared to the 89.2% accuracy of the optimization
settings.
Table 2.3: A comparison of the estimated national capacity added in 2006-2015
with the actual data
Capacity
addition
Cumulative
2006-2015
Technology mix:
Coal
Natural gas

Empirical
data

LEAP estimates
Accounting
Settings

Difference

Optimization
Settings

Difference

15.6 GW

15.5 GW-0.7%

15.4 GW

-1.8%

81%
19%

81%
19%

0%
0%

80%
20%

-1%
+1%

The model results also accurately represented the actual technology mixof the capacity added over the period 2006-2015 with 100% and 99%
accuracy in accounting and optimization settings, respectively. The
optimization setting results, which accurately reproduced the technology
mix, indicate that the Indonesian power sector development during thisperiod, was based on the least-cost principle. This is in line with the PLN’s
capacity expansion policy as stipulated in the Electricity Supply Business
Plan (RUPTL) (PLN, 2005).
The results of total electricity production – Step 2 in LEAP – and GHG
emissions are shown in Table 2.4. The calibrated LEAP model calculated
precisely the total electricity production in the period between 2006-2015,
both in the accounting and optimization settings, i.e., 100% and 99.93%
accuracy, respectively. Meanwhile, the model overestimated the CO2
40

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

emissions by 2.6% and 2.5% in the accounting and optimization settings,
respectively.
Table 2.4: A comparison of the estimated national electricity production and
GHG emissions with the actual data
Electricity
production and
GHG emissions(2006-2015)
Cumulativeelectricity
production

Empirical
data

LEAP estimate
Accounting DiffeSetting
rence

Optimization
SettingDifference

1,398
terawatthour/TWh

1,398 TWh0%

1,397 TWh-0.07%Cummulative
GHG emissions

1030
MtCO2e1057
MtCO2e+2.62%

1056 MtCO2e+2.53%

Based on these results, I concluded that LEAP calculations were accurate.
However, as is the case with any other energy model, the simulations in
LEAP depend on input data and assumptions. Hence, it may generate
uncertainty in outcomes when long-run future perspectives are taken.
2.3.3.

Future scenarios development

The validation phase (Section 2.3.2) indicated that the LEAP model
estimates of the capacity added, technology mix, cumulative electricity
production, and CO2 emissions were reliable. I moved to scenario analysisof possible future developments of the Java-Bali power system. I
developed four scenarios of capacity expansion for the Java-Bali powersystem. The scenarios were developed based on the changes in LEAP
assumptions on the choice of power generation technologies and types ofenergy sources. All scenarios aimed to reduce CO2 emissions in line with
the Indonesian pledge to the Paris Agreement.
The first scenario (reference) represents the continuity of the present
trend that sets the benchmark. The second and third ones –namely:
natural gas scenario and renewable energy scenario, respectively– follow
The National Energy Policy 2014 (NEP) (Government of The Republic of41

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

Indonesia, 2014). NEP aimed to increase the use of natural gas and new5and renewable energy (NRE) to attain a minimum of 22% and 23% shares,
respectively, in the national energy mix by 2025. The NRE target in NEP
includes nuclear. Nonetheless, NEP emphasizes that nuclear is the least
preferable option. These three scenarios use the accounting settings in
LEAP, which enable the analysis of different paths of the future of power
supply, based on different policy assumptions considering the realistic
constraints Indonesia currently faces. Meanwhile, the fourth scenario(optimization scenario) uses the optimization setting of LEAP to find theleast-cost solution for the capacity expansion and electricity dispatch. As
such, this scenario does not account for the realistic policy constraints
per se but instead serves as a normative benchmark from the cost
minimization perspective.
The objective of all scenarios is to meet the growing electricity demandof the future while fulfilling the Indonesian commitment under the Paris
Agreement. According to the Indonesian First NDC document, Indonesia
is committed to reducing 29% of GHG emissions by 2030 (unconditionaltarget/CM1), compared to the business as usual scenario (Government of
The Republic of Indonesia, 2016). A more ambitious target is set at 41%
reduction (conditional target/CM2), subject to the availability of financial
support from developed countries. Given 29% and 41% targets, 11% and14%, respectively, should be reduced by the energy sector alone. Thesetargets are lower when compared to those of the forestry sector, whichare 17% (own effort) and 23% (with international support). This is expected
as the forestry sector6 contributed 51% of total emissions over the periodof 2000-2012, while the energy sector contributed 32% over the same
period (Government of The Republic of Indonesia, 2015b). With no
mitigation policy, the energy sector alone is expected to produce up to
1,669 million tCO2e emissions in 2030 (Government of The Republic of
5

The term “new energy” is defined as energy that is stemmed from new technologies
such as nuclear and hydrogen (Government of The Republic of Indonesia, 2014)
6
Including land use change

42CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

Indonesia, 2015b). These emissions consist of emissions from power,transportation, and other energy sub-sectors. The emission reduction
targets for the energy sector are 314 million tons (CM1) and 398 million
tons (CM2) or equivalent to 19% and 24% of the energy sector’s baseline
emission in 2030, respectively, under CM1 and CM2. Yet, the NDC
document does not specify the CO2 emission baseline for the power sector
alone. Hence, for all practical purposes, I assume that the power sector
contributes proportionally to the national energy sector target.
Accordingly, I assume that the Java-Bali power system should reduce its
emissions at least at the same pace as agreed upon at the national level.
Thus, 19% and 24% by 2030 are the targets for setting up the scenarios.
To quantify possible pathways of reaching these competing targets, I
specify four scenarios of the Java-Bali power system expansion for theperiod 2016-2030 as follows:(i). Reference scenario (REF): In the year 2015, coal-fired power plants(CFPPs) were the dominant technology in the Java-Bali power system.
The net capacity of the coal power plants was 17.3 MW or 55% of thetotal capacity in the Java-Bali power system (P2B, 2016a). The natural
gas power plant constituted 34% of the total capacity, while therenewable capacity share was only 11%, which consisted of geothermal(8%) and hydro (3%). Biomass, wind power, and solar PV technologies
were not present in the 2015 capacity mix. The REF scenario assumes
that the power system expands with the 2015 technology mix, which
persists until the end of the modeling horizon. This pathway does notincorporate any CO2 mitigation policies and continues the historical
capacity expansion policy based merely on cost-optimization (PLN,
2016b). The abundance of coal resources in Indonesia and the low costof coal technology leads to the lock-in with the coal technology along
the REF pathway.
(ii). Natural gas scenario (NGS): This scenario assumes an increasing rateof the natural gas power plant’s development. This is in line with the43CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATIONNEP 2014, which aims to increase the share of natural gas in thenational energy mix. The substitution of coal by ‘cleaner’ natural gas
is expected to reduce GHG emissions, due to the lower emission factor
of natural gas when compared to that of coal. I ran this scenario in two
versions. The NGS1 scenario aimed to achieve the 19% CO2 reduction
target compared to the REF scenario, relying entirely on own efforts
without any support from international partners. The NGS2 scenario
assumes international partners provide support to Indonesia, and thiscurbs the CO2 emissions by 24% compared to the REF scenario.
(iii). Renewable energy scenario (REN): This scenario assumes an increasein the development of renewable energy, which includes geothermal,hydro, biomass, wind, and solar PV. In this scenario, the power system
expansion maximizes the utilization of the renewable energy potentialof the Java-Bali islands to achieve the Paris target. This scenario is
in line with the NEP 2014, which aims to increase the use of new andrenewable energy in the national energy mix. The REN1 scenario aims
for the 19% CO2 reduction target and REN2 scenario for the 24% CO2
reduction target.
(iv). Optimization scenario (OPT): This scenario uses LEAP’s optimization
settings to obtain the least-cost solution for the Java-Bali power
capacity expansion while satisfying both the increasing demand andCO2 reduction targets. The least-cost solution in LEAP is defined as
the power system with the lowest total net present value of costs ofthe system over the entire time horizon. In the OPT scenario, alltechnologies that are included in the REF, NGS, and REN scenarios
are considered for capacity expansion. The OPT1 and OPT2 scenarios
assume 19% and 24% CO2 reduction targets, correspondingly.
2.3.4.

Input data

The data source and methodology to calculate the parameters of theJava-Bali power system are provided in Table 2.5. Rather than relying on
the generic LEAP default values, I use national and regional data. Thus,
44

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

most of the input parameters were collected from governmental reports
and PLN. Since PLN is the sole owner and operator of Indonesia’s T&D
networks, it also records and manages electricity dispatch from IPPs.
Table 2.5: Summary of model input parameter
Input Data

Value

SourceAnnual demand growth
2016-2030
T&D losses

6.8% - 7.5%

System load shape

Appendix 2.C

Reserve margin

35%

Capacities of existing
power plants
Merit order* in theaccounting setting:- Baseload power plants- Intermediate/peakload power plants
Environmental
parameter

Appendix 2.C

Refers to the RUPTL estimates (PLN,
2017b)
Refers to the draft RUKN estimates(KESDM, 2015)
Based on hourly demand data in 2014
(P2B, 2016b)
Refers to the RUKN criteria (KESDM,
2015)(P2B, 2016a)

Discount rate

12%7.9% – 8.5%

Maintained according to the P2B
dispatch order (P2B, 2016a)
Coal, geothermal
Natural gas, hydroPer technology

The IPCC Tier 1 default emission
factors, embedded in the LEAP
technology database (Heaps, 2017)
The discount rate used by PLN (JICA,
2010)

*

In the accounting setting of LEAP, merit order is the order in which a power plant
will be dispatched. **P2B = the Java-Bali load control center

The demand projection for the year 2016 to 2025 is calculated based on
the estimated demand growth in RUPTL (PLN, 2017b). Meanwhile, thedemand projection for the remaining years (2026 to 2030) was calculated,assuming that demand for electricity continues to grow at the same rate
as the estimated growth in the year 2026, i.e., 7%. It is assumed thattransmission losses will gradually decrease from 8.6% in the year 2015 to
7.9% in the year 2030 (KESDM, 2015). The energy load shape is drawn in
the LEAP model based on the historical hourly load data collected from
P2B, as shown in Appendix 2.C, Figure 2C.1 (P2B, 2016b). The planning45

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

reserve margin was set at 35% following the criteria in the draft National
Electricity General Plan (RUKN) 2015-2034 (KESDM, 2015).
The calculation of electricity generation in LEAP depends on the input ofthe projected electricity demand in the demand module (Chapter 1, Fig.
1.2). Thereafter, the electricity generation module in LEAP assigns
technologies to satisfy the electricity demand. One limitation in LEAP isthat the model does not provide for the expansion of T&D networks.
Hence, I assume that there are no constraints in the T&D networks,
meaning that the electricity supply can be transmitted at any time to any
T&D substation.
The type of existing power generation technologies in the Java-Bali powersystem includes CST, NGCC, NGOC, diesel generator, HEPP, andgeothermal. For capacity expansion, I consider adding unexploitedrenewable potentials, namely, biomass, wind turbine, and solar PV.
The existing coal power plants in Java-Bali use conventional technologies,
which have relatively lower efficiencies compared to the supercritical (SC)and USC technologies. According to RUPTL, to improve efficiency andreduce CO2 emissions, the future coal power plant’s development in theJava-Bali power system would only consider SC and USC technologies(PLN, 2016b). Thus, this dissertation only considers USC coal technology
for the new coal capacity addition. To date, there are no nuclear power
plants in Indonesia. In the future, the development of large-scale nuclear
energy supply is possible, as indicated in NEP. However, it is consideredas the last option after maximizing the use of renewable energy sources.
Therefore, this chapter assumes no nuclear deployment during the time
horizon of the simulations (2016-2030).
Technology data of the existing power plants in the Java-Bali system werecollected from PLN. These data include the power-generating capacity ofeach technology, planned retirement, heat rate, historical production, andcapacity factor. The accuracy of the existing power plant data is essential
to ensure reliable base year data, which was used as a reference for46

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

developing power expansion scenarios. The capacities of existing power
plants in the Java-Bali power system are presented in Appendix 2.C, Table
2C.1.
Meanwhile, technology data for future capacity expansion were retrieved
from various studies, as shown in Table 2.6. The cost characteristic ofpower generation technologies refers to the cost assumptions of the worldenergy outlook (WEO) model (OECD/IEA, 2017). Costs data that was notpresented in the WEO model 2016 were taken from the Indonesian Energy
Outlook (IEO) 2016, which relied on data from the International Energy
Agency (IEA) database of the WEO 2015. These parameters were assumed
to be constant throughout the entire simulation.
Fuel costs of coal and natural gas were taken from the PLN Statistics
2015 (PLN, 2016c), while biomass fuel cost was sourced from ASEAN
Energy Centre (ACE) study (ACE, 2016). The calculation of theresources/fuel requirements in LEAP depends on the outputs of theelectricity generation module. LEAP allocates resources needed for theelectricity generation in accordance with the fuel efficiency of eachtechnology.
It is essential to take into consideration the availability of the energy
resources for the Indonesian LEAP model, particularly concerning
renewable energy, as it can only be utilized locally. Hence, the simulationof renewable energy expansion takes into account the potential ofrenewable energy in the Java-Bali islands. For practical reasons, I assume
that the publicly available data for the Indonesian renewable energy
potential (Chapter 1, Table 1.2) can be exploited over the time horizon ofthis study. Furthermore, I assume that Indonesian natural gas reserves
could be utilized for power generation without any constraint.

47

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION
Table 2.6: Characteristics of technologies in the Java-Bali LEAP model
Technology

Lifetime(years)a

Efficiency(%)bMaximum
Availability(%)*cCapacity
credit(%)**dCapitalcost (2015
US$/kW)b

Fixed OM
cost (2015
US$/kW)b

Variable OM
Cost (2015
US$/MWh)c

Fuel coste (2015US$)

USC coal
30
44
80
100
1867
64
3.8
51.8 US$/ton
NGCC
25
57
80
100
817
24
3.8
7.6 US$/MMBTU
NGOC
25
38
80
100
439
21
3.8
7.6 US$/MMBTU
Hydro
35
100
41
51
2200
56
3.8
Mini hydro
35
100
46
58
3350
67
3.8
c
c
c
Hydro P/S
35
95
20
25
1050
54
3.8
Geothermal
20
10
80
100
2675
53
0.7
Solar PV
20
100
17
22
1953
20
0.4
Wind power
20
100
28
35
1756
44
0.8
h
Biomass
20
35
80
100
2228
78
6.5
11.67 US$/toniNote: kW= kilowatt; MWh = megawatt-hours; OM = operation and maintenance; MMBTU = million British thermal units.
*Maximum availability in LEAP is defined as the ratio of the maximum energy produced to what would have been produced if theprocess ran at full capacity for a given period (expressed as a percentage) (Heaps, 2017).
**Capacity credit in LEAP is defined as the fraction of the rated capacity considered firm for calculating the reserve margin,
which is calculated based on the ratio of availability of the intermittent plant to the availability of a standard thermal plant (Heaps,2017).
a
IEA and OECD/IEA (2015); bOECD/IEA (2017)w; cDEN (2016); dcalculated based on the ratio of availability of the intermittent plant
to the availability of a standard thermal plant (Heaps, 2017); ePLN (2016c); fRothwell and Rust (1997); gIEA and NEA (2015); hIRENA(2012); iACE (2016).

48

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

2.4. Balancing the Paris climate target and the Java-Bali
capacity expansion: LEAP results
2.4.1.

Reference scenario (REF)

In the REF scenario, the total capacity added during 2016-2030 is 63.8
GW. Thus, the power generation capacity in 2030 reaches 94.2 GW (Fig.
2.1.a). The capacity mix in 2030 is equivalent to the base year, when coal,natural gas, and renewables constituted 55%, 33%, and 12% of the total
capacity, respectively. A small portion of oil capacity (0.3%) remains
present in the capacity mix.
In 2030, the corresponding electricity production from coal increases from108 TWh in 2015 to 350 TWh. Coal contributes 75% of the electricity
production in 2030, while natural gas and renewable energy contribute 18%
and 7%, respectively (Fig. 2.1.b).
100

Gigawatt

80
60
40

(a)

20
0
2016 2018 2020 2022 2024 2026 2028 2030
Coal

REF 2030

Natural Gas

Oil

Geothermal
5%

Hydro

Hydro
2%

Geothermal

Oil0%
Natural Gas
18%

(b)
Coal
75%

Fig. 2.1: Reference scenario: Power generation capacity 2015-2030 (a);Electricity generation mix in 2030 (b)49CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATIONThe resulting CO2 emissions in 2030 are 339 million tCO2e or nearly
threefold of the emissions in the base year. These are the baseline for theCO2 reduction in the mitigation scenarios (NGS, REN, and OPT). Table 2.7
shows the CO2 emissions baseline and reduction targets.
Table 2.7: Emission reduction targets against REF scenario
CO2 emissions level in 2030
(REF), million tCO2e

CO2 reduction target
in 2030 (%)

CO2 emission reduction
in 2030, million tCO2e

339

19%

64.4

339

24%

81.4

2.4.2.

Natural gas scenario (NGS)The results of the LEAP simulations for the NGS scenario indicate thatswitching from coal to natural gas alone can already deliver both 19% and
24% CO2 reduction targets. This switching requires around 0.36 billion toeof natural gas, which is equivalent to 9% of the Indonesian natural gas
reserve.

NGS1 scenario aiming at the 19% CO2 reduction:
In NGS1, the natural gas capacity in 2030 expands up to 56.5 GW, a fivefold
increase in the capacity over 2015. It is a 25% increase in the gas share in
the capacity mix as compared to REF (Fig. 2.2.a). Consequently, the coalcapacity share decreases from 52% in REF down to 31% in NGS1.
Compared to REF, the corresponding electricity production from natural
gas increases significantly. In NGS1, natural gas constitutes 50% of theelectricity generation in 2030, nearly threefold compared to its share in
REF. Accordingly, coal power generation decreases from 75% in REF to
42% in NGS1.

NGS2 scenario aiming at the 24% CO2 reduction:
In order to reduce the CO2 emissions by 24% against REF, further
expansion of natural gas capacity is required. The simulation results
indicate that it takes an additional 6.2 GW of natural gas capacity when50

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

compared to NGS1. Consequently, the share of natural gas power
generation in 2030 rises to 59%, which is 9% higher than NGS1 (Fig. 2.2.b).
Meanwhile, the coal capacity share declines to 24%, leading to a reduced
percentage of the coal power generation to 33% as compared to 75% in
REF.
100

Gigawatt

80
60
40

(a)

20
0
2016
Coal

2018

2020

Natural Gas

2022

2024

Oil

Hydro

2026

2028

2030

Geothermal

2030: 465 TWh
Geothermal
5%

Hydro Oil
3% 0%

Coal
33%

(b)
NaturalGas
59%

Fig. 2.2: Natural gas scenario: Power generation capacity (NGS1, 19% target) (a);Electricity generation mix in 2030 (NGS2, 24% target) (b)2.4.3.

Renewable energy scenario (REN)In REN, the capacity of renewable energy increases significantly in order
to attain both 19% and 24% CO2 reduction targets. Accordingly, thecorresponding electricity production from renewable energy also grows inboth REN1 and REN2 scenarios (Fig. 2.3). The deployment of renewableenergy in REN refers to RUPTL, which stipulates the plan to maximize the51CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

utilization of hydro and geothermal potentials and to add up to 5 GW and
2.5 GW of solar PV and wind power capacities, respectively by 2025 (PLN,
2017b). By 2030, the solar PV capacity adds up to 8 GW and 14 GW,
respectively, in REN1 and REN2. Furthermore, the capacity of wind power
reaches 4 GW and 6 GW by 2030 in REN1 and REN2, respectively.
Meanwhile, no specific target is set for biomass, although the RUPTL
mentions the plan to add 0.1 GW of municipal waste power plants. I assume
that 4 GW and 5 GW of biomass capacities are added in REN1 and REN2,
respectively, out of the total 7.4 GW biomass potentials of the Java-Baliislands.
2030: 465 TWh

Solar
Wind
3%
2%

Biomass6%

Natural
Gas
19%

Coal
57%

Geothermal
10%
Hydro
3%
Oil
0%

(a)

120

Gigawatt

100
80
60

(b)

40
20
0
2016

Coal

2018

Natural Gas

Oil

2020
Hydro

2022

2024

Geothermal

2026
Biomass

2028
Wind

2030
Solar

Fig. 2.3: Renewable energy scenario: Electricity generation mix in 2030 (REN1,
19% target) (a); Power generation capacity (REN2, 24% target) (b)52

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

REN1 scenario aiming at the 19% CO2 reduction:
The renewable capacity in REN1 adds up to 33 GW or threefold of therenewable capacity addition present in REF. Renewables now account for
31% of the total power generation capacity in 2030, split between hydro(9.8%), geothermal (6.3%), wind power (3.8%), biomass (3.8%), and solar PV(7.6%). Consequently, the share of renewable electricity generation
increases by up to 24% of the total power generation in 2030, which isthree times higher than that in REF.

REN2 scenario aiming at the 24% CO2 reduction:
To accomplish the 24% CO2 reduction target, 9 GW of renewables areadded on top of their capacity in REN1. In 2030 the renewables capacity
reaches 42 GW constituting 38% of the total power generation capacity.
The capacity is shared between hydro 10.3 GW, geothermal 6.7 GW, wind
6 GW, biomass 5 GW, and solar PV 14 GW. Meanwhile, the share of
renewable electricity generation adds up to 28% as compared to 7% in REF.
2.4.4.

Optimization scenario (OPT)

Based on the simulation results of the OPT scenario, the least-cost option
for meeting the Paris target is to expand both renewable energy andnatural gas capacities, resulting in a significant decrease in coal capacity
compared to REF (Fig. 2.4).

OPT1 scenario aiming at the 19% CO2 reduction:
In OPT1, by 2030, the renewables capacity accounts for 20 GW (Fig. 2.4.a).
This is a significant increase from 10.8 GW in REF, and consists of hydro(8.4 GW), geothermal (6.6 GW), biomass (4 GW), wind (0.5 GW), and solar
PV (0.5 GW). The natural gas capacity increased by 4% from 31.3 GW in
REF to 32.5 GW in OPT1. Meanwhile, the coal capacity decreases
significantly (25%) from 51.8 GW in REF down to 38.8 GW in OPT1.
Thus, the OPT1 cost-efficient electricity generation mix in 2030 consistsof coal (56%), natural gas (23%), and renewables (21%). Despite its
decreased capacity compared to REF, the production share of coal53

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

remains the highest due to its dominance in the base year (65% of the total
production) and the cheap price of coal resources. This indicates a highchance of lock-in with the coal technology infrastructure, if no othercriteria, other than cost minimization, are at play.
100

Gigawatt

80
60

(a)

40
20
0
2016
Coal

2018

Natural Gas

2020

Oil

2022

Hydro

2030: 465 TWh
Solar
0%

2024

Geothermal

Wind
1%

2026

2028

Biomass

2030

Wind

Solar

Biomass
6%
Geothermal
10%

Coal
46%

Oil
0%

Hydro
4%

(b)

Natural Gas
33%

Fig. 2.4: Optimization scenario: Power generation capacity (OPT1, 19% target)(a); Electricity generation mix 2030 (OPT2, 24% target) (b)

OPT2 scenario aiming at the 24% CO2 reduction:
The results of the OPT2 scenario show that the 5% supplementary CO2
reduction can be attained through partial substitution of coal by naturalgas. These produce an electricity generation mix comprising of coal (46%),
renewables (21%), and natural gas (33%), in the year 2030 (Fig. 2.4.b).

54

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

2.4.5.

CO2 Mitigation Costs

In LEAP, the total costs of power system expansion consist of capitalcosts, fixed operation and maintenance (OM) costs, variable OM costs,
and fuel costs throughout the planning horizon. Table 2.8 shows the total
costs for all scenarios. The costs of CO2 mitigation are the difference
between the total costs of the Java-Bali power system expansion in REF
and in each mitigation scenario. The total cost of the Java-Bali powersystem expansion in REF is equal to 0.1% of the cumulative national GDP
during the study period7. The model estimates the increased costs of 4.7%,
5.3%, and 6% in OPT1, REN1, and NGS1, respectively, to reduce 19% of theCO2 emissions compared to REF. Furthermore, reducing 24% of emissions
leads to the increased costs of 6.1%, 7.1%, and 7.3% in OPT2, NGS2, andREN2, respectively, which can be covered by climate finance as mandated
by the Paris Agreement. The CO2 mitigation costs in each scenario range
from 0.005% to 0.008% of the total GDP.
Table 2.8: Total costs of power system expansion in different scenarios
Scenarios

CO2 reduction
against REF in
2030 (million
tCO2e)

CO2
mitigation
costs(billion USD)

Costeffectiveness of
CO2 mitigation(USD/tCO2e)

0
64.6

Total
costs
(billion
USD)
49.9
52.9

Reference (REF)
Natural gas 1(NGS1)Natural gas 2(NGS2)
Renewableenergy 1 (REN1)
Renewableenergy 2 (REN2)
Optimization 1(OPT1)
Optimization 2(OPT2)

3.01

46.6

82.0

53.4

3.52

42.9

64.6

52.6

2.66

41.2

81.5

53.5

3.63

44.7

64.7

52.3

2.35

36.3

81.5

53.0

3.05

37.5

7

Assuming annual GDP growth of 4.5%

55

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATIONThe NGS scenarios impose the highest total costs among the three setsof mitigation scenarios, while the OPT scenarios offer the lowest. Hence,
the most cost-effective CO2 mitigation scenario is OPT, followed by REN.
The optimization method in OPT ensures the lowest total costs of thepower system expansion in meeting the mitigation target. The OPT
scenario covers all technologies employed in REF with the addition of
biomass, wind, and solar PV technologies. However, the optimal mix canbe sensitive to the input assumptions, such as the relative performanceof different technologies or future costs. I keep it aside as a subject for
Chapter 3.
Table 2.8 also shows the cost-effectiveness of the CO2 mitigation
scenarios. For the 19% CO2 reduction target, the abatement costs are 36.3,
41.2, and 46.6 US$/tCO2e in OPT1, REN1, and NGS1, respectively.
Meanwhile, for the 24% CO2 reduction target, the abatement costs are
37.5, 42.9, and 44.7 US$/tCO2e in OPT2, NGS2, and REN2, respectively.

2.5. Discussions
The analysis results obtained can be summarized as follows:1). In the reference case where the power capacity expansion of the Java
Bali power system continues as per the present pattern (REF
scenario), the fossil-fueled power plants remain dominant until the endof the study period. As a result, the CO2 emissions in 2030 are nearly
triple those witnessed in 2015.
2). In the context of the Java-Bali power system, the power sector target
associated with the Paris pledge – 19% or 24% reduction – is achievable
under the proposed capacity expansion scenarios. The NGS scenario
results in a substantial addition of natural gas capacity, i.e., 47 GW and
54 GW under the NGS1 and NGS2 scenarios, respectively. This
indicates that the single substitution of coal by natural gas can reduce
emissions by 19% and 24% from REF. REN scenarios lead to theaddition of the renewable capacity of 29 GW and 38 GW under the56CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

REN1 and REN2 scenarios, respectively. The OPT scenarios that
focus on the cost-minimization result in expansions of both renewableenergy and natural gas capacities.
3). Total costs of the Java-Bali power system expansion under the REF
scenario are equal to 0.1% of the Indonesian GDP during the analysis
period. Any of the CO2 mitigation efforts aligned with the Java-Bali
capacity expansion increases the costs from 5% to 7% of the REF cost.
The cost-effectiveness of CO2 mitigation scenarios ranges between
36.3 and 46.6 US$/t CO2e.
Overall, these findings indicate that, in the contexts of the Java-Bali
power system, the Paris target can be met solely by fuel switching fromcoal to natural gas. Though gas is a cleaner fuel, this strategy would not
use the Indonesian potential of its renewable energy. The national
electrification goal can also be achieved without breaking the Paris
Agreement by escalating the development of renewable energy. However,
the most cost-effective measure is by increasing renewable energy
development in combination with an expansion of natural gas power
generation capacity. As far as costs are concerned, results of this chapter
are aligned with other studies regarding the implication of the Paris
Agreement on the power sector in developing countries, for example, thecase of Botswana (Baek et al., 2019) and Kenya (Dalla Longa and van der
Zwaan, 2017).
Providing reliable and affordable electrical energy for the entire population
is a vital development goal of the Indonesian Government. Therefore, theelectricity tariff is determined by the government, and subsidies areallocated for low-income households to ensure electrical energy isaffordable for all people. In 2017 alone, the electricity subsidy accounts
for 3% of the Government’s revenue (Gumelar, 2017; Direktorat Jenderal
Anggaran, 2017). Thus, it is paramount to maintain low electricity
production costs. As such, the Indonesian power sector development
follows the ‘least costs’ principle, with electricity supply options are
57

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

chosen based on the lowest cost (PLN, 2016b). Any additional cost, suchas the cost incurred due to the compliance with the Paris Agreement, will
increase the electricity production costs, which eventually can increase
the price paid by the consumer and the size of the government subsidy.

2.6. Conclusions
Assuring electricity access for the entire population is still a vital national
development goal for many developing countries, including Indonesia.
Likewise, climate change mitigation and adaptation are also central policies
due to the country’s vulnerability to climate change impacts. This chapter
analyzed plausible trade-offs between electrification and climate change
mitigation goals in the Java-Bali islands. The focus was on alternatives
that could allow Indonesia to satisfy the future electricity demand, while
also meeting its Paris climate targets. LEAP was chosen for performing
the analysis after a systematic review of different models (Appendix 2.A).
At first, the model was carefully validated using the actual data of theIndonesian power system in the period 2005-2015. Then, four sets of
scenarios of the power system expansion for the period 2016-2030 weredeveloped and analyzed.
Results demonstrate that any effort to comply with the Paris climate
target will impact the electricity generation costs. Currently, there is no
regulation in place in Indonesia to limit CO2 emissions from the power
sector. Development of such a policy needs to consider the cost
implications for the power generation companies, as these costs,
eventually, are bound to pass onto the consumers or have to be coveredby the government’s subsidy.
This chapter has incorporated Indonesia’s NDC targets into power
capacity expansion. However, there is a number of important
methodological issues that should be incorporated in any future work. In
particular, these types of assessments would benefit greatly by taking into
consideration climate change impacts on the future power system, andpossible adaptation strategies in the power sector. It is also vital to58

CHAPTER 2 TRADE-OFFS BETWEEN ELECTRIFICATION AND CLIMATE CHANGE MITIGATION

account for the evolution of energy technology - as most technologies intime become more cost-effective (Rubin et al., 2015) - and analyzes theirimpact on the robustness of the power system expansion scenarios within
the CO2 mitigation constraints.

