An Analysis of Optimal and Near-Optimal Decarbonisation Strategies for Malaysia Using a Hybrid Model

An analysis of optimal and near-optimal decarbonisation strategies for Malaysia using a hybrid model

Maragatham Kumar

UCL Energy Institute

University College London

A thesis submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

March 2020

Declaration

I, Maragatham Kumar, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis.

Maragatham Kumar March 2020

Acknowledgements

Firstly, my heartfelt thank you to my parents, Eesan Pasupathi and Rajespari Pasupathi for all the motivation. Without your trust and support, I wouldn’t be here and have completed the PhD. There are no words to express my gratitude to you both.

To both my supervisors, Neil Strachan and Marianne Zeyringer, I really appreciate and am grateful to both of you for your continuous guidance and inspiration. Especially to Neil for your supervision to complete this research. My dearest friends, thank you so much for standing by me and giving endless support all through this PhD journey.

My big thank you to the Malaysian government for the support and funding to conduct this research at UCL Energy Institute. Finally, to God, my gratefulness for the opportunity.

Abstract

This thesis studies the challenges of formulating strategies for decarbonising the energy systems in many countries that are battling to reduce carbon emissions and seriously considering incorporating environmental issues in the process of energy planning and policy-making. It presents the development of mathematical models and analysis to obtain insights on optimal and near-optimal decarbonisation strategies. The energy system of Malaysia is used as a case study to analyse energy related issues and investigate the decarbonisation of the energy systems.

Under the landscape of demand–supply uncertainties at a multiregional level, a novel Modelling to Generate Alternatives (MGA) hybrid (MAED-OSeMOSYS) approach has been developed to capture the electricity trade option between three regions of Peninsular, Sabah and Sarawak, analysing in detail the end-use technologies of various sectors and the integration of end-use technologies with the power sector. It analysed the possibility of achieving least-cost optimal decarbonisation targets in Malaysia and concluded that the development of advanced and clean technologies needed in the system across all sectors, mainly power, industry and transportation. Further, it also investigated the near-optimal decarbonisation strategies that provided different insights on the possible evolution of a low carbon electricity sector in Malaysia with the implementation of the MGA technique. Additional investments and flexibility in categories of technology constraints imposed in the system. These constraints influence the type of technology to be deployed and make a difference in the diffusion of the power technologies, which result in carbon emissions reduction in the system.

The application of the MGA technique provides researchers with the flexibility to explore alternative pathways within a cost optimal solution range, which provides new knowledge in the application of this technique to the OSeMOSYS model. This thesis provides insights to policy makers of middle-sized countries on feasible decarbonisation and investment strategies that may have important investment, trade and policy implications and relevance at a national and international level. This research on its own merits gives Malaysia and other similar middle-sized developing countries a high added value to its energy modelling analysis, which is scarce in comparison to energy modelling research of developed countries.

Impact Statement

This research addresses a gap in energy system modelling analysis for middle-sized developing countries, which is scarce in comparison to energy modelling research of developed countries. It adds a deep understanding and technical expertise in modelling the full energy system of Malaysia, which is very important to address the energy use changes in the country, especially to achieve a decarbonised system. It seeks a balance between the various energy policy goals that could assist in the future policy planning of a developing country like Malaysia. It provides insights to policy makers of middle-sized countries on feasible decarbonisation and investment strategies that may have important implications for investment, trade and policy decisions and relevance at a national and international level. The modelling method developed in this project addresses uncertainties in demand projections, supply pathways (looking in depth at the technology profiles) and the policy-driven structural uncertainties. By developing these modelling techniques and examining the case of Malaysia in detail, the methodology developed for this research provides new knowledge and adds value to the energy modelling field to analyse decarbonisation strategies.

This research contributes to:

1. Literature that focuses on decarbonisation strategies in developing countries, that examines the correlation and trade-offs between the end-use sectors and growth of key socio-economic parameters.

2. Literature on policy-driven analysis based on the structural uncertainty, that focuses on investment and decarbonisation strategies as well as on identifying competitive drivers for energy sector policy planning.

The impact of this research will be bought about through disseminating the outcome of this doctoral thesis in academic journal papers that would be of interest to energy modelling researchers and energy policy makers in Malaysia and research communities internationally. The journal papers are under preparation for submission. The paper will provide detailed analysis and methodology to explore the long-term optimal and near-optimal decarbonisation strategies in Malaysia under the landscape of demand-supply uncertainties. The paper will inform policy makers of the optimal technology options under the demand drivers as well as

7 investment strategies in adopting new technologies under near-optimality conditions and the constrained environment of decarbonisation strategies.

This research was presented at the 40th IAEE International conference: “Meeting the Energy Demands of Emerging Economies: Implications for Energy and Environmental Markets in 2017. A proceeding paper on Long-Term Decarbonisation Energy Pathways for Malaysia using a Hybrid Demand-Supply Model was prepared and submitted to this conference. This work on modelling approaches to assess Malaysia’s decarbonisation targets was also presented at CIRED Paris Summer School in Economic modelling of Environment, Energy and Climate in 2017.

Contents

CHAPTER 1 INTRODUCTION ...... 26

1.1 Background and Research Context ...... 26

1.2 Objectives and Research Questions ...... 33

1.3 Research Scope and Methodology Adopted ...... 34

1.4 Thesis Structure ...... 35

CHAPTER 2 LITERATURE REVIEW ...... 37

2.1 Introduction and Classification of Energy Systems ...... 37 2.1.1 Top-down Energy Models ...... 39 2.1.1.1 Macro-economic models ...... 39 2.1.1.2 General Equilibrium models ...... 39 2.1.2 Bottom-up Energy Models ...... 40 2.1.2.1 Optimisation models ...... 40 2.1.2.2 Simulation models ...... 43 2.1.2.3 Accounting models ...... 44

2.2 Application of Energy Systems Model/Hybrid Models ...... 45 2.2.1 Energy systems models in developed countries ...... 45 2.2.2 Energy systems models in developing countries ...... 48 2.2.3 Energy Systems models for Malaysia ...... 56

2.3 The Rationale for using Hybrid models (Simulation-Optimisation) for Malaysia ...... 64

2.4 Uncertainties in the optimisation models ...... 69 2.4.1 Modelling to Generate Alternative (MGA) techniques ...... 71 2.4.2 Historical application of the MGA technique ...... 71 2.4.3 The Application of MGA technique in the energy-economy optimisation model ...... 72

2.5 Research Gap and Conclusion ...... 75

CHAPTER 3 METHODOLOGIES ...... 78

3.1 Simulation of the demand pathways ...... 78 3.1.1 The Model for Analysis Energy Demand (MAED) conceptualisation and formulation ...... 79 3.1.2 Population growth assumptions ...... 80 3.1.3 GDP growth assumptions ...... 82 3.1.4 Documentation and assumptions of household sector ...... 86 3.1.5 Documentation and assumptions of commercial sector ...... 87 3.1.6 Documentation and assumptions of industrial sector ...... 88 3.1.7 Documentation and assumptions of transportation sector ...... 89

3.2 Optimisation of the supply pathways ...... 91 3.2.1 The Open Source Energy Modelling System (OSeMOSYS) conceptualisation and formulation91

3.2.1.1 Structure of the Household sub-module ...... 95 3.2.1.2 Structure of the Commercial sub-module ...... 97 3.2.1.3 Structure of the Industrial sub-module ...... 99 3.2.1.4 Structure of the Transportation sub-module ...... 101 3.2.1.5 Structure of the Power sub-module ...... 104 3.2.2 Documentation and assumptions of OSeMOSYS model ...... 106 3.2.2.1 Key energy policies, energy resources, potentials and constraints ...... 106 3.2.2.2 Existing and Future technologies ...... 111 3.2.2.3 Trend of carbon emissions in Malaysia ...... 112 3.2.2.4 Fuel prices assumptions and constraints ...... 114

3.3 MGA-Hybrid Modelling Framework ...... 114 3.3.1 MGA technique ...... 114 3.3.2 The MGA-OSeMOSYS modelling concept ...... 115

CHAPTER 4 BUILDING THE MGA-HYBRID MODEL SCENARIOS ...... 121

4.1 Systematic formulation of the scenarios ...... 121 4.1.1 Demand–supply pathways concept ...... 123 4.1.2 Optimal decarbonisation pathways ...... 127 4.1.3 Near-optimal decarbonisation pathways ...... 128

4.2 Conclusions ...... 131

CHAPTER 5 RESULTS AND DISCUSSIONS ...... 134

5.1 Optimal energy system results ...... 134 5.1.1 Energy demand scenarios ...... 134 5.1.1.1 Reference (REF) demand scenario ...... 135 5.1.1.2 High demand scenario ...... 137 5.1.1.3 Low demand scenario ...... 138 5.1.2 Hybrid model systematic scenarios ...... 140 5.1.2.1 Reference (REF) supply scenario ...... 140 5.1.2.2 High supply scenario ...... 144 5.1.2.3 Low supply scenario ...... 146 5.1.2.4 Conclusion ...... 148 5.1.3 Decarbonisation pathways ...... 149 5.1.3.1 Reference (REF) supply deep decarbonisation scenario ...... 149 5.1.3.2 High supply deep decarbonisation scenario ...... 159 5.1.3.3 Low supply deep decarbonisation scenario ...... 165 5.1.3.4 Conclusion ...... 170

5.2 Near-optimal energy system results ...... 178 5.2.1 Near-optimal decarbonisation scenarios ...... 178 5.2.1.1 Near-optimality approach under the REF supply scenario ...... 179 5.2.1.2 Near-optimality approach under high and low supply scenarios ...... 192 5.2.1.2.1 Near-optimality approach under High supply scenario ...... 192 5.2.1.2.2 Near-optimality approach under Low supply scenario ...... 196 5.2.2 Conclusions on near optimal scenarios ...... 199

5.3 Discussion and Summary ...... 202

CHAPTER 6 CONCLUSION ...... 206

6.1 Restatement of Research Problem ...... 206

6.2 Main Findings ...... 207 6.2.1 Optimal Decarbonisation Strategies of Malaysia’s Energy Sector ...... 207 6.2.2 Near-Optimal Decarbonisation Strategies of Malaysia’s Energy Sector ...... 211

6.3 Limitation and Future Work ...... 214

6.4 Research Contribution and Conclusion ...... 215

REFERENCES ...... 220

APPENDIX A: APPENDIX TO CHAPTER 3 ...... 245

APPENDIX B: APPENDIX TO CHAPTER 5 ...... 272

List of Figures

Figure 1.1: Map of Malaysia: Peninsular Malaysia, Sabah and Sarawak ...... 27 Figure 1.2: Historical Energy Consumption by Energy Forms and Sectors ...... 28 Figure 1.3: Historical Electricity Generation and Consumption ...... 29 Figure 1.4: Timeline of energy policies in Malaysia ...... 30 Figure 2.1: Example of classifying energy models (van Beeck 1999) ...... 38 Figure 3.1: Sectorial demand disaggregation in the MAED model for Malaysia ...... 80 Figure 3.2: Population Projections (Malaysia) in the MAED model ...... 81 Figure 3.3: Historical Population and GDP growth (Malaysia) ...... 82 Figure 3.4: GDP Projections (Malaysia) in MAED model ...... 86 Figure 3.5: MAED and OSeMOSYS framework ...... 93 Figure 3.6: RES diagram represented in the OSeMOSYS model ...... 94 Figure 3.7: Structure of the household sub-module in OSeMOSYS ...... 96 Figure 3.8: Structure of the Commercial sub-module ...... 99 Figure 3.9: Structure of the Industrial sub-module ...... 101 Figure 3.10: Modes of transportation divided into road, air and sea ...... 104 Figure 3.11: Electricity load profile in 2013 (Malaysia) ...... 105 Figure 3.12: Total installed capacity and share of capacity by fuel in Malaysia (2013) ...... 112

Figure 3.13: Primary energy supply by fuel and CO2 emissions in Malaysia ...... 113 Figure 4.1: Summary of MGA-Hybrid model framework ...... 123 Figure 4.3: Useful Energy Demand of Reference scenario (Malaysia) in the MAED model ..... 125 Figure 4.3: Useful Energy Demand of High scenario (Malaysia) in the MAED model ...... 125 Figure 4.3: Useful Energy Demand of Low scenario (Malaysia) in the MAED model ...... 125 Figure 4.4: Three demand scenarios (Malaysia) – output of OseMOSYS model ...... 126 Figure 4.5: Emission Reduction Scenarios ...... 127 Figure 4.6: The Optimal and Near-Optimal Scenarios Building Concept ...... 128 Figure 4.7: Summary of systematic development of optimal and near-optimal decarbonisation scenarios under the long-term demand pathways ...... 132 Figure 5.1: Useful Energy Demand from the MAED model (REF scenario) ...... 135 Figure 5.2: Final energy demand by sector in Malaysia from the OSeMOSYS model (REF demand pathway) ...... 136 Figure 5.3: Useful Energy Demand from the MAED model (High scenario) ...... 137

Figure 5.4: Final energy demand by sector in Malaysia from the OSeMOSYS model (High demand pathway) ...... 138 Figure 5.5: Useful Energy Demand from the MAED model (Low scenario) ...... 138 Figure 5.6: Final energy demand by sector in Malaysia from the OSeMOSYS model (Low demand pathway) ...... 139 Figure 5.7: Final energy demand by scenarios (REF, High and Low) in Malaysia ...... 139 Figure 5.8: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (REF supply scenario) ...... 142 Figure 5.9: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (High supply scenario) ...... 145 Figure 5.10: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (Low supply scenario) ...... 147 Figure 5.11: Electricity generation by fuel mix (Low supply, REF and High supply scenarios) . 147 Figure 5.12: Total Electricity Generation and Installed Capacity in Malaysia over modelled period (Low, REF and High supply scenarios) ...... 148 Figure 5.13: Decarbonisation Scenarios (benchmarking the REF scenario) ...... 150 Figure 5.14: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (20% decarbonisation of The REF scenario) ...... 151 Figure 5.15: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (40% decarbonisation of the REF scenario) ...... 153 Figure 5.16: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (60% decarbonisation of the REF scenario) ...... 156 Figure 5.17: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (80% decarbonisation of the REF scenario) ...... 157 Figure 5.18: Electricity generation by fuel mix (decarbonisation scenarios benchmarking the REF scenario) ...... 158 Figure 5.19: Decarbonisation Scenarios (benchmarking High supply scenario) ...... 159 Figure 5.20: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (20% decarbonisation of High supply scenario) ...... 160 Figure 5.21: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (40% decarbonisation of High supply scenario) ...... 161 Figure 5.22: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (60% decarbonisation of High supply scenario) ...... 162 Figure 5.23: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (80% decarbonisation of High supply scenario) ...... 163

Figure 5.24: Electricity generation by fuel and electricity consumption by sectors (High decarbonisation scenarios) ...... 164 Figure 5.25: Decarbonisation Scenarios (benchmarking Low scenario) ...... 165 Figure 5.26: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (20% decarbonisation of Low supply scenario) ...... 166 Figure 5.27: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (40% decarbonisation of the low supply scenario) ...... 167 Figure 5.28: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (60% decarbonisation of the low supply scenario) ...... 168 Figure 5.29: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (80% decarbonisation of Low supply scenario) ...... 169 Figure 5.30: Electricity generation by fuel and electricity consumption by sectors (Low decarbonisation scenarios) ...... 169 Figure 5.31: Total Electricity Generation and Installed Capacity in Malaysia over modelled period (Decarbonisation scenarios) ...... 172 Figure 5.32: Abatement cost of power system over the modelled period ...... 173 Figure 5.33: Total Electricity Generation by Scenarios ...... 175 Figure 5.34: Optimal Decarbonisation Scenarios (depending on defined technology constraints and introduction of additional technologies in the system) ...... 176

Figure 5.35: Distribution of REF near-optimal decarbonisation scenarios: minimise CO2 of system (benchmarking the REF scenario) ...... 180 Figure 5.36: Electricity generation pathways of REF near-optimal decarbonisation scenarios: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%) ...... 181 Figure 5.37: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia: minimise CO2 of system (REF near-optimal scenario: REF5%_NOL2 (5% slack value)) ...... 181

Figure 5.38: Electricity generation by fuel and electricity consumption by sectors: minimise CO2 of system (Near-optimal decarbonisation scenario: REF5%_NOL2 (5% slack value)) ...... 182

Figure 5.39: Electricity generation by fuel and electricity consumption by sectors: minimise CO2 of system (Near-optimal decarbonisation scenario: REF10%_NOL2 (10% slack value)) ...... 182 Figure 5.40: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia: minimise CO2 of system (REF near-optimal scenario: REF20%_NOL4 (20% slack value)) ...... 183

Figure 5.41: Electricity generation by fuel and electricity consumption by sectors: minimise CO2 of system (Near-optimal decarbonisation scenario: REF20%_NOL4 (20% slack value)) ...... 184

Figure 5.42: Electricity generation by fuel and electricity consumption by sectors: minimise CO2 of system (Near-optimal decarbonisation scenario: REF30%_NOL4 (30% slack value)) ...... 184

Figure 5.43: Electricity generation by fuel and electricity consumption by sectors: minimise CO2 of system (Near-optimal decarbonisation scenario: REF30%_NOL2 (30% slack value)) ...... 185 Figure 5.44: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise CO2 of system (REF near-optimal decarbonisation scenario) ...... 186

Figure 5.45: Comparison of CO2 emission trajectories in power sector for 16 REF near-optimal pathways: minimise CO2 of system (slack values 5%, 10%, 20% and 30%) ...... 187 Figure 5.46: Distribution of REF near-optimal decarbonisation scenarios-minimise coal in power system (benchmarking the REF scenario) 5.2 12.8 ...... 188 Figure 5.47: Electricity generation pathways of REF near-optimal decarbonisation scenarios: minimise coal in power system (slack values of 5%, 10%, 20% and 30%) ...... 188 Figure 5.48: Electricity generation by fuel and electricity consumption by sectors: ...... 189 Figure 5.49: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia: minimise coal in power system (REF near-optimal scenario: REF20%_NOL2 (20% slack value)) ...... 190 Figure 5.50: Electricity generation by fuel and electricity consumption by sectors: minimise coal in power system (Near-optimal decarbonisation scenario: REF30%_NOL2 (30% slack value)) 191 Figure 5.51: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise coal in power system (REF near-optimal decarbonisation scenario) ...... 191

Figure 5.52: Comparison of CO2 emission trajectories in power sector for 16 REF near-optimal pathways: minimise coal in power system (slack values 5%, 10%, 20% and 30%) ...... 192 Figure 5.53: Electricity generation pathways of High near-optimal decarbonisation scenarios: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%) ...... 193 Figure 5.54: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise CO2 of system (High near-optimal decarbonisation scenario) ...... 194 Figure 5.55: Electricity generation pathways of High near-optimal decarbonisation scenarios: minimise coal in power system (slack values of 5%, 10%, 20% and 30%) ...... 195 Figure 5.56: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise coal in power system (High near-optimal decarbonisation scenario) ...... 196 Figure 5.57: Electricity generation pathways of Low near-optimal decarbonisation scenarios: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%) ...... 197 Figure 5.58: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise CO2 of system (Low near-optimal decarbonisation scenario) ...... 198

Figure 5.59: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise coal in power system (Low near-optimal decarbonisation scenario) ...... 199

Figure A. 1: Summary of household structure in the MAED model ...... 248 Figure A. 2: Historical fuel consumptions (1997–2010) in the household sector ...... 248 Figure A. 3: Summary of commercial structure in the MAED model ...... 251 Figure A. 4: Historical fuel consumptions (1997-2010) in the commercial sector ...... 252 Figure A. 5: Trend of total GDP and historical fuel consumptions (1997-2010) in the commercial sector ...... 252 Figure A. 6: Summary of industrial structure in the MAED model ...... 255 Figure A.7: Historical fuel consumptions (1997-2010) in the industrial sector ...... 257 Figure A. 8: Trend of total GDP and historical fuel consumptions (1980-2008) in the industrial sector ...... 257 Figure A. 9: New Registration of Private Motor Vehicles (2000-2013) ...... 261 Figure A. 10: Historical fuel consumptions (1997-2010) in the transportation sector ...... 263 Figure A. 11: Trend of total energy demand in transportation and total GDP growth ...... 263 Figure A. 12: Summary of transportation structure in the MAED model ...... 263

Figure B. 1: Electricity generation by time slices (2040-2050) (REF supply pathways) ...... 280 Figure B. 2: Electricity generation by time slices (2040-2050) (20% decarbonisation of the REF scenario) ...... 280 Figure B. 3: Electricity generation by time slices (2040-2050) (40% decarbonisation of the REF scenario) ...... 280 Figure B. 4: Electricity generation by time slices (2040-2050) (60% decarbonisation of the REF scenario) ...... 281 Figure B. 5: Electricity generation by time slices (2040-2050) (80% decarbonisation of the REF scenario) ...... 281 Figure B.6: Electricity generation pathways of REF near-optimal decarbonisation scenarios: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%) ...... 282 Figure B.7 : Electricity generation pathways of REF near-optimal decarbonisation scenarios: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%) ...... 283 Figure B.8: Electricity generation pathways of High near-optimal decarbonisation scenarios: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%) ...... 284 Figure B.9: Electricity generation pathways of High near-optimal decarbonisation scenarios: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%) ...... 285

Figure B.10: Electricity generation pathways of Low near-optimal decarbonisation scenarios: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%) ...... 286 Figure B.11: Electricity generation pathways of Low near-optimal decarbonisation scenarios: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%) ...... 287 Figure B.12: Electricity generation pathways of Low near-optimal decarbonisation scenarios: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%) ...... 287

List of Tables

Table 2.1: Criteria of bottom-up optimisation model (Subhes and Govinda 2010; pg. 502) ..... 41 Table 2.2: Characteristics of some key optimisation models based on Table 2.1 criteria (Subhes and Govinda 2010; pg. 503-504) ...... 43 Table 2.3: Application of energy systems model in developed and developing country ...... 55 Table 2.4: Summary of the model classification approach adapted from van Beeck (1999) ..... 63 Table 2.5: Summary on studies involving optimisation model and MGA technique ...... 74 Table 3.1: GDP growth scenario assumptions in the MAED model ...... 85 Table 3.2: Two phases of the near optimality analysis (Inflexible and Flexible models) with alternate objective function ...... 116 Table 3.3: Summary of Policies and Constraints in inflexible and flexible models ...... 120 Table 3.4: Summary of Flexible Scenarios ...... 120 Table 4.1: Policies and Constraints imposed in the inflexible and flexible models ...... 129 Table 4.2: Summary of Flexible Scenarios ...... 130 Table 5.1: Total system cost and emission by scenarios over the modelled period ...... 177 Table 5.2: Summary of Flexible Scenarios based on constraint categories ...... 179

Table A. 1: Population growth scenario assumptions in the MAED model ...... 245 Table A. 2: Population scenarios (Malaysia) using the MAED model (million) ...... 245 Table A. 3: GDP scenarios (Malaysia) using the MAED model at constant 2010 prices ($USD billion) ...... 246 Table A. 4: Total population, urbanisation level, population inside city and total dwellings by region (base year 2013) ...... 246 Table A. 5: Distribution of fuel consumptions in household (base year 2013) in ktoe ...... 246 Table A. 6: Number of dwellings, share of dwellings and energy requirement for cooling per dwellings ...... 247 Table A. 7: Types of dwellings in Malaysia ...... 247 Table A. 8: Key input assumptions for household structure in the MAED model ...... 247 Table A. 9: Household sector variables in the MAED model ...... 248 Table A. 10: Summary of key parameters, demand drivers and equations to calculate the useful energy demand projection of household sector in MAED model ...... 249 Table A. 11: Distribution of fuel consumptions in commercial (base year 2013) in ktoe ...... 250

Table A. 12: Potential labour force, labour force in commercial, age structure, floor area and energy requirement for cooling per floor area ...... 250 Table A. 13: GDP of commercial sector by region (2013) at constant 2010 prices (USD billion) ...... 250 Table A. 14: GDP scenarios (Malaysia) using the MAED model at constant 2010 prices (USD billion) ...... 250 Table A. 15: Commercial sector variables in the MAED model ...... 251 Table A. 16: Key input assumptions for commercial structure in the MAED model ...... 251 Table A. 17: Summary of key parameters, demand drivers and equations to calculate the useful energy demand projection of commercial sector in MAED model ...... 253 Table A. 18: Consumption of manufacturing sub-sectors by fuel (base year 2013) in ktoe ..... 254 Table A. 19: Summary of fuel distribution in industry sub-sectors (base year 2013) in ktoe .. 254 Table A. 20: GDP growth scenario assumptions in the MAED model ...... 255 Table A. 21: GDP by region and economic activity (2013) at constant 2010 prices (USD billion) ...... 255 Table A. 22: GDP aggregation by sub-sector in industry in 2013 (billion USD) ...... 255 Table A. 23: lndustrial sector variables in the MAED model ...... 256 Table A. 24: Key input assumptions for industrial structure in MAED model ...... 256 Table A. 25: Summary of key parameters needed in MAED model, demand drivers and equations to calculate the useful energy demand projection of industry sector ...... 256 Table A. 26: GDP contributions by sector (base year 2013) at constant 2010 prices (USD billion) ...... 258 Table A. 27: GDP by region and kind of economic activity (2013) at constant 2010 prices ($USD billion) ...... 259 Table A. 28: GDP by region and kind of economic activity (2013) - percentage (%) ...... 259 Table A. 29: GDP (agriculture) by region (2013) at constant 2010 prices ($USD billion) ...... 259 Table A. 30: GDP (mining and quarrying) by region (2013) at constant 2010 prices ($USD billion) ...... 259 Table A. 31: GDP (manufacturing) by region (2013) at constant 2010 prices ($USD billion) ... 259 Table A. 32: GDP (commercial) by region (2013) at constant 2010 prices ($USD billion) ...... 259 Table A. 33: Total number of vehicles in Malaysia by region (base year 2013) ...... 260 Table A. 34: New registered vehicles by type and fuel (base year 2013) ...... 260 Table A. 35: Average distance travelled in a year per person in Malaysia (base year 2013) ... 260 Table A. 36: Average annual passenger travel in Malaysia: Train (base year 2013) ...... 260 Table A. 37: Average annual passenger mileage in Malaysia: Road vehicle (base year 2013) . 260

Table A. 38: Average annual freight mileage in Malaysia: Truck (base year 2013) ...... 260 Table A. 39: Car ownership projection (2013-2050) – per 1000 people in Malaysia ...... 261 Table A. 40: Freight transportation activity ...... 262 Table A. 41: Transportation sector variables in the MAED model ...... 264 Table A. 42: Key input assumptions for transportation structure in MAED model ...... 264 Table A. 43: Summary of key parameters needed in MAED model, demand drivers and equations to calculate the useful energy demand projection of transportation sector ...... 265 Table A. 44: Energy efficiency of household technology ...... 266 Table A. 45: Energy efficiency of commercial technology ...... 266 Table A. 46: Energy efficiency of industry technology ...... 266 Table A. 47: Energy efficiency of transport technology ...... 266 Table A. 48: Renewable energy targets by 2050 (MW) ...... 267 Table A. 49: Installed capacity of commissioned renewable installations (MW) ...... 267 Table A. 50: List of existing power plants in Malaysia (MW) ...... 267 Table A. 51: List of future power plants in Malaysia (MW) ...... 268 Table A. 52: Key data and figures for power and end-use technologies ...... 270 Table A. 53: Life span and cost of end-use technologies ...... 271 Table A. 54: Fossil-fuel prices (oil, gas and coal) ...... 271 Table A. 55: Capacity credit for technologies ...... 271 Table A. 56: Discount Rate ...... 271

Table B. 1: Reference (REF) supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 272 Table B. 2: High supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 272 Table B. 3: Low supply scenario-Electricity production (PJ) and Total installed capacity (GW) 273 Table B. 4: 20% decarbonisation of REF supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 273 Table B. 5: 40% decarbonisation of REF supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 274 Table B. 6: 60% decarbonisation of REF supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 274 Table B. 7: 80% decarbonisation of REF supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 275

Table B. 8 :20% decarbonisation of high supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 275 Table B. 9: 40% decarbonisation of high supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 276 Table B. 10: 60% decarbonisation of high supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 276 Table B. 11: 80% decarbonisation of high supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 277 Table B. 12: 20% decarbonisation of low supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 277 Table B. 13: 40% decarbonisation of low supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 278 Table B. 14: 60% decarbonisation of low supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 278 Table B. 15: 80% decarbonisation of low supply scenario-Electricity production (PJ) and Total installed capacity (GW) ...... 279 Table B.16: Distribution of near-optimal results benchmarking REF scenario over the modelled period: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%) ...... 281 Table B.17: Distribution of near-optimal results benchmarking REF scenario over the modelled period: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%) ...... 282 Table B.18: Distribution of near-optimal results benchmarking High scenario over the modelled period: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%) ...... 283 Table B.19: Distribution of near-optimal results benchmarking High scenario over the modelled period: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%) ...... 284 Table B.20: Distribution of near-optimal results benchmarking Low scenario over the modelled period: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%) ...... 285 Table B.21: Distribution of near-optimal results benchmarking Low scenario over the modelled period: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%) ...... 286 Table B. 22: Total system cost and emission by near-optimal scenarios (benchmarking REF scenario: minimise CO2 emission of the system) over the modelled period ...... 288 Table B. 23: Total system cost and emission by near-optimal scenarios (benchmarking REF scenario: minimise coal in power sector) over the modelled period ...... 289 Table B.24: Total system cost and emission by near-optimal scenarios (benchmarking High scenario: minimise CO2 emission of the system) over the modelled period ...... 290

Table B.25: Total system cost and emission by near-optimal scenarios (benchmarking High scenario: minimise coal in power sector) over the modelled period ...... 291 Table B. 26: Total system cost and emission by near-optimal scenarios (benchmarking Low scenario: minimise CO2 emission of the system) over the modelled period ...... 292 Table B.27: Total system cost and emission by near-optimal scenarios (benchmarking Low scenario: minimise coal in power sector) over the modelled period ...... 293

Abbreviations

ACROPOLIS Assessing Climate Response Options: Policy Simulations-Insights from Using National and International Models

CCGT Combined Cycle Gas Turbine

DOSM Department of Statistics Malaysia

EC Energy Commission Malaysia

EIA U.S. Energy Information Administration

EPU Economic Planning Unit Malaysia

ETP Economic Transformation Programme

ETSAP Energy Technology Systems Analysis Programme

EXSS Extended Snapshot Tool

GDP Gross Domestic Product

GHG Greenhouse Gas Emissions

IAEA International Atomic Energy Agency

IEA International Energy Agency

IIASA International Institute for Applied Systems Analysis

KRI Khazanah Research Institute

LEAP Long-Range Energy Alternatives Planning System

LULUCF Land Use, Land-Use Change and Forestry

MAED Model for Analysis of Energy Demand

MARKAL MARKet ALlocation

MEDEE-2 Model for Long-Term Energy Demand Evaluation

MESSAGE Model for Energy Supply Strategy Alternatives and their General Environmental Impact

MGA Modelling to Generate Alternative

MIEEIP Malaysian Industrial Energy Efficiency Improvement Project

23 mmBtu million British Thermal unit

MMSCFD Million standard cubic feet per day

MTOE million tonnes of oil equivalent

NAP National Automotive Policy

NEMS National Energy Modeling System

OCGT Open Cycle Gas Turbine

OSeMOSYS Open Source Energy Modelling Systems

POLES Prospective Outlook on Long-term Energy Systems

PRIMES Price-Induced Market Equilibrium System

SAVE Sustainability Achieved via Energy Efficiency

SCORE Sarawak Corridor Renewable Energy Master Plan

SPAD Land Public Transport Commission, Malaysia

TIMES The Integrated MARKAL-EFOM System

Chapter 1 Introduction

1.1 Background and Research Context

The developing countries are facing dynamic structural changes in terms of industrial development, socioeconomic activities and energy use. The key driving forces that drive the changes in these countries, such as patterns of urbanisation, economic growth, economic structures, technological diffusion, and institutional and legal mechanisms, need crucial attention for future policy planning, especially for energy and environment strategies (Jung, La Rovere et al. 2000). As developing countries are transitioning from agricultural to industrial and service-based economies, these countries encounter constant challenges in balancing their energy needs. The energy use in most developing countries is fast increasing due to rapid economic growth and demographic changes (Shuddhasattwa and Ruhul 2011, Bayar and Özel 2014). Hence, policymakers face challenges in the planning of energy supply infrastructure and infrastructure investment strategies to support energy growth. As natural resources are abundant and need to be explored in these countries, the accessibility of these infrastructures could help in tapping these natural resources for economic development as well as increase international market potential for technology transfer from industrialised countries.

Besides economic growth, increases in population also drive the demand for energy. Population growth is projected to grow faster in developing countries than in industrialised countries, which will drive energy consumption growth (EIA 2017). Population growth also drives the movement of rural people to urban areas to expect and earn higher levels of income and attain higher living standards. The lifestyles of people with higher income in developing countries potentially influence energy consumption patterns. According to EIA (2014), energy consumption is predicted to increase by 2.2% per year in developing countries. The energy use of these countries is forecasted to grow from 54% of total world energy use in 2010 to 65% in 2040. Rapid urbanisation in developing countries also influences the restructuring of the social and economic system at national and global levels. Urbanisation pattern changes drive developing countries toward industrialisation and the modernisation of infrastructures, leading to the modification of production, consumption behaviour and institutional frameworks. Understanding the changes in these key driving forces that will radically influence the energy systems of developing countries, is also important for developing future scenarios and planning long term strategies.

Malaysia, as a developing country, is also experiencing these structural changes in terms of socioeconomic activities and energy use. The country is anticipating steady economic growth in the future and foresees increasing energy demand in various sectors. The country is divided into three main regions: Peninsular Malaysia, Sabah and Sarawak. Sarawak and Sabah are located on the island of Borneo and are separated from Peninsular Malaysia by approximately 640 km of the South China Sea (see Figure 1.1). The country’s economy was growing by at least 4.7% in 2013. The total GDP contribution in 2013 was USD 250 billion and the increase of GDP contribution from services (54% of total GDP contribution) and manufacturing (23% of total GDP contribution) sectors mainly drives the economy (BNM 2013, DOSM 2010a). Parallel to the economic growth in 2013, the total primary energy supply also increased by 4.9% to fulfil the energy demand (see Figure 1.2), mainly from residential, industrial, commercial, transportation and agricultural sectors. Transportation and industry sectors are the major consumers of final energy demand (NEB 2013).

Figure 1.1: Map of Malaysia: Peninsular Malaysia, Sabah and Sarawak

Source: World Atlas (2018)

Currently, the country is undergoing some major challenges and changes in relation to energy use in the country such as the depletion of oil and gas, fuel supply shortages, rising domestic energy prices and overdependence on natural gas and coal as main cheap resources (Zamzam Jaafar, Kheng et al. 2003, Shekarchian, Moghavvemi et al. 2011, Ali, Daut et al. 2012, Khor and Lalchand 2014). There have been many attempts to overcome these challenges, especially on the supply side to fulfil the increasing energy demand.

Source: MEIH (2017) Figure 1.2: Historical Energy Consumption by Energy Forms and Sectors

Economic growth is one of the key drivers of energy use changes in the country. With the introduction of the Eleventh Five Year Malaysia Plan in 2016, the GDP of the country is expected to grow by 5%-6% per annum. All economic sectors are projected to expand steadily, with manufacturing and services sectors to contribute more than 75% of GDP generated in future. Moreover, the implementation of economic policies such as the New Economic Model (NEM) policy, the Economic Transformation Programme (ETP) and the Tenth Malaysia Plan are expected to drive the growth of the economy. The transformation of economic structure from an agriculture-based economy to a manufacturing and service-based economy increases the demand for energy and electricity in Malaysia. In 2013, the industrial sector was the major consumer of electricity, followed by commercial and residential (see Figure 1.3). Shares of electricity consumptions of industry, commercial and residential were 45%, 33% and 21% of total electricity consumption of approximately 440 PJ in 2013. Electricity demand for the industrial and commercial sectors is expected to increase with the implementation of these economic policies, which target the expansion of these sectors in near future. The energy planning could be supported with some insights from this thesis analysing the technological details and investment strategies. Based on the literature review of modelling studies in Malaysia (discussed in Section 2.2.3), there has been no research conducted on detailed energy systems focusing on demand and supply uncertainties at a multiregional level capturing the electricity trade option between regions, analysing in detail the end-use technologies of various sectors and the integration of end-use technologies with power sectors. This doctoral research extends to capture these components, especially the multiregional demand-supply pathway uncertainties and policy-driven structural uncertainties which adds the novelty to this research.

Other key drivers for the changes in energy use are the growing population, the expansion of the urban population and an increase in the electrification rate. The population is also expected to grow at 1.6% in 2014 along with the increasing urbanisation rate, leading to increase in energy demand (DOSM 2011, DOSM 2017a). In the same year, 74% of the population resided in urban areas and this proportion is expected to reach 82% by 2020. With the changes in economic structure, further improvements in infrastructure and better job opportunities will encourage the movement of people to urban areas. With the integration of major cities in the country based on the national city planning e.g., the Greater Kuala Lumpur (KL)/Klang Valley plan, the urban areas are also expected to develop. Urbanisation leads to the development of new transportation modes such as cars and busses that utilise gas and electric. The country also plans to expand the electric rail network and public buses in order to improve intercity and intracity connectivity and services by 2020. The improvements in the transportation infrastructure influence the pattern of production and consumption of energy, especially electricity. According to EPU (2011), the electrification level will be further improved in urban and rural areas to achieve 100% electrification level. The electrification rate in 2013 was 96.86% for Malaysia (Peninsular Malaysia, 99.72%; Sabah, 92.94%; and Sarawak, 88.01%).

Source: MEIH (2017)

Figure 1.3: Historical Electricity Generation and Consumption

In order to meet this fast growing energy demand, fossil fuels are widely consumed in various sectors. The country has abundant natural resources, especially oil and gas, and these fossil fuels are the cheapest resources utilised in all layers. The transportation and manufacturing sectors heavily depend on these fossil fuels to meet the growing energy demand. Serious concerns have been raised over the country’s dependence on fossil fuel consumption. Malaysia is a major oil

29 and gas exporter within ASEAN countries, yet the country expected to be the net oil importer in near future (Rahim and Liwan 2012).

Figure 1.4 summarised the implemented energy-related policies and initiatives from 1979-2015. In order to diversify the generation mix, the country introduced important policies such as the Five Fuel Diversification Policy (2001), Small Renewable Energy Programme (2001) and National Renewable Energy Policy (2009) to balance the use of fossil fuels in the power sector and to promote renewable energy. The core national policies that drive the resource supply to the country are the National Energy Policy (1979), National Depletion Policy (1980), Four-Fuel Diversification Strategy policy (1981) and Five-Fuel Diversification Strategy policy (1990). With the implementation of these policies, oil is decoupled from the electricity sector and the use of oil has gradually decreased from 1980 until 2000. In order to reduce reliance on fossil fuels and support the efficient use of natural resources, the Five-Fuel Diversification Strategy was introduced to diversify and seek new alternatives in the energy sector, especially harvesting renewable resources (Rahman Mohamed and Lee 2006, Oh, Pang et al. 2010). In order to reduce oil consumption in the transportation sector, the country initiated plans to promote LNG based vehicles. To initiate the use of renewable energy in Malaysia, the government introduced the Small Renewable Energy Programme (SREP) in May 2001 to facilitate grid-connected renewable energy resources based on small power plants, although the success rate was low. To further move toward achieving renewable energy targets, the country conducted a study and introduced the National Renewable Energy Policy and Action Plan (2009) in order to enhance the utilisation of indigenous renewable energy (RE) resources to contribute towards the national electricity supply.

Source: Khor and Lalchand (2014) Figure 1.4: Timeline of energy policies in Malaysia

In the power sector, fossil fuels, mainly gas and coal, are utilised for electricity generation. In 2013, the share of energy input in power sectors was mainly from gas (43.7%) and coal (43.7%). This was followed by hydro (8.7%), oil (1.3%), diesel (2.0%) and renewables –solar and biomass (0.7%). Some studies projected that there will be a shift from the use of gas to coal in near future, especially with the commissioning of coal power plants in Peninsular Malaysia (Khor and Lalchand 2014, EC 2014, WEO 2013). The recent shift from gas to coal is due to the allocation of gas for export. However, it was predicted in 2013 that gas would deplete in 40 years and a limited amount of gas, 1350 mmscfd, is supplied to the power sector. The shift towards coal utilisation also raises the issue of energy security, as coal power plants fully depend on coal imported from Indonesia (70%), Australia (19%), South Africa (12%) and Russia (2%). Moreover, securing a long-term coal supply at a feasible fuel price is important taking into consideration the commissioning of new coal power plants in the near future their operational lifespan of 30– 35 years.

The infrastructure of the power sector is also due for some critical changes. The country’s total installed capacity was 29 748 MW by end of the year 2013, with gross electricity generation of 143 497 GWh (NEB 2013). However, significant amounts of these current generation capacities (approximately about 30% of actual installed capacity) are scheduled for retirement within the next ten years. Detailed planning is needed in commissioning new infrastructures to replace the retired power plants. Any investment in building new power plants needs to take into account the resource potentials, the security of fuel supply and potential fuel price fluctuation over the lifetime of the power plants. Furthermore, the power sector has been the major contributor toward CO2 emissions since 2000 due to fossil fuel combustion and is expected to increase with future coal power plants that are expected to become operational in the near short term (EC 2014, EC 2016).

The introduction of energy policies is an effort by the country to explore alternative technologies such as renewable energy or nuclear, which will also contribute toward carbon emissions reduction. Furthermore, escalating fuel prices are a concern, as the power generation fully depends on coal imports and the gas supply to the power sector is partially subsidised by the government. Gradual gas subsidy removal in the coming years will influence the deployment of gas technologies. Therefore, there is significant importance in exploring alternative technology pathways for Malaysia.

In the effort to explore alternatives, the country is also considering electricity trade between Peninsular Malaysia, Sabah and Sarawak. About 80% of the population is in Peninsular Malaysia. This region also generates about 84% of total GDP contribution and consumes 86% of total electricity demand as of 2013. With limited fuel resources in Peninsular Malaysia, there is an urge to diversify generation mix including to import hydro potentials from Sarawak through 660- km undersea transmission cable (The Malaysian Reserve, 2014). Sarawak, through SCORE master plan, will be exploring the hydropower potential of 20,000MW that create opportunities for electricity import from Sarawak (Sovacool and Bulan 2011; pg. 4843). The multi-regional interconnectivity between Peninsular Malaysia and Sarawak could be an option for Malaysia in the attempt to discover alternative technology pathways. In very long term, the interconnection between neighbouring countries could be established to promote regional cooperation in electricity trade (Stich, Mannhart et al. 2014, Huber, Roger et al. 2015). However, this project needs prominent political cooperation and collaboration between neighbouring countries as each country has its own agenda toward future energy planning.

These crucial changes in Malaysia are leading to increasing demand and variation in the use of future fuels. The country is trying to meet the increasing demand for energy in the most cost effective way while ensuring the sustainability of the energy sector. Furthermore, the country is also committed to reduce its GHG emission intensity of GDP by 45% by 2030 relative to the emissions intensity of GDP in 2005 (UNFCCC 2015). Earlier, in 2009, Malaysia had also committed to voluntarily reducing its emissions intensity of GDP by up to 40% by 2020 based on 2005 levels. This initiative is conditional upon technology transfer and financial support from developed countries (NRE 2011).

Therefore, much attention is required to effectively manage the demand-supply energy needs and to decarbonise various sectors in order to achieve the pledge of reducing the carbon intensity of GDP. Many policies and measures such as the National Policy on Climate Change, the National Green Technology Policy and the National Renewable Energy Policy and Action Plan have been initiated to accomplish this pledge. However, like many other developing countries, scenario-making of future energy and environmental problems is a challenging task due to the lack of energy systems modelling research relative to industrialised countries. Urban et al. (2007) highlighted the limitation of using the energy models in developing countries and the gap in energy systems modelling research between the industrialised and developing countries. The authors suggest that the energy systems and economies of developing countries are different and therefore uncertainties such as structural economic change, performance of power sector

32 or fuel subsidies need to be modelled differently (Urban, Benders et al. 2007). In Malaysia, there is a gap in the use of energy models for future scenario-making as discussed in Section 2.2.3. Thus, this modelling research addresses the demand uncertainties, supply pathway uncertainties looking in depth the technology profiles and structural uncertainties to seek the balance between different energy policy goals that could assist in the future policy planning of Malaysia. This energy system modelling research for Malaysia is also useful for other similar middle-sized developing countries as most energy systems studies of developing countries focused on big economic countries such as India, China or Brazil (as discussed in Section 2.5).

1.2 Objectives and Research Questions

This doctoral thesis aims to explore the long-term least-cost optimal and near-optimal decarbonisation strategies in Malaysia. Under the landscape of demand-supply uncertainties, the research focuses on analysing the fuel-switching of end-use technologies in the industrial, transportation, household, services and power sectors when gradual carbon emission constraints are imposed until 2050. The study on the comprehensive energy system is very important to address the energy use changes in the country, especially to achieve a decarbonised system. The research will inform policy makers of the optimal technology options under the demand drivers as well as investment strategies in adopting new technologies under near-optimality conditions and the constrained environment of decarbonisation strategies.

The key research questions posed for the study are:

1. What are the optimal decarbonisation strategies under long-term demand drivers in Malaysia? 2. How do the different decarbonisation targets influence the deployment of technologies, resources and inter-regional electricity trade? 3. How robust are the near-optimal decarbonisation strategies with respect to technological- environmental-economic objectives for long-term planning?

1.3 Research Scope and Methodology Adopted

The study focuses on exploring the research questions in the context of decarbonising the energy system of Malaysia by the year 2050. Energy industries are leading emitters of CO2 in the country. These emissions are produced through heavy fossil fuels usage by power and auto producers (self-energy producers) in order to produce electricity, petroleum refining and natural gas transformation. Therefore, in order to decarbonise the energy system, the country is seriously considering incorporating environmental issues into the process of energy planning and policy-making. The decarbonisation target is also framed as a parallel approach to achieve the pledge the country made at the 2015 Paris Climate Summit to reduce its GHG emissions intensity of GDP by 45% by 2030 relative to the emissions intensity of GDP in 2005. The carbon emission projection in the model is calculated based on the Tier 1 approach. Tier 1 emissions are estimated based on fuel combusted in the source category and default emission factors (IPCC 2006). The research only models demand and supply sides of the energy systems, excluding the LULUCF sector.

Although the decarbonisation strategies implemented in this study are very ambitious (Sections 5.1.3.1 and 5.2.1), it provides an insight on the possible decarbonisation strategies that could be implemented in future not only by focusing on the power sector but also by emphasising the end-use technologies in various sectors. The transformation of energy systems to achieve decarbonisation pathways could also significantly make a difference in terms of cost. To deploy future technology for the long term, the financial constraints will be an issue for a developing country like Malaysia.

This doctoral study interrogates the key research questions largely from the perspective of demand-supply pathways that are economically optimised under decarbonisation constraints, and robustness of the near-optimal decarbonisation strategies with respect to technological- environmental-economic objectives for long-term planning. This study provides an outlook to national policy makers of feasible decarbonisation and investment strategies that the country could adopt in order to achieve the policy objectives and frame future policy planning. If the country decides to aim for ambitious strategies, the study could also provide perspectives in terms of the technological-environmental-economic aspect, which is an ongoing interest for policy-makers in Malaysia and for most of the leaders in developing countries.

To conduct this analysis, a novel hybrid model (MAED-OSeMOSYS) has been developed to examine the demand-supply energy system and to further analyse the fuel combustion

34 emissions in the energy sector. The detailed demand model, MAED captures the range of possible future energy demands. The energy systems supply model, OSeMOSYS captures the technological and resource characteristics of Malaysia as well as its geographical aspects via a multi-regional approach. The OSeMOSYS model is reformulated to include the Modelling to Generate Alternatives (MGA) technique to analyse a range of near-optimal decarbonisation pathways for future energy policy planning. The application of the MGA technique provides researchers with the flexibility to explore alternative pathways within a cost optimal solution range.

1.4 Thesis Structure

The thesis is organised as follows:

Chapter Two presents an assessment of the literature on the application of existing energy systems models in developed and developing countries as well as at a national level. The chapter also explains the rationale of using Hybrid models (Simulation-Optimisation) for the case study of Malaysia, and elaborates the uncertainties in the optimisation model, including the application of the Modelling to Generate Alternatives (MGA) technique. In addition, it identifies the research gaps.

Chapter Three describes the details of the methodological approach in addressing the research. It outlines the Model for Analysis Energy Demand (MAED) conceptualisation and formulation as well as the documentation and assumptions to develop the model. The second part of the chapter illustrates Open Source Energy Modelling Systems (OSeMOSYS) conceptualisation and formulation, assumptions in the model and MGA in OSeMOSYS model.

Chapter Four outlines the idea and concept of building MGA-Hybrid model Scenarios. The first part of the chapter details the demand-supply pathways concept to obtain outlook on technology profiles and trends of fuel mixes in future. The second part of this chapter describes the energy system configurations that are developed to evaluate the least-cost decarbonisation strategies. The last part of this chapter elaborates the near-optimal approaches focusing on introducing new objective functions to evaluate the policy-driven structural uncertainties and examines two policy directions-decarbonising the power sector and decarbonising the energy systems.

Chapter Five discusses the results of demand scenarios, hybrid model systematic scenarios, optimal and near-optimal pathways. This chapter is divided into three parts. The first part of this chapter analyses the results of the demand-supply scenarios of the umbrella scenarios:

35 reference, high and low scenarios. The second part of this chapter discusses the deep decarbonisation scenarios benchmarking the umbrella scenarios to evaluate the least-cost decarbonisation strategies. The last part of the chapter discusses the near-optimal decarbonisation pathways focusing on two objective functions: minimising of the carbon emission of the system and minimising coal consumption in the power sector, taking into account policy-driven structural uncertainties.

Chapter Six gives an overview of the research, summarises the key research findings and outcomes, and discusses areas for further research. This chapter is followed by the references and appendices.

Chapter 2 Literature Review

This chapter presents and discusses the literature reviews of this research. It has five sections. Section 2.1 outlines the introduction and classification of energy systems. Section 2.2 discusses the application of energy systems models in developed and developing countries. This section also presents the application of energy systems models in Malaysia, followed by Section 2.3 that focuses on the rationale for using simulation and optimisation models in Malaysia. Section 2.4 discusses the uncertainties in the optimisation models focusing on Modelling to Generate Alternative (MGA) techniques. Section 2.5 presents the research gap based on the reviewed literature.

2.1 Introduction and Classification of Energy Systems

An energy system model is a schematic representation consisting of mathematical formulations that try to represent reality and make predictions. Beaujean et al. (1977) explain that a model only captures a broad perspective of a problem because it is impossible to capture mathematically the complex interconnection among various sectors and there is a need to clearly define the boundaries and scope of the problems that need to be examined. Modelling techniques originated from operational research during the Second World War. By the 1950s, the practice of developing models had started in most countries. However, the development of the first global models was actually begun in the mid-1960s and further expanded with the advancement of computers and programming capacity availability in the 1970s. The availability of advanced computers created a platform for the energy modellers to analyse the effects of energy sectors and their economic impact on the energy market at the regional and world levels. Beaujean and Charpentier (1978) review fourteen energy models and their application to analyse different issues such as energy supply planning, policy needs for the natural gas industry, demand forecasting, the impact of the oil crisis on the economy and the behaviour of crude oil production across eleven countries. These early models are classified into four groups based on the areas of the models applied to obtain an outlook purpose of these models.

Grubb et al. (1993) argue that there is no specific methodology to differentiate or classify these models, yet they can be categorised according to the economic paradigm and engineering paradigm, which are closely related to the top-down and bottom-up modelling approaches. On

37 the other hand, Messner and Strubegger (1995) and Kleinpeter (1995) discuss the type of the models employed based on their methodology, such as econometric, macro-economic, optimisation or simulation. Furthermore, Hourcade et. al. (1996) took a different approach to classifying these models: in terms of types of mitigation costs or technology representation. van Beeck (1999) concluded that there are nine ways to classify the models based on the representation of the models in the literature (for instance, Grubb et. al. (1993), Messner and Strubegger (1995), Kleinpeter (1995), Hourcade et. al. (1996)). The nine ways of classifying the models are represented in the Figure 2.1. Based on these nine classifications, the following section focuses on the analytical approach, the bottom up and top down models.

Purpose of Analytical Geographical Model Mathematical Sectoral Data Methodology Time Horizon Energy models Approach Coverage Structure Approach Coverage requirements

Medium/long Global/ Optimisation term national Bottom up Short National/ Energy term/target Accounting local/ sectoral Models: year Linear/mixed- Assumptions Energy Qualitative/ Global/ integer General or Macro- Short/ sectors national/ programming quantitative Specific medium term economic sectoral Top down Medium/long General Global/ term/target national Equilibrium year

Figure 2.1: Example of classifying energy models (van Beeck 1999)

The IPCC (2001; pg. 489) indicates that the ‘bottom’ and ‘top’ approaches are derived from the terms ‘aggregated models’ and ‘disaggregated models’, which are based on how the model behaviour is endogenised and extrapolated over the long study period. According to Klinge Jacobsen (1998), the different characteristics of these two approaches lead to the diverse application of these models, which give insights to policy makers depending on how they are developed to capture the multi-disciplinary energy, economic and environmental issues. Hence, using both approaches help policy makers to address different interests and problems at the regional and global levels. Nakata (2004; pg. 424) summarised the characteristics of top-down and bottom-up models, which are explained in the following.

2.1.1 Top-down Energy Models1

The top down model is based on an equilibrium framework, whereby macroeconomic theory and econometric techniques are applied in evaluating the economics of technologies. Econometric techniques are applied to historical data to analyse past trends on fuel or resource prices, energy consumption or to estimate income elasticity for final demand goods and services. Therefore, this framework studies the macroeconomic impacts or market behaviour between the energy sector and other economic sectors. For example, ENERPLAN was developed by Tokyo Energy Analysis Group, Japan to forecast or explore energy supply and energy demand. MICRO-MELODIE was developed by Commissariat à l'Energie Atomique (CEA), France to analyse the macroeconomics of energy and environment linkages. The top-down models provide a sophisticated tool to analyse financial elements across the whole economy level and capture little information on technology. The following sections (Section 2.1.1.1 and 2.1.1.2) describe the macro-economic and general equilibrium methodologies mainly used in the top-down models.

2.1.1.1 Macro-economic models

The macro-economic methodology focuses on the entire economy of a society and the interaction between energy sectors. The macroeconomic model integrates economic techniques and input-output analysis (Hoffman and Jorgenson 1977). Input-output tables are used to define transactions among economic sectors and assist in energy-economy interactions analysis. Macro-economic models do not represent specific technologies and focus on exploring the economy as a whole.

2.1.1.2 General Equilibrium models

An equilibrium model explores the behaviour of the economic elements and requires knowledge of economic theory and econometric metrics to produce insights into the effects of policies in an economic system (Peter and Dale 2012). The models simulate economic behaviour such as factors of production or products and foreign exchanges, taking into account the relations of supply and demand behaviour equations. The model produces results, for example, parameters on wages, prices and exchanges rates and plays an essential role in analysing the economic behaviours to bring the supply and demand needs into equilibrium.

1 This section provides a short description of the top-down energy models as described in Nakata (2004; pg. 424). The following section (Section 2.1.2) further describes bottom-up energy models, which is the focus of this study. 39

2.1.2 Bottom-up Energy Models

The bottom-up models capture technology in the engineering perspective and represent deep supply technological details using disaggregated data. The models are independent of observed market behaviour and interaction between energy sector and other economic sectors are not considered. Bottom-up energy models can be further aggregated to major different groups e.g., optimisation, simulation and accounting models, which are explained in the following.