59

60

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

Abstract
This chapter analyzes a diffusion of renewable energy in a power system
considering technological learning. The chapter explores long-term
scenarios for capacity expansion of the Java-Bali power system inIndonesia, considering the country’s renewable energy targets, by applying
the LEAP model with an integration of technological learning. Results
reveal that, at the medium and high pace of technological learning, thetotal costs of electricity production to achieve the long-term renewableenergy target are 4% to 10% lower than the scenario without considering
technological learning. With respect to technology, solar PV and windbecome competitive with other types of renewables and nuclear.
Moreover, the fulfillment of the renewable energy targets decreases CO2
emissions by 25% compared to the reference scenario. Implications ofthese findings indicate that energy policies should focus on the early
deployment of renewables, upgrading the grid capacity to accommodate
variable renewable energy, and enabling faster local learning.

This chapter is published as a journal article:
Handayani, K., Krozer, Y. and Filatova, T. (2019). From fossil fuels to
renewables: An analysis of long-term scenarios considering
technological learning. Energy Policy, 127.

63

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

3.1. Introduction
Renewable energy is a critical component for combating climate change.
In fact, most of NDCs submitted by countries under the Paris Agreement
include renewable energy as their measure to address climate change(IRENA, 2017). The implementation of NDCs will add at least 1.3 terawatts
to the global renewable installed capacity. This ambitious target would
need a considerable investment cost—1,700 billion USD by 2030, according
to the International Renewable Energy Agency (IRENA) estimate.
The high upfront expense for the installation of renewable technologies is
one of the factors that hinder the deployment of renewable energy.
However, the capital costs of energy technologies are known to decline
over time due to cost-reducing technological changes, usually referred toas learning (IEA, 2000; Lafond et al., 2017; McDonald and Schrattenholzer,
2001; Rubin et al., 2015; Watanabe, 1995). The concept of the learning
effect has been widely used and analyzed empirically in many applications(Grübler et al., 1999). The earliest example is Wright (1936), who reported
that unit labor costs in airframe manufacturing declined with accumulative
experience measured by cumulative output.
Cost savings brought up by technological learning are especially attractive
for developing countries, which are still facing rapid growths of electricity
demand while also pledging their NDCs. This case applies to Indonesia, thefourth most populous country in the world. Electricity demand in the
country is projected to grow at an average of 8.3% per year in the next
decade (PLN, 2017b). Meanwhile, it pledges to reduce 29% of its
greenhouse gas emissions against its business-as-usual scenario by 2030
(Government of The Republic of Indonesia, 2016). While, in 2015, renewable64

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

energy only accounted for 4% of the national energy mix8 (DEN, 2016c),
the most recent national energy policy (NEP) requires it to increase by
23% in 2025 and 31% in 2050 (Government of The Republic of Indonesia,2014). In the context of the power sector, renewable energy currently
accounts for 10% of the national electricity generation mix9 (PLN, 2016a).
RUPTL 2016-2025 estimates an increase in unit costs of electricity
production by 22% to realize the NEP target in the power sector by 2025.
Such a raise, if it occurs, will cause a burden on the power sector and, inturn, the national economy. Yet, these projections neglect the learning
process of electric power technologies. As discussed above, technologicallearning may reduce the unit costs of electricity production, making
investments in renewable energy technologies more economicallyattractive.
Given the essential role of technological learning, it is necessary toconsider it when projecting a long-term electricity supply. Accordingly,
this chapter integrates technological learning into the LEAP model toexplore a number of electricity expansion scenarios. LEAP has been
actively used for assessing renewable energy expansion in many countries,
such as Pakistan (Ishaque, 2017), Bulgaria (Nikolaev and Konidari, 2017),
Ghana (Awopone et al., 2017), Thailand (Wongsapai et al., 2016), Iran
(Eshraghi and Maleki, 2016), Indonesia (Kumar, 2016), India (Kumar andMadlener, 2016), Malaysia (Samsudin, 2016), Brazil (De Andrade Guerra etal., 2015), Korea (Park et al., 2013), and Lebanon (Dagher and Ruble, 2011).
Despite its extensive use, little attention has been paid to incorporating
technological learning in the LEAP model, which likely results inunderestimating the future deployment of renewable energy.
Focusing on the Java-Bali power system, this chapter develops scenarios
for the system’s capacity expansion from 2016 through to 2050.
8Energy mix is a set of various primary energy sources used to meet energy needs ina region (ton oil equivalent and % contributions)9Electricity generation mix is a set of primary energy sources that constitute the total
electrical energy production in a region (Megawatt hour and % contributions).

65

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

Moreover, this chapter applies a learning model for each power generation
technology into the LEAP cost function. The simulation results areanalyzed in terms of energy, costs, and CO2 emissions.
The innovative contribution of this chapter is twofold. First,
methodologically, it moves beyond the current practice of LEAP usage byincorporating a learning model with respect to energy technologies into
the LEAP cost function. Although there are some energy system models
that have included technology cost learning, such as MESSAGE-MACRO,MARKAL-TIMES, NEMS, POLES, ERIS, GALLM, and EXO-XEL (Heuberger
et al., 2017), to the best of our knowledge, this dissertation is the first to
include technological learning endogenously in LEAP. Secondly, thischapter adds an understanding regarding the role of technological learningin driving the transition from fossil-fuel-based power generation to alower-carbon power system in the context of developing countries.
The remainder of this chapter is organized as follows: Section 3.2 presents
the literature review; Section 3.3 explains the methodology, scenarios, andthe model input parameters for the future Java-Bali power system;
Section 3.4 discusses the results of model simulations; Section 3.5
presents the main findings of this chapter; Section 3.6 concludes thediscussions.

3.2. Technological learning
The development of technology is not an autonomous, independent
process. Instead, it evolves from many interactions within social systems
as well as from experience in using the technology itself (Barreto, 2001).
The process of technological change requires considerable time frominnovation to widespread diffusions, such as what has occurred in the past
concerning the global technology transitions from traditional biomass tocoal-based technology and from coal-based technology to electricity andpetroleum-based technologies (Wilson and Grubler, 2011).

66

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

The development and introduction of new technologies involve a learning
process that results in the improvement of the production process andproduct, which, in turn, often makes the costs lower (GEA, 2012). The
learning process starts from the first practical use of a new technology
until its maturation stage (Sagar and van der Zwaan, 2006). Learning is a
crucial element of the early adoption of technologies, and it indicates theexperiences gained through the practical use of technology andcontributes to cost reduction over time (Sagar and van der Zwaan, 2006).
Since learning is a self-enforcing process, more accumulated experiencesin technology lead to lower cost, and more increase in technology
competitiveness leads to even more accumulated experience (Gillingham
et al., 2008). As such, it is not always the case that new technology isused because it becomes cheap, but also technology becomes cheap
because of its increased use and learning process (Berglund and
Söderholm, 2006). In addition to cost reduction, learning can also lead togreater proficiency in technology operation as well as institutionaltransformation necessary to allow the widespread use of new technologies(Sagar and van der Zwaan, 2006).
The concept of the learning effect has been widely used since its first
appearance in the economic literature, and it was empirically verified(Berglund and Söderholm, 2006). The learning process triggering cost
reduction is expressed as a function of the accumulation of knowledge andexperience related to R&D expenditures, the production, and the use oftechnology (Kahouli-Brahmi, 2008). Quantification of these learning
patterns is presented in the literature using the so-called one-factor andmulti-factor learning curves (Kahouli-Brahmi, 2008; Rubin et al., 2015). The
former is the most widely used method for endogenously forecastingchanges in technology costs. Its experience performance is indicated by
the cumulative installed capacity or cumulative production.
The term “learning rate” is used to express the experience gained fromusing technology and the corresponding technological progress in its
production. It denotes the percentage by which a unit cost declines with67

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

every doubling of the cumulative production. In other words, it is a fraction
by which the unit price of energy service (such as electricity) declines
with every doubling of its installed capacity (Sagar and van der Zwaan,2006). The corresponding change in price compared to its previous pricewith each doubling of capacity is referred to as the “progress ratio”(Berglund and Söderholm, 2006). A progress ratio of 75% indicates that thecosts of technology have declined to 75% of its previous level after adoubling of its cumulative capacity. In this case, the corresponding learning
rate is 25%.
The multi-factor approach includes factors beyond the cumulative
installed capacity or production that contribute to technology cost
reduction, such as R&D spending, knowledge spill-overs, and economies
of scale (Rubin et al., 2015). However, due to data requirements andtheoretical limitations, this approach is less prevalent in the literaturecompared to the one-factor model (Farmer and Lafond, 2016; Rubin et al.,2015).

3.3. Methodology and data
3.3.1.

The LEAP model

A summary of LEAP methodology has been discussed in Section 1.6. The
following sections elaborate on the LEAP framework for calculating
electricity demand, capacity expansion, total costs, and GHG emissions

3.3.1.1. Electricity demand projection
In this chapter, the demand for electricity is calculated based on the
demand growth projection stipulated in RUPTL 2016-2025 and IEO 2014.
Hence, the electricity demand in a specific year is the sum of electricity
demanded in the previous year and its anticipated growth:
𝐸𝐷𝑡 = (𝐸𝐷𝑡−1 ∗ 𝐸𝐷𝐺𝑡) ) + 𝐸𝐷𝑡−1 ,Eq. 3.1

where 𝐸𝐷𝑡 is the electricity demand in year 𝑡, and 𝐸𝐷𝐺𝑡 is the percentage
of growth in the electricity demand in year 𝑡.
68

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

Total electricity demand in the power system for a specific year (𝑇𝐸𝐷𝑡 ) is
calculated as the sum of electricity demanded and electricity losses (𝐸𝐿𝑡 )during the electricity transfer through the T&D networks in that year:
𝑇𝐸𝐷𝑡 = 𝐸𝐷𝑡 + 𝐸𝐿𝑡 andEq. 3.2

𝐸𝐿𝑡 = 𝐸𝐷𝑡 ∗ 𝑇𝐿𝑡 ,Eq. 3.3

where 𝑇𝐿𝑡 is the percentage of T&D losses in year 𝑡.

3.3.1.2. Capacity expansion in LEAPThe capacity of a set of technologies can be added both exogenously andendogenously in LEAP. I specify exogenously the previously existing
capacities as well as committed additional capacities, such as the power
plants that are currently under construction. Furthermore, the capacity ofhydro-pumped storage is also added exogenously following the RUPTL
assumptions (PLN, 2016b)
For the endogenous capacity addition, LEAP calculates the amount ofcapacity to be added using the Eqs. 3.4, 3.5, and 3.6 below (Awopone etal., 2017; Heaps, 2017). In the reference scenario, which uses theaccounting setting, I specify the types of the power plant to add, but LEAP
decides when they will be added based on the system’s requirement. In
the renewable energy scenarios, which make use of LEAP’s optimization
capability, LEAP decides what types of technology should be added andwhen it will be added based on the least-cost principle and the set
constraints. In these scenarios, I set the minimum capacity of natural gas
power plants to be added each year as intermediate and peak plants, aswell as for balancing the variability of intermittent renewable energies.
𝐶𝐸𝑛 = 𝐷𝑝 (𝑃𝑅𝑀 − 𝑅𝑀),
𝐷𝑝 =𝐸𝐷
𝐿𝐹 ∗ 8760

𝑅𝑀 =𝐶𝑝 −𝐷𝑝
𝐷𝑝

,

, and

Eq. 3.4
Eq. 3.5
Eq. 3.6

where 𝐶𝐸𝑛 is the endogenous capacity addition, 𝐷𝑝 is the peak electricity
demand, 𝑃𝑅𝑀 is planning reserve margin, 𝑅𝑀 is the reserve margin before69CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

addition, 𝐸𝐷 is electricity demand, 𝐿𝐹 is the load factor (calculated as theratio of the average load and the peak load), and 𝐶𝑝 is the capacity before
addition.

3.3.1.3. Total costs calculation
The total cost of the power system is the total net present value of thesystem costs over the entire period of calculation:𝑁

1

𝑇𝐶 = ∑𝑡 𝑡 ∑𝑝 (1+𝑑)𝑡 (𝐾𝑐 ∗ 𝐶𝑎𝑡 + 𝑓𝑜𝑐𝑡 ∗ 𝐶𝑎𝑡 + 𝑉𝑜𝑐𝑡 ∗ 𝑃𝑡 + 𝐹𝑐𝑡 ), Eq. 3.7

where 𝑇𝐶 is the total cost, 𝑁𝑡 denotes the entire years from 2016 throughto 2050, 𝑝 is the process (technology), 𝑑 is the discount rate, 𝐾𝑐 is theinitial capital cost, 𝐶𝑎𝑡 is the capacity in year 𝑡, 𝑓𝑜𝑐𝑡 is the fixed operationand maintenance costs in year 𝑡, 𝑉𝑜𝑐𝑡 is the variable operation andmaintenance costs in year 𝑡, 𝑃𝑡 is the output power in year 𝑡, and 𝐹𝑐𝑡 isthe fuel cost in year 𝑡.

3.3.1.4. CO2 emissions calculation
CO2 emissions from electricity production are calculated as follows (Fengand Zhang, 2012):
𝐶𝐸 = ∑𝑝 ∑𝑓 𝐸𝐹𝑓,𝑝 ∗1
𝐸𝑝∗ 𝑃𝑝 ,

Eq. 3.8

where 𝐶𝐸 is the CO2 emissions, 𝐸𝐹𝑓,𝑝 is the CO2 emission factor from one
unit of primary fuel type 𝑓 consumed for producing electricity throughtechnology 𝑝, 𝐸𝑝 is the efficiency of technology 𝑝, and 𝑃𝑝 is the output
power from technology 𝑝.
3.3.2.

Integration of the learning model

LEAP does not provide a built-in expression for capturing technologicallearning. Thus, I integrate the one-factor learning model into LEAP byadding expression in LEAP, representing the learning curve of electric
power technologies. I build the syntaxes in LEAP that enable thecalculation of changes in capital costs for each technology type along withchanges in cumulative capacity and in learning rate value of the technology
for each learning phase.
70

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

The capital cost of electric power technology in a specific year is
calculated using the following formulas (Kim et al., 2012):𝐶𝛽

𝐾𝑡 = 𝐾𝑐 ( 𝑡 ) andEq. 3.9

LR = 1 − 2𝛽 ,Eq. 3.10

𝐶0

where 𝐾𝑡 denotes the capital cost in year t, 𝐶𝑡 is the cumulative capacity
until year t, 𝐶0 is the capacity in the base year, and 𝛽 is the positive
learning parameter (learning index), which characterizes the inclination of
the learning curve, and LR is the learning rate.
The shapes of learning curves for different energy technologies depend on
two factors: initial learning rates and the speed of their change. To initialize
the learning curve model, I retrieve the learning rate values for electric
power technologies from Rubin et al. (2015) and Heuberger et al. (2017),(see Appendix 3.A). Further, with respect to the speed of learning, I
assume four learning phases throughout the time horizon of this study
where the learning rate of each electric power technology decreases withevery phase, as shown in Table 3.1. This assumption refers to the WEO
model 2016, which assumes reductions in the capital costs of renewableenergy over time. WEO distinguishes the capital costs in four-time steps:
2015, 2020, 2030, and 2040 (OECD/IEA, 2017). The learning curve of solar
PV, as the results of its substantial deployment along the time horizon ofthis study, is depicted in Appendix 3.B (Fig. 3B.1).

71

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES
Table 3.1: Assumptions of learning rates of electric power technologies 2016-2050.
Technology

REN-Low LR scenarioa:
REN-Medium LR scenariob:
REN-High LR scenarioc:
Low value of the initial learning rate Medium value of the initial learning rate High value of the initial learning rate
20162020

20212030

20312040

2040- 20162050 202020212030

20312040

20402050

20162020

20212030

20312040

20402050

10.0%

7.1%

4.0%

1.6%

23.0%

16.3%

9.1%

3.7%

47.0%

33.4%

18.7%

7.7%

Wind Turbine -11.0%

-7.8%

-4.4%

-1.8%

12.0%

8.5%

4.8%

2.0%

32.0%

22.7%

12.7%

5.2%

Biomass

0.0%

0.0%

0.0%

0.0%

11.0%

7.8%

4.4%

1.8%

24.0%

17.0%

9.5%

3.9%

USC Coal

5.6%

4.0%

2.2%

0.9%

8.3%

5.9%

3.3%

1.4%

12.0%

8.5%

4.8%

2.0%

NGOC

10.0%

7.1%

4.0%

1.6%

15.0%

10.7%

6.0%

2.4%

22.0%

15.6%

8.7%

3.6%

NGCC

-11.0%

-7.8%

-4.4%

-1.8%

14.0%

9.9%

5.6%

2.3%

34.0%

24.1%

13.5%

5.5%

Hydro

1.4%

1.0%

0.6%

0.2%

1.4%

1.0%

0.6%

0.2%

1.4%

1.0%

0.6%

0.2%

Nuclear

-6.0%

-4.3%

-2.4%

-1.0%

-1.0%

-0.7%

-0.4%

-0.2%

6.0%

4.3%

2.4%

1.0%

Solar PV

Note: The learning rates for the first phase (2016-2020) is based on Rubin et al. (2015). For the other phases, I estimate learning
rates based on data from OECD/IEA (2017)a
The initial learning rates (LR), i.e., the LR values in the first phase refer to the lowest values in Rubin et al. (2015) and assumption
on the low LR value for nuclear in Heuberger et al. (Heuberger et al., 2017)b
The initial learning rates (LR), i.e., the LR values in the first phase refer to the mean values in Rubin et al. (2015) and assumption
on the medium LR value for nuclear in Heuberger et al. (Heuberger et al., 2017)c
The initial learning rates (LR), i.e., the LR values in the first phase refer to the highest values in Rubin et al. (2015)72

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

3.3.3.

Future Scenarios

I design five scenarios for the future development of the Java–Bali powersystem. The first one is the reference scenario, which assumes thecontinuation of the present technology mix in the Java-Bali power system.
The other four are scenarios for meeting the NEP’s NRE targets.
The NEP 2014 proposes ambitious NRE targets for increasing the role ofrenewable energy. The NRE targets relate to two stages. In Stage 1, by
2025, the share of NRE should be at least 23% of the national energy mix,
which refers to the total domestic energy use coming from various
sources. In Stage 2, by 2050, the share of NRE should be at least 31% of
the national energy mix. NEP also mentions that the economic aspects ofrenewables are taken into consideration in achieving its targets(Government of The Republic of Indonesia, 2014). Furthermore, NEP also
considers nuclear as alternative energy to meet its NRE targets, although
it is the last option after maximizing the use of renewable energy sources.
This chapter assumes that 23% and 31% of NRE targets are applied to thepower sector’s energy mix. Accordingly, this chapter analyzes four
scenarios for maximizing the use of renewable energy —as mandated by
NEP— in the context of the Java-Bali power system and assess theirimpacts on costs and CO2 emissions. The LEAP optimization method is
employed to analyze the least-cost options of meeting the NRE targets
with and without technological learning. The assumptions for each scenario
are as follows:a. Reference scenario (REF): The reference scenario assumes acontinuity of fossil-fuel-based power generation in the Java-Bali
power system. Hence, the technology mix in the future is expected tobe equivalent to the present situation. The main characteristics of this
scenario are as follows:-

Deployment of technology is limited to conventional technologies
that have been deployed up to 2015, mainly CFPPs

73

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES-

Renewable capacity expansion only limited to geothermal andhydro, as they are the only renewable technologies that existed in
the base year-

No limitation on the domestic fossil fuels uses-

Geothermal and hydro expansions are dependent on theiravailability (potentials) in the Java and Bali islands-

No specific target is set for renewable energy deployment-

No technological learning is consideredb. Renewable energy scenario (REN): In the renewable energy scenario,
the NRE targets are taken into consideration when projecting thepower system expansion. Besides hydro and geothermal that alreadyoperate in Indonesia, three types of renewable energy are added over
the time horizon of this study: solar, wind, and biomass. Moreover, inline with NEP, nuclear is considered a new technology to be added
after maximizing renewable energy uses. This scenario includes thefollowing characteristics:-

The capacity expansion aims at achieving the NRE targets. Thus,
the NRE targets are set as constraints in the model-

The types of technology that are considered for future capacity
expansion include USC coal, NGCC, NGOC, HEPP, geothermal,
wind power, biomass, solar PV, and nuclear-

The renewables’ capacity expansions are dependent on theiravailability (potentials) in the Java and Bali islands-

LEAP will choose the types of technology to be employed basedon costs and the set objectives-

No technological learning is considered

In addition, I suggest three variations of the REN scenario, which vary in
the initialization of technological learning for electric power technologies.
I consider the learning rate of not only renewable energy technologies but
also of non-renewables. Following the setup of ETL in Section 3.3.2, Iassume the technological learning for all energy technologies occurs in74

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

four phases. LEAP is run with ETL, assuming three different initial values
for the learning rate: low (REN-low LR in Table 3.1), medium (REN-medium
LR in Table 3.1), and high (REN-high LR in Table 3.1).
(i). Renewable energy scenario with low learning rate (REN-low LR): theinitial learning rate values refer to the minimum learning rate values (RENlow LR in Table 3.1).
(ii). Renewable energy scenario with medium learning rate (REN-medium
LR): the initial learning rate values refer to the mean learning rate values(REN-medium LR in Table 3.1).
(iii). Renewable energy scenario with high learning rate (REN-high LR): theinitial learning rate values refer to the maximum learning rate values (RENhigh LR in Table 3.1).
3.3.4.

Data

Most of the model input data were collected from PLN and governmental
reports, rather than relying on default data provided by LEAP. Therefore,
this dissertation represents the actual characteristics of the Indonesian
power system, making policy projections more reliable. Table 3.2 presents
the model input parameters and their sources. The Java-Bali electricity
demand projection for 2016 through to 2025 is based on RUPTL with anannual average of 7.3% (PLN, 2017b). Meanwhile, the demand growth
projections for 2026 onwards refer to IEO 2014 with an annual average of
5.6% from 2026 through to 2040 and 4.3% from 2041 through to 2050 (DEN,
2014). The transmission and distribution losses data come from RUKN
2015-2034, which estimates a reduction from 8.5% in 2015 to 7.9% in 2030
onwards. The planning reserve margin is set at 35%, in accordance with
the RUKN criteria (KESDM, 2015).

75

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES
Table 3.2: Summary of model input parameter
Input Data

Value

SourceAnnualdemandgrowth 2016-2050

4.3% - 7.3%

Refers to the RUPTL and IEO estimates(DEN, 2014; PLN, 2017b)

T&D losses

7.9% – 8.5%

Refers to the draft RUKN estimates(KESDM, 2015)

System load shape

Appendix
2C

Based on hourly demand data recorded by
P2B (P2B, 2016b)

Reserve margin

35%

Refers to the RUKN criteria (KESDM, 2015)

Environmental
parameterPer
technology

The IPCC Tier 1 default emission factors,
embedded in the LEAP’s technology
database (Heaps, 2017)

Discount rate

12%

The discount rate used by PLN (JICA,
2010)

The energy load shape in LEAP is drawn based on the hourly load data of
the Java-Bali power system (see Appendix 2C, Fig. 2C.1). In thisdissertation, the LEAP model considers the Java-Bali’s load
characteristics by dividing the demand in a year into 48 time-slices, which
represent four variations for each month. This approach is based on
historical load characteristics where there are four main variations inelectricity demand, which occur during the day, night, weekend, andweekday.
The technological characteristics data of existing power plants arecollected from PLN. Meanwhile, the characteristics of newly added
technologies, including USC coal, biomass, wind turbine, solar PV, andnuclear power, were retrieved from various studies (see Table 3.3). Mostof the technology costs assumptions are taken from the RUPTL cost data
(PLN, 2017c), complemented with Indonesia Energy Outlook (DEN, 2016b),
ACE (2016), OECD/IEA (2017), and the IEA & NEA (2015). The fuel costs
data for coal and natural gas were retrieved from the PLN Statistics 2015
(PLN, 2016d), while nuclear and biomass fuel costs data were taken fromIEA and ASEAN Energy Centre studies (ACE, 2016; IEA and NEA, 2015),
respectively. For renewable, I assume that the publicly available data of76

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

the Indonesian renewable energy potential (Chapter 1, Table 1.2) can be
exploited over the time horizon of this study without any constraints.
Furthermore, since NEP lists nuclear as the least preferred option for
meeting the NRE targets, I assume that nuclear will be deployed for the
first time in 2035 when all renewable energy potentials have mainly been
exploited. Coal, nuclear, and biomass power plants are expected to cover
the baseload, while natural gas and hydropower plants are expected tocover the peak load. With regard to the supply characteristics ofintermittent renewable power plants (wind and solar), I specify
exogenously capacity addition for hydro-pumped storages as well as set
a minimum amount of natural gas power plant to be added each year tobalance the intermittent renewable energies.

77

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES
Table 3.3: Characteristics of technologies in the Java-Bali LEAP model
Technology

Lifetime(years)a

Efficiency(%)aMaximum
availability(%)bCapacity
credit(%)

Capitalcost(2015
US$/kW)aFixed OM
cost (2015
US$/kW)a

Variable
OM cost(2015
US$/MWh)a

Fuel costc (2015US$)

USC coal

30

40

80

100

1400

31.3

2

51.8 US$/ton

NGCC

25

55

80

100

800

19.2

1

7.6 US$/MMBTU

NGOC

20

36

80

100

700

18

1

7.6 US$/MMBTU

Hydro

50

100

41

51

2000

6.6

1

-

Mini hydro

25

100

46

58

2400

6.6

1

-

50

95

b

20

25

800

6.6

1

-

d

80

100

3500

30

1

-

17

22

2069e

24.8e

0.4b

-

2200

d

b

-

g

8.6

9.33 US$/MWhf

6.5b

11.67 US$/tonhHydro P/S
Geothermal

25

10

Solar PV

20

100

Wind power

20

100

28

Nuclear

40

f

34

85

Biomass

20h

35d

80

a

35
g

44

100

6000

164

100

2228d

78d

0.8
d

PLN (2017b) bDEN (2016a); cPLN (2016c); dOECD/IEA(2017); eACE(2016); fRothwell & Rust (1997); gIEA & NEA (2015); IRENA(2012b).

78

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

3.4. Results
The LEAP model calculation of the demand growth (Eq. 3.1) results in 305
TWh of electricity demand in 2025, doubling values recorded in 2015.
Furthermore, in 2050, it increases up to 1,068 TWh—over threefold of
those in 2025. The total electricity supply calculated by the LEAP model
is higher than the projected electricity demand, as the electricity
generation module also needs to compensate electricity losses in the T&D
networks (Eq. 3.2). The following sections discuss the results of five
scenarios for the Java-Bali power system’s expansion to satisfy the
projected future demand.
3.4.1.

Reference scenario (REF)

In the REF scenario, with the business as usual technology composition,
the coal capacity is added expansively over the time horizon of thesimulation, followed by natural gas (see Fig. 3.1). Consequently, theelectricity generation mix in the Java-Bali power system is dominated bycoal. In total, fossil fuels (coal and natural gas) account for 92% and 94%
of electricity supply in 2025 and 2050, respectively. Interestingly,
renewable energy share reduces from 8% in 2025 to 6% in 2050 despite thefull utilization of geothermal and hydro potentials of the Java-Bali islands.
Therefore, the electricity generation mix in REF (Fig. 3.2) is far from whatis expected by NEP. These results indicate that it is not possible toincrease renewable energy share if the sector is to rely on geothermal andhydro alone without exploiting other types of renewable energy.

79CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

Capacity (Gigawatt)

250
200
150
100
50
0
2015

Coal

2020

2025

Natural Gas

Oil

2030

2035

Hydro

2040

2045

Geothermal

2050

Solar

Electricity generation (TWh)Fig. 3.1: Installed capacity, REF scenario

1,200
1,000
800
600
400
200
0
2015

2020

2025

2030

2035

Coal

Natural Gas

Oil

Hydro

2040

2045

2050

Geothermal

Fig. 3.2: Electricity generation mix, REF scenario.

3.4.2.

Renewable energy scenario (REN)

The total power generation capacity of the Java-Bali power systemreaches 69.6 Gigawatt (GW) at the end of NEP’s Stage 1 (2025) under the
REN scenario without technological learning (Fig. 3.3). In this scenario, therenewable capacity expands up to 15.2 GW—a nearly twofold increase
compared to REF. Accordingly, there is a 22% decrease in coal capacity.
During the NEP Stage 2 period in the REN scenario, the system’s capacity
expands further, reaching 244.4 GW in 2050. Interestingly, nuclear capacity
is added significantly during this time. It is first installed in 2035 and adds

80

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

up to 22 GW by 2050. In the same year, renewable capacity reaches 41
GW—threefold of that in REF.
Looking more closely into the electricity generation mix, the total
electricity generation in 2025 and 2050 is 332 TWh and 1159 TWh,respectively (Fig. 3.4). Renewable energy accounts for 23% of the JavaBali electricity generation mix in 2025, compared to 8% in REF. Hence, theNRE target Stage 1 is achieved solely by exploiting renewable energy.
However, in 2050, renewable energy share constitutes only 17.3% despite
full utilization of hydro, geothermal, and biomass potentials (Fig. 3.4b),which is below the NRE target Stage 2. This gap is filled by nuclear, which
accounts for 14.2% of the Java-Bali electricity generation mix. These
results imply that the least-cost option to achieve the NRE target Stage2, assuming no changes in relative costs of energy technologies, is tocombine renewables and nuclear. However, this scenario neglects
technological learning, whereas most of these technologies become morecost-effective over time.

Capacity (Gigawatt)

250
200
150
100
50
0

2015 2020 2025 2030 2035 2040 2045 2050
Coal

Natural Gas

Oil

Hydro

Geothermal

Solar

NuclearFig. 3.3: Installed capacity in REN scenario, no technological learning81

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

2025: 332 TWh

2050: 1159 TWh

0% 1%
14%

11%

1%
4%

7%
4%

5%

44%
5%

56%

21%
3%
24%

(a)

(b)

Fig. 3.4: Electricity generation mix in REN scenario, no technological learning

The following sections discuss the results of the alternative REN
scenarios, which take into account the learning curves of both renewable
and non-renewable technologies. In these scenarios, the capital costs ofelectric power technologies change along with their increased capacity
depending on their learning rates.