2.1.2.1 Optimisation models

Optimisation is one of the economic approaches to select within the different alternatives. It represents the best possible solution according to a basic objective function. Optimisation requires an accurate formulation of a system of equations and the existence of a reliable database. The relations in an optimisation model are to be formulated with mathematical equations in such way that a minimum or maximum value can be determined. Besides mathematical formulations, accurate data with respect to both quantity and reliability must be available. Optimisation models are based on the allocation of energy resources to specific demands that depend on the energy technologies structured for production, transportation and distribution to derive optimal investment strategies under given constraints.

With optimisation models, the need to match energy supply to demand capturing the technological details will force reconsideration of the entire energy system. This has particular importance for long-term planning. A balance is needed to acknowledge the evolution of energy supply and demand. An energy balance, describing demand and supply is established for a specific area and a specific time period and will match inputs and outputs. These models are designed based on the Reference Energy System (RES) approach developed by Hoffman (cited in Hoffman and Wood 1976; pg. 441). Each path through the systems from resources to a specific end-use technology is represented by a single activity in the model. The complexity of RES layers depends on the scope and the questions to be answered by using the optimisation models.

The different steps in the energy chain are defined as follows (Hoffman and Jorgenson 1977, Messner and Strubegger 1995, Subhes and Govinda 2010):

• Primary energy is energy which has not been subjected to a conversion or transformation process.

• Secondary energy is energy which has been produced by the conversion or transformation of primary energy or another secondary energy. • Final energy is energy made available to the consumer before its final conversion. • Useful energy is energy made available to the consumer after its final conversion.

The optimisation approach is based on cost minimisation that can be formulated as a linear programming problem (Chazelle 2000). Linear programming is the problem of minimising a linear functional that can be solved in time linear, t under a number of constraints.

Algebraically, one of the ways of stating the problem is

Minimise Cx,

Subject to the constraints Ax ≤ b and x ≥ 0, where b and C are column vectors in ℛ� and ℛ, respectively and A is an n-by-d real matrix. The number d of variables is the dimension of the ambient space. This method is generally used for a broad class of optimisation problems. Table 2.1 summarises the characteristics and scope of the bottom-up optimisation model.

Table 2.1: Criteria of bottom-up optimisation model (Subhes and Govinda 2010; pg. 502)

Criteria Bottom-up, Optimisation Geographical coverage Local to global, but mostly national Activity coverage Energy systems, environment, trading Level of disaggregation High Technology Coverage Extensive Data need Extensive Skill requirement Very high Capability to analyse price induced policies High Capacity to analyse non-price policies Good Time Horizon Medium to long-term Computing requirement High end requiring commercial LP solvers

Optimisation models are applied in the evaluation of energy policies, analysing the environmental impact of alternative energy policies, estimating capital investment in implementing new technologies and studying the effects of energy price changes on primary energy resources (Hoffman and Jorgenson 1977). The Brookhaven Energy System Optimisation (BESOM) model, was one of the early optimisation models developed in the 1970s at

Brookhaven National Laboratory (BNL) to analyse the energy systems in the USA. A range of other optimisation models was developed at different stages in the 1970s focusing on various issues to find, e.g., optimal solutions of fuel mix, capacities of technologies or the optimal utilisation of diverse energy forms (Beaujean and Charpentier 1978). For example, the development of other optimisation models started in the Europe (The Energy Flow Optimization Model (EFOM) at the European Commission) and through international collaboration, MARKAL at the IEA and the MESSAGE at the IIASA.

Over the years, the utilisation of the models evolved to cover a wide range of problems from policy planning to environmental issues such as analysing global patterns of energy supply or revolutionary technologies’ requirement for emissions reductions (Nakata 2004, Bazmi and Zahedi 2011, Pfenninger, Hawkes et al. 2014). MARKAL/TIMES is one widely used optimisation model developed through cooperative projects by ETSAP of the IEA. Fishbone and Abilock (1981) describe that two versions of MARKAL exist; one was developed Brookhaven National Laboratory (BNL) and the other at Kernforschungsanlage Juelich (KFA). Both attempts to understand the behaviour of possible future national energy systems. MARKAL is a direct successor to two other energy models, Brookhaven Energy System Optimisation (BESOM) and Dynamic Energy System Optimization Model (DESOM) at BNL. The MARKAL model computes energy balances at all levels of an energy system: primary resources, secondary fuels, final energy, and energy services based on a RES approach. The model aims to supply energy services at minimum cost by simultaneously making the technological investment and operating decisions.

MESSAGE, developed at the IIASA, is based on a similar concept (Messner 1997). The energy optimization model MESSAGE is a dynamic linear programming model of the overall energy system. It models flows of energy through the energy system, from primary energy extraction via conversion up to final utilisation in various sectors of the economy. The objective function generally applied in MESSAGE is to minimize the sum of the discounted costs, or the net present value, of the overall energy system.

Both MARKAL/TIMES and MESSAGE are widely used still to address current research. There are various other key models, as described in Table 2.2. Despite these established and dominant models being applied broadly in many types of researches, there are also ongoing efforts to develop new energy systems models. For example, OSeMOSYS is an open source energy model with open source codes, which is being developed based on a traditional energy systems

42 modelling concept (Howells, Rogner et al. 2011). OSeMOSYS is a full-fledged systems optimisation model for long-run energy planning and the objective of the model is to estimate the lowest net present value (NPV) cost of an energy system to meet given demand(s) for energy or energy services. Unlike long established energy systems models such as MARKAL/TIMES or MESSAGE, OSeMOSYS potentially requires a less significant learning curve and time commitment to build and operate.

The OSeMOSYS code is relatively straightforward, elegant and transparent and allows for simple refinements and the ability to conduct sophisticated new analyses. OSeMOSYS is designed to be easily updated and modified to suit the needs of a particular analysis. To summarise, the choice of an optimisation model by researchers depends on the level of complexity of long-term scientific application, geographical coverage or issues to address. This will be discussed further in Section 2.2.

Table 2.2: Characteristics of some key optimisation models based on Table 2.1 criteria (Subhes and Govinda 2010; pg. 503-504)

Criteria MARKAL TIMES MESSAGE OSeMOSYS Geographical National/Global Local, regional, Multi- Multi- coverage national/ Global regional/nationa regional/national/global l/global Activity Energy System Energy System Energy System Energy System & Energy coverage & Energy Trading Trading Level of User-Defined User-defined User-defined User-defined disaggregation Technology Extensive Extensive Extensive Extensive Coverage Data need Extensive Extensive Extensive Extensive Skill High to Very Very High High to Very Low to Very High requirement High High Economic Not Covered Can be Covered Not Covered Not Covered Transition

2.1.2.2 Simulation models

Parallel to optimisation models utilisation, the second largest bottom-up group of national and global energy systems models used in the current research are simulation models. Choucri and Heye (1990) discuss some basic assumptions for simulation approaches, which are econometric- based simulations and system dynamics simulations. These two approaches are widely used in the simulation models to generate alternative scenarios. Unlike the optimisation models with detailed mathematical formulations, the simulation model approach is much simpler. The main

43 objective of the simulation models is to understand the structure of a system and process the alternative scenario or behaviours outcomes based on some parameters as drivers.

The simulation model has a detailed representation of energy demand and supply technologies. Exogenous scenario assumptions based on econometric forecasts usually drive the demand. Beaujean and Charpentier (1978) compiled some of the early simulation models developed to conduct analysis on electricity demand, the simulation of crude oil behaviour that affects the shaping of future economic and policies as well as perform time series analyses of parameters that influence the future energy supply planning. For example, Prospective Outlook on Long- term Energy Systems (POLES) is a simulation model used to analyse international energy markets, national energy balances or long-term energy supply and demand scenarios. Another simulation model, used by IAEA, is called the Model for Analysis of Energy Demand (MAED). The model is a simplified version of MEDEE-2, which was adapted by B. Lapillonne to conduct studies on global energy assessment at IIASA, Laxenburg, Austria (Lapillonne 1978). While sustaining the general structure of MEDEE-2, important modifications were introduced by the IAEA in the MAED model to provide a systematic framework for analysing the medium to long-term energy demand scenarios of socioeconomic and demographic developments (IAEA 2006).

2.1.2.3 Accounting models

Accounting models are tools for long-term projections of supply or demand configurations. Based on a range of parameters such as economic growth, population and technology, the accounting models calculate how the energy in various sectors is consumed and produced (Nakata 2004). The long-range energy alternatives planning system (LEAP) is an example of an accounting model. LEAP is a software tool for energy policy analysis and climate change mitigation assessment developed at the Stockholm Environment Institute. LEAP’s approach is relatively simple. The model is an integrated, scenario-based modelling tool that can be used to account for energy consumption, production and resource extraction in all sectors of an economy (Heaps 2016). The model analyses energy assessment in developing and industrialised countries at a local or multi-regional level.

2.2 Application of Energy Systems Model

Bottom-up energy systems models have become powerful tools for researchers to analyse many emerging issues in countries. Policymakers capture the suggestions from these models as a recommendation in order to develop comprehensive energy policies. These models serve to analyse some basic energy issues in a country. The complexity level in developing an energy systems model depends on the researchers and the depth of the issues analysed in the various countries. The following section discusses the application of the energy models in developed and developing countries. This section further discusses the application of energy models at the national level.

2.2.1 Energy systems models in developed countries

Beaujean et al. (1977) reviewed some of the early global and international energy models to give some background to major research groups and organisations involved in energy modelling. Based on this paper, the United States, the United Kingdom, Sweden, France, Germany, the Netherlands and Japan initiated the early development of energy models. Initially, the researchers focused more on energy security and costs using these energy system models. In recent years, researchers have been focusing on climate change issues. The European Commission (EC) and the International Energy Agency (IEA) took many other initiatives to support the development and use of different models on a global scale such as TIMES and POLES. Another study reflecting the electricity sector on a global scale was the development of the ACROPOLIS project, whereby up to fifteen energy models were used to analyse and compare the costs of GHG emissions reduction and technology mixes (Das, Rossetti di Valdalbero et al. 2007).

Bazmi and Zahedi (2011) reviewed the papers related to the use of the optimisation modelling techniques in the power sector. This technique is widely applied to analyse various power technologies and the paper elaborated the role of optimisation to address different issues in the power sector, e.g., evaluating the economic implications of different energy policies for the power sector in Europe through linear dynamic optimisation model application focusing on the electricity system. This model incorporated investment and generation decisions for all types of power plants. Another study that focused on a comprehensive set of technically feasible and economically efficient pathways was developed for the Europe’s power sector by 2050 through the development of thirty-six scenarios (Jägemann, Fürsch et al. 2013).

Other well-established optimisation models such as MARKAL were widely used by many institutions to study a various range of issues such as CO2 abatement technologies or the role of nuclear energy. (Goldstein and Tosato 2008, Vaillancourt, Labriet et al. 2008, Taylor, Upham et al. 2014). In the UK, for example, Strachan et al. (2009) used the MARKAL model to analyse the development of different technology pathways, costing related to decarbonisation scenarios and underlying outcome uncertainties. The study focused on how policymakers understand these insights and applied these understandings in developing the national policy. MESSAGE, another well-established model, is used to examine technology pathways in the electricity sector at the global scale. The model is divided into eleven regions to examine the energy system in a simplified way (Sullivan, Krey et al. 2013).

Many other studies in developed countries in recent years also focus on deep decarbonisation strategy analysis. For example, Denmark and Germany analysed the potential of utilising 100% renewables in the energy system. In Denmark, the energy system analysis model EnergyPLAN was used to examine a 100% renewable energy supply strategy based on domestic resources and concluded that the country needs to harvest mostly biomass resources or wind power to achieve this target. The study suggested at least three major technological changes in the energy systems such as energy savings on the demand side, efficiency improvements in the energy production or the replacement of fossil fuels needed to achieve a 100% renewable target (Lund and Mathiesen 2009). In Germany, a new simulation model, REMod-D (Renewable Energy Model-Deutschland), was used to analyse potential future German energy system based on 100% renewable energies harvested domestically to cover the electricity and heat demand target. The results are intended to identify robust solutions in order to help policy makers in future policy planning. To improve the design of the model system to analyse very high renewable energy shares, the study suggested including the techno-economic criteria (Henning and Palzer 2014).

Ireland aims to achieve ambitious emissions targets of 80% and 95% reductions relative to 1990 levels. The Irish TIMES energy systems modelling tool is used to investigate these targets and concluded that these challenging targets can be technically achieved with the deployment of energy efficiency and renewable energy technologies (Chiodi, Gargiulo et al. 2013). In the Swiss multi-region TIMES electricity model (Pattupara and Kannan 2016) and the TIMES Energy system Model (Kannan and Turton 2016), the cost optimisation framework is used to analyse the ambitious carbon dioxide (CO2) emission reductions in the power sector with the phase-out of

46 nuclear technology. The model investigated a set of long-term scenarios of Switzerland’s and its neighbouring countries’ electricity systems. The modelling results foresee a combination of natural gas, renewables or electricity imports as possible substitutes for nuclear power plants. In the US, the SWITCH (Solar and wind energy integrated with transmission and conventional sources) investment optimisation model is used to explore the cost and technology mix under the stringent carbon cap of 85% below 1990 emissions levels. The model details information of eleven western US states, two Canadian provinces, and northern Baja California, Mexico and examines the long-term capacity planning for the accurate economic evaluation of intermittent renewables, storage technologies, and other integration alternatives (Mileva, Johnston et al. 2016). In California, the CA-TIMES model is used to analyse the technology transformations and policies required to decarbonise the energy system over the long term. The model is used to explore the low-carbon scenarios, focusing on the potential evolution of the transportation, fuel supply, and electric generation sectors. The study concluded that significant efficiency improvement in end-use technologies, the reduction of travel demand in the transportation sector as well as the complete decarbonisation of the power sector is required in order to achieve deep carbonisation strategies (McCollum, Yang et al. 2012).

A number of other studies focus on decarbonisation strategies at the global level. The International Energy Agency studies2, for example, conducted an analysis to examine 50% reduction in energy-related CO2 emissions by 2050 compared to 2005 levels. The global scenarios in these studies are developed to investigate the costing of implementing future strategies to reduce GHG emissions (IEA 2008a; IEA 2010). Syri et al. 2008 and Labriet et al. 2012 used the TIAM (TIMES Integrated Assessment Model) to analyse the adaptation of low-carbon technologies and impact of emission reduction at the global level. The TIAM-UCL is used to generate global scenarios, exploring the role of fossil fuels in a CO2-constrained global system. The study examines the trade flows of fossil commodities between producing and consuming regions, reflecting how demand drives production under climate and energy policy constraints. The model results provide a basis for the comparing fossil fuel production and supply. The study suggested that fossil fuels will play a role in a deeply decarbonised system (Pye, McGlade et al. 2016).

2 The International Energy Agency (IEA) flagship publication, World Energy Outlook (WEO) uses World Energy Model (WEM) which is large-scale simulation model used to generate detailed projections for WEO scenarios (IEA 2017b). Meanwhile, the IEA- Energy Technology Perspectives (ETP) publication uses ETP-TIMES model to generate technologically detailed supply side of the energy system (Loulou et al., 2005).

2.2.2 Energy systems models in developing countries

There are a number of changes in developing countries in relation to socioeconomic activities and energy use. Jung et al. (2000) examined the key driving forces such as patterns of urbanisation, economic and industrial structures, technological diffusion, and institutional and legal mechanisms that are closely related to the patterns and strategies for economic development in developing and industrialised countries. The structure of GDP in developing countries is evolving due to global economic transformation. The share of the manufacturing sector mainly contributes toward the rapid expansion of GDP in developing countries. The changes in manufacturing shares have a strong relationship to economic development, and these changes can be observed in industrialised and developing countries. For example, in the 1970s, Japan's rapid economic growth was based on the high share of the manufacturing sector and declined after that period. On the other hand, China and Malaysia as emerging economies show a continuous increase in manufacturing share in total GDP. However, it can also be observed in most industrialised countries that eventually, as the economic structure becomes more stable, the share of GDP in manufacturing decreases and shift toward the service sector. Therefore, based on the economic structural trends of industrialised countries, developing countries may also experience economic structural changes from agricultural to industrial and service-based economies. Furthermore, long-term economic output may be increasingly generated from service activities due to the decline of heavy industry investment by industrialised countries in developing countries.

With the expansion of the GDP structure of developing countries, there is a need for infrastructure to support economic growth. Since a major part of the infrastructure is still widely needed for development, the spectrum of future modern technology options such as biotechnology, solar energy, wind and small-scale hydro is considerably broader, and these technologies could be transferred from industrialised countries. With these modern technologies, a large amount of natural resources available in developing countries could be tapped. The changes in the economic structure in developing countries could not only achieve higher income levels, but also expand their international market potential and allow them to become more integrated with the world economy. This will provide greater market opportunities and high technology transfer between industrialised and developing countries. However, the recent experience of economic recession in a substantial set of developing countries demonstrates that access to superior technologies may be limited.

Besides economic growth, the increase in population growth also drives the need for energy and commodity demand. The population increase, especially the urban population with a higher income level, motivates a desire for higher living standards. Energy consumption is predicted to increase by 2.2% per year in developing countries and total energy use is forecasted to grow from 54% of total world energy use in 2010 to 65% in 2040 (EIA 2014). However, the spatial distribution of population and access to energy are still not settled in most developing countries. This situation offers the possibility of adopting policies in rural development and meeting energy needs. In summary, the increasing use of energy in developing countries is mainly due to their growing economies, high population, and urbanisation and these issues vary in each country depending on the institutional structure, social and cultural processes, and technological adoption rate in various sectors.

Since the socioeconomic activities and demand needs vary between developing and developed countries, capturing these characteristics specific to developing countries in energy models is a challenging task for energy policy modellers. There is lack of experience in the application of the energy system models described in Section 2.1 to address issues in developing countries. Munasinghe and Meier (1993a) highlighted some of the key issues faced in the developing countries and the difficulties in capturing these issues in energy models. The study discusses the lack of experience in energy modelling, as most of the energy models are developed by institutions in industrialised countries to address policy and planning in the context of these countries. Although there were some energy models such as BESOM, MARKAL, MESSAGE applied to China, India, Indonesia, Nigeria and Tunisia in the 1980s (Munasinghe and Meier 1993b; pg 250), the knowledge of energy modelling was not completely transferred or gained by these countries. The established energy planning institutions in these developing countries were not well integrated and largely depended on consultants with sufficient knowledge of the code to make changes in the models in order to provide decision-makers with a clear understanding of the models’ assumptions and limitations. Furthermore, many computer- modelling projects consumed vast resources for computer programming, model formulation, and debugging, leaving little time for on-the-job training in the developing countries.

The energy system models also require a detailed and comprehensive database as well as knowledge in energy modelling, which are not readily available in developing countries. Subhes and Govinda (2010) argue that most of the energy models are not suitable for developing country contexts, as the models explicitly do not cover the essential characteristics of developing countries. However, issues like the improvement of operational performance, trajectories of

49 technology mix and fuel mix, estimating the cost of greenhouse gas emissions, the impact on the environment as well as the sustainable use of natural resources in a developing country can be studied using models developed for similar objectives in industrialised countries (Pandey 2002). Others studying modelling energy systems in developing countries investigated the various energy models and the application of these models to capture issues in developing countries. The studies concluded that main issues such as electrification, urbanisation levels, the use of natural resources and fuel subsidies could be addressed using selective top-down and bottom-up models e.g., AIM, LEAP, MARKAL, MESSAGE, WEM and MiniCam (Urban, Benders et al. 2007, DEA, OECD et al. 2013).

China, being the highest populated developing nation with a fast-growing economy, is currently a major force in global energy and emissions (Hubacek, Guan et al. 2007). China actively conducted many studies using energy systems models. China has abundant coal and hydro resources and is mainly dependent on coal for electricity generation. In 1999, researchers first developed the China MARKAL model. A number of studies using this energy model looked at energy systems, mainly technology selections and technology influences on emission reductions (De Laquil, Wenying et al. 2003, Mischke and Karlsson 2014). Other MARKAL studies analysed the MARKAL-ED integration with MARKAL-MACRO models focusing on the power sector to analyse the fossil-fuel resources and technologies transitions in future (Larson, Zongxin et al. 2003, Chen, Wu et al. 2007, Jiang, Wenying et al. 2008). A few other studies focused on power sector, such as TIMES (Jia, Wenying et al. 2011), LEAP (Cai, Wang et al. 2007), MAED-MESSAGE (UN-ENERGY 2007) and TIMES G5 (Rout, Voβ et al. 2011). These studies analysed the impact of introducing energy policies at different time phases and technological changes over the period. The China-TIMES model study with updated details on the building sector further focuses on the use of renewable energy in buildings to achieve decarbonisation up to 2050 (Shi, Chen et al. 2016). The China-TIMES model is also used to analyse the decarbonisation of the transportation sector (Zhang, Chen et al. 2016). Kejun et al. (2010) used three models with a mixed framework approach, (the Integrated Policy Assessment Model China (IPAC)- CGE model, IPAC-Emission global model, and the IPAC- AIM/technology model) to analyse the role of technologies in mitigating climate change as well as meeting the requirements of energy savings and environment protection in the short and long term. The main aim of these models is to look at how the technological transitions in the future will influence the emission and decarbonisation pathways in the country. To analyse uncertainties, these studies develop different scenarios to look at the impact of policy and technology influences and ways to reduce the country’s greenhouse gas emissions.

In India, a few energy studies were conducted using the MARKAL model for the power sector (Ghosh, Shukla et al. 2002, Mallahand Bansal 2010, IEA 2011, Mallah 2011). These studies looked at the transitions of different technologies in the power sector and the reduction of greenhouse gases in the country. Mallah and Bansal (2011) have conducted uncertainty analyses based on the sensitivity analysis approach to look at the changes of the technology mix in the power sector. In Shukla et al. 2008, an integrated modelling framework (MARKAL-AIM) approach is used to determine India’s carbon emissions pathways for long term planning. Another analysis was conducted on various technologies availability to meet the electricity demand for the given supplies and requirements using the MESSAGE model (Saradhi, Pandit et al. 2009). The TIMES G5 model aimed to look at the electricity generation and sectoral energy demands and emission in India (Rout 2011). Other studies actively looked at India’s CO2 emissions pathway until 2050 using the TIAM-UCL model. These authors used this model to analyse technology transitions influencing the low-carbon pathways and details on renewable technology influences toward emission reduction (Gambhir, Anandarajah et al. 2013, Anandarajah and Gambhir 2014). TIAM- UCL is also used to examine the impact of emissions trading in the power sector (Gambhir, Napp et al. 2014). India’s economy is growing at fast pace and is highly dependent on fossil fuels to meet its energy needs. Researchers use these energy systems models to help with the planning of future energy in the country to meet the growing electricity demand and emissions reduction targets. These studies mainly analyse the alternatives through a parametric uncertainty approach. Scenarios and sensitivity analysis were developed to look at alternative technology patterns.

Brazil’s power sector relies mainly on hydropower and renewable energy. A number of studies were conducted to look at resource diversity in the power sector. MAED-MESSAGE3 is the most commonly used tool to analyse the power sector compared to other models. Researchers integrated MAED-MESSAGE models to look at supply-demand technologies and to calculate the least-cost adaptation options for a given set of climate impacts in the power sector (de Lucena, Schaeffer et al. 2010, Dale, Lucena et al. 2013). Another analysis looked at new renewable technologies in the system and the co-benefits of introducing these technologies using the same model (Pereira Jr, Cunha da Costa et al. 2013). A similar study was conducted to analyse the

3 The MAED-MESSAGE model investigates only the industrial, agricultural and service electricity demand linked to supply model focusing on the Brazilian power sector. This doctoral research extends to capture the full energy system of Malaysia including the electricity trade option between regions, analysing in detail the end-use technologies of various sectors and the integration of end- use technologies with power sectors under the landscape of demand–supply uncertainties at a multiregional level using MAED- OSeMOSYS.

51 costs of abatement of CO2 emissions in Brazil using the MARKAL model (La Rovere, Legey et al. 1994). Coelho and Frey (2001) used the MARKAL model to develop a technology database for the energy mix in Brazil. This study looked at the technology transitions in future. The authours conducted uncertainty analysis through scenario development based on two assumptions, the economic growth and share of thermal power plants. Changing these parameters in scenarios produced different sets of results in terms of fossil fuel use in power plants and emissions produced. These results gave the researchers a broader perspective of the energy mix in Brazil to meet carbon reduction targets and insights on the future power plant development.

Some other developing countries such as South Africa used energy systems models to analyse the energy situation in the country. The MESSAGE model was used to develop renewable energy scenarios and look at the potential renewable energy penetration in the electricity sector. This study intended to assist the policy makers in assessing the future role of renewable energy generation (Miketa and Merven 2013). A similar study for South Africa was conducted using the MARKAL model. Winkler et. al (2007) focused on modelling the future electricity supply options based on two key drivers of future energy trends, the economic and population growth. Researchers conducted a few other analyses using the LEAP model to look at China, India, Brazil, and South Africa. The focus of these analyses is in the power sector, looking at the structure of different technologies and policy influences toward implementing alternative technologies in these countries (Erickson, Heaps et al. 2009). Another report compiled the experiences of ten developing countries4 in modelling to analyse alternative technologies in electricity generation to reduce greenhouse gases. Most of these countries depend on bottom-up models such as LEAP, MARKAL/TIMES, MESSAGE/MEAD or purpose-developed models (DEA, OECD et al. 2013).

Similar energy issues, such as the increasing use of fossil fuels, resource imports, efficiency gaps, economic diversification, lack of institutional infrastructures and political conflicts are faced by the ASEAN5 countries, as highlighted by Karki, Mann et al. (2005). A number of studies were developed using modelling tools in these countries to look at these energy problems, including issues in the electricity sector.

4 The research on greenhouse gas mitigation focused on ten developing countries: Brazil, China, Ethiopia, India, Indonesia, Kenya, Mexico, South Africa, Thailand and Vietnam 5 The Association of Southeast Asian Nations (ASEAN) has 10 member states: Malaysia, Thailand, Vietnam, Indonesia, Philippines, Lao PDR, Cambodia, Singapore, Myanmar and Brunei

a. A study conducted by the National University of Singapore used the SG-TIMES_ELC model to develop electricity demand projections for the country. An analysis on the end- use electricity demand and total installed generation capacity until 2050 was conducted through scenario development. Singapore is an island-state with no indigenous fossil fuel reserves. The country has limited alternative energy potential and relies on imported fuels for its power generation needs. The study is in the preliminary stage. The researchers are currently developing the model to represent the entire Singapore energy economy (NUS 2014).

b. Thailand conducted a few energy studies using models such as ExSS (Thammasat University, 2010), AIM (Shrestha, Malla et al., 2007), MARKAL (Watcharejyothin and Shrestha, 2009), WASP (Santisirisomboon, Limmeechokchai et al. 2001, Santisirisomboon, Limmeechokchai et al. 2003, Nakawiro, Bhattacharyya et al. 2008). These studies analysed issues on fossil fuel use, fuel imports and renewable energy implementation in the power sector and contribution toward carbon emissions. Other studies analysed some of these energy issues using the LEAP model. The results suggested that the country needs improvement in existing technologies and alternative fuels exploration. Similarly, the LEAP model is also used to explore future scenarios without the nuclear and coal power plants (Mulugetta, Mantajit et al. 2007, Chayawatto, Fungtammasan et al. 2011, Wangjiraniran, Nidhiritdhikrai et al. 2013, Wongsapai, Ritkrerkkrai et al. 2016).

c. Vietnam has conducted a few studies using energy models such as MARKAL (Nguyen 2007, Nguyen 2008) and LEAP (Kumar, Bhattacharya et al. 2003). The MARKAL model

was used to analyse the reduction of CO2 emission in the power sector by developing different scenarios. The findings showed the changes in the power generation, especially the reduction in fossil fuel power plants and preferences toward renewable power plants. The LEAP study focused on the utilisation of different biomass technologies in various sectors. The analyses focused on developing scenarios and evaluating the implication of these biomass technologies toward greenhouse gas reduction.

d. Das and Ahlgren (2010) examined the potential role of clean and advanced energy technologies for power generation using a MARKAL model for Indonesia, Philippines and Vietnam. Although the energy situation in these countries may vary, the common

problem faced by these countries are fossil fuels utilisation and high CO2 emissions.

e. Pagnarith and Limmeechokchai (2014) analysed the energy situations in Thailand, Vietnam, Lao PDR and Cambodia using the LEAP model. The model was utilised to forecast electricity demand and supply in the business-as-usual (BAU) scenario for these countries. Other scenarios developed in this study include the transmission & distribution (T&D) loss scenario, the demand side scenario, the mitigation scenario and the RE scenario. The study suggested that the improvement of T&D to prevent losses, efficiency improvement on electric appliances and renewable energy use could reduce the dependency on imported fossil fuels and improve electricity supply.

f. Another study focused on supply technological details and the impact on the power supply if emissions restrictions were applied to Indonesia, Malaysia and Singapore. An optimisation model based on the URBS methodology was used to develop the system for these countries. Although details such as policies in these respective countries were not considered, this study gives insight into the technology generation mix by 2035 and future possible electricity trades between these countries (Stich, Mannhart et al. 2014).

g. Stich, Mannhart et al.’s (2014) approach further expanded and applied the URBS model to ASEAN countries to analyse the technology generation mix and renewable energy potentials located in these countries. Although the details of each country are not captured and an hourly load curve of one specific country is applied to all other countries, this study provides general insight into the future of electricity trade between ASEAN countries. However, there are a number of challenges in implementing the electricity trade modelled in the study, especially to get these countries to collaborate, as each country has its own political agenda and current energy issues to deal with (Huber, Roger et al. 2015).

h. The APEC (2013) study also conducted analyses for the power sector in ASEAN countries using the APERC’s Energy Demand and Supply Model. The study included some of the key policies especially for Malaysia and analysed the supply-demand generation until 2035, which will be useful as a reference. A similar study was conducted by IEE (2011) to analyse the future energy needs in ASEAN using LEAP model, considering the socioeconomic factors. Many developing countries are facing issues such as the shifting of the economy from agricultural-to industrial-focused, the disaggregated distribution of electricity within urban and

54 rural areas, and the overall centralisation of the power sector. Studies covering major changes in the power sector of developed countries is a good starting point for analysing the implications of similar changes in developing countries (Pandey 2002; Urban, Benders et al. 2007). Jebaraj and Iniyan (2006) highlighted the use of optimisation models in developing countries especially China and India to determine the best course of technology options at the least cost, which will be useful in energy planning. Simpler models, for example LEAP, ExSS and AIM, are mainly used in ASEAN countries to conduct analyses on issues in power sector. Table 2.3 summarises the literature and scope of the selected energy models applications in developed and developing countries.

Table 2.3: Application of energy systems model in developed and developing country

Developed Countries Developing Countries

Models MARKAL, TIMES, MESSAGE, WEM, TIAM, MARKAL, TIMES, MESSAGE, TIAM, LEAP, NEMS, POLES, PRIMES ExSS , AIM

Studies Hadley and Short (2001) La Rovere, Legey et al. (1994) Das, Rossetti di Valdalbero et al. (2007) Coelho and Frey (2001) Kydes (2007) Ghosh, Shukla et al. (2002) Goldstein and Tosato (2008) Larson, Zongxin et al. (2003) IEA (2008a) Cai, Wang et al. (2007) Syri, Lehtilä et al. (2008) Chen, Wu et al. (2007) Vaillancourt, Labriet et al. (2008) Shrestha, Malla et al. (2007) Geisbrecht and Dipietro (2009) UN-ENERGY (2007), Winkler H, et al. (2007) Strachan, Pye et al. (2009) Jiang, Wenying et al. (2008) IEA (2010) Peter Erickson, Charles Heaps, and Michael Lazarus Labriet, Kanudia et al. (2012) (2009), Saradhi, Pandit et al. (2009) Jägemann, Fürsch et al. (2013) Watcharejyothin and Shrestha, (2009) Sullivan, Krey et al. (2013) Das and Ahlgren (2010) Williams et al. (2014) de Lucena, Schaeffer et al. (2010) Mallahand Bansal (2010) Thammasat University (2010), IEA (2011) Jia, Wenying et al. (2011), Mallah (2011) Rout (2011), Rout, Voβ et al. (2011) Gambhir, Napp et al. (2012) Dale, Lucena et al. (2013) Miketa and Merven (2013) Pereira Jr, Cunha da Costa et al. (2013) Anandarajah and Gambhir (2014) Gambhir, Napp et al. (2014)

Summary GHG emission reduction Technologies transitions of Low carbon technology GHG emission reduction Research Deep decarbonisation Pathways CO2 abatement technology Scope CO2 abatement technology Renewable technology transitions 100% renewables

2.2.3 Energy Systems models for Malaysia

Pandey (2002) and Urban et al. (2007) discussed some of the energy systems characteristics for developing countries that need to be addressed in energy models for an adequate developing countries representation. These papers elaborate on how to improve energy models for developing countries and give suggestions on modelling techniques and data requirements. Malaysia has similar characteristics to many other developing countries, like having a rapidly growing demand for electricity, low efficiency of power plants, increasing attempts of technology transfer and a heavy dependence on imports. The country is going through a transition from a primarily rural to a primarily urban economy based on industry and services, which leads to increasing demand of the electricity sector. The country is also struggling to mitigate energy-electricity issues, especially the use of fossil fuels, dependence on fuel imports, the implementation of alternative energy resources and fuel price issues. There have been many attempts taken recently by the government and universities to use energy modelling tools and increase the expertise in the modelling field for long term planning in the country. The use of suitable models to address these characteristics is important. The results of the model are crucial in providing insight to energy planners and decision makers in preparing an energy system framework and guidelines for energy policy making.

Since the 1990s, Malaysia has used energy systems models to address different energy dimensions and problems. For example, models have been used to analyse energy demand, energy supply mix, greenhouse gas limits, CO2 abatement strategies and low carbon strategies. A national team from Petronas, the Ministry of Energy, Telecommunications and Post, the Ministry of Transport, Nuclear Energy Units, local universities and the Tenaga National Berhad formed in the early 1990s to study energy plans using the Energy and Power Evaluation Program (ENPEP) model. This model allows several other sub-modules used to conduct analyses, such as a model for analysis of energy demand (MAED) and Wien Automatic System Planning Package (WASP) tools. Several energy scenarios developed until the period of 2010. Later, under the Australia Economic Cooperation program, the Malaysia Energy Centre (PTM) adopted the MARKAL tool to study long-term energy planning. Petronas, TNB, the Ministry of Energy, Telecommunications and Post and local universities participated in the program. The study was not complete due to the lack of data collection system and local experts in the modelling field. Many efforts have been taken to improve the use of energy models for policy planning in the country since then. The following discussion provides the strengths and limitations of utilising energy systems models in current energy studies in Malaysia:

a. Hosseini et al. (2013) discussed sources of GHGs in Malaysia. The study reviewed

preventive measures such as CO2 capturing methods and carbon sequestration, alternatives fuels as well as increasing the efficiency of power plants. The paper reviewed a study on the utilisation of a comprehensive modelling tool, LEAP, to analyse energy demand for electricity generation for the industrial, transport and residential sectors. The main drawback of the study is that no modelling results were developed using energy models to support the suggestions. The paper only reviewed possible suggestions that the Government should take to mitigate the issues on GHG emission sources.

b. Safaai et al. (2011) used the LEAP model to forecast CO2 emissions from the year 2000 to 2020. The analysis was conducted for the residential, transportation, industrial and electricity generation sectors. A business-as-usual (BAU) scenario was developed. This paper highlighted some limitations in the modelling, such as the lack of national data.

The authors concluded that energy demand and CO2 emissions would grow at different

rates. By 2020, would electricity generation would emit the most CO2 emissions as coal will be mainly used as fuel for combustion. The study suggested alternative energy exploration to substitute fossil fuels in future.

The main results were the energy demand projections and CO2 emissions from residential, transportation, industrial and power sectors. Due to economic development and a high demand for a better standard of living, energy demand is expected to grow at different growth rates. Yet, the paper did not discuss the economic growth and energy demand for Peninsular Malaysia, Sabah and Sarawak as separate geographical entities, as the economic activities and electricity demands for these three regions differ significantly. Energy policies such as the Five-Fuel Diversification Strategy was mentioned in the paper and the study estimates that coal will be the dominant fuel used in the power sector. However, no modelling analysis was conducted to prove that with the implementation of the energy policies, coal would dominate the fuel mix and produce the most carbon emission in the power sector.

c. Koh et al. (2011) analysed the availability of renewable energy and GHG emission reduction technologies in Sabah. Sabah is one of the 13 states in Malaysia. The objective

of the study was to quantify the economic and environmental benefits with the implementation of emission reduction technologies for the developing countries.

The LEAP model used to analyse emissions reduction technologies. The scope of the study was to look at renewable energy, supply-side energy efficiency (EE), demand-side EE and transmission & distribution (T&D). Nine scenarios, including solar photovoltaic (PV) cells, hydropower, biomass from palm oil waste, supply-side energy efficiency (such as advanced combustion technology, and carbon capture technology), demand-side energy efficiency (such as efficient building, energy saving bulbs and energy imports from Bakun) were developed for the years 2010–2030. For example, conventional power plants with lower efficiency operating before 2016 and 2020 were upgraded to a thermal efficiency of 60%. These technologies were introduced in the EE power plant scenario. In carbon capture scenario, the integrated gasification combined cycle (IGCC) technology with a thermal efficiency of 33.9% replaced a new coal power plant.

Some key findings of the study include: (i) Electricity cost was reduced with the implementation of the Malaysian Industrial Energy Efficiency Improvement Project (MIEEIP). However, the adoption of PV or the implementation of advanced technologies increased electricity cost. (ii) Carbon capture technology was the most efficient in the GHG emissions reduction strategy with the implementation of IGCC-CCS technology in coal power plants, although the electricity cost was the highest. (iii) Gas and electricity is currently subsidised by the government. The gas subsidy in power sector is fixed at RM 13.70/mmBtu. Removing the subsidy on natural gas would influence the electricity cost. The author concluded that implementation of non-renewable energy or advanced technologies would be more effective for developing countries.

There are some limitations to this study. The study excluded coal subsidies and fossil fuel imports, which could influence the electricity cost. The study also excluded some national policies such as the National Renewable Energy Policy and Action Plan (2009) for the development of the three renewable energy scenarios. Although the approach in introducing carbon capture technology in coal power plants is efficient in reducing emissions, the study excluded no coal policy in Sabah for near-term.

d. Fairuz et al. (2013) used the MESSAGE model to analyse the cost of energy expansion and carbon emissions for Peninsular Malaysia from 2009 until 2030. Eleven

comprehensive scenarios were developed looking into various stand-alone technology options such as nuclear, hydropower import and renewable energy. The objective of this study was to analyse the long-term strategy for electricity generation in Peninsular Malaysia. The results indicated that the two best scenarios are with a balanced fuel mix of natural gas, coal, nuclear, hydropower, renewable, import hydropower. The combination of these fuels produced the lowest carbon emissions compared but with a higher expansion cost.

However, the study has some shortcomings. Firstly, the objective of the study is to analyse the long-term energy strategy for Peninsular Malaysia, but no national policies such as the Five-Fuel Diversification Strategy or Economic Transformation Programme (ETP) have been used as a benchmark to decide on the best optimal solution. Secondly, imported hydro from was Sarawak considered, yet the cost of building the submarine transmission line to import electricity from Sarawak on Borneo Island to Peninsular Malaysia was excluded. The best case suggested was a 10.82% import from Sarawak. This result may vary if the transmission line cost is included. The results for nuclear power will also change if the fuel cost included. Potential hydro from Peninsular Malaysia was not included in this case study. Thirdly, as this paper focused on carbon emissions and most of the cases show high emissions due to coal power plants, no clean coal technology was considered, as Tenaga National Berhad (TNB) is planning toward building new clean coal power plants. Janamanjung coal power plant is an example of a new clean coal power plant. Finally, no subsidised gas supply limit was discussed. Currently, Petronas is supplying limited gas resources to TNB for gas power plants. Gas imports from new regasification plant are excluded as well. Fifthly, renewable energy potential targets were used from the National Renewable Energy Policy and Action Plan. However, the total potential value for Malaysia was calculated for Peninsular Malaysia, which provides misleading results for renewable energy scenarios. The National Renewable Energy Policy and Action Plan aggregated the potential renewable resources by three regions.

e. The US National Renewable Energy Laboratory conducted a study on Clean Energy Options for Sabah. This study analysed information on the potential of renewable energy in Sabah to provide a cost-effective alternative to the proposed coal plants. McNish et al. (2010) used the HOMER (Hybrid Optimization of Multiple Energy Resources) modelling software to simulate how each scenario meets Sabah’s load on an

hourly and monthly basis. The objective of the study was to show how relatively low- cost portfolios can meet demand on an hour-by-hour and month-by-month basis. Three scenarios were developed for the period 2010–2020: the BAU scenario was based on current power plant planning; the SREP (Small Renewable Energy Power programme) scenario was based on an accelerated expansion of small biomass and hydro production; a utility-scale renewable scenario was based on solar and geothermal investment. The production of each grid-attached power plant was simulated over the course of an 8760- hour year based on a low-cost dispatch model.

The results analysed if each proposed capacity addition to the portfolio could meet Sabah’s demand profile and simulate electricity production and emissions by fuel and technology type. The results suggested a number of measures for renewable energy. The main limitation of this study is that the renewable energy policies were not considered in the modelling and discussion. These policies could provide insight into the possible implementation of renewable energy in Sabah.

f. The Ministry of Natural Resources and Environment Malaysia conducted a study for Malaysia’s Second National Communication to the UNFCCC (United Nations Framework Convention on Climate Change) NRE (2011). The LEAP model was used to generate

energy demand and CO2 emissions projections. The model structure was developed based on the Energy Balance format, which included power generation, residential and commercial, industry, transport, non-energy as well as losses of oil and gas. These

sectors were identified as the key contributors to CO2 emissions. A business-as-usual (BAU) baseline projection from 2000 until 2020 was developed to look at final energy

demand by sector and fuel. Three other scenarios were developed to analyse CO2 emissions: Energy Efficiency and Conservation (EEC) for the industrial sector, Renewable Energy (RE) and the combination of EEC and RE.

The results on electricity growth rate, commercial energy supply by fuel type and CO2 emissions were generated for the power sector. However, issues such as fuel price, imports, gas supply limitation, power plant efficiencies and clean coal technologies were not considered in the modelling and reflected in the supply projections. Energy policies were highlighted in the study but were not captured in the LEAP modelling. A combination of all these issues in one scenario could provide a better analysis of the energy situation in the country. Furthermore, there was no uncertainty analysis

conducted to look at possible pathways in future. The study considered only one scenario (BAU) for the projection of energy supply by fuel type.

g. Ho et al. (2013) conducted a study on Iskandar City using ExSS model. The model

estimated energy demand and CO2 emissions for power generation, industry, residential, commercial, passenger transportation and freight sectors. The objective of the study was to examine the concept of low-carbon cities and explore scenarios toward sustainable cities. Two scenarios developed, a business-as-usual (BAU) scenario and a mitigation countermeasures (CM) scenario for the period of 2005–2025. The BAU scenario considered various high population and economic targets adopted in next 20 years. The CM scenario looked at technologies and potential low carbon measures available to reduce greenhouse gas emissions by 2025. Major results suggested energy efficiency improvements in the industrial sector; fuel shifting in the transport sector and efficiency improvement in power generation. The study focused on mitigation strategies at the city level, with a representation of two scenarios mainly looking at the demand side sectors. For power generation, only the efficiency improvement of power plants was considered. Yet, no scenario was developed to look at the supply side and generation mix at the city level.

h. Simson et al. (2013) conducted an additional study on Iskandar City. This study also used

the ExSS tool to estimate energy demand and CO2 emissions for industry, residential, commercial, passenger and freight transport and power sectors. The model estimated

future CO2 emissions levels in Iskandar City. Compared to the previous study, this study

forecasted CO2 emissions per capita and CO2 emission intensity. The study was limited to demand-side countermeasures analysis.

i. Beginning with the low carbon study on Iskandar City, another low carbon analysis was conducted at the national level by combining two tools, ExSS for socioeconomic indicators and the AFOLUB (agriculture, forestry and other land use bottom-up model) tool for calculating the allowable abatement costs of the energy and waste sectors. The study investigated future socio-economic scenarios and GHG mitigation potentials using the integrated modelling methodology and back-casting approach until 2030. The study suggested that in order to achieve national emissions reduction targets, the deployment of energy efficiency and renewable technologies is needed for the long term as well as tackling issues in the LULUCF sector (Yuzuru and Siong 2013).

The above is a summary of the literature focusing on the energy systems model to analyse the energy situations in Malaysia. The scope of these studies is on emissions reduction, city planning or demand side management. These studies are broad and focus on one region. Mainly, a bottom-up simulation model, LEAP was used to simulate outlook for the industrial, transport, residential and electricity sectors. The main purpose of using this model is to look at energy, economic and environmental aspects at national levels. The model was used to generate simple results to reflect the economic aspects such as demand growth based on GDP. The assumptions and constraints defined in the model are not comprehensive due to the lack of data. Comprehensive details on current and advanced technology are not included in the model. Most of these studies representing the national level only generate one to three scenarios to predict the energy situation in the country. There is no uncertainty analysis conducted to look at possible future scenarios. These papers mentioned key policies in the country such as the Five- Fuel Diversification Policy or the National Renewable Energy Policy and Action Plan, but these policies are not reflected in the modelling.

Khor and Lalchand (2014; pg. 953) highlighted all the energy related policies in Malaysia that are currently implemented. Modelling the energy system of the country capturing these policies in the model may produce a significant electricity outlook. For example, reflecting the renewable energy policy in the model may give a different perspective on technology profile mix. Furthermore, a suitable choice of model is important in addressing the research scope. Some of these studies intended to look at the socioeconomic as well as technological aspects. Therefore, one specific model may not be suitable. A combination of optimisation and simulation models might be suitable to address these issues. However, no multi-modelling approach studies were conducted in Malaysia yet. The drawback of these studies is their lack of technical details or assumptions, their lack of complete data in the models, their lack of comprehensive scenario development, and their lack of policy reflection in the modelling and suitable model selection. van Beeck (1999) provided an overview of nine ways of classifying energy models and elaborated the purpose of a model developed to answer specific questions. This method was adopted to classify the models used in Malaysia and application of these models to address specific characteristics and issues in energy systems. From the classification as seen in Table 2.4, only three papers specifically addressing the power sector used the bottom-up and hybrid model. Only two of these papers actually used the optimisation methodology to analyse the results. LEAP studies included the electricity sector in the modelling, yet the results produced did not focus on the electricity generation mix, technology details or issues in the power sector.

Although Fairuz et al (2013) and Koh et al. (2011) focused on the power sector, but these studies looked only at one specific region. Fairuz et al (2013) is the only study using a bottom-up optimisation model to analyse the technology and cost details in Peninsular Malaysia. Therefore, there has been no research conducted on detailed energy systems focusing on both the demand and supply sides at a multiregional level, analysing in detail the end-use technologies of various sectors and the integration of end-use technologies with power sectors. There were also no studies at the national level looking at a range of uncertainties of decarbonisation strategies taking into account technological details, fuel prices, resources and policy constraints using an energy systems model. Table 2.4: Summary of the model classification approach adapted from van Beeck (1999)

Papers Energy Analytical Methodology Geographical Sectoral Time Model Model/tool Approach coverage Coverage Horizon Structure Hosseini et al. LEAP Simulation/ Bottom-up National Electricity, Long Energy- (2013) Accounting industrial, term economy- transport, environment residential Safaai et al. LEAP Simulation/ Bottom-up National Electricity, Long Energy- (2011) Accounting industrial, term economy- transport, environment residential Koh et al. LEAP Simulation/ Bottom-up Local Electricity Long Energy (2011) Accounting (State: Sabah) term renewable- economy- environment Fairuz et al. MESSAGE Optimisation Bottom-up Local Electricity Long Energy- (2013) (State: term environment Peninsular Malaysia) McNish et al. HOMER Optimisation Hybrid Local Electricity Long Energy (2010) (State: Sabah) term renewable NRE (2011) LEAP Simulation/ Bottom-up National Electricity, Long Energy- Accounting residential, term economy- commercial, environment industrial, transport, non-energy Ho et al. AIM/ExSS Simulation/ Bottom-up Local (City) Electricity, Long Energy- (2013) Accounting residential, term economy- commercial, environment industrial, transport, Yuzuru and ExSS & Simulation/ Bottom-up National Energy Long Socio- Siong (2013) AFOLUB Accounting LULUCF term economic & environment APEC LEAP Simulation Bottom-up National Electricity, 2010- Energy- (2013; pg. 97) IEEJ (Japan) Macro- Top-down industrial, 2035 Environment economic transport, residential, commercial WEO (2013) WEM Simulation Bottom-up National Electricity 2011- Energy- Industry 2035 environment Transport Building

2.3 The Rationale for using Hybrid models (Simulation-Optimisation) for Malaysia

Like many other developing countries experiencing issues related to energy and dynamic economic changes (as discussed in Section 2.2.2), Malaysia is also facing similar problems in relation to energy use in the country (as discussed in Section 1.1). Capturing these issues in a complex energy model is a continuing challenge for developing countries and Malaysia is also part of these cluster countries aiming to tackle the hurdles in using comprehensive energy models to represent the specific characteristics of energy systems in the country. Table 2.4 summaries the national studies related to energy systems modelling and most of these studies applied simulation or accounting models to address the energy-economy-environment issues. Although simulation models are adequate to represent simple energy studies and development trends to be projected through technology development scenarios, these models lack representation of the possible evolution of energy systems as well as detailed technological details to find the lowest cost for an energy system. In developing countries where financial constraint in adopting future technologies is an important factor, the application of simulation models is not adequate to give alternative options on future energy system representation to policy makers. Furthermore, uncertainty studies on future socio-economic scenarios is also lacking in these studies.

As discussed in the literature on various available energy models in Section 2.1 and the literature on the application of these models in developed and developing countries in Section 2.2, two main issues need to be given attention in order to develop a modelling study for developing countries. Firstly, demand pathways in a developing country need to be analysed in detail, taking into account the changes in socio-economic activities, mainly GDP and population growth. Based on the literature discussed in Section 2.2.2, GDP and population growth are the main drivers of future energy demand in a country. The possible changes of these two parameters need to be captured in order to analyse future demand uncertainties. Secondly, supply pathways representing detailed infrastructures, resources, prices, technology efficiencies need to be investigated. The supply uncertainties can significantly alter long-term technology mix, fuel mix and consumption patterns. Most of the investment decisions are subject to changes in economic structure and policies implemented with respect to technology implementation, fuel prices, and trade norms that can significantly influence consumption patterns in various end-use sectors over the long run.

Therefore, it is important to model the demand and supply sides for a developing country over the long term in order to capture the future supply-demand uncertainties. Based on the literature, not many developing countries focus on both supply and demand uncertainties. The studies conducted either focus on one or the other. Only China, India and Brazil conducted studies integrating simulation and optimisation models to analyse demand and supply balances, yet these studies mainly focus on the power sector. For an economically advancing country like Malaysia, it is very crucial to conduct an analysis looking at both demand and supply uncertainties. Urban et al. (2007) also highlighted that a bottom-up optimisation model is a useful tool for developing countries because technologies in an optimisation model are explicitly modelled to give a representation of energy systems at least cost. Pye and Bataille (2016) study argued that deep decarbonisation pathway analysis requires a fundamental change in technology from the present situation and a bottom-up modelling is about the representation of the energy-using technology stock. Therefore, it is rational to use a hybrid model (bottom-up simulation-optimisation) to investigate in detail the future demand and supply pathways focusing on the complete energy system of the country, especially the decarbonisation strategies. The focus on the comprehensive energy system is very important to address the energy use changes in the country especially to achieve the commitment to reduce carbon emission using the hybrid model. The application of the hybrid model to analyse the optimal decarbonisation strategies will provide crucial insights to policy makers for energy and policy planning.

Therefore, two energy systems models, the Model for Analysis of Energy Demand (MAED) and the Open Source Energy Modelling System (OSeMOSYS) are selected to achieve the objective of the research as described in Chapter 1.

Bottom-up Simulation Model: MAED

The MAED model evaluates future energy demand, disaggregating it into a large number of end- use categories. The social, economic and technological influences are the driving factors for estimating demand scenarios. The model is a simplified version of MEDEE-2, which was first adapted by B. Lapillonne to conduct studies on global energy assessment at International Institute for Applied Systems Analysis (IIASA, Laxenburg, Austria) (Lapillonne 1978). While sustaining the general structure of MEDEE-2, important modifications were introduced by the International Atomic Energy Agency (IAEA) in the MAED model in order to provide a systematic

65 framework for analysing the medium to long-term energy demand scenarios of socioeconomic and demographic developments (IAEA, 2006).

The MAED model used by countries such as Nepal, Tanzania, Nigeria and Syria to forecast the long-term energy demand of various sectors and scenarios was developed to examine plausible demand needs in various countries. In Syria, a comprehensive analysis of the possible future long-term development of energy and electricity demand was conducted. Starting from the base year, final energy consumption distributed by energy forms and consumption sectors, the future energy and electricity demand was projected according to three different scenarios reflecting the possible future demographic, socio-economic and technological development of the country (Hainoun, Seif-Eldin et al. 2006). Another study in Nigeria was conducted focusing on the construction sector, which includes activities involving the building of residential, commercial (warehouses, schools, retail stores), industrial and administrative buildings. The MAED model was used to estimate energy demand in construction sector based on the gross domestic product (GDP) including the factors such as economic growth rates, sectoral value added, total population and population growth rates, the energy intensity of energy sources, urbanisation rates, etc. (Ahmed, Isa et al. 2014). To forecast the energy demand of Tanzania, the MAED model was used to generate simulations of future demand based on social, economic and technological development. Future energy demands are disaggregated into end-use categories e.g., lighting or air-conditioning as a function of several determining factors including population growth, transportation modes, national priorities for the development of certain industries or economic sectors, and energy forms, among others (Kichonge, John et al. 2014). For the case of Bosnia and Herzegovina, the MAED model methodology applied to forecast energy demand and electricity for various sectors (S. Avdaković 2015). Nepal was the only analysis conducted based on the hybrid concept, integrating MAED–MARKAL models for assessing different pathways for the development of energy systems in the country. The integrated modelling framework provides a tool for analysing several energy pathways for energy development in Nepal (Nakarmi, Mishra et al. 2016).

Much of these studies highlighted that although MAED model requires intensive data, the model is suitable for energy demand simulations in developing countries. Therefore, the MAED model is selected for the case of Malaysia to generate future energy demand pathways based on main drivers such as GDP and population growth rate.

Bottom-up Optimisation Model: OSeMOSYS

OSeMOSYS is a full-fledged systems optimisation model for long-run energy planning, which is designed to fill a gap in the analytical toolbox available to the energy research community and energy planners in developing countries (Howells, Rogner et al. 2011). The model requires less significant curve and time commitment to develop a study on energy systems in a country. The model is built on non-commercial programme language and solvers, therefore no upfront financial investment is required. Hence, this is an advantage to extend the availability of this energy modelling tool to students, business analysts and energy researchers in developing countries. Furthermore, OSeMOSYS is designed to be easily updated and modified to suit the needs of a particular analysis. The model is built in a series of component ‘blocks’ functionality and a collection of these functional blocks combines to form a customised model, which is another advantage of this model.

A number of studies conducted using the OSeMOSYS model focuses on expanding the codes of this open source energy model. Given its open source nature, OSeMOSYS provides the flexibility of recoding the model to represent specific smart grid options. The study focuses on amending the codes to allow for the modelling of renewable electricity generation that prioritises demand types, demand shifting and storage options in order to access the potential contribution of Smart Grid options (Welsch, Howells et al. 2012). Another study looked at a local multi-regional energy system model (MELiSsa) of a specific area, the Lombardy region. The MELiSsa model is based on the basic logical structure of OSeMOSYS, developed to analyse the residential sector (space heating in detached households and apartment blocks). Some changes and addition to the OSeMOSYS codes on technology capacity and aggregated emission limits were included in order to adapt its structure to this case study (Fattori, Albini et al. 2016). The flexibility of the OSeMOSYS codes further allows the hard-linking of OSeMOSYS with a top-down model (share of choice model describing end-user’s preferences regarding the energy consumption) to provide a coherence to the technology distribution by integrating behavioural patterns of energy end-users (Fragnière, Kanala et al. 2017). In order to analyse the risk of excess emissions, Niet et al. (2017) coded the financial portfolio analysis methodology in the OSeMOSYS model. The method is applied to a case study of electricity system in Alberta, Canada to investigate strategies in reducing the excess emissions.

OSeMOSYS codes were further enhanced by introducing a multi-objective function considering both costs and emissions. The study analysed the impact of domestic and international fuel

67 prices on the power sector market of the Kingdom of Saudi Arabia under cost minimisation and environmental impact minimisation objectives (Groissböck and Pickl 2016). A similar study was conducted introducing the concept of Pareto Optimality involving two objective functions, the cost and CO2 emission minimisation (Lavigne 2017a). This study also introduced passenger and freight transportation demands and end-use technologies to the existing version of the UTOPIA OSeMOSYS model (which is available at the OSeMOSYS homepage). This study was further extended to introduce price elasticity within OSeMOSYS to allow the possibility to reduce endogenously end-use demands to satisfy given GHG emission objectives (Lavigne 2017b). Löffler et al. (2017) designed a new energy model the GENESYS-MOD based on OSeMOSYS to calculate global low carbon scenarios. An additional block called ‘Transportation’ was added to the GENESYS-MOD, which implements a model split for the distribution of passenger or freight kilometres of a particular type of transportation. The study investigated the possibility of a globally sustainable 100% renewable energy supply by 2050 and concluded that gradual shift toward renewables under increasing carbon constraints can be achieved with more time slices as well as day types added including more storage technologies introduced into the model.

Other studies focused on optimising the energy systems of developing countries such as Africa and South America. A model called OSeMOSYS SAMBA (The Open Source South American Model Base) was developed to capture the electricity supply systems of thirteen countries in South America individually and link them together via trade links. The study focused on generating scenarios of generation mix evolution and analysing the electricity trade potentials between these countries (Moura and Howells 2015). Based on the structure of OSeMOSYS, another model called TEMBA (The Electricity Model Base for Africa) was developed to examine the potentials for electricity investments and power trade between countries in Africa. The electricity supply systems of forty countries were individually modelled and linked via trade options to analyse the potential of electricity investment between these countries (Taliotis, Shivakumar et al. 2016). In Kenya, analyses were conducted focusing on electrification pathway targets by soft- linking a spatial model with OSeMOSYS to capture the optimal generation mix (Nandi, Alexandros et al. 2017).

Besides the modelling of case studies in developing countries, Romania, Canada and Cyprus also used the OSeMOSYS model to analyse their energy systems. The model was used to optimise the energy systems, specifically on household energy consumption in Romania. The study focuses on residential end-use technologies such as lighting, space heating/cooling and hot water (Smeureanu, Reveiu et al. 2015). For the case of Alberta, Canada, OSeMOSYS was used to

68 explore the impact of the carbon pricing of power systems. The study examines potential pathways to low-carbon power systems and uses carbon pricing to reveal cost effective transitions for reducing carbon emissions from Alberta’s power sector (Lyseng, Rowe et al. 2016). In Cyprus, the study investigated the viability of a range of electricity generating options, especially the development of renewable generation. Scenario approaches used to explore least-cost generating strategies as well as carrying out assessments regarding the flexibility, economic robustness and energy security impacts to meet an exogenously determined electricity demand (Taliotis, Rogner et al. 2017).

Therefore, the OSeMOSYS model is selected for two main reasons:

1. OSeMOSYS is based on an optimisation approach and a linear programming method to minimise a linear functional that can be solved under a number of constraints, satisfying the demand of energy services of a considered region. The model can be tailored to represent detailed technologies in various sectors taking into account domestic resources, fuel imports, fuel prices, power plant technologies, technology efficiencies, renewable potentials, network infrastructure and sectoral end-use technologies as well as to address the issues described in Chapter 1.

2. Some changes and additions to the OSeMOSYS code are necessary in order to adapt its structure to conduct structural uncertainties analysis. The OSeMOSYS code is relatively straightforward and transparent, which allows for simple refinements as well as the ability to conduct sophisticated new analyses. The OSeMOSYS codes are amended to include the Modelling to Generate Alternatives (MGA) technique, which adds value and new knowledge to the OSeMOSYS model framework (discussed in Section 2.5). The revised OSeMOSYS with new objective functions and codes is used to analyse structural uncertainties (discussed in Section 2.4) evolved beyond an optimal energy pathway for energy systems in Malaysia.

2.4 Uncertainties in the optimisation models

Uncertainties are high when the energy systems model gets larger and complex in the long-term trajectory, with various assumptions on future technologies, infrastructure development, prices of imported energy fuels and technologies, and the availability of natural resources like fossil

69 fuels both domestically and internationally. Energy policy modellers used different techniques to measure parametric uncertainties in energy models, which include running detailed scenarios, sensitivity analysis, or Monte Carlo simulations. The purpose of the scenario method is to create several possible future scenarios and explore the differences of each one. In order to make the scenario-based study useful, Kleinpeter (1995) suggested that the developed scenarios must consider not only the investigated phenomenon (e.g., energy planning) but also the environment as a whole. The study also must analyse dynamic aspects of the systems and take into the consideration economic factors such as consequences of oil price increases. For example, Strachan, Pye et al. (2008) implemented a set of scenarios based on international drivers e.g., technology costs, fossil fuel resources prices, and international aviation emissions to analyse how the UK energy system could respond to these international drivers under long- run deep carbon constraints. Other global studies such as Annual Energy Outlook (EIA 2009), World Energy Outlook (IEA 2016a), and the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC 2014) examine different pathways based on running various scenarios, e.g., fuel prices or fuel supply. Trutnevyte et al. (2016) suggested that the analysts could extract tailored subsets of scenarios using techniques and criteria e.g., policy relevant scenarios or maximally diverse scenarios to analyse uncertainties.

Sensitivity analysis is another approach widely used to analyse uncertainties. Saltelli et al. (2008) argue that a sensitivity study is a study of how uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input. The sensitivity analysis technique is widely used in many studies to measure uncertainties. Saltelli and Annoni (2010) discussed local sensitivity analysis that allows modellers to identify the key parameters that have the largest effect on key model outputs. Another study analysed the systematic approach of multiple uncertainties via probability distribution using the UK energy system model, ESME, and further combined the global sensitivity analysis to identify parametric uncertainties affecting the cost-effective pathway (Pye, Sabio et al. 2015). The global sensitivity analysis is also used to investigate uncertainties in low-carbon technologies that can influence long-term decarbonisation pathways for the UK energy system. The study concluded that technology-oriented analysis in an optimisation model UKTM could provide robust insights and deeper understanding of decarbonisation uncertainties to policy makers (Fais, Keppo et al. 2016).

Although identifying parametric uncertainty is a feasible way of measuring uncertainties in energy models, this research takes a different approach to look at near-optimal pathways in the

70 optimisation model in order to measure structural uncertainties. The following section elaborates on Modelling to Generate Alternative (MGA) techniques and studies on the application of MGA in the optimisation model to generate near-optimal pathways.

2.4.1 Modelling to Generate Alternative (MGA) techniques

Structural uncertainty refers to uncertainties of mathematical equations in the model that relate to the real-world situation the model is representing. One way of looking at this uncertainty in the optimisation model is the application of the MGA technique to generate near optimal pathways. An optimisation model is an important learning tool, as the model is capable of handling a large amount of input data and optimising the parameters to find the most effective least cost solution of energy technology mix. The model is also used to examine efficient pathways toward achieving certain goals e.g., emissions limits subject to least cost objective.

Börjeson et al. (2006) discussed one risk with optimising modelling: when an optimisation model is used in a normative way to find least cost solutions, the model eliminates solutions that are just a little more expensive but relevant to some other topics, such as the environment or energy security. Brill (1979) suggested that the role of the optimisation model should be reconsidered in order to acknowledge this limitation and the relevance to the planning process. The author highlighted that the model should be used to generate planning alternatives and to facilitate comparisons among the alternative pathways, e.g., simple cost comparisons can be facilitated. New alternatives can be generated by modifying parts of the solutions. New objectives can be identified and old objectives can be well understood. Hence, an optimisation model can be designed to generate alternative pathways, which provide broader insights to decision makers and analysts in examining different solutions within the range of an optimal solutions.

2.4.2 Historical application of the MGA technique

The Modelling to Generate Alternatives (MGA) technique was developed to generate alternative pathways by relaxing the cost targets to a certain percentage of the optimal costs. The MGA technique was applied in water and land use planning problems to generate different pathways in order to provide alternatives to planners and analysts for better decision-making (Chang, Brill et al. 1982a, Chang, Brill et al. 1982b). The idea of developing the MGA approach is to have good represent of real-world problems by producing a number of different alternatives. These different solutions allow decision makers to select the pathway that best resembles a solution

71 to the real-world problem. Brill et al. (1990) explained the MGA approach based on two important assumptions in the study related to joint cognitive systems. Firstly, a solution produced by a mathematical programming model will not always represent the characteristics of a real-world problem. A decision maker will need additional information to make decisions based on the solution provided, as not all the elements are reflected in the model. Secondly, a human’s ability to make better decisions is based on the number of different alternatives provided and the degree of difference among these alternatives.

The optimisation techniques can be amended to provide a subset of solutions with respect to modelled objective functions but different from each other with respect to values of decision variables. There are different approaches to the MGA techniques as described by Chang et al. (1982a), including the Hop, Skip, and Jump (HSJ) method and a random generation method. Chang et al. (1982b) highlighted that a random generation method is used to obtain solutions by maximising the sum of several randomly selected decision variables. The number of variables selected can be arbitrarily specified or randomly determined. The feasible options are limited by constraining the cost objective to meet a specified target. On the other hand, the Hop, Skip and Jump (HSJ) method is designed to generate alternatives that are good with respect to objectives included in the model and are significantly different from one another. The method limits the model to explore a prescribed inferior region near the original optimal solution (Brill, Chang et al. 1982). The following section discusses these two methods, efficient random generation and Hop, Skip, and Jump (HSJ) applied in the energy-economy optimisation model.

2.4.3 The Application of MGA technique in the energy-economy optimisation model

This technique is applied in the energy systems model to generate alternative pathways. The application of MGA technique provides researchers with the flexibility to explore for alternative pathways within the range of a cost-optimal solution. A slack value, which is a certain deviation value allowed from the cost-optimal value applied in the optimisation model.6

A number of studies investigated the application of MGA techniques in the energy-economy optimisation model to generate insights on future pathways and suggestions toward policy implementations. DeCarolis (2011) introduced and demonstrated the MGA technique as a useful method to generate alternative solutions. A simple least-cost linear optimisation model of the

6 The equations and steps of the MGA technique application explained in Section 3.3.2

US electricity sector was constructed to demonstrate the application of the HSJ MGA technique. The study highlighted that in order for the original model formulation to produce the MGA solution through linear combination, the previously inactive technology must be cost- competitive with other active technologies. A slack value of 25% is used to constrain the model. The study concluded that choosing a value for the slack parameter is subjective and could strongly influence the outcome of the alternative pathways. DeCarolis et al. (2016) also applied the HSJ MGA technique in the Tools for Energy Model Optimisation and Analysis (TEMOA), focusing on the electricity and transportation sector in the US. The analysis was conducted aiming on using the MGA technique to explore the different pathways to achieve a low carbon energy future. The study demonstrated how the MGA slack value and discount rate can produce a diverse set of energy futures. The cost differences obtained from the emissions reduction scenarios as compared to the base year were used to calibrate the slack values in the MGA runs. The MGA runs were conducted with slack values of 1%, 2%, 5% and 10% over the base case presenting the cost of energy supply.

Trutnevyte et al. (2012) applied the efficient random generation (ERG) technique to construct feasible heat supply alternatives for a Swiss region. The scope of this case study was extended to analyse the potential of renewable energy sources in heat supply mix. Trutnevyte (2013) introduced a methodology called EXPANSE (EXploration of PAtterns in Near-optimal energy ScEnarios) for evaluating the economic potential of renewable energy sources from an energy mix perspective. The efficient random generation technique is applied to generate multiple near-optimal energy mixes. The study suggested that at least a 30%–40% deviation in costs from the cost-optimal heat supply mix is needed to explore the maximum potential of renewable energy in the results. This efficient random generation technique was also adopted in developing a dynamic energy model, D-EXPANSE, which provided the cost-optimal and multiple near- optimal temporal distributions of investment (Trutnevyte and Strachan 2013). The focus of this study is to explore different patterns and technology mixes within the range of optimal solutions, which suggest alternative solutions to the decision makers. This model is used to analyse the cost-optimal and near-optimal transition pathways of the UK power system until 2050. The alternative pathways are analysed in terms of maximally different patterns of newly installed technologies and the temporal distribution of investment for a slack value of 20%.

As most of these studies explore a range of slack values and as choosing a slack value is very subjective, Trutnevyte (2014) modelled a historic transition between 1990 and 2010 using D- EXPANSE to obtain a deviation of 17% in terms of cumulative total system costs. The study

73 explores the implications of an actual deviation from the cost-optimal solution on the future modelling results for the UK power sector. A similar study was conducted using the D-EXPANSE model to generate the UK electricity system transition from 1990 to 2014 in order to obtain an actual deviation from the cost-optimal solution (Trutnevyte 2016). The Monte Carlo simulation runs were conducted to examine whether the deviation originates in parametric uncertainties with respect to energy demand, technology data and costs. Over a period of 25 years, the total cumulative costs of the UK real-world transition exceed the costs of the cost-optimal scenario by 13%–23% (3.5% discount rate) and 9%-17% (8% discount rate).

The deterministic D-EXPANSE model based on efficient random generation technique is used to generate near-optimal scenarios and identify the area covered by near-optimal scenarios in relation to cost-optimal scenarios. Li and Trutnevyte (2017) further combined this deterministic D-EXPANSE model with the energy systems model, UKTM to analyse cost-optimal and near- optimal UK electricity generation pathways. The study used the advance approach of combining Monte Carlo simulation with MGA technique to generate near-optimal pathways and analyse uncertainties in policy, technology and costs. A deviation of up to 15% in total system costs from the cost-optimal pathway is assumed based on the Trutnevyte study. Price and Keppo (2017) for the first time focused on the multi-sector, global view by adjusting the structural assumptions of cost optimality and exploring nearly cost optimal solutions in a global model, TIAM-UCL. The maximally different solutions are analysed in terms of primary energy portfolio with slack values of 1%, 5% and 10%.

Table 2.5: Summary on studies involving optimisation model and MGA technique

Papers Method Geographical Sector Coverage Time Slack Value coverage Horizon DeCarolis Optimisation National Power System 2010–2050 25% (2011) model + HSJ MGA Trutnevyte et ERG MGA Region Residential Heat n/a 30% al. (2012) Supply Trutnevyte EXPANSE + Region Residential Heat n/a 10%–40% (2013) ERG MGA Supply Trutnevyte and D-EXPANSE + National Power System 2010–2050 20% Strachan (2013) ERG MGA Trutnevyte D-EXPANSE + National Power System 1990–2010 deviation of 17% (2014) ERG MGA DeCarolis et al. TEMOA+ HSJ National Power System 2015–2050 1%, 2%, 5% & (2016) MGA Light duty 10% transportation Trutnevyte D-EXPANSE + National Power System 1990–2014 deviation of 9%- (2016) ERG MGA 23%

Li and UKTM & National Power System 2010–2050 up to 15% Trutnevyte D-EXPANSE + (2017) ERG MGA Price and Keppo TIAM-UCL+ Global Multi-sector 2010–2050 1%, 5% and 10% (2017) ERG MGA

Based on Table 2.5, there is no previous research conducted considering policy-driven structural uncertainties focusing on energy system of a developing country under the landscape of demand-supply uncertainties at a multiregional level. This PhD thesis aims to obtain policy- driven structural uncertainties covering multi end-use sectors in nearly cost optimal space focus on a developing country application as explained in Section 2.5. The outcome of the study intends to provide insights of plausible energy system configuration to achieve decarbonisation strategies and the policy directions to transform the energy systems.

2.5 Research Gap and Conclusion

There is an increasing interest among policy makers and energy modellers to focus on deep decarbonisation strategy analysis, as many countries are taking climate change issues seriously and have committed to reduce national emissions. While much work has been done in developed countries analysing the decarbonisation strategies of energy system taking into consideration socio-economic factors using bottom-up optimisation models such as in the UK (MARKAL-MARCO/ESME), the US (CA-TIMES), Portugal (TIMES-PT), Switzerland (STEM/CROSSSTEM), Ireland (Irish-TIMES), not many such analyses have been conducted in developing countries. Only three studies conducted to date focus on the decarbonisation analysis of the energy systems, capturing socio-economic activities in China and India using TIMES and TIAM-UCL models. Decarbonisation pathway analysis requires a fundamental change in technology from the present situation and a bottom-up optimisation modelling is important to represent socio-economic-technology activities. Urban et al. (2007) highlighted the limitations of using the optimisation models in developing countries. For many developing countries, complex analytical tools such as TIMES or MARKAL may not be accessible or available, as these models use registered software or commercial programming languages and solvers which require financial investment. Moreover, these models require high skill requirements to understand the details of the underlying code or the structure of the model and adapt the model to address specific issues. These models also require significant a learning curve and time commitment to develop and use.

As illustrated in Section 2.3 and Section 2.4, it is important to analyse uncertainties in optimisation models under long-term demand drivers. Optimisation models are used to optimise energy investment decisions by finding best solutions. However, in the attempt to optimise investments, especially for the case of developing countries, the inadequate investments may lead to a sub-optimal solution instead of a best solution. The MGA approach was used recently in the optimisation model to explore uncertainties and near-optimal pathways in the power sector, as summarised in Table 2.5. As most of the MGA studies focus on the power sector, it is also important to conduct near-optimality analysis covering multi-sectors at the national level7, taking into consideration the decarbonisation of energy systems. In the energy system optimising models, the structure of all sectors e.g., the household, commercial, industrial, transport and power sectors, are closely linked and provide an outlook on the fuel transition patterns emerging across end-use and power sectors in order to meet least-cost objective function. If the near-optimality analysis focuses only on the power sector, the model may overlook the trade-off of fuel transitions between multi-sectors when MGA technique with a new user-specified objective function is incorporated into the model, especially under the emission constraints.

Therefore, the current research proposes an open source hybrid model (MAED-OSeMOSYS) integrating the MGA technique. This novel hybrid model (MAED-OSeMOSYS) will fill an important gap in the literature by combining demand and supply models integrated with the MGA technique to analyse the optimal and near-optimal decarbonisation strategies. There has so far been no study conducted using this MGA hybrid approach to look at near-optimal decarbonisation strategies for multiregional power integration systems at a national level focusing on developing countries. The detailed demand model (MAED) generates energy service demands, taking into account socio-economic factors. The MAED generated energy service demands are soft-linked with the supply model (OSeMOSYS) developed to capture the deployment of technologies, resource characteristics and geographical differences at a multiregional level. The OSeMOSYS model is reformulated to include the Modelling to Generate Alternatives (MGA) technique in order to analyse a range of near-optimal decarbonisation pathways for future energy policy planning.

7 Most MGA studies conducted at national level focusing on power sector of developed countries (e.g. the UK and US power sectors). Price and Keppo (2017) study focused on the multi end-use sectors, investigated fuel transitions of end-use technologies and considered decarbonisation of energy systems at a global level. Hence, it is important to conduct near-optimality analysis focus on multi-sectors for middle-sized developing countries. 76

The methodology developed for this research provides new knowledge in the application of the MGA technique in the OSeMOSYS model. Although a number of studies have been conducted using the OSeMOSYS model focusing on expanding its codes, this is the first time the MGA technique has been applied in the OSeMOSYS model. The research on the MGA hybrid model development further adds value to the energy modelling field to analyse decarbonisation strategies, especially for the developing countries. Although the case study focuses on a developing country like Malaysia, the insights obtained from this research may have important investment, trade and policy implications and relevance at national and international level.

To conclude based on the reviewed literature, the research gap and opportunities for knowledge contribution are identified. This research contributes to:

1. Literature that focuses on decarbonisation strategies in developing countries, that examines the correlation and trade-offs between the end-use sectors and growth of key socio-economic parameter.

Chapter 3 Methodologies

This chapter presents the methodology of this research. It has three sections. Section 3.1 discusses the simulation of demand pathways using the MAED model. Section 3.2 presents the conceptualisation and formulation of supply pathways optimisation using the OSeMOSYS model. Section 3.3 outlines the MGA-OSeMOSYS modelling framework and discusses the development of two new objective functions derived based on the policy-driven uncertainty analysis.

3.1 Simulation of the demand pathways

This section discusses the framework of the MAED model, including the assumptions of demographic and economic activities in Malaysia, followed by the documentation and assumptions of household, commercial, industry and transportation sectors.

Framework of Model for Analysis of Energy Demand (MAED)

The MAED model is a simplified version of MEDEE-2. Initially, MEDEE-2 was developed and adapted by Lapillonne to conduct studies on global energy assessment at IIASA (Lapillonne 1978). While sustaining the general structure of MEDEE-2, important modifications were introduced by the IAEA in the model in order to provide a systematic framework for analysing the medium to long-term energy demand scenarios of socioeconomic and demographic developments (IAEA 2006). The model is used by countries such as Nepal, Tanzania, Nigeria and Syria to forecast the long-term energy demand of various sectors in these countries (Hainoun, Seif-Eldin et al. 2006, Ahmed, Isa et al. 2014, Kichonge, John et al. 2014, Nakarmi, Mishra et al. 2016).

The MAED model is used to analyse the long-term energy demand for Malaysia. The useful energy demands are projected using the model for the study period 2013–2050. The model requires collecting and compiling essential data from different sources, deriving and calculating various input parameters as well as adjusting the database to establish the base year energy balance. The model is developed to generate industry, transportation, household and services demand projections for Peninsular Malaysia, Sabah and Sarawak separately based on key inputs and parameters elaborated in Appendix A (see Section A.1:household, Section A.2:commercial, Section A.3:industry and Section A.4:transportation). Summary of these key parameters and

demand drivers are listed in Table A. 10, Table A. 16, Table A. 25 and Table A. 43 of Appendix A. The useful energy demand projections of industry, transportation, household and services sectors obtained from the MAED model are soft-linked with the OSEMOSYS model for each of the regions.

3.1.1 The Model for Analysis Energy Demand (MAED) conceptualisation and formulation

The MAED model is a simulation model designed to reflect energy demand of a country by projecting alternative forms of energy for the medium and long term planning. The output uncertainties of the MAED model can be evaluated based on the scenario approaches. As explained in the MAED-2 manual, a scenario approach for the demand modelling can take into account the possible long-term development patterns of a country based on the directions of governmental socioeconomic policies (IAEA 2006). Therefore, the policy planners can be provided with assumptions about the possible evolution of the social, economic, and technological development patterns of a country forecasted over the long term based on the current trends and governmental objectives. The consistency of the assumptions and scenario approaches are very important considerations in order to attain realistic results.

In summary, the MAED methodology comprises the following sequence of operations (IAEA 2006):

(1) disaggregating the total energy demand of the country or region into a large number of end-use categories in a coherent manner;

(2) establishing in mathematical terms the relationships which relate to energy demand and the factors affecting this demand;

(3) developing (consistent) scenarios of social, economic and technological development for the given country;

(4) evaluating the energy demand resulting from each scenario

Useful energy demand by end-use categories and alternative forms of energy (output of MAED) model

Multiregional end-use sectors & sub-categories

Transport Household Commercial Industry

Freight Urban Agriculture

Intercity Rural Construction

Intracity Manufacturing

Figure 3.1: Sectorial demand disaggregation in the MAED model for Malaysia

For Malaysia, four economic sectors are considered in the MAED model based on national statistics (NEB 2013). Figure 3.1 shows the transportation, household, commercial and industry sectors that are further subdivided into respective sub-categories of similar energy intensities for the calculation of useful energy demand projections from 2013 to 2050. The GDP and population factors in the MAED model are used as the key functions for calculating the useful energy demand by alternative forms of energy for each of the end-use categories portrayed in Figure 3.1. The GDP and demographic distributions are different for Peninsular Malaysia, Sabah and Sarawak. Therefore, three separate MAED models are developed respectively. GDP and population growth are exogenously defined as scenario parameters and are based on statistical data assumptions, as explained in Section 3.1.2-3.1.3 and the demand results discussed in Section 5.1.1. Other key activity parameters for each of the end-use categories are taken into account in the projections of useful energy demand as explained in Section 3.1.4-3.1.6 and summarised in Section A.1-A.4 of Appendix A.

3.1.2 Population growth assumptions

Population growth rate is one of the key drivers, assumed exogenously as an input in the MAED model to obtain the total population projection. The population growth rate used in the model is obtained from the World Population Prospects report, which provides the statistics of global population growth, including for Malaysia (UNDESA 2015). The population growth is projected by taking into account data on fertility growth, population ageing and life expectancy at birth. There is a degree of uncertainty about how fertility growth, population ageing and life

80 expectancy at birth projections will be in the future, therefore the median, high and low growth variants are developed to capture these uncertainties by UNDESA. These projections are used in the MAED model to develop three population growth scenarios as shown in Figure 3.2. Details on the population growth rates are shown in Table A. 2 of Appendix A.

Projected Population Scenarios (Malaysia) 50 Medium Growth rate 45 High Growth Rate

40 Low Growth Rate

35 million 30 25

20 2013 2015 2020 2025 2030 2035 2040 2045 2050 Years

Figure 3.2: Population Projections (Malaysia) in the MAED model

Understanding the demographic changes such as urbanisation level, the share of urban and rural population, persons per household and labour force structure are also important in order to derive the demographic inputs in the MAED model (see Table A.4 and Table A.12 of Appendix A) and to predict the future energy demand for Malaysia. As the economic activity is gradually increasing as elaborated in Section 3.1.3, Kuala Lumpur, Johor Baharu, Kuching, and Kota Kinabalu are the major cities forecasted to increase the GDP contributions in the service sector and undergo infrastructure transformation (DOSM 2015, EPU 2015a). The Greater Kuala Lumpur/Klang Valley (GKL/KV) is another transformation plan to integrate the surrounding cities to create a platform for the growth of the service sector. GLK/KV is also a component of the economic plan to transform the country into a high-income nation in the future (ETP 2010a).

With the expansion of these cities and increasing economic activity, the urbanisation rate is predicted to reach 75% in 2020 and 82% by 2035 (WEO 2015, EPU 2015b). The increasing urbanisation rate changes the structure of the urban and rural population. Figure 3.3 shows the shifting of rural population to increasing urban population in the 1990s due to the transformation from an agricultural and mining economy to an industrial- and service-based economy. With the implementation of the five-year Malaysia plans and the New Economic Policy (NEP) and the economic structural changes that they entail, the population is now more concentrated in the urban areas (Masron, Yaakob et al. 2012). The historical urban population

81 growth and the movement of people to urban areas in the country are also due to improved infrastructure, facilities accessibility, education and employment opportunities (Shahbaz, Loganathan et al. 2016). An analysis conducted for Malaysia looking at the impact of urbanisation in the country on energy consumption for the study period of 1970–2011 shows that there is a positive correlation between urbanisation rate and energy consumption (Shahbaz, Loganathan et al. 2015). Therefore, the increasing share of urban population in the future is forecasted to increase the energy consumption in urban areas, especially the electricity demand. Hence, assumptions related to the demographic changes based on these studies are defined for the near term and long term in the model. The assumptions about the labour force statistics and total population age structure correlated to labour force potentials included in the model are also obtained from national statistics (DOSM 2013a, EPU 2016). Details on these parameters as inputs of base year energy balance are illustrated in Table A. 4 and Table A. 12 of Appendix A.

Historical Population and GDP growth 30.0 250.0 Urban Population Rural Population 25.0 Total Population Total GDP 200.0 20.0 150.0 15.0 100.0 10.0

GDP (billion USD) Population (million) 50.0 5.0 0.0 0.0 1970 1975 1980 1985 1990 1995 2000 2005 2010 Years

Source: UNDESA 2014, DOSM 2017b

Figure 3.3: Historical Population and GDP growth (Malaysia)

3.1.3 GDP growth assumptions

Economic activity is predicted to grow rapidly, driven by the implementation of economic policies such as the New Economic Model (NEM), the Economic Transformation Programme (ETP) and the Eleventh Malaysia Plan 2016–2020. As discussed in Section 1.1, the economic activity is mainly focused in Peninsular Malaysia and the service sector was the main contributor to the total GDP, followed by the manufacturing sector in the year 2013. The Eleventh Malaysia Plan projected that these economic sectors will grow strongly with the manufacturing and services sectors, contributing to more than 75% of the GDP by 2020. Based on this predicted economic activity, Malaysia will achieve an advanced economy status by 2020, with a national per capita income of USD 15,000.

Some other predictions were also conducted by international economic forecasters: that GDP growth rate for Malaysia is expected to increase on average by 4.0% to 5.0% in the future (World Bank Group 2017, APERC 2016, WEO 2015, IMF 2015). These studies used different approaches and assumptions to predict the national GDP growth. The APERC analysis uses a macroeconomic model using the Cobb-Douglas production function considering three major factors – capital, labour and total productivity – to forecast future GDP growth. The APERC study projected that the average GDP of Malaysia would increase by 4.0% for the years 2013–2040 (APERC 2016). The WEO analysis on Southeast Asian countries derived the GDP growth based on projections by various economic forecasting bodies such as the International Monetary Fund (IMF), World Bank databases and IEA databases. These economic projections are based on assessments of developments in the labour force, the accumulation of capital investment and total factor productivity. The WEO analysis assumed that the GDP of Malaysia will grow on average of 4.2% for the years 2013–2040 (WEO 2015). The World Bank analysis estimated that between years 2014–2019, the average GDP growth rate would be 5.0% (World Bank Group 2017). The IMF also predicted in the World Economic Outlook database that the average GDP growth rate of the country would be about 5.0% for the years 2015–2020 (IMF 2015).

The macroeconomic structure in the MAED model is subdivided to four main sectors as agriculture, construction, manufacturing and commercial. The GDP contribution of these sectors varies between Peninsular Malaysia, Sabah and Sarawak as detailed in Tables A.27–A.32 of Appendix A. The analysis by the international economic forecasters took into account in the formulation of the assumptions for medium-term and long-term GDP projections to develop the macroeconomic structure in the model. Three scenarios using medium, high and low growth rates are developed to capture the uncertainties of GDP growth in Malaysia (Table A. 3 of Appendix A).

The economic activities of Malaysia are based on contributions mainly from the industrial and service sectors and are much influenced by regional and global economic market stability. Regionally, the country is a member of the Association of Southeast Asian Nations (ASEAN), which aims to create a platform to increase trade and investment among the member states as well as to establish a competitive economic base at a regional and global level. The ASEAN economic market would be one of the platforms for Malaysian businesses, as ASEAN economics continue to experience rapid economic growth that results in increasing investment, trade, tourism and cross-border infrastructure (Bhattacharyay 2009, Zhuang 2014). The country’s economic involvement at the global level is expected to be driven by steady economic growth in the emerging economies of Asian countries, especially in the People’s Republic of China (PRC)

83 and India (ADB 2015). Therefore, the country will remain an open economy, integrated regionally and globally post-2020. With the increasing economic integration, the Malaysia gross domestic product (GDP) is expected to reach USD 570 billion by 2030 with an average growth rate of 6.2% p.a. (EPU 2015b, EPU 2015c). These underlying assumptions are captured in the median GDP growth rate scenario whereby steady economic growth is anticipated throughout the study period.

Apart from the ASEAN current economic collaboration, the association is also initiating the formation of the ASEAN Economic Community to further integrate the economic structure of the member states. A blueprint of the ASEAN Economic Community until 2025 was introduced in 2015 to envision the strategic measures to strengthen the regional economy network (ASEAN 2015). Hence, the initiative to strengthen regional economic growth will also boost the growth of the national economy. The economy of Malaysia is anticipated to increase further with the implementation of the blueprint in future to facilitate regional trade, investment, services and skills. Moreover, the ASEAN+3 partnership establishment that includes Japan, Korea and the PRC in order to create business collaboration and opportunities will also build a stronger platform to further strengthen economic cooperation in the region (ASEAN 2017). The ASEAN+3 will strongly benefit Malaysia, as the country is forming good bilateral connections with these countries. Through the ASEAN+3 platform, Korea and the PRC are investing in the manufacturing and infrastructure sectors and are seeking further bilateral cooperation in future (MITI 2010; MITI 2015a; MITI 2015b). Japan has been one of the top industrial investors and has signed the Malaysia–Japan Economic Partnership Agreement (MJEPA) in 2006 to establish a platform for further investment and trade in the manufacturing and services sectors (MITI 2006). Bilateral cooperation with these countries is expected to have an impact on the future economic growth of Malaysia, especially in the manufacturing and service sectors’ contributions toward the country’s GDP growth. Assuming that the country continues to establish economic cooperation at the regional and global levels as well as continuing to sustain the financial system’s stability, the country is anticipated to attract more investment in the future in the manufacturing and service sectors. Therefore, the GDP growth rate is predicted to grow higher (the High GDP growth scenario) as compared to the Median GDP growth scenario.

The country has been experiencing rapid economic growth since independence (Figure 3.3), and has been undergoing structural transformation from a largely agricultural-based economy to an export-oriented manufacturing economy, focusing on commodities such as electrical and electronic products (Lim 1987, Yusof and Bhattasali 2008, Doraisami 2015). However, two financial crises, the Asian financial crisis in 1997 and the global financial crisis in 2007–2008

84 affected the economic performance of the country. The Asian financial crisis in 1997 collapsed the Malaysian currency as well as the financial system and the country experienced economic slowdown (Wade 1998). To recover the economy, capital control measures were introduced in the country to insulate its financial markets and to temporarily control the capital outflows by closing down the offshore ringgit market (Sharma 2003, Athukorala 2008). Yet another financial crisis in 2007 affected the growth of the Malaysian economy. The 2007–2008 global financial crisis, which started in United States (US) affected many countries through the global financial system, including Malaysia (Berkmen, Gelos et al. 2012, Cevik, Dibooglu et al. 2016). These studies discuss how the global crisis affected export-driven countries like Malaysia, where the demand for manufacturing exports that generated high revenues for the country decreased. The demand for major export commodities in the country such as petroleum, palm oil, rubber and timber dropped drastically. Although the country recovered from these economic downturns, the crisis indicates how the regional and international financial market influences and impacts the Malaysian economic market. Therefore, assuming that the country may face such financial crisis again and not able to recover from the economic downturn, the low GDP growth scenario is developed to capture the lack of certainty due to the possibility of economic crisis in the country.

A summary of assumptions in the MAED model for GDP growth scenario development is provided in Table 3.1.

Table 3.1: GDP growth scenario assumptions in the MAED model

Scenarios Assumptions Median Growth rate • Implementation of economic policies e.g., the Eleventh Malaysia Plan (5.0 %–6.0%) • Increasing trade, investment, tourism and cross-border infrastructure through regional business market e.g., the ASEAN platform High Growth rate • Major expansion of the manufacturing and service sectors through (5.0 %–7.5%) global and regional business markets e.g., ASEAN +3 bilateral collaboration Low Growth rate • Financial crisis e.g., the Asian financial crisis (1997) and the global (5.0 %–1.4%) financial crisis (2007–2008)

Hence, taking into account the GDP growth predicted by the international economic forecasters as well as capturing the uncertainties on the issues and influences of regional and international economic growth discussed above and summarised in Table 3.1, three GDP scenarios are generated in the MAED model, as shown in Figure 3.4.

GDP Projections (Malaysia) 3000 Medium Growth rate High Growth Rate 2000 Low Growth Rate

billion USD 1000

0 2013 2015 2020 2025 2030 2035 2040 2045 2050 Years Figure 3.4: GDP Projections (Malaysia) in MAED model

3.1.4 Documentation and assumptions of household sector

Demographic parameters are the main drivers used to calculate the household useful energy demand projection and there are many studies conducted which showed population growth and household income are key drivers that affect energy consumption in the household sector. Daioglou et al. (2012) projected that the trend of useful energy demand in developing countries increases along with increased household income and economic growth. This study considered drivers such as population, household size, floor space and electrification rate and projected how these drivers would shape the energy demand transitions of developing countries in future. The projections showed that the trend of useful energy demand increases with increased income and fuel switching from traditional to modern fuels occurs as the lifestyle of society changes.

Another analysis conducted by Sivak (2009) found that individual income in developing countries increases, so does the trend for electrical appliance ownership e.g., household air- conditioning, which consumes high energy. Meier et al. (2013) investigated the relationship between household income and expenditure on energy services in the United Kingdom from 1991 to 2007 and concluded that there is a dynamic link between energy consumption and household income levels. As household incomes gradually increase, the household spending on energy consumption tends to increase. Hence, the level of energy demand can change with income levels. A similar study by Schulte (2017) analysing income elasticities of household energy demand in Germany concluded that there is inter-relationship between household sizes and household incomes.

Based on these studies, population and GDP per capita growth rates are taken into account for the energy demand projections of the household sector using the MAED model for Malaysia.

Historical fuel consumptions (1997–2010) in the household sector are shown in Figure A. 2 of Appendix A, which shows the fuel consumption trend whereby the electricity consumption in the household sector has been increasing since 1997, along with GDP per capita in Malaysia. The NEB (2013) statistics provided data which aggregated by fuel for household sector whereby LPG is used for cooking and electricity is used for cooling, lighting and other appliances. Assumptions on fuel consumption in household sector also obtained from Saidur (2007) and Saidur (2007a). These studies conducted analysis on the use of different types of electrical appliances in the houses and the energy flow diagram for household sector in Malaysia for the year 2004 are used as a reference in order to develop the household structure in the MAED model and to calculate the useful energy demand (Saidur, Masjuki et al. 2007). This is the only study that analysed energy use by different types of household appliances in the country. The study concluded that the air-conditioning is one of the household electricity appliances used widely and consumes large amounts of energy. Meanwhile, LPG is specially used for cooking appliances.

Therefore, the useful energy demand projections by fuels for household appliances are divided to three clusters (cooking, electrical appliances and air-conditioning). Details on inputs needed in MAED model such as urbanisation level, total number of dwellings, type of dwellings, share of dwellings, energy requirements per dwellings per year for cooling, cooking and electricity appliances are listed in Table A. 1-Table A. 9 of Appendix A. The key parameters, demand drivers and equations used in the MAED model are summarised in Table A. 10 of Appendix A are taken into account in order to calculate the household useful energy demand projection. Figure 5.1, Figure 5.3 and Figure 5.5 shows the useful energy demand projection by sectors for three umbrella scenarios: reference (REF), high and low scenarios. The energy demand projections obtained for cooking, electrical appliances and air-conditioning serve as inputs to the OSeMOSYS model, which will be further discussed in Section 3.2.1.1.

3.1.5 Documentation and assumptions of commercial sector

The IEA (2008b) highlighted that level of economic activity is the main factor affecting energy use in the commercial sector. Increasing economic activity in a country influences the growth in the stock of buildings and increases the employment rate of this sector. Both building stocks and employment factors lead to growth in the demand of this sector. According to Ürge-Vorsatz et al. (2015), the energy use in commercial buildings for cooling or heating is expected to grow substantially, driven by the dynamic increase in commercial economic activity in most countries. Another analysis conducted on drivers of recent energy consumption trends sectors across

European Union (EU) also showed that there is a positive correlation between commercial sector energy consumption and the economic activity of the sector (Thomas, S. 2018). Similarly, for the case of Malaysia where the commercial sector is expected to expand and the GDP contribution of this sector is expected to increase, the energy use of this sector is estimated to escalate as well. Based on these studies, the GDP contribution of commercial sector is used as the key function in influencing useful energy demand as defined in the MAED model. Historical fuel consumptions (1997–2010) in the commercial sector are shown in Figure A. 4 of Appendix A and the fuel consumption trend shows that the fuel consumption in the commercial sector has been increasing since 1997, along with GDP in Malaysia as shown in Figure A. 5 of Appendix A.

Based on NEB (2013) statistics, electricity, LPG, motor fuel and gas are mainly consumed in the commercial sector. The statistics from NEB (2013) and a study focused on energy and exergy analysis of the commercial sector in Malaysia (Saidur, Sattar et al. 2007a; pg. 1960) are used as references to develop the commercial structure in the MAED model. This study provided an insight on LPG consumed by cooking appliances and air-conditioning as well as the electricity consumed by electrical appliances and equipment. This is the only study that provides an analysis of energy consumption by end-use categories and gives a brief structure of the commercial sector by fuel type. Therefore, the useful energy demand projections by energy form for the commercial sector in the MAED model are divided to four clusters (LPG, motor fuels, electricity and other fuels). Inputs on GDP growth, GDP of commercial sector, number of labour force, floor area per person in commercial sector and age structure are needed in the MAED model to calculate the commercial energy demand projections. These inputs are assumed based on national statistics summarised in Table A. 11-Table A. 17 of Appendix A. The key parameters, demand drivers and equations used in the MAED model are summarised in Table A. 17 of Appendix A are taken into account in order to calculate the commercial useful energy demand projection. The energy demand projections of these four fuel clusters serve as inputs to the OSeMOSYS model, which will be discussed in Section 3.2.1.2.

3.1.6 Documentation and assumptions of industrial sector

The industrial sector is the second largest energy-consuming sector. Hu et al. analysed the relationship between industrial energy consumption and economic growth in China based on data from thirty-seven industrial sectors in China covering the period from 1998 to 2010, and concluded that both energy consumption and economic growth are co-integrated. Another study conducted by Lee and Chang (2008) concluded that there is a positive long-run cointegrated relationship between real GDP and energy consumption based on data from

88 sixteen Asian countries during the 1971–2002 period. Warr et al. analysed supply of energy that contributed to economic growth in the UK, the US, Austria and Japan, throughout the last century and the long-term empirical evidence showed that the economic development is directly proportional to the amount of useful energy consumed. Based on these studies, GDP growth rates are taken into account for the useful energy demand projections of the industry sector using the MAED model for Malaysia. Historical fuel consumptions (1997-2010) in the industrial sector shown in Figure A.7 of Appendix A and trend of fuel consumption in Malaysian industry increased along with GDP growth as shown in Figure A. 8 of Appendix A.

The structure of the industry sector for the base year is prepared based on NEB (2013) and Saidur et al. (2009). The authors conducted an analysis on end-use electricity consumption of equipment or machinery based on energy auditing in manufacturing factories (Saidur, Rahim et al. 2009; pg. 155). The analysis provided insights on the end-use electricity used in the industrial production processes for the year 2006. The national energy balance classifies the fuel consumption by sub-sector in the manufacturing sector (NEB, 2013; pg. 89). The statistics on the agricultural sector are based on the energy balance and Ahamed, Saidur et al. (2011; pg. 7928), which estimates energy and exergy consumption in this sector. As detailed in Table A. 18 of Appendix A, these assumptions from these studies are taken into account to conclude the distribution of fuel consumptions by sub-sectors in the MAED model. Details on inputs needed in MAED model such as distribution of fuel consumptions by industrial subsector and GDP aggregation by sub-sector in industry are listed in Table A. 18-Table A. 24 of Appendix A. The key parameters, demand drivers and equations used in the MAED model are summarised in Table A. 25 of Appendix A are taken into account in order to calculate the industry useful energy demand projection. The useful energy demand projections by fuel type serves as an input to the OSeMOSYS model, which will be discussed in Section 3.2.1.3.

3.1.7 Documentation and assumptions of transportation sector

The transportation sector is the largest energy-consuming sector. Medlock and Soligo (2002) examines the effect of economic development on the demand for private motor vehicles and concluded that any growth in GDP per capita links to increase in the number of vehicles ownership. Hanly and Dargay (2000) studied household car ownership and changes in household car ownership in United Kingdom. The authors summarised that income is an important determinant of household car ownership levels. Similarly, Dargay et.al (2001) examines the effect of income on car ownership and the results of study indicated that car ownership

89 responds more strongly to rising income. In China, Wu et. Al (2014) forecasted the vehicle ownership through to 2050, and the study showed that vehicle ownership showed a rapid growth rate along with GDP per capita growth rate.

IEO (2016) analysis on transportation sector energy consumption showed that freight travel demand is related explicitly to economic activity. Similarly, Thomas, S. (2018) conducted an analysis on changes in transport sector activity levels and noted that there’s a strong correlation between economic growth and commercial road freight transport demand in the EU and at global level. For example, EU freight tonne-kilometres decreased as the GDP growth decreased between 2007 and 2009, which showed a relationship of transportation demand occurred in line with economic growth.

Based on these studies, population, GDP and GDP per capita growth rates are taken into account for the energy demand projections of the transportation sector using the MAED model for Malaysia. For example, GDP per capita is used as a reference to drive the increase of car ownership in Malaysia (see Table A.39 of Appendix A for car ownership projections in Malaysia). A number of studies have analysed the reason for the increase of private passenger ownership in the country. The outcome of the studies conducted in the Klang valley (Nurdden, Rahmat et al. 2007) and Kuala Lumpur (Mohamad and Kiggundu 2007) show that the population in the city prefers to travel by private car as there were inadequate public transportation services available and also commuters prefer to drive cars due to savings in travelling time and cost. Surveys conducted in Penang City (the state with the second highest population density) elaborates that the determinant of private vehicle ownership in this city is mainly due to household characteristics (e.g., the economic status of the household), travel characteristics (e.g., rapid urban development versus poor public transportation facilities) and the government policy of promoting the national automotive industry (Shariff 2012, Bakar, Chee et al. 2013). The statistics on the new registered motor vehicles by type and fuel (Figure A. 9 of Appendix A) shows that private passenger vehicles (cars and motorcycles) are mainly consuming petrol and diesel compared to other private motor vehicles. The sales of these private passenger vehicles have been increasing since 2000 at an average rate of 5.5%.

The transportation sector consumes about 45% of the total energy consumption in Malaysia, mainly diesel and petrol (NEB 2013). Historical fuel consumptions (1997–2010) in the transportation sector are shown in Figure A. 10 of Appendix A and the fuel consumption trend shows that the fuel consumption in the transportation sector has been increasing since 1997, along with GDP in Malaysia (see Figure A. 11 of Appendix A). The structure of the transportation

90 sector for the base year in the MAED model is prepared based on key input assumptions detailed in Section A.4 of Appendix A. Another study on exergy efficiencies for energy resources consumption in the transportation sector is also used as a reference to prepare the transportation structure (Saidur, Sattar et al. 2007b). Details on inputs and assumptions needed in MAED model such as total passenger activity, average distance travelled and car ownership as outlined in Table A. 33-Table A. 42 of Appendix A. The key parameters, demand drivers and equations used in the MAED model are summarised in Table A. 43 of Appendix A are taken into account in order to calculate the transportation useful energy demand projection. The useful energy demand projections by fuel type for transportation sector in the MAED model are divided to three clusters (intracity passenger transportation, intercity passenger transportation and freight transportation) as shown in Figure A. 12 of Appendix A. Figure 5.1, Figure 5.3 and Figure 5.5 shows the projection on passenger transportation (passenger-km) and freight transportation (tonne-km). The energy demand projections from these three clusters serve as inputs to the OSeMOSYS model, which will be discussed in Section 3.2.1.4.

3.2 Optimisation of the supply pathways

The multiregional energy demand projections of the industrial, transportation, household and commercial sectors for the timeframe 2013–2050 are obtained from the MAED model and are soft-linked with the OSeMOSYS model for Malaysia’s three regions: Peninsular Malaysia, Sabah and Sarawak.

3.2.1 The Open Source Energy Modelling System (OSeMOSYS) conceptualisation and formulation

The Open Source Energy Modeling System (OSeMOSYS) is a full-fledged energy systems optimisation model (Howells, Rogner et al. 2011, Welsch, Howells et al. 2012, Moksnes, Welsch et al. 2015). The model was developed on an open source programming language (GNU MathProg) and optimised using a GLPK solver (Howells, Rogner et al. 2011). As discussed in Section 2.3, some studies have applied this model to analyse the cross-border electricity trade and multi-regional energy systems (Moura and Howells 2015, Fattori, Albini et al. 2016, Taliotis, Shivakumar et al. 2016), while other studies focused on investigating decarbonisation strategies in the power sector (Groissböck and Pickl 2016, Lyseng, Rowe et al. 2016). Another study expanded the OSeMOSYS model code to examine the features associated with smart grids in a single framework (Welsch, Howells et al. 2012). The OSeMOSYS model was also used to evaluate

91 energy needs in specific sectors such as the residential sector (Smeureanu, Reveiu et al. 2015) or to examine technological details in the power sector (Hasibi, Hadi et al. 2013).

The OSeMOSYS platform is based on an optimisation framework, similar to the other established energy systems models8 discussed in Section 2.1.2.1. The relations in the OSeMOSYS model are formulated with mathematical equations in such a way that the model is solved by minimising the discounted total energy system costs. The OSeMOSYS model is developed to capture different types of current and future technologies, types of fuels and end-use commodities to meet the demands of different types of energy services assumed exogenously. In the process of optimising to obtain least-cost solution, the supply must satisfy the constraint that production must be greater than or equal to the sum of time-specific use and exogenous demand.

The objective function, OF, in the OSeMOSYS model in time frame t, is given by the equation below:

where t = the number of years T= 1,….,N where N = type of technologies CI: capital investment cost in time frame t for technology N FC: fixed operational cost in time frame t for technology N VC: variable operational and fuel cost in time frame t for technology N St: storage cost for technology N

8The history and characteristics of other long established energy systems models such as MARKAL, TIMES, MESSAGE or NEMS are discussed in Section 2.1.2.1. The application of these models in different parts of the world to address various energy issues is discussed in Section 2.2.

Figure 3.5: MAED and OSeMOSYS framework

The OSeMOSYS model is developed for Peninsular Malaysia, Sabah and Sarawak respectively, incorporating the supply-demand side balances for the period 2013–2050 (see Figure 3.5 and Figure 3.6). The model also captures the required network infrastructure and connectivity for the electricity trade between these three regions. The details of these technologies are based on the ETSAP supply-demand technology database9. The OSeMOSYS model takes into account the domestic resources, fuel imports, fuel prices, power plant technologies, technology efficiencies, renewable potentials, network infrastructure and sectoral end-use technologies.

9 Refer https://iea-etsap.org/index.php/energy-technology-data

Figure 3.6: RES diagram represented in the OSeMOSYS model

3.2.1.1 Structure of the Household sub-module

The useful energy demand projections for the household sector are modelled using the MAED model. As discussed in Section 3.1.4, the key drivers for household demand are population growth and household income. The studies conducted in many other countries show that these key drivers are crucial and affect energy consumption in the household sector. Isaac and van Vuuren (2009) conducted an analysis on the global energy demand for heating and cooling in the household sector and concluded that as developing countries grow wealthier, their citizens’ lifestyle patterns as well as demand trends become similar to those of developed countries. A similar study conducted by Daioglou et al. (2012) and Sivak (2009) also projected that the trend of useful energy demand in developing countries increases along with increased household income and economic growth. Santamouris (2016) argued that although the penetration of air conditioning is very high in developed countries such as the US and Japan, the penetration level is expected to reach a possible saturation level. However, significant growth is expected in developing countries such as China and India as household incomes increase and electricity access becomes more available (Pachauri and Jiang 2008).

As the economic activity and household income in Malaysia are projected to increase further in future, similar energy demand patterns and trends in air-conditioning ownership are also anticipated for the country. Therefore, air-conditioning demand is specifically modelled, as air- conditioning ownership and electricity consumption by this technology is expected to increase in the future. Moreover, with the introduction of policies such as the ETP and the Eleventh Malaysia Plan, the urbanisation and electrification levels are estimated to increase. Hence, the urbanisation and electrification rates are expected to shape the pattern of electrical appliance ownership and household demand trends.

McNeil and Letschert (2010) suggested that there is a close relationship between appliance ownership and electrification rate, as most households with electricity access will purchase electrical appliances if they can afford them. The authors concluded that the electricity consumption in the developing countries is anticipated to grow with the increase in ownership of electrical appliances such as refrigerators and washing machines. Since the markets for these appliances are not yet saturated as compared to developed countries, the demand for these products is likely to grow rapidly with the economy in the coming decades. According to Dzioubinski and Chipman (1999), the trend in household demand in developed countries is different compared to that of developing countries. Some of the important factors such as fuel

95 conservation, fuel substitution and the proliferation of electric appliances influence household energy demand trends in developed countries. For example, increases in the energy efficiency of new appliances or better insulation of buildings for heating and cooling in developed countries affect the demand growth and saturation in the use of appliances as compared to developing countries.

Note: compact fluorescent lamps (CFL), Light Emitting Diode (LED)

Figure 3.7: Structure of the household sub-module in OSeMOSYS

Therefore, similar energy demand trends are foreseen in the household sector of Malaysia. The energy demand projections for cooking and electrical appliances obtained from the MAED model are soft-linked in the OSEMOSYS model. In the OSeMOSYS model, a range of household end-use technologies is modelled to meet these demand projections (Figure 3.7). The household sector includes energy activities such as cooling, lighting and the use of electric appliances. The electricity and LPG are the energy carriers incorporated in the model based on NEB 2013, as described in Figure 3.7. Each technology is linked to alternate energy carriers, for example, either LPG or electricity to fulfil cooking appliances’ demand.

A study by Saidur et al. (2007) on the use of different types of electrical appliances in the houses and the energy flow diagram for household sector in year 2004 is used as a reference in developing the structure of supply system in the OSEMOSYS model. Two end-use categories are modelled, the cooking and electrical appliances, which represent the key household end-use functions in the country. Within the cooking appliances category, two end-use technologies are modelled, electricity and LPG oven/hob. Existing LPG cooking appliances are widely used in Malaysia due to the cheap availability of gas. The electric cooking appliances are linked to this category to give the model the flexibility of switching to alternate options in the future. This flexibility is important as the country had introduced an energy efficiency programme and also

96 initiated measures to reduce the use of fossil fuels to meet the emission targets discussed in Section 1.1.

Initiatives such as the SAVE (Sustainability Achieved via Energy Efficiency) programme and the NEEAP (National Energy Efficiency Action Plan) have been introduced in Malaysia to identify and implement energy saving measures (see Section 3.2.2.1 on the SAVE and NEEAP initiatives captured in the modelling). SAVE was launched in July 2011 to create awareness of energy efficiency electrical products such as refrigerators and air-conditioning and to stimulate the sales of these appliances. The NEEAP was initiated in 2014 and the focus of this plan is to promote efficient electricity use in the industrial, commercial and household sectors. With the implementation of this programme in the household sector, the electricity is expected to be efficiently managed in the future with the use of more energy efficient electrical appliances in homes.

As shown in Figure 3.6, the end-use sectors are closely linked to power sector in this model. The electricity growth in the end-use sectors will initiate the building of new power plants in the model. Hence, capturing these energy efficiency initiatives in the model is important in order to obtain long-term investment and electricity supply projections. Within the electric appliances’ category, three end-use categories are modelled: air-conditioning, lighting and other electrical appliances. Air-conditioning is specifically modelled as the demand for air-conditioning ownership is expected to increase as the household income trend increases. Existing air- conditioning and energy efficient air-conditioning are introduced into the model to comply with the energy efficiency policies. Similarly, existing and energy efficiency lighting, e.g., incandescent bulbs, LED (Light Emitting Diode) and CFL (compact fluorescent lamps) are introduced into the model. The details of the technologies, such as costing, are modelled based on the ETSAP Residential and Commercial demand technologies database (IEA-ETSAP E-TechDS). Details on existing and future technologies are discussed in Section 3.2.2.2.