3.4.2.1. Renewable energy scenario with low learning rate values (REN-low
LR)
REN-low LR scenario assumes the minimum value of the learning rate ofeach technology in Phase I, which evolves throughout the other phases.
The results indicate that there is a significant change in the 2025’s
electricity generation mix when compared to REN (Figs. 3.5a vs. 3.4a).
Solar now accounts for 6% of the electricity generation mix, partiallyreplacing biomass and geothermal, in contrast to 1% in REN. It implies thatin the early phase of technological learning, even in its minimum learning
rate value, solar becomes competitive with other renewables.
Furthermore, the natural gas share slightly increases as compared to REN,
compensating a slight reduction in coal.

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CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

These changes are seen substantially by 2050. The share of nuclear andwind power present in REN is replaced by solar (compare Figs. 3.5b and3.4b). The solar share is now 18%, which also slightly replaces the coalshare. Hence, this result suggests that when the minimum learning rate
values for all technologies are considered, solar becomes more
economically attractive compared to other technologies. Remarkably, evenunder the most modest assumptions regarding technological progress,
solar proliferates from less than 1% to become the third most used energy
source after coal and natural gas. A reasonable explanation could be that,in this scenario, the initial learning rate of solar PV (10%) is the highest,
compared to those of nuclear (-6%), wind power (-11%), and coal (5.6%).
This also explains why nuclear and wind power hardly appear in the 2050’s
electricity generation mix.
2025: 332 TWh

2050: 1159 TWh

0%

1%
1%

6%
7%

18%

5%
4%

0%
5%

43%
4%

54%

3%

23%
26%

(a)

(b)

Fig. 3.5: Electricity generation mix in REN-low LR scenario, technologicallearning occurs at the minimum pace

3.4.2.2. Renewable energy scenario with medium learning rate values(REN-medium LR)
When the medium learning rate value for each technology is applied, asignificant change is also seen in the 2025’s electricity generation mix(Fig. 3.6a) as compared to REN. Renewable energy is now shared between
83CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

biomass (7%), solar (6%), geothermal (5%), hydro (4%), and wind (1%). This
result suggests that in the early phase of technological learning when amedium learning rate for each technology is assumed, solar and biomass
compete with each other. Comparing to REN, the coal share now increases
by 2%, compensating the 2% decrease of the natural gas share. Together
they still support 77% of the Java-Bali electricity production while therenewables share accounts for 23% of the electricity generation mix, as
targeted.
2025: 332 TWh

2050: 1159 TWh
1%

1%
6%
7%

14%

4%

5%
4%

44%

4%
4%

56%
21%

3%

26%

(a)

(b)

Fig. 3.6: Electricity generation mix in REN-medium LR scenario, averagepace of technological learning

Turning now to the electricity generation mix in 2050, Fig. 3.6b shows that
renewables account for 30% of the electricity generation, supplying nearly
350 TWh of electricity to the Java-Bali system. With an additional 10 TWh
electricity supply from nuclear, NRE now constitutes 31% of the electricity
generation mix, as targeted. An interesting finding reveals when comparing
these results with those in REN-low LR. It can be seen that the solar
share in this scenario is 4% lower than that in REN-low LR despite thefact that its learning rate in this scenario is higher than that in REN-low
LR. The 4% portion is replaced by wind, which hardly appears before (Fig.
3.5b). This result indicates that in the later phases of technological learning
84

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

when the learning rate values of each technology diminish, the wind poweris more competitive than it is in the earlier phases.

3.4.2.3. Renewable energy scenario with high learning rate values (RENhigh LR)
In the scenario with the high learning rate value for each technology, it
can be seen that the 2025 electricity generation mix is comparable withthose in the REN-low LR and REN-medium LR scenarios (Figs. 3.5a vs.
3.6a and 3.7a). This finding suggests that, regardless of the initial learning
rate values, solar is competitive against other renewables when it isdeployed in the early phases.
2050: 1159 TWh

2025: 332 TWh

1%

1%
6%

14%

7%

4%

5%
4%

43%

5%
4%

58%

3%

19%
26%

(a)

(b)

Fig. 3.7: Electricity generation mix in REN-high LR scenario, the high pace oftechnological learning

Results for 2050 under the intensive technological learning show thathydro, geothermal, and biomass expand up to 28.1 GW, reaching theirmaximum plausible capacities. Meanwhile, wind capacity adds up to 20.5
GW, almost reaching its maximum potential of 24 GW. In total, theserenewables account for a 16% share of the electricity generation mix (Fig.
3.7b). After these renewables reach their maximum plausible capacities,
solar and nuclear are the only options for meeting the NRE targets.
85

CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

Together with 14% of solar and 1% of nuclear shares, NRE constitutes 31%of the Java-Bali electricity generation mix, satisfying the NRE target Stage
2. This is comparable with REN-medium LR, indicating that, at the medium
and high initial learning rate values, all types of renewable compete witheach other to achieve the NRE target Stage 2.
Comparing results from the three technological learning scenarios, several
significant findings emerge. Firstly, the integration of ETL in LEAP reveals
comparable results for the early phase of technological learning (20162025). In this phase, hydro, geothermal, biomass, and solar PV competewith each other to meet the NRE target Stage 1. Meanwhile, in the later
phases, when the learning rate value of all technologies decreases, theresults are slightly different between the REN-low LR scenario and thetwo other technological learning scenarios. While, in the former scenario,
the wind power share is negligible, in the latter scenarios, the wind power
is competitive with other renewables. Secondly, as far as technologicallearning scenarios are concerned, they meet the NRE targets mostly
through renewables, barely depending on nuclear.
3.4.3.

Costs

Given the pressure from the competing socio-economic priorities,
including poverty eradication and other SDGs, any effort in achieving the
NRE targets relies on making NRE technologies economically feasible.
Hence, assessing the costs of meeting these targets is essential. Here, I
compare the results of simulations from the five scenarios with respect totheir total costs (Fig. 3.8). The total costs to achieve the NRE target by
2050 are 103.1 USD in REN in the absence of technology learning, which
is nearly 16% higher when compared to 88.9 USD in REF.
The technological learning has an impact on the total costs, which vary
non-linearly with the change in the learning pace. Results show that the
total costs of REN-medium LR and REN-high LR become 4% and 10%
lower, respectively, when compared to REN (Fig. 3.8). Meanwhile, the total
costs of REN-low LR are 2% higher than REN, which is due to the86CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

Billion US$

assumptions of negative learning rates of NGCC, wind, and nuclear in this
scenario (see Table 3.1). Interestingly, when the high learning rates areapplied (REN-high LR), the total costs become lower than REN even by2025. This indicates that if progress in energy technologies and theiradoption intensifies, the cost reduction starts earlier, and the total costsof the expanding power sector increase at a slower rate already before
2025, making it attractive to invest in renewable energy.
110
100
90
80
70
60
50
40
30
20
10
0

REN-medium LR
REN-high LR
REN-low LR
REN
Reference2020 2025 2030 2035 2040 2045 2050

Fig. 3.8: Total costs of electricity production under reference and four
renewable energy scenarios

3.4.4.

CO2 emissions

Aligned with the Paris Agreement on mitigation of climate change, theGovernment of Indonesia has announced its targets to reduce greenhousegas emissions by 29% in 2030 against the business-as-usual scenario.
Eleven-percent of the 29% target is allocated to the energy sector. Results
indicate how much the NRE targets will contribute to the achievement of
the Indonesian CO2 reduction target (Fig. 3.9). As expected, all renewable
scenarios, regardless of technological learning, result in lower emissions
compared to REF. In 2030, CO2 emissions from REN reaches 239 millionton CO2e, as compared to 318 million-ton CO2e in REF. This is equal to
25% of CO2 reduction, 6% higher than what is targeted for the power sector.

87CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

Million tons CO2e

From 2030 onwards, the CO2 emissions gap between the reference andthe renewable energy scenarios becomes higher. In 2050, CO2 emissions
under REN reaches 533 million ton, compared to 798 million-ton CO2e
under REF, promising 33% emission reduction. Since the contribution fromachieving the NRE targets goes beyond the country’s NDC target, it cancontribute to the roadmap for rapid decarbonization, which aims atachieving zero net emissions by mid-century or soon thereafter(Rockström et al., 2016).
800
700
600
500
400
300
200
100
0
2030
REN-medium LR

REN-high LR

2050
REN-low LR

REN

Reference

Fig. 3.9: CO2 emissions under the reference scenario and the renewable energy
scenario

3.5. Discussions
The simulation results suggest the following findings.
1). In the reference scenario, the future capacity mix reflects the situationin 2015. This results in fossil fuels continuing to dominate in the futureJava-Bali’s electricity generation mix. In 2025 and 2050, fossil fuelsaccount for 92% and 94% share of the electricity generation mix,
respectively.
2). The renewable energy scenario fulfills the NRE target Stage 1. In theabsence of technological change, it is driven mainly by expanding
geothermal and hydro capacity and by adding biomass. Meanwhile, the88CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

NRE Stage 2 target is achieved by expanding renewables (17%) and
deploying nuclear (14%).
3). The inclusion of technological learning rates significantly alters theelectricity generation mix. Regardless of technological learning pace,solar PV is competitive against other renewables and nuclear.
Meanwhile, in the long-run, wind power is competitive in the scenarioswith medium and high learning rate values.
4). Without considering technological learning, the fulfillment of the NREtargets increases the total costs of electricity production by 15%. The
incremental costs become 4% and 10% lower, respectively, when themedium and high paces of technological learning are considered, but
the effect changes non-linearly with the learning rate.
The fulfillment of NRE targets provides co-benefits in terms of reducing
CO2 emissions. By 2030, CO2 emissions decrease by 25% as compared to
the reference scenario, thereby assuring the achievement of the power
sector’s CO2 emission reduction target.

3.6. Conclusions
The aim of this chapter is to analyze the long-term capacity expansion in
the Java-Bali power system in Indonesia, considering the national NRE
targets. To the best of our knowledge, this dissertation is the first toassess the impacts of this national policy quantitatively considering
technological learning. On the methodological side, this chapter makes aninnovative contribution to the literature by accounting for ETL in theanalysis of future electricity supply using LEAP. This chapter employs aunique detailed dataset to simulate five scenarios for capacity expansionof the Java-Bali system. The reference scenario assumes a business-asusual electricity generation mix and no technological change. Renewableenergy scenarios assess power system expansion under the nationalenergy policy in Indonesia, using new and renewable energy targets asconstraints. Furthermore, I differentiate between the standard renewable89CHAPTER 3 FROM FOSSIL FUELS TO RENEWABLES

energy scenario with fixed technological costs and three renewable energy
scenarios that include technological learning of electric power
technologies.
Overall, the results of this chapter indicate that the incorporation of ETL
into the LEAP simulation for the capacity expansion of the Java-Bali
power system enables accelerating the renewable energy penetration inJava-Bali islands.
This chapter provides a framework to explore the cost-reducing effect oftechnological learning in the power sector using LEAP. Furthermore, it also
indicates the least-cost option for the Java-Bali power system to meet
the NRE targets. Looking further ahead, more detailed researches areneeded to cover transmission capacity and spatial analysis of each power
plant including data on the supply characteristics of intermittent energy
sources, such as wind speeds and solar radiation in each region.

90

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

Abstract
The power sector is a key target for reducing CO2 emissions. However,
little attention has been paid to the sector’s vulnerability to climatechange. This chapter investigates the impacts of severe weather eventsand changes in climate variables on the power sector in developing
countries, focusing on Indonesia as a country with growing electricity
infrastructure, yet being vulnerable to natural hazards. This chapter
obtains empirical evidence concerning weather and climate impacts
through interviews and focus group discussions with electric utilities along
the electricity supply chain. These data are supplemented with reviews ofutilities’ internal reports and published energy sector information. Results
indicate that severe weather events often cause disruptions in electricity
supply—in the worst cases, even power outages. Weather-related power
outages mainly occur due to failures in distribution networks. Furthermore,while severe weather events infrequently cause shutdowns of power
plants, their impact magnitude is significant if it does occur. Meanwhile,transmission networks are susceptible to lightning strikes, which are theleading cause of the networks’ weather-related failures. This chapter alsopresents estimates of financial losses suffered by utilities due to weatherrelated electricity supply disruptions and highlights their adaptation
responses to those disruptions. The findings of this chapter imply thatenergy policies should consider climate change adaptation in the sectoraldevelopment plans.

This chapter is published as a journal article:
Handayani, K., Filatova, T., & Krozer, Y. (2019). The Vulnerability of thePower Sector to Climate Variability and Change: Evidence fromIndonesia. Energies, 12 (19), 3640.

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CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

4.1. Introduction
Climate change has already greatly affected economies around the world(World Bank, 2010). Further warming of the earth by 1.5ºC above the preindustrial level would entail $54 trillion costs in damage to the global
economy by the year 2100, as warned by the IPCC in its special report on
1.5ºC global warming (Hoegh-Guldberg et al. 2018). More frequent andintense extreme weather events, rising temperatures, and shifting
precipitation patterns are expected to impact businesses. These havebrought attention to financial institutions and credit rating agencies, which
now take into account climate change risks into their assessment criteria(Connell et al., 2018; Mufson, 2019). Accordingly, the new international
standard, i.e., ISO 14090, has emerged to assist companies in assessing
climate change impacts and developing action plans for effective
adaptation (Naden, 2019).
The power sector is not immune to climate change impacts; it is affected
through acute, disruptive, extreme weather events, and gradual long-termchanges in climate parameters (Sieber, 2013). Extreme weather events
cause damage in electricity supply infrastructures, leading to disruptions
in electricity supply. Meanwhile, long-term changes in climate parameters,
although not disruptive, can affect the quality and quantity of electricity
supply and demand. For example, an increase in surface air temperature
reduces the power output of gas turbine power plants (de Lucena et al.,2010; Bartos and Chester, 2015; Henry and Pratson, 2016) and changes inprecipitation alter the reliability of hydropower (Syariman and Heru, 2011;
Popescu et al., 2014).
The societal need to assure climate-resilient development has triggeredscientific attention to assessing the vulnerability of the power sector toclimate change. Audinet et al. (2014) discuss climate risks for the power
sector and reveal the management approaches to deal with these risks
taken by 12 electric utility companies operating in Canada, Taiwan, India,
Australia, South Africa, and the UK. Similar studies were conducted for94CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

Asia and the Pacific (Peter et al., 2012), the European region (Tobin et al.,2018), some areas of United States (Li et al., 2014; Vine, 2012), and Norway(Seljom et al., 2011). Other studies focus only either on the supply or
demand side: for thermal power plants (van Vliet et al., 2012; Sieber, 2013;
Cook et al., 2015), hydroelectric power plants (Popescu, Brandimarte andPeviani, 2014; de Lucena et al., 2009; Syariman and Heru, 2011), biofuel(de Lucena et al., 2009), solar power (Jerez et al., 2015), wind power(Pereira de Lucena et al., 2010), T&D networks (Panteli and Mancarella,
2015; Ward, 2013), and for the electricity demand (Véliz et al., 2017;
Kaufmann et al., 2013).
In general, scholars agree that thermal power plants are affected by rising
air ambient and water temperatures. Similarly, renewable energy, such as
wind, solar, and hydropower, are also affected by climate variability andchange, raising a concern on climate change mitigation and adaptation
nexus. Alterations in wind speed may influence the optimal match between
the wind energy source and the wind turbines power curve (Schaeffer etal., 2012). Furthermore, increasing air temperature reduces the efficiency
of solar power. Moreover, extreme weather events and change in climate
variables also affect the hydropower operation with uneven impacts
between regions (de Lucena et al., 2009; Popescu et al., 2014; Syarimanand Heru, 2011). Similarly, impacts on electricity demand vary between
regions, indicating a net decrease in demand in colder regions while, on
the contrary, implying a net increase in demand in warmer regions (Seljom
et al., 2011; Véliz et al., 2017)
Most of these studies use simulation models to assess the projectedclimate change impacts on future electricity demand and supply. However,assessments of such effects based on empirical evidence are scarce.
Moreover, current literature focuses on assessing one segment of theelectricity supply chain: the power generation. Meanwhile, there is little
information regarding climate change impacts on other segments of thepower sector, which include transmission and distribution networks(Cronin et al., 2018). This chapter contributes to covering this gap in the95

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

literature on the relationship between energy and climate change byaddressing the two research questions: How do severe weather eventsand gradual changes in climate variables affect the entire segments of thepower sector? What are the current adaptation practices taken by electricutility companies?
The chapter focuses on the power sector in developing countries. It ischaracterized by supply shortages, aging equipment, poor performances ofenergy utilities, capital flow barriers, yet rapid expansions of the grid, andslow technology diffusion (Bhattacharyya, 2010; Nicolas et al., 2019).
These factors reduce the reliability of the power sector and increase its
vulnerability to natural shocks (Nicolas et al., 2019). As such, it is alreadyvulnerable to present-day weather and climate, let alone future climate(Audinet et al., 2014).
Moreover, developing countries have a low adaptive capacity—socially,
technologically, and financially (UNFCCC, 2007b). Hence, climate change
impacts might undermine the vital role of the power sector in allowing
socio-economic development that is so crucial for enabling any progressalong UN SDGs in these countries. At the same time, the IPCC’s fifth
assessment report highlights the scarcity of literature on climate change
vulnerability, impacts, and adaptation in developing countries (Clarke et al.,2014). By taking Indonesia as a case study, this chapter investigates thehistorical effects of severe weather events and gradual changes in climate
variables on the power sector and estimates the losses suffered byelectric utility companies due to weather and climate-related electricity
disruptions. Furthermore, this chapter identifies adaptation measuresalready taken by electric utilities to deal with those disruptions. The
investigation relies on extensive fieldwork in Indonesia, which comprisedof interviews, focus group discussions (FGD), and reviews of utilities’internal reports and published energy sector information.
This chapter makes several original contributions to the literature. Firstly,
it is among the first attempts to thoroughly examine the historical impacts
96CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

of severe weather events and changes in climate variables on different
segments along the power sector supply chain. Hence, this chapter
provides new data that add to our understanding concerning weather andclimate impacts on the production, transmission, and distribution ofelectricity, focusing on the direct damage costs suffered by electricutilities. Secondly, this chapter identifies measures taken by electricutilities to adapt to severe weather events and gradual climate change;
therefore, it offers some important insights into firms’ capacity to adapt
to the changing climate. Finally, this chapter focuses on the power sector
in a developing country, thereby adding to the scarce literature on climatechange impacts, adaptation, and vulnerability of the sector in developing
countries.
The remaining part of the chapter proceeds as follows: Section 4.2
presents the methodology; Section 4.3 elaborates the effects of weather
and climate on the Indonesian power sector and discusses responses andadaptations measures taken by the electric utilities; Section 4.4 highlights
the major findings; Section 4.5 draws the conclusions.

4.2. Methodology
To understand the actual effects of severe weather events and changes
in climate variables on the Indonesian power sector, an extensive fieldwork
was carried out in February-March 2018. An overview of Indonesia’svulnerability to climate change is provided in Appendix 4.A. The fieldwork
focuses on the Java-Bali power system, covering the entire key segmentsof the power system: power generation, transmission, and distribution.
While the power generation segment produces the electrical energy, thetransmission networks, which consist of substations and transmission
lines, transfer the high voltage electricity over long distances before
reaching end users. Finally, the distribution networks facilitate theelectricity delivery from the grid to the end user’s meter at reduced,
usable voltage levels.

97

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

The sources of the Indonesian electricity supply are primarily coal, naturalgas, and hydropower. Hence, the investigated power generation plants
comprised of major power plants of these types (Table 4.1). In total, thefieldwork covered power plants that constitute 35% of the total power
generation capacity in the Java-Bali electricity grid.10 Any disruptions that
occur in these power plants, including weather and climate-related
outages can cause power shortage throughout the whole Java-Bali powersystem. For the T&D segments, this chapter includes the entire T&D
networks throughout the Java-Bali islands (Fig. 4.1).
Table 4.1: Indonesian power plants where primary data collection has taken place
Power plant

Utility

Saguling
CirataPriok
Muara Karang
Tambak Lorok
Pesanggaran
SuralayaTanjung Jati B
PJB Paiton
Paiton #9

IP
PJB
IP
PJB
IP
IP
IP
PLN
PJB
PLN

Installed
Capacity
(MW)
797
1,008
1,900
909
1,350
325
3,400
2,640
800
660

LocationWest JavaWest Java
JakartaJakarta
Central Java
Bali
BantenCentral Java
East Java
East Java

PrimaryenergysourceHydro
Hydro
Natural gas
Natural gas
Natural gas
Natural gas
Coal
Coal
Coal
Coal

Data
collection
FGD
FGD
Interview
Interview
Interview
FGD
Interview
Interview
Interview
Interview

Note: IP = PT Indonesia Power, PJB = PT Pembangkitan Jawa Bali

The fieldwork consisted of in-depth semi-structured interviews (n = 28)
and five FGDs. The interviews involved key informants, holding various
position levels (e.g., high and middle management levels, supervisory level,
staff, and operators) of three electric utilities, who work across multiple
business units (e.g., head office, load control center, transmission office,distribution office, and power plant). Meanwhile, the FGD was carried out
at three power plants, one transmission office, and one distribution office.

10

Source: own calculation based on KESDM (2017) and PLN (2017)98

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

The respondents are employees of the utilities who are responsible for theoperation and maintenance of power plants, transmission, and distribution
infrastructures at different position levels.
Prior to the fieldwork, I conducted a systematic review of studies that areconcerned with the impacts of climate change on the power sector. After
which, I drew six questionnaires (Tables 4B.1 to 4B.6, Appendix 4.B) for
guiding interviews and FGDs, which were structured differently for eachtarget group.
1

RENCANA KELISTRIKAN SISTEM 500 kV JAWA-BALI

SRLYA
~
CLGON

23

LNTAR

DKSBI
KMBNG

7
CWANG

BLRJACKUPACWGBR
LGKNG

XBOGORMTWAR~

MKRNG PRIOKCBATU
CIBNGDLTMS

DEPOK

UBRUG

LEGEND

IDMYU

BKASI
TMBUN

GNDUL

4
5

TJATI B

CRATA
MDCAN
BDSLN
CKLNG

UJBRG

7
RMBNG

6
TBLRK

TJWAR

GRSIK

UNGAR

BTANG

NGBNG SBBRT

TASIK

RWALO
AMPEL
Case studies’ power plants
1. Suralaya coal-based power plants
PEDAN
2. Muara Karangnatural gas-based power plant
3. Priok natural gas-based power plant
4. Saguling hydroelectric power plant
5. Cirata hydroelectric power plant
6. Tambak Lorok natural gas-based power plant
7. Tanjung Jati B coal-based power plant
8. Paiton #1 & #2 coal-based power plant
9. Paiton #9 coal-based power plant
500 kV substation
Planned 500 kV substation
10. Pembangkit
Pesanggaran gas
and oil-based
power2017-2026
plant*) COD
Berdasarkan
RUPTL

WARU
SBSLN

GRATI
KDIRI
TWLAN

BNGIL

8

9

PITON

WTDDL
NTSRI

10
500 kV transmission line

Planned 500 kV substation

3

Fig. 4.1: Power plants and extra-high-voltage Java-Bali transmission networks
covered by this chapter, modified from PLN (2018). While this figure depicts only
extra-high-voltage transmission networks, this chapter also includes highvoltage transmission networks and low-voltage distribution networks.

The interviews and FGDs were supplemented with reviews on secondary
data, which consisted of utilities’ internal reports and published energy
sector information (Table 4.2). These data were used to validate andtriangulate the results derived from the interviews and FGDs.
The findings of the fieldwork are summarized in a matrix that presents theidentified impacts of weather and climate for each segment of the powersystem, including estimates of financial losses suffered by the utilities andtheir adaptation responses. I estimate financial losses using the energynot-supplied (ENS) data associated with the weather- and climate-related
disruptions in power plants, transmission lines, and distribution networks.
99

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

The ENS data is multiplied by the average electricity tariff for a disruption
event in the corresponding year. Meanwhile, there is only limited data
available regarding the cost incurred by the utilities to repair the physicaldamages of their assets caused by severe weather events. Therefore, thechapter reports these costs per case, data permitting. There is hardly any
data available regarding the probabilities of adverse events, which prohibits
me from reporting the expected annual damages. Likewise, the data
regarding costs associated with adaptation actions taken by the utilities
is hardly available, due to proprietary data protection or lack of proper
documentation.
Table 4.2: Sources of secondary data
Data

SourcesYear Covered

Disruptions in power
plants- P2B internal reports- Media covers- Suralaya CFPP’s
internal report

2011-20172007, 2008, 2011, 20132011-2017

Disruptions intransmission lines- P2B internal reports- Media cover

2011-20172013

Disruption in distribution
networks- PLN internal reports- Media cover

2014-2015
2013

Precipitation, water
inflow, and water spill

Cirata and Saguling
HEPPs internal reports

Cirata: 1988-2017
Saguling: 1986-2017

The Java-Bali peak load

P2B internal report2014

Air temperature

BMKG database center

2014

Average electricity tariff

PLN statistics

2011-2017

Note: BMKG = the Agency for Meteorology, Climatology, and Geophysics

4.3. Results: Weather and climate effects and adaptive
responses of the power sector
The fieldwork reveals that weather and climate affect all segments of thepower system: generation, transmission, and distribution. Disruptive
severe weather events cause most of the identified effects. These include
heavy wind, heat waves, lightning, and heavy precipitation, which can lead100

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

to floods and landslides. The effects of gradual long-term changes inclimate variables revealed during the fieldwork include rising ambient air
and seawater temperatures, changes in precipitation patterns, and sealevel rise. Tables 4.3 to 4.5 summarize findings regarding weather andclimate impacts on power generation, transmission, and distribution
segments of the power sector, respectively, including financial losses
suffered by the utilities and their adaptation responses. The following
sections elaborate findings of the fieldwork.
4.3.1.

Impacts of severe weather events

4.3.1.1. Disruptions due to heavy precipitation
Flood and landslide affect electricity supply infrastructure
Heavy precipitation increases the chances of flooding in low-lying delta
areas, making coastal thermal power plants and their associated T&D
substations are vulnerable to flooding. According to the interviews andreviews on various documents, flooding generally results in acutesituations. A notable example is a severe flood that occurred on the
Northern coast of Jakarta in January 2013 that forced a 909-megawatt
natural-gas-based power plant to shut down for 12 days.
Moreover, at least 546 units of an inundated distribution substation wereturned off. These instances led to an estimated loss of 15.3 million USD
suffered by electric utilities due to the disruptions of electricity productionthat month and physical damages of the power plant, transmission, anddistribution equipment. The societal loss could be much more significant,considering the vital function of the power plant and its associated T&D
networks as the backbone of the electricity supply to the capital city.
Assessing these indirect effects are out of the scope of this fieldwork andrequire modeling efforts.
Besides the severe flood in 2013, PLN documentation also reveals flooding
events for the years 2014 and 2015. There were 354 and 19 floods
recorded, respectively, which affected the Java-Bali distribution networks.
During the flooding, PLN deliberately turned off the inundated distribution
101

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

substations and lines for safety reasons. The ENS due to these events
reached 6.3 gigawatt-hours (GWh), with an estimated loss of nearly 0.5
million USD suffered by the utility over the two years.
Heavy precipitation often causes failures in distribution networks
Respondents of distribution offices and results of a review on PLN internalreports indicate heavy precipitation as the second primary cause of
weather-related outages in distribution networks. By the end of 2015, thelength of distribution lines throughout the Java-Bali islands was a 22,553kilometer circuit (PLN, 2017a). These lines pass through many trees, which
include community, agricultural, and forest trees. Since the majority ofthese lines consist of bare overhead conductors, they are sensitive tocontact with tree branches or other objects. During heavy precipitation,
wet tree branches along the distribution line become heavier and often
touch the bare conductors causing short circuits and triggering theprotection system to turned off the power. Moreover, if heavy precipitationis accompanied by severe lightning, PLN deliberately cuts off the power
supply to avoid the channeling of lightning current to houses through the
distribution lines’ conductors.
Between 2014 and 2015, heavy precipitation caused 1,048 events of power
outages in the Java-Bali distribution network. These events result in an
8.3 gigawatt-hour ENS that is equal to an estimated loss of more than 0.5
million USD over the two years. The FGD confirms that the utility is wellaware of the risks posed by heavy precipitations. Therefore, preventive
measures have been taken to minimize the risks (Table 4.5).

102

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE
Table 4.3: Impacts of severe weather events on power generation and the utilities’ adaptation responses. Source: own fieldwork.
Weather- andclimate-related event

Identified impacts

Estimated utilities’ financial
losses (USD)

Adaptation responses

Heavy precipitation



Soaked dry coal  reduced
burning efficiency

Suralaya CFPP, 2011-2017:21.5 million.



Wet coals clogged the coalfeeders  reduced power output

 Construction of sheds toprotect the coal storage areafrom rainwaterIncreased river flow  morewaste in the sea  impeded
power plants’ water uptake

Muara Karang, Priok, andTambak Lorok NGCC power
plants, 2011-2017: 15 million

 Additional work hours for waste
cleaning and transportationDays of heavy rainfall  highwater inflow to the hydropower
reservoir  reservoir’s water
spills  flooding downstream

N/A Adjust hydropower operation
pattern to maintain a normal
reservoir’s water level

Flooded electricity infrastructure

Muara Karang NGCC power
plant, 2013: 6.2 million

 Construction of flood control
systems
 Mobilization of portable water
pumps for draining the water Elevation of some areas ofpower plants

Interruptions in coal shipping to
CFPPsreducedpower
outputs/shutdowns

Suralaya power plant, 20112017: 1.2 million

 Additional coal shipping fleets Changes in the type of fleets(to a stronger and larger vessel)

Heavy wind and highsea waves103

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

Weather and climaterelated event

Identified impacts

Estimated utilities’ financial
losses (USD)

Adaptation responses

Jellyfish inflow

Jellyfish inflow into the cooling water
system of thermal power plants 
reduced power output/shutdown

Paiton #9 CFPP, 2016: 21.3
million Installation of fishnets
 Cleaning of the water intake
area from jellyfish

HeatwavesWarm ambient air temperature 
reduced efficiency of gas turbine andgas/diesel engine power plants

Muara Karang NGCC power
plant, 2016: 4,250

 Installation of an inlet air
cooling system in gas turbineunitstomaintainits
performance during hot ambient
air temperatures

Drought

Critically low water inflow  reduced
power output

Saguling and Cirata HEPPs,
2011: 51.5 million Adjustmentsofmonthly
operation pattern plans Applicationofweathermodification technology

Sea level rise

Tidal flooding  interrupted daily
activities of the employees

Coastalflooding
occursperiodically in Tambak Lorok
and Priok power plants

 Elevation of some areas ofpower plants

Sea surface
temperatureWarm seawater  reduced efficiencyof the cooling water system

N/A Monitoringof
temperatures104

seawaterCHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE
Table 4.4: Impacts of severe weather events on transmission networks and the utility’s adaptation responses. Source: own
fieldwork.
Weather and climaterelated events

Identified impacts

Estimated utility’s
financial losses (USD)

Adaptation responses

LightningLightning strikes  overvoltage
and flashover  power failures andtransmission equipment damages

2011-2017: 524,091

Installation of themonitoring systemImprovement of the lightning
protection systemlightning

Heavy windPower failures due to objects being
blown onto the conductors or
heavy wind detaching the
conductors from the isolator

2011-2017: 22,139

Repair the damaged equipment.

Flood



Inundated substations weredeliberately turned off for safety
reasons

Flood in Jakarta, 2013:
9.1 million



Elevated the flood-prone
substations

105

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE
Table 4.5: Impacts of severe weather events on distribution networks and utility’s adaptation responses. Source: own fieldwork
Weather andclimaterelated event

Identified impacts

Estimated
utility’s financial
losses (USD)

Adaptation responses

Heavy windUplifted objects damage bareconductors  power failures

2014-2015:
millionConduct routine inspections
Alert the 24-hour technical service teams
Apply segmentation of protection systems to
avoid widespread power outages

Heavy
precipitation



Wet and heavy branches touched
the bare conductors  power
failures2014-2015:
575,152



Check and maintain distribution networks (e.g.,clear distribution lines from tree branches) before
the wet season come
Alert the 24-hour technical service teams

13.1


Flood



Inundated substations weredeliberately turned off for safety
reasons.

2014-2015:
455,605

Landslide106

Heavy precipitation and heavy
winds triggered landslides 
damaged distribution networks

2014-2015: 9,234



Identify and elevate flood-prone substations
Replace old underground conductors that were
floodedEstablish a computer application to monitor thearea affected by flood-induced power outages
Establish a disaster recovery center and a special
service team for post-flood recovery.
Identify landslide-prone poles
Improve poles’ foundation

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

Heavy precipitation reduces coal quality
CFPPs generally store coals in open areas (coal yards). As heavy rainfall
soaks the dry coal, the plants’ burning efficiency reduces. Moreover,rainwaters generate excessive coal runoff, which in severe cases,
inundates the coal yards. Respondents highlight that, subsequently, thecoals turn into sticky sludge, causing clogging in the coal feeder, which
prevents a continuous, reliable coal supply to the power plants’ burningchambers and reduces power output. For example, the document reviews
reveal that the Suralaya CFPP has experienced 544 occurrences of apower output reduction due to a clogged coal feeder during 2011-2017.
Consequently, the total ENS over the seven years for this power plantalone reached 277.6 GWh, resulting in estimated losses of 21.5 million USD.
The power plant reacted by building a shed in its coal yard, which currently
accommodates around 10% of the coal storage area.
Heavy precipitation brings waste to power plants’ water intake area
Heavy precipitation increases river flows, which in turn bring more waste
into the sea. An increased volume of waste in the sea creates problems
for coastal power plants. The respondents of coal- and gas-based power
plants stress that small-size waste escapes water intake filters and enters
the cooling water system, causing clogging in the water-circulating pump,and plugging in the condenser. Interviews and utilities’ documentations
point to the fact that waste has forced some units of natural gas-based
plants to shut down temporarily in severe cases.
The fieldwork finds that increased waste driven by heavy rainfall affected
three natural-gas-based power plants of the case studies: Muara Karang,
Tambak Lorok, and Priok. A high-quantity of waste on water intake areas
have caused reductions in their power outputs. In total, the ENS due tointerruptions in these three power plants nearly reached 190 GWh, causing
an estimated loss of 15 million USD during 2011-2017. Furthermore, theMuara Karang power plant recorded eight events of waste-related forced
shutdowns over the considered seven-year period.
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The massive amount of waste requires a significant number of workforce.
For instance, at the Muara Karang power plant alone, the waste cleaning
in its two water intake areas involves at least 80 personnel who work in
shifts for 24 hours in two water intake facilities. The collected waste istransported outside the plant twice a week. However, during the peakamount of waste, daily transportation is needed, as respondents reported.

4.3.1.2. Disruptions due to heavy wind
Heavy wind damages electricity supply infrastructure
Heavy wind is by far the main threat for the Java-Bali distribution
networks (Table 4.4). For example, during 2014-2015, it was responsiblefor 95% of the weather-related power outages. The interviews withrepresentatives of distribution offices indicate that heavy wind can knock
down trees and billboards and throw them and other objects onto the bareconductors of the distribution networks, thereby causing power outages.
Multiple occurrences of heavy wind over the two years resulted in 275
GWh of ENS, leading to an estimated loss of 13.1 million USD for PLN over
the two years.
Meanwhile, in the transmission networks, there were only 12 heavy-windrelated power outages recorded from 2011 through to 2017, owing to theirstronger tower structure and higher conductors as compared to
distribution networks. These events resulted in 0.28 GWh of ENS, whichequals to 22,139 USD of financial losses over the seven years (Table 4.3).
Respondents identify three reasons for the heavy-wind-related outages
in T&D networks: (i) heavy wind blew tree branches or other objects onto
the conductors; (ii) heavy wind caused the conductors to swing and hit
the pole/tower’s body; (iii) Heavy wind detached the conductors from theinsulators.
Respondents of the distribution offices state some proactive measuresthey have taken responding to heavy-wind-related disruptions. They
regularly monitor weather forecasts; in case a heavy wind is approaching,
they alert its 24-hours technical service teams and ensures sufficient108CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

people and materials available to overcome any damage that may occur
when the heavy wind comes. Beside corrective measures, they also
include preventive measures by conducting inspections on its distribution
networks. The inspection includes cutting tree branches that could
potentially harm the distribution network when heavy wind occurs. The
control also checks and maintains pole stands to ensure that they aresupported by an adequate foundation. In case a power outage occurs due
to severe weather, they prevent widespread power outages by applying
the segmentation of the distribution’s protection system. Meanwhile, theresponse of transmission offices to heavy-wind-related disruptions so far
has been on the investigation of the cause of power transmission failures
and repairing the damaged equipment without any ex-ante adaptation
actions.
Heavy winds also cause damages to power plants’ facilities. For example,
in 2007, a heavy wind slammed two coal ships, causing them to crash into
the coal loading facility of the Tanjung Jati B CFPP. Consequently, thecoal unloading facility did not function for two weeks, which resulted in a
critical stock of fuel coal in the power plant, forcing the plant to shut down
for some time. Furthermore, in March 2017, a heavy wind lifted the roof ofthe coal yard’s shed of Paiton #9 CFPP and crooked some of the shed’s
pipes. Respondents state that nothing has been done in terms of improving
the resiliency of infrastructure against the severe weather.
Heavy wind disrupts the fuel-coal stock
The interviews with representatives of head office and coal fired-power
plants reveal that heavy wind and high waves in the Java Sea affect coaltransportation to the Java island. The CFPPs sited in Java rely on seashipped coals from coalmines of other islands. During heavy winds, coalloading and unloading activities are prohibited, which causes a longer
waiting time for coal deliveries. Moreover, high waves in the Java Sea,
which often occur during the wet season, prevent coal barges fromcruising. These circumstances jeopardize the stock of coal at CFPPs,
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which often lead to reductions in the plants’ power outputs. For instance,
in 2007, Tanjung Jati B CFPP experienced critical fuel-coal stocks twice–in March and December– due to severe weather in the Java Sea. The
media reported forced shutdowns of the CFPP for days due to thedisruptions in coal supply (Damayanty, 2008; ‘Tanjung Jati’, 2007).
Consequently, the costly oil-fueled power plants were operated to
substitute such capacity loss in the Java-Bali grid, causing an additional
cost of 1.6 million USD per day during those critical days in March andDecember 2007 (Detikfinance, 2007).
Recent data from the fieldwork show that severe weather events in theJava Sea continue posing threats to the security of coal supply for CFPPs.
In December 2017, the average coal stock of Tanjung Jati B was only
enough for 13 days of operation, which was halved of the planned stock of
25 days. By the end of January 2018, the stock was only enough for threedays of operation, leaving the plant vulnerable to a forced shutdown.
Similarly, the average coal stock of PJB Paiton power plant in January
2017 reached a critical point, i.e., less than three days. In Suralaya CFPP,
the disturbances in coal stock between 2011 and 2017 resulted in an
estimated loss of 1.2 million USD in total.
As per the interviews, to avoid critical fuel-coal stocks during severe
weather events, CFPPs responded by revising their contracts to add thenumber of coal shipments and to use stronger and larger vessels.

4.3.1.3. Heatwave effects on electricity supply and demand
Heatwave reduces generation capacity of natural-gas-based power plants
The heatwave is particularly a concern for natural-gas-based power
plants. Interviews with representatives of these power plants indicate thatwarm air temperatures affect the gas turbines and gas engines of thepower plants. The operation of this type of power plant requires ambient
air for compressor intake, which is then pumped into the burning chamber.
The higher the air ambient temperature, the lower the air density and,hence, the burning efficiency, which subsequently reduces power outputs.
110CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

Utilities recorded disruptions in several natural-gas-based power plants
attributable to heatwaves. On March 17, 2015, two units of gas turbines in
the Muara Karang power plant reduced their power outputs by 14% and
17% each due to the hot temperature outside, which was measured at
35ºC. Similarly, on May 3, 2016, two other units of the same power plant
experienced a reduction in power output by 3% of their capacity due tohigh ambient air temperature. The latter caused an estimated loss of 4,250
USD suffered by the power plant on that particular month alone. Anothercase that is worth mentioning is a disruption that occurred in the
Pesanggaran power plant. During the fieldwork visit on March 1, 2018, theambient air temperature was 33ºC. At this temperature, the engine could
not deliver the optimum power output. The screen in the control roomshows "automatic derating" notification with an explanation of the cause:a high ambient air temperature. The information means that the engine
automatically reduces its output due to high air temperature.
The FGD and interviews reveal that reductions in power outputs due towarm-ambient air temperature have not been a substantial issue for theJava-Bali electricity grid as a whole. However, for the Bali subsystem, it
was a significant problem in 2010 due to its limited power generation
capacity then, while the power transfer from the Java subsystem wasconstrained by a technical barrier in the transmission networks. Hence, it
was essential to optimize power output from local plants in Bali. Therefore,
the Pesanggaran power plant in Bali adapted by installing an inlet air
cooling system in its gas turbine units to maintain the machine
performance during hot temperatures.
Heatwave increases electricity use
Respondents confirm an increase in electricity demand during hottemperature days. An IP internal report reveals that in the Bali subsystem
alone, the difference in peak demand between hot and cold days/nights
can reach 100 MW—around 12% of the peak demand in 2017. Hence, asurge in electricity demand due to warmer temperature directly affects
111

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

the power system, as the system should be able to accommodate any
surge in electricity demand. In the long-term, an increased mean
temperature will require a more substantial investment in power
generation, transmission, and distribution networks to cope with theincreased energy use (MWh) and higher peak demand (MW).

4.3.1.4. Disruptions due to lightning strikes
Respondents from the transmission offices point to lightning strikes as theleading cause of weather-related disruptions in high-voltage transmission
lines. According to the interviews, a lightning strike can induce an
overvoltage on transmission lines and cause a flashover, which results in
power failures, and damages power transmission equipment. Therefore,transmission lines generally equipped with shield wire and insulation toprotect them from a lightning strike (Table 4.3). However, lightning strikes
that occur too frequently can reduce the lifetime of the protective
equipment, causing an increase in transmission’s failure and its
maintenance costs. The fieldwork reveals 107 events of lightning strikerelated power outages in transmission networks throughout the Java-Bali
power system during 2011-2017. The total ENS during the same period
was 67 gigawatt-hour, which is equal to 0.52 million USD over the six
years.
Being aware of the risks posed by lightning, PLN established a lightning
monitoring system, which derives the data regarding the occurrence oflightning strikes in the transmission network areas. The monitoring systemproduces a lightning density map and data regarding the exposure oflightning to the transmission network. The transmission offices compare
these data with the disruption events in the transmission networks toconclude whether a disruption occurred due to lightning or something else.
In case lightning strikes are found responsible for any disruptions in a
specific transmission line or substation, PLN improves the lightning
protection facilities at this location. Such improvements include theinstallation of transmission line arresters and the improvement of112

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

grounding systems and earth wire conductor materials. PLN also uses
lightning data as a consideration for the planning of the transmission
network’s development.

4.3.1.5. Disruptions due to jellyfish inflows
This chapter adds the ‘jellyfish outbreak’ into the extreme category
because, in this context, it is associated with a drastic seawatertemperature change (Rafiq, 2016). Jellyfish inflow on thermal power plants
is an extreme event that occasionally occurs on the Northern coast ofJava. According to interviews, jellyfish often slip off screening facilities forthe inlet water, causing a problem in the cooling water system of CFPPs.
A severe jellyfish inflow can cause a reduction in power generation
capacity or even force the power plant to shut down. For example, in April
2016, the jellyfish inflow to Paiton #9 CFPP forced the plant to shut downfor 20 days, causing an estimated loss of 21.7 million USD due to ENS
alone. According to BMKG, the jellyfish outbreak was triggered by anextremely cold temperature in the Australian sea, forcing jellyfish to
migrate to the North Java Sea (Rafiq, 2016).
The first emergency response to the jellyfish inflow at that time wascleaning and spraying the traveling screen, flushing the inlet of water
intake, and installing fishnet on the water intake canal. However, these
responses were not enough to block the jellyfish inflow, and in the end,
the plant was forced to shut down. After the plant was shut down, manual
cleaning of the water intake canal was carried out, involving 90 personnel,
including divers. The damaged equipment was also repaired. The shutdown
of Paiton #9 CFPP caused a power supply shortage in the Java subsystem.
Consequently, the Java subsystem had to import the expensive oil-based
electricity from the Bali subsystem.
Jellyfish inflow also occurred in the water intake facility of Tanjung Jati B
CFPP. The jellyfish inflow blocked the water uptake of the cooling water
system, which eventually caused reductions in power output. From 20112017, there were 11 occurrences of jellyfish inflow that affected the113CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

Tanjung Jati B electricity productions. The problem was generally solved
within 0.5 to a few hours. The estimated losses suffered by Tanjung Jati
B due to the jellyfish-induced reductions in the plant’s energy production
reaches 0.3 million USD over the seven years. Apart from the jellyfish
migration from the Australian sea, the jellyfish outbreak can be attributed
to among others, a warmer sea temperature (Purcell, 2005; Aljbour et al.,2019). So far, the power plant has taken ex-post measures such as
installing fishnets and cleaning the water intake facility from the jellyfish.
4.3.2.

Impacts of gradual changes in climate variables

4.3.2.1. Changes in precipitation pattern affect hydroelectric power plants
The FGDs with representatives of two hydropower plants conclude thatchanges in precipitation patterns affect the hydropower operation alreadytoday, raising concerns about future climate conditions. My analysis basedon the power plants’ data confirms the strong correlation between
precipitation, water inflow to the reservoir, and electricity production ofhydropower (Figure 4C.1 and 4C.2 Appendix 4.C). The primary climateinduced challenge for hydropower is, therefore, to manage the situation
during extreme wet and dry seasons. For example, reductions in generated
capacity due to long-lasting drought were observed in 1997, 2003, 2006,
and 2011. In 2011 alone, both Saguling and Cirata HEPPs only utilized 87%
and 75% of their design capacities, respectively, resulted in 51.5 million of
ENS-related financial losses. Furthermore, in September 2011, they were
only able to operate half of their capacity and only to serve the peakdemand (Musim Kering, 2011).
Table 4.64.6 compares the energy generated in extremely dry years with
their design capacities and estimates the ENS due to the lack of water.
In contrast, days with heavy rainfalls result in high water levels of thereservoirs. This situation gives a positive impact enabling the HEPPs toproduce electricity at their maximum capacity. However, excessive water
inflow can lead to water spills, which should be avoided as much as
114

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

possible as it can increase the downstream flow and cause flooding. For
example, heavy precipitations along the Citarum watershed in March-April
2010 led to a significant inflow to the two reservoirs and also to anotherreservoir of the same watershed, causing lasting downstream flooding(Syariman and Heru, 2011). Fig. 4.2 presents historical water levels andenergy spills of the Saguling reservoir, indicating that peaked water inflows
are followed by water spills, which calculated as energy spills.
Table 4.6: Percentage of electricity production in dry years compared to design
capacities. Source: own analysis

1997
2003
2006
2011

Percentage of energy generated
compared to design capacity
Saguling
Cirata
61%
60%
81%
67%
70%
62%
87%
75%

Total ENS for both
HEPPs in GWh

200

644
643
642
641
640
639
638
637
636
635
634
633
632

150

GWh

1,400
882
1,174
637

100
50

1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016

0

mmsl

Dry years

Energy spill (GWh)

Water level max (mmsl)

Fig. 4.2: Annual maximum water levels and energy spills: Saguling HEPP from1987 through to 2016. Source: own fieldwork.

Both HEPPs take managerial and technological adaptation responses toaddress low water inflows. The former is done through an adjustment of
the HEPPs’ operation pattern plans. In the normal situation, Cirata andSaguling HEPPs can be called to supply electricity at any time to meetdemand and to stabilize the frequency of the power system. However,115

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

during low water levels in reservoirs, they are only operated to serve theincreasing energy demand at peak hours (Musim Kering, 2011). The
technological response is through an application of weather modification
technology that creates artificial rain, which was done in 2011 whenextreme drought occurred. Meanwhile, when there is excessive water
enter the reservoirs, the HEPPs should maintain the standard reservoir’swater level by maximizing water use for producing electricity. However, insevere cases, water spill could not be avoided.

4.3.2.2. Warmer ambient air temperature increases electricity use
The mean annual temperature in Indonesia has increased by 0.04℃ per
decade between 1985 and 2015 (GERICS, 2015). As discussed in Section
4.3.1.3, the interviews indicate a rise in electricity demand during warmer
temperatures. To validate this, this section analyzes the hourly load data,
and the peak electricity demand data collected from P2B, relating thesedata to the average temperature data from 23 weather stations retrieved
from the BMKG database center. As a result, a strong relationshipbetween an increase in electricity use and air temperature rise is found(Fig. 4.3). Therefore, an increase in the mean temperature is expected toboost the average electricity consumption.
Peak Electricity Demand (MW)24,000y = 521.55x + 5788.3
R² = 0.6078

23,50023,00022,50022,00021,50021,00020,50020,000
26

28

30

32

34

36

Average Maximum daily Temperature (ºC)
Temperature
Linear (Temperature)

Fig. 4.3: Peak electricity demand in the Java-Bali grid as a function of maximum
daily temperature: weekdays 2014. Source: own fieldwork.

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CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

4.3.2.3. Seawater temperature affects the cooling water system
The seawater temperature is crucial for coastal power plants, which relyon seawater for their cooling systems. The seawater temperaturecorrelates with the performances of a condenser of a cooling water
system. Respondents of the PJB Paiton CFPP revealed that higher
temperatures resulted in higher pressures of the condenser, which, in turn,
reduced the efficiency of the power plant. Thus, this section examines therelationship between the cooling water temperature and the performanceof a condenser, based on the condenser monitoring data provided by PJB
Paiton. The result shows a strong correlation between the cooling watertemperature with the condenser pressure (Fig. 4.4). Although PJB Paiton
monitors the seawater temperature data and evaluates its impact on the
power plant’s performance, no concrete capital or technological
adaptation response has been taken.

Condenser Pressure (mmHg)710
y = -3.4339x + 796.33
R² = 0.8684

705
700
695
690
685
680
26

27

28

29

30

31

32

33

Temperature ºC

Fig. 4.4: The condenser vacuum as a function of cooling water temperature:PJB Paiton CFPP, half-hourly, December 2015. Source: own fieldwork.

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CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

4.3.2.4. Sea-level rise contributes to tidal flooding
Indonesia has recorded 2 to 10 mm of annual sea-level rise from 1993 to2012 (USAID, 2017). Sea-level rise threatens coastal power plants andtheir associated T&D substations. Tidal flooding often occurs in coastal
power plants, especially during high tide and heavy precipitation. For
example, Priok and Tambak Lorok gas-based power plants are often hit bytidal flooding. Priok, which is located on the Northern coast of Jakarta, liesin the altitude of 0-2 m above sea level. It is a flood-prone area where
nine rivers and two flood canals meet. The lower parts of the power plant
have often inundated, disturbing some activities such as employees’
mobility between buildings. The water inundation level is considereddangerous when it reaches 35 cm, which has occurred in 2007 and 2008.
Respondents point out that tidal flooding has never caused production
failure so far because the powerhouse is located on the higher altitude of
the power plant area. However, in 2007, one of the Priok power plant’stransformers was flooded (Fig. 4.5a). Hence, it was deliberately
disconnected from the grid during the flooding for safety reasons. During
the 2007 and 2008 flooding, the Priok power plant mobilized a portablewater pump for draining the water. After the floods, some areas of theplants were also elevated.
Tambak Lorok power plant is located on the Northern Coast of Semarang,
Central Java at the elevation of 0.75-1.5 m above sea level (IP Semarang,
2017). The sea-level rise in combination with land subsidence makes theplant vulnerable to tidal flooding and future changes in these due to climatechange. The flooding occurs periodically; in fact, when the field visit was
conducted in February 2018, the front gate of the plant was flooded (Fig.
4.5b). As an adaptation to this situation, the Tambak Lorok power plant
constructed a flood control system, which included a polder, water sump
pumps, a wall, and upgraded its drainage system to improve its resilienceagainst seawater surge. Moreover, an additional polder is currently underconstruction to manage water inflow from the front side of the plant.

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CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

(a)

(b)

Fig. 4.5: Tidal flooding affects coastal power plants: Priok in 2007 (a) andTambak Lorok in 2018 (b). Source: own fieldwork

4.4. Discussions
The findings of this chapter reveal the adverse impacts of weather andclimate to the power sector, including past losses both in energy-notsupplied and in USD, and indicate the adaptation measures already takenby the actors in the Indonesian power sector.
119CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

Impacts: Severe weather events and gradual changes in climate variables
affect all segments of the power sector, namely generation, transmission,and distribution. My findings suggest that, in terms of the frequency offailures, the distribution network segment is the most vulnerable to severe
weather events. Meanwhile, weather-related disruptions occur lessfrequently in the power generation segment. Nevertheless, whenever itdoes happen, the magnitude is significant, knocking out power plants fordays and causing tens of millions of USD in the direct damage alone.
Severe weather mainly affects the fuel-coal quality and the coal stock ofcoal-fired power plants, and the reliability of the cooling water system inboth coal- and gas-based power plants. These effects raise concerns
regarding climate-resilience of fossil-fuel-based electricity production,
which to date outprices green electricity options in Indonesia and forms
the core of its future electrification plans. Changes in precipitation
patterns also affect hydropower operation, undermining its role in serving
electricity demand during peak hours. Concerning the transmission
network, the primary cause of weather-related disruptions is the lightning
strike. Yet, the weather-related damage in this segment is minor whencompared to those in the distribution and generation segments.
Adaptation: To some extent, actors throughout Indonesia’s electricity
supply chain already implement adaptation actions in response to severe
weather events, combining managerial and technological interventions. The
technological responses include investments in flood control systems in
power plants, a lightning monitoring system for the transmission networks,
and an application of weather modification technology to create artificial
rain to increase water inflow to hydropower reservoirs. Managerial
responses include altering coal shipment contracts, increasing routine
distribution network checks, and altering hydropower operation patterns.
However, these adaptation responses are limited to ex-post reactions toweather- and climate-related disruptions encountered by utilities.
Furthermore, the responses are fragmented rather than a part of a national
strategy and focus only on assets already adversely affected. Meanwhile,120

CHAPTER 4 THE VULNERABILITY OF THE POWER SECTOR TO CLIMATE CHANGE

a long-term strategic plan that ex-ante anticipates future climate changerelated risks and systematic adaptation responses have not been
considered.

4.5. Conclusions
This chapter aims to systematically investigate the effects of severe
weather events and changes in climate variables on the electricity supply
chain, taking Indonesia as an example of a developing country where
natural hazards already undermine the resilience of the power sector
today. Relying on extensive fieldwork carried out in 2018, this chapter
reveals which, and how severe weather events and changes in climate
variables adversely affect the Indonesian power sector.
The estimation of weather-related damage to the power sector in thischapter is limited to the direct losses suffered by electric utilities, primarily
including energy-not-supplied. It also includes direct economic losses
from damaged assets and equipment when data permits. This chapter,
however, does not account for complete economic losses due to weatherand climate-related widespread power cuts. Hence, business interruptions
and other cross-sectoral impacts of hazards are out of the scope of thischapter.
This chapter contributes to the literature by providing empirical insights
into the actual effects of severe weather events and changes in climate
variables on the entire electricity supply chain in a developing country,
both in physical and monetary terms where data permitted. Moreover, it
provides unique information on the variety of the actual adaptation
responses of actors throughout different segments of the electricity
supply chain to severe weather events. Future research might integrate
the observed and projected climate change impacts into simulations of theelectricity system expansion for realizing a climate-resilient electricity
system and estimating costs and benefits of adaptation of the power
sector to climate change.

121

122

CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

Abstract
The power sector is in the spotlight for reducing carbon emissions.
However, little attention has been paid concerning the impacts of climatechange on the sector. This chapter analyzes the interplay between
electrification, climate change mitigation and climate change impacts andadaptation in the power sector. Taking Indonesia as an example, thischapter integrates both climate change mitigation goals and the impactsof climate change into simulations of an Indonesian power system
expansion using LEAP. First, this chapter quantifies the impacts of climatechange on electricity demand and supply based on historical climaterelated impacts and climate change projections. Thereafter, the quantified
effects, together with renewable energy targets, then integrated into the
LEAP modeling architecture. Results show a substantial alteration intechnology composition driven by the joint climate mitigation–adaptation
scenario compared to the reference scenario. Furthermore, there is anincrease in installed capacity and electricity generation relative to thereference scenario to balance the climate-induced rise in electricity
demand and to compensate for the reductions of the power-generating
capacity under new climate conditions. Consequently, the total costs ofelectricity production increase significantly.

This chapter is based on:
Handayani, K., Filatova, T, Krozer, & Y., Anugrah, P. (2019). Seeking
for climate change mitigation and adaptation nexus: Analysis of along-term power system expansion. Applied Energy. (Under review).

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CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

5.1. Introduction
The energy sector is a significant contributor to global warming.
Accordingly, most of the NDCs submitted by the UNFCCC parties include
reducing emissions from this sector as their strategies to meet theirpledge under the Paris Agreement (UNFCCC, 2016). However, the energy
sector is also vulnerable to the impacts of climate change. Extreme
weather events and gradual changes in climate variables have implications
for the reliability, cost, and environmental effects of the energy supply(Schaeffer et al., 2012; Cronin et al., 2018). Therefore, it is a greatchallenge for the energy sector to meet their climate mitigation goals whileat the same time, cope with climate change impacts. For developing
countries, this challenge coincides with their vital electrification objective
as they face rapid growth in electricity demand.
Significant research has addressed the decarbonization of the power
sector in response to the Paris Agreement. Grande-Acosta and IslasSamperio (2017) provide an alternative scenario to decarbonize the
Mexican power sector by assessing 36 mitigation options using LEAP.
Handayani et al. (2017) also employ LEAP to analyze scenarios for theIndonesian power system to meet both electrification and Paris climate
goals. Dalla Longa and van der Zwaan (2017) adopt TIAM-ECN model toinvestigate the role of low carbon technologies in achieving Kenya’s Parisclimate target. A study for the Malaysian power sector was conducted byHaiges et al. (2017), which utilizes TIMES model to assess the long-term
power generation options considering climate change mitigation. Lee andHuh (2017) propose a diffusion model to evaluate the impact of South
Korean renewable energy policies triggered by the Paris Agreement, on
the diffusion of renewable electricity. Overall, these studies indicate that
the Paris Agreement is achievable but entails various consequences suchas increasing costs and alterations in technology and energy mixes.
While mitigation efforts are vital, it is evident that adverse impacts ofclimate change affect various sectors of social-economic systems already126

CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

today, and these impacts may facilitate or hinder mitigation efforts of thepower sector (Cronin et al., 2018). The previously-mentioned studies pay
little attention to adverse climate change impacts, and yet they are
expected throughout the entire elements of the power system. On the
supply side, climate change impacts include changes in water availability
and seasonality of hydropower, alteration in wind speed frequency anddistribution, reduction in solar cell efficiency, a decline in generation cycle
efficiency and cooling water availability of thermal power plants, as well as
failure and reduction in the capacity of transmission and distribution lines.
On the demand side, climate change alters the balance of heating anddemand patterns (Schaeffer et al., 2012; Cronin et al., 2018; Audinet et al.,2014). Neglecting these impacts can undermine efforts to decarbonize theenergy sector (Cronin et al., 2018).
Furthermore, the IPCC AR5 has recognized climate change impacts andadaptation responses of the energy system as a research gap and urge
their integration into the assessment of climate stabilization path (Clarke
et al., 2014). This is crucial as climate change impacts could influence,
among others, the effectiveness of mitigation options. AR5 also highlights
the scarcity of publications regarding climate change impacts, adaptation,and vulnerability from developing countries (Field et al., 2014).
This chapter aims to address this gap in the literature by conducting aholistic analysis of a low-carbon pathway of the power sector, accounting
for the adverse climate change impacts. It integrates both climate change
mitigation goals and climate change impacts into modeling simulations of
the Java-Bali power system expansion in Indonesia. Indonesia, anemerging developing economy with fast-rising electricity demand, embarks
on a low-carbon energy system by setting out a target to increase theshare of renewable energy by 23% in 2030 and 31% in 2050. This target isin line with the country’s NDC to reduce 29% of CO2 emissions from thebusiness as usual scenario.

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LEAP is used for conducting simulations of the long-term expansion of
the Java-Bali power system. Prior to commencing the simulations, theimpacts of climate change on the electricity demand and supply were
quantified based on fieldwork in Indonesia, revealing the pastconsequences of climate-related events (Chapter 4) and on the secondary
data of global climate change projections. The quantified effects, together
with the renewable energy targets, are then converted into constraints
within the LEAP optimization modeling architecture.
This chapter offers several contributions to the current literature andpractice. Firstly, to the best of our knowledge, this chapter is the first to
jointly consider climate change mitigation goals and climate change
impacts on various elements of the power sector into simulations of powersystem expansion using LEAP. This chapter is novel in the sense that it
explicitly links the sectoral efforts to pursue the low-carbon pathway with
its adaptation to the adverse climate change impacts and fills an important
gap in the literature on the climate-resilient energy sector. Secondly, thischapter adds to the scarce literature on climate change impacts,
adaptation, and vulnerability of developing countries and may serve as areference for policies facilitating a low-carbon and climate-resilient power
sector.
The remaining part of this chapter proceeds as follows. Section 5.2 lays
out the existing literature on modeling a power system expansion underclimate change and outlines knowledge gaps. Section 5.3 describes theemployed methodology, and Section 5.4 presents the results of the LEAP
modeling for each scenario. Section 5.5 discusses the significance of thefindings of the modeling work. Finally, Section 5.6 provides conclusions andlimitations of this chapter as well as recommendations for future work.