3.2.1.2 Structure of the Commercial sub-module

Commercial sector activities in Malaysia include activities such as communication, finance, insurance, food and beverages, accommodation, community, social and personal services as well as real estate business services. The energy consumption in the commercial sector is related to the energy consumed by these economic activities. According to Schipper et al. (1986), the majority of energy consumption in this sector is related to building functions such as heating

97 and cooling, ventilation and other electrical operations and is shaped by economic activities and physical attributes e.g., the floor area or structure of the building.

The IEA (2008b) highlighted that level of economic activity is the main factor affecting energy use in the commercial sector. Increasing economic activity in a country influences the growth in the stock of buildings and increases the employment rate of this sector. Both building stocks and employment factors lead to growth in the demand of this sector. Commercial sector activities in different countries use different energy commodities based on the availability of fuels and fuel prices. For example, in OECD Europe, US and Canada, the sector largely consumes natural gas and electricity. In Mexico and China, oil and electricity are mainly used for commercial sector activities. Yet in India, this sector depends mostly on coal, renewables and electricity.

The economic activity of commercial sector is taken into account to generate the useful energy demand projections using the MAED model as discussed in Section 3.1.5. The distribution of energy consumption among various end-use technologies for Malaysia is difficult to determine due to insufficient documentation of the commercial sector. Saidur et al. (2007a; pg 1960) is the only study that was conducted to examine energy consumption distribution of LPG and electricity by end-use technologies such as cooking, air-conditioning and general equipment. This study is used as a reference to model the supply energy system in the OSeMOSYS model.

Three end-use categories are modelled, cooking, general equipment and air-conditioning technologies. As shown in Figure 3.8 , each of these end-use technologies is linked to alternative energies to provide for the flexibility of fuel-switching in the OSeMOSYS model. Within the general equipment category, two end-use technologies are modelled, motor power based equipment and electrical appliances. Assuming that motor power equipment currently consumes oil products based on NEB 2013, this end-use sector is also linked to electricity for alternative energies. The structure of cooking appliances and air-conditioning are modelled in a similar way to the household sector. As discussed in the previous section on air-conditioning technology modelling in the household sector, demand for cooling of commercial buildings is also anticipated to increase in the future.

According to Ürge-Vorsatz et al. (2015), the energy use in commercial buildings for cooling or heating is expected to grow substantially, driven by the dynamic increase in commercial economic activity in most regions. Similarly, for the case of Malaysia where the commercial sector is expected to expand and the GDP contribution of this sector is expected to increase, the energy use of this sector is estimated to escalate as well. A number of policies implemented in the country, for example the Economic Transformation Programme (ETP), the Services Sector

Blueprint10 and the Eleventh Malaysia Plan (2016–2020) aim to increase investment in the commercial sector and the GDP contribution of this sector is expected to be at least 58% in 2020 compared to about 54% in 2013. The details of the technologies such as costing are based on the ETSAP Residential and Commercial demand technologies database. The energy demand projection of commercial sector for cooking appliances, general equipment and air conditioning are obtained from the MAED model and are soft-linked in the OSeMOSYS model.

Figure 3.8: Structure of the Commercial sub-module

3.2.1.3 Structure of the Industrial sub-module

The industrial sector is the second largest energy-consuming sector. The energy consumed in the industrial sector is based on a composition of sub-sectors, energy intensity, industrial gross output and technology development. Energy is consumed by a range of uses, such as process and assembly, steam and generation, and heating and cooling. IEO (2017) argued that the energy consumption is increasing by an average of 2% annually across all three industry types for rapidly growing industrialised countries (non-OECD Asian countries excluding China and India). In Malaysia, the energy-intensive manufacturing subsectors are iron and steel, non-metallic mineral products, food, beverages & tobacco and chemicals. The iron, steel and food subsectors mainly consume natural gas meanwhile non-metallic products depend on coal (NEB 2013; pg. 89).

10 The Services Sector Blueprint, launched in 2015, aims to explore knowledge-intensive commercial activities and liberate the commercial subsectors in the areas of health, social, tourism, transportation, business and computer related services (MIDA 2018).

IEO (2016) argue that in most industrialised OECD nations, electricity penetration in the industrial sector has been linked with fuel substitution in production processes by replacing inefficient blast furnaces with electric ore furnaces. However, in the growing Asian market, industry is expanding through investment in new industrial facilities that incorporate the latest and most energy-efficient technologies. For the case of Malaysia, electricity and natural gas are mainly consumed in this sector for the year 2013 based on the composition of the sub- subsectors and the implementation of energy efficiency policies in the country.

The shift from an agricultural-based economy to a manufacturing-based one began in the early 1970s when output from oil and gas become significant. With the implementation of the National Industrial Policy and the Industrial Master Plan in the mid-1980s, the structure of the manufacturing sector changed, with the electrical and electronic sub-sectors started to dominate. The changes toward an energy-intensive manufacturing sub-sector in the 1980s required the use of mainly oil products in the sector. To reduce over-dependence on the use of oil and to improve energy efficiency in the industrial sector, measures such as MIEEIP (1999– 2007) was introduced to enhance the efficient use of energy in this sector. The MIEEIP mainly focused on improving the energy efficiency in eight energy-intensive industries (wood, rubber, food, ceramics, glass, pulp & paper, iron & steel and cement). After the implementation of this project, manufacturing sector began to consume more natural gas and electricity and gradually increase the trend of consumption of these fuels.

These changes in fuel consumption and economic activity of the industrial sector are taken into account to generate the useful energy demand projections using the MAED model as discussed in Section 3.1.6. However, modelling the distribution of end-use technologies in the OSeMOSYS for the industrial sector is challenging due to the lack of documentation on the fuel consumption of end-use technologies. Three studies are used as references to model the end-use technologies of the industrial sector in OSeMOSYS. Saidur et al. (2009; pg. 156) had conducted a study on end-use equipment or machineries that uses electricity by conducting an energy audit in targeted manufacturing factories. The NEB (2013; pg. 89) compiles the final energy consumption by sub-sectors in manufacturing for Peninsular Malaysia, however, it does not classify them accordingly to equipment or machinery consumption. The MIEEIP also only provides the total energy consumed by sub-sectors for the year 2007.

100

Figure 3.9: Structure of the Industrial sub-module

Based on these studies, two end-use categories are modelled, machinery & equipment and electrical equipment as shown in Figure 3.9. Each of these end-use categories is linked to alternative energy carriers for the flexibility of fuel switching in the OSeMOSYS model.11 Within the machinery & equipment category, the end-use technologies are introduced as oil, gas, coal and LPG machinery & equipment. As there are no further details on the type of electrical equipment used, only one technology, known as electrical equipment, is introduced. The details of the technologies, such as costing, are based on the ETSAP Industrial demand technologies database.

3.2.1.4 Structure of the Transportation sub-module

The transportation sector is the largest energy-consuming sector. It is a challenge to model the energy demand projection for transportation sector in Malaysia as there are limited studies conducted on it. To analyse the energy consumption and emission projection for the road transport sector in Malaysia from 2012 until 2040, Azam et al. (2016) used the LEAP model to analyse the use of different fuels that produce emissions. However, this study excluded factors such as changes in the growth of population, urbanisation or specific end-use technologies to model future energy consumption. Another study focused on using the multiple regression technique to analyse the factors that contribute to CO2 emissions, such as fuel consumption,

11 The industrial end-use technologies linked for total fuel-switching flexibility and the industrial machinery could operate on one type of fuel.

101 fuel efficiency, fuel price and distance travelled (Mustapa & Bekhet, 2015). Furthermore, Ong et al. (2012) discussed the trend of fleet patterns and policies to reduce the consumption of fuel or fuel switching to manage the increasing demand in the transportation sector.

Although most of the studies conducted in the country focus on the trend of future transportation demand, these studies excluded some of the important determinants and factors such as total population and urban population, which influence passenger vehicle ownership. Moreover, these studies exclude the modelling of end-use technologies of the transportation sector. Saidur et al. (2007b) is the only study relevant to this research, which focused on energy consumption by three main sub-sectors: road, air and marine vehicles. These sub-sector categorisations are used as references to build the transportation sector structure in the MAED and OSeMOSYS models. Vehicles such as cars, buses, motorcycles and trucks are categorised as the road sub-sector. Cargo and passenger planes are included in the air sub-sector, whereas ships and ferries are included in the marine sub-sector. The energy consumption of the transportation sector is detailed based on these sub-sector categories in the study.

At the global level, World Energy Council (2011) predicted that the transportation sector fuel mix will continue to be mainly gasoline, diesel, fuel oil and jet fuel in 2050 and the total number of cars in the world is expected to increase mainly in developing countries. The report predicted that in the case of Malaysia, higher income levels, an increasing urbanisation rate and improvements in transportation infrastructure would drive the increase in transportation demand and the ownership of private vehicles. In recent years, the increase in the private vehicle sales increased the energy consumption especially petrol. Figure A.9 of Appendix A shows the increase in new registration of private motor vehicles, especially cars and motorcycles since year 2000, contributing to increase in petrol and diesel consumptions. With increasing private passenger vehicles, implementation of urban transportation planning is a challenge in big cities. Morikawa (2001) conducted an analysis looking at cities such as Bangkok, Kuala Lumpur, Manila and Nagoya that face increasing private vehicle ownerships. The author discussed the challenges faced to control the urban vehicles and suggested the comprehensive policies needed to manage the increasing traffic in these cities. Hence, considering the issues of urban transportation planning and ways to improve transportation infrastructure, the NLPTMP (National Land Public Transport Master Plan) for Greater KL/Klang Valley Region was drafted to mitigate urban vehicle challenges by enhancing the services of public transportation and the rail and bus systems in this region.

102

According to World Energy Council (2011), developing countries in Asia are expected to consume about 16 mtoe of biofuels by 2030, mainly in China, India and Indonesia. Southeast Asian countries that produce palm oil could meet the demand by competitive biofuel production. Malaysia, as a palm oil producing country had taken initiatives to diversify the transportation fuel mix by introducing the National Biofuel Policy in 2006. Through the implementation of this policy, a mixture of 5% blend of processed palm oil with 95% petroleum derived diesel, known as Envo Diesel (B5), was introduced. Furthermore, natural gas vehicles (NGVs) using compressed natural gas (CNG) were also introduced in the country. The implementation of these policies in the OSeMOSYS modelling perspective will be further discussed in Section 3.2.2.1.

The projections for road transport, rail transport, air transport and sea transport obtained from the MAED model and are soft-linked in the OSEMOSYS model. Five end-use categories are modelled in the OSeMOSYS, which are road transport (car travel), road transport (light/heavy), rail transport, air transport and sea transport. As shown in Figure 3.10, each of these end-use technologies are linked to alternative fuels to give the optimisation model for fuel switching. The details on the technologies and costing are based on the ETSAP transportation demand technologies database.

103

Gasoline car Petrol from Refinery Diesel/Bio-diesel car Road Transport (Car Travel) Diesel from Refinery NGV hybrid car

Electric Hybrid car NGV from Refinery Diesel Bus

NGV hybrid Bus Road Transport Electricity from Grid (Light/Heavy) Diesel Trucks

NGV hybrid Trucks Biodiesel Two-wheelers (Gasoline & Electric)

Electric Rail Jet Fuel Rail Transport

Diesel Rail Air Transport Fuel Oil Air Crafts

Marine Sea Transport

Figure 3.10: Modes of transportation divided into road, air and sea

3.2.1.5 Structure of the Power sub-module

The RES diagram represents the inter-linkages between the multi-regional power sectors in the OSeMOSYS as shown in Figure 3.6. The system represents various technologies and energy carriers or resources in the system. The power plant sector modelled in the OSeMOSYS treated Peninsular Malaysia, Sabah and Sarawak separately and linked to each other, allowing for the need for electricity trade between these three regions. The methodology to link the electricity supply system and transmission lines between the regions to enable multiregional electricity trade was adopted from the studies conducted on the electricity supply infrastructure of South America and Africa (Moura and Howells 2015, Taliotis, Shivakumar et al. 2016). The electricity trade between Peninsular Malaysia and Sarawak requires the development of a 660-km undersea transmission cable and the details of the undersea transmission cable are captured in the OSeMOSYS model based on HVDC Submarine Power Cables in the world report (Ardelean and Minnebo 2015).

104

All power plants in the country, amounting to a total installed capacity of 29,748 MW, are modelled in the OSeMOSYS. The demand for electricity is based on the need for electricity instigated by the end-use technologies in household, commercial, industry and transportation sectors. The increase in electricity demand is also based on the constraints imposed in the model, which will instigate the building of new power plants. For example, a gas power plant (a technology) uses gas (a resource) to generate electricity consumed by a specific end-use sector or vice versa. Under certain constrains, for example emission reductions, the specific electricity demand could be contributed through electricity trade.

The existing and future power plants are modelled based on data obtained from national reports, which will be further explained in Section 3.2.2.2. The power sector mainly depends on gas, hydro and coal as the input for these power plants. In 2013, the sector highly depended on the gas and coal supply (43.7% each) for power generation. However, the limited supply of gas to the power sector is substituted with high cost alternatives such as distillate. Furthermore, the power sector depends on 100% coal imported from China, Australia and South Africa, which raises the question of future fuel security with a number of coal power plants under construction and in planning stages. The power sector is also facing the issue of aging infrastructure, whereby most existing power plants are expected to retire by 2030. In the system, the technologies’ details such as capital, fixed and variable costs, efficiencies, existing capacities, and discount rates are defined in the model (see Table A. 48-Table A. 56 of Appendix A outlines the details). The OSeMOSYS framework is developed with a timeslice resolution aggregated at two seasonal (northwest and southwest monsoons) and three daily levels (level one: 8 am to 1 pm; level two: 1 pm to 6pm; level three: 6pm to 8am). The number of timeslices are decided based on an analysis of the electricity load profile for the year 2013 as shown in Figure 3.11.

Source: TNB (2013) Figure 3.11: Electricity load profile in 2013 (Malaysia)

105

Based on the analysis across the weekdays and weekends, there are some differences in load profile, however the patterns of the load curves are almost the same. Therefore, the timeslices of the weekdays and weekends are assumed to be the same as in the model as shown in Figure 3.11. According to the Malaysian Meteorological Department, Malaysia faces two types of monsoons, the northwest and southwest, based on wind flow patterns. The northwest monsoon between November and March, brings heavy rainfall and cold air from the north. During the southwest monsoon between May and September, the climate is relatively dry with minimum rainfall. Therefore, there is a variation between the northwest and southwest monsoons, especially during early mornings and at night. The demand for more electricity, especially for cooling, is anticipated during the southwest monsoon compared to the northwest monsoon.

3.2.2 Documentation and assumptions of OSeMOSYS model

3.2.2.1 Key energy policies, energy resources, potentials and constraints

The model embeds several assumptions to improve the realism associated with future energy pathways. Due to the key nature of linear programming models, these models optimise extreme end-use technology switching in many cases. The realism is improved by introducing constraints in the model. These constraints are designed to take into account resources potentials, limited or planned infrastructures, future fuel prices, technology learning curves and technology building rates.

End-use sectors

A number of key policies of end-use sectors are captured in the modelling. Energy efficiency measures were first highlighted in the Ninth Malaysia Plan and continuously given importance in every Malaysian five year plan since then. Besides the Malaysian Five-Year Plans, SAVE and NEEAP are two other measures introduced to tackle the issues on energy efficiency in the country. For the household sector, measures on energy efficiency of technologies such air- conditioning and lighting are modelled. For example, the NEEAP initiated the introduction of compact fluorescent lamps (CFL) in the market to encourage customers to switch to more efficient lighting systems at home which consume five times less electricity compared to incandescent bulbs. However, incandescent bulbs are still widely used, as these bulbs cost 1/10th of what CFLs cost. In the OSeMOSYS, CFLs are introduced as an option for technology switching, and similarly, for air-conditioning that is widely used in the household and commercial

106 sectors. According to the NEEAP, a household with higher income could easily own up to three air-conditioners at home. As discussed in Section 3.2.1.1 and Section 3.2.1.2, air-conditioning ownership in the country is expected to increase in the future. Therefore, this measure introduced the 5-star labelling system to promote efficient air-conditioning and encourage customers to purchase the air-conditioners that are at least 10% more efficient than conventional systems. These forecasted changes toward owning more efficient air-conditioning is captured in the OSeMOSYS, whereby higher efficiency technology is introduced into the model for future technology switching in the household and commercial sectors.

In industry, motors are widely used as stand-alone or integrated systems. The MIEEIP launched in 1999 aims to improve the energy efficiency in energy-intensive industries. Furthermore, the NEEAP initiated a plan to create awareness among the industries about the benefits of switching to higher energy efficiency motors and phase out low efficient technologies. These features are captured in the model by introducing alternative efficient electrical equipment compared to fossil-fuel based equipment. For transportation, the NAP in 2014 outlined a framework to transform the domestic automotive industry and integrate it into the increasingly competitive regional and global industry, especially by introducing energy-efficient vehicles in the transportation system. To execute the implementation of the NAP 2014, roadmaps such as the MATR (Malaysia Automotive Roadmap) was outlined. Through the implementation of the MATR, the automotive technology is projected to create a market for switching from ICE (Internal Combustion Engine) based vehicles to electric vehicles by 2025. The introduction of natural gas vehicles (NGVs) using compressed natural gas (CNG), especially in buses and taxis is another initiative to diversify the fuel mix in the transportation sector. Public vehicles, specifically taxis, are retrofitted with conversion tools for switching between petrol and gas.

Furthermore, biofuel was introduced with the implementation of the National Biofuel Policy in 2006 to support the five fuel diversification policy, which aims to reduce the dependence on fossil fuels and promote palm oil utilisation. The use of palm methyl esters and the blend of processed palm oil (5%) with petroleum diesel (95%), known as Envo Diesel (B5), is a suitable fuel for the transport and industrial sectors. These measures are captured in the modelling of the transportation sector by introducing alternative electric and gas vehicles for future fuel switching. The rate of the switching of these fuels also depends on the constraints imposed in the model. To prevent drastic switching of end-use technologies in the model, constraints on

107 the building rate of new technologies are introduced whereby gradual phase-out of existing technologies and switching to new technologies occurs.12

Power sector

Some important key policies for the power sector are captured in the OSeMOSYS model. In order to diversify the energy mix in the model, renewable energy strategies in the policies such as National Renewable and Action Plan, the Eleventh Malaysia Plan, the Small Renewable Energy Power Programme (SREP) and the Fifth Fuel Policy are reflected in the model. Refer Section 1.1 (Figure 1.4) for the implementation of these policies in chronological order. The renewable energy policies are implemented to harvest indigenous renewable energy resources such as biomass, solar or hydro and to contribute toward electricity generation. These policies aim to achieve at least a cumulative capacity target of 11,544 MW of renewable energy by 2050. Table A. 48 of Appendix A outlines the details on the cumulative capacity targets of biomass, biogas, mini hydro, solar PV and solid waste until 2050 outlined in National Renewable and Action Plan (KeTTHA 2009). The model also has the flexibility to select additional renewable technologies. The wind potential is not considered as part of the renewable strategies in these policies. According to Ho (2016), there is a lack of spatial and temporal variability studies on wind and it is difficult to measure the potential of implementing wind technology in the country. Furthermore, Malaysia is located in a low wind region with variations in the wind speed due to northwest and southwest monsoons. Thus, the country faces greater challenges in developing wind energy.

In 2016, a cumulative of 348.17 MW of renewable installed capacities was achieved under the FiT (feed-in tariff) mechanism. These initiatives are parallel with other policies such as the National Green Technology Policy to promote green technology development as well as the National Depletion Policy and the Four Fuel Diversification Policy, which aim to reduce the exploitation of oil and gas reserves and to diversify the generation mix in the power sector. Table A. 49 of Appendix A for details on renewable installed capacity from 2012 until 2016. Currently, the generation fuel mix mainly depends on gas and coal. Hence, the details13 on the near-term planning of gas, coal and hydro power plants that are under construction and in the planned stages are also modelled in the OSeMOSYS, which will be further discussed in

12 The assumption based on some studies investigating the benefits of demand-side flexibility mechanism and smoothening the demand profiles with predefined technology penetration rates and flexible technology deployment in long-term transitions to balance the energy system (Kesicki and Anandarajah 2011, Sanders, Hart et al. 2016, Li and Pye 2018) 13Near term planning of new power plants obtained from Peninsular Malaysia Electricity Supply Industry Outlook 2014, Sabah Electricity Supply Industry Outlook 2014 and RECODA Annual Sarawak Report 2014 (EC 2014a, EC 2014b, RECODA 2014) 108

Section 3.2.2.2. Furthermore, to avoid the extreme switching of technologies or being dependent on a specific technology in the optimisation model, upper and lower constraints of power technology availability as well as natural resource potentials are introduced based on these policies in the model. For the case of renewables, the potentials and constraints of biomass, biogas, solar and hydro are imposed.

Biomass and biogas potentials are based on empty fruit bunch (EFB)14 and other agriculture waste as fuel for power plants. According to KeTTHA (2009), 60% of palm oil factories are in Peninsular Malaysia and 30% are located in Sabah and Sarawak. Biomass and biogas potentials are based on empty fruit bunch (EFB) and palm oil effluent waste (POME) as fuels for power plants. As stated by KeTTHA, based on a survey of 100 palm oil mills conducted by PTM (currently known as GreenTech Malaysia), 20% of EFB produced from fresh fruit bunch (FFB) potentially can be used for power generation. POME is also produced when palm oil fresh fruit bunches are processed. To reduce the impact on the environment, the POME is treated and initiatives are taken to capture the methane gas released during the milling and treatment processes for electricity generation connected to the grid or for palm oil mills’ own use. The report also discusses the cost of transporting biomass and biogas potentials and highlighted that it is much more feasible to explore these potentials on-site as fuel for power plants, considering the processes of the harvesting and treatment of palm oil waste.

Based on the ETP (2010b), 4.7 million hectares were already in use for oil palm cultivation in Malaysia. Therefore, the potential for further national expansion is limited. The expansion potential is estimated at a maximum of 1.3 million additional hectares, of which 75 percent or 1 million hectares is located in Sarawak. Currently, a total of 6.6 million hectares of land are for agricultural use in Malaysia, of which 4.7 million hectares (71 percent of total) are already used for oil palm plantations. Therefore, taking into account the limited land available for palm oil plantations, the amount of potential waste for power generation and the growth of the industry by 7.1 percent over the next ten years as predicted in 2009, the reliable maximum capacity of biomass is 1,340 MW by 2030. For biogas, the maximum potential by 2030 is 410 MW. These resource potentials are used as upper constraints in the model.

Mekhilef et al. (2012; pg. 388) highlighted that the monthly solar radiation in Malaysia is approximately about 400–600 MJ/m2 and is mainly concentrated in the states of Penang and Sabah. The country has a favourable climate for solar energy potential, whereby the average

14 Empty fruit bunch (EFB) is one of the by-products generated from palm oil mills and has the potential for production of biomass pellet fuel (Soom, Aziz et al. 2009, Abdul, Elsholkami et al. 2017, Nasrin, Vijaya et al. 2017) 109 sunshine duration is in the range of 4–8 hours per day and has a daily average solar radiation of 4000–5000 Wh/m2 (Mekhilef, Safari et al. 2012; pg. 391). KeTTHA (2009), based on these data as well as information on suitable roof top areas of household and commercial buildings for PV installation, concluded that a potential of 9 GW of solar energy could be achieved by 2050. This potential target is modelled in the model. Lau et al. (2016) argue that the household sector has high potential for grid-connected PV installations and the possibility of 9 GW of solar PV installations by 2050. The authors conducted an analysis using HOMER software on the implementation of grid-connected PV systems in the household sector and concluded that it is highly feasible to have PV systems in this sector. However, they suggested that battery storage is not feasible in the grid-connected PV systems as it increases the net present cost (NPC) of the system. Furthermore, Amin et al. (2009) conducted an analysis comparing the performances of different types of solar panels that are commercially available and concluded that thin film technology solar cells works best under low light conditions which make photovoltaic usage more efficient and cause more power to be generated from low sunlight. Hence, the suggestions about the types of solar panels and battery storage are taken into account in the OSeMOSYS model.

However, the potential of solar installations in the country could be unlimited as long as the society is willing to invest in solar panel installation and begin to become aware of the benefits of grid-connected PV installations. Therefore, the upper constraints on the solar potential in Malaysia until 2050 were derived based on the information obtained from the MAED model inputs on potential types of dwellings and household income.

Through the Sarawak Corridor of Renewable Energy (SCORE), Sarawak has formulated a comprehensive development programme to identify the possibility of exploring a hydro potential of 20GW in the state (RECODA 2011, RECODA 2012). According to Sovacool and Bulan (2012; pg. 118), SCORE involves the development of at least 12 hydro power plants with a total installed capacity of 20 GW. The authors concluded that the building of these power plants would benefit the state in terms of an increase in the electrification level, an expansion of the industry sector with the availability of electricity supply and probable electricity export to Peninsular Malaysia via undersea cables. The hydro potential of 20GW is captured in the OSeMOSYS with the possibility of electricity trade between Peninsular Malaysia and Sarawak through an undersea cable connection.

Besides the renewable energy potentials, constraints on gas resources to the power sector are also reflected in the model. According to the EC (2017; pg. 17), gas supply is limited by PETRONAS

110 to up to 1,000 million standard cubic feet per day (mmscfd) at a controlled price to the power sector with any additional volume to be charged at a market rate based on a government- approved gas pricing formula. Other constraints such as the technology learning curve and the introduction of new technologies (energy demand and supply technologies) are adopted from the IEA-ETSAP database discussed in the following section. Additionally, smooth technology building rates are introduced to avoid the extreme switching of end-use technologies.

3.2.2.2 Existing and Future technologies

For the case of the power sector, each existing power plant, with specific details such as costing, efficiency and life-span, are modelled in the OSeMOSYS. Information on the existing, under construction and planned power plants are obtained from the EC (2014a), EC (2014b), NEB (2013), RECODA (2011) and RECODA (2012) reports as well as MEIH (2017) database. Table A. 50-Table A. 51 of Appendix A list all the existing and future power plants modelled in OSeMOSYS. Figure 3.12 illustrates the installed capacity by region and comprised a total installed capacity of 29,748MW in 2013. In Peninsular Malaysia, mainly gas (OCGT and CCGT) and coal power plants are installed. In the case of Sabah, the power plants mostly depend on natural gas and diesel. Currently, Sabah has no coal power plants. The state has committed to not building coal power plants in order to protect its natural environment, which contributes to the state’s economy (The Sun daily, 2011). For Sarawak, hydro and coal are contributing to power generation.

However, as discussed in Section 1.1, most of these existing power plants are scheduled for retirement in the next ten years. Therefore, detailed planning on future power plants is needed to replace the decommissioned power plants. A number of coal, gas and hydro power plants are planned or under construction in Peninsular Malaysia. Sabah is focusing more on future gas, hydro and renewables. Under the SCORE, Sarawak is tapping the potential of hydro power plants as well as gas and coal power plants. In the OSeMOSYS model, a reserve margin of 23% is applied for each region. Conventional power plants have availability factors approaching 100% and capacity credit which counts 100% towards reserve margin based on IEA-ETSAP. A constant capacity credit of 20% is used in the model based on IRENA (2017). The capacity credit (to peak load) for each of the power technologies as presented in Table A.55 of Appendix A. Lamont (2008) and McClurg (2012) studied the system value of intermittent of power generation technologies. The authors concluded that the marginal value of intermittent generation such as solar and wind declines as penetration of these technologies increases.

111

The existing end-use technologies are captured in the model based on policies and studies conducted related to these four sectors: household, commercial, industrial and transportation as discussed in Section 3.2.2.1. Data on the costing, efficiency and life-span of future end-use technologies are obtained from the IEA-ETSAP database. Table A. 52 of Appendix A outlines the key data and figures for power and end-use technologies. For example, in the household and commercial sectors, existing and energy efficient air-conditionings are modelled. In the household sector, lighting technologies such as compact fluorescent lamps (CFL) and incandescent bulbs are modelled to give the alternative options. For the industrial sector, alternative equipment based on fuel types are modelled in the OSeMOSYS. A similar methodology is applied to model the transportation sector by introducing alternative vehicles based on fuels.

Source: NEB (2013)

Figure 3.12: Total installed capacity and share of capacity by fuel in Malaysia (2013)

3.2.2.3 Trend of carbon emissions in Malaysia

According to the IEA (2017a), the trend of global CO2 emissions from fuel combustion had increased from almost zero in 1870 to approximately 33 GtCO2 in 2015. The growth of CO2 emissions is due to growth in the total global primary energy supply that relies on fossil fuels

(accounting for almost 82% in 2015). Coal and oil contributed to about 40% of global CO2 emissions from the late 1980s to the early 2000s. Although the total global primary energy supply consisted of only 28% coal in 2015, the fuel represented 45% of the global CO2 emissions due to the carbon content per unit of energy produced. The IEA study argued that the contribution of CO2 emissions from non-Annex I countries has tripled since 1990 mainly due to an increase in coal consumption as compared to Annex I countries, where oil is the major source of fuel combustion emissions. In 2015, non-Annex I countries represented approximately 58%

112 of global CO2 emissions. The contribution to emissions is mainly due to an increase in the demand of the primary energy supply of non-Annex I countries, especially coal, which increased from 39% in 2002 to 45% in 2015.

Source: MEIH (2017) and IEA (2017a) *TPES: Total primary energy supply

Figure 3.13: Primary energy supply by fuel and CO2 emissions in Malaysia

In Malaysia, the trend of CO2 emissions has increased since the 1980s as the country gradually increased the use of natural gas, oil and coal. Since the implementation of the National Depletion Policy in 1980, oil consumption in the power sector gradually reduced and the fuel was mainly consumed in the transportation sector by 2010. Natural gas and coal were used as alternatives to oil, mainly in the power sector, which contributes to the increase in CO2 emissions from fuel combustion. As discussed in Section 1.1, the power sector in Malaysia has been the major contributor to CO2 emissions due to increasing fossil fuel consumption (EC 2014, EC 2016). For example, 20% of coal supply in 2010 contributes about 31% of CO2 emissions from fuel combustion as compared to other fuel emission contribution in the country (Figure 3.13). Due to the lack of information such as country-specific emission factors, combustion technology, operating conditions and data on the age of equipment used to burn fuel, a default emission factor is used to calculate the CO2 emissions from fuel combustion. Therefore, the Tier 1

113 approach is applied in the OSeMOSYS model to calculate future CO2 emissions for each source category and fuel. Tier 1 is defined as fuel combustion from national energy statistics and default emission factors (IPCC, 2006, pg. 2.11).

3.2.2.4 Fuel prices assumptions and constraints

Gas supply is limited by PETRONAS to up to 1,000 million standard cubic feet per day (mmscfd) at a subsidised price to the power sector with any additional volume to be charged at market rate based on a government-approved gas pricing formula. Gas subsidy is at the fixed price of RM 13.70/mmBtu as of in 2012(EC 2014; pg. 63). The constraints on gas supply and price are imposed in the OSeMOSYS model. An option of gas supply at market price for the power sector is also introduced into the model to provide flexibility for future gas power plant development. Gas prices for the non-power sector are modelled based on data obtained from the EC (2014) report and MEIH database. For future fuel prices assumptions as outlined in Table A. 54 of Appendix A, are obtained from the IEA (2015) and WEO (2017). The Southeast Asia Energy Outlook forecasted the fuel cost of coal and gas in 2030 (IEA 2015). The WEO (2017) demonstrated the fossil fuel price assumptions until 2050. Assumptions from both reports are taken into account in order to capture the fuel prices in the OSeMOSYS model (see Table A.54 of Appendix A for fuel prices).

3.3 MGA-Hybrid Modelling Framework

3.3.1 MGA technique

As discussed in Section 2.4.1, MGA is an algorithm that can be applied to any optimisation model, whereby the technique is customised and applied in a specific modelling context or to address specific research questions. To deal with the structural uncertainties, the optimisation model is used to generate alternatives and to provide different insights to facilitate the evaluation and elaboration of the energy system planning process. Liebman (1976) highlighted that optimisation models are useful in addressing public systems issues, yet public sector solutions are highly uncertain, with different members of society often disagreeing on the solutions or on the different mechanisms to achieve the goals.

Therefore, Liebman suggested that optimisation models should be used to provide “insight, intuition and understanding” to decision makers by generating alternatives. Brill (1979)

114 discussed the Liebman’s philosophy of using optimisation models to assist planning processes and proposed the need to use models differently with different types of formulations and computer codes. In order to provide “insight, intuition and understanding” recommendations as suggested by Liebman, the author suggested tailoring an available algorithm in an optimisation model such as identifying new objectives or understanding the old objective function.

Brill et al. (1982) discusses the important role of programming models, especially optimisation models to generate a small number of alternative solutions that are feasible, perform well with respect to modelled issues, and are significantly different with respect to the decisions space. Such a set of alternatives may aid analysts and decision makers in understanding the problem and may serve as a catalyst for human creativity and invention. A number of possible approaches to modelling to generate alternatives (MGA) have been developed specifically for the purpose of generating alternative solutions that are different, and they are discussed in this paper. Some of these methods, such as HSJ (Hop, Skip and Jump) and the random generation method, are applied in the energy-economy optimisation model to generate insights on future energy pathways, as elaborated in Section 2.4.3.

3.3.2 The MGA-OSeMOSYS modelling concept

One of the MGA techniques suggested in Brill et al. (1982) is implemented in the OSEMOSYS model to explore alternative pathways. The author suggested that one of the approaches to MGA is to obtain near optimal within a given region in objective space to generate alternatives that are different from the preceding solutions. Adopting this technique, this research used the MGA approach of coding new objective functions with a range of slack values based on one initial solution to obtain near optimal in order to examine the near-optimal decarbonisation strategies.

The MGA steps described in Brill et al. (1982) are adopted in the OSeMOSYS model as follows:

Step 1. Obtain an initial optimal solution generated based on the least-cost objective function in the OSeMOSYS model.

minimize cost: sum {r in REGION, y in YEAR} TotalDiscountedCost[r,y];

115

Step 2. Formulate a new objective function that minimises the sum of decision variables e.g., a new objective function based on policy-driven uncertainty analysis, described in detail below. Step 3. Encode the least-cost objective function value as an additional upper bound constraint and add a user-specified amount of slack to the value of this objective function Step 4. Iterate the re-formulated optimisation to obtain a new solution.

This approach is different from the methodologies used in the literature discussed in Section 2.4.3, whereby the authors seek alternative pathways that are maximally different from each other using the HSJ (Hop, Skip and Jump) and random generation methods. The MGA technique applied in this research aims to explore the policy-driven structural uncertainties and approaches to the decarbonisation issues in the context of policy. The objective of this approach is to assist and provide an outlook to decision makers of the near-optimal technology profiles if extra investment is financed to achieve decarbonisation strategies.

Step 2: New objective functions derived based on policy-driven uncertainty analysis

Two new objective functions are considered for the near-optimal decarbonisation strategies analysis under a range of slack values as summarised in Table 3.2. The near-optimal decarbonisation strategies are analysed in two perspectives or two directions: decarbonising the power sector and decarbonising the energy systems.

Table 3.2: Two phases of the near optimality analysis (Inflexible and Flexible models) with alternate objective function

Inflexible model Objective Functions: Slack Values: 1%, 5%, 10%, 15%, 20%, 25%, i. Min coal import in power sector: 30% minimize production: sum {r in REGION, l in TIMESLICE, CoalImpRF in TECHNOLOGY, f in FUEL, s.t. DC_DiscountedCost1 {r in Flexible model y in YEAR} REGION}: (Four different ProductionByTechnology[r,l,CoalImpRF,f,y]; ModelPeriodCostByRegion[r] scenarios to <= slack value; illustrate the ii. Min CO2 emission of the system: flexibility of the minimize emission: sum {r in REGION, e in model – see Table EMISSION} ModelPeriodEmissions[r,e]; 3.8)

116 i. Objective function: Minimise coal in power system

The first alternate objective function, the minimisation of the coal imports coded in the OSEMOSYS model focuses on the multi-regional power sector. As elaborated in Section 1.1, coal is mainly imported for power generation from Indonesia, Australia, South Africa and Russia, which raises the issue of energy security. Further, the construction of a number of coal power plants is expected in the short term. As most power plants are due for retirement after a 10- year phase, the shift toward coal power plant is proposed for short term planning. With a lifetime of approximately 30 years for a coal power plant, securing coal supply for the long term is a challenging task. Based on the IEA report on medium term coal 2016 (IEA 2016b), coal remains the world’s number one fuel for electricity generation and the coal demand is shifting toward Asian countries. These countries, with growing economies and population, are in need of affordable energy sources to meet the demand. Furthermore, the demand for coal for power generation is expected to increase in South East Asian countries, especially Malaysia, Indonesia and Vietnam as the number of coal power plants are expected to increase in the next decade. As end of 2015, these countries already had a combined of 54GW of coal-fired capacity. High investment in coal capacity also raises the issue of the environment in this region. Due to low cost and resources consideration, these countries are planning to build more coal power plants. The IEA 2016 report highlighted that initiatives to phase out or retire early some of the coal units would not significantly improve the environmental impact due to large scale of existing and planned coal capacity in the future.

Therefore, concentrating on this issue, the near-optimality analysis focuses on the minimisation of multiregional cumulative coal imports within a range of slack values to obtain insights on alternative strategies in the power sector. If the policy makers would want to phase out coal power plants or become less dependent on coal imports, what future technology investments and policy approaches would the policy makers propose, given the willingness to spend more money on adapting alternative technologies in the long term? The alternative pathways are analysed in relation to technological-environmental-economic objectives for the long term.

ii. Objective function: Minimise CO2 emissions of the system

As the country is also committed to reducing carbon emission and to seek alternatives of energy system technologies, the second alternate objective function focuses on the minimisation of system carbon emissions at a range of slack values. This near optimal technique analyses the decarbonisation of the energy system and the changes of the technology portfolios in various sectors to meet the new objective function and slack value constraint. As discussed in Section

117

1.1, the country also committed to reduce its greenhouse gas (GHG) emissions intensity of GDP by 45% by 2030 relative to the emissions intensity of GDP in 2005. The energy system is the largest carbon emission contributor and a number of significant policies such as the National Renewable and Action Plan 2009, the Eleventh Malaysia Plan 2016–2020, the Small Renewable Energy Power Programme (SREP), the National Depletion Policy (1980) and the Four-Fuel Diversification Policy (1981) were introduced to tackle this issue.

Step 3: Slack values

Slack values of 1%–30% are introduced into the OSEMOSYS model (see Section 4.1.3 and Section 4.2 (Figure 4.8) for details on scenario development as well as Section 5.2 (Results and Discussions) on near-optimal results). As discussed in Section 2.4.3, Trutnevyte (2016) provides the evidence that the real world transition pathways in terms of cumulative system costs deviates from the optimisation solution by 9%–23% under various technology, cost, demand and discount rate assumptions. This analysis raises the uncertainties of the optimal solution in projecting future pathways. The bottom-up electricity model EXPANSE is used to model from 1990–2014 for the UK electricity sector (an ex post modelling exercise to understand whether cost optimisation approximates the real-world energy transitions). The study concluded that ex- post analysis gives an idea of cumulative cost deviation from real world energy transitions and near-optimal scenarios are considered. The alternative pathways may not able to project the actual energy transitions but could provide an outlook of options or, as the author mentions, the ‘envelope of predictability’ for decision makers in assisting them with future policy planning. Therefore, this research considers slack values of 1%–30% in order to obtain the near-optimal decarbonisation strategies. The results are analysed in terms of cumulative primary energy production, electricity production, installed capacity and technology energy consumption in various sectors, discussed in Section 5.2.

Phases of the near-optimal decarbonisation analysis (Inflexible and Flexible models)

The near-optimal decarbonisation analysis implemented in two phases:

1. In the first phase of the analysis, the REF model or inflexible model is incorporated with assumptions captured from the energy policies such as the National Renewable and Action Plan 2009, short-term and long term power plant planning, natural resource potentials as illustrated in Section 3.2.2. Assuming that all the energy policies assumed in the model are implemented as planned, the optimal decarbonisation and near-optimal decarbonisation

118

analyses examine how the energy profiles evolve in the future. The REF or inflexible model is run separately with the minimum coal import objective function (focusing on power

sector) and minimum CO2 emission (focusing on energy system) for the slack values described as in Table 3.2.

2. In the second phase of the analysis, the flexible model, the assumptions or constraints described in Table 3.14 are removed in stages to give the model the flexibility of technology selections given the new objective functions. The purpose of this analysis is to investigate the diversity of the energy profiles, assuming that some of these energy policies may change in future as socioeconomic activities and socio-political structures change. Giving the flexibility to the model provides an outlook on how different the energy system could be and if the current energy policies are applicable in achieving decarbonisation of the system as well as examining how the policies should be improved to achieve the decarbonisation target. Furthermore, a highly constrained or inflexible model may not give the model many options to provide different near-optimality strategies when the MGA technique is implemented in the model. Based on DeCarolis (2011), in order for the original model formulation to produce the MGA solutions, the previously inactive technology must be cost-competitive with the other active technologies. In the inflexible model, the technologies are not cost-competitive and strictly constrained and the MGA formulation in the model, thus not providing much flexibility in terms of selecting technology options in the power sector.

Table 3.4: Summary of Flexible Scenarios

Flexible Scenarios Constraint Categories and Constraints removed from the model Scenario 1 (L1) • Renewable energy strategies (2011–2050) Scenario 2 (L2) • Renewable energy strategies (2011–2050) • Near term planning of new power plants that are under construction and planned for construction Scenario 3 (L3) • Renewable energy strategies (2011–2050) • Near term planning of new power plants that are under construction and planned for construction • Potential solar installation Scenario 4 (L4) • Renewable energy strategies (2011–2050) • Near term planning of new power plants that are under construction and planned for construction • Potential solar installation • Technology learning curve and introduction of new technologies

119

Table 3.5 describes the four flexible scenarios generated as the constraints from the four categories are excluded from the model.

Table 3.3: Summary of Policies and Constraints in inflexible and flexible models

Models Policies Constraints in the model15 Constraints removed Inflexible Policy • Renewable energy strategies (2011–2050) /REF (Near term and • Biomass and biogas potentials long term) • Gas supply limited

Policy Near term planning of new power plants that (Near term) are under construction and planned for construction Resources • Potential solar installation (Near Term and • Hydro potential in Peninsular Malaysia and long Term) Borneo Technology • Technology learning curve and introduction (Near Term) of new technologies • Smooth technology building rates • Operation of each existing power plants • Operation of existing end-use technologies

Flexible Policy • Biomass and biogas potentials Renewable energy strategies (Near term and • Gas supply limited (2011–2050) long term) Policy Near term planning of new power (Near term) plants that are under construction and planned for construction Resources Hydro potential in Peninsular Malaysia and Potential solar installation (Near Term and Borneo Islands long Term) Technology • Operation of each existing power plants Technology learning curve and (Near Term) • Operation of end-use technologies introduction of new technologies (coal and nuclear)

Table 3.4: Summary of Flexible Scenarios

15 Detailed descriptions of policies and constraints in Section 3.2.2

120

Chapter 4 Building the MGA-Hybrid Model Scenarios

This chapter outlines the systematic development of the scenarios in the MAED and OSeMOSYS models. Section 4.1 presents the formulation of the scenarios using the methodology approaches discussed in Chapter 3 including a summary of the MGA-Hybrid model framework. This section also describes optimal and near-optimal decarbonisation pathways development. Section 4.2 provides a summary of systematic development of optimal and near-optimal decarbonisation scenarios under the long-term demand pathways.

4.1 Systematic formulation of the scenarios

The primary purpose of developing the scenarios using the methodology approaches discussed in Chapter 3.0 is to explore the uncertainties of the technology profiles and decarbonisation pathways as well as the drivers that influence these trends. These uncertainties provide an outlook on different policy directions that could be considered for the country. This chapter summarises the coherent ways to describe the scenarios formulated based on the discussion in previous chapters. The descriptions of scenarios developed are important in order to understand the analysis of the results generated based on the assumptions and constraints discussed in Section 3.2.2. The three parts of the methodology, as discussed in Chapter 3, are summarised as follows:

The first part of the research methodology is to simulate the demand pathways in order to understand how these pathways in a developing country evolve under the influence of socioeconomic factors. GDP and population growth are the important variables that drive the patterns of future energy needs in a country. Understanding the growth patterns of these variables is crucial in order to capture the details in the process of developing the MAED model and obtain realistic demand pathways for the future. The demand pathways simulated from the MAED model are soft linked with the OSeMOSYS model to generate the demand–supply pathways.

The second part of the research methodology is to optimise the demand–supply pathways to obtain outlook on the technology profiles and the trends of fuel mixes in future. The demand–

121 supply pathways are generated using the OSeMOSYS model, which captures the ongoing energy policy implementation and the energy objectives in the country (see Figure 4.2-4.4 showing demand pathways under three umbrella scenarios: REF, high and low soft linked to the OSeMOSYS model). Most of the energy policies introduced are already in the process of implementation, therefore it is essential to impose the key energy policies, energy resource potentials and constraints as discussed in Section 3.2.2.1 in the model. Capturing these elements is also important in order to derive the optimal decarbonisation strategies in the country and to evaluate the impact of these policies in relation to the carbon emissions target to which the country is committed. The stringent emission constraints imposed (discussed in Section 4.1.2) in the model provide the outlook on the technology profiles and views on which sectors could be tackled to achieve the emissions target with all the current energy policies and technology options in place and implemented accordingly.

The last part of the research methodology discusses about the MGA technique implemented in the OSeMOSYS model to analyse the policy-driven structural uncertainties. The MGA methodology implementation provides insights into the flexibility and alternative policy directions that a country could select if additional financial investments were provided to the energy sector. Firstly, with all the ongoing policies imposed in the model that are in the process of implementation, and given the flexibility in terms of additional financial investment (or slack value), how different could the technology profile be? (see Figure 4.7 referring to the Near- optimal pathway_OF1_Inflex model or Near-optimal pathway_OF2_Inflex model as well as the scope and definition of the Inflex model (Inflexible model) as described in Table 4.1). Secondly, if some of the soft constraints are excluded and the model is given the freedom within the new objective function and investment flexibility to optimise the demand–supply pathways, then how could the technology profile be shaped? (see Figure 4.7 referring to the Near-optimal pathway_OF1_Flex model_L1-L4 or Near-optimal pathway_OF2_Flex model_L1-L4 as well as the scope and definition of the Flex model (flexible model) as described in Table 4.1 and the scope of L1–L4 scenarios as described in Table 3.14 of Section 3.3.2). This MGA methodology with flexibility on both constraints and investment is able to provide suggestions to energy planners and decision makers on how the current policies and technology implementation could be taken in different directions to achieve the emissions target. The technology profile suggests which sectors could be changed and modified in term of policy direction to achieve the target, e.g., in the household, commercial, industrial and transportation sectors or in power sectors where the other end-use sectors are switched to electricity. These methodologies, and details on the scenarios developed based on these methodologies, are discussed in the following sections.

122

4.1.1 Demand–supply pathways concept

Figure 4.1 summaries the methodologies of the MAED and OSeMOSYS models with the integration of MGA technique as discussed in Section 3.3.2. The MAED model is used to simulate long-term demand pathways for the household, commercial, industry and transportation sectors for Peninsular Malaysia, Sabah and Sarawak separately. The GDP and population growth variables are important scenario parameters assumed exogenously and used as the key function to calculate these demand projections. Changes in economic activity or population dynamics are anticipated in the future for a developing country such as Malaysia, as discussed in Sections 1.1 and 2.3. Therefore, understanding the range of assumptions on the possible evolution of the social, economic, and technological factors based on the current trends and governmental policies as well as capturing the uncertainties of the demand pathways in the MAED model is important (as discussed in Section 3.1.2 and 3.1.3). The multiregional and sectoral demand pathways obtained from the MAED model are soft-linked to the OSeMOSYS model.

Demand Profile Supply-Demand Profile

• Sectoral demands for 3 • multiregional (3 regions) • cross-border electricity trade regions generated separately • Based on GDP and population options growth Energy Resources Technologies/Parameters • Import/export (oil, gas, coal) • Household sector • Renewable energy potentials § number of urban and rural • Fossil fuel reserves dwellings Soft -linked to generate projections for year 2015-2050 § type of dwellings Technologies/Parameters § size of dwellings • Future power technologies MAED OSeMOSYS • Existing oil/gas/coal supply • Commercial sector Model Model • Installed capacity § potential labour force • Efficiency § energy intensity Simulation of the Optimisation of the • Availability factor

demand pathways supply pathways • Variable costs • Industrial sector • O&M costs § subsector GDP contribution • Fuel costs § energy intensity Policy Objectives/Constraints • Transportation sector • National policies § average distance travelled Output: • Fuel supply constraints § load factor Optimal and Near-optimal • Resource constraints § energy intensity • CO target 2 § private vehicle ownership Decarbonisation strategies

Policy Objectives/Constraints Objective Functions • National policies • Minimise cost • GDP growth changes • Minimise CO2 MGA • population growth changes • Minimise coal import technique

Figure 4.1: Summary of MGA-Hybrid model framework

123

The OSeMOSYS model is used to optimise the multiregional energy system pathways and to meet the demand projections generated exogenously from the MAED model. The multiregional structures of the household, commercial, industrial, transportation and power sectors are developed in the model, including the cross-border electricity trade options as discussed in Section 3.2.1. A number of assumptions are introduced in the model to improve the realism of the solutions, due to the key nature of the optimisation models to optimise the extreme switching of end-use technologies. These assumptions are based on the current energy issues, governmental energy objectives, technologies availability and resource potentials, as discussed in Section 3.2.2.

MAED model Scenarios

Figure 3.2 in Section 3.1.2 shows the three population scenarios on the median, high and low growth rates derived based on a set of assumptions elaborated in that section. Figure 3.4 in Section 3.1.3 shows the possible GDP projection scenarios in order to capture the uncertainties of future economic activities in the country. Three possible scenario combinations are derived in order to identify the spectrum of demand projection uncertainties based on demand drivers discussed in Section 3.1.2-3.1.7. This spectrum of demand projections is analysed in detail at the household, commercial, industry and transportation sectoral levels to identify the influence of social and economic parameters in each of these sectoral energy demand projections.

Section 3.1.4 outlines the studies that highlight the drivers and assumptions that were used to drive the energy demand projection of household, commercial, industry and transportation sectoral levels. Table A. 10, Table A. 16, Table A. 25 and Table A. 42 of Appendix A summarised the demand drivers considered based on the studies to driver the household, commercial, industry and transportation demand projections. For example, the industrial energy demand projections are significant with different GDP growth rates and GDP per capita is used as a reference to drive the passenger transportation demand scenarios. Similarly, freight transportation demand projections are in relation to different GDP growth rates.

To conclude, demand projection scenarios under the umbrella scenarios: REF, high and low for the industry, transportation, household and commercial sectors – are projected using the MAED model as shown in Figure 4.2-4.4. The details on the results of these demand scenarios are

124 further discussed in Section 5.1.1. These sectoral useful demand projections from the MAED model are soft-linked with the OSeMOSYS model for the three regions separately.

Figure 4.2: Useful Energy Demand of Reference scenario (Malaysia) in the MAED model

Figure 4.3: Useful Energy Demand of High scenario (Malaysia) in the MAED model

Figure 4.4: Useful Energy Demand of Low scenario (Malaysia) in the MAED model

125

OSeMOSYS model Scenarios

The structures of the household, commercial, industry and transportation sectors are developed in the OSeMOSYS model as discussed in Section 3.2 and each of these sectors is linked to a range of end-use technologies. The different end-use technology options for each sector are important for alternative fuel-switching. For example, the cooking appliances category of the household sector is linked to the gas and electric oven/hob technologies in order to provide the option for switching between them depending on the cost and constraints imposed in the OSeMOSYS model. The power sector is modelled to be linked with the end-use technologies of the four other sectors that consume electricity, as shown in Section 3.0. The need for more electricity under certain constraints (e.g., carbon constraints) will instigate the building of new power plants due to the switching of end-use technologies from fossil fuel-burning technologies to electric options.

There are three supply scenarios developed in the OSeMOSYS model based on the three demand scenarios soft-linked from the MAED model. The OSeMOSYS model optimises the system in order to meet these demand projections. Three supply pathways are generated to describe the spectrum of future energy systems and technology profiles in the country. The results of the optimal strategies (Reference (REF), high supply and low supply scenarios) under the long-term demand drivers in Malaysia are discussed in Section 5.1.2.

Figure 4.5: Three demand scenarios (Malaysia) – output of OseMOSYS model

In the following sections, the Reference (REF) supply scenario generated from the OSeMOSYS model based on the REF demand pathway is used as a benchmark scenario (also as an example scenario) to describe the flow of generating the optimal and near-optimal decarbonisation pathways. The REF scenario or Inflex model in Table 4.1 and Figure 4.7 is described as an

126 inflexible model and has all the ongoing policies and constraints imposed. The optimal and near- optimal decarbonisation pathways generated under the three umbrella scenarios (REF, high supply and low supply) discussed in Section 5.1 and Section 5.2.

4.1.2 Optimal decarbonisation pathways

This section describes the decarbonisation strategy that analyses the energy supply under the aspect of the lowest cost to society with the highest supply security in the long term (see Figure 4.7 which refers to the Optimal pathway_OF0_Inflex model). The optimal decarbonisation strategies analysed for the long-term demand drivers that take into account socioeconomic activity, policy changes, planned short- and long-term power plants, future technology learning curves and build rates of the technologies. The hybrid model is further optimised to analyse the different decarbonisation strategies under stringent carbon emission targets to investigate the deployment of technology, resources and an inter-regional electricity trade, as shown in Figure 4.6.

Figure 4.6: Emission Reduction Scenarios

Five energy system configurations are developed to evaluate optimal decarbonisation strategies. The REF scenario is constructed to provide a benchmark against the four other carbon emission reduction scenarios, namely the 20% carbon reduction scenario, the 40% carbon reduction scenario, the 60% carbon reduction scenario and the 80% carbon reduction scenario. The REF scenario is the least-cost optimal pathway which provides supply technology patterns in the absence of a carbon reduction target. The carbon emission projection produced in the REF scenario is used as a new constraint to generate four different emission scenarios. The carbon emission projection in the model is calculated based on the Tier 1 approach. Tier 1 emissions are estimated based on the fuel combusted in the source category and default emission factor (IPCC 2006). These stringent constraints are imposed starting from the year 2030 due to

127 introduction of the new technologies post-2025, for example, nuclear or multiregional undersea grid connectivity. The results of these different decarbonisation targets that influence the deployment of technologies, resources and an inter-regional electricity trade are discussed in Section 5.1.3.

OSeMOSYS Model

(Scenario analysis)

Optimal Decarbonisation Strategies Near-Optimal Decarbonisation Strategies

High Supply Scenario High Supply Scenario Low Supply Scenario Low Supply Scenario Flexible scenarios REF Supply Scenario REF Supply Scenario (Table 4.2)

Optimal Emission Scenarios Near- Optimal Scenarios (Emission Constraints) (Slack Values)

Objective Function: New Objective Function: New Objective Function: CO minimisation Least cost minimisation Import coal minimisation 2

Figure 4.7: The Optimal and Near-Optimal Scenarios Building Concept

4.1.3 Near-optimal decarbonisation pathways

This section focuses on analysing the near-optimal16 decarbonisation pathways. The near- optimal decarbonisation scenario approach focuses on introducing new objective functions to evaluate the policy-driven structural uncertainties. As discussed in Section 3.3, the structural uncertainty analysis focused on energy policy objectives examines two directions: decarbonising the power sector and decarbonising the energy systems. Figure 4.8 refers to the Near-optimal pathway_OF1 or Near-optimal pathway_OF2 and Figure 4.6 shows the methodology and scenario development comparisons in order to generate the optimal and near-optimal decarbonisation pathways under three umbrella scenarios (REF, high supply and low supply).

The analysis, which focuses on decarbonising the power sector and energy systems in a way that captures the energy policy objectives of the country, is important for evaluating the alternative supply pathways if additional investment is provided (Table 3.2 in Section 3.3.2). Two new

16 See Section 3.3.2 in Chapter 3 that explains the steps of introducing the MGA technique in the OSeMOSYS model to generate the policy-driven structural uncertainties. The cost generated based on the REF scenario is used as a new constraint in the model to generate the slack values of 1%, 5%, 10%, 15%, 20%, 25% or 30%. 128 objective functions are imposed in the model: an objective function to minimise coal imports, focusing on decarbonising the power sector and an objective function to minimise CO2 emissions, aiming to decarbonise the energy systems. The slack values of 1% to 30% are imposed in the model to obtain a range of near-optimal solutions. A range of assumptions and constraints related to the policies and energy objectives of the country are imposed in the OSeMOSYS model, as discussed in Section 3.2.2. To analyse the policy-driven structural uncertainties, the model is relaxed with the exclusion of constraints by categories in order to provide the flexibility for fuel-switching with the new objective functions and slack values of 1% to 30%.

As shown in Table 4.1, the policies and constraints are divided into four categories in the REF/inflexible model: near and long-term policies, near-term policies, near and long-term resources and near-term technologies. For the case of the flexible model, some of these policies and constraints are removed. For example, the renewable energy strategies on the planned introduction of solar, biomass and mini hydro until 2050 are removed to provide the model with the flexibility to introduce these technologies based on new objective functions and slack values. Other constraints are removed as well, such as near-term planning of new power plants, potential solar installation and introduction of new technologies (e.g., nuclear) based on the technology learning curve.

Table 4.1: Policies and Constraints imposed in the inflexible and flexible models

Models Policies Constraints in the model Constraints removed Reasons for the removal of constraints Inflexible Policy • Renewable energy strategies /REF (Near term (2011–2050) and long • Biomass and biogas potentials term) • Gas supply limited Policy Near term planning of new (Near term) power plants that are under construction and planned for construction Resources • Potential solar installation (Near Term • Hydro potential in Peninsular and long Malaysia and Borneo Term) Technology • Technology learning curve and (Near Term) introduction of new technologies • Smooth technology building rates • Operation of each existing power plants • Operation of existing end-use technologies Flexible Policy • Biomass and biogas potentials Renewable energy • These constraints (Near term • Gas supply limited strategies maybe subject to and long (2011–2050) changes in future term)

129

Policy Near term planning of depending on policy (Near term) new power plants implementation and that are under the planning phase of construction and future power plants planned for • These constraints construction were removed to Resources Hydro potential in Peninsular Potential solar obtain different (Near Term Malaysia and Borneo Islands installation policy directions in and long contrast to the Term) current planned Technology • Operation of each existing Technology learning commissioning of (Near Term) power plants curve and new power plants • Operation of end-use introduction of new • These constraints technologies technologies (coal were removed to and nuclear) provide the optimisation of flexibility in the model subject to the new objective functions and increase of cost investment

However, hard constraints such biomass or biogas potentials still remain in the flexible model due to the fact that, because of land use limitations for oil palm cultivation in the country, the maximum capacity of these technologies is expected to be achieved by 2030 (discussed in Section 3.2.2.1). Other constraints such as a hydro potential of 20GW remain in the model. The constraints on the build rate of existing power plants and end-use technologies are retained as well to maintain the realism of the modelling results and to prevent the extreme switching of these technologies in the short term.

Table 4.2: Summary of Flexible Scenarios Flexible Scenarios Constraint Categories and Constraints removed from the model Scenario 1 (L1) • Renewable energy strategies (2011–2050) Scenario 2 (L2) • Renewable energy strategies (2011–2050) • Near term planning of new power plants that are under construction and planned for construction Scenario 3 (L3) • Renewable energy strategies (2011–2050) • Near term planning of new power plants that are under construction and planned for construction • Potential solar installation Scenario 4 (L4) • Renewable energy strategies (2011–2050) • Near term planning of new power plants that are under construction and planned for construction • Potential solar installation • Technology learning curve and introduction of new technologies

130

Four near-optimal scenarios (L1–L4) are generated as shown in Table 4.2 (see Figure 4.8 that refers to the Near-optimal pathway_OF1_Flex model_L1-L4 or Near-optimal pathway_OF2_Flex model_L1-L4). These four scenarios are developed based on the gradual removal of constraints from the model to analyse the significance of these constraints in generating different technology profiles under the new objective functions and slack values. The results of these scenarios provide the policy oriented structural uncertainties and show the alternative policies approaches that Malaysia could implement. The robustness of these near-optimal decarbonisation strategies with respect to technological, environmental, and economic objectives for long-term planning is discussed in Section 5.2.

4.2 Conclusions

Figure 4.8 summaries the systematic scenario approaches based on the methodologies summarised in Section 4.1. Three demand scenarios are generated using the MAED model to capture the uncertainties of GDP and population growth in the future for Malaysia. These multiregional demand pathways are linked to the OSeMOSYS model. As described in Section 4.1, the model optimises the end-use technologies for alternative fuel options at sectoral level to meet the demand. The electricity trade option in the model is also important to understand the possibility of electricity trade at the multiregional level under various decarbonisation constraints.

The optimal pathway under the long-term demand drivers provide an outlook on the deployment of technologies, resources and inter-regional electricity trade options and how the different decarbonisation targets influence these options. Near-optimal pathways provide an outlook on how different and robust the technological-environmental-economic objectives for long-term planning compared to the current energy policies and the trade-off between different sectors in order to achieve decarbonisation strategies. The methodology also provides an outlook on the trade-off between sectors in terms of end-use technology switching if additional financial investment is allocated to the energy sector. These results can be compared to help decide the different ways in which the energy policy directions could be implemented.

Furthermore, it is also interesting to compare the near-optimal decarbonisation (see Figure 4.8 refers to the Near-optimal pathway_OF2_Inflex_L0 scenario and Near-optimal pathway_OF2_Flex_L1-L4 scenarios) and optimal decarbonisation (see Figure 4.8 refers to the

131

Optimal pathway_OF0_Inflex_20%-80% scenarios) methodologies. Although both methodologies are aimed at reducing the carbon emissions of the energy system, the differences in these approaches are important. The optimal decarbonisation methodology focuses on stringent carbon emission constraints in order to evaluate the emissions target at the least cost. The near optimal decarbonisation methodology focuses on the different ways in which the emissions targets could be achieved given the flexibility of cost investment in the energy system. Hence, these two methodologies produce different results on technology profiles, which provides suggestions about which methodology could be approached in the near and long term in order to achieve the emissions targets. This will be further discussed in Chapter 5.

Note: • The Median GDP/Median Population scenario referred to as the Reference (REF) supply scenario in the OSeMOSYS model. • The High GDP/High Population scenario referred to as the High supply scenario in the OSeMOSYS model. • The Low GDP/Low Population scenario referred to as the Low supply scenario in the OSeMOSYS model. • The REF/High/Low supply scenarios are considered as the ‘Inflexible Model’ (Inflex Model) as illustrated in Table 4.1. This model captures the key policies and constraints in the OSeMOSYS model. • As described in Table 4.1, the ‘Flexible Model’ refers to the model where the policies and constraints are gradually removed. Table 4.2 shows the four scenarios (L1–L4) generated using the Inflexible Model to demonstrate the significance of excluding the policy or constraints gradually and to generate an outlook on different policy directions that a country could possibly adopt given the flexibility of financial investment as well.

Figure 4.8: Summary of systematic development of optimal and near-optimal decarbonisation scenarios under the long-term demand pathways

To conclude, the formulation of these systematic scenarios’ development is important to address the issues discussed in Chapter 1. Malaysia is facing dynamic structural changes in terms of industrial development, socioeconomic activities and energy use. Thus, it is crucial to address

132 the demand–supply uncertainties. The country is also consuming large amounts of fossil fuels in various sectors due to growing demand. Yet domestic resources such as oil and gas are predicted to be depleted in the near future, which raises energy security issues, especially in the power sector. The power sector mainly depends on coal imports and limited gas supply; therefore, the country is considering alternative technology options such as renewables or a multiregional electricity trade. Moreover, most of the infrastructure in the power sector is due for retirement in the near term. The planning of new power infrastructure and investment in this infrastructure for the long-term is crucial. Besides addressing these issues, the country is also aiming to decarbonise various sectors in order to achieve the carbon emissions target to which Malaysia has committed.

As elaborated in this chapter, the methodologies developed and discussed in Chapter 3 as well the systematic scenarios developed based on these methodologies, aim to address these issues. The methodologies and the scenarios developed also aim to address the literature gap discussed in Chapter 2 and contribute to:

1. Literature that focuses on decarbonisation strategies in developing countries, that examines the correlation and trade-offs between the end-use sectors and growth of key socioeconomic parameters;

133

Chapter 5 Results and Discussions

This chapter presents and discusses the main findings of this research. It has three sections. Section 5.1 presents and discusses the results of the energy models, MAED (MAED-2, 2006) and OSeMOSYS (Howells et al., 2011) models for demand and supply modelling, respectively. The structure of these models is described in Chapter 3. This section also discusses the results of the deep decarbonisation scenarios. Section 5.2 outlines the near-optimal decarbonisation pathways. Section 5.3 provides a discussion of the findings and gives a summary of the results and highlights of the main findings.

5.1 Optimal energy system results

This section addresses the first and second research questions of this thesis:

The first research question is: what are the optimal decarbonisation strategies under the long- term demand drivers in Malaysia? This question focusses on understanding how the energy demand would influence the technology profile mix and the investments of supply technology profile towards achieving decarbonisation strategies.

The second research question is: how do the different decarbonisation targets influence the deployment of technologies, resources and inter-regional electricity trade? This question investigates and explores the targets on deep decarbonisation imposed in the system that would shape the supply technology profiles in the future and policy frameworks of the country.

This section is divided into three sub-sections on energy demand scenarios, hybrid model systematic scenarios and decarbonisation pathways in order to explore the answers to these questions. A summary of the hybrid model framework (Figure 4.1) is described in Section 4.1.1.

5.1.1 Energy demand scenarios

The assumptions of socio-economic parameters and the importance of these parameters in modelling the energy demand in household, commercial, industrial and transportation sectors is discussed in Sections 3.1.2 and 3.1.3 (Chapter 3). Figure 3.2 in Section 3.1.2 shows the three population scenarios on median, high and low growth rates derived from a set of assumptions elaborated in that section. Figure 3.4 in Section 3.1.3 shows the possible GDP projection

134 scenarios in order to capture the uncertainties of future economic activity in the country. As discussed in Section 4.1.1, three umbrella scenarios: REF, high and low scenarios are identified based on assumptions on demographic and economic activity discussed in Section 3.1.2 – 3.1.7.

5.1.1.1 Reference (REF) demand scenario

The OSeMOSYS model requires exogenous specification of useful energy demands for each sector, which have been calculated using the MAED model. Figure 5.1 shows the useful energy demand generated based on documentation and assumptions of household, commercial, industrial and transportation sectors described in Section 3.13 -3.17, and inputs and equations summarised in Section A.1-A.4 of Appendix A. These demand projections are used as inputs to OSeMOSYS model. Figure 3.7-Figure 3.10 shows the sub-module structures for household, commercial, industry and transportation sectors to develop the OSeMOSYS model and Table A. 44-Table A. 56 of Appendix A details the inputs of technologies captured in the OSeMOSYS.

Figure 5.1: Useful Energy Demand from the MAED model (REF scenario)

Figure 5.2 shows the energy demand of four major sectors (output of OSeMOSYS model) whereby the demand of each sector increases at different growth rates. The total final energy demand of this scenario increases at an average annual growth rate of 4.7% from 1875 PJ (2013) to 10125 PJ. As of 2013, the transportation sector was the major energy consuming sector; it accounted for about 51% of total energy consumption. Road transport, mainly passenger vehicles, consumes most of the energy in the transportation sector. Hence, the transportation sector is projected to grow at an average modest rate of 4.8% per year to reach an energy

135 demand of 5352 PJ in 2050. Meanwhile, the industrial sector is growing at the average rate of 5.0% per year from 611 PJ (2013) to 3732 PJ (2050).

Figure 5.2: Final energy demand by sector in Malaysia from the OSeMOSYS model (REF demand pathway)

The commercial sector accounts for about 10% of total energy in 2013, and the commercial share increases at an average growth rate of 4.1% per year to reach at least 816 PJ in 2050. The household sector is the lowest energy consuming sector, consuming only 6% of total energy in 2013. The demand is projected to grow at an average growth rate of 1.7% per year, from 122 PJ (2013) to 225 PJ (2050). Fossil fuels are expected to dominate the trend of fuel consumption in these sectors until 2050 (refer Section 5.1.2.1). In 2013, about 76% of total energy consumed by these sectors was based on fossil fuels, consisting mainly of oil, followed by gas and coal. The share of fossil fuel consumption in the transportation sector is about 66% in 2013. Oil is mainly consumed in the transportation sector and without carbon constraints; oil consumption remains the major source of energy in this sector over the modelled period. Gas is mainly consumed in the industry sector, followed by oil and coal: this account for about 29% of total fossil fuel consumption across all sectors in 2013. Meanwhile, electricity is mainly consumed by the household and commercial sectors, compared to fossil fuels. According to Saidur (2007) and Saidur (2007a), fossil fuels are mainly used for cooking purposes in these sectors and a fraction of gas is consumed in commercial cooling technology (discussed in Section 3.2.1.1 and Section 3.2.1.2).

136

5.1.1.2 High demand scenario

Figure 5.3 shows the useful energy demand generated based on documentation and assumptions of household, commercial, industrial and transportation sectors described in Section 3.13 -3.17, and inputs and equations summarised in Section A.1-A.4 of Appendix A, which are used as an input to OSeMOSYS. As mentioned earlier, Figure 3.7-Figure 3.10 shows the sub-module structures for household, commercial, industry and transportation sectors to develop the OSeMOSYS model and Table A. 44-Table A. 56 of Appendix A details the inputs of technologies captured in the OSeMOSYS.

Figure 5.3: Useful Energy Demand from the MAED model (High scenario)

In the high demand scenario, the total final energy demand is projected to grow at an average rate of 5.7% per year as shown in Figure 5.4. Transportation as the major energy consuming sector will expand from 958 PJ (2013) to 6755 PJ (2050) at average growth rate of 5.4% annually and remain as the major energy consuming sector by 2050. The industrial sector grows annually at an average rate of 6.4%, from 611 PJ (2013) to 6096 PJ (2050). The industrial sector accelerated and remain as the second major energy consuming sector, due to higher GDP growth rate from 5.0% in 2013 to 7.5% by 2050, as compared to REF (GDP growth rate of 5.0% to 6.0% by 2050). Meanwhile, commercial energy is projected to increase from 185 PJ to 1282 PJ with an average annual rate of 5.4%. The household sector is the least energy consuming sector, which is expected to grow from 122 PJ (2013) to 258 PJ (2050) at an average growth rate of 2.1% per year. The outlook of final energy demand by various sectors is illustrated in Figure 5.4.

137

Figure 5.4: Final energy demand by sector in Malaysia from the OSeMOSYS model (High demand pathway)

5.1.1.3 Low demand scenario

Figure 5.5 shows the useful energy demand generated based on inputs and equations described in Section A.1-A.5 of Appendix A for low scenario, which are used as an input to OSeMOSYS.

Figure 5.5: Useful Energy Demand from the MAED model (Low scenario)

With GDP growth rate decreasing from 5.0 % to 1.4% and population growth rate from 1.9% to 0.1% over the modelled period, this scenario estimated that the total final energy demand will grow from 1875 PJ (2013) to 5506 PJ (2050) at an average annual growth rate of 2.9% (Figure 5.6). As compared to previous REF and high demand scenarios, the industrial sector only grows at an average growth rate of 2.8% to reach a total demand of 1674 PJ in 2050. Meanwhile, the transportation sector increases at an average rate of 3.4% annually to achieve 3238 PJ in 2050. Commercial and household usage increase at average annual rates of 2.2% and 1.1%,

138 respectively. The energy demand of the commercial sector increased to 415 PJ while the household sector increased to 179 PJ by 2050.

Figure 5.6: Final energy demand by sector in Malaysia from the OSeMOSYS model (Low demand pathway)

Figure 5.7 reveals the growth of energy demand by scenarios, where each pathway increases gradually at different growth rates based on the assumptions discussed in Section 3.1.2-3.1.7.

Figure 5.7: Final energy demand by scenarios (REF, High and Low) in Malaysia

139

5.1.2 Hybrid model systematic scenarios

This section presents and discusses the results of the supply infrastructure that would be required to meet the energy demand scenarios presented in Section 5.1.1. The OSeMOSYS model is used to optimise the multiregional energy system pathways and to meet the demand projections generated exogenously from the MAED model. The multiregional structures of the household, commercial, industrial, transportation and power sectors are developed in the OSeMOSYS, including the cross-border electricity trade options as discussed in Section 3.2.1. The fuel consumptions by end-use technologies in all sectors including power sector modelled in the system and calibrated accordingly based on national energy balance (NEB 2013). A number of assumptions are introduced in the model to improve the realism of the solutions, due to the key nature of the optimisation models to optimise the extreme switching of end-use technologies. These assumptions are based on the current energy issues, governmental energy objectives, technologies availability and resource potentials, as discussed in Section 3.2.2. The following sections discuss the outlook on the technology profiles and the trends of fuel patterns in the future for the reference (REF), high supply and low supply scenarios. All scenarios are constructed without limitations on carbon emissions.

5.1.2.1 Reference (REF) supply scenario

The system is inter-linked between the multi-regional power sectors and all other sectors. The trade-off options between the least cost technologies across all sectors are modelled in the system. In the household sector, as discussed in Section 3.2.1.1, a range of existing and efficient end-use technologies are modelled and linked to alternate upstream fuel supplies. For example, the air-conditioning and lightings are introduced in the model as existing and efficient technologies to allow the options for fuel-switching in the model. The total household energy demand increases from 122.0 PJ in 2013 to 225.0 PJ in 2050, with electricity and LPG consumption dominating this sector over the modelled period, as shown in Figure 5.8. The household energy consumption by end of the modelled period is only 2% of the cumulative energy consumed (the total energy of the four main sectors) in 2050 and the trend of fuel consumptions by end-technologies is mainly based on electricity and LPG consumption without carbon constraints.

In the commercial sector, three end-use categories – cooking, general equipment and air- conditioning are modelled within each category. This is linked to alternative end-use

140 technologies for fuel switching flexibility in the model, as discussed in Section 3.2.1.2. Similar to the household sector, electricity and LPG are mainly consumed with total commercial energy demand increases from 184.0 PJ in 2013 to 816.0 PJ in 2050. Electricity is mainly consumed by machinery and air-conditioning, while LPG is used for cooking. This sector consumes about 8% of the energy by end of the modelled period with the existing air-conditioning switches to gas- based air-conditioning in 2028 due to cost competitiveness of gas air-conditioning.

For the industry sector, two end-use categories are modelled, machinery & equipment and electrical equipment, as described in Section 3.2.1.3. Within the machinery & equipment category, the alternative fuel options, e.g. oil, gas, coal, LPG and electricity are inter-linked with end-use technologies. The energy-intensive based industry consumes about 37% of the energy by end of the modelled period. The total industry energy demand increases from 611.0 PJ in 2013 to 3732.0 PJ in 2050. The results show that after the existing technologies phase out, the industrial end-use technologies will mainly consume coal due to cost and efficiency of the technologies modelled in the system.

In the transportation sector, five end-use categories are modelled to capture the road transport (passenger vehicles), road transport (light/heavy vehicles) as well as rail transport, air transport and sea transport sectors, which mainly consume petrol and diesel, as described in Section 3.2.1.4. Each of these end-use technologies are linked to alternative fuels to provide options for fuel switching in the system, yet there’s no fuel switching in this scenario observed, as the model is not subjected to carbon emission constraints. However, significant fuel-switching is observed when such constraints are imposed in the model, which will be further discussed in Section 5.1.3. The total transportation energy demand increases from 958.0 PJ in 2013 to 5352.0 PJ in 2050 and is mainly dominated by oil as the cheapest option.

In the power sector, fossil fuels, mainly gas and coal, are utilised for electricity generation. In 2013, the share of fuel input in power sectors was mainly from gas (43.7%) and coal (43.7%). This was followed by hydro (8.7%), oil (1.3%), diesel (2.0%) and renewables (0.7%). The country’s total installed capacity was 29.7 GW by end of the year 2013, with gross electricity generation of 143 497 GWh. However, a significant number of these current generation capacities are scheduled for retirement within the next ten years. Some of the other issues faced in the power sector are limited biomass and biogas potential due to natural resource availability and land use allocation for oil palm cultivation, potential exploration of solar installation, abundant hydro resources on the island of Borneo with high electricity demand in Peninsular Malaysia and

141 challenges for wind energy development. Natural resources constraints which differ at regional level, due to the geographical landscape as discussed in Section 3.2.2.1, were modelled in the system. Limited gas supply for the power sector, e.g. 1350 mmscfd, based on the subsided price in Peninsular Malaysia, and reduced domestic natural oil supply for electricity generation are further issues faced in the power system. These issues are captured as constraints based on current policies in the model, including technology build rates to improve the realism of future scenario forecasts.

Figure 5.8: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (REF supply scenario)

The structure of the power sector is modelled as explained in Section 3.2.1.5 and the model has ten technology definitions representing existing multiregional generators: coal, two types for natural gas, hydropower, biomass, biogas, geothermal, solar, solid waste and diesel. The initial capacities for these generator types are set to match reported assets of year 2013 for Peninsular, Sabah and Sarawak (NEB2013). The power plants that are under construction and planned are introduced in the model, especially the coal and gas power plants (EC 2014, EC 2014a, EC 2016, EC 2017, RECODA 2011, RECODA 2012, RECODA 2014). In order to diversify the energy mix in the model, renewable energy strategies in the policies such as the National Renewable and Action Plan, the Eleventh Malaysia Five-Year Plan, the Small Renewable Energy Power Programme (SREP) and the Fifth Fuel Policy are reflected in the model. These renewable energy policies are implemented in the country to harvest indigenous renewable energy resources such

142 as biomass, solar PV and solid waste. Additional generation technologies such as new supercritical coal power plants, new natural gas-fired power plants, new hydropower plants, nuclear power plants and the expansion of renewables are introduced as alternatives in the optimised system to meet future demand.

As most of the current power plants will be retired by 2032, the structure of the energy mix profile shifts mainly towards coal, gas and hydro power plants as shown in Figure 5.11. The coal power plant options become more economical compared to other power plant options for to meet future demand, followed by gas and hydro technologies. The current system consists of mainly gas power plants that retire by 2032 and will be replaced with new gas and coal power plants. The limit on the subsided gas supply to the power sector is consistently consumed throughout the study period and there is the option of building more of gas power plants in future based on gas supply at market prices. Hence, coal technologies facilitate the transition away from gas (~2025–2050) and take a primary role in the system. The flexibility in the system to generate electricity from hydro technologies in Sabah and Sarawak due to the abundant availability of hydro resources (hydro potential is limited in Peninsular Malaysia) facilitates the system, with increasing hydro capacities (~2030–2050) after coal and gas power plants.

Other technology options make smaller contributions to the generation mix. Renewable technologies play minor roles in the system due to the potential availability of these technologies defined in the model. They also are disadvantaged by high capital costs compared to gas and coal technologies. Biomass generated from palm oil waste used as fuel to generate electricity and biomass capacity increases to reach maximum electricity generation of 4.0 TWh by 2040 and remains constant until the end of the modelled period. Other renewable technologies, such as geothermal and solid waste, also contribute towards electricity generation, which gradually increases and remains constant after 2040. Due to the technology learning curve, the capital cost of solar PV gradually decreases compared to biomass technology. Yet, due to solar PV having a lower capacity factor compared to other RE technologies and not being able to be used to generate electricity at night, electricity generation from solar PV is less significant, given that the REF scenario is also not taking into account the constraints on carbon emissions. By 2045, the cumulative installation of 5GW solar in the system is planned in the renewable policy (KeTTHA 2009). In addition, to ensure that solar PV is not generating electricity at night in the OSeMOSYS model, electricity generation by fuel type according to the respective time slices (see Section 3.2.1.5 on time slices details) are run for the following scenarios; REF

143 supply pathways, 20%, 40%, 60% and 80% decarbonisation of the REF scenario as presented in Section B.2 of Appendix B.

Hence, a detailed technology and resources evaluation for Malaysia is important in order to plan for future capacity developments in the energy system. For example, the system forcasted that the electricity generation based on coal increases from 40% (2013) to 60% (2050), while gas decreases from 45% (2013) to 21% (2050). The CO2 of power systems is about 92 mton (2013) and is expected to increase to 286 mton (2050). If the country wants to achieve decarbonisation strategies in future, the power system, which is based on mainly coal, needs to be revised.

5.1.2.2 High supply scenario

The pattern of energy consumptions by end-use technologies in household, commercial and transportation sectors is similar to the fuel consumption patterns observed in these sectors in the REF scenario, as shown in Figure 5.9. For example, the total household energy demand increases from 122.0 PJ in 2013 to 258.0 PJ in 2050 (approximately about 2% of total fuel consumption in 2050) with electricity and LPG consumptions dominating this sector. In the commercial sector, electricity and LPG are mainly consumed, with total commercial energy demand increasing from 185.0 PJ in 2013 to 1282.0 PJ in 2050, which is about 9% of total energy consumption of the modelled sector in 2050. For transportation, energy demand increases from 958.0 PJ in 2013 to 6755.0 PJ in 2050, mainly dominated by oil, where total energy consumption is about 47% of the modelled sectors in 2050. In the industry sector, the total energy demand increases from 610.0 PJ in 2013 to 6096.0 PJ by 2050, which is the second major energy consuming sector, totalling up to 42% of the cumulative energy consumption of sectors in the system by 2050.

The results show that coal is gradually consumed in industry sector. It is observed in the system that fuel-switching in this sector is related to trade-offs across all sectors due to the cost competitiveness and efficiency of end-use technologies. In the case of industry sector, coal consumption increased due to higher coal industrial boiler efficiency as compared to gas, oil and electricity boilers based on the data obtained from ETSAP database (IEA-ETSAP E-TechDS). Nuclear development is gradually increasing beginning in 2036. The electricity demand increases to 36% more in 2050 as compared to the REF scenario and this additional increase in electricity demand is met by gas, nuclear and RE technologies. The results show that the gas installation gradually increases after 2042 to meet the demand until 2050 (~104.0 TWh electricity

144 generation in 2042 as compared to REF scenario of 53.0 TWh in the year 2042, based on gas). The limited gas supply at subsidised prices is completely utilised and further installation of gas power plant instigated the consumption of the gas supply at the market price. Total electricity generation from gas over the study period is approximately 4278.0 TWh (about twice the electricity generation based on gas compared to REF scenario).

Figure 5.9: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (High supply scenario)

The pattern of coal installation is similar to REF scenario as coal capacity is fully installed by the end of the period (the installation cap on coal is constrained based on national policies and planned coal power plants). Hydro potentials are exhausted with a total of 2757.0 TWh electricity generated over the study period. Hydro potentials in Peninsular Malaysia are fully explored and also hydro potentials from Sarawak are harvested to meet this additional electricity demand. Nuclear installation started in the year 2036 as compared to REF scenario, whereby nuclear capacity was not introduced in the modelled system. Cumulative electricity generation from nuclear over the modelled period is about 782.0 TWh for the high supply scenario. The cumulative electricity generation from renewables increased to 731.0 TWh compared to the REF scenario with a cumulative electricity generation of 464.0 TWh over the modelled period. Biomass capacity installation gradually increases starting 2028 to reach a maximum generation of approximately 12.0 TWh by 2032. Renewables, including solar

145 technology, are projected to follow similar installation patterns as of the REF scenario due to the competitive capital investment of renewable technologies over the modelled period and capacity factor of the solar technology. To conclude, the increase of the electricity demand is fulfilled by gas, hydro, nuclear and biomass technologies as shown in Figure 5.11 and details on total cost and emission of the system is shown in Table 5.1 of Section 5.1.3.

5.1.2.3 Low supply scenario

The possibility of low demand scenario in the country is discussed in Section 3.1.2 and Section 5.1.1. The fuel transitions across all sectors are illustrated in Figure 5.10. In the household sector, the energy demand increases from 122.0 PJ in 2013 to 179.0 PJ in 2050 and the pattern of fuel consumption is similar to the REF scenario. Similarly, in the commercial sector, the total energy demand increases from 185.0 PJ in 2013 to 415.0 PJ in 2050 and the types of fuel consumed are similar as in the commercial REF scenario. The industry sector in this scenario also reflects similar patterns of fuel consumption as in REF industry scenario, where coal and oil are largely consumed throughout the modelled period. The industrial energy demand increases from 611.0 PJ in 2013 to 1674.0 PJ in 2050. The transportation sector has a similar use of fuels as of in the REF scenario, where energy demand increases from 958.0 PJ in 2013 to 3238.0 PJ in 2050.

Coal technology still dominates the power sector, followed by hydro technology. As the existing gas power plants retire, coal and hydro power plants facilitate the transitions to meet the demand and gas power plants are phased out of the system. The contribution of the RE technologies is less significant in this case with cumulative electricity generation of 327.0 TWh over the modelled period. Solar demonstrated a similar installation pattern as in the REF scenario whereby a maximum electricity generation of approximately 7.2 TWh is achieved by 2023. Other renewables consist of mainly geothermal, introduced in 2016, which increases electricity generation gradually to a maximum gtoeneration of ~1.0 TWh by 2028. A cumulative of 1521.4 GW of total installed capacity consists of existing and newly installed power systems forecasted to meet the low demand compared to cumulative of 1905.9 GW total installed capacity in the REF scenario over the modelled period.

146

Figure 5.10: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (Low supply scenario)

Figure 5.11: Electricity generation by fuel mix (Low supply, REF and High supply scenarios)

147

5.1.2.4 Conclusion

To conclude, this sub-section discusses the technology profiles of three scenarios based on demand pathways generated from the MAED model inter-linked with the OSeMOSYS model. The structure of the power sector and all other sectors (discussed in Section 3.2) are significantly interlinked, which contributes towards the fuel-switching of end-use technologies and fuel resources utilisation in the power sector. Analysing the power sector in detail across all scenarios shows that the future capacity development in the system is dominated by coal technologies (Figure 5.11 and Figure 5.12). It is observed that the crucial period for the changes in power technology installations is between 2020–2032, where most of the existing gas power plants are retired and new coal power plants are developed. After all the gas and hydro potentials introduced in the model are installed, nuclear and renewable technologies are developed (in the high supply scenario, as explained in Section 5.1.2.3). In all three scenarios, the end-use technologies of household, commercial, industrial and transportation are mostly based on fossil fuels.

Figure 5.12: Total Electricity Generation and Installed Capacity in Malaysia over modelled period (Low, REF and High supply scenarios)

148

If the energy demand is higher than the high supply scenario projection (taking into account that all the potential availability of the technologies based on current policies are modelled and captured in the energy system), the country would need to introduce additional solar and nuclear technologies to meet the demand. For example, in the high supply scenario, the coal and gas as scheduled in the policies already installed provides the largest amount of energy, followed by total new installed capacity of hydro (~17 GW), nuclear (~12 GW) and solar (~22 GW) over the modelled period. So, if the demand increases further, the available options are renewables or nuclear technologies. Nuclear technology introduced in the model based on the government plan to have nuclear power plant build in Peninsular Malaysia by the year 2030 (Saad et. al. 2017). Hence, deployment of various technologies and potential capacities, for example renewables, would significantly influence the outlook of capacity development as well as the policy planning and implementation if the demand changes in future. This means that if the country would want to adopt decarbonisation targets, then serious consideration on the deployment of renewables, nuclear or multiregional electricity trade need to be done to assess the alternative options.

5.1.3 Decarbonisation pathways

This subsection details the results of the supply infrastructures to meet increasing electricity demand due to the fuel switching of end-use technologies across all sectors under different decarbonisation targets. These targets are imposed on three scenarios: the REF, high and low supply-demand pathways presented in Sections 5.1.1 and 5.1.2 The decarbonisation targets of 20%, 40%, 60% and 80% carbon emission reductions by 2050 are imposed on these three scenarios to obtain results on how these targets influence the deployment of technologies, resources consumptions and infrastructure development for multiregional electricity trade. The discussion on structures of end-use technologies across four main sectors linked to power systems is illustrated in Section 3.2.

5.1.3.1 Reference (REF) supply deep decarbonisation scenario

Five energy system configurations were developed to evaluate the decarbonisation strategies. The Reference (REF) scenario was constructed to provide a benchmark against the four other carbon emission reduction scenarios. The REF scenario is the least cost optimal pathway that provides supply technology patterns in the absence of a carbon reduction target. The carbon emission projection produced in the REF scenario is used as a new constraint to generate four

149 different emission scenarios. This REF carbon emission projection gradually decreased the beginning year 2030 to 20%, 40%, 60% or 80% emission reduction levels by 2050 as shown in Figure 5.13.

Figure 5.13: Decarbonisation Scenarios (benchmarking the REF scenario)

20% decarbonisation target benchmarking the REF scenario

The total final energy consumption by fuel for all sectors in Malaysia under this decarbonisation scenario is illustrated in Figure 5.14. In the household sector, the fuel transitions for cooking technologies are visible between years 2046–2050. The results suggest that gas-cooking technology switches to electrified stoves beginning in 2047 and electricity consumption in cooking gradually increasing towards the end of the modelled period (approximately 6% of electricity and 94% of gas consumption in cooking by 2050). A similar fuel-switching pattern in the commercial sector is observed for cooking technologies between the years 2046–2050, where commercial gas-cooking technology switches to electrified stoves beginning in 2046. The installation of electrified stoves gradually increases to about 6% of electricity consumption and 94% of gas in commercial cooking by 2050.

Meanwhile, the consumption of electricity by commercial machinery gradually increases compared to commercial oil-machinery to a proportion of 7% of machinery oil and 93% of machinery electricity consumption by 2050. In the industry sector, industrial machineries consume mainly coal, followed by electricity and oil by 2050. For the transportation sector, fuel- switching to electric and gas cars will be observed to achieve shares of petrol (64%), diesel (7%), gas (28%) and electricity (1%) by 2050. Also, electric motorcycles are observed by 2050 with shares of petrol (25%) and electricity (75%). At this point of carbon constraint, no fuel transitions for trucks and buses vehicles are observed in the results. The air and marine technologies meet

150 a single energy demand respectively, therefore no changes are anticipated from these end-use technologies.

Figure 5.14: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (20% decarbonisation of the REF scenario)

The demand for electricity increases in the 20% decarbonisation scenario, as the end-use technologies in the household, commercial and transportation sectors switches from fossil fuel- based to electricity-based technologies starting in 2040. The electricity consumption by 2050 increases by 14% as compared to the REF scenario due to electrification of end-use technologies across all sectors. Figure 5.33 shows the details on total electricity generation trends over the modelled period for four energy system configurations benchmarking REF scenario. To achieve this emission target by the end of the study period, the fuel-switching to electricity in all sectors instigated the power system transition from coal-based technology to nuclear, hydro and renewables. The results show that these technologies are installed towards the end of study period, for example, nuclear installation starts in 2043, additional hydro in 2030 and solar installations in 2036. Most of the additional power technologies installed approximately in the last 12 years of the study period to meet the electricity demand of the end-use categories.

In the process of decarbonising the power sector, cumulative coal consumption in the power sector is gradually reduced starting in 2033, and by at least 23% decreased in 2050 as compared to the REF scenario in 2050 and switching to low carbon technology options is increased. For

151 example, cumulative gas consumption in the power sector in 2050 is increased by at least 5% as compared to the REF scenario in 2050. Furthermore, there is a total new installed capacity of nuclear (~12GW), hydro (~25GW), biomass (~2.2GW) and solar (~22GW) needed to achieve this carbon emission target over the modelled period. The total system cost of this decarbonisation target over the modelled period is about 1.1 times higher than the REF scenario (Table 5.1) with total system carbon emissions of 15 826 Mton. In the effort to decarbonise the power system, the total carbon emissions produced from electricity generation is about 5091 Mton, which is about 11% less than carbon emissions produced as compared to the emission of power the REF scenario.

40% decarbonisation of the REF scenario

To achieve this stringent target, the fuel switching in end-use technologies to alternative low carbon fuels and electricity are observed between years 2036–2050 as shown in Figure 5.15. The fuel switching from LPG to electricity for cooking stoves in the household sector are visible starting in 2038 and gradually increase to achieve a fraction of electricity (6%) and LPG (94%) consumption by 2050. Similarly, the commercial cooking technologies consuming LPG switching to electricity beginning in 2037 to reach approximately the same levels as electricity (6%) and LPG (94%) consumption by 2050. The fuel switching trends in both household and commercial cooking technologies are similar to the 20% decarbonisation scenarios. The results show that commercial machineries consuming electricity gradually increasing until the end of the modelling period, which accounts for about 58% electricity of total energy consumed in commercial sector.

In the industrial sector, the transition from fossil fuel to electricity are observed. By 2050, coal (49%) mainly consumed in industry followed by electricity (42%), oil (8%) and LPG (1%). For the transportation sector, the system suggests that there will be fuel switching for cars towards gas starting 2031.17 Diesel and petrol fuels are gradually reduced and terminated in 2046 and 2045 respectively, where the share of fuels for cars is about 1% of electricity and 99% of gas by 2050. It is also observed that the diesel buses and trucks are switched to gas vehicles starting in 2043. By 2050, the fuel transition in buses achieve a fraction of 7% of diesel and 93% of gas consumption, while in trucks there is approximately 10% of diesel and 90% gas consumption.

17 The fuel transition from electric vehicles and then to natural gas vehicles requires supply chain modelling. Spatial supply chain modelling and infrastructure of the optimisation model capturing the transport fuel transitions could be carried out as future work.

152

Similarly, fuel transitions from diesel and petrol to electricity consumption are observed for motorbikes and trains.

Figure 5.15: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (40% decarbonisation of the REF scenario)

The demand for electricity due to fuel transitions of end-use technologies from fossil-based to electricity starts in 2036, where the cumulative electricity demand by 2050 increases about 14% as compared to the REF scenario (Figure 5.30Figure 5.33). Figure 5.15 shows that electricity demand increases due to fuel switching to electricity started in 2036. For example, commercial cooking stoves switching to electricity from LPG in 2037 and household cooking stoves gradually switch to electricity from LPG consumption in 2038. In the power sector, all the potential installed capacities introduced in the model are installed to meet the electricity demand and to achieve the 40% emission constraint imposed by 2050. To meet the electricity demand, installation of gas power plants gradually increases after 2032 (additional gas installation in this case is based on gas supply at market price), hydro increases after 2036, solar increases drastically after 2037 and nuclear installation starts in 2035 and increases further to reach full capacity.

The increasing electricity requirements are met by cumulative electricity generated by gas (27.8%), coal (34.7%), hydro (24.9%), other renewables18 (5.6%) and nuclear (6.7%), as shown in

18 Other renewables: solar, biomass, geothermal and solid waste

153

Figure 5.31. A total new installed capacity of nuclear (~12GW), hydro (~25GW) and other renewables (~25GW) is needed to achieve 40% carbon emission targets over the modelled period. The total system cost is about 1.3 times higher relative to the REF scenario, as shown in Table 5.1, where the total carbon emission of the system is about 14 217 Mton (about 17% less of the REF scenario’s total carbon emissions). With the introduction of nuclear, hydro and other renewables to decarbonise a power system, the total carbon emission produced in the power sector is about 21% less as compared to the REF scenario with a total investment cost of USD 93 billion to develop these power capacities over the modelled period.

60% decarbonisation of the REF scenario

To meet this deep decarbonisation constraint, the fuel transitions are more concentrated in major energy consuming sectors, such as the industry and transportation sectors, as shown in Figure 5.16. The industrial machineries are recommended to switch to low-carbon fuels to achieve this carbon constraint. These industrial technologies are gradually replaced by machinery consuming gas starting in 2046 and electricity in 2036. By 2050, the industry sector consumes about 99% electricity and 1% of gas. In the transportation sector, fuels such as petrol and diesel consumed by cars are eventually terminated in 2040. These fuels are switched to electricity and gas to reach a fraction of 97% gas fuelled cars and 3% electric cars by 2050. Similarly, both diesel buses and diesel trucks switch to gas-based vehicles starting in 2038. By 2050, the buses are consuming about 93% gas and 7% of diesel, while the trucks are forecasted to consume about 90% gas and 10% diesel.

For the household sector, the cooking technologies switch from LPG to electricity starting in 2036. During this period, the power sector is starting to further increase the installation of power plants to meet the electricity demand. As the electricity demand increases from other sectors and due to the cost competitiveness of end-use household technologies as well as to satisfy the carbon constraint at the same time, fuel transitions in the household sector are also observed to switch to more efficient air-conditioning technologies beginning 2028. In the commercial sector, the fuel switching of cooking technologies from LPG to electricity starting in 2034 is observed in the results. Furthermore, the commercial machineries also optioning to electricity and electricity consumption by the commercial machineries gradually increasing as compared to oil-based commercial machineries.

Due to electrification of end-use technologies in the transportation sector (electric cars and electric motorbikes introduced in 2033), the industrial sector (fuel switching to electricity beginning year 2036) and the commercial sector (electricity machinery gradual increase starting

154 in 2036), the cumulative electricity generation is observed to increase by at least 6% compared to the REF scenario by 2050 (Figure 5.18). Imposing a 60% carbon emissions reduction in the model shows that as the end-use technologies in all sectors switch fuels to gas and electricity, the increasing demand for electricity is fulfilled by installation of nuclear, solar power and a multi-regional electricity trade. By 2050, the coal fuel in power sector is phased out (coal in 2045) and all the potential future power plants introduced in the model (the potentials as of the REF scenario) are completely installed. All hydro resources in Sarawak are exhausted to meet the electricity demand, which include an average of 64.2 TWh of electricity imported from Sarawak to Peninsular Malaysia starting in 2050.

Therefore, additional flexibility given in the model for optional technologies19 to meet this increasing electricity demand and to satisfy the carbon constraints of 60% reduction by 2050. The model results suggest the abundant installation of nuclear, hydro, solar or electricity trade infrastructure. This includes the electricity trade between regions, from Sarawak to Peninsular Malaysia with an average of 64.2 TWh starting in 2050 and from Sabah to Sarawak, with an average of 17.2 TWh starting in 2050. Hence, the system requires total new installed capacities of nuclear (~20 GW), hydro (~25GW) or solar (~142GW) over the modelled period as shown in Figure 5.51. The switching of the end-use technologies as well as the decarbonisation of the power sector would cost at least two times more compared to the total system cost of the REF scenario and would achieve a system carbon emissions reduction of 12335 mton (28% less as compared to REF) over the modelled period. The decarbonisation of the power sector includes a high level of installation of solar and multi-regional trade infrastructure, requiring a total investment cost of USD 164 billion. Over the modelled period, this would result in at least a 43% emissions reduction as compared to the REF scenario in the power sector, as shown in Table 5.1.

19 The additional options e.g. solar and nuclear introduced to give the model flexibility to achieve the deep carbon constraints (these technologies are in addition to the technologies introduced in the model based on key energy policies and energy resource potentials explained in Section 3.2.2.1) 155

Figure 5.16: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (60% decarbonisation of the REF scenario)

80% decarbonisation of the REF scenario

The fuel transitions in this scenario are similar to the previous 60% scenario, focused mainly on the transportation and industrial sectors as illustrated in Figure 5.17. For the case of industry sector, machinery technologies switch between coal, gas, oil and LPG during the modelled period. Eventually, these machineries start to switch to electricity in 2033 to achieve a fraction of 84% electricity, 11% oil, 3% coal and 1% LPG by 2050. In the transportation sector, the electric cars are gradually increasing starting in 2042. By the end of the modelled period, cars are forecasted to consume about 99% electricity and 1% diesel. It is observed in the system that the gas is consumed starting year 2028 before gas consumption is reduced and phased out by 2050, and gradually replaced with electricity vehicles.

156

Figure 5.17: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (80% decarbonisation of the REF scenario)

During this phase where the vehicles switch to gas consumption, the results show that new power installations are emerging, as most power plants are decommissioning by 2034, to meet the increasing electricity demand in the household, commercial and industry sectors. Hence, the system is introducing gas during this phase as the least cost option and to achieve the decarbonisation strategy. As switching to electric vehicles increases (for example cars, motorbike and trains switching to electricity consumption) under emission constraints, electricity generation increases further to meet the demand and to balance the trade-off between all sectors in the system, as shown in Figure 5.33. The system projects that switching to electric vehicles would consume approximately 17% electricity of total energy in transportation sector by the end of the modelled period. In the household sector, the switch to higher efficient air-conditioning and electrical appliances is observed starting in 2027 while electricity for cooking is introduced in 2032. In the commercial sector, the machinery technologies are gradually increasing electricity consumption after 2036, whereas electricity for commercial cooking is gradually introduced starting in the year 2032. By 2050, appliances in the household sector consume about 97% electricity of the total household energy consumption,

157 while commercial end-use technologies consume about 73% electricity of total commercial energy consumption.

Figure 5.18: Electricity generation by fuel mix (decarbonisation scenarios benchmarking the REF scenario)

The system also projected that all power technologies’ capacities introduced in the model are installed (including electricity trade between regions) as the end-use technologies in all sectors switching towards electricity. Yet, as the constraints outlined in Section 3.2.2 are still restricted in the model, the model requires an additional power technology installation starting in the year 2040 to meet the carbon constraints. Therefore, given the flexibilities in the model in terms of technology capacities, the results show the preferences for the high installation of nuclear, solar or trade infrastructure. For example, in the 80% decarbonisation scenario, at least 257 GW of installed capacity of solar and 20 GW installed capacity of nuclear are needed to meet the electricity requirement in 2050 (the installed capacity mix in that year consist of solar (57%), biomass (18%), other RE (10%), hydro (6%), nuclear (4%), gas (4%) and coal (1%)). During the 80% decarbonisation of REF scenario, the total new installed capacity for solar is 54% percent out of the total new installed capacity during this period. Furthermore, in the 80% decarbonisation of REF scenario in 2050, the total installed capacity mix consist of solar (57%),

158 biomass (18%), other RE (10%), hydro (6%), nuclear (4%), gas (4%) and coal (1%). Therefore, 43% of the installed capacity mix (equivalent to 194 GW) is contributing by technologies other than solar. High solar installation faces system balancing and peak load issues, and the results on total installed capacity of 80% decarbonisation of REF supply scenario (Table B.7 of Appendix B: page 273) show that gas and coal installed as back-up plants and not generating electricity. The result also comprises 57 TWh of multiregional electricity trade starting in the year 2041. The feasible electricity generation is renewable and nuclear-powered based on technology constraints introduced in these mitigation scenarios. The switching of the end-use technologies as well as the decarbonisation of the power sector would cost four times more compared to the total system cost of the REF scenario, reducing the total system carbon emission at least by 41% as compared to the REF scenario. The development of the power capacities cost at least four times higher as compared to the REF scenario, achieving power emissions reduction of at least 58%, as shown in Table 5.1.

5.1.3.2 High supply deep decarbonisation scenario

A similar methodology was used to generate five energy system configurations as assumed for the REF scenario. The emission projection of the high cost optimal scenario is used as benchmark to generate 20%, 40%, 60% or 80% emissions reduction scenarios, as shown in Figure 5.19.

Figure 5.19: Decarbonisation Scenarios (benchmarking High supply scenario)

20% decarbonisation of high supply scenario

The energy demand projection generated from the MAED model soft-linked to OSeMOSYS is presented in sub-section 5.1.2. To meet this emission constraint, the fuel transition patterns occur in all sectors, especially fuel-switching to gas starting in 2031, focusing on the transportation sector as shown in Figure 5.20. Electricity and gas are mainly consumed by cars

159 towards the end of modelled period. In the commercial sector, at least 64% of the total fuel consumed by 2050 is electricity. To meet the increased electricity demand by 10% more as compared to the high supply scenario in 2050, nuclear installation started in 2035, while solar and hydro installations increased starting in 2037 and coal capacities are reduced beginning in 2045. By 2050, all the potential power capacities are exhausted (potentials as outlined in Section 3.2.2) to reduce total carbon emissions of the system by at least 10% as compared to the high supply scenario over the modelled period, as shown in Table 5.1. The total power capacity investment cost is about 1.1 times more as compared to the high supply scenario to achieve cumulative new installed capacities of 12GW nuclear, 25GW hydro, 22GW of solar and other renewables of 3.5GW. Over the time horizon, the total system cost is about 1.2 times higher to reduce the total carbon emission by 10% as compared to the high supply scenario.

Figure 5.20: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (20% decarbonisation of High supply scenario)

40% decarbonisation of high supply scenario

Similar to fuel transition patterns in the previous decarbonisation of the system (20% scenario), fuel-switching to gas and electricity is observed in the all sectors (Figure 5.21). Fuel-switching to electricity consumption starting in 2034 and gas consumption by cars in 2029 is suggested to meet this constraint. By 2050, at least 1% of the electricity of the total energy in transportation sector is consumed by cars. Meanwhile, electricity consumption by commercial cooking technologies is introduced in 2034 and commercial machineries consuming electricity gradually

160 increasing towards the end of modelled period. By 2050, around 64% of total consumed energy in the commercial sector will be electricity.

As the end-use technologies switch to electricity, the cumulative electricity demand over the modelled period increases about 10% as compared to the high supply scenario. To meet this increasing electricity demand, cumulative new capacities of 12GW nuclear, 25GW hydro, 142GW solar and 22GW other renewables are installed. As all the potential capacities introduced the system are developed, additional solar, biomass and solid waste capacities are needed to meet the increasing demand towards the end of the modelled period. The total new capacity investment is about twice higher and the total power emissions are reduced by at least 22% as compared to the high supply scenario. As fuel-switching occurs across all sectors, the total system cost increases by 1.4 times and the total system emissions are reduced by 21% as compared to the high supply scenario, as shown in Table 5.1.

Figure 5.21: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (40% decarbonisation of High supply scenario)

60% decarbonisation of high supply scenario

To meet this stringent constraint in the system, fuel transitions occur across all sectors as shown in Figure 5.22. Fuel-switching is observed in transportation sector, where electricity consumption by cars increases beginning in 2049 and gas consumptions by cars, buses and

161 trucks increases in 2037. By 2050, total energy consumption in transportation sector as observed in the results will be 20% electricity and 61% gas. Commercial cooking technologies switch to electricity in 2032 and commercial machineries gradually switch to electricity. Meanwhile, household cooking technologies are projected to switch to electricity in 2033 and air- conditioning technologies are forecasted to switch to higher efficiency cooling technologies. In the industry sector, the fuel switches occur with increasing electricity and gas consumption towards the end of the time horizon.

Figure 5.22: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (60% decarbonisation of High supply scenario)

With most end-use technologies switching to electricity over the modelled period, the electricity demand in 2050 is increased by at least 34% as compared to the high supply scenario in 2050. To meet this increasing demand and to decarbonise the power sector, about 277GW of solar installed along with 20 GW of nuclear and 26 GW of hydro are installed in 2050. An average multiregional electricity trade of 57 TWh from Sarawak to Peninsular (2048–2050) and about 17 TWh from Sabah to Sarawak (2048–2050) is suggested for the system. The investment to develop these recommended capacities is about three times more expensive in order to reduce approximately 46% cumulative power emissions as compared to the high supply scenario over the modelled period. As most end-use technologies across all sectors switch to electricity, this instigates the further development of power capacities, as shown in Figure 5.24. The total

162 system cost over the modelled period is about 2.1 times more expensive relative to total system cost of the high supply scenario, as illustrated in Table 5.1.

80% decarbonisation of high supply scenario

The system struggles to solve in order to meet this deep decarbonisation target, as the most of the end-use technologies are electrified towards the end of the time horizon, creating tension in the system to decarbonise the power sector, as shown in Figure 5.24 and Figure 5.33. Similar to earlier fuel transition patterns, the household cooking technologies switch to electricity in 2032 while higher efficient air-conditioning is introduced in 2027. Meanwhile, commercial cooking technologies start to consume electricity in 2032 and, as observed in earlier stringent decarbonisation strategies, commercial machineries gradually increase to electricity consumption. In the industrial sector, electricity is increasingly consumed and by 2050; at least 87% of total energy in this sector is based on electricity.

Figure 5.23: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (80% decarbonisation of High supply scenario)

By 2050, in the transportation sector, electricity and gas dominated the total energy consumption. Fuel switching in the system by vehicles to electricity consumption starts in 2031 while gas consumption observed for cars, buses and trucks begins in 2028. Electricity demand is

163 predicted to increase by at least 59% compared to the high supply scenario, with total capacities of 12GW nuclear, 26GW hydro and 277GW of solar suggested to be installed in the system in 2050. Decarbonisation of the power sector reduces the power emissions by at least 60% compared to the high supply scenario, yet capacity development and investment is really expensive, especially with the development of the electricity trade infrastructures as presented in Table 5.1.

Figure 5.24: Electricity generation by fuel and electricity consumption by sectors (High decarbonisation scenarios)

164

5.1.3.3 Low supply deep decarbonisation scenario

The carbon emission produced in the Low supply scenario is used as the benchmark to produce four decarbonisation scenarios as shown in Figure 5.25.

Figure 5.25: Decarbonisation Scenarios (benchmarking Low scenario)

20% decarbonisation of low supply scenario

The total energy demand for this scenario is discussed in sub-section 5.1.1. The results show that there is fuel switching of end-use technologies to electricity in transportation for this decarbonisation scenario (Figure 5.26). Compared to previous decarbonisation strategies in the REF and high supply scenarios, where fuel transitions to electricity consumption are anticipated towards the end of the modelled period in sectors such as the household or commercial sectors, it is observed that the decarbonisation of the system is mainly focused on power sector in this low 20% decarbonisation scenario. As compared to the low supply scenario, cumulative coal capacities are reduced and a potential nuclear capacity of 12GW is installed (Figure 5.31) to meet this decarbonisation strategy reducing the cumulative carbon emissions of the system by at least 8% as shown in Table 5.1.

165

Figure 5.26: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (20% decarbonisation of Low supply scenario)

40% decarbonisation of low supply scenario

With this emission constraint, fuel switching to electricity in transportation, commercial and industry sectors is observed in the system. In transportation, fuel transitioning to electricity consumption by cars starts in 2033 and electricity consumption gradually increases, meanwhile gas consumption in cars is introduced towards the end of modelled period. At the same time, motorcycle is recommended to switch to electricity consumption in 2035 to meet the carbon constraint. The fuel switching to electricity consumption by commercial cooking stoves starts in 2037 and commercial machineries gradually depend on electricity increased over the modelled period. Household cooking technologies are observed to switch to electricity beginning in 2035. The fuel transitions to electricity across these sectors increase the electricity demand of the system in 2050 by at least 19% compared to the low supply scenario in 2050. Additional capacities of gas, hydro and solar are installed to meet the electricity demand, as shown in Figure 5.31. The investment of new capacity developments is about 1.3 times more and the decarbonisation of the system reduces the cumulative carbon emission by approximately 17% compared to the low supply scenario.