5.2. Modeling climate change impacts on the power sector
The recognition of the power sector vulnerabilities to the effects of thechanging climate is growing, as does the need to quantify these impacts,
usually with the help of models. Tobin et al. (2018) investigate the impacts
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of climate change on four electricity supply technologies (wind, solar PV,
hydro and thermal power supply) in 28 EU countries based on EUROCORDEX regional climate model projections. Other studies focus mainlyon the impacts on thermoelectric power plants. Cook et al. (2015) develop
a methodology for predicting thermal power plants' risks of violating
thermal pollution limits due to climate-induced drought and heatwave in
the Upper Mississippi River Basin and Texas. Furthermore, van Vliet et al.
(2012) analyze the impact of changes in temperature and water availability
under climate change on thermoelectric power plants in the U.S. andEurope using a physically-based hydrological and water temperaturemodeling framework in combination with an electricity production model.
Likewise, Zheng et al. (2016) investigate the vulnerability of the Chinese
thermoelectric power plants to water scarcity problems by performing ahigh-resolution evaluation and projection of spatial vulnerability.
Collectively, these studies agree that the adverse effects of changes inclimate variables such as temperature and precipitation reduce the power
generation capacity of thermoelectric power plants.
Meanwhile, adverse impacts of gradual changes in climate variables onwind and solar PV are found to be limited (Tobin et al., 2018; Jerez et al.,2015; Pereira de Lucena et al., 2010). Moreover, Lucena et al. (2010) andPašičko et al. (2012) estimate an increase in wind power production under
the future climate in some parts of Brazil and Croatia, respectively.
Nonetheless, off-shore wind power plants may need to invest in adaptation
measures against sea-level rise (Lise and van der Laan, 2015).
Results with respect to climate change impacts on hydropower are mixed.
Anugrah et al. (2015) evaluate Bayang micro hydropower system inIndonesia using WEAP-LEAP models and find that climate change will
reduce power production. Obahoundje et al. (2017) also employ WEAP to
explore the combined effect of land use and land cover change and climatechange on water availability for all demand sectors, including hydropower.
The study shows contradictory results regarding the power generation
between two climate change scenarios. Zhang et al. (2017) evaluate the129CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

vulnerability of hydropower of the Yangtze river basin to climate changeusing WEAP and also find large fluctuations in the power generating
capacity. Namely, the supply increases under the high-temperature
scenario and decreases under the low-temperature scenario, thereby
increasing the vulnerability and uncertainties of hydropower supply
systems.
Compared to other energy sources, climate change impacts onhydropower have been extensively incorporated into simulations of longterm power system expansion. Such popularity is due to the hydropower
dominance in some countries, its long asset lifetime, and its apparent
dependence on climate (Cronin et al., 2018). The integration of climatechange impacts into simulations of a power system expansion allows
analyzing their associated impacts on the power system as a whole. To
mention a few studies: Lucena et al. (2010) for the Brazilian power sector,
Teotónio et al. (2017) for the Portuguese power system, Arango-Aramburo
et al. (2019) for the Colombian power sector, and Spalding-Fecher (2017)
for the Southern African Power Pool. Overall, these studies indicate adecline in hydropower generation under climate change, although the
declining percentage is marginal is some countries while significant inothers. Consequently, extra capacity from other energy sources isrequired to compensate for the lack of reliability of hydropower.
On the demand side, previous studies often indicate an increase inelectricity consumption under future climate change, such as in USA(Baxter and Calandri, 1992; Sailor and Pavlova, 2003; Mansur et al., 2008),
the Southeast Mediterranean region (Cartalis et al., 2001) and Brazil (deLucena et al., 2010). A net increase in demand is also indicated in Austria
and German (Totschnig et al., 2017). In contrast, the total EU’s energy
demand is expected to decrease (Dowling, 2013). Similarly, a study for
Norway (Seljom et al., 2011) also suggests a net decrease in energy
demand.

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Several studies integrate climate change multiple impacts on energy
demand and various types of energy supply into the simulation of longterm power system expansion, thereby allowing to trace their combined
effects on the system. Lucena et al. (2010) incorporate climate change
impacts on electricity demand, hydropower, and natural gas turbines into
the long-term expansion of the Brazilian power sector using MAEDMESSAGE models. Seljom et al. (2011) employ MARKAL to include climatechange impacts to hydro and wind power as well as energy demand into
the analysis of future Norwegian energy system. Meanwhile, Dowling (2013)
analyzes the long-term European energy system considering climatechange impacts on energy demand and various types of power supply,
including thermoelectric, wind, solar PV, and hydroelectric by utilizing
POLES. A similar study for Europe was conducted by Mima and Criqui(2015) with a smaller scope, covering only energy demand, thermal power
plants, and hydropower. Totschnig et al. (2017) employ HiREPS tointegrate impacts of climate change and fuel price shocks on energy
demand and various types of energy supply into simulations of the future
Austrian and German power sectors. Finally, Li et al. (2014) incorporate
effects of projected changes in temperature, precipitation, and extreme
events on electricity demand and various power supply types into thepower system expansion for several U.S. regions using a robust electric
power Generation Expansion Planning (GEP) optimization model. All thesestudies agree that thermal power plants' reliability will decrease underclimate change. Meanwhile, climate change impacts on solar PV and wind
are estimated to be minor (Seljom et al., 2011; Dowling, 2013). Results for
Norway reveal lower system costs due to the net decrease in demand.
Furthermore, the country benefits from an increase in hydropower
production. Meanwhile, Brazil suffers from reduced hydropower reliability,needing extra installed capacity, consequently requiring extra investment
costs.
Nevertheless, to the best our knowledge, research explicitly linking theimpacts of climate change with the sector’s climate mitigation goals is131

CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

still lacking. Hence, the interferences of climate change impacts on the
power sector’s mitigation efforts for achieving national long-term climate
mitigation goals, remain unexplored. An integrated assessment of climatechange mitigation and adaptation for the power sector is especially vitalfor developing countries, which already face adverse climate change
impacts and electrification needs on top of climate change mitigation goals.
Therefore, this chapter addresses this important gap by pursuing acomprehensive assessment of the nexus between electrification, climatechange mitigation, and climate change adaptation in developing countries,
taking Indonesia as an example.

5.3. Methodology and data
This chapter simulates the future development of Indonesia’s power
sector, considering both climate change mitigation (low-carbon
electrification) and adaptation, focusing on the country’s primary powersystem. Fig. 5.1 depicts the conceptual framework of this chapter.
To account for climate change mitigation, this chapter sets a long-term
scenario for the Java-Bali power system expansion taking into accountthe country’s low-carbon development policy (Fig. 5.1, left flow).
Subsequently, LEAP is used to analyze the low-carbon pathway. For theadaptation11 part, I take a 3-step approach (Fig. 5.1, right flow). Firstly, I
collect data on the current impacts of severe weather events and gradualchanges in climate variables on different power plants, transmission anddistribution networks by conducting semi-structured interviews and focus
group discussions between February-March 2018 (as reported in Chapter
4). Secondly, I perform a literature review on climate change scenarios toidentify the likely trends in temperature, precipitation, and sea surface

11

Here, adaptation id defined as the variations in the power system configuration (e.g.,
installed, capacity, technology composition) due to the climate change-induced
reduction in power supply reliability and changes in the electricity demand132

CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

temperature for the Indonesian archipelago. Finally, I identify the variables
and functions in the LEAP model architecture to parameterize theprojected impacts of climate change on the Java-Bali power system.
This chapter considers the impacts of climate change on both demand andsupply sides. On the demand side, this chapter estimates how changing
temperatures might affect electricity needs in Java-Bali. Meanwhile, on
the supply side, it quantifies climate change impacts on power generation,
focusing on four power plant types: CFPP, NGPP, HEPP, and solar
photovoltaic (solar PV). The first three are chosen because they
constitute most of Indonesia’s power generation capacity, i.e., 50%, 28%,
and 9%, respectively (KESDM, 2018c). This chapter also includes solar PV
because, as indicated in Chapter 2, it plays a significant role in Indonesia’stransition to the low carbon energy system.
This chapter focuses on analyzing the impacts of gradual changes inclimate variables, including surface air temperature, precipitation, and sea
surface temperature on the power system. Although extreme weather
events often cause power failure, especially in distribution networks (seeChapter 4), they are not included in this chapter. This will be an essential
topic for future research. WEAP is employed to analyze the impacts ofchanges in precipitation and temperature on the water availability forhydroelectric power plants. Meanwhile, this chapter relies on findings from
Chapter 4 and on the current literature to parameterize expected climatechange impacts on thermal power plants (TPPs) and solar PV.
Finally, I simulate the Java-Bali power system expansion taking into
account the national low-carbon policy targets and climate change
impacts on the power system. These simulations quantify electricity mix
and corresponding costs of a climate-resilient power system that assuresa nexus of electrification, climate change mitigation, and adaptation goals.

133CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

Low-carbon
electrification pathway

Assumptions anddata for the lowcarbon powersystem expansion

Climate change impacts
on the power sector

Historic climaterelated impacts on
the power system

LEAP electricity
supplyoptimization

Impacts onelectricity
demandImpacts on
HEPP
LEAP results:
Low carbon,
climateadapted
power system

WEAP results:
HEPP availability
under futureclimateProjected
climate change

Impacts onelectricity supply

Impacts on
TPPs andSolar PV

Reliability of TPPs
& Solar PV under
future climate

Fig. 5.1: The conceptual framework for integrating climate change mitigation andadaptation using WEAP-LEAP models

5.3.1.

Scenario development

5.3.1.1. Climate change scenarios
The climate change projection in this chapter is based on the
Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios
adopted in AR5. RCP4.5 is one of the medium stabilization scenarios,
assuming a stable radiation intensity at approximately 4.5 W/m2 or
equivalent to 650 ppm CO2e after 2100. RCP8.5 is the one very highbaseline emission scenario, which assumes a constant rise pathway
leading to 8.5 W/m2 of the radiation intensity (more than 1,370 ppm CO2e)134

CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

in 2100 (Zhang et al., 2017). Accordingly, this chapter uses climate change
projections for Indonesia under these scenarios. The projection oftemperature and precipitation (Table 5A.1 of Appendix 5.A) are based on
the ensemble values of 35 CIMP models available at the World Bank
Climate Knowledge Portal’s website (The World Bank Group, 2019), whichwere derived from Harris et al. (2014). These data are available for two
periods within the study’s time horizon: 2020-2039 and 2040-2059.
Meanwhile, the projected sea surface temperature (SST) data comes froma global climate model’s output of the Institut Pierre Simon Laplace, which
was retrieved from the International Pacific Research Center’s website12(International Pacific Research Center, 2018). Based on these data, thischapter quantifies the effects of the changes in these climate variableson the electricity demand and on the key technical characteristics ofvarious power plants.

5.3.1.2. Power system expansion scenarios
I develop three scenarios of the Java-Bali power system’s expansion forthe period of 2018-2050:

1. Reference scenario (REF): the power system’s expansion scenario
without climate change considerations. Hence, the objective of this
scenario is to satisfy solely the growing future demand for electricity
in the Java-Bali islands at the lowest cost.

2. Climate change mitigation scenario (CCM): the power system’s
expansion assumes a shift to a low-carbon pathway following the NREtargets stipulated in NEP, which is to increase the share of renewablesin the national energy mix up to 23% by 2025 and 31% by 2050. In
addition to these mitigation targets, this scenario accounts for

12

The Asia-Pacific Data Research Center is a part of the International PacificResearch Center at the University of Hawaiʻi at Mānoa, funded in part by the National
Oceanic and Atmospheric Administration (NOAA).

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CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

electrification but neglects the sector’s adaptation to the changing
climate.

3. Climate change mitigation and adaptation scenarios (CCMA): thepower system’s expansion assumes a shift to a low-carbon pathway
and integrates the effects of projected climate change on theelectricity supply and demand. Following the climate change scenarios
discussed in Section 5.3.1.1, this chapter differentiates between two
CCMA scenarios:a. CCMA RCP4.5: climate change impacts on power plants arequantified based on the IPCC RCP4.5 scenario
b. CCMA RCP8.5: climate change impacts on power plants arequantified based on the IPCC RCP8.5 scenario
Chapter 2 and Chapter 3 have explained the setup of the Java-Bali LEAP
model in detail. In addition to the methodological advances with the CCMA
scenario, this chapter updates the previous ones by including the mostrecent historical data of energy demand and supply. Accordingly, the LEAP
base year is updated from 2015 to 2017. Furthermore, this chapter updates
the electricity demand projection by the most recent RUPTL 2018-2027(PLN, 2018), which indicates a lower growth in electricity demandcompared to the previous projection. Likewise, the capital cost of electric
power technologies is now assumed to decrease every five years following
the percentage of technology cost reduction in the World Energy Outlook(OECD/IEA, 2017).
5.3.2.

Integrating adverse climate change impacts into LEAP

5.3.2.1. Demand-side
Projecting impact of higher temperatures on the electricity demand
By relying on the relationship between air temperature and electricity
demand analyzed in Chapter 4 (Fig. 4.3, Chapter 4), I carry out a firstorder estimate of the effects of projected higher temperatures on theannual peak demand.
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Results show that the annual demand for electricity increases by 1.2% and1.4% by 2020s under the CCMA RCP4.5 and CCMA RCP8.5 scenarios,
respectively, relative to REF. Furthermore, by the 2040s the electricity
demand increases by 2% and 2.8%, respectively.

5.3.2.2. Supply-side
As a bottom-up energy model, LEAP allows setting detailed
characteristics of energy technologies through its input data. Hence, it ispossible to alter the technical features of a power plant, integrating theeffects of the changing climate. This chapter quantifies these effects
based on the results of Chapter 4 and transforms them into changes inthe technical characteristics of power plants. The altered technicalfeatures include the capacity factor and the efficiency of power plants,
which are expected to influence the technology mix and installed capacity,
as well as reflect the costs of the power system adapting to the changing
climate.
Projecting impacts of temperature and precipitation changes to HEPPs
This chapter employs WEAP to simulate future hydrology in the Citarum
river basin (Fig. 5B.1 of Appendix 5.B) based on projected changes in air
temperatures and precipitations. The Citarum river provides water for thetwo largest HEPPs in Indonesia, i.e., Saguling and Cirata; each has theinstalled capacity of 1,008 MW and 797 MW, respectively. As discussed in
Chapter 1, WEAP has a built-in link with LEAP, where the hydropower
availability modeled by WEAP becomes an input for LEAP. Hence, the useof both software tools enables dynamic analyses of climate change
implications on hydropower production (Spalding-fecher, 2018). Table 5B.1
of Appendix 5.B lists WEAP input data for the HEPPs simulations.
Results show that under the CCMA RCP4.5 scenario, the average annual
capacity factor of both Saguling and Cirata HEPPs varies across years
when compared to REF (Fig. 5B.2 of Appendix 5.B). From 2022 to 2039,
both Saguling and Cirata produce slightly less electricity (<1%) compared
to REF. In contrast, their electricity production overshoots that of REF
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CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

from 2040 onward. However, the aggregate HEPP production over the
study period is higher than that in REF. Meanwhile, no significant difference
found in the annual average capacity factor across the years between REF
and the RCP8.5 scenario. Nonetheless, the aggregate HEPP production in
RCP8.5 is slightly lower compared to REF. These results are extrapolated
to the rest of HEPPs when simulating the Java-Bali power system
expansion using the LEAP-WEAP combination.
Sea surface temperature and efficiency of coal-fired power plants
A cooling water system is an essential part of coal-fired power plants.
CFPPs in the Java-Bali power system rely on seawater for their coolingsystems, thereby will likely be impacted by higher seawater temperatures
expected in a climate-changed world. The CFPPs employ once-throughcooling systems, which take water from the sea and circulate it throughpipes in a condenser to absorb heat from the steam. The warmer water isthen discharged back to the sea. A higher cooling water temperature
results in a higher condenser pressure, which reduces the energy
efficiency of a condenser (Haldkar et al., 2013). In Chapter 4, I drew thecorrelation between seawater temperature and condenser pressure of the
Paiton PJB CFPP, which is a typical CFPP using a once-through coolingsystem (Fig. 4.4, Chapter 4). Here, this relationship is combined with thedata of the primary energy consumption of the power plant. According to
the power plant’s performance report, every 1ºC increase in cooling watertemperature above 25ºC, the power plant efficiency drops by 0.32%.
This chapter adopts this relationship to estimate the alteration of CFPP’s
efficiency following the changes in the sea surface temperature
anticipated with climate change. Results show that the reductions of theefficiency of the Java-Bali CFPPs range from 0.1% to 0.4% under the
RCP4.5 scenario and from 0.03% to 0.3% in the RCP8.5 scenario. Thesedrops of efficiency are expected to increase fuel consumption, leading toa rise in fuel costs and CO2 emissions.

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Precipitation and capacity factors of coal-fired power plants
The fieldwork reveals that heavy precipitation dampens coals in CFPPs’
open storage areas, thereby reducing the CFPPs’ burning efficiency. Thisinstance often occurs during the wet season when rainfall is intense andfrequent. Suralaya CFPP, the largest CFPP in Indonesia, provides theoperation disruption data from 2011 to 2017, which includes pastdisruptions due to wet coals. Based on this data, the average annual
reduction of the CFPP’s capacity factor is calculated, following thedecrease in its energy generation attributable to wet coals.
Furthermore, this chapter combines the historical data of the CFPP
disruptions and local precipitation, with projected precipitation underclimate change scenarios to estimate reductions in the CFPP’s capacity
factor under future climate. Results indicate that the CFPP’s capacity
factor will reduce by 0.29% and 0.30%, respectively, by 2020 and 2040.
These estimates are adopted in LEAP simulations for the rest of JavaBali CFPPs.
Ambient air temperature and power output of natural gas power plants
There exist two types of technologies in NGPPs of the Java-Bali powersystem: NGOC, which produces electricity solely from a gas combustion
turbine, and NGCC, which produces electricity from the gas combustion
turbine and generates extra electricity by routing the waste heat from thegas turbine to the nearby steam turbine. High ambient air temperatures
affect gas turbines of NGPPs. The operation of the gas turbines requires
ambient air for the compressor intake and routed into the burning chamber.
The higher the ambient air temperature, the lower the air density and,hence, the burning efficiency, which in turn reduces power outputs of thegas turbines (Henry and Pratson, 2016). According to the field data from
the Muara Karang NGOC, every 1ºC increase in ambient air temperature
above 16ºC, the power output of the NGOC drops by 0.6%. Accordingly,
the power output of the Muara Karang NGOC correlates with air ambient
temperature following Eq. 5.1. This data is comparable with findings
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CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

reported in the literature, which indicate that with each one-degree
temperature increase above 30ºC, the power output of gas turbines drops
by 0.50%–1.02% (Drbal et al., 1996; Kehlhofer et al., 2009; AsianDevelopment Bank, 2012).
Furthermore, the literature indicates that the overall net reduction of thepower output of an NGCC is within the range of 0.3% - 0.6%. Here, I assume
that every 1ºC increase in ambient air temperature, the power outputs of
NGCCs drop by 0.45%, which is the middle value of the range. Accordingly,
the power output of NGCCs correlates with air ambient temperature
following Eq. 5.2.
Thus, this chapter estimates the monthly energy output of NGOC and
NGCC under climate change scenarios based on projections of averagemonthly temperature, using Eq. 5.1 and Eq. 5.2, respectively. Accordingly,
the annual capacity factor under climate change scenarios is calculatedusing Eq. 5.3. Results show that reductions in the capacity factor of theJava-Bali NGPPs in 2040 range from 0.8% to 4.3%.
𝑃(𝑇) = 𝑃 ∗ (−0.006𝑇 + 1.0933)

Eq. 5.1

𝑃(𝑇) = 𝑃 ∗ (−0.0043𝑇 + 1.0662)

Eq. 5.2

𝐶𝐹𝑇𝑚 =

𝐸𝑇𝑚
𝑃𝑙𝑎𝑛𝑡 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (𝑀𝑊) ∗ 8760 ℎ𝑜𝑢𝑟𝑠

Eq. 5.3

where 𝐸𝑇𝑚 is the annual energy output in MWh, 𝑇𝑚 is the average monthly
temperature, 𝑃(𝑇) is the power output at temperature 𝑇 in MW, and 𝐶𝐹𝑇𝑚is the capacity factor.
Air temperatures and efficiency of Solar PV
A rise in air temperature may negatively impact the performance of solar
PVs. It manifests as a decrease in the solar PV efficiency determined by
the so-called temperature coefficient, which is a correction factor of
efficiency as a function of temperature (Simioni and Schaeffer, 2019). This
chapter adopts the estimate by Pašičko et al. (2012), where every 1ºC
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CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

increase in air temperature decreases PV efficiency by 0.5% relative tothe referent value at 25ºC. This value is consistent with the averagetemperature coefficient specified in Dubey et al. (2013). Based on thisassumption, this chapter estimates the reduction in the efficiency of theJava-Bali solar PV following the climate-driven rise in temperature13.
Results indicate that the reduced efficiency under future air temperatureleads to a reduction in the solar PV capacity factor by 0.5% to 0.6% during
the 2020s and by 0.8% to 1.1% during the 2040s.

5.4. Climate change mitigation-adaptation synergies: LEAP
Results
The following sections discuss the output of the climate change
mitigation-adaptation integrated framework using WEAP and LEAP models
applied to the case of the Java-Bali power system. First, the capacity
added and electricity mix for each scenario are presented, followed bydiscussions on their associated costs and CO2 emissions.
5.4.1.

Installed capacity and electricity mix

5.4.1.1. Reference scenario
In the reference scenario, which assumes the least-cost electrification
without considering climate change mitigation and adaptation, the capacityof fossil fuel technologies increases rapidly. The coal and natural gas
capacity reach 115 GW and 53 GW by 2050, respectively, hence increase
sixfold and fivefold compared to 2017. As a result, the capacity mix overthe study period locks into the fossil fuels’ regime (Fig 5.2a). Therefore,
in 2050, coal maintains its domination (Fig. 5.2b), comprising 72.4% of theJava-Bali electricity mix, followed by natural gas (20.7%), hydro (3.8%), andgeothermal (3%).

13This study assumes that other parameters (e.g., wind speed, solar radiation) remainunchanged141

CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

200

Gigawatt

160
120
80

(a)

40
0
2020

2025

2030

2035

2040

2045

2050

Biomass

Wind

Solar

Year
Coal

Natural Gas

Hydro

Geothermal

2050: 982 TWh 3.0% 3.8%

20.7%

(b)

72.4%

Fig. 5.2: Capacity mix over the study period (a) and electricity
mix in 2050 (b) in the reference scenario

5.4.1.2. Climate change mitigation scenario
Results show that the implementation of NEP’s NRE policy (climatechange mitigation targets assumed in the CCM scenario) dramaticallyalters the Java-Bali capacity mix. In 2050, the coal capacity in CCM
reduces by 41% compared to REF. The reduction in the coal capacity iscompensated by the increased capacity of natural gas (23%), hydro (7%),
and geothermal (27%) as well as by the penetration of solar (117.7 GW),
biomass (7.4 GW), and wind (1.6 GW). With this capacity mix, all of thehydro, geothermal, and biomass potentials in Java-Bali islands (Table 5B.1
of Appendix 5.B) are utilized, leaving wind and solar as the only renewableenergy source still potentially available.
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Accordingly, the Java-Bali electricity mix mimics the change in its
capacity mix. While fossil fuels serve most of the demand for electricity inthe base year, they gradually reduce their share in the Java-Bali electricity
mix. Complying with the NRE target, by 2025, renewables compose 23% of
the electricity mix, shared between geothermal (9.6%), hydro (5.6%),
biomass (6.5%), wind (1%), and solar (0.3%) (Fig. 5.3).
Furthermore, by 2050, the renewable share increases up to 31% astargeted by NEP. Interestingly, the electricity production from solar
encompasses 17.4% of the total electricity mix, the highest among otherrenewable owing to the relatively faster reduction in its investment costs.
Meanwhile, although nuclear is available as an option for meeting the NREtargets, it is not competitive against solar and other renewables due to its
low technology-learning rate.
2017: 182 TWh

5%

2025: 282 TWh

1%

2050: 982 TWh

4%
6%
10%
24%
67%

6%

17.4%

5.3%

47.4%
56%

0.4%

3.9%

21%
21.6%

4.0%

Fig. 5.3: The Java-Bali electricity mix in 2017, 2025, and 2050; climate change
mitigation scenario (CCM)

5.4.1.3. Climate change mitigation and adaptation scenarios
When impacts of global climate change are integrated on top of the climatechange mitigation and electrification assumptions, the results depict the
optimal choice of shifting the Java-Bali power system to a low-carbon
pathway while also responding to the effect of new climate conditions. The
results show that extra capacities are required to serve the climateinduced electricity demand surge and to cope with the decreased powergenerating capacity (Fig. 5.4). Under CCMA RCP4.5, the extra capacity of143

CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

solar PV, coal, wind, and biomass are 7.5%, 5.4%, 19.9%, and 0.7%,
respectively higher from that in CCM. In contrast, the natural gas capacity
reduces by 2.2% compared to CCM. Naturally, the capacity of solar PV,
coal, wind, and biomass increase to balance the growth in demand and tocompensate for the declined power-generating capacities.
Extra capacity (GW) in CCMA RCP4.5 relative to CCM
8.7

4.0

Coal

Natural Gas

Hydro

-

0.1

Geothermal Biomass

(a)

0.5
Wind

Solar

-1.6

Extra capacity (GW) in CCMA RCP8.5 relative to CCM

5.0

4.9

Coal

Natural Gas

Hydro

-

5.8

(b)

0.1

Geothermal Biomass

Wind

Solar

-2.4

Fig. 5.4: Extra capacity in CCMA RCP4.5 (a) and CCMA RCP8.5(b). CCM is taken as a benchmark

Under CCMA RCP8.5, the extra installed capacity is even higher, whichindicates that the higher temperature scenario intensifies impacts on theelectricity demand and power supply (e.g., coal, natural gas, solar PV). The
extra capacity is now shared almost evenly between solar, coal, and wind
(Fig. 5.4b). The increased penetration of wind power indicates the tradeoff between climate change mitigation and adaptation. Firstly, windresource potential is still available to facilitate the power system meeting
the NRE targets while the addition of the coal capacity is constrained by
NRE targets. Secondly, the simulation excludes the impacts of climate
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change on wind power. Thus, under the higher temperature scenario(CCMA RCP8.5), wind power replaces a portion of solar PV’s extra
capacity, making the extra capacity of solar PV lower than that in CCMA
RCP4.5. However, the role of solar PV is still significant in anticipating theclimate-induced surge demand, adding extra capacity above CCM by 5%.
Furthermore, the extra installed capacity to anticipate climate change
adds 299 TWh and 388 TWh of electrical energy, respectively, in CCMA
RCP4.5 and CCMA RCP8.5, over the period of 2020-2050. In CCMA
RCP4.5, the additional electrical energy mainly comes from coal and solar
PV (Fig. 5.5a). Meanwhile, although solar adds extra capacity in CCMA
RCP8.5 (Fig. 5.4b), its electricity generation is 6% lower than that in CCM
likely due to a reduction in its efficiency, which is compensated by asignificant increase in wind power production (Fig. 5.5b).
Extra energy production (TWh): CCMA RCP4.5 relative to CCM
356.4
(a)

65.2
5.2
Coal

Natural Gas-126.2Hydro-5.3
Geothermal Biomass

4.8
Wind

Solar

-0.6

Extra energy production (TWh): CCMA RCP8.5 relative to CCM
(b)

418.8

172.5

7.4

Coal

Natural Gas-132.8

-3.1
Hydro

-5.3
Geothermal Biomass

Wind

Solar
-69.3

Fig. 5.5: Extra electricity production in CCMA RCP4.5 (a) and CCMA RCP8.5(b). CCM is taken as a benchmark

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5.4.2.

O2 emissions and total costs

5.4.2.1. CO2 emissions
Indonesia’s NDC outlines the country’s target of reducing 29% of its
emission from business as usual (BAU) by 2030, where the energy sector
is targeted to contribute 11% of the national CO2 reduction target or 19%
reduction from its emission baseline. The simulation results reveal that
under the reference scenario, the Java-Bali power system emits 215
million tons of CO2 in 2030 (Fig. 5.6). This value is lower than that wascalculated in Chapter 3, mainly because of the lower electricity demand
growth assumed in the present chapter - following the latest RUPTL compared to Chapter 3. Meanwhile, under the CCM scenario, the CO2
emissions decrease to 188 million tons, which is 13% lower than that in
REF. Therefore, under the updated assumption, additional efforts beyond
implementing NEP’s NRE targets are required to achieve the NDC target
by 2030. By 2050, however, the CO2 emissions drop further, being 34%
lower than that in REF. Therefore, when the NRE target in 2050 is fulfilled,
Indonesia can reduce its emission further, moving beyond its current NDC.

200
180

Million ton CO2e

160
140
CCM

120
100

CCMA RCP4.5

80

CCMA RCP8.5

60

REF

40
20
0
2020

2025

2030

2035

2040

2045

2050

Fig. 5.6: CO2 emissions under reference, mitigation, and adaption scenarios.
CCM is taken as a benchmark

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The CO2 emissions increase in both CCMA scenarios, i.e., 20 and 25 million
ton CO2, respectively, in RCP4.5 and RCP8.5 (Fig. 5.6) compared to CCM.
The additional CO2 emissions are attributed to the increased electricity
production from coal to compensate for the climate-induced surged
electricity demand. Moreover, CFPPs efficiencies drop under futureclimate, needing additional fuel coal consumption per unit energy
produced. However, even with this increase, the CO2 emissions stilldecrease by 30% compared to REF in 2050.