166

Figure 5.27: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (40% decarbonisation of the low supply scenario)

60% decarbonisation of low supply scenario

The significant changes are mainly observed in the industry and transportation sectors under this decarbonisation scenario. The fuel switching by industrial machineries to electricity starts in 2036, where 44% of total energy consumption in industry sector is electricity based. The system suggests that fuel consumption of cars switches to electricity in 2033 and electricity consumption gradually increases over the modelled period. Gas consumption in the transportation sector is also observed beginning in 2036, as shown in Figure 5.28. New capacities of solar, hydro and other renewables contribute to the electricity generation fuel mix as shown in Figure 5.31, with electricity demand in 2050 increased about 23% as compared to the low supply scenario in 2050.

167

Figure 5.28: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (60% decarbonisation of the low supply scenario)

80% decarbonisation of the low supply scenario

Decarbonisation of the system shows that end-use technologies dramatically change to electricity consumption across all sectors in order to satisfy this stringent constraint, as shown in Figure 5.29 beginning in 2028. By 2050, most of the end-use technologies entirely switch to electricity, where electricity demand increases by at least 2.3 times more in 2050 and decarbonisation of the system reduces the system emissions by at least 36% as compared to the low supply scenario. The decarbonisation of the system costs at least 2.5 times more as compared to the total system cost of the low supply scenario.

168

Figure 5.29: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia (80% decarbonisation of Low supply scenario)

Figure 5.30: Electricity generation by fuel and electricity consumption by sectors (Low decarbonisation scenarios)

169

5.1.3.4 Conclusion

This subsection concludes the insights obtained from the decarbonisation scenarios subject to the three demand pathways generated exogenously using the MAED model (discussed in Section 5.1.1) and used as inputs in the OSeMOSYS model. The study analyses the possibility of achieving deep decarbonisation targets in Malaysia, especially in the power sector, where CO2 emissions have been the major contributor since 2000 due to fossil fuel combustion and are expected to increase with future coal power plants that are expected to become operational in the near term (EC 2014, EC 2016). As discussed in Section 5.1.2 (also refer to Figure 4.8 in Section 4.1 for a systematic summary of possible deep decarbonisation scenarios), three umbrella scenarios (REF, high supply and low supply) are used to generate different sets of decarbonisation scenarios, where the results show the switching of end-use technologies to electricity and the installation of further power plants in the system. The results provide possible pictures of the trend of fuel transitions across all sectors and energy pathways, which are also subject to different service demand pathways. The results are reflected in terms of technological deployment in the power sector, resources utilisation and multiregional electricity trade infrastructure development in the future under different decarbonisation targets.

Achieving the 20% and 40% carbon reductions benchmarking the REF scenario is still feasible with all the potential resources introduced in the system completely utilised, including under- construction and planned power plants. In both scenarios, the decarbonisation of the power sector starts in the year 2030, with introduction of low carbon technologies to meet the electricity demand. The trade-off between power sector and other sectors to meet two main constraints on the least cost and carbon emissions reductions in the model reveals that end-use technologies in the commercial and transportation sectors switch to electricity followed by technologies in the industry and household sectors, parallel to the installation of nuclear and additional solar power plants to meet the electricity demand in the system. Optimising both 20% and 40% decarbonisation scenarios shows that all the potential resources are exhausted and fully utilised to satisfy the carbon emissions constraints and the growing electricity demand.

For example, in both 20% and 40% REF decarbonisation scenarios, a maximum capacity of ~2GW of other renewables (biomass, geothermal and solid waste) are installed, as outlined in the renewable energy policy. Although only 9GW of solar aimed as discussed in this renewable

170 policy, an upper constraint on the solar potential (~280GW)20 was allowed in the system. A total maximum capacity of 17GW of solar was installed. Furthermore, ~26GW of hydro potential is recommended in the system, which almost achieves the maximum hydro resources21 available in Malaysia. The industrial and transportation sectors are the major energy consuming sectors in the system. The end-use technologies of these sectors, for example, industrial machineries, are suggested to switch to gas and the introduction of electric and gas-fuelled vehicles is recommended in order to achieve emission reductions. These recommendations for fuel switching are aligned with some measures currently introduced in the country, such as the NAP (2014) policy (discussed in Section 3.2.2.1) to introduce electric and gas vehicles as well as the MIEEIP initiative implementation to create awareness among industries to switch fuels to gas and the use of higher efficiency technologies. Yet, further measures or initiatives need to be taken and introduced in the end-use sectors to achieve these decarbonisation targets.

Achieving deep decarbonisation strategies of the 60% and 80% carbon reduction scenarios would be a challenge for Malaysia. Electricity generation is almost all renewable22 and nuclear powered in these scenarios (Figure 5.31). To meet these ambitious emission constraints, additional installations of solar or nuclear capacities are needed, which included electricity imports from Sarawak (in addition to earlier constraints on nuclear and solar installed capacity23 introduced in the model). These installations are needed as the end-use technologies in all sectors switch to electricity (Figure 5.33) shows the electricity demand increasing trends for these scenarios). Achieving these targets in the power sector with the abundant installation of nuclear, solar and hydro capacities is a challenge for a developing country like Malaysia. For example, according to Renewable Energy Policy (2009), the country is aiming to achieve a cumulative installation of 9GW of solar by 2050. This target is achievable in the case of reducing emissions by 20% or less by 2050. Hence, achieving deep decarbonisation targets of above 20% requires the widespread installation of solar by 2050. For example, at least a total installed capacity of 257GW in 2050 for the 80% decarbonisation scenario needed compared to the 9GW target as outlined in the policy. Therefore, the country needs to consider new approaches to introduce solar technologies in the country to achieve deep decarbonisation strategies.

20 The upper constraint in the system on solar potential in Malaysia is derived based on the information obtained from the MAED model inputs on the potential availability of dwelling rooftop and household income. 21 Through the Sarawak Corridor of Renewable Energy (SCORE), Sarawak has formulated a comprehensive development programme to identify the possibility of exploring hydro potential of 20GW (RECODA 2011, RECODA 2012) 22 Renewables include hydro, mini hydro, solar, biomass, solid waste and geothermal 23 Set of constraints outlined in Section 3.1.2 171

Similarly, the widespread installation of nuclear power plants is recommended for achieving deep decarbonisation targets. The nuclear power option is introduced post- 2030 as alternative strategy besides renewables and electricity imports from Sarawak. This strategy is introduced in the model post- 2030 including the nuclear technology building rate constraint. Flexibility in the nuclear building rate constraint recommends a high level of installation of this technology, especially towards the end of study period to achieve the deep carbonisation strategies. 12 GW to 20 GW of new capacity would be needed, yet this may be a challenge for a developing country that is still in the phase of its initial consideration of implementing a nuclear power programme. This means that if the country decided to achieve and adopt deep decarbonisation strategies (or other related decarbonisation policies), the current development of coal and gas power plants could be affected. Hence, considering the current availability of technologies in the Malaysian energy system, the country only has solar, nuclear or electricity trade options to meet the supply deficit and achieve deep decarbonisation strategies. Other available potentials in the system, e.g. biomass or geothermal technologies potential, are fully developed.

Figure 5.31: Total Electricity Generation and Installed Capacity in Malaysia over modelled period (Decarbonisation scenarios)

172

A number of social, political and environmental aspects need to be considered in order to achieve the high deployment of specific technology, for example, solar, nuclear or undersea cables to facilitate Sarawak electricity exports. The deployment and commissioning of new infrastructures would entail high investment in the power sector and the decision to make such an investment needs to be explored. For example, Figure 5.31 and Table 5.1 show the estimated total investment needed to decarbonise the power sector, where high capital investments range at least three times higher as compared to the REF scenario. The decarbonised energy supply system exhibits fuel transitions across all sectors as well as installations of nuclear power and solar. In extreme constraints, the multiregional trade infrastructure developed to meet the electricity growth in the REF 60% and 80%, high supply 60% and 80% as well as in the low supply

80% scenarios. Calculating the impact of different mitigation strategies through CO2 abatement cost is also one of the insights obtained from an optimisation systems model such as OSeMOSYS.

The abatement costs are defined as the incremental cost of a low-emission technology compared to the reference case which measured as a dollar per tonne of CO2 avoided and are calculated based on the equation stated in McKinsey & Company (2009) as follows:

Incremental abatement cost (IAC) =

The abatement costs shown in Figure 5.32 are generated from different stringent emission target runs. The abatement cost increases at a higher rate for emission reductions above 60%, where optimal solutions are dominated by new technologies such as nuclear or solar. For

example, a mitigation cost by 2050 of 91$/tonne of CO2 in the REF 60% and 232$/tonne of CO2 in the REF 80% scenarios required under the conditions of limited options availability, especially in the power sector, to deliver the final part of the supply system optimisation.

Figure 5.32: Abatement cost of power system over the modelled period

173

Therefore, the country needs to evaluate short-term decision on building the fossil fuel-based power plants to meet the fast-growing electricity demand compared to long-term clean technologies requiring high investment in the initial stage but contributing to emissions reductions in the long term. The financial impact to achieve deep carbonisation strategies, especially in the power sector, needs to be examined as well as the country’s commitment to reduce carbon emissions. For example, the development of nuclear technologies requires the consideration of factors such as public perceptions towards the acceptance of nuclear technology, power plant decommissioning cost and nuclear waste management cost in the long term. Therefore, to embark upon these deep decarbonisation pathways may require significant international cooperation for technology transfers and detailed assessment of financial requirement to adopt these advanced technologies.

Besides investment decisions, other factors that need to be considered are the development of national infrastructure and expertise in order to adopt and build technologies such as nuclear or undersea infrastructures for electricity trade. Long term planning needed for international technology transfer includes studies on country-specific technology requirements, infrastructure development to facilitate the building of these technologies locally or human capacity building to operate these technologies. If the decision makers decide to introduce these technologies in the country, the social acceptance of these technologies and the land-use planning to develop these technologies are crucial factors that need to be considered. This may lead to reframing the energy policy to create the platform to implement these initiatives.

174

Figure 5.33: Total Electricity Generation by Scenarios

It is also observed in the system that if the energy demand increases, for example, due to increasing GDP and population growth as modelled in the high supply scenario, the results are reflected in the 60% and 80% decarbonisation targets, where the system accelerates the diffusion of end-use technologies switching to low carbon fuel options, mainly electricity (Figure 5.24 and Figure 5.31). As the demand is higher and to meet this demand requirement, for example, the end-use technology based on electricity for cooking is deployed earlier as compared to the REF scenario. Similarly, the system accelerates the deployment of other end- use technologies for the high supply scenario.

In order to achieve decarbonisation strategies, for example in 2050, it is important to analyse and recommend the possible implementation of industrial energy transformation and

175 investment in transportation infrastructure to deploy low carbon vehicles. Hence, the recommendation of policies is needed in order to promote the research and development of advanced and highly efficient technologies across all sectors, mainly power, industry and transportation. To conclude, the decarbonisation of the system on the supply side in each scenario could be achieved through the rationalisation of energy utilisation as well as development and deployment of new technologies. These decarbonisation targets could be achieved under the acceleration of research specific to the country on resource availability and utilisation across all sectors as well as a feasibility study on the development and deployment of new technologies.

High Scenario REF Scenario Low Scenario Part A: Optimal within technology constraints defined Part B: Optimal with additional technologies introduced

Figure 5.34: Optimal Decarbonisation Scenarios (depending on defined technology constraints and introduction of additional technologies in the system)

To conclude, five optimal decarbonisation strategies were identified under the long-term demand drivers. The optimal decarbonisation strategies are REF 20%, REF 40%, High 20%, Low 20% and Low 40%, as demonstrated in Figure 5.34 (Part A). These optimal decarbonisation strategies are achievable within the technology and potential constraints defined in the power system. In all these optimal decarbonisation scenarios, which also depend on demand changes in the system, that all the resource potentials are harvested and the maximum possible future technologies (~25GW hydro, ~270GW solar and ~20GW nuclear) are installed by 2050. On average, for these optimal decarbonisation scenarios over the modelled period, total electricity generation increased by 6% due to the electrification of end-use technology, which costs the system at least 1.2 times more in order to reduce the carbon emissions of the power system by approximately 12% as compared to umbrella scenarios (REF, high supply and low supply). For example, in the high supply 20% decarbonisation scenario over the modelled period, the total electricity generation increased about 10% due to the electrification of end-use technology as

176 compared to high supply scenario. In this decarbonisation scenario, it costs about 1.1 times more to install new power technologies as compared to the high supply scenario, resulting in total carbon emissions reduction in the power sector by at least 2%. To achieve beyond these optimal decarbonisation strategies (Part B), as discussed earlier, additional technologies, resources and infrastructures need to be developed and deployed in the country. This requires the reform of policies and measures across all sectors as well as further feasibility studies on the possibility of deploying these technologies in the future.

Table 5.1: Total system cost and emission by scenarios over the modelled period

Decarbonisation Scenarios (benchmarking the REF scenario) REF 20% 40% 60% 80% Total System Cost of modelled period (USD billion) 580 614 754 1020 2216

Total System CO2 of modelled period (Mton) 17185 15826 14217 12335 10131 Total Power System Investment of modelled period (USD billion) 77 89 93 164 313

Total Power System CO2 of modelled period (Mton) 5713 5091 4522 3246 2414

Decarbonisation Scenarios (benchmarking the high supply scenario) High 20% 40% 60% 80% Total System Cost of modelled period (USD billion) 805 945 1139 1699 5063

Total System CO2 of modelled period (Mton) 20798 18642 16382 14176 11477 Total Power System Investment of modelled period (USD billion) 100 111 181 307 456

Total Power System CO2 of modelled period (Mton) 6299 6153 4910 3408 2742

Decarbonisation Scenarios (benchmarking the low supply scenario) Low 20% 40% 60% 80% Total System Cost of modelled period (USD billion) 466 490 547 637 1182

Total System CO2 of modelled period (Mton) 13142 12099 10965 9774 8388 Total Power System Investment of modelled period (USD billion) 53 60 69 127 211

Total Power System CO2 of modelled period (Mton) 4420 3861 3137 2533 2114

177

5.2 Near-optimal energy system results

As discussed in previous section on Malaysia’s optimal energy system and further investigation on the different decarbonisation targets that would shape the supply technology profiles in future, this section addresses the third research question: How robust are the near-optimal decarbonisation strategies with respect to technological-environmental-economic objectives for long-term planning? This section takes a different direction to analyse near-optimal solutions that provide significant supply technology profiles of Malaysia’s power system and possible energy outlooks for the consideration of future policy planning. The method on MGA-hybrid modelling framework is described in Section 3.3, the concept of Malaysia’s near-optimal decarbonisation pathways is outlined in Section 4.1.3 and the systematic scenario development is portrayed in Section 4.2 (Figure 4.8).

5.2.1 Near-optimal decarbonisation scenarios

This section discusses the results of near-optimal decarbonisation scenarios focusing on decarbonised power systems and the alternative supply pathways generated with additional investment provided. The near-optimal decarbonisation scenario approach focuses on two objective functions to evaluate the policy-driven structural uncertainties, with slack values of 1% to 30% that are imposed in the model to obtain a range of near-optimal solutions. The concept of near-optimality is applied in three umbrella scenarios (REF, high supply and low supply), which was discussed in Section 5.1.2 above, to obtain a comparison of the results and to understand the broad patterns and trends in terms of policy-driven structural uncertainties that emerge from the analysis. Table 4.1 in Section 4.4.3 also explains the concept of flexible and inflexible models generating these uncertainty pathways.24 Table 5.2 describes the list of constraints that are excluded by category to give the system the flexibility to optimise based on new objective functions within slack values defined in order to obtain near-optimal pathways. The near- optimal scenarios are generated based on these constraint categories (L1, L2, L3 and L4) are described in table below and will be discussed in the following sections.

24 A more flexible model as compared to an inflexible model gives additional freedom for alternate solutions for a given slack cost constraint as demonstrated in Section 5.2.1.1. 178

Table 5.2: Summary of Flexible Scenarios based on constraint categories

5.2.1.1 Near-optimality approach under the REF supply scenario

Objective function: Minimise CO2 emissions of the system

Twenty-eight near-optimal pathways benchmarking the REF scenario were generated focusing on objective function to minimise the CO2 emissions of the system under the slack values of 1% to 30%. Based on the constraint categories defined in Table 5.2 above, the near-optimal scenarios are generated within investment flexibility, as shown in Figure 5.35 (see Table B.16 of Appendix B for detailed results on these distributions of near-optimal scenarios). Based on these distributions of near-optimal scenarios and the electricity generation of these scenarios, only sixteen significant near-optimal pathways (slack values of 5%, 10%, 20% and 30%) are discussed in this subsection. The near-optimal results show that the end-use technologies switch fuels to satisfy the new objective function. Based on the investment flexibility, the demand for electricity increases, leading to clean energy access in power system (see Figure B.6 of Appendix B illustrating the electricity generation of 28 near optimal scenarios over the modelled period). Figure 5.36 shows the increase in electricity generation due to the electrification of end-use technologies. The trend of electricity generation varies under different slack values and increased constraint flexibility in the system.

179

Figure 5.35: Distribution of REF near-optimal decarbonisation scenarios: minimise CO2 of system (benchmarking the REF scenario)

For example, the electricity generation of near-optimal pathway NOL2 increased the most in 2050 compared to other near-optimal pathways (NOL1, NOL3 and NOL4) under a 5% slack value constraint. The increase in electricity generation of REF5%_NOL2 scenario is due to the electrification of end-use technologies in the household, commercial and transportation sectors, as shown in Figure 5.37. In the transportation sector, it is observed that passenger vehicles are electrified in 2034 and increase in electrified trains by 2050, with a total electricity consumption of about 4% of total energy consumed (4460 PJ) in this sector by 2050 (Figure 5.38). Meanwhile, the cooking technologies in commercial and household sectors are electrified in 2042 and 2037, respectively. Electrification of these technologies increases the installation of power plants based on clean energy (Figure 5.44). The cumulative carbon emissions produced by the power system over the modelled period is about 3258 mton and the CO2 emission trajectory reduces towards the end of modelled period with the installation of mainly nuclear, hydro and solar power plants (Figure 5.45). The investment cost to install these power technologies is about USD 114 billion over the modelled period. Hence, an increase of 5% investment costs reduces the total carbon emissions of the system by at least 19% as compared to the REF scenario.

180

Figure 5.36: Electricity generation pathways of REF near-optimal decarbonisation scenarios:

minimise CO2 of system (slack values of 5%, 10%, 20% and 30%)

Figure 5.37: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia: minimise CO2 of system (REF near-optimal scenario: REF5%_NOL2 (5% slack value))

181

Figure 5.38: Electricity generation by fuel and electricity consumption by sectors: minimise CO2 of system (Near-optimal decarbonisation scenario: REF5%_NOL2 (5% slack value))

Similar increasing trends in electricity generation are observed for near-optimal decarbonisation scenarios with slack values of 10%, 20% and 30% as illustrated in Figure 5.36. Due to the electrification of end-use technologies in the household, commercial and transportation sectors, the increase of electricity consumption is observed from approximately 2040 for NOL1, NOL2 and NOL3 pathways under the 10% slack constraint. The cumulative electricity generation of near-optimal pathway NOL2 increased the most over the modelled period compared to other near-optimal pathways (NOL1, NOL3 and NOL4).

Figure 5.39: Electricity generation by fuel and electricity consumption by sectors: minimise CO2 of system (Near-optimal decarbonisation scenario: REF10%_NOL2 (10% slack value))

Hence, taking the REF10%_NOL2 pathway as an example scenario to analyse the electrification of end-use technologies in detail, Figure 5.39 shows the trend of fuel-switching in the major sectors, which instigates access to clean energy in power sector. The trend of fuel switching is similar to the REF5%_NOL2 scenario, yet the deployment of electrified end-technologies is accelerated, whereby passenger vehicles are electrified starting in 2020 compared to the REF5%_NOL2 scenario. Meanwhile, electrified commercial and household cooking technologies are deployed in 2029 and 2023, respectively. The cumulative carbon emissions produced by the

182 power system over the modelled period for this scenario are about 3247 mton, yet Figure 5.45 shows that carbon emissions were mainly reduced in NOL4 scenarios due to flexibility to deploy additional solar and nuclear technologies under the 10% slack constraint. With an increase of 10% investment cost, the total carbon emissions of the system are reduced by at least 22% for REF10%_NOL2 compared to the REF scenario. Thus, a maximum total carbon emissions reduction of 24% is estimated for REF10%_NOL4 with the deployment of high nuclear technologies compared to the REF scenario under the 10% slack constraint.

Figure 5.40: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia: minimise CO2 of system (REF near-optimal scenario: REF20%_NOL4 (20% slack value))

Under a 20% slack constraint, the trend of electricity generation for all four near-optimal scenarios (NOL1, NOL2, NOL3 and NOL4) is observed to increase from approximately 2035 as compared to the REF pathway due to the acceleration of end-use technology electrification across major sectors. Taking the REF20%_NOL4 as an example scenario to analyse the details of the fuel-switching and deployment of power technologies, Figure 5.40 shows the trend of fuel switching in the major sectors.

183

Figure 5.41: Electricity generation by fuel and electricity consumption by sectors: minimise CO2 of system (Near-optimal decarbonisation scenario: REF20%_NOL4 (20% slack value))

In this scenario, the industry sector is observed to switch to electrified machineries, which prompts the increase in electricity consumption at the end of the modelling period as compared to other pathways under this slack constraint. The switching trends to electrified end-use technologies in the household, commercial and transportation sectors are observed to be similar, under 5% and 10% slack values. Yet, due to flexibility in technology deployment in the power sector, the system is mainly nuclear- and hydro-based (Figure 5.41). These technologies are preferred compared to solar due to the availability and capacity factors of solar technology. As hydro, biomass and solid waste potentials are exhausted, the suggestion is for mainly nuclear installation to start in 2031. Although the power system is almost decarbonised, with carbon emissions producing only about 5 mton yearly towards the end of the modelling period, a total of 21GW nuclear new installed capacity is recommended in 2050, and such to install such high capacities within 20 years over the modelled period may not be feasible.

Figure 5.42: Electricity generation by fuel and electricity consumption by sectors: minimise CO2 of system (Near-optimal decarbonisation scenario: REF30%_NOL4 (30% slack value))

Similar to the pattern of fuel-switching of technologies in previous slack value scenarios, the 30% slack scenario also demonstrated the electrification of technologies across all sectors.

184

Figure 5.36 shows the electricity generation pathway produced by the REF30%_NOL4 scenario increasing radically beginning 2029 due to the increasing electricity consumption across all sectors mainly transportation sector. The increasing electrification of cars beginning 2026 and motorcycles in 2020 further increases the electricity consumption, followed by the electrification of commercial cooking technologies and household cooking technologies in 2029. Given the constraint flexibility in the system, the electrification of these end-use technologies instigated the widespread installation of nuclear technologies (Figure 5.43). Electricity is mainly generated by nuclear and hydro starting 2031, whereby the power system is decarbonised by 2050 with, electricity generation shared by nuclear (72%), hydro (9%) and other renewables (18%) in 2050.

Figure 5.43: Electricity generation by fuel and electricity consumption by sectors: minimise CO2 of system (Near-optimal decarbonisation scenario: REF30%_NOL2 (30% slack value))

Since the flexibility in terms of technology constraints imposed in the system makes a difference on the diffusion of the power technologies in the system, REF30%_NOL4 is compared with REF30%_NOL2 focusing on the electrification of end-use technologies and access to clean energy in the power sector. Since the flexibility of power technology deployment (e.g. solar and nuclear) is limited, the trend of power technology installation is different from the REF30%_NOL4 scenario in order to reduce the carbon emissions of the system with additional investment given. In the power sector for the REF30%_NOL2 scenario, the maximum potential of nuclear, hydro, solar and biomass is completely installed. The model is given flexibility to introduce these technologies at any point of time frame in the system. For example, nuclear is introduced in the system starting in 2031, yet only a maximum potential of 12 GW is allowed. The installation of these technologies is instigated by increased electricity demand through the electrification of end-use technologies in the household, commercial and transportation sectors. For example,

185 motocycles are electrified beginning 2019, respectively. In the commercial and household sectors, cooking technologies are electrified in 2020 and 2015.

Figure 5.44: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise CO2 of system (REF near-optimal decarbonisation scenario)

Hence, additional investment flexibility and categories of technology constraint imposed in the system influence the type of technology deployed in the system, which result in carbon emissions reduction in the system. The near-optimal results also show that the investment cost decreases in NOL4 as compared to NOL3 for REF, high supply and low supply umbrella scenarios (Figure 5.44, Figure 5.54 and Figure 5.58). In the NOL3 scenario, the system deploys more solar power plants to meet the electricity demand due to the low capacity factor of solar. The widespread solar deployment in the NOL3 needs more investment as compared to nuclear installation in the NOL4 scenario. The summary of the results with respect to technological- environmental-economic objectives will be discussed in Section 5.2.2 (Conclusion).

186

Figure 5.45: Comparison of CO2 emission trajectories in power sector for 16 REF near-optimal pathways: minimise CO2 of system (slack values 5%, 10%, 20% and 30%)

Objective function: Minimise coal in power system

Focusing on the power sector, twenty-eight near-optimal pathways benchmarking the REF scenario were generated, focusing on objective function to minimise coal imports under slack values of 1% to 30% in order to obtain insights on possible decarbonised pathways in the power system. Constraint categories defined in Table 5.2 were also imposed in these near-optimal scenarios with investment flexibility as shown in Figure 5.43. Table B.2 and Figure B.2 of Appendix B details the results on these distributions of near-optimal scenarios. Significant near- optimal pathways (slack values of 20% and 30%) are discussed in this subsection based on distributions of near-optimal scenarios and the electricity generation pathways of these scenarios.

187

Figure 5.46: Distribution of REF near-optimal decarbonisation scenarios-minimise coal in power system (benchmarking the REF scenario) 5.2 12.8

Since the analysis focused on the power sector, the electrification of end-use technologies is not notable for these near-optimal scenarios. For example, the electricity generation pathways of scenarios (slack values of 1%, 5%, 10% and 15%) are similar to the electricity generation trends of the REF scenario. In fact, in some scenarios, the electricity consumption is reduced due to switching from electrified end-use technologies to other alternative fuels in order to satisfy the constraints and to decarbonise the power sector (Figure 5.47). For example, the near-optimal scenarios of 20% and 30% slack values are analysed in detail. Figure 5.48 shows the trend of electricity generation pathways by fuels and total electricity consumption by sectors for REF20%_NOL2 and REF20%_NOL4.

Figure 5.47: Electricity generation pathways of REF near-optimal decarbonisation scenarios: minimise coal in power system (slack values of 5%, 10%, 20% and 30%)

188

For example, the electricity generation of near-optimal pathway NOL2 under the 20% slack value constraint is the lowest in 2050 compared to other near-optimal pathways (NOL1, NOL3 and NOL4). This is due to the switching of commercial air-conditioning from electricity to gas consumption, which gradually increases starting 2029 (Figure 5.49). The pattern of fuel consumptions in other sectors changed as well, for example, the industry sector is observed to switch to electrified machineries. Since the objective of the system is to reduce the coal consumption in the power sector in a constrained environment and the available options in REF20%_NOL2 is to deploy gas, hydro, nuclear or renewables based on the maximum availability of resources and technologies. Therefore, the electricity demand is reduced in order to decarbonise the power sector. By 2050, the total electricity demand is reduced by at least 12% with electrified air-conditioning substitution with gas-based cooling technologies in the commercial sector as compared to the REF scenario. However, the situation is different for the REF20%_NOL3 and REF20%_NOL4 scenarios if flexibilities in solar and nuclear technology installations are allowed for. Coal consumed in the power sector is reduced in both of these scenarios and the abundant installation of solar or nuclear power is observed in the system. Yet, no fuel switching of end-use technology is observed for both REF20%_ NOL3 and REF20%_NOL4 and the pattern remain the same as the REF20%_ NOL2.

Figure 5.48: Electricity generation by fuel and electricity consumption by sectors: minimise coal in power system (Near-optimal decarbonisation scenario: REF20%_NOL2 and REF20%_NOL4 (20% slack value))

189

Compared to the 20% slack value scenario, the electricity generation projections of the 30% slack value scenario (Figure 5.47) show that the pathways of REF30%_NOL1, REF30%_NOL2 and REF30%_NOL3 (compared to REF30%_NOL4) are reduced towards the end of modelling period. The changes in the trend of electricity generation pathways due to the accelerated switching of commercial cooling technology from electricity to gas consumption beginning in 2019 are illustrated in Figure 5.50 (compared to REF20%_NOL2, where the fuel-switching of this technology only started in 2029). It is observed in the results that the near-optimal system options for electricity generation reduction are constrained due to alternative power technologies in the system. The potential power technology of hydro, nuclear, solar and other renewables25 is exhausted and the system has the options of coal and gas. With the objective of reducing coal consumption in power sector as well as satisfying constraint on gas resources, limits on gas and coal technologies are installed. This constrained environment instigates the switching of electrified end-use technology to gas that leads to reduction in total electricity generation.

Figure 5.49: Total final energy consumption by fuel for household, commercial, industrial and transportation sectors in Malaysia: minimise coal in power system (REF near-optimal scenario: REF20%_NOL2 (20% slack value))

25 Biomass, solid waste and geothermal

190

Figure 5.50: Electricity generation by fuel and electricity consumption by sectors: minimise coal in power system (Near-optimal decarbonisation scenario: REF30%_NOL2 (30% slack value))

Figure 5.51: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise coal in power system (REF near-optimal decarbonisation scenario)

Figure 5.51 demonstrates the increase in the share of solar in electricity generation as the system is given investment flexibility, especially in the 20% and 30% slack value scenarios. Coal consumption is reduced as much as possible and all other potential resources are developed. Access to clean energy in the power sector with the installation of mainly nuclear, hydro and solar power plants leads to a reduced CO2 emissions trajectory towards the end of modelled

191 period, as demonstrated in Figure 5.52. The investment cost of installing these power technologies also varies depending on the type of technology deployed in the system.

Figure 5.52: Comparison of CO2 emission trajectories in power sector for 16 REF near-optimal pathways: minimise coal in power system (slack values 5%, 10%, 20% and 30%)

5.2.1.2 Near-optimality approach under high and low supply scenarios

This sub-section summarises the results of the near-optimality scenarios under the high and low supply scenarios. Two new objective functions, minimising CO2 emissions of the system and minimising coal in the power system are imposed in the system to generate twenty-eight near- optimal pathways for each objective function under the two umbrella scenarios, high and low supply, as described in Section 5.1.1.

5.2.1.2.1 Near-optimality approach under High supply scenario

High supply pathways described in Section 5.1.2 used as the benchmark scenario to generate near-optimal pathways for two objective functions, which are discussed in the following subsection.

192

Objective function: minimise CO2 emissions of the system

The near-optimal results under this high supply scenario (Figure 5.53) demonstrates the acceleration of electricity generation pathways due to the electrification of end-use technologies as compared to the REF near-optimal pathways (Figure 5.36). Table B.18 and Figure B.8 of Appendix B shows detailed results on these distributions of near-optimal scenarios. Similar to the trend of fuel-switching observed in the REF near-optimal scenarios, the electrification of end-use technologies increases electricity generation.

Figure 5.53: Electricity generation pathways of High near-optimal decarbonisation scenarios: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%)

Depending on the investment flexibility and constraint categories (NOL1, NOL2, NOL3 and NOL4) illustrated in Table 5.2, the trend of end-use technology fuel switching to electricity consumption across all sectors and access to clean energy in the power sector through the installation of hydro, nuclear and renewable technologies observed. For example, in the NOL3 scenario, under 5% or 10% slack values, the electrification of end-use technologies mainly occurs in the household, commercial and transportation sectors. Given the power technology constraint flexibility for this NOL3 scenario, the electrified end-use technologies instigated high installation of solar technologies (Figure 5.54). Yet, with higher investment flexibility (20% or 30% slack values), NOL4 demonstrated a different electricity generation pathways and trends of end-use technology fuel switching. The electricity generation pathways were observed to increase

193 dramatically towards the end of modelling period due to the electrification of industrial machineries as well as cars and motorbikes. Increases in electricity generation, at least by 1.6 times as compared to the high supply scenario in 2050, instigated the widespread installation of nuclear technology to meet the electricity demand (Figure 5.54).

Total Electricity Generation Mix in Malaysia (Near-Optimal Decarbonisation Scenarios) 100% 7000 90% 6000 80% 70% 5000 60% 4000 50% 40% 3000 30% 2000 (percentages) 20% 1000 10%

0% 0 Sector (modelled period) mton Cumulative carbon emission from Power Electricity Generation in Malaysia by Fuel

Total New Installed Capacity in Malaysia (Near-Optimal Decarbonisation Scenarios) 100% 700 90% 600 80% 70% 500 60% 400 50% 40% 300 30% 200 20% 10% 100

0% 0 Total New Capacity Investment (billion)

Total New Installed Capacity (percentages)

Figure 5.54: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise CO2 of system (High near-optimal decarbonisation scenario)

Objective function: minimise coal in power system

Different trends of electricity generation pathways and installation of power technologies were observed for this objective function, as it mainly focused on reducing the coal consumption in the power sector. For example, the electricity generation pathways of the NOL2 scenario under 5%, 10%, 20% and 30% slack values reduced the fuel-switching of electrified commercial cooling technology to gas in order to satisfy the constraints imposed in the system (Figure 5.55). Table B.19 and Figure B.9 of Appendix B shows the detailed results on these distributions of near- optimal scenarios. Meanwhile, in the power sector, under this scenario with investment flexibility, the widespread deployment of solar (maximum potential of solar installation) is

194 observed, as coal consumption is reduced as much as possible. Similar patterns of electricity generation pathways and installation of power technologies are observed for the NOL1 scenario across all slack value constraints. The electricity generation pathways of NOL3 and NOL4 scenarios remain unchanged, yet it is observed that installation of power technologies varies depending on investment flexibility (Figure 5.56). For example, NOL4, with 10% more investment, recommended the widespread installation of nuclear technology to meet the electricity demand. However, given the flexibility for nuclear and solar with 30% more cost investment for this NOL4 scenario, the system suggests the widespread deployment of solar to meet the electricity demand.

Figure 5.55: Electricity generation pathways of High near-optimal decarbonisation scenarios: minimise coal in power system (slack values of 5%, 10%, 20% and 30%)

195

Total Electricity Generation Mix in Malaysia (Near-Optimal Decarbonisation Scenarios) 100% 7000 90% 6000 80% 70% 5000 60% 4000 50% 40% 3000 30% 2000 (percentages) 20% 1000 10%

0% 0 Sector (modelled period) mton Cumulative carbon emission from Power Electricity Generation in Malaysia by Fuel

Total New Installed Capacity in Malaysia (Near-Optimal Decarbonisation Scenarios) 100% 900 90% 800 80% 700 70% 600 60% 500 50% 40% 400 30% 300 20% 200 10% 100

0% 0 Total New Capacity Investment (billion) Total New Installed Capacity (percentages)

Figure 5.56: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise coal in power system (High near-optimal decarbonisation scenario)

5.2.1.2.2 Near-optimality approach under Low supply scenario

Low supply pathways described in Section 5.1.3 used as the benchmark scenario to generate near-optimal pathways for two objective functions, which are discussed in the following subsection.

Objective function: minimise CO2 emissions of the system

With low supply pathways, the near-optimality results demonstrated similar electricity generation trends to the REF and high supply near-optimality scenarios. The electrification of end-use technologies is focused mainly in the household, commercial and transportation sectors, as household and commercial cooking technologies are electrified along with cars switching from petrol consumption to electricity towards the end of modelling period. An additional 5% investment cost does not produce significant changes in terms of electricity generation and installed power capacities. Under the 10% slack value, NOL2, NOL3 and NOL4 scenarios demonstrated changes in electricity generation pathways (Figure 5.57) and the

196 installation of new power technologies. Table B.20 and Figure B.10 of Appendix B shows the detailed results on these distributions of near-optimal scenarios. It is also observed that since the maximum solar potential is installed under conditions of additional 10% and 20% cost investment, both NOL2 and NOL3 scenarios portrayed similar patterns of new installed capacities and electricity generation mix by fuel (Figure 5.58). With 20% and 30% investment flexibility, NOL4 demonstrated increasing electricity generation due to the electrification of end- use technologies across all sectors. The electrification of cars and motorbikes is observed in the near-optimal system, along with household and commercial cooking technologies and industrial machineries.

Figure 5.57: Electricity generation pathways of Low near-optimal decarbonisation scenarios: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%)

197

Figure 5.58: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise CO2 of system (Low near-optimal decarbonisation scenario)

Objective function: minimise coal in power system

Imposing this objective function under the low supply scenario with additional investment flexibility produced no significant changes in terms of electricity generation pathways for all near-optimal scenarios and the changes are mainly focused on the power sector (Figure 5.59). Table B.21 and Figure B.11 of Appendix B shows the detailed results on these distributions of near-optimal scenarios. Given 5% more investment flexibility, the results show no significant changes in NOL2, NOL3 and NOL4 scenarios in terms of installed capacities or total electricity generation by fuel. Yet, with 20% and 30% additional investment cost, the power system mainly focused on reducing and phasing out the coal consumption and alternative technologies, mainly solar power plants. A high share of solar installation is needed due to the low capacity factor along with the installation of hydro, nuclear, gas and renewable power plants to meet the electricity demand, which is observed across all near-optimal scenarios.

198

Figure 5.59: Total Electricity Generation and Installed Capacity in Malaysia over modelled period: minimise coal in power system (Low near-optimal decarbonisation scenario)

5.2.2 Conclusions on near optimal scenarios

This subsection concludes the insights obtained from the near-optimal decarbonisation scenarios under three umbrella scenarios, REF, high supply and low supply (discussed in Section 5.1.2). Two objective functions were imposed in the system to obtain a range of near-optimal pathways. These two objective function approaches provide different perspectives on decarbonising the system, especially the power sector, when the system is given flexibility in terms of additional investment and the exclusion of technology constraints by category (Table 5.2).

The near-optimal runs with the objective function of minimising CO2 emissions of the system under three umbrella scenarios, REF, high supply and low supply showed that flexibility on investments and technological options significantly influences the near-optimal results. To summarise, it is observed that electricity demand increases due to electrified end-use technologies, mainly in the household, commercial and transportation sectors. With additional investments (increasing slack values), the rate of end-use technologies switching to electricity

199 consumption increased e.g. electrification of cooking appliances and cars leading to increasing electricity demand (Figure 5.36, Figure 5.53 and Figure 5.57). Different demand pathways also influence the near-optimal runs. For example, near-optimal results benchmarking the high supply scenario showed that end-use technologies switching to electricity consumption accelerated as compared to the REF near-optimal scenarios. The rate of electrified end-use technologies and increase in electricity generation also depend on power technology availability and installation flexibility.

To capture the uncertainties in the system, the stringent technology constraints in the power sector are removed by categories (Table 5.2). At the same time, additional investment flexibility was given in order to obtain a different set of technology profile mixes. Hence, based on the constraint categories (Table 5.2), it is observed that for the NOL1, NOL2 and NOL3 scenarios, the near-optimal system suggested for access to clean energy in the power sector with the reduction of coal consumption and higher installation of solar as the slack value increases. Under the REF umbrella scenario, the NOL2 and NOL3 scenarios demonstrated a similar trend of electricity generation by fuel mix, whereby both scenarios reduced almost the same amount of cumulative carbon emissions as additional investment flexibility increased. However, given the flexibility for higher solar and nuclear installation options in the NOL4 scenario, the near-optimal system suggested the widespread installation of nuclear as electricity generation dramatically increases towards the end of modelling period (mainly in 20% and 30% slack values). This is due to the electrification of industrial machineries along with cars, motorbikes as well as household and commercial cooking technologies. Similar trends of end-use technology electrification, increased electricity consumption and similar technology profile mixes are observed for the near-optimal results under high and low umbrella scenarios. Yet the rate of technology deployment – for example, the deployment of solar or nuclear – varies under different constraint scenario categories.

The near-optimal runs with the objective function of minimising coal in the power sector under the three umbrella scenarios demonstrated a different outlook on the technology profile mix of the system. The main focus of introducing this objective function is to obtain insights on the power technology profile in the absence of or with minimal coal installation. The near optimal results illustrated that electricity generation pathways are reduced (depending on the scenarios explained in Table 5.1) due to the fuel-switching of commercial cooling technologies from electricity to gas under the REF and high supply umbrella scenarios (Figure 5.47 and Figure 5.55).

200

For example, decreases in electricity consumption are estimated for near-optimal pathways benchmarking REF scenario under the slack values of 20% and 30% (no changes in electricity consumption are anticipated for 5% and 10% slack values). For near-optimal pathways benchmarking the high supply scenario, significant changes in electricity generation pathways are observed with 20% and 30% additional investment. The electricity consumption is reduced approximately in 2033–2035 due to the accelerated switching of commercial cooling technologies to gas-based technologies. Yet these changes are only observed for the NOL1 and NOL2 scenarios (in the case of both benchmarking REF and high supply scenarios), whereby these scenarios are subject to technology constraints and the alternative options are only to deploy gas, hydro, nuclear or renewables based on the maximum availability of resources and technologies. The electricity generation pathways of the NOL3 and NOL4 scenarios remain identical to the REF and high supply umbrella scenarios. However, changes in terms of installed capacity and total electricity generation profile by fuels are observed (Figure 5.51 and Figure 5.56). The electricity generation pathways of near-optimal scenarios benchmarking the low supply scenario remain unchanged as well, but changes in terms of installed capacity and total electricity generation profile by fuels are foreseen in the results.

To summarise, in the effort to minimise coal consumption in the power sector, the near-optimal system alternates mainly between gas, hydro, solar or nuclear options (depending on the NOL1, NOL2, NOL3 or NOL4 scenarios). In the NOL1 and NOL2 scenarios, since these scenarios are not given the flexibility to develop more solar or nuclear power plants, the near-optimal system recommends a reduction in electricity consumption in commercial sector in order to satisfy the objective function constraint. In the power sector, the system minimises coal power capacities as the slack value increases with maximum potential of solar installed. A similar installation pattern can be observed for NOL1 and NOL2 under the REF, high supply and low supply umbrella scenarios and the electricity generation profiles by fuel mainly depend on gas, hydro, nuclear and solar.

Meanwhile, the NOL3 and NOL4 scenarios (given the flexibility for additional solar or nuclear) portrayed different electricity generation and technology profile patterns. Under the REF and high supply umbrella scenarios, for example, NOL3, with additional 5% to 10% investment, recommended the widespread installation of solar. In the case of NOL4, with additional 5% to 10% investment, the near-optimal system suggested the widespread installation of nuclear. However, both NOL3 and NOL4 scenarios with additional 20% to 30% investments in the REF and high supply umbrella scenarios, suggested the widespread installation of solar. Since the

201 capital cost of nuclear has been competitive with solar in the last five years and with additional investment flexibility, the near-optimal system installs solar technologies instead of nuclear. A summary of costing and carbon emissions by slack values is illustrated in Table B.22-Table B.27 in Appendix B.

5.3 Discussion and Summary

This section discusses the findings of the research on the optimal and near-optimal decarbonisation strategies. Based on the literature review and rationale for using hybrid models for Malaysia (Section 2.3), these analyses capture the future supply-demand uncertainties, which provide significant outcomes of energy system representation for economically advancing countries like Malaysia. These analyses investigated in detail the future demand and supply pathways as well as focused on the optimal decarbonisation strategies, which provided crucial insights on future energy systems in terms of the technological, environment and economical perspective.

The technology profiles of the optimal scenarios are modelled based on demand scenarios. To capture the uncertainties on the demand side, three demand scenarios (REF, high and low), driven by GDP and population growth, are modelled in order to obtain final demand pathways (2013–2050). These demand pathways are exogenously linked to the OSeMOSYS model to obtain three supply scenarios (REF, high and low) that demonstrated different trends of electricity generation mix by fuel and technology profiles of the optimal system. Further, in order to obtain insights on possible implementation of decarbonisation strategies in the country and how these targets would influence the deployment of the technology, resources and inter- regional electricity infrastructure, the system is imposed with stringent emission targets of 20%, 40%, 60% and 80% starting in 2030 under the umbrella scenarios. Decarbonisation pathways (benchmarking REF, high and low supply scenarios) showed that the system accelerates the diffusion of end-use technologies switching to low carbon fuel options, mainly electricity, as the percentage of carbon emissions reduction constraint (Figure 5.13, Figure 5.19 and Figure 5.25) increased towards the end of modelling period.

As summarised in Section 5.1.3.4, only five optimal decarbonisation strategies are identified as achievable within the technological and potential constraints defined in the power system (set of constraints outlined in Section 3.1.2). These optimal decarbonisation strategies are REF 20%, REF 40%, High 20%, Low 20% and Low 40% as demonstrated in Figure 5.34 (Part A). In all these

202 optimal decarbonisation scenarios, which also depend on demand changes in the system, all resource potentials are harvested and all maximum possible future technologies (~25GW hydro, ~3.5GW other renewables, ~17GW solar and ~12GW nuclear) are installed by 2050. To achieve beyond these optimal decarbonisation strategies (Part B of Figure 5.34Figure 5.34), additional technologies, resources and infrastructures need to be developed and deployed in the country. For example, additional solar and nuclear technologies are introduced to achieve optimal solutions for 60% and 80% scenarios under the REF, high supply and low supply umbrella scenarios. This includes the recommendation for inter-regional electricity trade infrastructure for the 60% and 80% scenarios, benchmarking the REF and high supply scenarios respectively.

The system suggests the widespread installation of solar to achieve the optimal decarbonisation of the system. For example, at least a cumulative new installed capacity of 142GW solar (REF 60% scenario) to a maximum cumulative new installed capacity of 264 GW solar (High 80% scenario) over the modelled period is suggested to achieve deep decarbonisation targets, which is a very ambitious goal. The IEA (2016c) report highlighted that a cumulative installed capacity of about 303 GW was achieved globally by the end of 2016, with at least 75 GW of PV systems installed and connected to the grid in 2015. Some of the leading countries using PV capacities to produce electricity are China, Japan, Germany, USA, Italy and the UK. Some studies that analysed the evolution of the PV industries in these countries highlighted that for the development of PV industry for long-term targets, key actions such as the allocation of government funds for R&D and technology deployment needs to be optimised, cost-effective PV incentive scheme need to be implemented and international collaboration in PV research needs to be expanded (Dinçer 2011, Zheng and Kammen 2014). Hence, key actions and research studies taken by these countries to deploy solar technologies can be a benchmarking example to encourage policy and market research for solar power in Malaysia.

To obtain insights on near-optimal decarbonisation strategies scenarios under three umbrella scenarios, REF, high supply and low supply (discussed in Section 5.1.2), two objective functions are introduced into the system separately. The results of these near-optimal scenarios are concluded in Section 5.2.2. To summarise, the near-optimal results of the system embedded with an objective function minimising CO2 emission of the system showed that the end-use technologies are electrified, leading to an increase in the electricity demand. An increase in electricity consumption across all sectors (depending on the constraint scenarios outlined in Table 5.2 and additional investments in terms of slack values introduced in the system), the system recommended high levels of installation of solar or nuclear power. The potentials of gas,

203 hydro, biomass, solid waste and geothermal are exhausted and the system installed solar and nuclear as alternatives to meet the increasing electricity demand. The electricity generation by fuel mainly depends on gas, hydro and nuclear, and as additional investments are imposed in the system, increasing electricity generation by solar is captured in the system. With increasing deployment of clean technologies in the power sector, carbon emissions in this sector are reduced (benchmarking the REF, high supply and low supply scenarios) as portrayed in Figure 5.44, Figure 5.54 and Figure 5.58.

The near-optimal results of the system embedded with objective function on minimising coal in power systems under the NOL1 and NOL2 scenarios across all slack values showed that the electricity generation pathways reduced due to a constrained technology environment, were unable to deploy additional capacities as the coal options are reduced to the minimum possible. Hence, the system switches from electrified commercial cooling technology to gas with additional investments leading to reduction in electricity demand while satisfying the constraint on minimisation of coal technology deployment in power sector. Yet for the NOL3 and NOL4 scenarios, the electricity generation pathways remain unchanged for all slack values. As these scenarios are given the flexibility to build more solar or nuclear, these technologies (nuclear and solar) are installed along with gas, hydro and other renewables to meet the electricity demand. Coal consumption reduction in the power sector can also significantly benefit local air pollutant abatement. For example, a number of studies highlighted that the strategy of reducing coal consumption contributes to carbon emission mitigation and local pollutant control (Changhong, Bingyan et al. 2006, Zhao, Wang et al. 2008, Yang and Teng 2018). Hence, the strategy of reducing coal consumption is highly important and cannot be ignored. Since high coal deployment is forecasted in the Malaysian power sector, strategies to mitigate carbon emissions which co-benefit local air pollutant abatement should be addressed by introducing alternative low carbon technologies.

Both near-optimal approaches with respective new objective functions provided different perspectives on uncertainties in technology deployment and carbon emissions reduction with additional investments in the system. These perspectives contribute to future policy planning. Reducing the carbon emissions of the system suggested the electrification of end-use technologies with low-carbon technology deployment in power sector. Meanwhile, reducing coal consumption in the power sector meant that the system would be recommended to focus mainly on the development of low-carbon power technologies while reducing electricity consumption in the commercial sector.

204

To conclude, the optimal and near-optimal decarbonisation strategies provided different insights on the possible evolution of a low carbon electricity sector in Malaysia. As highlighted by Hughes and Strachan (2010), the challenges of the uncertainty of low carbon scenarios are huge and unprecedented, whereby radical technological changes are anticipated to achieve the decarbonisation objective and also are subject to uncertainty compounded by long time frames. Hence, this PhD study involves the consideration of uncertainties around future technological changes, carbon emissions reduction and investment flexibility in Malaysian energy systems, mainly the power sector, for the long term. The important findings based on the optimal results are that coal technology is projected to play a key role in electricity generation in the future on the supply side, followed by gas, hydro and renewables. The optimal decarbonisation results projected that nuclear, solar or multiregional electricity trade infrastructure developed as stringent carbon emission targets are imposed in the system. The near-optimal decarbonisation results forecasted that electricity generation by fuels is based mainly on gas, hydro, nuclear or solar as additional investment flexibility is introduced in the system. Moreover, technology learning for solar leads to the significant penetration of solar technology in the energy supply mix, subject to the investment flexibility imposed in the system. The findings of this study could contribute to the context of policy planning to accelerate technological development in main sectors (household, commercial, industrial and transportation) and especially in the power sector to reduce carbon emissions.

205

Chapter 6 Conclusion

The conclusion chapter summarises the findings of each research question along with the main research contributions of this study. Future work and research limitations also stated in this chapter. The research gaps of past literature are reviewed in Section 2.5.

6.1 Restatement of Research Problem

This research aims to address the energy use changes in Malaysia with the hopes of helping to achieve a decarbonised system. The research intends to inform policy makers – both in Malaysia and similar fast developing economies – of the optimal technology options under the demand drivers as well as investment strategies in adopting new technologies under near-optimality conditions and the constrained environment of decarbonisation strategies. The research focuses on exploring the long-term optimal and near-optimal decarbonisation strategies under the landscape of demand–supply uncertainties.

Malaysia is faced with the challenges of addressing the supply side infrastructure, especially in the power sector, to fulfil the increasing demand and at the same time explore the implementation of clean energy options in this sector. The transformation of the power system to access clean energy makes a difference in terms of investment. The fuel-switching of end-use technologies under the decarbonisation conditions increases the electricity demand, instigating for clean energy access in the power sector. The connections between the demand–supply pathways that are economically optimal under decarbonisation constraints and the possibility of achieving near-optimal decarbonisation strategies are explored in order to provide an outlook to national policy makers of feasible decarbonisation and investment strategies that the country could implement.

Hence, three research questions were posed to address these challenges in exploring the strategies of decarbonising the energy system of Malaysia by 2050:

1. What are the optimal decarbonisation strategies under the long-term demand drivers in Malaysia? 2. How do the different decarbonisation targets influence the deployment of technologies, resources and inter-regional electricity trade? 3. How robust are the near-optimal decarbonisation strategies with respect to technological- environmental-economic objectives for long-term planning?

206

6.2 Main Findings

This section outlines the main findings of the research, summarised and grouped by research question as the following:

6.2.1 Optimal Decarbonisation Strategies of Malaysia’s Energy Sector

Two research questions are addressed here:

Research Question 1: What are the optimal decarbonisation strategies under the long-term demand drivers in Malaysia?

This question considered demand, supply and supply-demand decarbonisation modelling parts in order to generate findings. The demand modelling focused on projection of three long-term demand scenarios across the main sectors. The supply modelling investigated the supply technology profile mix to meet the demand. The decarbonisation modelling under the REF, High and Low umbrella scenarios considered the capital investment cost and CO2 emissions reduced under optimal decarbonisation strategies that are achievable within the technology and potential constraints imposed in the system based on the energy policies in Malaysia.

The main findings are:

1. Demand modelling: The projected energy demand modelled in Malaysia is mainly driven by GDP and population growth uncertainties. Three demand scenarios (REF, High and Low) were obtained to reflect demand uncertainties in future based on demand drivers discussed in Section 3.1.2-3.1.7. The growth of energy demand as projected by the scenarios is as follows: In all three scenarios considered, the transportation sector was the dominating demand need, followed by the industry, commercial and household sectors. The total final energy demand in Malaysia would increase by at least between 2.9 to 7.7 times more by 2050 relative to 2013 demand (1875 PJ), depending on the demand scenarios.

2. Supply modelling: The technology profiles of these scenarios modelled changes based on demand pathways. Analysing the power sector in detail across all scenarios showed that future capacity development in the system is dominated by coal technologies and the crucial period for the changes in power technology installations is between 2020–2032, where most of the existing gas power plants retire and new coal power plants are developed. In all three supply scenarios (REF, High and Low), the end-use technologies of

207

household, commercial, industrial and transportation are mostly based on fossil fuels. The total installed capacity in power sector increased from 26 GW (2013) to at least 47 GW (2050) in the low supply scenario (81% of capacities based on coal and gas), 98 GW (2050) in the REF supply scenario (70% of capacities based on coal and gas) and 148 GW (2050) in the high supply scenario (66% of capacities based on coal and gas). If the energy demand is higher than in the high supply scenario projection (taking into account all the potential availability of the technologies based on current policies modelled and completely installed in the energy system), the country would need to explore additional solar and nuclear technologies to meet the demand. Total carbon emissions produced in power sector increased from 92 Mton (2013) to at least 148 Mton (the low supply scenario), 286 Mton (the REF supply scenario) and 350 Mton (the high supply scenario) in 2050. The total investment cost over the modelled period that is needed to develop these capacities is about USD 53.0 billion (the low supply scenario), USD 77.0 billion (the REF supply scenario) and USD 100.0 billion (the high supply scenario).

3. Supply-demand decarbonisation modelling: Five moderate optimal decarbonisation strategies were identified under the long-term demand drivers, which required no radical technological changes. These optimal decarbonisation strategies are achievable within the technology and potential constraints defined in the power system. The moderate optimal decarbonisation strategies are REF 20%, REF 40%, High 20%, Low 20% and Low 40% (summarised in Figure 5.34 in Section 5.1.3.2). In all these optimal decarbonisation scenarios, which depend on demand changes in the system, all the resource potentials harvested and all maximum possible future technologies (~25GW hydro, ~3.5GW other renewables, ~270GW solar and ~20GW nuclear) are installed by 2050. Estimation of minimum total power system investment is from about 60 USD billion to a maximum of 111 USD billion, reducing carbon emissions between 3137 Mton to 6153 Mton for these optimal decarbonisation scenarios over the modelled period. On average, for these moderate optimal decarbonisation scenarios over the modelled period, total electricity generation increased by 6% due to the electrification of end-use technology, which costs the system at least 1.2 times more in order to reduce the carbon emissions of the power system by approximately 12% as compared to umbrella scenarios (REF, high supply and low supply).26 The increase in cost is due to clean energy access in the power sector through the widespread installation of solar and nuclear.

26 Refer Section 5.1.2.4 and Section 5.1.3.2 (Conclusion)

208

Research Question 2: How do the different decarbonisation targets influence the deployment of technologies, resources and inter-regional electricity trade?

This question considered the influences of different carbon emission constraints on the technology profiles in the system and increased access to clean energy in the power sector as the electricity demand increased across all sectors under stringent carbon constraints. The main findings included the following:

1. Projections showed that as stringent carbon emissions restrictions are imposed in the system, access to clean energy through electrification of end-use technologies in household, commercial, industry and transportation sectors is observed. It is observed that the system also accelerates the diffusion of end-use technologies switching to low carbon fuel options, mainly electricity, as the percentage of emission constraints increases by 2050.

2. With 20% and 40% emission constraints imposed in the system in all scenarios, the electricity demand by 2050 is forecasted to increase around 2.5–8.1 times more compared to electricity demand in 2013. To support the increasing electricity demand across all sectors, the maximum deployment of new installed technologies of ~25 GW hydro, ~12 GW nuclear and ~142 GW solar are required in the system. The feasible electricity generation is powered by mainly renewable and nuclear technology and these solutions are generated within the technology constraints introduced in these mitigation scenarios.

3. Gas and coal resources (20% and 40% emission constraints) also contribute to electricity generation in this decarbonised power system, although the share of these resources’ contribution is reduced over the modelled period. Gas and coal consumptions in 2013 are 784 PJ and 567 PJ respectively; the consumption trends of these fuel resources in the power system varies based on the decarbonisation scenarios. Gas resources consumption in 2050 is about 681 PJ (the Low 20% scenario) to a maximum of 877 PJ (the REF 40% scenario). Meanwhile, coal is gradually reduced in the REF 40% and Low 40% scenarios and the maximum consumption is about 995 PJ (REF 20% scenario) by 2050. This means that if the country decided to achieve and adopt decarbonisation strategies (or other related decarbonisation policies), the current development of coal and gas power plants would be affected.

4. To achieve deep decarbonisation strategies of 60–80%, the system produces feasible solutions if additional capacities of solar and nuclear are introduced. The solar share of total electricity generation mix by fuel is projected to increase from 1% (for the REF, High and Low supply umbrella scenarios) in 2013 to a maximum of 14% (the High 80% scenario) by

209

2050. The solar share of the total electricity generation mix in this high scenario translates to about 277 GW of total installed capacity by 2050 (approximately 40 GW of new solar installed capacity in 2050). Maximum nuclear total installed capacity is forecasted to about 12 GW for the High 80% scenario. Hydro resources (a maximum of 26 GW installation in Sarawak) necessitate the development of a multiregional electricity trade infrastructure to achieve deep decarbonisation strategies of 60–80%.

5. The maximum multiregional electricity trade between the regions of Sarawak and Peninsular Malaysia in the 80% decarbonisation strategy is an average of 21.0 TWh yearly starting in 2039–2050 and between Sabah and Sarawak it is an average of 4.3 TWh yearly, starting in 2043–2050. The maximum electricity trade in the 60% decarbonisation strategy between Sarawak and Peninsular Malaysia is an average of 5.4 TWh yearly, starting in 2048–2050 and between Sabah and Sarawak it is an average of 1.6 TWh yearly, starting in 2048–2050. The total investment to develop these infrastructures is about an average six times higher than the REF, High and Low supply umbrella scenarios.

6. Under decarbonisation of the system, end-use technologies further electrified, prompting clean energy access in the power sector through the development of solar, nuclear or hydro technologies. With 60% and 80% emission constraints imposed in the system in all umbrella scenarios, the electricity demand by 2050 was forecasted to increase around 3 to 18 times more than the REF, High and Low supply umbrella scenarios. Electrification of the end-use technologies and increasing access to clean energy would have a significant impact on the outlook of the future energy structure in Malaysia if the country decided to adapt to deep decarbonisation targets.27

A number of environmental, social and political aspects needs to be considered in order to achieve the high deployment of specific technology, for example, solar, nuclear or interregional electricity trade infrastructure. Long term planning needed for international technology transfer includes studies on country-specific technology requirements, infrastructure development to facilitate the building of these technologies locally or human capacity building to operate these technologies. If the decision makers decide to introduce these technologies in the country, the social acceptance of these technologies and the land-use planning to develop these technologies

27 Refer Figure 5.28 and Figure 5.33 in Section 5.1.3.2 (Conclusion)

210 are crucial factors that need to be considered. This may lead to reframing the energy policy to create the platform to implement these initiatives.

For example, the development of nuclear technologies requires the consideration of factors such as power plant decommissioning cost, nuclear waste management cost in the long term and public perceptions towards the acceptance of nuclear technology. The deployment and commissioning of interregional electricity trade infrastructure would entail high investment in the power sector and needs prominent political cooperation between neighbouring countries. Therefore, the decision to make such an investment also needs to be explored. To conclude, the decarbonisation of the system on the supply side in each scenario could be achieved under the acceleration of research specific to the country on resource availability and utilisation across all sectors as well as a feasibility study on the development and deployment of new technologies. Hence, the recommendation of policies is needed in order to promote the research and development of advanced and highly efficient technologies across all sectors to achieve decarbonisation targets

6.2.2 Near-Optimal Decarbonisation Strategies of Malaysia’s Energy Sector

Research Question 3: How robust are the near-optimal decarbonisation strategies with respect to technological–environmental–economic objectives for long-term planning?

This question considered flexibility in terms of additional investments and the exclusion of technology constraints by category with two objective function approaches embedded separately in the system to analyse near-optimal solutions that provide significant supply technology profiles for Malaysia’s power system and possible energy outlooks for the consideration of future policy planning. The analysis focused on investigating the near-optimal runs with higher investment cost flexibility (more slack values) as well as with the stringent technology constraints in the power sector are removed by categories (NOL1, NOL2, NOL3 and NOL4 scenarios – fewer constraints and more flexibility allowed) in order to obtain a different set of technology profile mixes. The main findings are:

Objective function: minimising CO2 emissions of the system

1. The near-optimal results of the system embedded with an objective function minimising

the CO2 emissions of the system showed that the rate of end-use technologies switching to electricity consumption increased for cooking appliances and cars, leading to increased electricity demand with additional investment (Figure 5.36, Figure 5.53 and Figure 5.57 in

211

Section 5.2.1). Increasing electricity generation fulfilled by mainly gas, hydro and nuclear power plants, and as additional investments are imposed in the system, increasing electricity generation by solar is captured in the system.

2. In the NOL1, NOL2 and NOL3 scenarios, access to clean energy in the power sector with the reduction of coal consumption is maximised (the potentials of gas, hydro, biomass, solid waste and geothermal are exhausted) and solar installation increases as the slack value increases. In the NOL4 scenario, the near-optimal system suggests the widespread installation of nuclear as electricity generation dramatically increases towards the end of modelling period (mainly in 20% and 30% slack values). This is due to the electrification of industrial machinery along with cars, motorbikes as well as household and commercial cooking technologies. With increasing deployment of clean technologies in the power sector, carbon emissions in this sector are reduced (benchmarking the REF, high supply and low supply scenarios) as portrayed in Figure 5.44, Figure 5.54 and Figure 5.58 in Section 5.2.1.

Objective function: minimising coal in power systems

3. The near-optimal results of the system embedded with objective function on minimising coal in power systems under the NOL1 and NOL2 scenarios (20% and 30% slack values) showed that the electricity generation pathways are reduced due to a constrained technology environment (Figure 5.47). It is limited to deploy additional capacities, as the coal options are reduced to the minimum possible. Electricity consumption is reduced approximately in 2033–2035 due to the accelerated switching of commercial cooling technologies to gas consumption, with additional investments leading to reduction in electricity demand while satisfying the constraints on the minimisation of coal technology deployment in the power sector.

4. In the NOL1 and NOL2 scenarios, since these scenarios are not given the flexibility to develop more solar or nuclear power plants, the near-optimal system recommends a reduction in electricity consumption in the commercial sector in order to satisfy the objective function constraint. In the power sector, the system minimises coal power capacities as the slack value increases with the maximum potential of solar installed. In the NOL3 and NOL4 scenarios (given the flexibility for additional solar or nuclear), these technologies (nuclear and solar) are installed along with gas, hydro and other renewables

212

to meet the electricity demand. The carbon emissions are reduced in the power sector due to the deployment of clean technologies (benchmarking the REF, high supply and low supply scenarios) as shown in Figure 5.51, Figure 5.56 and Figure 5.59 in Section 5.2.1.

Both near-optimal approaches with respective new objective functions provided different perspectives on uncertainties in technology deployment and carbon emissions reduction with additional investments in the system. Reducing the carbon emissions of the system suggested the electrification of end-use technologies with low-carbon technology deployment in the power sector. Meanwhile, reducing coal consumption in the power sector meant that the system would be recommended to focus mainly on the development of low-carbon power technologies while reducing electricity consumption in the commercial sector. The near-optimal decarbonisation results forecasted that electricity generation by fuels is based mainly on gas, hydro, nuclear or solar as additional investment flexibility is introduced in the system. The findings of this study could contribute to the context of policy planning to accelerate technological development in main sectors (household, commercial, industrial and transportation) and especially in the power sector to reduce carbon emissions.

It is observed that for the NOL1, NOL2, NOL3 and NOL4 scenarios, the near-optimal system suggested for access to clean energy in the power sector. The rate of technology deployment – for example, the deployment of solar or nuclear – varies under different constraint scenario categories as the slack value increases. These perspectives contribute to future policy planning. If the policy makers need to make a decision to trade-off higher costs with the removal of prior power system commissioning plans (technology boundaries defined in the model) with goals to reduce carbon emission of the system, the Malaysian energy systems could be developed through the implementation of NOL1 and NOL2 near-optimal runs under the REF, high supply and low supply umbrella scenarios with an additional 20% and 30% investment cost (20% and 30% more slack values).

For the NOL3 near-optimal runs under the REF, high supply and low supply umbrella scenarios, high penetration of solar technology observed in the energy supply mix (5% to 30% more slack values). If the policymakers would opt for a renewable-powered system, different approaches may be required in terms of international cooperation for technology transfers and detailed assessment of financial requirement to adopt this technology. For the NOL4 near-optimal runs under the REF, high supply and low supply umbrella scenarios (5% to 30% more slack values), high widespread of nuclear installation may be a challenge for a developing country that is still in the phase of its initial consideration of implementing a nuclear power programme.

213

6.3 Limitation and Future Work

There are some limitations of this research that need to be addressed in the future in order to further improve the results as the following:

6.4.1 Dispatch and spatial modelling linkage

The optimal and near-optimal decarbonisation results suggest the widespread installation of solar technology towards the end of the modelling period. Capturing the spatial and temporal variability of solar energy would help to further improve the solar modelling projections. The optimisation model should be linked with a dispatch model to determine the technically feasible investment strategies for the power sector in Malaysia and a temporal demand distribution that would give a detailed insight into the solar energy dispatching to meet the electricity demand. Investigating and identifying the spatial pattern of solar technology diffusion could indicate of level and rate of solar technology deployment in the country. The detailed modelling of the power sector captured in the optimisation model, combined with a dispatch model, would provide comprehensive outcomes on the detailed operation of the power plants for the short and long term.

6.4.2 Modelling of end-use technologies in sectors

The representation of comprehensive energy system models is important for analysing the decarbonisation of the energy system across all sectors and the trade-offs of fuel-switching between end-use technologies. The structure of the energy model could be improved with greater detail in modelling of end-use technologies to allow flexibility for fuel-switching. For example, disaggregation of end-use technologies in the industrial sector (e.g. existing and efficient machinery) would improve the sectoral energy projections. Hence, comprehensive end- use technology modelling across all sectors would provide detailed fuel-switching trends to low carbon fuels at the sectoral level.

6.4.3 Land-use modelling linkage

This thesis research found that maximum biomass resources must be captured in the system to achieve optimal and near-optimal decarbonisation targets. To improve the realism of the biomass modelling results, they should be linked with a land-use model to investigate spatial land availability for bioenergy crop production in order to further identify and harvest bioenergy potentials.

214

6.4.4 Near-optimal modelling approach

This thesis research found that near-optimal solutions with two objective functions embedded separately in the system provide significant differences in terms of supply technology profiles for Malaysia’s power system and possible energy outlooks for the consideration of future policy planning. Different objective functions should be introduced in the system in order to investigate politically different near-optimal scenarios. Transportation and industry sectors are projected to be the major energy consuming sectors, mainly depending on fossil fuels. Imposing objective functions such as maximum electricity consumption by industrial machinery, maximum introduction of gas and electricity fuelled road-vehicles or minimum petrol consumption by cars would provide politically different insights to reduce carbon emissions in the system. These insights could also significantly contribute to improving current energy policies on industry or transportation, as well as future energy planning along with efforts to reduce carbon emissions.

6.4.5 Energy supply chain modelling

The decarbonisation system showed that the transportation sector requires a transition from oil-based vehicles to electric vehicles and then to natural gas vehicles. The modelling of comprehensive energy supply chains to investigate future energy needs and gaps may provide dynamic perspectives which energy systems models do not fully account for. Energy supply chain modelling paired with energy system models would assist in multi-criteria decision-making and policy planning in order to address the energy security issues in the power sector as well to reduce the carbon emissions of the energy systems.

6.4 Research Contribution and Conclusion

To conclude, the study analyses the possibility of achieving decarbonisation targets in Malaysia, especially in the power sector. The power sector has been the major CO2 emissions contributor since 2000 due to fossil fuel combustion and is expected to increase with future coal power plants that to become operational in the near term (EC 2014, EC 2016). For example, an outlook on 10-year supply-demand projection for Peninsular Malaysia (2016-2026) showed that the share of generation from the coal power plant is forecasted to increase as compared to generation share from gas and hydropower plants (SB 2016). Since high coal deployment is predicted in the Malaysian power sector, strategies to mitigate carbon emissions which co- benefit local air pollutant abatement should be addressed by introducing alternative low carbon technologies such as nuclear, solar or biomass.

215

(see Section 5.2.1.1 on near optimality with an objective function to minimise coal in power system). Hence, the strategy of reducing coal consumption is highly important and cannot be ignored. For example, several studies highlighted that the approach of reducing coal consumption contributes to carbon emission mitigation and local pollutant control (Changhong, Bingyan et al. 2006, Zhao, Wang et al. 2008, Yang and Teng 2018). The authors suggested that there should be a policy introduced to cap coal consumption in the power sector. Similar policy on coal consumption cap could be introduced in Malaysia, and future coal power sector development needs to be revised if the country intended to reduce CO2 emissions in this sector as well as to meet the national strategy of reducing its GHG emissions intensity of GDP by 45% by 2030 relative to the emissions intensity of GDP in 2005.

The results provide possible trends of fuel transitions across all sectors under different decarbonisation targets and suggested deployment of various technologies and potential capacities, for example, renewables, that would significantly influence the outlook of capacity development as well as the policy planning in Malaysia. For instance, according to Renewable Energy Policy (2009), the country is aiming to achieve a cumulative installation of 9GW of solar by 2050. This target is achievable in the case of reducing emissions by 20% or less by 2050 (see Section 5.1.3.1 on 20% decarbonisation target benchmarking the REF scenario). Although the government had introduced, for example, Feed-in-Tariff (FiT) for solar PV development and the Large Scale Solar program to increase the renewable energy share in the electricity generation mix, the government should further be proactive to introduce policies to subsidise green energy sources and provide financial incentives to support R&D program on renewable technologies if the country would want to achieve deep decarbonisation strategies. For example, the Japanese government introduced a program known as "New Sunshine Program" to promote new energy introduction in Japan including the development of low-cost manufacturing technology for solar power generation and allocated grants to implement this program (Tatsuta, M. 1996). Similar approach or program could be funded by the government to promote renewable energy in Malaysia.

The country needs to evaluate short-term decision on building the fossil fuel-based power plants to meet the fast-growing electricity demand compared to long-term clean technologies requiring high investment in the initial stage but contributing to emissions reductions in the long term. The financial impact on achieving deep carbonisation strategies, especially in the power sector needs to be examined. The fossil fuel consumption still dominates the Malaysian energy system, and a major issue in transitioning to low carbon technology system is on how to obtain finance and steer the investments into renewable directions. The results on optimal

216 decarbonisation scenarios (Table 5.1) shows that total system cost increases as the system adopts low-carbon technologies to reduce the total CO2 emission of the system. Furthermore, the results on near-optimal decarbonisation scenarios (see Section 5.2.1.1) outlines the trade- offs and technology options if there is flexibility in terms of current policies implementation as well as allowing the system's costs to increase beyond strict optimality. The results portray that increasing investment needed to reduce a given CO2 reduction level and at the same time, providing flexibility to existing policy implementation. For example, Germany has a diversified market for renewable energy. The implementation of the regulatory framework in the energy sector created a market for investment in the renewable energy sources and mainly driven by political momentum to comply with German climate policy by increasing renewable integration in the power sector (Steffen 2018). Mazzucato and Semieniuk (2016) discussed and mainly concluded that it is crucial to understand the relevance of different types of financial investments to plan the renewable implementations and design technology-specific benefits based on historical trend renewable financing. Hence, if deep overall CO2 emission reduction is the objective of the government, then more money needs to be invested in low-carbon technologies, and the government needs to set the political momentum and create a low-risk market for investment in the renewable technologies.

Besides investment decisions, other factors such as social, political and environmental aspects need to be considered to achieve the high deployment of specific technology, for example, solar, nuclear or undersea cables to facilitate Sarawak electricity exports. The results of this study suggested high deployment of specific technology to achieve decarbonisation strategies in Malaysia. Long term planning needed for international technology transfer including studies on country-specific technology requirements, infrastructure development to facilitate the building of these technologies locally or human capacity building to operate these technologies. If the decision-makers decide to introduce these technologies in the country, the social acceptance of these technologies and the land-use planning to develop these technologies are crucial factors that need to be considered. These factors may lead to reframing the energy policy to create the platform to implement these initiatives on introducing new technologies such as nuclear. For example, a study by Corner et al. (2011) on public attitudes towards nuclear power in the UK concluded that people would increasingly accept the nuclear power if people are more concern about climate change and energy security. Hence, the development of nuclear technologies requires the consideration of factors such as public perceptions towards the acceptance of nuclear technology, power plant decommissioning cost and nuclear waste management cost in the long term. Therefore, to embark upon decarbonisation pathways may require significant

217 international cooperation for technology transfers and creating social awareness on acceptance of advance technology such as nuclear as well as detailed assessment of financial requirement to adopt these advanced technologies. Moreover, a political decision also needed to create a liberalised market in Malaysia. A liberalised market environment needs to tackle the issue of under-pricing of electricity and to create a stable regulatory environment to promote investment in the power system (Rudnick and Velasquez 2018).

The results also show the energy transition of end-use technologies in the various sector; for example, the introduction of electric and gas-fuelled vehicles is recommended to achieve emission reductions. These recommendations for fuel switching are aligned with some measures currently introduced in the country, such as the NAP (2014) policy (discussed in Section 3.2.2.1) to introduce electric and gas vehicles. However, additional policy measures needed, for example, a report by C2ES (2008) outlined that car manufacturers could provide allowances to cover the lifetime GHG emission of a new vehicle sold based on car use estimation and efficiency. This approach could provide incentives for consumers to select vehicles with low GHG emissions, whereby the car prices could be adjusted by the manufacturers to reflect the number of allowances needed to cover the vehicle lifetime emissions. Hence, if the government plans to adopt deep decarbonisation strategies, then the government could enforce such approaches to be taken by the car manufacturers in Malaysia such as PROTON Holdings Berhad and PERODUA.

This thesis reviewed literature and used a novel hybrid model to fill an important research gap by combining demand and supply models integrated with the MGA technique to analyse the optimal and near-optimal decarbonisation strategies for multiregional power integration systems at a national level. As much of the past work focused on decarbonisation analysis of the energy system, this thesis considered near-optimal scenarios covering multiple sectors at the national level, which includes multiregional power integration under the landscape of socioeconomic uncertainties. To conduct this analysis, the MGA hybrid (MAED-OSeMOSYS) approach was used to investigate a range of near-optimal decarbonisation pathways for future energy policy planning. This thesis provides insights to policy makers of middle-sized countries on feasible decarbonisation and investment strategies that may have important investment, trade and policy implications and relevance at a national and international level. This research on its own merits gives Malaysia and other similar middle-sized developing countries a high added value to its energy modelling analysis, which is scarce in comparison to energy modelling research of developed countries.

218

Under the landscape of demand–supply uncertainties, this thesis analysed the possibility of achieving optimal decarbonisation targets in Malaysia and concluded that the development of advanced and clean technologies was needed in the system across all sectors, mainly power, industry and transportation. This thesis also investigated the near-optimal decarbonisation strategies that provided different insights on the possible evolution of a low carbon electricity sector in Malaysia. Additional investments and flexibility in categories of technology constraints imposed in the system influence the type of technology to be deployed and make a difference in the diffusion of the power technologies, which result in carbon emissions reduction in the system. Hence, this study contributed to the body of the knowledge of achieving decarbonisation strategies through investigating the correlation and trade-off between the end- use sectors and examining structural uncertainties with the investment flexibility imposed in the model.

219

References

Abdul, A., M. Elsholkami, A. Elkamel and L. Simon (2017). "Multi-products productions from Malaysian oil palm empty fruit bunch (EFB): Analyzing economic potentials from the optimal biomass supply chain." Journal of Cleaner Production 168: 131-148.

ADB (2015). Asian Development Outlook 2015. Financing Asia’s Future Growth. [ebook] Philippines: Asian Development Bank, 2015. Available at:https://www.adb.org/sites/default/files/publication/154508/ado-2015.pdf

Ahamed, J. U., R. Saidur, H. H. Masjuki, S. Mekhilef, M. B. Ali and M. H. Furqon (2011). "An application of energy and exergy analysis in agricultural sector of Malaysia." Energy Policy 39(12): 7922-7929.

Ahmed, A. S., A. H. Isa, J. S. Olayande and J. O. Ojosu (2014). "Modelling of Energy Demand in the Building and Construction Sector of the Nigerian Economy." International Journal of Energy Engineering 4(3): 54-60.

Ali, R., I. Daut and S. Taib (2012). "A review on existing and future energy sources for electrical power generation in Malaysia." Renewable and Sustainable Energy Reviews 16(6): 4047-4055.

Amin, N., C. W. Lung and K. Sopian (2009). "A practical field study of various solar cells on their performance in Malaysia." Renewable Energy 34(8): 1939-1946.

Anandarajah, G. and A. Gambhir (2014). "India’s CO2 emission pathways to 2050: What role can renewables play?" Applied Energy 131: 79-86.

APEC (2013). APEC Energy Demand and Supply Outlook Japan, Asia Pacific Energy Research Centre (APERC). 5th Edition.

APERC (2016). APEC Energy Demand and Supply Outlook 6th Edition 2016. Japan: Asia Pacific Energy Research Centre (APERC), Institute of Energy Economics.

Ardelean, M. and P. Minnebo (2015). HVDC Submarine Power Cables in the World. JRC Technical Report. Netherlands, EUR 27527 EN. doi:10.2790/95735

ASEAN (2015). ASEAN Economic Community Blueprint 2025. [online] Jakarta: ASEAN Secretariat, November 2015. Available at:http://www.asean.org/wp- content/uploads/images/2015/November/aec-page/AEC-Blueprint-2025-FINAL.pdf

ASEAN (2017). Overview of ASEAN Plus Three Cooperation. [online] Jakarta: ASEAN Secretariat Information Paper, June 2017. Available at:http://asean.org/storage/2017/06/Overview-of-APT-Cooperation-Jun-2017.pdf

Athukorala, P. (2008). "The Malaysian Capital Controls: A Success Story?" Asian economic papers 7(1): 31-74.

220

Azam, M., J. Othman, R. A. Begum, S. M. S. Abdullah and N. G. M. Nor (2016). "Energy consumption and emission projection for the road transport sector in Malaysia: an application of the LEAP model." Environment, Development and Sustainability 18(4): 1027- 1047.

Bakar, M. N. B. A., W. L. Chee and J. L. Fernandez (2013). "PSU-USM International Conference on Humanities and Social Sciences Factors that Influence the Choice of Mode of Transport in Penang: A Preliminary Analysis." Procedia - Social and Behavioral Sciences 91: 120-127.

Bayar, Y. and H. A. Özel (2014). "Electricity Consumption and Economic Growth in Emerging Economies." Journal of Knowledge Management, Economics and Information Technology 4(2).

Bazmi, A. A. and G. Zahedi (2011). "Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review." Renewable and Sustainable Energy Reviews 15(8): 3480-3500.

Beaujean, J. M., J. P. Charpentier and N. Nakicenovic (1977). "Global and International Energy Models: A Survey." Annual Review of Energy 2(1): 153-170.

Beaujean, J.-M. and J.-P. Charpentier (1978). A Review of Energy Models No. 4-July 1978. IIASA Research Report. Laxenburg, Austria, IIASA. RR-78-012 Copyright © July 1978 by the author(s). http://pure.iiasa.ac.at/831/

Berkmen, S. P., G. Gelos, R. Rennhack and J. P. Walsh (2012). "The global financial crisis: Explaining cross-country differences in the output impact." Journal of International Money and Finance 31(1): 42-59.

Bhattacharyay, B. N. (2009). Infrastructure Development for ASEAN Economic Integration. ADBI Working Paper 138. [online]Tokyo: Asian Development Bank Institute. Available at: http://hdl.handle.net/11540/3726. License: CC BY 3.0 IGO.

BNM (2013). Bank Negara Malaysia Annual Report 2013. Malaysia, Bank Negara Malaysia. Available at: http://www.bnm.gov.my/index.php?ch=en_publication&pg=en_ar&ac=19

Börjeson, L., M. Höjer, K.-H. Dreborg, T. Ekvall and G. Finnveden (2006). "Scenario types and techniques: Towards a user's guide." Futures 38(7): 723-739.

Brill, E. D. (1979). "The Use of Optimization Models in Public-Sector Planning." Management Science 25(5): 413-422.

Brill, E. D., Jr., J. M. Flach, L. D. Hopkins and S. Ranjithan (1990). "MGA: a decision support system for complex, incompletely defined problems." Systems, Man and Cybernetics, IEEE Transactions on 20(4): 745-757.

Brill, E. D., S.-Y. Chang and L. Hopkins (1982). "Modeling to Generate Alternatives: The HSJ Approach and an Illustration Using a Problem in Land Use Planning." Management Science 28(3): 221-235.

Cai, W., C. Wang, K. Wang, Y. Zhang and J. Chen (2007). "Scenario analysis on CO2 emissions reduction potential in China's electricity sector." Energy Policy 35(12): 6445-6456.

221

Cevik, E. I., S. Dibooglu and T. Kenc (2016). "Financial stress and economic activity in some emerging Asian economies." Research in International Business and Finance 36(Supplement C): 127-139.

Chamon, M., P. Mauro and Y. Okawa (2008). "Mass car ownership in the emerging market giants." Economic Policy(54): 243-296.

Chang, S.-Y., E. D. Brill and L. D. Hopkins (1982a). "Use of mathematical models to generate alternative solutions to water resources planning problems." Water Resources Research 18(1): 58-64.

Chang, S.-Y., E. D. Brill and L. D. Hopkins (1982b). "Efficient Random Generation Of Feasible Alternatives: A Land Use Example." Journal of Regional Science 22(3): 303-314.

Changhong, C., W. Bingyan, F. Qingyan, C. Green and D. G. Streets (2006). "Reductions in emissions of local air pollutants and co-benefits of Chinese energy policy: a Shanghai case study." Energy Policy 34(6): 754-762.

Chayawatto, N., B. Fungtammasan, N. A. Utama, T. Tezuka and K. N. Ishihara (2011). Energy Sector Scenario for Low Carbon Society in Thailand towards 2050 2011 IEEE First Conference on Clean Energy and Technology CET.

Chazelle, B. (2000). Linear Programming and Extensions. The Discrepancy Method: Randomness and Complexity. Cambridge, Cambridge University Press: 307-315.

Chen, W., Z. Wu, J. He, P. Gao and S. Xu (2007). "Carbon emission control strategies for China: A comparative study with partial and general equilibrium versions of the China MARKAL model." Energy 32(1): 59-72.

Chiodi, A., M. Gargiulo, F. Rogan, J. P. Deane, D. Lavigne, U. K. Rout and B. P. Ó Gallachóir (2013). "Modelling the impacts of challenging 2050 European climate mitigation targets on Ireland’s energy system." Energy Policy 53: 169-189.

Choucri, N. and C. Heye (1990). "3.5. Simulation models." Energy 15(3): 363-378.

Coelho, M. J. and H. C. Frey (2001). Evaluation Of Alternative Future Energy Scenarios For Brazil Using An Energy Mix Model. Annual Meeting of the Air & Waste Management Association, Orlando, Florida, A&WMA, Pittsburgh, PA.

Corner, A., D. Venables, A. Spence, W. Poortinga, C. Demski and N.Pidgeon (2011), “Nuclear power, climate change and energy securi-ty: Exploring British public attitudes”, Energy Policy 39, 4823–4833.

C2ES (2008). Report on Policies to Reduce Emissions from the Transportation Sector. Center for Climate and Energy Solutions. Arlington.

Daioglou, V., B. J. van Ruijven and D. P. van Vuuren (2012). "Model projections for household energy use in developing countries." Energy 37(1): 601-615.

222

Dale, A. T., A. F. P. d. Lucena, J. Marriott, B. S. M. C. Borba, R. Schaeffer and M. M. Bilec (2013). "Modeling Future Life-Cycle Greenhouse Gas Emissions and Environmental Impacts of Electricity Supplies in Brazil." Energies 6(7): 3182-3208.

Dargay, J., D. Gately and M. Sommer (2007). "Vehicle Ownership and Income Growth, Worldwide: 1960-2030." The Energy Journal 28(4): 143-170.

Das, A. and E. O. Ahlgren (2010). "Implications of using clean technologies to power selected ASEAN countries." Energy Policy 38(4): 1851-1871. Das, A., D. Rossetti di Valdalbero and M. R. Virdis (2007). "ACROPOLIS: An example of international collaboration in the field of energy modelling to support greenhouse gases mitigation policies." Energy Policy 35(2): 763-771.

Dargay, J.M. The effect of income on car ownership: Evidence of asymmetry. Transportation Research Part A: Policy and Practice. 35 (9): 807–821.

De Laquil, P., C. Wenying and E. D. Larson (2003). "Modeling China's energy future." Energy for Sustainable Development 7(4): 40-56.

de Lucena, A. F. P., R. Schaeffer and A. S. Szklo (2010). "Least-cost adaptation options for global climate change impacts on the Brazilian electric power system." Global Environmental Change 20(2): 342-350.

DEA, OECD and URC (2013). National Greenhouse Gas Emissions Baseline Scenarios: Learning from Experiences in Developing Countries. Research Report. Danish Energy Agency (DEA), Organisation for Economic Co-operation and Development (OECD) and UNEP Risø Centre (URC).

DeCarolis, J. F. (2011). "Using modeling to generate alternatives (MGA) to expand our thinking on energy futures." Energy Economics 33(2): 145-152.

DeCarolis, J. F., S. Babaee, B. Li and S. Kanungo (2016). "Modelling to generate alternatives with an energy system optimization model." Environmental Modelling & Software 79: 300- 310.

DECC (2015). DECC 2015 Fossil Fuel Price Assumptions. UK: Department of Energy and Climate Change.

Dinçer, F. (2011). "The analysis on photovoltaic electricity generation status, potential and policies of the leading countries in solar energy." Renewable and Sustainable Energy Reviews 15(1): 713-720.

Doraisami, A. (2015). "Has Malaysia really escaped the resource curse? A closer look at the political economy of oil revenue management and expenditures." Resources Policy 45(Supplement C): 98-108.

DOSM (2010a). Annual Gross Domestic Product, 2010-2015. Department of Statistics, Malaysia. [online]. Available at: https://newss.statistics.gov.my/newss- portalx/ep/epFreeDownloadContentSearch.seam?cid=2997 (Accessed: 29 May 2016).

223

DOSM (2010b). Population Distribution and Basic Demographic Characteristic Report 2010. Department of Statistics, Malaysia (DOSM). [online] Available at:https://www.statistics.gov.my/index.php?r=column/ctheme&menu_id=L0pheU43NWJwR WVSZklWdzQ4TlhUUT09&bul_id=MDMxdHZjWTk1SjFzTzNkRXYzcVZjdz09

DOSM (2011). Population Distribution and Basic Demographic Characteristic Report 2010. Department of Statistics Malaysia. [online]. Available at: https://www.dosm.gov.my/v1/index.php?r=column/cthemeByCat&cat=117&bul_id=MDMx dHZjWTk1SjFzTzNkRXYzcVZjdz09&menu_id=L0pheU43NWJwRWVSZklWdzQ4TlhUUT09. [Accessed 24 August 2017].

DOSM (2013a). Key Statistics of Labour Force in Malaysia 2013 Report. Department of Statistics, Malaysia (DOSM). [online] Available at:https://www.dosm.gov.my/v1/index.php?r=column/ctwoByCat&parent_id=123&menu_i d=U3VPMldoYUxzVzFaYmNkWXZteGduZz09

DOSM (2013b). Population Projection, Malaysia 2010 – 2040 (Summary Findings). Department of Statistics, Malaysia (DOSM). [online] Available at: https://www.statistics.gov.my/images/stories/files/LatestReleases/population/Ringkasan_P enemuan-Summary_Findings_2010-2040

DOSM (2014a). Salaries & Wages Survey Report 2014. Department of Statistics, Malaysia (DOSM). [online] Available at:https://www.dosm.gov.my/v1/index.php?r=column/ctheme&menu_id=U3VPMldoYUxzV zFaYmNkWXZteGduZz09&bul_id=R1pZQ0RqRjY0aFJjcUM4cS9zcUdTZz09

DOSM (2014b). Press Release: Report of Household Income and Basic Amenities Survey 2014. Department of Statistics, Malaysia (DOSM). [online] Available at: https://www.dosm.gov.my/v1/index.php?r=column/pdfPrev&id=aHhtTHVWNVYzTFBua2dS UlBRL1Rjdz09

DOSM (2014c). Press Release: Report on Selected Demographic Statistics Estimates Malaysia 2014. Department of Statistics, Malaysia (DOSM). [online] Available at: https://www.dosm.gov.my/v1/index.php?r=column/ctwoByCat&parent_id=115&menu_id= L0pheU43NWJwRWVSZklWdzQ4TlhUUT09 [Accessed: 20 April 2016]

DOSM (2015a). Press Release: GDP by State, 2010-2014. Department of Statistics, Malaysia (DOSM). [online] Available at: https://www.dosm.gov.my/v1/index.php?r=column/pdfPrev&id=OFN3NkVtT0xGWU1wQ1Y 1YnRPNXRUQT09 [Accessed 29 May 2016].

DOSM (2015b). Annual Gross Domestic Product, 2010-2015. Department of Statistics, Malaysia (DOSM). [online] Available at: https://newss.statistics.gov.my/newss- portalx/ep/epFreeDownloadContentSearch.seam?cid=2997 [Accessed 29 May 2016].

DOSM (2015c). Press Release: Selected Demographic Statistics Estimates Malaysia 2015. Department of Statistics, Malaysia (DOSM). [online] Available at: https://www.dosm.gov.my/v1/index.php?r=column/ctwoByCat&parent_id=115&menu_id= L0pheU43NWJwRWVSZklWdzQ4TlhUUT09 [Accessed: 20 April 2016]

224

DOSM (2015d). Population Statistics 2015. Department of Statistics, Malaysia (DOSM). [online] Available at: https://www.statistics.gov.my/dosm/uploads/files/3_Time%20Series/Malaysia_Time_Series _2015/22Perangkaan_Penduduk.pdf [Accessed: 29 May 2016]

DOSM (2016). Press Release: Gross Domestic Product First Quarter 2016. Department of Statistics, Malaysia (DOSM). [online] Available at: https://www.statistics.gov.my/index.php?r=column/pdfPrev&id=MmFJTitpalZJcFQ4ZzRhM WhYNTR0Zz09 [Accessed: 29 May 2016]

DOSM (2017a). Malaysia Economics Statistics: Time Series on National Accounts and Population. Department of Statistics, Malaysia (DOSM). [online] Available at: https://www.dosm.gov.my/v1/uploads/files/3_Time%20Series/Malaysia_Time_Series_2016 DOSM (2017b). Population Statistics. Department of Statistics Malaysia. [online]. Available at: https://www.dosm.gov.my/v1/uploads/files/3_Time%20Series/Malaysia_Time_Series_2016 /22_Penduduk.pdf. [Accessed 25 August 2017].

Dzioubinski, O. and R. Chipman (1999). Trends in consumption and production: Household energy consumption. DESA Discussion Paper No. 6. New York, N.Y, United Nations.

EC (2014). Peninsular Malaysia Electricity Supply Industry Outlook 2014. Energy Commission Malaysia. [online]. Available at: http://www.st.gov.my/index.php/component/k2/item/606- peninsular-malaysia-electricity-supply-industry-outlook-2014.html

EC (2014a). Sabah Electricity Supply Industry Outlook 2014. Malaysia: Energy Commission (EC). [online]. Available at: file:///C:/Users/ucqbkum/Downloads/Sabah%20Electricity%20Supply%20Industry%20Outlo ok%202014.pdf

EC (2014b). Malaysia Energy Statistics Handbook 2014. Malaysia: Energy Commission (EC).

EC (2016). Peninsular Malaysia Electricity Supply Industry Outlook 2016. Energy Commission Malaysia. [online]. Available at: http://st.gov.my/index.php/en/component/k2/item/680- peninsular-malaysia-electricity-supply-industry-outlook-2016-download.html

EC (2017). Peninsular Malaysia Electricity Supply Industry Outlook 2017. Malaysia: Energy Commission (EC). [online]. Available at: file:///C:/Users/ucqbkum/Downloads/Peninsular%20Malaysia%20Electricity%20Supply%20 Outlook%202017%20(1).pdfMEIH

EIA (2009). "Annual Energy Outlook 2009 with Projections to 2030." Energy Information Administration Office of Integrated Analysis and Forecasting, U.S. Department of Energy.

EIA (2014). International Energy Outlook 2014. US, U.S. Energy Information Administration. Available at: https://www.eia.gov/outlooks/ieo/pdf/0484(2014).pdf

EIA (2017). International Energy Outlook 2017. US, U.S. Energy Information Administration. Available at: https://www.eia.gov/outlooks/ieo/pdf/0484(2017).pdf

EPU (2001). Population & Labour Force Statistics. Economic Planning Unit (EPU). [online] Available at: http://www.epu.gov.my/en/economic-statistics/population-and-labourforce

225

EPU (2010). Tenth Malaysia Plan 2011-2015. [online] Malaysia: Economic Planning Unit (EPU). Available at: https://www.pmo.gov.my/dokumenattached/RMK/RMK10_Eds.pdf

EPU (2011). Tenth Malaysia Plan 2011-2015. The Economic Planning Unit, Malaysia. [online]. Available at: https://www.pmo.gov.my/dokumenattached/RMK/RMK10_Eds.pdf

EPU (2013). Time Series Economic Statistics on Transportation (2000-2013). Economic Planning Unit (EPU). [online] Available at: http://www.epu.gov.my/en/economic- statistics/transportation

EPU (2014). Percentage Distribution of Households by Income Class (1970-2014) in Malaysia. Economic Planning Unit (EPU). [online] Available at:http://www.epu.gov.my/sites/default/files/Jadual%201%20- %20Taburan%20Peratus%20Isi%20Rumah%20Mengikut%20Kelas%20Pendapatan%2C%20M alaysia%2C%201970%20-%202014.pdf

EPU (2015a). Malaysia Beyond 2020. In: Eleventh Malaysia Plan 2016-2020. [online] Malaysia: Economic Planning Unit (EPU). Available at: http://rmk11.epu.gov.my/book/eng/Chapter%2010/Chapter%2010.pdf

EPU (2015b). Eleventh Malaysia Plan 2016-2020. [online] Malaysia: Economic Planning Unit (EPU). Available at: http://rmk11.epu.gov.my/index.php/en/muat-turun-dokumen

EPU (2015c). Strengthening macroeconomic resilience for sustained growth. In: Eleventh Malaysia Plan 2016-2020. [online] Malaysia: Economic Planning Unit (EPU). Available at: http://rmk11.epu.gov.my/book/eng/Chapter-2/Chapter%202.pdf

EPU (2016). Statistics on number of employed persons by industry (2010-2016). [online] Malaysia: Economic Planning Unit (EPU). Available at: http://www.epu.gov.my/en/economic-statistics/population-and-labourforce

Erickson, P., C. Heaps and M. Lazarus (2009). Greenhouse Gas Mitigation in Developing Countries:Promising Options in China, Mexico, India, Brazil, South Africa, and South Korea. Working Paper WP-US-0903. USA, Stockholm Environment Institute.

ETP (2010a). Greater Kuala Lumpur/Klang Valley NKEA Fact Sheet. [online] Malaysia: Economic Transformation Programme (ETP). Available at: http://etp.pemandu.gov.my/upload/NKEA_Factsheet_Greater_KLKV.pdf

ETP (2010b). Palm Oil. In: Economic Transformation Programme: A Roadmap for Malaysia Handbook. [online]. Malaysia: Economic Transformation Programme (ETP). Available at: http://etp.pemandu.gov.my/upload/etp_handbook_chapter_9_palm_oil.pdf

Fairuz, S. M. C., M. Y. Sulaiman, C. H. Lim, S. Mat, B. Ali, O. Saadatian, M. H. Ruslan, E. Salleh and K. Sopian (2013). "Long term strategy for electricity generation in Peninsular Malaysia - Analysis of cost and carbon footprint using MESSAGE." Energy Policy 62: 493-502.

Fais, B., I. Keppo, M. Zeyringer, W. Usher and H. Daly (2016). "Impact of technology uncertainty on future low-carbon pathways in the UK." Energy Strategy Reviews 13: 154- 168.

226

Fattori, F., D. Albini and N. Anglani (2016). "Proposing an open-source model for unconventional participation to energy planning." Energy Research & Social Science 15: 12- 33.

Fishbone, L. G. and H. Abilock (1981). "Markal, a linear-programming model for energy systems analysis: Technical description of the bnl version." International Journal of Energy Research 5(4): 353-375.

Fragnière, E., R. Kanala, F. Moresino, A. Reveiu and I. Smeureanu (2017). "Coupling techno- economic energy models with behavioral approaches." Operational Research 17(2): 633- 647.

Gambhir, A., G. Anandarajah, T. Napp, C. Emmott and L. Vallejo (2013). India's CO2 Emissions Pathways To 2050. London, Grantham Institute for Climate Change, Imperial College.

Gambhir, A., T. A. Napp, C. J. M. Emmott and G. Anandarajah (2014). "India's CO2 emissions pathways to 2050: Energy system, economic and fossil fuel impacts with and without carbon permit trading." Energy 77: 791-801.

Geisbrecht, R. and P. Dipietro (2009). "Evaluating options for US coal fired power plants in the face of uncertainties and greenhouse gas caps: The economics of refFishing, retrofitting, and repowering." Energy Procedia 1(1): 4347-4354.

Ghosh, D., P. R. Shukla, A. Garg and P. V. Ramana (2002). "Renewable energy technologies for the Indian power sector: mitigation potential and operational strategies." Renewable and Sustainable Energy Reviews 6(6): 481-512.

Goldstein, G. and G. Tosato (2008). Global Energy Systems and Common Analyses: Final Report of Annex X (2005-2008), International Energy Agency.

Groissböck, M. and M. J. Pickl (2016). "An analysis of the power market in Saudi Arabia: Retrospective cost and environmental optimization." Applied Energy 165: 548-558.

Grubb, M., J. Edmonds, P. ten Brink and M. Morrison (1993). "The Costs of Limiting Fossil- Fuel CO2 Emissions: A Survey and Analysis." Annual Review of Energy and the Environment 18(1): 397-478.

Hadley, S. W. and W. Short (2001). "Electricity sector analysis in the clean energy futures study." Energy Policy 29(14): 1285-1298.

Hainoun, A., M. K. Seif-Eldin and S. Almoustafa (2006). "Analysis of the Syrian long-term energy and electricity demand projection using the end-use methodology." Energy Policy 34(14): 1958-1970.

Hasibi, R. A. A., S. P. Hadi and A. N. Widiastuti (2013). "Optimizing Geothermal Energy and Hydro Power in Capacity Expansion at the Electrical System of Java-Madura-Bali." International Journal of Engineering & Computer Science IJECS-IJENS 13(01).

Hanly, M. and Dargay, J. (2000). Car ownership in Great Britain - A panel data analysis. London: Univesity College London. [online] Available at: https://journals.sagepub.com/doi/pdf/10.3141/1718-11

227

Heaps, C.G., 2016. Long-range Energy Alternatives Planning (LEAP) system. [Software version: 2017.0.11] Somerville, MA, USA, Stockholm Environment Institute. https://www.energycommunity.org

Henning, H.-M. and A. Palzer (2014). "A comprehensive model for the German electricity and heat sector in a future energy system with a dominant contribution from renewable energy technologies-Part I: Methodology." Renewable and Sustainable Energy Reviews 30: 1003-1018.

Ho, C. S., Y. Matsuoka, J. Simson and K. Gomi (2013). "Low carbon urban development strategy in Malaysia – The case of Iskandar Malaysia development corridor." Habitat International 37(0): 43-51.

Ho, L.-W. (2016). "Wind energy in Malaysia: Past, present and future." Renewable and Sustainable Energy Reviews 53: 279-295. Hoffman, K. C. and D. O. Wood (1976). "Energy System Modeling and Forecasting." Annual Review of Energy 1(1): 423-453.

Hoffman, K. C. and D. W. Jorgenson (1977). "Economic and Technological Models for Evaluation of Energy Policy." The Bell Journal of Economics 8(2): 444-466.

Hosseini, S. E., M. A. Wahid and N. Aghili (2013). "The scenario of greenhouse gases reduction in Malaysia." Renewable and Sustainable Energy Reviews 28(0): 400-409.

Hourcade J.C., Richels R., Robinson J. (1996). Estimating the Cost of Mitigating Greenhouse Gases. In: J. P. Bruce, H. Lee and E. F. Haites. (eds.) Climate Change 1995: Economic and Social Dimensions of Climate Change. USA: Cambridge University Press. 263-296

Howells, M., H. Rogner, N. Strachan, C. Heaps, H. Huntington, S. Kypreos, A. Hughes, S. Silveira, J. DeCarolis, M. Bazillian and A. Roehrl (2011). "OSeMOSYS: The Open Source Energy Modeling System: An introduction to its ethos, structure and development." Energy Policy 39(10): 5850-5870.

Hubacek, K., D. Guan and A. Barua (2007). "Changing lifestyles and consumption patterns in developing countries: A scenario analysis for China and India." Futures 39 (9):1084-1096.

Huber, M., A. Roger and T. Hamacher (2015). "Optimizing long-term investments for a sustainable development of the ASEAN power system." Energy 88: 180-193.

Hughes, N. and N. Strachan (2010). "Methodological review of UK and international low carbon scenarios." Energy Policy 38(10): 6056-6065.

Hu, Y., Wang, S.Y. (2015). The relationship between Energy consumption and Economic Growth: Evidence from China’s Industrial Sectors. Energies 8: 9392–9406

IAEA (2006). Model for Analysis of Energy Demand (MAED-2) : User’s Manual. Computer Manual Series No. 18. Vienna, International Atomic Energy Agency.

IEA (2008a). Energy Technology Perspectives 2008: Energy Scenarios & Strategies to 2050. Paris, International Energy Agency.

228

IEA (2008b). Worldwide Trends in Energy Use and Efficiency: Key Insights from IEA Indicator Analysis. [online] France: International Energy Agency (IEA). Available at:https://www.iea.org/publications/freepublications/publication/Indicators_2008.pdf

IEA (2010). Energy Technology Perspectives 2010: Energy Scenarios & Strategies to 2050. Paris, International Energy Agency.

IEA (2011). “Technology Development Prospects for the Indian Power Sector”. Information Paper. Paris, International Energy Agency.

IEA (2012).Energy Technology Perspectives 2012: Pathways to a Clean Energy System. [online] France: International Energy Agency (IEA). Available at: https://www.iea.org/publications/freepublications/publication/ETP2012_free.pdf

IEA (2015). Southeast Asia Energy Outlook 2015. [online] France: International Energy Agency. Available at: https://www.iea.org/publications/freepublications/publication/WEO2015_SouthEastAsia.pd f

IEA (2016a). "World Energy Outlook 2016 Excerpt Water Energy Nexus." International Energy Agency, France.

IEA (2016b). Medium-Term Coal Market Report 2016: Market Analysis and Forecasts to 2021. [online] France: International Energy Agency (IEA). Available at: https://www.iea.org/publications/freepublications/publication/MTCMR2016.pdf

IEA (2016c) : Snapshot of Global Photovoltaic Markets - IEA PVPS http://www.iea- pvps.org/fileadmin/dam/public/report/statistics/IEA-PVPS_-_A_Snapshot_of_Global_PV_- _1992-2016__1_.pdf

IEA (2017a). CO2 Emissions from Fuel Combustion: Overview. [online] France: International Energy Agency (IEA). Available at:http://www.iea.org/publications/freepublications/publication/CO2EmissionsFromFuelCo mbustion2017Overview.pdf

IEA (2017b). World energy model documentation (2017 version). [online] France: International Energy Agency (IEA). Available at: http://www.iea.org/media/weowebsite/2017/WEM_Documentation_WEO2017.pdf

IEA-ETSAP E-TechDS. Energy Demand Technologies Data on Residential and Commercial. International Energy Agency (IEA). https://iea-etsap.org/index.php/energy-technology- data/energy-demand-technologies-data

IEA-ETSAP E-TechDS. Energy Demand Technologies Data on Transportation. International Energy Agency (IEA). https://iea-etsap.org/index.php/energy-technology-data/energy- demand-technologies-data

IEE (2011). The 3rd ASEAN Energy Outlook. Japan, The Institute of Energy Economics.

229

IEO (2016). International Energy Outlook 2016. [online] US: Energy Information Administration. Available at: https://www.eia.gov/outlooks/ieo/pdf/industrial.pdf

IEO (2017). International Energy Outlook 2017. [online] US: Energy Information Administration. Available at: https://www.eia.gov/outlooks/ieo/pdf/0484(2017).pdf

IMF (2015). Report for Selected Countries and Subjects. World Economic Outlook Database, April 2015. [online] Available at: http://www.imf.org/external/pubs/ft/weo/2015/01/weodata/weorept.aspx?sy=2013&ey=2 020&scsm=1&ssd=1&sort=country&ds=.&br=1&pr1.x=21&pr1.y=7&c=548&s=NGDP_R%2CN GDP_RPCH%2CLP&grp=0&a=#download

IPCC (2001). Climate Change 2001: Mitigation. IPCC Working Group III Report. Available at: https://www.ipcc.ch/ipccreports/tar/wg3/pdf/WGIII_TAR_full_report.pdf

IPCC (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Prepared by the National Greenhouse Gas Inventories Programme. Eggleston H.S., Buendia L., Miwa K., Ngara T., and Tanabe K. (eds). Published: IGES, Japan.

IPCC (2014). Working Group III Report "Climate Change 2014: Mitigation of Climate Change". Intergovernmental Panel on Climate Change Fifth Assessment Report. Intergovernmental Panel on Climate Change. https://www.ipcc.ch/publications_and_data/publications_and_data_reports.shtml

Isaac, M. and D. P. van Vuuren (2009). "Modeling global residential sector energy demand for heating and air conditioning in the context of climate change." Energy Policy 37(2): 507- 521.

Jägemann, C., M. Fürsch, S. Hagspiel and S. Nagl (2013). "Decarbonizing Europe's power sector by 2050-Analyzing the economic implications of alternative decarbonization pathways." Energy Economics 40: 622-636.

Jebaraj, S. and S. Iniyan (2006). "A review of energy models." Renewable and Sustainable Energy Reviews 10(4): 281-311

Jia, L. I. U., C. Wenying and L. I. U. Deshun (2011). "Scenario analysis of China's future energy demand based on TIMES model system." Energy Procedia 5: 1803-1808.

Jiang, B., C. Wenying, Y. Yuefeng, Z. Lemin and D. Victor (2008). "The future of natural gas consumption in Beijing, Guangdong and Shanghai: An assessment utilizing MARKAL." Energy Policy 36(9): 3286-3299.

JPPH (2013). Property Stock Report: Residential Property Stock Table Q4 2013. Valuation and Property Services Department (JPPH), Ministry of Finance, Malaysia. [online] Available at:file:///C:/Users/ucqbkum/Downloads/1415003240972q413residential%20(1).pdf

Jung, T. Y., E. L. La Rovere, H. Gaj, P. R. Shukla and D. Zhou (2000). "Structural Changes in Developing Countries and Their Implication for Energy-Related CO2 Emissions." Technological Forecasting and Social Change 63(2–3): 111-136.

230

Kannan, R. and H. Turton (2016). "Long term climate change mitigation goals under the nuclear phase out policy: The Swiss energy system transition." Energy Economics 55: 211- 222.

Karki, S. K., M. D. Mann and H. Salehfar (2005). "Energy and environment in the ASEAN: challenges and opportunities." Energy Policy 33(4): 499-509.

Kejun, J., L. Qiang, Z. Xing and H. Xiulian (2010). "Technology roadmap for low carbon society in China." Journal of Renewable and Sustainable Energy 2(031008).

Kesicki, F. and G. Anandarajah (2011). "The role of energy-service demand reduction in global climate change mitigation: Combining energy modelling and decomposition analysis." Energy Policy 39(11): 7224-7233.

KeTTHA (2009). National Renewable Policy and Action Plan. Malaysia: Ministry of Energy, Green Technology and Water (KeTTHA). [online]. Available at: http://www.seda.gov.my/?omaneg=00010100000001010101000100001000000000000000 000000&s=31

Khor, C. S. and G. Lalchand (2014). "A review on sustainable power generation in Malaysia to 2030: Historical perspective, current assessment, and future strategies." Renewable and Sustainable Energy Reviews 29(0): 952-960.

Kichonge, B., G. R. John, I. S. N. Mkilaha and S. Hameer (2014). "Modelling of Future Energy Demand for Tanzania." Journal of Energy Technologies and Policy 4(7).

Kleinpeter, M. (1995). Energy Planning and Policy. Chichester, England, John Wiley & Sons Ltd.

Klinge Jacobsen, H. (1998). "Integrating the bottom-up and top-down approach to energy– economy modelling: the case of Denmark." Energy Economics 20(4): 443-461.

Koh, S. L., Y. S. Lim and S. Morris (2011). "Cost Effective Options for Greenhouse Gas (GHG) Emission Reduction in the Power Sector for Developing Economies-A Case Study in Sabah, Malaysia." Energies 4(5): 780-803

KRI (2014). The State Of Households Report. [online] Malaysia: Khazanah Research Institute (KRI). Available at:http://www.krinstitute.org/assets/contentMS/img/template/editor/SOHmain_3.pdf

Kubota, T., Jeong, S., Toe, D. H. C., and Ossen, D. R., (2011). Energy Consumption and Air- Conditioning Usage in Residential Buildings of Malaysia. Journal of International Development and Cooperation 17: 61-69.

Kumar, A., S. C. Bhattacharya and H. L. Pham (2003). "Greenhouse gas mitigation potential of biomass energy technologies in Vietnam using the long range energy alternative planning system model." Energy 28(7): 627-654.

Kydes, A. S. (2007). "Impacts of a renewable portfolio generation standard on US energy markets." Energy Policy 35(2): 809-814.