5.4.2.2. Total costs
Total costs are calculated based on the net present value of the total
costs over the entire study period, which includes capital costs, operation,and maintenance costs, and fuel costs. Under REF, the total costs ofelectricity production over the simulation period 2018-2050 reach 70.4
billion USD, which is equal to 0.1% of the cumulative national GDP during
the study period14. This is lower than estimated in previous work (Chapter3), partly owing to the lower electricity demand growth assumption in thepresent research following the update in the latest RUPTL (PLN, 2018)
and due to the assumption of technology cost reduction, which is now
included in all scenarios.
Meanwhile, under the CCM scenario, the total costs of expanding theJava-Bali power system over the period of 2018-2050 reach 75.1 billion
USD. Hence, shifting from the current regime to the low-carbon pathway
adds 4.7 billion USD extra or 6.8% higher costs compared to the no
mitigation-adaptation scenario (REF) (Fig. 5.7). Furthermore, when climatechange impacts are considered, the costs grow by 10.7% and 11.4% in
CCMA RCP4.5 and CCMA RCP8.5, respectively, relative to REF. Thisimplies that shifting to a low-carbon power system while coping withclimate change impacts increase the sector costs relative to the no
mitigation-adaptation scenario. Thus, the climate change mitigationadaptation costs for the Java-Bali power system are 2.7 and 3.2 billion
14

Assuming annual GDP growth of 4.5%

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Cost of production (2017 million USD)

USD, respectively higher than the costs when only climate change
mitigation is considered (compare CCMA RCP4.5 and CCMA RCP8.5 with
CCM in Fig. 5.7). These costs are high: they constitute more than half ofthe budget allocated by the Indonesian Government for climate change
mitigation and adaptation for all sectors in 2017. These additional costs
are due to the extra installed capacity and energy production of coal, solar
PV, and wind power required to cope with the adverse impacts of futureclimate.
4,000
2,000
0

CCM
CCMA RCP4.5-2,000CCMA RCP8.5-4,000

REF-6,0002020 2025 2030 2035 2040 2045 2050

Year

Fig. 5.7: Total production costs under reference, mitigation, and adaption
scenarios. CCM is taken as a benchmark.

5.5. Discussion
The integration of climate change mitigation and adaptation objectives into
the simulations of the Java-Bali power system expansion reveals thefollowing findings. First, the Java-Bali technology composition issignificantly altered, driven by the constraints set in LEAP, i.e., theminimum amount of renewable energy share and the adverse climatechange impacts. Second, the Java-Bali installed capacity increases tobalance the climate-induced increase in electricity demand and tocompensate for a loss of reliability of the Java-Bali power generation due
to new climate conditions. Finally, while CO2 emissions drop due to the148CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

mitigation efforts, the total costs of the electricity production increase by
11.4% in 2050 attributed to the mitigation and adaptation measures.
The NEP’s NRE targets force the power system to shift to a low-carbon
pathway, resulting in a 34% CO2 emission reductions by 2050. The main
driver of this transition is solar PV owing to its vast remaining exploitablepotential, whereas the exploitable potential of other renewables such ashydro and geothermal is limited. Likewise, solar PV also plays a significant
role in compensating for the reduction in capacity and energy generation
under adverse climate change impacts, especially under CCMA RCP4.5.
However, under the higher temperature changed scenario (CCMA RCP8.5),
solar PV reduces its reliability, which is compensated by wind power.
Meanwhile, coal plays a significant role in satisfying the climate-induced
increase in electricity demand and compensating for the declined powergenerating capacity in both CCMA scenarios, resulting in an increase of
CO2 emissions compared to CCM. These findings clearly show theinterplay between climate change mitigation-adaptation objectives andsuggest that climate change impacts might hinder climate change
mitigation efforts of the power system, which agrees with the hypothesisin the literature (Fisher-Vanden et al., 2013; Turner et al., 2017; Cronin etal., 2018).
Given that climate change impacts are unavoidable due to the past emitted
CO2 and the delayed mitigation actions, these findings suggest several
courses of action for policymakers. First, a key policy priority should be torecognize the power sector’s vulnerability to climate change, prepare for
its adaptation action plans, and further integrate the plans into the national
adaptation strategies. Secondly, there is a defined need for ensuring asufficient budget allocation for the power sector’s adaptation to climatechange. Since the power sector adaptation to climate change hinders theCO2 mitigation efforts, a nexus between mitigation and adaptation energy
policy should be exploited in national budget allocation. Finally, moreconsiderable efforts are needed for conducting detailed investigations ofclimate change impacts on individual power plants, including a full analysis
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of the cost and benefit of their adaptation. The latter may include local
investments in preventing reductions in capacity factor and efficiency at
individual vulnerable power plants.

5.6. Conclusions
The main goal of this chapter is to integrate both climate change mitigation
and adaptation objectives into the long-term capacity expansion of apower system. This chapter takes Indonesia as an example of a developing
country struggling to meet its electrification needs, focusing further on its
primary power system, i.e., Java-Bali. Four scenarios for the Java-Bali
power system expansion from 2018 through to 2050 are analyzed, includinga reference scenario, a climate change mitigation scenario, and two
scenarios that integrate climate change mitigation and adaptation. For
climate change mitigation, this chapter considers the renewable energy
target of the Indonesian government that aims to increase the share of
new and renewable energy by 23% in 2025 and 31% in 2050.
Meanwhile, this chapter quantifies climate change impacts on power supply
and demand by employing various methods. The WEAP model is used whenquantifying climate change impact on hydropower availability, and combine
empirical data from the fieldwork with findings from the literature whenassessing climate change impacts on thermal power plants and on theelectricity demand. The estimated impacts include the changes in HEPP’s
capacity factor, a reduction in CFPP’s efficiency and capacity factor, areduction in NGPP’s capacity factor, a reduction in solar PV capacity
factor, and an increase in electricity demand. Furthermore, this study
integrates these quantified impacts into the Java-Bali power system
expansion using LEAP. The simulation results are discussed in terms of
installed capacity, electricity mix, costs, and CO2 emissions.
This chapter provides an insight into economically optimal options for theelectricity generation in the Java-Bali power system under a set of climatechange mitigation-adaptation regimes. It includes impacts on the demand
as well as on the supply sides. Although this chapter does not cover150

CHAPTER 5 SEEKING FOR CLIMATE CHANGE MITIGATION AND ADAPTATION NEXUS

climate change impacts for all types of power plants, results indicate
significant deviations from the reference scenario in terms of installedcapacity, electricity generation, and the total costs of electricity
production, attributable to climate change mitigation and adaptation.
Furthermore, even though this chapter excludes the impacts of extreme
weather events, the costs of power system adaptation to gradual changes
in climate variables such as temperature and precipitation are comparablewith the costs of climate change mitigation.
The methodological approach of this chapter could be replicated in othercases that aim at analyzing the interplay between climate change
mitigation and adaptation in the power sector. This chapter, however, has
several limitations, mainly regarding the assessment of climate change
impacts on power generation. Firstly, the projection of climate change
impacts on the Indonesian power sector is preliminary, driven by theestimates found in the literature rather than a case-specific downscaled
climate change scenario. Yet, this secondary data provides the basis for
an initial evaluation regarding climate change impacts on the power sector.
Furthermore, this chapter generalizes the effects of climate change on
CFPPs and HEPPs based on the fieldwork data from several power plants.
Future work may focus on collecting detailed data on individual power
plants to assess site-specific costs of climate change and possible
adaptation solutions.
Secondly, the modeling approach in this chapter does not include impactsof extreme weather events, whereas they may intensify under futureclimate change and lead to physical infrastructure damage, energy losses,
and indirect effects such as business interruption. This would be a fruitfularea for further work. Thirdly, due to data limitations and the scope of
LEAP, climate change impacts on transmission and distribution are
excluded from this assessment. Further research may explore thequantification of climate change impact on the entire segments of thepower sector: generation, transmission, and distribution. Finally, theanalysis in this chapter relies on global climate change projection for which151

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there is a great deal of uncertainty. Therefore, the modeling of climatechange impacts on electricity supply and demand inherits the uncertaintyof the chosen climate change scenarios.
Despite its limitations, this chapter provides a methodological framework
for an integrated analysis of climate change mitigation and adaptation in
the power sector, which allows an assessment of the nexus between
climate change mitigation and adaptation that crucial for developing
optimal mitigation and adaptation strategy. This framework can beextended to account for other climate change impacts such as extreme
weather events, impacts of gradual changes in climate variables ontransmission and distribution networks, and impacts on other types ofelectric power technology such as wind power, geothermal, nuclear, andstorage technologies.

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6.1.

Introduction

The global power sector faces a triple challenge: to satisfy fast-rising
electricity demands, to meet the sector’s CO2 reduction targets, and tocope with the adverse impacts of climate change. These challenges require
the power sector’s transformation to low-carbon and climate-resilientsystems. Moreover, financial institutions and credit rating agencies haveincluded climate change risks into their assessment criteria, calling for theenhancement of the electric industry’s resilience to climate change(International Finance Corporation, 2012; Connell et al., 2018; Mufson,2019). Accordingly, international standards are emerging to assist electricutilities in examining climate change impacts and developing adaptation
strategies. For example, the World Bank produced a guideline for climateresilient hydropower (World Bank, 2017), while the International
Organization for Standardization recently established ISO 14090:
Adaptation to climate change — principles, requirements, and guidelines(Naden, 2019)
A practical barrier to the low-carbon and climate-resilient transformationis the fact that expanding the power system, even without climate change
consideration, involves enormous investment costs (Bhattacharyya, 2011;
Nicolas et al., 2019). Furthermore, while technological change is required
to facilitate this transformation (Nakicenovic et al., 2000; Berglund and
Söderholm, 2006; IPCC, 2007; Wilson and Grubler, 2011), it needs to besupported by institutions and economic incentives (Pachauri et al., 2014).
Meanwhile, the scientific challenge concerning this societal problem lies indeveloping a method for an integrated analysis of electrification, climatechange mitigation, and climate change adaptation in the power sector.
Likewise, modeling technological change to support low-carbon transition
remains a challenge for energy modelers (Grübler et al., 1999; Frei et al.,
2003; Berglund and Söderholm, 2006).
Thus, the goal of this doctoral research was to contribute to addressing
the above scientific challenges by assessing the triple nexus of155CHAPTER 6 SYNTHESIS OF THE DISSERTATION

electrification, climate change mitigation, and climate change adaptation in
the power sector. I have addressed four core research questions basedon the Java-Bali power system in Indonesia by employing energy andwater sectors simulation modeling using LEAP and WEAP, respectively,
and by conducting extensive fieldwork. The fieldwork involved in-depth,
semi-structured interviews and focus group discussions withrepresentatives of electric utilities within the Java-Bali power system.
Furthermore, I have conducted this doctoral research project in fourstages, as discussed in greater detail in Chapter 2 through Chapter 5.
This chapter proceeds as follows: Section 6.2 summarizes the findings
from each study, followed by the highlights of the innovative contributionsof this dissertation (Section 6.3). Section 6.4 provides the policy
implications of this doctoral research, while Section 6.5 reflects on thelimitations of this dissertation and highlights the agenda for future
research that aims at modeling power system expansion under the climatechange regime.

6.2.

Summary of the findings

RQ1: How could the power sector align electrification and the Paris
Agreement goals?
To address RQ1, I conducted modeling simulations of the Java-Bali powersystem expansion, as reported in Chapter 2. To decide on a suitable model
for performing simulations, a systematic screening of 48 energy models
was performed. The screening resulted in the LEAP energy model being
chosen for a number of reasons. Firstly, it can accommodate the detailed
characteristics of electric power technologies. Secondly, its free accessfor developing countries increases the chances of reproducing theoutcomes of this work and further development of methods beyond thescope of this dissertation. Lastly, its extensive use makes resultscomparable across countries.

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Furthermore, I carried out a validation of the Indonesian LEAP model totest the accuracy of the LEAP analysis, comparing the LEAP simulation
results for power system expansion from 2006 through to 2015 with thehistorical data. The results of this validation check conclude that LEAP
predicted the expansion of the Java-Bali system from 2006 through to2015 with an accuracy of 88.2%-100%. Relying on these results, I
proceeded further by conducting simulations of the Java-Bali powersystem expansion from 2016 through 2030.
Three scenarios of the Java-Bali power system’s responses to the Paris
Agreement were simulated. These scenarios defined the power sector’s
CO2 emission reduction target stipulated in Indonesia’s NDC document asa constraint in the simulation. Hence, the future power generation needs
to comply with the Paris Agreement while satisfying the national
electrification objective.
The results revealed that in the context of the Java-Bali power system,
electrification and Paris climate goals could be achieved solely through: (i)fuel switching from coal to natural gas or (ii) escalating renewable energy
deployment. However, the economically optimal strategy was achieved dueto the combination of an increase in natural gas power generation and in
renewable capacities. This strategy led to a reduction of 65 million tons ofCO2, thereby meeting the NDC target at 18% and 12% lower costs thanmerely switching to natural gas or renewable energy, respectively.
The chapter concluded that the Paris Agreement could enforce the powersystem’s transition from a fossil-fuel-based power generation to a lowcarbon power system in Java-Bali, thereby lowering CO2 emissions.
However, this transition imposes higher total costs of the power system
expansion compared to BAU, which should be considered in the national
budget plan, as the Indonesian power sector is still highly subsidized.

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RQ2: How could technological change affect the deployment of renewableenergy?
To answer this research question, I incorporated the one-factor learningmodel into the LEAP cost function, which allowed a quantification of theendogenous technology cost reduction as a function of the accumulationof installed capacity of electric power technologies. With this modification,
I have simulated the Java-Bali power system expansion from 2016 through2050, taking into account the NRE targets.
Chapter 3 has elaborated the methodological framework to explore thecost-reducing effect of technological learning using LEAP and then,thereafter, discussed the findings. The learning rate of various electric
power technologies, including fossil fuels and renewables, were taken into
account in the simulations. Based on the WEO model assumption of capitalcosts reduction of electric power technologies (OECD/IEA, 2017), thechapter has parameterized four phases of technological learning in LEAP.
The high speed of learning occurred during the initial phase, while its pace
reduced gradually throughout the other three phases.
The study has shown that in the absence of technological learning, nuclear
power is more cost-effective at meeting the NRE targets than solar PV
and wind power. However, empirical data has proven that the costs ofrenewables decrease over time, while the costs of nuclear tend to increase(Rubin et al., 2015). Accordingly, when ETL was included in the LEAP
simulations, renewables became competitive against nuclear. By 2050,
solar PV will lead other renewables in assuring the achievement of NREtargets. As a result, the costs for achieving the NRE targets are 4% and
10% lower under the scenario of the medium and high pace of technologicallearning, respectively, compared to no technological learning scenarios.
Furthermore, the achievement of NRE provides a co-benefit in terms of
reducing emissions, assuring the fulfillment of the power sector’s NDC
target.

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Overall, the chapter has concluded that endogenous technological learning
accelerated the penetration of renewable energy in the Java-Bali powersystem and lowered the costs of achieving the NRE targets.
RQ3: How do severe weather and gradual changes in climate variables
affect the power system? What are current adaptation practices?
Chapter 4 moved the focus of this dissertation to climate change
adaptation. The research question of this chapter sought empiricalevidence that could explain how and to what extent weather and climate
have implications for the power sector. Data were collected throughextensive fieldwork that was carried out in Indonesia between February
and March of 2018. First, I designed questionnaires that were structured
differently for each target group. After which, I conducted semi-structured
interviews and focus group discussions involving the representatives ofthree electric utility companies. The latter was comprised of three head
offices, ten power generation plants, a load control center (grid operator),
two transmission offices, and two distribution offices, thus covered theentire chain of the electricity supply. The ten power plants are the major
ones in the Java-Bali power system, accounting for 35% of the Java-Bali
power generation capacity. Moreover, this chapter covered the entire
Java-Bali T&D networks.
The results of this investigation revealed that severe weather and gradualchanges in climate variables have implications for power generation,transmission, and the distribution of electricity. However, in terms offrequency of disruption event, distribution networks are affected most by
severe weather events. Weather-related power cuts commonly occur due
to failures in distribution networks. Meanwhile, the Java-Bali power
generations recorded several forced outages due to extreme weather- andclimate-related events, such as flood and jellyfish inflow into the coolingwater system. For transmission networks, lightning strikes have become
the leading cause of their failures.

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Furthermore, Chapter 4 provided empirical evidence regarding thecorrelation between climate variables, such as air temperature,
precipitation, and sea surface temperature, with the performances of thepower plants. The evidence suggests that higher temperatures reduce thereliability of thermal power plants. Meanwhile, changes in precipitation
patterns affect the operations of hydroelectric power plants, which in turnaffect the operational pattern of the Java-Bali power system as a whole.
Another interesting finding was that weather- and climate-related
disruptions caused financial losses for utility companies due to damaged
equipment and energy-not-supplied. Subsequently, utilities have taken
ex-post adaptation responses, which include managerial and technologicalinterventions.
To summarize, these findings suggested that the Java-Bali power system
is vulnerable to severe weather and gradual changes in climate variables.
Therefore, future climate change is expected to increase the vulnerabilityof the power system, demanding that the system improve its resilience toclimate change.
RQ4: How can one integrate climate change impacts into the powersystem expansion modeling? How might this affect electrification andclimate change mitigation goals?
To answer this set of research questions, Chapter 5 brought together
climate change mitigation and climate change adaptation within an
integrated framework. Within this framework, the chapter has incorporated
the anticipated climate change impacts into simulations of a low-carbon
power system expansion from 2018 through 2050. Climate change impacts
were quantified based on the empirical data from the fieldwork and thensupplemented by the secondary data from the literature; for example, theglobal climate change projections downscaled for Indonesia. Furthermore,
the WEAP software tool was used to simulate water availability forhydropower under the future climate. Meanwhile, quantifications of climate160

CHAPTER 6 SYNTHESIS OF THE DISSERTATION

change impacts on coal and natural gas were based on empirical evidence
from case study power plants and findings from the literature.
Thus, the research has shown that future climate change could have
implications on electricity supply and demand. On the supply side, climatechange reduced power-generating capacity while on the demand side, it
increased the demand for electricity. Given these constraints as well as
the requirement to achieve the NRE and CO2 mitigation targets, the resultsof modeling simulations showed an alteration in technology composition
compared to the electrification-only scenario.
Furthermore, extra capacities were required to compensate for the climatechange-induced reduction in power-generating capacity and the increased
demand in the above 2℃ warmer world. Consequently, the total costs ofpower system expansion increased by 11.4% compared to no mitigationadaptation scenario. Moreover, in the mitigation-adaptation scenario, CO2
emissions in 2050 increased by 4% compared to the scenario that only
considered the mitigation and electrification objectives.
The simulation results indicated that future climate changes would
increase electricity demand and reduce the reliability of the power
generation. These changes will cause an increase in CO2 emissions andcosts, indicating that climate change impacts on the power system might
hinder the electrification and climate change mitigation goals if no climatechange adaptation policy is developed for the sector.

6.3.

Innovative contributions to science

This doctoral dissertation has provided insights into the interplay between
electrification, climate change mitigation, and climate change impacts andadaptations. Accordingly, this dissertation makes several innovative
scientific contributions to the literature on climate change mitigationadaptation in the energy sector.

Electrification and Paris climate goals: Firstly, to the best of the author’s
knowledge, the study discussed in Chapter 2 was the first to assess the161CHAPTER 6 SYNTHESIS OF THE DISSERTATION

trade-offs between electrification and the Paris climate goals in thecontext of the Indonesian power sector using a technology-rich energy
system model. Furthermore, the modeling simulations in this dissertation
employed a high-quality dataset of the 67 power generation units in theJava-Bali power system, which included technical parameters crucial for
LEAP input data. Hence, it enabled more accurate results for theIndonesian context than relying on the default values provided by the
LEAP technology database. As reported in Chapter 2, the research results
showed an agreement with other studies, assessing the implications of theParis Agreement on the power sector (Grande-Acosta and IslasSamperio, 2017; Baek et al., 2019; Kumar and Madlener, 2018) and inparticular, those concerning an increase in the investment costs of thepower system expansion, as well as in the penetration of renewable energy
technologies.

Power system expansion and endogenous technological learning:
Secondly, Chapter 3 demonstrated how ETL could influence renewableenergy deployment in Java-Bali. Although LEAP has been used worldwide,
to the best of the author’s knowledge, this chapter was the first to
integrate ETL into LEAP and applied it to simulations of a power system
expansion. Therefore, this chapter adds to the understanding regarding therole of technological learning in driving the transition from a fossil-fuelbased power generation to a lower-carbon power system in the context
of developing countries. Similar results regarding the role of ETL in energy
transition are indicated in other studies, although with different contexts,
such as Heuberger et al., which incorporate ETL into the ESO-XEL model
for analyzing the UK’s power system and Liu et al., which integrate ETL
into TIMES for assessing the Tianjin power sector in China.

Empirical evidence of the impacts of weather and climate on the power
sector: Thirdly, Chapter 4 has been one of the first attempts to thoroughly
examine the historical impacts of weather and climate on the power
sector. Hence, this chapter provides new data that adds to ourunderstanding concerning weather and climate impacts on the sector,
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including direct damage costs suffered by utilities and their adaptation
responses. Moreover, given the absence of vulnerability analysis of theIndonesian power sector to climate change, this chapter offers valuable
insights into this important subject. Compared with few empirical studies
on this subject (Audinet et al., 2014; Syariman and Heru, 2011), newfindings have emerged, such as extreme weather events that caused
jellyfish inflow and waste increase, which adversely affected the coolingwater system of thermal power plants.

Integrated analysis of the power sector expansion under climate change
mitigation and adaptation: Fourthly, Chapter 5 has explicitly linked thepower system’s climate change mitigation efforts with the interferences
brought by climate change impacts to the power sector. Thus, this chapter
contributes to filling the gap in the literature on the integrated analysis ofclimate change impact and adaptation in the energy sector, as highlighted
by the IPCC AR5 (Clarke et al., 2014).

Assessment of climate change mitigation and adaptation of the energy
sector on the global South: Finally, this dissertation takes Indonesia as acase study. In doing so, it adds to the scarce literature in developing
countries regarding (i) the implications of the Paris Agreement on the
power sector, and (ii) climate change impacts, adaptation, and vulnerability.
In addition, this dissertation creates two methodological innovations byadvancing the LEAP model application:
Firstly, Chapter 3 extended the current practice of LEAP by incorporating
the ETL of electric power technologies into the LEAP cost function. The
modeling approach was reported in detail, thereby allowing its replication
and application to other cases. Secondly, Chapter 5 advanced the LEAP
model practice by incorporating climate-driven changes in technologicalcharacteristics (i.e., efficiency and capacity factor) of various power
generation types. Thus, providing LEAP modeling framework for an
integrated analysis of climate change mitigation and adaptation.

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6.4.

Policy implications

This doctoral research focused on one critical contemporary societal
problem: electrification and climate change. The findings of thisdissertation can be used to develop targeted interventions aimed atbalancing electrification goals with climate change mitigation andadaptation goals. This is particularly important for the global South, where
electrification is a precondition for the development and achievement of
most social and economic UN SDGs. Accordingly, this dissertationsuggests several courses of action for the Indonesian power sector:

Early deployment of renewable energy: Indonesia’s NDC mitigation target
is aligned with the national policy to increase the share of renewableenergy in the national energy mix. The results in Chapter 3 have shownthat the costs of renewables decrease following the increase in theirinstalled capacity, with technological learning occurring at the highest
speed during the initial phase of technology deployment. These findings
suggest that the deployment of currently unexploited renewable energy
should start as early as possible to gain the benefits of technologicallearning and to avoid further technological lock-ins of the newly installed
infrastructure into fossil-fuel-based electricity production. Furthermore,this will help to avoid excessive investments in coal-fired power plants,
which could result in stranded coal power plant assets in the future.

Enabling faster local learning: Developing countries like Indonesia generally
receive technology transfers from industrialized countries, allowing
Indonesia to take advantage of global technological learning that has
already taken place. Additionally, local learning processes also play asignificant role in reducing the costs of renewables. Therefore, theconditions that facilitate faster local learning should be made available.
Such a condition includes a stable regulatory framework, an adequate
skilled workforce, and the establishment of a sustainable business model(Huenteler et al., 2016). Infrastructure improvements that enable remoteareas to become more accessible would also create a faster diffusion of164CHAPTER 6 SYNTHESIS OF THE DISSERTATION

renewable energy technologies. Most importantly, it is critical that there
should be interactions between all involved parties, including users,
suppliers, competitors, universities, and regulators for the learning andinnovation process to occur (Lundvall, 2016).

Improvements in the grid capacity: Integration of vast renewable energy
capacities presents new challenges to any power system operations andplanning. Variable energy resources, such as wind and solar, haveintermittent characteristics that will likely change the way electricity isdispatched and transmitted by the grid operator. Therefore, theacceleration of renewable energy deployment should go hand-in-hand with
the improvement of grid capacity, which includes technical and humancapital capacities. Furthermore, other disruptive technologies, such as theinternet of energy, energy storage, and electric vehicles, require the global
utility sector to transform into a smarter grid.

Moving toward a low-carbon and climate-resilient power sector: Chapter
4 has shown that severe weather and changes in climate variables havecaused widespread power outages, resulting in tremendous financial losses
for utilities and have affected the economy as a whole. Given the vital roleof the sector in meeting the electrification and climate change mitigation
goals, it is crucial for the country to improve the sector’s resilience toclimate change.
The first step toward a low-carbon and climate-resilient power sector
would be to acknowledge the vulnerability of the sector to climate change
and to integrate the sector’s climate change adaptation plans into thenational action plans for climate change adaptation. The critical question
here is to determine whether it is enough to scale up the existing
adaptation responses or if a transformational action is required to assurea climate-resilient development path. Synergies between climate
mitigation goals –the massive introduction of renewables into the existingfossil fuel dominated energy mix– and climate adaptation efforts should be
explored to increase the climate resilience of the sector.
165

CHAPTER 6 SYNTHESIS OF THE DISSERTATION

Moreover, this dissertation has shown that the sector’s adaptation toclimate change has been reactive following weather and climate-related
damages and losses. Ex-post adaptation focused on the extent andprobability of past adverse events faces limitations in the climate-changed
world. The expected increase in severity and probability of hazard eventsin a future climate calls for ex-ante strategies, especially when an
expansion of the power system is considered. The future sector’sresilience is co-developed today, requesting the integration of climatechange projections, assessment of physical and economic impacts, andpossible incremental or transformational adaptation responses (Kates etal., 2012) into the sector’s long-term development plan.
Finally, given the adverse impact of climate change on the power sector,
it is crucial for electric power utilities to include climate change adaptation
into their long-term business strategies and capacity building. Increasingawareness of the power sector’s stakeholders regarding climate change
consequences for the sustainability of their business operations is an
essential milestone.

Ensuring budget allocations for climate change mitigation and adaptation:
In the context of Indonesia, achieving a 100% electrification ratio is a vital
national development goal that affects human capital and other essentialsocial and economic objectives. Therefore, electricity should be not only
reliable but also affordable so that every citizen, including the poor, canenjoy electricity. To date, the government controls the electricity tariff
and allocates subsidy for low-income households through the stateelectricity company. This subsidy is used to cover the difference between
costs and the subsidized tariff for low-income households.
As discussed in Chapter 2 and Chapter 5, achieving the power sector’s
target under the Paris Agreement would add to the costs of electrification.
Given that climate change is already affecting the power sector today due
to past-emitted CO2 and delayed mitigation actions, adaptation to climatechange is unavoidable. Therefore, it is critical for the power sector to166

CHAPTER 6 SYNTHESIS OF THE DISSERTATION

increase its resilience to climate change; for example, by taking ex-ante
adaptation measures, such as investing in technology that is lessvulnerable to climate change and upgrading electricity infrastructure. This
would entail supplementary costs in addition to mitigation costs. Naturally,
these additional costs could affect the electricity price and the size of thegovernment subsidy. However, if climate change impacts and adaptations
are considered at the early stages of electricity system expansion, it
affects the choice of technology in the projected energy mix. This
indicates that anticipatory climate change adaptation can bring cobenefits.

6.5.

Limitations and the agenda for future research

While this dissertation makes a number of innovative contributions, it also
opens doors for further research. Firstly, due to the scope of LEAP anddata limitations, the power system expansion neglected constraints in the
T&D networks, assuming that electricity can be transmitted at any timeto any load station. Moreover, since the simulations in this doctoral
research were carried out with an annual time-step over a long-term time
horizon, hourly demand variations could not be accommodated due tocomputational feasibility. In this dissertation, the Java-Bali’s load
characteristics were divided into 48 time-slices, which represent four
variations for each month (weekday, weekend, day, and night). Hence, thevariations in demand were represented by a segmented load duration
curve, making the temporal dynamic, such as continuous (hourly)
variations in demand and intermittency and variability in supply, wereignored. Further modeling works will have to cover transmission capacity
and spatial analysis of each power plant and substation, as well as include
data on temporal characteristics of electricity demand and intermittencyof renewable energy supply.
Secondly, with respect to ETL, this dissertation has taken into accountthe one-factor learning model, which relates technology costs with its
cumulative capacity or production. An interesting extension could be to167

CHAPTER 6 SYNTHESIS OF THE DISSERTATION

consider other factors, such as cumulative expenditure for research anddevelopment in the ETL simulations.
Thirdly, the quantifications of climate change impacts on the power systemin this dissertation were preliminary, relying on the empirical data fromseveral power plants and findings found in the literature. Further research
may proceed with a systematic data collection to enable detailed
estimations based on individual power plants, thereby enabling evaluating
the site-specific impacts of climate change. Comparison of the empiricalfacts across power plants based on the same technology and acrosscountries will allow for generalizations and estimation of probabilities anddamage curves caused by the changing climate.
Finally, the integrated mitigation-adaptation modeling approach in thisdissertation omitted the impacts of extreme weather events due toexisting tools having a limited ability to capture extreme events (Cronin etal., 2018). While my fieldwork delivered data on both extreme events andgradual changes in climate variables, only the latter –i.e., increased air andseawater temperatures and changes in precipitation– were considered in
the modeling part. Nonetheless, extreme events are expected to intensify
under future climate change and will lead to physical infrastructuredamages, energy losses, and indirect effects, such as businessinterruptions. Therefore, an important direction of future research would
be to explore how extreme weather events would hinder long-term
electrification and the climate change mitigation goals of the power sector
due to damages and their social costs throughout the electricity value
chain.

168

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196

APPENDICES

Appendix 2.A: Systematic screening of energy models
Table 2A.1: Inventory of energy system models
Suitability Criteria
No.

Model

1

AEOLIUS (Connolly et al., 2010; Rosen
and Rentz, 2007)
BALMOREL (Connolly et al., 2010;
Ravn, 2001)
BHCP Screening Tool (Connolly et al.,2010; MacDonald and Fischer, 2007)
Compare Options for Sustainable
Energy/COMPOSE (Connolly et al.,2010; Blarke, 2008)
E4cast (Connolly et al., 2010; Akmal etal., 2004)
Electricity Market Complex Adaptive
Systems/EMCAS (North et al., 2002)
Early Market Introduction of New
Energy Technologies/EMINEN(Connolly et al., 2010; Klemeš et al.,
2009)

2
3
4

5
6
7

Able to simulate
capacity expansion ofa large power system

Wide options of
power generation
technologies

Calculatecosts

Simulation
period of min
10 yearsCalculateCO2emissions

No

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

No

No

Yes

No

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

No

Yes

Yes

Yes

No

Yes

199

APPENDICES

No

Model

8

EFI’s
Multi-Area
Powermarket
Simulator/EMPS (Connolly et al., 2010;
Doorman et al., 2004)
EnergyPLAN (Lund, 2013)l
EnergyPRO (Connolly et al., 2010; EMD
International A/S, 2017)
EnergyandPower
Evaluation
Program/ENPEP-BALANCE (Connolly
et al., 2010; CEEESA, 2017)
Generation
andTransmission
Maximisation Tool/GTMax (Connolly
et al., 2010; Veselka, 2014)
H2RES (Connolly et al., 2010; Duić andda Graça Carvalho, 2004)
HOMER (Connolly et al., 2010; Lambert
et al., 2006)
Hydrogen
EnergyModels/HYDROGEMS (Connolly et al.,2010; Ulleberg and Morkved, 2008)
IKARUS (Connolly et al., 2010;
Martinsen et al., 2006)

9
10
11

12

13
14
15

16

200

Able to simulate
capacity expansion ofa large power system

Wide options of
power generation
technologies

Calculatecosts

Simulation
period of min
10 yearsCalculateCO2emissions

Yes

Yes

Yes

Yes

Yes

Yes
No

Yes
Yes

Yes
Yes

No
Yes

Yes
Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

No

No

Yes

Yes

Yes

Yes

No

Yes

Yes

No

Yes

No

Yes

Yes

No

Not
clear*

Yes

Yes

Yes

Yes

Yes

APPENDICES

No

Model

17

International Network for Sustainable
Energy/INFORSE (Connolly et al.,2010)
Long-range
EnergyAlternative
Planning System/LEAP (Heaps, 2017)
MARKetALlocation
model(MARKAL)/The Integrated MARKALEFOM System/TIMES (Loulou andLabriet, 2008)
Model for Energy Supply Strategy
Alternatives and their General
Environmental
impact/MESSAGE(Connolly et al., 2010; Tot, 2012)
Mini Climate Assessment Model(MiniCAM)/Global
ChangeAssessment
Model/GCAM(The
JGCRI GCAM Team, 2016)
National
EnergyModelling
System/NEMS (EIA, 2009)
Oak Ridge Competitive Electricity
Dispatch/ORCED (Hadley, 2008)

19
20

21

22

23
24

Able to simulate
capacity expansion ofa large power system

Wide options of
power generation
technologies

Calculatecosts

Simulation
period of min
10 yearsCalculateCO2emissions

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

201

APPENDICES
Able to simulate
capacity expansion ofa large power system

Wide options of
power generation
technologies

Calculatecosts

Simulation
period of min
10 yearsCalculateCO2emissions

Programme-package for Emission
Reduction Strategies in Energy Use
andSupply-Certificate
Trading/PERSEUS (Rosen and Rentz,
2007)
PRIMES (Mantzos and Capros, 1998)
ProdRisk (Connolly et al., 2010)

Yes

Yes

Yes

Yes

Yes

Yes
No

Yes
No

Yes
Yes

Yes
Yes

RAMSES (Connolly et al., 2010)
Renewable
EnergyTechnology
Screening
Model/RETScreen(Connolly et al., 2010)
Simulation of Renewable Energy
Networks/SimREN (Lehmann, 2003)
SIVAEL (Connolly et al., 2010)
Sustainable Technology Research andEnergy Analysis Model/ STREAM(Risø, 2008)
TRNSYS16 (Solar Energy Laboratory
University of Wisconsin-Madison,
2013)

Yes
No

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Not
clear*
Yes
Yes

Yes

Yes

Yes

Yes

No
Yes

No
Yes

Yes
Yes

No
Yes

No

No

Not
clear*

Yes

No

Model

25

26
27
28
29

30
31
32

33

202

Not
clear*Yes
Yes

Not
clear*

APPENDICES

No

Model

34
35

UniSyD3.0 (Connolly et al., 2010)
Wien Automatic System Planning
Package/WASP (IAEA, 2006)
WILMAR Planning Tool (Meibom, 2005)
Regional
EnergyScenario
Generator/RESGEN (Foell et al., 1995)
EnergyFlow
Optimisation
Model/EFOM (De Kruijk, 1994)
Prospective outlook on long-term
energy systems/POLES (Enerdata,
2012)
World Energy Model/WEM (OECD/IEA,
2017)
System for the analysis of global
energy markets/SAGE (DOE, 2003)
Electricity Generation Expansion
Analysis System/EGEAS (Ng, 2015)
Asia-Pacific Integrated Model/AIM(AIM Project Team, 2016)
Atmospheric
Stabilization
Framework/ASF (IPCC, 2000)

36
37
38
39

40
41
42
43
44

Able to simulate
capacity expansion ofa large power system

Wide options of
power generation
technologies

Calculatecosts

Simulation
period of min
10 yearsCalculateCO2emissions

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

No
Yes

Yes
Yes

Yes
Yes

No
Yes

Yes
No

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

203

APPENDICES

No

Model

45

TARGETS-IMAGE Energy Regional
Model/IMAGE-TIMER (De Vries et al.,2002)
Multiregional Approach for Resource
and Industry Allocation/MARIA (IPCC,
2000)
PowerPlan (Benders, 1996)
SecondGeneration
Model(SGM)/Phoenix (Wing et al., 2011)

46

47
48

*Unclear due to insufficient information

204

Able to simulate
capacity expansion ofa large power system

Wide options of
power generation
technologies

Calculatecosts

Simulation
period of min
10 yearsCalculateCO2emissions

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

APPENDICES
Table 2A.2: Accessibility of the suitable models
No

Tools

Accessibility

Link to free download

1.
2.
3.
4.
5.
6.
7.

BalmorelE4castEMPS
ENPEP-BALANCEH2RESIKARUSINFORSE

http://www.balmorel.com/index.php/downloadmodel

8.
9.
10.
11.
12.
13.

LEAP
MARKAL/TIMES
Mesap PLanet
MESSAGE
MiniCAM/ GCAM
NEMS

14.
15.
16.
17.
18.

ORCED
PERSEUSPRIMESSimRENSTREAM

Free*
Commercial
Commercial
FreeInternal use only
Commercial
Free to INFORSE members andcooperating European networks
Free**
Commercial
Commercial
Free***Free*
The model is free, but users must
purchase the simulator
Used to be free to download
Commercial
Commercial
Commercial
Free

Email to ceeesa@anl.gov

https://www.energycommunity.org/default.asp?action=download

http://www.globalchange.umd.edu/gcam/download/
https://www.eia.gov/bookshelf/models2001/NationalEnergy.html

http://streammodel.org/downloads.html

205

APPENDICES
No

Tools

Accessibility

19.

UniSyD3.0

20.
21.
22.
23.

WASP
POLES
WEM
SAGE

Contact: Prof. Jonathan Leaver:jleaver@unitec.ac.nz
Free IAEA member states
Commercial
Internal use
Model source codes are available*

24.
25.
26.
27.
28.
29.
30

EGEAS
AIM
ASF
IMAGE/TIMERMARIAPowerPlan
SGM/Phoenix

Commercial
Free*Internal use
Contact: image-info@pbl.nl
Internal use
Commercial
Model source codes are available*

Link to free download

http://www.globaloilwatch.com/reports/mdr-system-analysisglobal-energy-markets-eia-082003.pdf
http://www-iam.nies.go.jp/aim/data_tools/index.html

http://www.globalchange.umd.edu/data/models/phx_documentati
on_august_2011.pdf*The program is written in GAMS modeling language, which is a commercial software. **Free for students and non-profit institutions indeveloping countries. ***Free for academic purposes and the International Atomic Energy Agency (IAEA) member states.

206

APPENDICES

Appendix 2.B: Input data for the LEAP Indonesian model 2005-2015
Table 2B.1: Summary of model input parameters for the LEAP Indonesian model 2005-2015
Data input
Annual electricity demand 2005-2015
Transmission & distribution losses
System load shape
Planning reserve margin
Capacities of existing power plants

Value
2005: 107,032 GWh
2015: 202,846 GWh
9.7% - 11.6%
9% - 35%
Total capacity
2005: 22.2 GW
2015: 39.9 GW

Merit order in the accounting setting:Baseload power plants
Intermediate load power plants
Peak load power plants
Capacity factor

CST, solar PV, geothermal
NGCC, hydroNGOC, DG
CST: 65%, NGCC: 50%

Environmental parameters-

Lifetime of technologies
Discount rate

20-40 years
12%

Sources
PLN statistics 2005-2015
PLN statistics 2005-2015
P2B
Calculated based on historical peak load and capacity
data
PLN statistics 2005-2015

Maintained according to the P2B dispatch order

Calculated based on data from PLN Statistics 20052015
The IPCC Tier 1 default emission factors, embedded in
the technology database of LEAP
(58)
Refers to the discount rate used by PLN

207

APPENDICES
Table 2B.2: Cost data for the capacity addition 2006-2015

CST

Investment
Costa
(2005
US$/kW)
850

Variable OM
Costsb(2005
US$/MWh)
2.2

Fuel Costs
Costs
(2005
US$)b
25.7

NGCC

550

5.2

2.65

Technologies

Unit
Per metric ton ofcoal
Per MMBTU ofnatural gas

Sources: aRUPTL 2006-2015 (PLN, 2005); bPLN Statistics 2005 (PLN, 2006)

208

APPENDICES

Appendix 2.C: Input data for Java-Bali power system expansion 20162030

Fig. 2C.1: Load shape of the Java Bali power system (P2B, 2016a)
Table 2C.1: Power generation capacity of the Java-Bali Power System (P2B,
2016a)
Power plants

Net Capacity (MW)Coal steam turbine

17,339Natural gas combines cycle

8,971

Hydro

2,477

Geothermal

1,092

Natural gas open cycle

1066

Diesel generator

260

Natural gas engine

200

Total

31,405209

APPENDICES

Appendix 3.A: Input data for the LEAP simulations

3.A.1. Assumptions of Learning Rates
The assumptions of the initial learning rates are based on Rubin et al.
(2015), as presented in Table 3A.1. They provide a review of learning rates
from various studies. Due to the wide range of learning rate values
presented in that study, here, I use the minimum, mean, and maximum
values of the learning rate of each technology in this analysis (REN-low
LR, REN-medium LR, and REN-high LR). In the case of hydropower, only
one study is present in Rubin et al. (2015). Therefore, I use only one value
for hydro in all three REN LR scenarios. Moreover, this secondary data
specified a negative minimum learning rate value for nuclear, while its meanvalue is not provided. In this chapter, I assume the minimum learning rate
value for nuclear is -6%, while the mean learning value for nuclear isassumed to be -1%, following the assumptions in Heuberger et al.
(Heuberger et al., 2017). Since the learning rate for geothermal is notavailable, I assume it as 0% in all three scenarios.
Table 3A.1: Learning rate values of power generation technologies (Rubin et al.,2015)
Technologies

Learning Rates*
Minimum

Mean

Maximum

Years covered across thestudies

Solar PV
10%
23%
47%
1959-2011
Wind Turbine
-11%
12%
32%
1979-2010
Biomass
0%
11%
24%
1976-2005
Pulverized coal 5.6%
8.3%
12%
1902-2006
Gas turbine
10%
15%
22%
1959-1990
NGCC
-11%
14%
34%
1980-1998
NGOC
10%
15%
22%
1958-1990
Hydro
1.4%
1.4%
1.4%
1980-2001
Nuclear
Negative 6%
1972-1996*
The learning rate values are based on empirical data reported in the literature thatwere collected and reviewed by Rubin et al. (2015).

210

APPENDICES

Appendix 3.B: Learning curves of solar PV
The optimization simulations in LEAP result in a massive deployment ofsolar PV in all three technological learning scenarios. Hence, the capital
cost of solar PV reduced over time, as depicted in Fig. 3C.1 (for the RENmedium LR scenario). Fig. 3B.1 (a) shows a continues reduction in thecapital cost of solar PV along with the increase in its cumulative capacity.
As Fig. 3B.1 (b) illustrates, in Phases I and II, the cost reduction occurs
faster. Meanwhile, in the later phases, the costs reduce slower partly
because of the reduction in the learning rate value as well as the
cumulative capacity already being high. In Phase II, the cumulative capacity
reaches nearly 20 GW; thus, in Phase III, it requires another 20 GW ofadditional capacity to gain a 9% cost reduction, as assumed for the RENMedium LR scenario (see Table 3A.1). Furthermore, in Phase IV, it requires
another nearly 40 GW additional capacity to gain 4% of cost reduction.

211

APPENDICES

Capital cost ( 2015 US$/kW)

2500
2000
1500

(a)
1000
500
0
0

20,00040,00060,00080,000 100,000 120,000

Cummulative capacity (Megawatt)

Capital cost (2015 US$/KW)

2500
2000

Phase
I

1500

Phase II

(b)

1000
500
Phase III

Phase IV00

20,00040,00060,00080,000100,000

120,000

Cummulative capacity (Megawatt)

Fig. 2C.1: Learning curves of solar PV assuming the medium value of the initial
learning rate: (a) depicts a continuous cost reduction based on Eq. (9), while (b)depicts a linear approximation of the cost reduction in (a) that divides into fourphases.

212

APPENDICES

Appendix 4.A: Indonesia’s vulnerability to climate change
Being a vast archipelagic country with 81,000 km of coastline and 42
million inhabitants living in low-lying areas, Indonesia is highly vulnerable
to the adverse impacts of climate change (USAID, 2017). As many as 2,000
small islands are projected to sink by mid-century. Furthermore, by 2100,
an estimate of 5.9 million people every year will be affected by coastal
flooding. Other impacts of climate change include extended dry seasons,an increased frequency of extreme climate occurrences, and degradedbiodiversity (BAPPENAS, 2014). A World Bank analysis ranks Indonesia
12th among 35 countries with high mortality risks due to multiple hazards,
including climate-related hazards such as floods, landslides, and droughts(Dutch Sustainability Unit, 2016). Accordingly, an estimated 40% ofIndonesians are at risk of various hazards, including earthquakes, tsunamis,
volcanic eruptions, floods, landslides, droughts, and forest fires (World
Bank, 2011). Table 4A.1 summarizes the trends and projections of theIndonesian climate.

213

APPENDICES
Table 4A.1 Historical trends and future projections of the Indonesian climate.
Source: (GERICS, 2015; USAID, 2017)
Climateparameters
Temperature

Historical trends

Future projections

Mean
annualtemperature increased
0.04°C per decade
between 1985 to 2015

Mean annual temperature increaseof 1.1 to 3.2°C by 2085

Heatwaves

The frequency of hotdays
andnightsincreased +88 days and+95 nights, respectively,
since 1960.

-

Average annual rainfall
increased by 12% from1985 to 2015-

Rainfall--

Increase in the duration of heatwaves ranging from 23 to 1340
days by 2085
The decrease in the duration ofcold spell ranging from -10 to -1
day
Although projections for rainfall
differ, they point to increased
rainfall during the wet season
A slight increase in the durationof dry spells (+2 days).
Increase in frequency andintensity of heavy rainfall events
by
3-31%
and
2-13%,
respectively

Sea level

Sea level rise of 2-10
mm per year from 1993
to 2012

Sea level rise of 150-450 mm by2056

Tropicalglacier

Decreased of tropicalPapua glaciers cover by
78% from 1936 to 2006

The disappearance of Papua glaciers214

APPENDICES

Appendix 4.B: Questionnaires and secondary data
Table 4B.1: List of questions for head office representatives
No

Questions1.

Based on your experience, how do weather andclimate affect the power generation,transmission, and distribution networks?
Has flood ever occurred and affected theelectricity infrastructure? How often? What was
the cause? Did the infrastructure shut down?
How much was the financial loss?
What has been done to deal with the floods?
How much do these actions cost?
Were there any interruptions on electricity
supply attributable to extreme weather? What
and when? How much were the financial losses?
How did the operation interruption affect the
rest
of
the
economy(intheregion/island/country – choose the relevant)?
Are there any policies and programs addressing
climate change impact and adaptation? Is there
any budget allocated?

2.

3.
4.

5.

Expected supporting
documents

Documentation of theflood

Documentation ofoperation interruptions
due to extreme
weather.

Copy of policies andprograms$$ of investments
plannedTable 4B.2: List of questions concerning transmission networks
No

Questions1.

Has flood ever occurred in substations, andtransmission networks? How often? What werethe causes? Did they cause power cuts? Howmuch were the financial losses?
What has been done to deal with the floods?
How much do they cost?
Was there any operation interruption due toextreme weather? What and when? How often?
How much was the financial loss?
Are there any policies and programs addressing
climate change impact and adaptation? Is there
any budget allocated?

2.
3.
4.

Expected supporting
documents
Documentation of theflood
SOP of dealing withfloodsDocumentationofoperation interruptions
due to extreme weather
Copy of policies andprograms

215

APPENDICES
Table 4B.3: List of questions for grid operator (load control center)
No

Questions

1.

What is the load shape of the Java-Bali
power system?
In addition, Jakarta subsystem, West Javasubsystem, Central Java subsystem, EastJava subsystem and Bali subsystem.

2.

Has flood ever occurred in the dispatcher
units? How often? What were the causes?
Did they cause power cuts? How much were
the financial losses?

Expected supporting
documents
At least ten years hourlyload data
SystemOperation
Evaluation Annual Reports(at least 10 years)
Documentation
flood

of

the

SOP of dealing with floods

3.

What has been done to deal with the floods?
How much do they cost?

4.

Was there any operation interruption due toextreme weather? What and when? How
often? How much was the financial loss?

Documentation ofoperation interruptions
due to extreme weather

5.

Are there any policies and programs
addressing climate change impact andadaptation? Is there any budget allocated?

Copy ofprograms

216

policiesandAPPENDICES
Table 4B.4: List of questions concerning distribution networks
No

Questions1.

Is there any information about the amountof electricity use for space cooling? Do
you observe any trends in relationships
between T rise and changes in demand for
E (due to space cooling)?

2.

Has flood ever occurred in distribution
substations and networks? How often?
What were the causes? Did they cause
power cuts? How much were the financial
losses?

Expected supporting
documents

Documentation of the flood
SOP of dealing with floods

3.

What has been done to deal with thefloods? How much do they cost?

4.

Was there any operation interruption due
to extreme weather? What and when? How
often? How much was the financial loss?

Documentation of operationinterruptions due to extreme
weather

5.

Are there any policies and programs
addressing climate change impact andadaptation? Is there any budget allocated?

Copy ofprograms

policiesand217

APPENDICES
Table 4B.5: List of questions concerning hydropower plants
No

Questions

Expected supporting
documents

1.

What is the trend of precipitation andwater level? Has the water level changed
from the first year of operation?

Monitoring
dataof
precipitation, water inflow,
the water level2.

How does the trend affect electricity
production?

Monitoringdataelectricity production

3.

Was there any operation interruption due
to low water level/drought or excessive
water? How often? How much were thefinancial losses?

Water level and electricity
production data

4.

Have there been any actions done toincrease the water level? What are they?
How much do they cost?

5.

Has flood ever occurred in the power
plants? How often? What was the cause?
Did the plant shut down? How much was
the financial loss?

on

Documentation of the flood
SOP of dealing with floods

6.

What has been done to deal with thefloods? How much do they cost?

7.

Was there any operation interruption due
to extreme weather? What and when?
How often? How much were the financial
losses?

Documentation of operationinterruptions due to extreme
weather

8.

Are there any company’s policies andprograms addressing climate change
impact and adaptation? Is there any
budget allocated?

Copy ofprograms

218

policiesandAPPENDICES
Table 4B.6: List of questions concerning thermal power plants (coal- and gasbased power plants)
No

Questions

Expected
documents

supporting

1.

What is the trend of cooling watertemperature?

Monitoring data on coolingwater temperature in last (5,10, 15 years if possible)2.

Is there any correlation between coolingwater temperature and power plant
efficiency? What and how?

Monitoring data on coolingwater temperature and power
plant’s efficiency

3.

Has the sea level changed from the firstyear of operation?

Sea level data

4.

What has been done to maintain the coolingwater temperature at the desired level?
How much does it cost?
To what extent (in %?) were you able torecover the power output to the prewarming levels?

5.

Has flood ever occurred and affected thepower plants? How often? What was thecause? Did the plant shut down? How much
was the financial loss?

Documentation of the flood

6.

What has been done to deal with thefloods? How much do these actions cost?

7.

Was there any operation interruption due
to extreme weather? What and when? Howmuch were the financial losses?

Documentation of operationinterruptions due to extreme
weather.

8.

Are there any policies and programs
addressing climate change impact andadaptation? Is there any budget allocated?

Copy of policies and programs$$ of investments planned

219

APPENDICES

5,000
4,500
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0

180
160
140
100
80

(a)

60
40
20
0

Electricity production (GWh)

GWh

m3/s

120

Water inflow (m3/s)

3,000

300

2,500

250

2,000

200

1,500

150

1,000

100

500

50

0

0

Electricity production (GWh)

m3/s

GWh

Appendix 4.C: Historical data of hydroelectric power plants(b)

Water inflow (m3/s)

Fig. 4C.1: Historical data on electricity production and water inflow Saguling HEPP(a) and Cirata HEPP (b). Source: own fieldwork.

220

APPENDICES

Precipitation vs water inflow to the reservoir
450
y = -2E-08x3 + 8E-05x2 + 0.0025x + 99.71
R² = 0.619

Water inflow (m3/s)

400
350
300
250

(a)

200
150
100
50
0
0

500

1000

1500

2000

2500

3000

Precipitation (mm)Electricity production (GWh)

Eater inflow vs production
300
y = 5E-06x3 - 0.0027x2 + 0.8294x + 30.718
R² = 0.7447

250
200
150

(b)

100
50
0
50

150

250

350

450

Water inflow (m3/s)

Fig. 4C.2: Correlation between precipitation, water inflow, and electricity
production: Cirata HEPP 2013-2017. (a) Monthly water inflow as a function of
precipitation15; (b) Monthly electricity production as a function of water inflow.
Source: own analysis.

15

Precipitation data from seven weather stations

221

APPENDICES

Appendix 5.A: Projection of temperature and precipitation changes
Table 5A.1: Historical and projected changes in temperature
Month

January
February
March
April
May
June
July
AugustSeptember
OctoberNovember
DecemberTemperature (ºC)
1986-2005
Projected changed
2020-2039
RCP4.5
RCP8.5
25.88
0.69
0.76
25.94
0.73
0.82
26.22
0.72
0.81
26.43
0.75
0.83
26.41
0.71
0.82
26.05
0.72
0.81
25.73
0.73
0.84
25.74
0.71
0.82
25.92
0.77
0.82
26.20
0.77
0.83
26.21
0.74
0.84
25.95
0.69
0.79

2040-2059
RCP4.5 RCP8.5
1.03
1.37
1.09
1.45
1.06
1.40
1.05
1.42
1.07
1.43
1.07
1.45
1.06
1.42
1.07
1.41
1.10
1.43
1.09
1.43
1.09
1.41
1.02
1.39

Table 5A.2: Historical and projected changes in temperature
Month

January
February
March
April
May
June
July
AugustSeptember
OctoberNovember
December

222

Precipitation (mm)1986-2005
Projected changed
2020-2039
RCP 4.5
RCP 8.5
336.7318.08
9.42
266.76-3.62
4.48
343.740.83
15.38
327.12-5.28
4.32
240.61-6.10-1.65
115.71-3.43-7.23
109.90-8.02-7.95
101.13-1.69-0.22
137.69-4.24-2.18
287.00-8.80-9.24
398.001.99
8.53
340.32-1.83-8.89

2040-2059
RCP 4.5
RCP 8.5
29.90
20.87
0.52
14.48
15.36
15.56
10.43
13.86
-0.06
1.48
-0.25
-3.47
-10.32-17.47
-6.22
-3.98
-2.16
-6.87
-7.22
-10.95
13.27
4.36
15.28
-5.09

APPENDICES

Appendix 5.B: Assumptions for projecting climate change impacts
on the electricity supply

Fig. 5B.1: Schematic view of water demand and supply of the Citarum WEAP
model. Source: own simulations

223

APPENDICES
Table 5B.1: Input data for the Citarum WEAP model
Input Data

Value

Sources

Map

Fig. 5B.2

KLHK (KLHK, 2011), BIG(BIG, 2017b)

Area

Saguling: 222,000 ha

Own calculation based on
geospatial analysis

Catchment

Cirata: 416,000 ha
Precipitation (mm)Monthly, 1948-2010
Monthly, 1986-2017

Sheffield et al. (Sheffield
et al., 2006)
PLTA Cirata (PLTA
Cirata, 2018) and PLTA
Saguling (PLTA Saguling,2018)

Temperature (°C)

Mean monthly air temp,
1948-2010

Sheffield et al. (Sheffield
et al., 2006)

Wind speed (m/s)

Monthly, 1948-2010

Sheffield et al. (Sheffield
et al., 2006)

Total Storage Capacity
Cirata2,165 million m

Saguling

875 million m3

3

PLTA Cirata
Cirata, 2018)(PLTA

PLTA Saguling
Saguling, 2018)(PLTA

PLTA Cirata
Cirata, 2018)(PLTA

PLTA Saguling
Saguling, 2018)(PLTA

Max Turbine Flow
Cirata1,080 m3/s

Saguling

219.2 m3/s

Tailwater Elevation

224

Cirata84 m

Saguling

252 m

Kasiro et al. (Ibnu Kasiro,
Isdiana, D. Pangluar, CL
Nugroho, Attan Muchtar,
Hari Martadi, 1995)

APPENDICES

Saguling HEPP
50%

Availability

48%
46%
44%
42%

(a)

40%
38%
36%
2018

2022

CCM

2026

2030

2034

Year
CCMA RCP4.5

2038

2042

2046

CCMA RCP8.5

Cirata HEPP
23.00%

Availability

22.00%
21.00%
20.00%
19.00%
18.00%
17.00%
2018

2022

CCM

2026

2030

2034

Year
CCMA RCP4.5

2038

2042

2046

2050

(b)

CCMA RCP8.5

Fig. 5B.2: The average annual capacity factor of HEPPs under CCM and
CCMA scenarios: Saguling HEPP (a); Cirata HEPP (b). Source: WEAP
estimates.

225

226

SUPPLEMENTARY

Supplementary
List of Figures
Fig. 1.1: The national and Java-Bali power generation mixes in 2015 (PLN,
2016a)..................................................................................................................................... 7
Fig. 2.1: Reference scenario: Power generation capacity 2015-2030 (a);Electricity generation mix in 2030 (b) ..................................................................... 49
Fig. 2.2: Natural gas scenario: Power generation capacity (NGS1, 19%
target) (a); Electricity generation mix in 2030 (NGS2, 24% target) (b) ...... 51
Fig. 2.3: Renewable energy scenario: Electricity generation mix in 2030
(REN1, 19% target) (a); Power generation capacity (REN2, 24% target) (b)................................................................................................................................................ 52
Fig. 2.4: Optimization scenario: Power generation capacity (OPT1, 19%
target) (a); Electricity generation mix 2030 (OPT2, 24% target) (b) ........... 54
Fig. 3.1: Installed capacity, REF scenario. ............................................................. 80
Fig. 3.2: Electricity generation mix, REF scenario. ............................................ 80
Fig. 3.3: Installed capacity in REN scenario, no technological learning. ... 81
Fig. 3.4: Electricity generation mix in REN scenario, no technologicallearning. ............................................................................................................................... 82
Fig. 3.5: Electricity generation mix in REN-low LR scenario, technologicallearning occurs at the minimum pace. .................................................................... 83
Fig. 3.6: Electricity generation mix in REN-medium LR scenario, averagepace of technological learning.................................................................................... 84
Fig. 3.7: Electricity generation mix in REN-high LR scenario, the high paceof technological learning. .............................................................................................. 85
Fig. 3.8: Total costs of electricity production under reference and four
renewable energy scenarios. ...................................................................................... 87
Fig. 3.9: CO2 emissions under the reference scenario and the renewableenergy scenario. ............................................................................................................... 88
Fig. 4.1: Power plants and extra-high-voltage Java-Bali transmission
networks covered by this chapter, modified from PLN (2018). While thisfigure depicts only extra-high-voltage transmission networks, this chapter
also includes high-voltage transmission networks and low-voltage
distribution networks ..................................................................................................... 99

227

SUPPLEMENTARY

Fig. 4.2: Annual maximum water levels and energy spills: Saguling HEPP
from 1987 through to 2016. Source: own fieldwork. ...................................... 115
Fig. 4.3: Peak electricity demand in the Java-Bali grid as a function of
maximum daily temperature: weekdays 2014. Source: own fieldwork. ... 116
Fig. 4.4: The condenser vacuum as a function of cooling watertemperature: PJB Paiton CFPP, half-hourly, December 2015. Source: own
fieldwork. .......................................................................................................................... 117
Fig. 4.5: Tidal flooding affects coastal power plants. Source: own fieldwork.
............................................................................................................................................. 119
Fig. 5.1: The conceptual framework for integrating climate change
mitigation and adaptation using WEAP-LEAP models .................................. 134
Fig. 5.2: Capacity mix over the study period (a) and electricity mix in 2050(b) in the reference scenario ................................................................................... 142
Fig. 5.3: The Java-Bali electricity mix in 2017, 2025, and 2050; climatechange mitigation scenario (CCM) ........................................................................ 143
Fig. 5.4: Extra capacity in CCMA RCP4.5 (a) and CCMA RCP8.5 (b). CCM
is taken as a benchmark............................................................................................ 144
Fig. 5.5: Extra electricity production in CCMA RCP4.5 (a) and CCMA
RCP8.5 (b). CCM is taken as a benchmark ....................................................... 145
Fig. 5.6: CO2 emissions under reference, mitigation, and adaption
scenarios. CCM is taken as a benchmark .......................................................... 146
Fig. 5.7: Total production costs under reference, mitigation, and adaption
scenarios. CCM is taken as a benchmark. ......................................................... 148
Fig. 2C.1: Learning curves of solar PV assuming the medium value of theinitial learning rate: (a) depicts a continuous cost reduction based on Eq.
(9), while (b) depicts a linear approximation of the cost reduction in (a)that divides into four phases. .................................................................................. 212
Fig. 4C.1: Historical data on electricity production and water inflow.
Source: own fieldwork. ............................................................................................... 220
Fig. 4C.2: Correlation between precipitation, water inflow, and electricity
production: Cirata HEPP. Source: own analysis. ............................................. 221
Fig. 5B.1: Schematic view of water demand and supply of the Citarum
WEAP model. Source: own simulations ............................................................... 223
Fig. 5B.2: The average annual capacity factor of HEPPs under CCM and
CCMA scenarios: Saguling HEPP (a); Cirata HEPP (b). Source: WEAP
estimates. ........................................................................................................................ 225
228

SUPPLEMENTARY

List of Tables
Table 1.1: Capacities of power generation, transmission, and distribution:
the total Indonesia and Java-Bali system (KESDM, 2017; PLN, 2017a) .... 6
Table 1.2: Primary energy resources in Indonesia............................................... 8
Table 1.3: Renewable energy potential and current practices in Indonesia.................................................................................................................................................. 8
Table 2.1: Input-output parameters in the Indonesian LEAP model and data
used for validation........................................................................................................... 39
Table 2.2: Two alternative simulation settings in LEAP ................................. 39
Table 2.3: A comparison of the estimated national capacity added in 20062015 with the actual data ............................................................................................ 40
Table 2.4: A comparison of the estimated national electricity production
and GHG emissions with the actual data .............................................................. 41
Table 2.5: Summary of model input parameter ................................................... 45
Table 2.6: Characteristics of technologies in the Java-Bali LEAP model................................................................................................................................................ 48
Table 2.7: Emission reduction targets against REF scenario........................ 50
Table 2.8: Total costs of power system expansion in different scenarios................................................................................................................................................ 55
Table 3.1: Assumptions of learning rates of electric power technologies
2016-2050. ......................................................................................................................... 72
Table 3.2: Summary of model input parameter ................................................... 76
Table 3.3: Characteristics of technologies in the Java-Bali LEAP model................................................................................................................................................ 78
Table 4.1: Indonesian power plants where primary data collection has takenplace ..................................................................................................................................... 98
Table 4.2: Sources of secondary data ................................................................. 100
Table 4.3: Impacts of severe weather events on power generation and theutilities’ adaptation responses. Source: own fieldwork................................ 103
Table 4.4: Impacts of severe weather events on transmission networks
and the utility’s adaptation responses. Source: own fieldwork. ............... 105
Table 4.5: Impacts of severe weather events on distribution networks andutility’s adaptation responses. Source: own fieldwork ................................. 106
Table 4.6: Percentage of electricity production in dry years compared todesign capacities. Source: own analysis ............................................................. 115
229

SUPPLEMENTARY

Table 2A.1: Inventory of energy system models ............................................. 199
Table 2A.2: Accessibility of the suitable models ............................................ 205
Table 2B.1: Summary of model input parameters for the LEAP Indonesianmodel 2005-2015.......................................................................................................... 207
Table 2B.2: Cost data for the capacity addition 2006-2015...................... 208
Table 2C.1: Power generation capacity of the Java-Bali Power System(P2B, 2016a) ................................................................................................................... 209
Table 3A.1: Learning rate values of power generation technologies (Rubinet al., 2015) ..................................................................................................................... 210
Table 4A.1 Historical trends and future projections of the Indonesianclimate. Source: (GERICS, 2015; USAID, 2017)............................................... 214
Table 4B.1: List of questions for head office representatives .................. 215
Table 4B.2: List of questions concerning transmission networks............ 215
Table 4B.3: List of questions for grid operator (load control center) .... 216
Table 4B.4: List of questions concerning distribution networks .............. 217
Table 4B.5: List of questions concerning hydropower plants .................... 218
Table 4B.6: List of questions concerning thermal power plants (coal- andgas-based power plants) ........................................................................................... 219
Table 5A.1: Historical and projected changes in temperature .................. 222
Table 5A.2: Historical and projected changes in temperature .................. 222
Table 5B.1: Input data for the Citarum WEAP model.................................... 224

230

SUMMARY

Summary
Electricity is the backbone of the modern world. Yet, an electricity
connection is beyond the reach of nearly one billion people worldwide.
While the global power sector is expanding to meet the increasing
electricity demand, global warming calls for its transformation to a lowcarbon sector. Under the Paris Agreement, countries have committed tocurbing carbon emissions, with the power sector being one of the primarytargets for emissions reduction actions. At the same time, climate change
is affecting the global economy, including the power sector, and hence thepower sector needs to be greener and climate-resilient.
The goal of this dissertation is to address the above societal problem byassessing the triple nexus of electrification, climate change mitigation, andclimate change adaptation in the power sector of developing countries,
taking Indonesia as an example. This goal is pursued by employing a set of
methodological steps, including modeling with the LEAP and WEAP
software tools and extensive data collection through interviews and FGDs
with stakeholders from different stages of the power sector supply chain.
This dissertation gradually advances the current modeling practice by
sequentially taking into consideration endogenous technological learning
as well as climate change impacts and adaptation.
Chapter 2 investigates plausible trade-offs between electrification andclimate change mitigation in Indonesia’s primary power system – i.e.,
Java-Bali. Accordingly, Indonesia’s NDC pledge is included in thesimulation of the power system expansion. Results indicate that thenational electrification and the Paris Agreement goals can be achieved
through switching from coal to natural gas and renewable energy. The CO2
mitigation scenario requires 5% – 7% of additional costs compared to thereference (electrification only) scenario. Meanwhile, the costeffectiveness of CO2 mitigation scenarios ranges from 36.3 and 46.6
US$/tCO2e.

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SUMMARY

Chapter 3 extends the LEAP modeling practice by incorporating
technological learning to assess the diffusion of renewable energy in theJava-Bali power system in Indonesia. The chapter indicates that
endogenous technological learning accelerates the penetration ofrenewable energy and lowers the costs of achieving Indonesia’s renewableenergy targets. Furthermore, at the medium and high pace of technologicallearning, the total costs of electricity production to achieve the long-term
renewable energy target are 4–10% lower than the scenario without
considering technological learning.
Chapter 4 moves the discussions to the climate change adaptation
challenge. This chapter presents the empirical evidence of the impacts ofsevere weather events and gradual changes in climate variables on all
segments of Java-Bali’s electricity supply chains: generation,transmission, and distribution. Weather- and climate-related disruptions
cause financial losses for the electric utilities due to damaged equipment
and energy-not-supplied. Accordingly, utilities take autonomous
adaptation measures, which include behavioral, managerial, andtechnological responses.
Finally, Chapter 5 brings together the triple objectives of electrification,
climate change mitigation, and climate change adaptation within the LEAP
modeling framework. The results show that the joint objectives alter the
future technology mix when compared to the electrification-only scenario.
Moreover, there is an increase in the installed capacity and electricity
generation to balance the climate-induced rise in electricity demand andto compensate for the reduction in power-generating capacity underadverse impacts of the future climate. As a result, the total costs of power
capacity expansion increase significantly.
This dissertation provides a number of innovative contributions to science.
Firstly, to the best of our knowledge, it is the first to assess theimplications of the Indonesian NDC for the power sector, using a bottomup energy system model. Hence, it adds to the literature on balancing the232

SUMMARY

electrification and climate change mitigation goals. Secondly, it is the first
to integrate endogenous technological learning into the LEAP model,
thereby advancing the LEAP methodology and adding understanding
regarding the role of technological learning in driving the transition fromfossil-fuel-based power generation to a lower-carbon power system.
Thirdly, it presents historical evidence of the vulnerability of theIndonesian power sector to climate variability and change, which is likely
to be shared among other developing countries. Hence, it adds to thescarce literature on climate change impacts, adaptation, and vulnerability
in developing countries. Finally, it explicitly connects the objective of thepower sector’s climate change mitigation goal with the interferences
caused by the impact of climate change on the sector. This dissertation,
therefore, contributes to filling the gap in the literature on integrated
analysis of climate change mitigation and adaptation in the energy sector
highlighted by IPCC AR5.
On the practical side, this dissertation offers several policy implications.
First, the deployment of the currently unexploited renewable energy
should be started as early as possible to gain benefits of technologicallearning and avoid further technological lock-ins of fossil-fuel-based
technologies. Accordingly, the conditions that facilitate faster locallearning should be made available to reduce the costs of renewable energy
technologies even more quickly. Second, it is essential that theacceleration of renewable energy deployment go hand-in-hand with the
improvement of the grid capacity, which includes technical and humancapital capacities. Third, the power sector’s vulnerability to climatechange should be acknowledged at the national level, and its adaptationplans should be integrated into the national action plans for climate change
adaptation. Likewise, it is crucial for electric utilities to include climatechange adaptation into their long-term business strategies and capacity
building. Finally, the power sector needs to ensure budget allocations forclimate change mitigation and adaptation to realize a future low-carbon,
climate-resilient power sector.
233

234

SAMENVATTINGSamenvatting
Elektriciteit is een ruggengraat van de moderne wereld. Toch is een
elektriciteitsvoorziening buiten bereik van bijna een miljard mensen
wereldwijd. Terwijl de mondiale elektriciteitssector groeit om tegemoet te
komen aan de toenemende vraag naar elektriciteit, vraagt de opwarming
van de aarde om transformatie naar een koolstofarme sector. In het kader
van de overeenkomst van Parijs hebben landen zich ertoe verbonden dekoolstofemissies te reduceren, waarbij de elektriciteitssector een van debelangrijkste partijen is. Tegelijkertijd treft de klimaatverandering demondiale economie, met inbegrip van de elektriciteitssector. Ook daarom
moet de energiesector groener en klimaatbestendig zijn.
Het doel van dit proefschrift is na te gaan hoe het bovengenoemde
maatschappelijke probleem kan worden aangepakt. De verweving tussen
elektrificatie, mitigatie van klimaatverandering en aanpassing aan deklimaatverandering in de elektriciteitssector van ontwikkelingslanden
wordt beoordeeld, waarbij Indonesië als voorbeeld wordt genomen. Ik
streef dit doel na door een reeks methodologische stappen te zetten,
waaronder het modelleren met de LEAP-en WEAP-software tools en
uitgebreide gegevensverzameling via interviews en interviews met
belanghebbenden uit verschillende stadia van de toeleveringsketen van deelektriciteitssector. In opeenvolgende hoofdstukken modeleer ik de
praktijk, rekening houdend met endogene technologische leerprocessen en
aanpassing van de klimaatverandering.
In Hoofdstuk 2 worden plausibele afwegingen tussen elektrificatie en
mitigatie van klimaatverandering in het belangrijkste energiesysteem van
Indonesië - d.w.z. Java-Bali onderzocht. Het kader daarbij is de belofte
van Indonesië om koolstofemissie te reduceren en tevens groei van het
energiesysteem om aan de vraag voor elektriciteit te voldoen. De
resultaten geven aan dat de nationale elektrificatie en de doelstellingenvan de Overeenkomst van Parijs kunnen worden bereikt door over te235

SAMENVATTING

schakelen van steenkool naar aardgas en hernieuwbare energie. Het CO2beperkingsscenario vereist 5% - 7% extra kosten in vergelijking met het
referentiescenario (alleen elektrificatie). Ondertussen varieert dekosteneffectiviteit van CO2-beperkende scenario's van USD 36,3 and 46,6
per tCO2e.
In Hoofdstuk 3 breid ik de LEAP-modellering uit door technologischleerproces op te nemen. Vooral diffusie van hernieuwbare energie in het
Java-Bali energiesysteem in Indonesië wordt beoordeeld. Het hoofdstuk
geeft aan dat endogeen technologisch leerproces de verspreiding vanhernieuwbare energie versnelt en de kosten van het behalen van debeoogde duurzame energie in Indonesië versnelt. Bovendien zijn de totale
kosten van elektriciteitsproductie om het langetermijndoel voor
hernieuwbare energie te halen bij het gemiddelde en hoge tempo vantechnologisch leren 4% tot 10% lager dan het scenario zonder rekening tehouden met technologisch leerproces.
In Hoofdstuk 4 worden de discussies naar de aanpassing aan deklimaatverandering verschoven. Onderbouwd worden de effecten van
drastische weersveranderingen en geleidelijke klimaatsveranderingen op
alle segmenten van de stroomvoorzieningsketens van Java-Bali:
opwekking, transmissie en distributie. Storingen verbonden met de weeren klimaatsveranderingen veroorzaken financiële verliezen bij deelektriciteitsbedrijven als gevolg van beschadigde apparatuur en nietgeleverde
energie.
Dienovereenkomstig
nemen
nutsbedrijven
aanpassingsmaatregelen in het gedrag, bestuur en technologie.
Ten slotte worden in hoofdstuk 5 de drievoudige doelstellingen vanelektrificatie, beperking van de klimaatverandering en aanpassing aan deklimaatverandering gemodelleerd met behulp van LEAP. De resultaten
laten zien dat bij klimaatdoelstellingen de toekomstige technologiemix
veranderd in vergelijking met het scenario met alleen elektrificatie.
Bovendien is er een toename van de geïnstalleerde capaciteit en
236

SAMENVATTING

elektriciteitsopwekking om de door het klimaat veroorzaakte stijging van
de vraag naar elektriciteit te dekken en de vermindering van deelektriciteitsproductiecapaciteit te compenseren. Als gevolg hiervan
nemen de totale kosten van de hogere capaciteit aanzienlijk toe.
Dit proefschrift levert een aantal innovatieve bijdragen aan de wetenschap.
Ten eerste, voor zover wij weten, is het de eerste keer dat de gevolgen
van de Indonesische Nationale klimaatdoelstellingen voor de energiesectorzijn beoordeeld met behulp van een bottom-up model voor het
Indonesische energiesysteem. Daarom draagt het onderzoek bij aan deliteratuur over het balanceren van de doelstellingen voor elektrificatie ende beperking van de klimaatverandering. Ten tweede is het de eerste keer
dat technologische leerproces endogeen in het LEAP-model isgeïntegreerd. Daardoor wordt de LEAP-methodiek verbeterd en begrip
wordt vergroot over de rol van het technologisch leerproces bij deovergang van energieopwekking op basis van fossiele brandstoffen naar
een koolstofarm energiesysteem. Ten derde presenteert het historischbewijs van de kwetsbaarheid van de Indonesische elektriciteitssector voor
klimaatvariabiliteit en verandering, die waarschijnlijk ook bij andere
ontwikkelingslanden veel voorkomt. Daarom draagt het bij aan de schaarse
literatuur over de gevolgen van klimaatverandering, aanpassing en
kwetsbaarheid in ontwikkelingslanden. Ten slotte worden de doelstellingenvan de energiesector om de klimaatverandering te verminderen met
interventies
vandeelektriciteitssector
engevolgenvanklimaatsveranderingen voor de sector. Dit proefschrift draagt daarom bij
aan het opvullen van de leemte in de literatuur over geïntegreerde analyse
van de koolstofemissiereductie en aanpassing aan klimaatverandering inde energiesector.
Het proefschrift toont diverse implicaties voor de beleidspraktijk. Ten
eerste moet de inzet van de momenteel onbenutte hernieuwbare energie
zo snel mogelijk worden gestart om de voordelen van technologisch leren
te behalen en verdere technologische “lock-ins” van de nieuw te237

SAMENVATTING

installeren infrastructuur voor de op fossiele brandstoffen gebaseerde
elektriciteitsproductie te voorkomen. Dienovereenkomstig moeten devoorwaarden worden geschapen om een sneller leerproces door te maken
waarmee de kosten van technologieën voor hernieuwbare energie nog
sneller worden beperkt. Ten tweede is het van essentieel belang dat deversnelling in de inzet van hernieuwbare energie gepaard gaat met deverbetering van de netcapaciteit, waaronder technische en menselijke
capaciteiten. Ten derde moet de kwetsbaarheid van de energiesector voor
klimaatverandering op nationaal niveau worden erkend en moeten deaanpassingsplannen van de sector worden geïntegreerd in de nationale
actieplannen voor aanpassing aan de klimaatverandering. Evenzo is het
van cruciaal belang voor elektriciteitsbedrijven om aanpassing aan deklimaatverandering op te nemen in hun langetermijnstrategieën en opbouw
van hun capaciteit. Ten slotte moet de energiesector zorgen dat er
voldoende begroting beschikbaar is voor de klimaatmitigatie en aanpassing
van de klimaatverandering waarmee ze een toekomstige koolstofarme en
klimaatbestendige energiesector kan realiseren.

238

RINGKASAN

Ringkasan
Tenaga listrik merupakan tulang punggung kehidupan modern. Namun,masih terdapat hampir satu milyar populasi bumi belum memiliki
sambungan listrik. Oleh karena itu, sektor ketenagalistrikan di seluruh
dunia, khususnya di negara-negara berkembang berekspansi dalam rangka
memenuhi permintaan listrik yang meningkat. Di sisi lain, isu pemanasan
global menyerukan sektor ketenagalistrikan untuk bertransformasi menuju
sektor yang rendah karbon. Di bawah kerangka Perjanjian Paris, negaranegara bersepakat untuk membatasi emisi karbon, dan pada umumnya
mengikutsertakan upaya penguranagn emisi di sektor ketenagalistrikan.
Sementara itu, dampak perubahan iklim telah dirasakan oleh ekonomi global
saat ini, termasuk sektor ketenagalistrikan. Oleh karena itu, di masa yangakan datang, sektor ketenagalistrikan tidak hanya dituntut untuk lebihbersih, tetapi juga lebih tahan terhadap dampak perubahan iklim.
Tujuan dari disertasi ini adalah untuk memberikan kontribusi solusi
terhadap permasalahan di atas dengan melakukan kajian mengenai
keterkaitan tiga tujuan penting sektor ketenagalistrikan, yaitu: elektrifikasi,
mitigasi perubahan iklim, dan adaptasi terhadap dampak perubahan iklim.
Pencapaian tujuan ini diupayakan dengan menggunakan serangkaian
metodologi, termasuk pemodelan dengan perangkat lunak LEAP dan WEAP
dan melakukan pengumpulan data melalui wawancara serta diskusi
kelompok secara terarah dengan para pemangku kepentingan dari berbagai
elemen rantai pasokan penyediakan tenaga listrik. Disertasi ini secara
bertahap memperbaharui LEAP dengan secara bertahap memasukan
pertimbangan “endogenous technological learning” dan adaptasi sistem
ketenagalistrikan terhadap perubahan iklim.
Bab 2 menyajikan evaluasi beberapa skenario trade-off antara elektrifikasi
dan mitigasi perubahan iklim di sistem ketenagalistrikan Jawa-Bali. Dalam
hal ini, target penurunan emisi sektor energi Indonesia yang tercantum
dalam dokumen Nationally Determined Contribution (NDC) dijadikan
pertimbangan dalam simulasi pengembangan sistem ketenagalistikan
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RINGKASAN

Jawa-Bali. Hasil simulasi menunjukkan bahwa target elektrifikasi dan NDC
dapat dicapai melalui peralihan energi primer pembangkit listrik daribatubara ke gas alam dan energi terbarukan. Skenario mitigasi CO2
tersebut membutuhkan biaya tambahan 5% - 7% dibandingkan dengan
skenario dasar yang hanya mempertimbangkan target elektrifikasi.
Sementara itu, efektifitas biaya mitigasi CO2 berkisar antara USD 36,3 and46,6/tCO2e.
Pada Bab 3, metode kalkulasi biaya pada perangkat lunak LEAP
dimodifikasi dengan memasukkan formula “endogenous technologicallearning” dalam rangka mengkaji penetrasi energi terbarukan dalam sistem
ketenagalistrikan Jawa-Bali. Bab ini menyimpulkan bahwa “endogenous
technological learning” mempercepat penetrasi energi terbarukan dalamsistem ketenagalistrikan Jawa-Bali dan mampu menurunkan biaya
pencapaian target energi terbarukan Indonesia. Lebih jauh lagi, padaskenario “technological learning” dengan kecepatan sedang dan tinggi,
total biaya produksi listrik untuk mencapai target energi terbarukan jangka
panjang menjadi 4% - 10% lebih rendah dibandingkan skenario tanpa
mempertimbangkan “technological learning”.
Pada Bab 4, pembahasan disertasi ini dialihkan ke topik adaptasi perubahan
iklim. Bab ini membahas dampak kejadian cuaca buruk dan perubahan
variabel iklim pada proses bisnis penyediaan tenaga listrik di sistem JawaBali yang meliputi: pembangkitan, transmisi, dan distribusi. Berdasarkan
hasil wawancara dengan pemangku kepentingin penyediaan tenaga listrik
dan hasil kajian terhadap laporan gangguan pembangkit, transmisi, dandistribusi, dapat disimpulkan bahwa gangguan terkait cuaca dan iklim telah
menyebabkan kerugian finansial untuk perusahaan-perusahaan penyedia
tenaga listrik akibat peralatan yang rusak dan energi listrik yang tidak
tersalurkan. Perusahaan-perusahaan penyedia tenaga listrik telahmengambil langkah-langkah adaptasi terhadap kejadian cuaca buruk
dengan melakukan upaya-upaya teknis dan manajerial.

240

RINGKASAN

Bab 5 menyatukan tiga tujuan penting yaitu: elektrifikasi, mitigasi
perubahan iklim, dan adaptasi terhadap dampak perubahan iklim dalamkerangka pemodelan LEAP. Hasil penelitian menunjukkan bahwa ketika
ketiga tujuan tersebut dipertimbangkan dalam simulasi pengembangansistem ketenagalistrikan Jawa-Bali, maka bauran teknologi di masa depan
mengalami perubahan jika dibandingkan dengan skenario dasar yang hanya
mempertimbangkan elektrifikasi. Selain itu, diperlukan pula penambahankapasitas terpasang pembangkit dan produksi listrik untuk memenuhi
kenaikan permintaan listrik yang disebabkan oleh kenaikan suhu akibat
perubahan iklim serta untuk mengkompensasi penurunan keandalan
pembangkit listrik akibat dampak perubahan iklim. Hal-hal tersebut
menyebabkan total biaya ekspansi sistem ketenagalistrikan meningkat
secara signifikan.
Disertasi ini memberikan beberapa kontribusi inovatif bagi ilmu
pengetahuan. Pertama, sejauh pengetahuan kami, disertasi ini adalah yangpertama melakukan kajian implikasi target penurunan emisi sektor
ketenagalistrikan Indonesia di bawah Perjanjian Paris dengan menggunakanmodel bottom-up. Oleh karena itu, disertasi ini menambah literatur terkaitenergi dan perubahan iklim. Kedua, disertasi ini merupakan kajian pertamayang mengintegrasikan “endogenous technological learning” ke dalammodel LEAP, sehingga ada pembaharuan metodologi LEAP dan menambah
pemahaman tentang peran “technological learning” dalam mendorongtransisi dari energi listrik berbasis bahan bakar fosil ke energi rendah
karbon. Ketiga, disertasi ini menyajikan bukti historis kerentanan sektor
tenaga listrik Indonesia terhadap variabilitas dan perubahan iklim, yang juga
mewakili kerentanan negara-negara berkembang lainnya. Oleh karena itu,
disertasi ini menambah literatur yang masih terbatas mengenai dampak
perubahan iklim, adaptasi, dan kerentanan di negara-negara berkembang.
Keempat, disertasi ini secara eksplisit menggabungkan tujuan mitigasi dan
adaptati perubahan iklim di sektor ketenagalistrikan sehingga berkontribusi
untuk mengisi kesenjangan literatur tentang analisis terpadu mitigasi dan

241

RINGKASAN

adaptasi perubahan iklim di sektor energi sebagaimana ditekankan dalam
Laporan Kajian IPCC ke-5.
Disertasi ini juga menawarkan beberapa rekomendasi kebijakan yang dapat
diterapkan. Pertama, pemanfaatan energi terbarukan yang saat ini belum
dieksploitasi harus dimulai sedini mungkin untuk mendapatkan manfaat“technological learning” dan menghindari ketergantungan pada teknologi
berbasis bahan bakar fosil. Selain itu, kondisi yang dapat memfasilitasi“local learning” perlu diupayakan agar penurunan biaya teknologi energi
terbarukan dapat terjadi lebih cepat. Kedua, percepatan pemanfaatan
energi terbarukan harus berjalan seiring dengan penguatan kapasitas
sistem ketenagalistrikan, yang mencakup kapasitas teknis dan sumber
daya manusia. Ketiga, kerentanan sektor energi terhadap perubahan iklim
perlu diarusutamakan di tingkat nasional, dan rencana adaptasinya harus
diintegrasikan ke dalam rencana aksi nasional adaptasi perubahan iklim.
Demikian juga, sangat penting bagi perusahaan penyedia tenaga listrik
untuk memasukkan adaptasi perubahan iklim ke dalam strategi bisnis
jangka panjang dan program penguatan kapasitas. Terakhir, sektor
ketenagalistrikan perlu mengalokasikan anggaran untuk mitigasi dan
adaptasi perubahan iklim dalam rangka mewujudkan infrastruktur
ketenagalistrikan yang rendah karbon dan tahan terhadap dampak
perubahan iklim.

242

About the Author
Kamia Handayani was born in Garut, Indonesia. She
holds a Master of Science (Cum Laude) in Energy& Environmental Management from the
University of Twente, the Netherlands, and a
Bachelor of Environmental Engineering from
Bandung Institute of Technology, Indonesia.
After working for a decade at PT PLN(Persero), the state-owned electric powercompany in Indonesia, she took a study
leave in 2016 to pursue a doctoral degree atthe Department of Governance andTechnology for Sustainability, University ofTwente, the Netherlands.
Kamia represented the University of Twente at the UN Climate Change
Conference COP23 in Bonn (2017). She also shared a panel withresourceful people in COP24 in Katowice (2018), which included the Dutch
climate envoy and various young experts. She has presented her works atnumerous international conferences and regularly writes articles for The
Jakarta Post.
During her previous service in PLN, Kamia contributed to the developmentof a system for evaluating the environmental management performancesof PLN business units all over Indonesia. She also involved in thedevelopment of climate change mitigation strategies of the company.
Kamia’s expertise includes carbon credit for clean energy projects andenvironmental & social safeguards for electricity infrastructure projects,
among others. She is a member of numerous energy and environmental
communities, including the International Association for Energy Economics(IAEE), European Association of Environmental and Resources Economists(EAERE), Southeast Asian Women, 4TU Centre for Resilience Engineering(4TU RE), Energy Academy Indonesia (Ecadin), and Yayasan Pembangunan
Berkelanjutan-LEAD Indonesia. She is a volunteer at ‘Indonesia
Mengglobal’ and ‘I Care Indonesia’ and the coordinator of Komunitas
Hijau PLN (PLN Green Community).

243

List of Publications
Journal articles
1. Handayani, K., Filatova, T, Krozer, Y., & Anugrah, P. (2019). Seeking forclimate change mitigation and adaptation nexus: Analysis of a long-term
power system expansion. Applied Energy. (Under review).
2. Handayani, K., Filatova, T., & Krozer, Y. (2019). The Vulnerability of thePower Sector to Climate Variability and Change: Evidence fromIndonesia. Energies, 12(19), 3640.
3. Gonella, F., Almeida, C. M. V. B., Fiorentino, G., Handayani, K., Spanò,F., Testoni, R., & Zucaro, A. (2019). Is technology optimism justified? A
discussion towards a comprehensive narrative. Journal of cleaner
production, 223, 456-465.
4. Handayani, K., Krozer, Y., & Filatova, T. (2019). From fossil fuels to
renewables: An analysis of long-term scenarios considering
technological learning. Energy Policy, 127, 134-146.
5. Handayani, K., Krozer, Y., & Filatova, T. (2017). Trade-offs between
electrification and climate change mitigation: An analysis of the JavaBali power system in Indonesia. Applied energy, 208, 1020-1037.

Conference proceedings
1. Handayani, K., Filatova, T., Krozer, Y., & Anugrah, P. (2019). Toward a
low-carbon and climate-resilient power system: A case of theIndonesian power sector. Paper presented at 42nd IAEE International
Conference, Montreal, Canada.
2. Handayani, K., Krozer, Y., & Filatova, T. (2017). Assessment of
renewable energy expansion in the Java-Bali islands, Indonesia. In S.
Ulgiati, L. Vanoli, M. T. Brown, M. Casazza, & H. Schnitzer (Eds.), 10th
Biennial International Workshop Advances in Energy Studies: Energy
futures, environment and well-being (pp. 271). TU Graz.
3. Handayani, K., Krozer, Y. (2014). Cost-effectiveness of emission
reduction for the Indonesian coal-fired power plants. The 20th
Conference of Electric Power Supply Industry, International Convention
Center Jeju, South Korea.

244

Selected talks and presentations
1. Linking climate change mitigation and adaptation: An integrated powersystem expansion modeling. EMP-E conference 2019: Modelling theimplementation of A Clean Planet For All Strategy. 8-9 October 2019,
Brussels, Belgium.
2. Exploring young-led climate partnership towards COP25:‘What wehave brought from COP24 Katowice 2018’, 27 February 2019, The
Indonesian embassy, The Hague, the Netherlands.
3. Young Professionalism in climate resilience, UN Climate Change
Conference (COP24), 14 December 2018, Katowice, Poland.
4. Panel Discussion: How to create societal support for alternative
energies. 31 August 2018, University of Twente, the Netherlands.
5. Simulasi Pencapaian Target Penurunan Emisi Gas Rumah Kaca Sektor
Ketenagalistrikan, Webinar Energy Academy Indonesia, 29 October2017.
6. Opportunities for Climate Change Mitigations: A Case Study of theIndonesian Power Sector. International Symposium on Sustainability
Science, “Understanding Climate Change Phenomena for Human Well
Being”, 8-10 September 2014, Bandung, Indonesia.
7. Renewable Energy for Electrification in Flores Island, Indonesia. ICREP
Conference: “Improving energy Access through Climate Finance:
Picking the Winners”, 26-28 March 2013, Enschede, the Netherlands.

Media articles
1. Kamia Handayani (2018). Invest in ‘Technology Cost Learning’. The
Jakarta Post, 08 November.
http://www.thejakartapost.com/academia/2018/11/08/invest-intechnology-cost-learning.html
2. Kamia Handayani (2017). Securing electricity supply: Climate change
does matter. The Jakarta Post, 26 October.
http://www.thejakartapost.com/academia/2017/10/26/securingelectricity-supply-climate-change-does-matter.html
3. Kamia Handayani (2016). Trading off electrification and climatechange. The Jakarta Post, 28 October.
http://www.thejakartapost.com/academia/2016/10/28/trading-offelectrification-and-climate-change.html
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