231

La Rovere, E. L., L. F. L. Legey and J. D. G. Miguez (1994). "Alternative energy strategies for abatement of carbon emissions in Brazil A cost-benefit analysis." Energy Policy 22(11): 914- 924.

Labriet, M., A. Kanudia and R. Loulou (2012). "Climate mitigation under an uncertain technology future: A TIAM-World analysis." Energy Economics 34, Supplement 3: S366- S377.

Lamont, A.D (2008). “Assessing the long-term system value of intermittent electric generation technologies,” Energy Economics, vol. 30: 1208–1231

Lapillonne, B. (1978). MEDEE 2: A Model for Long-Term Energy Demand Evaluation. Laxenburg, Austria, IIASA. IIASA Research Report. RR-78-017 Copyright © November 1978 by the author(s). http://pure.iiasa.ac.at/826/

Larson, E. D., W. Zongxin, P. DeLaquil, C. Wenying and G. Pengfei (2003). "Future implications of China's energy-technology choices." Energy Policy 31(12): 1189-1204.

Lau, K. Y., N. A. Muhamad, Y. Z. Arief, C. W. Tan and A. H. M. Yatim (2016). "Grid-connected photovoltaic systems for Malaysian residential sector: Effects of component costs, feed-in tariffs, and carbon taxes." Energy 102: 65-82.

Lavigne, D. (2017a). "OSeMOSYS Energy Modeling Using an Extended UTOPIA Model." Universal Journal of Educational Research 5(1): 162-169.

Lavigne, D. (2017b). "OSeMOSYS: Introducing Elasticity." Universal Journal of Management 5(5): 254 - 260.

Lee, C.C. and Chang, C. P. (2008). " Energy consumption and economic growth in Asian economies: A more comprehensive analysis using panel data. Resource and Energy Economics. 30(1): 50-65.

Li, F. G. N. and E. Trutnevyte (2017). "Investment appraisal of cost-optimal and near-optimal pathways for the UK electricity sector transition to 2050." Applied Energy 189: 89-109.

Li, P.-H. and S. Pye (2018). "Assessing the benefits of demand-side flexibility in residential and transport sectors from an integrated energy systems perspective." Applied Energy 228: 965-979.

Liebman, J. (1976). "Some Simple-Minded Observations on the Role of Optimization in Public Systems Decision-Making." Interfaces 6(4): 102-108.

Lim, C.P. (1987). Changes in the Malaysian Economy and Trade Trends and Prospects, In: Colin I. Bradford, Jr. and William H. Branson (Eds.), Trade and Structural Change in Pacific Asia (pp. 435-466) Chicago, US: University of Chicago Press. Retrieved from http://www.nber.org/chapters/c6931

Löffler, K., K. Hainsch, T. Burandt, P.-Y. Oei, C. Kemfert and C. v. Hirschhausen (2017). Designing a Global Energy System Based on 100% Renewables for 2050: GENeSYS-MOD: An Application of the Open-Source Energy Modelling System (OSeMOSYS). Berlin, Deutsches Institut für Wirtschaftsforschung (DIW).

232

Loulou, R., Remne, U., Kanudia, A., Lehtila, A., Goldstein, G., 2005. Documentation for the TIMES Model - PART I 1–78. Available at: http://iea-etsap.org/docs/TIMESDoc-Intro.pdf

Lund, H. and B. V. Mathiesen (2009). "Energy system analysis of 100% renewable energy systems-The case of Denmark in years 2030 and 2050." Energy 34(5): 524-531.

Lyseng, B., A. Rowe, P. Wild, J. English, T. Niet and L. Pitt (2016). "Decarbonising the Alberta power system with carbon pricing." Energy Strategy Reviews 10: 40-52.

MAA (2014). National Automotive Policy (NAP) 2014. Malaysian Automotive Association (MAA). [online] Available at: http://www.maa.org.my/pdf/NAP_2014_policy.pdf

MAA (2017). Summary of New Passenger & Commercial Vehicles Registered In Malaysia (1980-2017). Malaysian Automotive Association (MAA). [online] Available at:http://www.maa.org.my/info_summary.htm

Mallah, S. (2011). "Nuclear energy option for energy security and sustainable development in India." Annals of Nuclear Energy 38(2–3): 331-336.

Mallah, S. and N. K. Bansal (2010). "Renewable energy for sustainable electrical energy system in India." Energy Policy 38(8): 3933-3942.

Mallah, S. and N. K. Bansal (2011). "Parametric sensitivity analysis for techno-economic parameters in Indian power sector." Applied Energy 88(3): 622-629.

Masron, T., U. Yaakob, N. M. Ayob and A. S. Mokhtar (2012). "Population and spatial distribution of urbanisation in Peninsular Malaysia 1957-2000." Malaysia Journal of Society and Space 8(2): 20 -29.

Matsuoka, Y., M. Kainuma and T. Morita (1995). "Scenario analysis of global warming using the Asian Pacific Integrated Model (AIM)." Energy Policy 23(4–5): 357-371

Mazzucato,M. and Semieniuk, G. (2018). Financing renewable energy: who is financing what and why it matters. Technological Forecasting and Social Change. Volume 127: Pages 8-22

McCollum, D., C. Yang, S. Yeh and J. Ogden (2012). "Deep greenhouse gas reduction scenarios for California – Strategic implications from the CA-TIMES energy-economic systems model." Energy Strategy Reviews 1(1): 19-32.

McNeil, M. A. and V. E. Letschert (2010). "Modeling diffusion of electrical appliances in the residential sector." Energy and Buildings 42(6): 783-790.

McNish, T., D. M. Kammen and B. Gutierrez (2010). Clean Energy Options for Sabah: An analysis of resource availability and unit cost. Berkeley, Energy & Resources Group, University of California.

McClurg, J. C. (2012). "Reduction of reserve margin with increasing wind penetration: a quantitative first-principles analysis." MS(Master of Science) thesis, University of Iowa.

Medlock, K. B. and Soligo, R. (2002). Car Ownership and Economic Development with Forecasts to the year 2015. Journal of Transport Economics and Policy 36 (2): 163-188

233

MEIH (2017) 'Malaysia Energy Information Hub: Statistics on Energy'. Malaysia Energy Information Hub (MEIH), Energy Commission (EC). [online] Available at: http://meih.st.gov.my/statistics (Accessed: 17 September 2017).

Meier, H., Jamasb,T. and Orea, L.(2013). "Necessity or luxury good? Household energy spending and income in Britain 1991–2007" Energy 34:109-129.

McKinsey & Company (2009). Pathways to a low carbon economy: Version 2 of the global greenhouse gas abatement cost curve.

Mekhilef, S., A. Safari, W. E. S. Mustaffa, R. Saidur, R. Omar and M. A. A. Younis (2012). "Solar energy in Malaysia: Current state and prospects." Renewable and Sustainable Energy Reviews 16(1): 386-396.

Messner, S. (1997). "Endogenized technological learning in an energy systems model." Journal of Evolutionary Economics 7(3): 291-313.

Messner, S. and M. Strubegger (1995). Model-Based Decision Support in Energy Planning. IIASA Working Paper. Laxenburg, Austria, IIASA. WP-95-119 Copyright © 1995 by the author(s). http://pure.iiasa.ac.at/4477/

MIDA (2018). Services Sector. Malaysian Investment Development Authority (MIDA). [online] Available at: http://www.mida.gov.my/home/services-sector/posts/

Miketa, A. and B. Merven (2013). Southern African Power Pool: Planning and Prospects for Renewable Energy. United Arab Emirates, The International Renewable Energy Agency (IRENA).

Mileva, A., J. Johnston, J. H. Nelson and D. M. Kammen (2016). "Power system balancing for deep decarbonization of the electricity sector." Applied Energy 162: 1001-1009.

Mischke, P. and K. B. Karlsson (2014). "Modelling tools to evaluate China's future energy system – A review of the Chinese perspective." Energy 69: 132-143.

MITI (2006). Malaysia's Free Trade Agreements: Agreement between the Government of Malaysia and the Government of Japan for an Economic Partnership. [online] Malaysia: Ministry of International Trade and Industry (MITI). Available at:http://fta.miti.gov.my/index.php/pages/view/Malaysia-Japan

MITI (2010). Bernama article on Malaysia's Total Trade with China Exceeds US$28.4 Billion In 2009. In: Ministry of International Trade and Industry (MITI), Malaysia website. [online] Available at: http://www.miti.gov.my/index.php/pages/view/1446

MITI (2015a). Bernama article on Malaysia-China Trade to Rise to US$110 Billion In 2015, says Malaysia External Trade Development Corporation (Matrade). In: Ministry of International Trade and Industry (MITI), Malaysia website. [online] Available at: http://www.miti.gov.my/index.php/pages/view/2979

234

MITI (2015b). Article on Look East Policy 2.0 to focus on Economic Cooperation with S. Korea. In: Ministry of International Trade and Industry (MITI), Malaysia website. [online] Available at: http://www.miti.gov.my/index.php/pages/view/2633

Mohamad, J. and A. T. Kiggundu (2007). "The Rise Of The Private Car In Kuala Lumpur, Malaysia: Assessing the Policy Options." IATSS Research 31(1): 69-77.

Moksnes, N., M. Welsch, F. Gardumi, A. Shivakumar, O. Broad, M. Howells, C. Taliotis and V. Sridharan (2015). 2015 OSeMOSYS User Manual. Working Paper Series: DESA/15/11. Sweden, KTH Royal Institute of Technology.

Morikawa, T. (2001). Travel Behavior Analysis and its Implication to Urban Transport Planning for Asian Cities: Case Studies of Bangkok, Kuala Lumpur, Manila, and Nagoya ICRA Project Report Japan, Nagoya University.

MOT (2013). Transport Statistics Malaysia. Ministry of Transport Malaysia (MOT). [online] Available at:http://www.mot.gov.my/en/Statistik%20Tahunan%20Pengangkutan/Transport%20Statist ics%20Malaysia%202013.pdf

Moura, G. and M. Howells (2015). Working Paper: “SAMBA” The Open Source South American Model Base: A Brazilian Perspective on Long Term Power System Integration. Stockholm, Sweden KTH Royal Institute of Technology.

Mulugetta, Y., N. Mantajit and T. Jackson (2007). "Power sector scenarios for Thailand: An exploratory analysis 2002–2022." Energy Policy 35(6): 3256-3269.

Munasinghe, M. and P. Meier (1993a). The role of modeling in energy policy analysis. Energy Policy Analysis and Modelling. Cambridge, Cambridge University Press: 20-34.

Munasinghe, M. and P. Meier (1993b). Energy sector models. Energy Policy Analysis and Modelling. Cambridge, Cambridge University Press: 248-262.

Mustapa, S. I. and H. A. Bekhet (2015). "Investigating Factors Affecting CO2 Emissions in Malaysian Road Transport Sector." International Journal of Energy Economics and Policy 5(4): 1073-1083.

Nakarmi, A. M., T. Mishra and R. Banerjee (2016). "Integrated MAED–MARKAL-based analysis of future energy scenarios of Nepal." International Journal of Sustainable Energy 35(10): 968-981.

Nakata, T. (2004). "Energy-economic models and the environment." Progress in Energy and Combustion Science 30(4): 417-475.

Nakawiro, T., S. C. Bhattacharyya and B. Limmeechokchai (2008). "Electricity capacity expansion in Thailand: An analysis of gas dependence and fuel import reliance." Energy 33(5): 712-723.

Nandi, M., K. Alexandros, M. Dimitrios and H. Mark (2017). "Electrification pathways for Kenya – linking spatial electrification analysis and medium to long term energy planning." Environmental Research Letters. at press: https://doi.org/10.1088/1748-9326/aa7e18

235

Nasrin, A. B., S. Vijaya, S. K. Loh, A. A. Astimar and W. S. Lim (2017). "Quality Compliance and Environmental Impact Assessment of Commercial Empty Fruit Bunch (EFB) Pellet Fuel in Malaysia." Journal of Oil Palm Research 29(December 2017): 570-578.

NEB (2013). National Energy Balance 2013. Energy Commission Malaysia. Available at: http://www.st.gov.my/index.php/download-page/category/111-national-energy- balance.html

Nguyen, K. Q. (2007). "Impacts of wind power generation and CO2 emission constraints on the future choice of fuels and technologies in the power sector of Vietnam." Energy Policy 35(4): 2305-2312.

Nguyen, K. Q. (2008). "Internalizing externalities into capacity expansion planning: The case of electricity in Vietnam." Energy 33(5): 740-746.

Niet, T., B. Lyseng, J. English, V. Keller, K. Palmer-Wilson, I. Moazzen, B. Robertson, P. Wild and A. Rowe (2017). "Hedging the risk of increased emissions in long term energy planning." Energy Strategy Reviews 16: 1-12.

NRE (2011). Malaysia’s Second National Communication to the UNFCCC. Ministry of Natural Resources and Environment, Malaysia. [online]. Available at: http://unfccc.int/resource/docs/natc/malnc2.pdf

Nurdden, A., R. A. O. K. Rahmat and A. Ismail (2007). "Effect of Transportation Policies on Modal Shift from Private Car to Public Transport in Malaysia." Journal of Applied Sciences 7(7): 1013-1018.

NUS (2014). Long Range Energy Analysis of Singapore’s Electricity Sector using the TIMES Modelling Framework. ESI Bulletin on Energy Trends and Development. Energy Studies Institute, National University of Singapore. Vol. 4/Issue 2 (June): page 9.

Oh, T. H., S. Y. Pang and S. C. Chua (2010). "Energy policy and alternative energy in Malaysia: Issues and challenges for sustainable growth." Renewable and Sustainable Energy Reviews 14(4): 1241-1252.

Ong, H. C., T. M. I. Mahlia and H. H. Masjuki (2012). "A review on energy pattern and policy for transportation sector in Malaysia." Renewable and Sustainable Energy Reviews 16(1): 532-542.

Pachauri, S. and L. Jiang (2008). "The household energy transition in India and China." Energy Policy 36(11): 4022-4035.

Pagnarith, K. and B. Limmeechokchai (2014). "Integrated Resource Planning for Long-term Electricity Supply in Selected GMS Countries, Part 1:Energy Efficiency and Renewable Energy." Energy Sources, Part B: Economics, Planning, and Policy 10(2): 167-175.

Pandey, R. (2002). "Energy policy modelling: agenda for developing countries." Energy Policy 30(2): 97-106.

Pattupara, R. and R. Kannan (2016). "Alternative low-carbon electricity pathways in Switzerland and it’s neighbouring countries under a nuclear phase-out scenario." Applied Energy 172: 152-168.

236

Pereira Jr, A. O., R. Cunha da Costa, C. d. V. Costa, J. d. M. Marreco and E. L. La Rovere (2013). "Perspectives for the expansion of new renewable energy sources in Brazil." Renewable and Sustainable Energy Reviews 23: 49-59.

Peter, B. D. and J. Dale (2012). Handbook of Computable General Equilibrium Modeling SET, Vols. 1A and 1B, Elsevier Science.

Pfenninger, S., A. Hawkes and J. Keirstead (2014). "Energy systems modeling for twenty-first century energy challenges." Renewable and Sustainable Energy Reviews 33(0): 74-86.

Price, J. and I. Keppo (2017). "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models." Applied Energy 195: 356-369.

Pye, S. and C. Bataille (2016). "Improving deep decarbonization modelling capacity for developed and developing country contexts." Climate Policy 16(sup1): S27-S46.

Pye, S., C. McGlade, C. Bataille, G. Anandarajah, A. Denis-Ryan and V. Potashnikov (2016). "Exploring national decarbonization pathways and global energy trade flows: a multi-scale analysis." Climate Policy 16(sup1): S92-S109.

Pye, S., N. Sabio and N. Strachan (2015). "An integrated systematic analysis of uncertainties in UK energy transition pathways." Energy Policy 87: 673-684.

Rahim , K. A. and A. Liwan (2012). "Oil and gas trends and implications in Malaysia." Energy Policy 50: 262-271.

Rahman Mohamed, A. and K. T. Lee (2006). "Energy for sustainable development in Malaysia: Energy policy and alternative energy." Energy Policy 34(15): 2388-2397.

RECODA (2011). Regional Corridor Development Authority (RECODA) Annual Report 2011. [online]. Available from: http://www.recoda.com.my/wp- content/uploads/2017/06/RECODA-Annual-Report-2011.pdf

RECODA (2012). Regional Corridor Development Authority (RECODA) Annual Report 2012. [online]. Available from: http://www.recoda.com.my/wp- content/uploads/2017/06/RECODA-AR-2012-Final.pdf

RECODA (2014). Regional Corridor Development Authority (RECODA) Annual Report 2014. [online]. Available at: http://www.recoda.com.my/wp-content/uploads/2017/06/RECODA- AR2014-20150623.pdf

Rout, U. K. (2011). "Prospects of India's energy and emissions for a long time frame." Energy Policy 39(9): 5647-5663.

Rout, U. K., A. Voβ, A. Singh, U. Fahl, M. Blesl and B. P. Ó Gallachóir (2011). "Energy and emissions forecast of China over a long-time horizon." Energy 36(1): 1-11.

Rudnick,H. and Velasquez, C. (2018). “Taking Stock of Wholesale Power Markets in Developing Countries: A Literature Review.” Policy Research Working Paper No. 8519. World Bank, Washington, DC

237

S. Avdaković, E. B., N. Hasanspahić, M. Musić, A. Merzić, A. Tuhčić, J. Karadža, D. Pešut, A. Kinderman Lončarević (2015). "Long-term forecasting of energy, electricity and active power demand – Bosnia and Herzegovina case study." Balkan Journal of Electrical & Computer Engineering 3(1).

Saad, S.M.A.; Ismail, F.A.; Fauzi, F.M.; Rahmat, M.K. Consideration for nuclear energy in Malaysia. In Proceedings of the 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T), Kuala Lumpur, Malaysia, 18–20 September 2017; pp. 1–5.

Safaai, N. S. M., Z. Z. Noor, H. Hashim, Z. Ujang and J. Talib (2011). "Projection of CO2 emissions in Malaysia." Environmental Progress & Sustainable Energy 30(4): 658- 665

Saidur, R., H. H. Masjuki and M. Y. Jamaluddin (2007). "An application of energy and exergy analysis in residential sector of Malaysia." Energy Policy 35(2): 1050-1063.

Saidur, R., M. A. Sattar, H. H. Masjuki, H. Abdessalam and B. S. Shahruan (2007a). "Energy and exergy analysis at the utility and commercial sectors of Malaysia." Energy Policy 35(3): 1956-1966.

Saidur, R., M. A. Sattar, H. H. Masjuki, S. Ahmed and U. Hashim (2007b). "An estimation of the energy and exergy efficiencies for the energy resources consumption in the transportation sector in Malaysia." Energy Policy 35(8): 4018-4026.

Saidur, R., N. A. Rahim, H. H. Masjuki, S. Mekhilef, H. W. Ping and M. F. Jamaluddin (2009). "End-use energy analysis in the Malaysian industrial sector." Energy 34(2): 153-158.

Saltelli, A. and P. Annoni (2010). "How to avoid a perfunctory sensitivity analysis." Environmental Modelling & Software 25(12): 1508-1517.

Saltelli, A., M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana and S. Tarantola (2008). Introduction to Sensitivity Analysis. Global Sensitivity Analysis. The Primer, John Wiley & Sons, Ltd: 1-51.

Sanders, D., A. Hart, M. Ravishankar and J. Brunert (2016). An analysis of electricity system flexibility for Great Britain Report. UK: Carbon Trust and Imperial College London. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_ data/file/568982/An_analysis_of_electricity_flexibility_for_Great_Britain.pdf

Santamouris, M. (2016). "Cooling the buildings – past, present and future." Energy and Buildings 128: 617-638.

Santisirisomboon, J., B. Limmeechokchai and S. Chungpaibulpatana (2001). "Impacts of biomass power generation and CO2 taxation on electricity generation expansion planning and environmental emissions." Energy Policy 29(12): 975-985.

Santisirisomboon, J., B. Limmeechokchai and S. Chungpaibulpatana (2003). "Least cost electricity generation options based on environmental impact abatement." Environmental Science & Policy 6(6): 533-541.

238

Saradhi, I. V., G. G. Pandit and P. V.D. (2009). "Energy Supply, Demand and Environmental Analysis – A Case Study of Indian Energy Scenario." International Journal of Civil and Environmental Engineering(1): 115–120.

SB (2016). 10-year Supply and Demand Outlook for Peninsular Malaysia. Single Buyer Malaysia. [online] Available at: https://www.singlebuyer.com.my/resources-outlook.php

Schipper, L., S. Meyers and A. N. Ketoff (1986). "Energy use in the service sector: An international perspective." Energy Policy 14(3): 201-218.

Schulte, I. and Heindl, P. (2017). "Price and income elasticities of residential energy demand in Germany." Energy Policy 102: 512-528.

SEDA (2016). Statistics & Monitoring: RE Installed Capacities. Malaysia: Sustainable Energy Development Authority of (SEDA). [online] Available at: http://seda.gov.my/?omaneg=0001010000000101010100010000100000000000000000000 0&s=538

Shahbaz, M., N. Loganathan, A. T. Muzaffar, K. Ahmed and M. Ali Jabran (2016). "How urbanization affects CO2 emissions in Malaysia? The application of STIRPAT model." Renewable and Sustainable Energy Reviews 57(Supplement C): 83-93.

Shahbaz, M., N. Loganathan, R. Sbia and T. Afza (2015). "The effect of urbanization, affluence and trade openness on energy consumption: A time series analysis in Malaysia." Renewable and Sustainable Energy Reviews 47(Supplement C): 683-693.

Shariff, N. M. (2012). Private Vehicle Ownership and Transportation Planning in Malaysia. International Proceedings of Computer Science & Information Tech, Singapore, IACSIT Press.

Sharma, S. D. (2003). "The Malaysian Capital Control Regime Of 1998: Implementation, Effectiveness, And Lessons." Asian Perspective 27(1): 77-108.

Shekarchian, M., M. Moghavvemi, T. M. I. Mahlia and A. Mazandarani (2011). "A review on the pattern of electricity generation and emission in Malaysia from 1976 to 2008." Renewable and Sustainable Energy Reviews 15(6): 2629-2642.

Shi, J., W. Chen and X. Yin (2016). "Modelling building’s decarbonization with application of China TIMES model." Applied Energy 162: 1303-1312.

Shrestha, R. M., S. Malla and M. H. Liyanage (2007). "Scenario-based analyses of energy system development and its environmental implications in Thailand." Energy Policy 35(6): 3179-3193.

Shuddhasattwa, R. and S. Ruhul (2011). "The linkage between energy consumption and income in six emerging economies of Asia: An empirical analysis." International Journal of Emerging Markets 6(1): 50-73.

Shukla, P. R., S. Dhar and D. Mahapatra (2008). "Low-carbon society scenarios for India." Climate Policy 8(sup1): S156-S176.

239

Simson, J. J., H. C. Siong, Y. Matsuoka and K. Gomi (2013). "Planning Strategic Sustainable Development in Malaysia by Incorporating the Concept of Low Carbon Society " Journal of Civil Engineering and Architecture 7(No.9): 1164 -1176.

Sivak, M. (2009). "Potential energy demand for cooling in the 50 largest metropolitan areas of the world: Implications for developing countries." Energy Policy 37(4): 1382-1384.

Smeureanu, I., A. Reveiu, M. Dardala, T. F. Furtuna and R. Kanala (2015). "Modeling Residential Space Heating Energy for Romania " International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering 9(8).

Soom, R. M., A. A. Aziz, W. H. W. Hassan and A. G. M. Top (2009). "Solid-state characteristics of microcrystalline cellulos from oil palm empty fruit bunch fibre." Journal of Oil Palm Research 21(June ): 613-620. Sovacool, B. K. and L. C. Bulan (2011). "Behind an ambitious megaproject in Asia: The history and implications of the Bakun hydroelectric dam in Borneo." Energy Policy 39(9): 4842-4859.

Sovacool, B. K. and L. C. Bulan (2012). "Energy security and hydropower development in Malaysia: The drivers and challenges facing the Sarawak Corridor of Renewable Energy (SCORE)." Renewable Energy 40(1): 113-129.

SPAD (2012). Urban public transport NKRA Performance Monitoring Survey. Land Public Transport Commission, Malaysia (SPAD). [online] Available at: https://www.spad.gov.my/sites/default/files/NKRA-Monitoring-Survey-2012.pdf

Steffen, B. (2018).The importance of project finance for renewable energy projects. Energy Economics. Volume 69: pages 280-294.

Stich, J., M. Mannhart, T. Zipperle, T. Massier, M. Huber and T. Hamacher (2014). Modelling a Low-Carbon Power System for Indonesia, Malaysia and Singapore. 33rd International Energy Workshop (IEW). Beijing.

Strachan, N., S. Pye and N. Hughes (2008). "The role of international drivers on UK scenarios of a low-carbon society." Climate Policy 8(sup1): S125-S139.

Strachan, N., S. Pye and R. Kannan (2009). "The iterative contribution and relevance of modelling to UK energy policy." Energy Policy 37(3): 850-860.

Subhes, C. B. and R. T. Govinda (2010). "A review of energy system models." International Journal of Energy Sector Management 4(4): 494-518.

Sullivan, P., V. Krey and K. Riahi (2013). "Impacts of considering electric sector variability and reliability in the MESSAGE model." Energy Strategy Reviews 1(3): 157-163.

Syri, S., A. Lehtilä, T. Ekholm, I. Savolainen, H. Holttinen and E. Peltola (2008). "Global energy and emissions scenarios for effective climate change mitigation—Deterministic and stochastic scenarios with the TIAM model." International Journal of Greenhouse Gas Control 2(2): 274-285.

Taliotis, C., A. Shivakumar, E. Ramos, M. Howells, D. Mentis, V. Sridharan, O. Broad and L. Mofor (2016). "An indicative analysis of investment opportunities in the African electricity

240

supply sector-Using TEMBA (The Electricity Model Base for Africa)." Energy for Sustainable Development 31: 50-66.

Taliotis, C., H. Rogner, S. Ressl, M. Howells and F. Gardumi (2017). "Natural gas in Cyprus: The need for consolidated planning." Energy Policy 107: 197-209.

Taylor, P. G., et al. (2014). "Energy model, boundary object and societal lens: 35 years of the MARKAL model in the UK." Energy Research & Social Science 4: 32-41

Thammasat University (2010). Thailand’s Low Carbon Society Vision 2020. Sirindhorn International Institute of Technology, Thammasat University. [online] http://www.siit.tu.ac.th/pdf/LCS_Thailand_2030.pdf

The Malaysian Reserve (2014). ‘Sarawak to Revive Mega Power Cable Project’. Accessed 1st December 2014. http://themalaysianreserve.com/new/story/sarawak-revive-mega-power- cable-project

The Sun daily (2011). Govt says no to coal power plants in Sabah. [online] Available at: http://www.thesundaily.my/node/132796 [Accessed 19 Jan. 2018].

TNB (2013). Electricity load profile data. (2013). [email].

Tatsuta, M. (1996). New sunshine project and new trend of PV R&D program in Japan. Renewable Energy: 8(1-4): pages 40-43

Trutnevyte, E. (2013). "EXPANSE methodology for evaluating the economic potential of renewable energy from an energy mix perspective." Applied Energy 111: 593-601.

Trutnevyte, E. (2014). Does cost optimisation approximate the real-world energy transition? Retrospective modelling and implications for modelling the future. International Energy Workshop 2014. Beijing, China.

Trutnevyte, E. (2016). "Does cost optimization approximate the real-world energy transition?" Energy 106: 182-193.

Trutnevyte, E. and N. Strachan (2013). Nearly perfect and poles apart: investment strategies into the UK power system until 2050. International Energy Workshop 2013. Paris.

Trutnevyte, E., C. Guivarch, R. Lempert and N. Strachan (2016). "Reinvigorating the scenario technique to expand uncertainty consideration." Climatic Change 135(3): 373-379.

Trutnevyte, E., M. Stauffacher, M. Schlegel and R. W. Scholz (2012). "Context-Specific Energy Strategies: Coupling Energy System Visions with Feasible Implementation Scenarios." Environmental Science & Technology 46(17): 9240-9248.

Thomas, S. (2018). Drivers of recent energy consumption trends across sectors in EU28. Energy Consumption Trends Workshop Report. Samuel Thomas Consulting.

UNDESA (2014). World Urbanization Prospects, the 2014 Revision. [online] New York: United Nations, Department of Economic and Social Affairs, Population Division. Available at: https://esa.un.org/unpd/wup/cd-rom/

241

UNDESA (2015). World Population Prospects, the 2015 Revision. [online] New York: United Nations, Department of Economic and Social Affairs, Population Division. Available at: http://esa.un.org/unpd/wpp/.

UN-ENERGY (2007). Assessing policy options for increasing the use of renewable energy for sustainable development: Modelling energy scenarios for Sichuan, China. Research Report. Department of Economic and Social Affairs (DESA), International Atomic Energy Agency (IAEA) and United Nations Environment Programme (UNEP).

UNFCCC (2015). Intended Nationally Determined Contribution of the Government of Malaysia. United Nations Climate Change Conference. Paris. [online]. Available at: http://www4.unfccc.int/submissions/indc/Submission%20Pages/submissions.aspx

Urban, F., R. M. J. Benders and H. C. Moll (2007). "Modelling energy systems for developing countries." Energy Policy 35(6): 3473-3482.

Ürge-Vorsatz, D., L. F. Cabeza, S. Serrano, C. Barreneche and K. Petrichenko (2015). "Heating and cooling energy trends and drivers in buildings." Renewable and Sustainable Energy Reviews 41: 85-98.

Vaillancourt, K., M. Labriet, R. Loulou and J.-P. Waaub (2008). "The role of nuclear energy in long-term climate scenarios: An analysis with the World-TIMES model." Energy Policy 36(7): 2296-2307.

van Beeck, N. M. J. P. (1999). Classification of Energy Models. FEW Research Memorandum Vol. 777. Tilburg: Operations research.

Wade, R. (1998). "The Asian debt-and-development crisis of 1997-?: Causes and consequences." World Development 26(8): 1535-1553.

Wangjiraniran, W., R. Nidhiritdhikrai and B. Eua-Arporn (2013). "Scenarios on Power Generation in Thailand: Uncertainty of Nuclear and Coal Options." Engineering Journal 17(3).

Watcharejyothin, M. and R. M. Shrestha (2009). "Effects of cross-border power trade between Laos and Thailand: Energy security and environmental implications." Energy Policy 37(5): 1782-1792.

Warr, B., Ayres, R., Eisenmenger, N., Krausmann, F. and Schandl, H. (2010). “Energy use and economic development: a comparative analysis of useful work supply in Austria, Japan, the United Kingdom and the US during 100 years of economic growth. Ecological Economics, 69: 1904-1917.

Welsch, M., M. Howells, M. Bazilian, J. F. DeCarolis, S. Hermann and H. H. Rogner (2012). "Modelling elements of Smart Grids – Enhancing the OSeMOSYS (Open Source Energy Modelling System) code." Energy 46(1): 337-350.

WEO (2013). Southeast Asia Energy Outlook: World Energy Outlook (WEO) special report. [online] France: International Energy Agency (IEA). Available at: https://www.iea.org/publications/freepublications/publication/SoutheastAsiaEnergyOutloo k_WEO2013SpecialReport.pdf

242

WEO (2015). Southeast Asia Energy Outlook: World Energy Outlook (WEO) special report. [online] France: International Energy Agency (IEA). Available at: https://www.iea.org/publications/freepublications/publication/weo2015_southeastasia.pdf

WEO 2017. Outlook for Natural Gas: Excerpt from World Energy Outlook 2017. International Energy Agency. http://www.iea.org/publications/freepublications/publication/WEO2017Excerpt_Outlook_f or_Natural_Gas.pdf

Williams, J.H., B. Haley, F. Kahrl, J. Moore, A.D. Jones, M.S. Torn, H. McJeon (2014). Pathways to deep decarbonization in the United States. The U.S. report of the Deep Decarbonization Pathways Project of the Sustainable Development Solutions Network and the Institute for Sustainable Development and International Relations.

Winkler, H., P. Mukheibir, S. Mwakasonda, A. Garg and K. Halsnaes (2007). Electricity Supply Options, Sustainable Development and Climate Change Priorities: Case Studies for South Africa. Roskilde, Denmark, UNEP Risoe Centre on Energy, climate and Sustainable Development. ISBN: 978-87-550-3629-1. September 2007

Wongsapai, W., C. Ritkrerkkrai and J. Pongthanaisawan (2016). "Integrated Model for Energy and CO2 Emissions Analysis from Thailand's Long-term Low Carbon Energy Efficiency and Renewable Energy Plan." Energy Procedia 100: 492-495.

World Atlas (2018). Map of Malaysia. [online] Available at: https://www.worldatlas.com/webimage/countrys/printpage/printpage.php?l=/webimage/c ountrys/asia/ciamaps/my.jpg

World Bank Data (2017). GDP per capita (current US$) | Data. [online] Available at: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=MY

World Bank Group (2017). Global Economic Prospects, June 2017: A Fragile Recovery. Washington, DC: World Bank. Washington, DC: World Bank. doi:10.1596/978-1-4648- 1024-4. License: Creative Commons Attribution CC BY 3.0 IGO

World Energy Council (2011). World Energy Scenarios: Global Transport Scenarios 2050.[online] United Kingdom: World Energy Council. Available at: https://www.worldenergy.org/wp- content/uploads/2012/09/wec_transport_scenarios_2050.pdf

Wu, T., Zhao, H. and Ou, X. (2014). Vehicle Ownership Analysis Based on GDP per Capita in China: 1963–2050. Sustainability, 6: 4877-4899.

Yang, X. and F. Teng (2018). "The air quality co-benefit of coal control strategy in China." Resources, Conservation and Recycling 129: 373-382.

Yusof, Z. A. and D. Bhattasali (2008). "Economic Growth and Development in Malaysia: Policy Making and Leadership." Working Paper No. 27 [online] Washington: Commission on Growth and Development. Available at:http://siteresources.worldbank.org/EXTPREMNET/Resources/489960- 1338997241035/Growth_Commission_Working_Paper_27_Economic_Growth_Developmen t_Malaysia_Policy_Making_Leadership.pdf

243

Yuzuru, M. and H. C. Siong (2013). Low Carbon Society Scenarios Malaysia 2030, University Teknology Malaysia. Kyoto University, Japan. National Institute for Environmental Studies, Japan.

Zamzam Jaafar, M., W. H. Kheng and N. Kamaruddin (2003). "Greener energy solutions for a sustainable future: issues and challenges for Malaysia." Energy Policy 31(11): 1061-1072.

Zhang, H., W. Chen and W. Huang (2016). "TIMES modelling of transport sector in China and USA: Comparisons from a decarbonization perspective." Applied Energy 162: 1505-1514.

Zhao, Y., S. Wang, L. Duan, Y. Lei, P. Cao and J. Hao (2008). "Primary air pollutant emissions of coal-fired power plants in China: Current status and future prediction." Atmospheric Environment 42(36): 8442-8452.

Zheng, C. and D. M. Kammen (2014). "An innovation-focused roadmap for a sustainable global photovoltaic industry." Energy Policy 67: 159-169.

Zhuang, J. (2014). Asian Development Outlook 2014 Supplement: Stable Growth Outlook for Developing Asia.[online] Philippines: Asian Development Bank. Available at: http://hdl.handle.net/11540/3093. License: CC BY 3.0 IGO.

244

Appendix A: Appendix to Chapter 3

A.1 Household Sector The NEB (2013) provided data aggregated by fuel for household sector whereby LPG is used for cooking and electricity is used for cooling, lighting and other appliances. Assumptions on fuel consumption in household sector also obtained from Saidur (2007) and Saidur (2007a) to populate data in MAED model. These studies analysed the household energy consumption and provided data on electricity and fossil fuel consumption in 2009 and 2011 and assumptions obtained from these studies are used to aggregate data in the MAED model and to calculate the useful energy of household sector. A summary of assumptions in the MAED model for population growth scenario in Table A. 1.

Table A. 1: Population growth scenario assumptions in the MAED model

Scenarios Assumptions (growth rate) Median 2.0 %–0.38% High 2.0 %–0.82% Low 2.0 %–-0.06% Source: DOSM (2013b), UNDESA (2015)

Table A. 2: Population scenarios (Malaysia) using the MAED model (million)

2013 2015 2020 2025 2030 2035 2040 2045 2050 Medium Growth rate (%) - 2.22 1.23 1.09 0.89 0.72 0.56 0.46 0.38 Medium Total Population (mil) 30.21 31.57 33.57 35.43 37.05 38.39 39.48 40.40 41.18 High Growth rate (%) - 2.491 1.521 1.439 1.240 1.022 0.873 0.818 0.815 High Total Population (mil) 30.21 31.74 34.23 36.76 39.10 41.14 42.96 44.75 46.60 Low Growth rate (%) - 1.95 0.94 0.71 0.53 0.37 0.22 0.08 -0.06 Low Total Population (mil) 30.21 31.40 32.91 34.09 34.99 35.66 36.05 36.19 36.09 Source: DOSM (2013b), UNDESA (2015)

Based on Table A. 2, which covers total population by region, population distributions are mainly concentrated in Peninsular Malaysia (79.0%), followed by Sabah (12.0%) and Sarawak (9.0%). The base year for the respective regions are prepared and the medium, high and low growth rates shown in Table A. 1.

Table A. 1 and are used in order to obtain the population projections for Peninsular Malaysia, Sabah and Sarawak.

245

Table A. 3: GDP scenarios (Malaysia) using the MAED model at constant 2010 prices ($USD billion)

2013 2015 2020 2025 2030 2035 2040 2045 2050 Medium Growth rate (%) 0.00 4.80 5.00 5.00 5.06 5.06 5.50 5.50 6.00 Medium Total GDP (USD bil) 252.72 277.56 354.24 452.11 578.67 740.66 968.02 1265.16 1693.07 High Growth rate (%) 0.00 4.80 6.00 6.40 6.40 6.40 7.40 7.40 7.50 High Total GDP (USD bil) 252.72 277.56 371.44 506.51 690.72 941.91 1345.95 1923.32 2761.17 Low Growth rate (%) 0.00 4.80 4.80 4.80 3.67 2.18 2.18 1.51 1.40 Low GDP (USD bil) 252.72 277.56 350.88 443.57 531.17 591.65 659.01 710.29 761.42 Source: IMF (2015), WEO (2015), APERC (2016), World Bank Group (2017)

Based on Table A. 26–Table A. 32, the GDP distribution of three regions for base year 2013 are prepared. The medium, high and low GDP growth rates shown in Table A. 20 are used in order to obtain the GDP projections for Peninsular Malaysia, Sabah and Sarawak. The multiregional GDP projections are summarised as GDP scenarios of Malaysia as shown in Table A. 3. Other inputs needed for household summarised in Table A.4-Table A.6).

Table A. 4: Total population, urbanisation level, population inside city and total dwellings by region (base year 2013)

Total Population by region million Percentage Peninsular Malaysia 23.87 79.0% Sabah 3.70 12.0% Sarawak 2.64 9.0% Malaysia 30.21 Urbanisation Level by region Percentage (%) Peninsular Malaysia 75.0% Sabah 54.0% Sarawak 53.8% Malaysia 71.0% Population inside City Percentage (%) Peninsular Malaysia 75.00 Sabah 54.00 Sarawak 53.80 Total Number of Dwellings million Peninsular Malaysia 6.40 Sabah 0.99 Sarawak 0.71 Malaysia 8.10 Source: DOSM (2010b), EPU (2010), ETP (2010), DOSM (2013a), DOSM (2013b), DOSM (2014c), DOSM (2015c), DOSM (2015d), EPU (2015a), EPU (2015b), UNDESA (2015), WEO (2015), EPU (2016)

Table A. 5: Distribution of fuel consumptions in household (base year 2013) in ktoe

Fossil Fuels Electricity Total Cooking 737 2 739 Cooling 0 269 269 Electric Appliances 0 1991 1991 Total 737 2262 2999 Source: NEB (2013)

246

Table A. 6: Number of dwellings, share of dwellings and energy requirement for cooling per dwellings

Item Unit 2013 Total Number of Dwellings million 8.10 Share of Villa [%] 3.000 Share of Apartment [%] 24.000 Share of High standard house [%] 40.000 Share of Low standard house [%] 33.000 Energy requirement for cooling per dwellings per year [kWh/dw/yr] 1167.000 Energy requirement for cooking per dwellings per year [kWh/dw/yr] 531.58 Energy requirement for appliances per dwellings per year [kWh/dw/yr] 1820.00 Source: Saidur, M. et al. (2007), JPPH (2013), DOSM (2014a), DOSM (2014b), EPU (2014), KRI (2014)

Note: Kubota et al. (2011) conducted a survey on household energy consumption in Johor, Malaysia to analyse energy requirements for cooling, cooking and appliances. Data from this study on energy requirement for cooking, cooling and electric appliances per household is used in MAED for the base year. GDP per household drives the increase of average energy requirement (cooling, cooking and electric appliances) of the household in future.

Table A. 7: Types of dwellings in Malaysia

Dwelling Classification in MAED model Types of dwellings (Malaysia) Villa • Single Storey Semi detach • 2-3 Storey Semi detach • Detach Apartment • Service Apartment • Condominium High standard house • Single Storey Terrace • 2-3 Storey Terrance • Flat • Town House Low standard house • Low Cost House • Low Cost Flat • Cluster Source: JPPH (2013; pg. 4)

JPPH (2013; pg. 4) summarises twelve types of dwellings in the country. These twelve types of dwellings are classified into four main groups and defined in the MAED model (Table A. 7). KRI (2014; pg. 14 & pg. 23) highlighted the percentage of household income and the price of houses that a person could afford to buy based on income in 2012. Based on this information, the following assumptions are derived:

• About 23% of households are earning less than MYR 3000 monthly and could afford a low standard house • About 69% of households are earning between MYR 3000–10,000 monthly and could afford high standard house or apartment. • About 8% of households are earning more than MYR 10,000 monthly and could afford villas

Table A. 8: Key input assumptions for household structure in the MAED model

Variables Assumptions Type of dwellings Types of dwellings described in household property Size of dwellings statistics (JPPH 2013, KRI 2014) are clustered into four main groups to simplify the classification of the dwellings’ sizes and base year data input. Share of dwellings The percentage share of dwellings are aggregated based on national household income and property statistics (DOSM 2014a, DOSM 2014b, EPU 2014, KRI 2014).

247

The household structure in the MAED model is divided to two parts: urban household sector and rural household sector as shown in Figure A. 1 and the household sector variables summarised in Table A. 9. The total number of urban and rural population is the function used to derive the total number of urban and rural households. The urban and rural sectors are further aggregated to different types of dwellings and component of household appliances by fuel type. The background on documentation and assumption of household sector are discussed in Section 3.1.4.

Urban Dwellings

Villa

Apartment Household Sector Energy Demand High standard houses

Low standard houses

Rural Dwellings

Village houses

Figure A. 1: Summary of household structure in the MAED model

A summary of important household sector variables in the MAED model is given in Table A. 9. Table A. 9: Household sector variables in the MAED model

Variables Unit Urban dwellings [million] Rural dwellings [million] Share of villa [%] Share of apartment [%] Share of high standard house [%] Share of low standard house [%] Share of rural dwelling [%] Villa dwelling size [sqm] Apartment dwelling size [sqm] High standard house dwelling size [sqm] Low standard house dwelling size [sqm] Rural houses dwelling size [sqm]

Source: MEIH (2017), World Bank Data (2017)

Figure A. 2: Historical fuel consumptions (1997–2010) in the household sector

248

KRI (2014; pg. 24) in 2012 showed that between 90–96% of households own electrical appliances such as refrigerators, TVs, electric stoves and washing machines. About 38% of households owns air-conditioning and internet at home. In 2013, about 75% of electricity was consumed in this sector as well as about 25% of LPG used for cooking. Figure A. 2 shows that the electricity consumption in the household sector has been increasing since 1997, along with household income. As discussed in Section 3.1.4, this increasing trend is similar to other developing countries, where changes in lifestyle and increases in household income are correlated to electrical appliance ownership (Pachauri and Jiang 2008, Sivak 2009, Daioglou, van Ruijven et al. 2012). Table A.10 summarised the key parameters, demand drivers and equations used in the MAED model to forecast the useful energy demand for cooking, electric appliances and air-conditioning in the household sector. Table A. 10: Summary of key parameters, demand drivers and equations to calculate the useful energy demand projection of household sector in MAED model

Key parameters Equations used in MAED model Demand Driver • total population (see Table A1, Useful energy demand for cooking in • population A.2 and A.4) time frame t: • GDP per = Total number of dwellings × energy capita • urbanisation level (see Table A.4) required for cooking per year per dwelling See Table A.6-A.7 for details on: • total number of dwellings Useful energy demand for electric • type of dwellings appliances in time frame t: • share of dwellings = Total number of dwellings × energy • total floor area air-conditioned required for appliances per year per • energy requirement for cooling dwelling per dwellings per year • energy requirement for cooking Useful energy demand for air- per dwellings per year conditioning in time frame t: = Total number of dwellings (I) × fraction • energy requirement for electricity per dwellings per year of dwellings air-conditioned (I) × energy required for cooling per year per dwelling (I)

where I = villa, apartment, high standard house and low standard house t = 2013, 2015,….., 2050 in 5-year time intervals The total number of dwellings in time frame t = Total population in respective year × (percentage of urban or rural population/ capita per household)

249

A.2 Commercial Sector Distribution of fuel consumption in commercial (base year 2013) based on aggregation of fossil fuel and electricity consumptions obtained from NEB (2013). Saidur (2007a) focused on energy and exergy analysis of the commercial sector in Malaysia and this study provided an insight on LPG consumed by cooking appliances; electricity consumed by air-conditioning and electrical appliances. This study is also used as reference to aggregate the fuel consumptions as in Table A. 11. Other inputs for commercial listed as in Table A. 12- Table A. 14.

Table A. 11: Distribution of fuel consumptions in commercial (base year 2013) in ktoe

Sector LPG Motor Fuels Electricity Cooking 693 0 0 Motive power 0 244 0 Electric Appliances 0 0 1248 Air conditioners 0 0 2218 Total 693 244 3466

Table A. 12: Potential labour force, labour force in commercial, age structure, floor area and energy requirement for cooling per floor area Item Unit 2013 Potential Labour Force (Malaysia) million 13.63 Labour force in Commercial Peninsular Malaysia million 6.25 Sabah million 0.84 Sarawak million 0.44 Age structure (Malaysia) Population aged 0-14 years million 7.80 Population aged 15-64 years million 20.79 Population aged 65 years and above million 1.66 Floor area per person [sqm/cap] 20.0 Total floor area air-conditioned [%] 55.0 Energy required for cooling per floor area [kWh/sqm/yr] 212.0

Table A. 13: GDP of commercial sector by region (2013) at constant 2010 prices (USD billion)

Peninsular Malaysia Sabah Sarawak Total Commerce, Restaurant and Hotels 37.19 2.54 2.54 42.26 Finance, Insurance, Real Estate and Business Services 26.10 1.78 1.78 29.66 Other Commercial activates 29.55 2.01 2.01 33.58 Transport and Communication 19.55 1.33 1.33 22.21 Energy 5.87 0.40 0.40 6.67 Total (Commercial) 118.25 8.06 8.06 134.37 Source: IMF (2015)

Table A. 14: GDP scenarios (Malaysia) using the MAED model at constant 2010 prices (USD billion)

250

The commercial sector is mainly structured to capture labour force inputs in the MAED model as shown in Figure A. 3 and summary of commercial sector variables summarised in Table A.15. Key assumptions on commercial sector in the MAED model summarised in Table A.16. Table A.17 summarised the key parameters, demand drivers and equations used in the MAED model to forecast the useful energy demand of commercial sector. The GDP and population variables are used to calculate the useful energy demand of commercial sector. Background on documentation and assumptions of commercial sector are discussed in Section 3.1.5.

Active Labour Force in Commercial Sector Labour Force

Floor Area per person

Commercial Sector Energy Demand Factors for Air-conditioning

Fuel consumption and GDP

Figure A. 3: Summary of commercial structure in the MAED model

A summary of the important commercial sector variables in the MAED model is given in Table A. 15. Table A. 15: Commercial sector variables in the MAED model

Variables Unit Labour force in Commercial Sector [million] Floor area per employee [sqm/cap] Floor area of Commercial Sector [million sqm] Air conditioning floor area [%]

Table A. 16: Key input assumptions for commercial structure in the MAED model

Variables Assumptions Potential labour force The total population growth is the key function used to Participating labour force calculate the total labour force. Statistical data for these Percentage of labour force in commercial variables are obtained from national data on labour force (EPU 2001, DOSM 2013). Floor area per person This is based on standard space allocation per person in office buildings (20 m2 per person) Fuel consumption These statistics are obtained from national energy balance on GDP contribution fuel consumption (NEB 2013) and GDP contribution of commercial sector (DOSM 2015b) to calculate the energy intensity of various forms of energy.

251

Source: MEIH (2017)

Figure A. 4: Historical fuel consumptions (1997-2010) in the commercial sector

The commercial sector represents about 54% of GDP of the country and this sector accounts for about 9% of the total final energy consumption. The sector’s contribution to the GDP is expected to increase to 58%, with a growth of 6.7% annually for the 2016 to 2020 period (MIDA 2018). The sector relies mainly on electricity (79%) and LPG (16%). Electricity is the most widely used energy commodity in this sector and has been increasing over the years. This increase reflects the importance of electricity consumption, which is used by technologies such as lighting, commercial equipment and air-conditioning.

Source: MEIH (2017) Figure A. 5: Trend of total GDP and historical fuel consumptions (1997-2010) in the commercial sector

252

Table A. 17: Summary of key parameters, demand drivers and equations to calculate the useful energy demand projection of commercial sector in MAED model

Key parameters Equations used in MAED model Demand Drivers • distribution of fuel Useful energy demand for air-conditioning • GDP consumptions (by fuel type) in in time frame t: • Population commercial (see Table A.11) = Total labour in commercial × floor area • Labour force per employee in commercial × percentage • GDP growth and GDP of of floor air-conditioned × energy required commercial sector (see Table for cooling per year per floor area A.13 – A.14) Useful energy demand for motor power in See Table A.12 for details on: time frame t: • labour force in commercial = GDP contribution of commercial sector × • age structure (motor fuel consumption in commercial • floor area per person sector/GDP of commercial sector) • total floor area air-conditioned • energy required for cooling per Useful energy demand for electricity in floor area time frame t: = GDP contribution of commercial sector × (electricity consumption in commercial sector/GDP of commercial sector)

Useful energy demand for cooking in time frame t: = GDP contribution of commercial sector × (LPG consumption in commercial sector/GDP of commercial sector)

t = 2013, 2015,….., 2050 in 5-year time intervals

253

A.3 Industry Sector The industrial sector is the second largest energy-consuming sector. Distribution of fuel consumption in industry (base year 2013) derived based on aggregation of fossil fuel and electricity consumptions by sub-sector obtained from NEB (2013) and Saidur (2010). Saidur (2009) also conducted an analysis on end-use electricity consumption based on energy auditing in manufacturing factories, which provided insights on the end-use electricity aggregations for the base year. Assumptions and aggregated data obtained from NEB (2013) and Saidur (2010) are used to aggregate the share of fossil fuels and electricity for industry in order to populate the MAED templates for base year (2013) and to calculate the useful energy demand of thermal uses and motive power for 2013-2050 as detailed in Table A. 18 and Table A. 19. Assumptions used on GDP growth and economic activity by region outlined in Table A.20 – Table A.22. Details on GDP activity by sector and region concluded in Table A.26-Table A.32. Table A. 18: Consumption of manufacturing sub-sectors by fuel (base year 2013) in ktoe

Fossil Fuels Motor Fuels Electricity Basic Materials 2797 1772 3563 Wood and Wood Products Including Furniture 17 85 280 Paper and Paper Products Printing and Publishing 128 111 443 Chemicals including Petroleum and plastic Products 568 294 536 Non-Metallic Mineral Products 190 111 863 Basic Metal Industries 1702 531 612 Miscellaneous 192 640 829

Machinery & Equipment 58 647 421 Fabricated Metal Products Machinery and Equipment 58 647 421

Nondurables 1572 170 490 Food, Beverages and Tobacco 1429 110 220 Textile, Wearing Apparel & Leather 143 60 270 Miscellaneous 61 71 335 Source: NEB (2013; pg. 89), DOSM (2013b)

Table A. 19: Summary of fuel distribution in industry sub-sectors (base year 2013) in ktoe

Industry sub-sector Fossil Fuels Motor Fuels Electricity Coke Basic Materials 2797 1772 3563 1539 Machinery & Equipment 58 647 421 0 Nondurables goods 1572 170 490 0 Miscellaneous 61 71 335 0 Agriculture and Fishery 0 1019 32 0 Total 4488 3679 4841 1539 Source: NEB (2013)

254

A summary of assumptions in the MAED model for GDP growth scenario development is provided in Table A. 20. Table A. 20: GDP growth scenario assumptions in the MAED model Scenarios Assumptions Median Growth rate • Implementation of economic policies e.g., the Eleventh Malaysia Plan (5.0 %–6.0%) • Increasing trade, investment, tourism and cross-border infrastructure through regional business market e.g., the ASEAN platform High Growth rate • Major expansion of the manufacturing and service sectors through global and (5.0 %–7.5%) regional business markets e.g., ASEAN +3 bilateral collaboration Low Growth rate • Financial crisis e.g., the Asian financial crisis (1997) and the global financial crisis (5.0 %–1.4%) (2007–2008) Source: IMF (2015), WEO (2015), APERC (2016), World Bank Group (2017)

Table A. 21: GDP by region and economic activity (2013) at constant 2010 prices (USD billion) Peninsular Malaysia Sabah Sarawak Total Agriculture 16.39 4.10 3.62 24.10 Mining and Quarrying 13.93 3.48 5.81 23.22 Manufacturing 50.71 1.42 5.80 57.93 Construction 9.07 0.41 0.82 10.22 Services 118.25 8.06 8.06 134.37 Total 208.35 17.21 24.11 249.90 Percentage distribution by region (%) 83% 7% 10% Source: IMF (2015)

Table A. 22: GDP aggregation by sub-sector in industry in 2013 (billion USD)

2013 Agriculture & Fishery 24.1 Construction & Mining 23.2 Manufacturing 57.9 Basic Materials 29.12 Machinery & Equipment 20.3 Nondurables 8.0 Miscellaneous 0.49 Source: DOSM (2015a), DOSM (2015b), DOSM (2016)

The industrial structure in the MAED model is subdivided into the manufacturing, agriculture and construction sectors (Figure A. 6). The model requires GDP contribution by sub-sector and energy intensity as input in order to calculate the useful energy demand for the industrial sector.

GDP distribution by sub-sector Manufacturing Industry Sector Energy Demand Agriculture Construction

Energy Intensity

Figure A. 6: Summary of industrial structure in the MAED model

A summary of important industry sector variables in the MAED model is given in Table A. 23 and assumption on key inputs for industrial structure in MAED model summarised in Table A.24. Table A.25 provides a summary on key parameters needed in MAED model and equations used to calculate useful energy demand for industry sector.

255

Table A. 23: lndustrial sector variables in the MAED model

Variables Unit GDP [billion US$] Energy intensity for industry sector [kWh/USD]

Table A. 24: Key input assumptions for industrial structure in MAED model

Variables Assumptions GDP contribution The percentages of GDP contribution by sub-sectors for industry sector are prepared based on national GDP statistics for the base year (DOSM 2015b). GPD growth drives the sub- sector GDP projections. Fuel consumption Statistics are obtained from the national energy balance on fuel GDP contribution consumption (NEB 2013) and GDP contribution (DOSM 2015b) of the industrial sector.

Table A. 25: Summary of key parameters needed in MAED model, demand drivers and equations to calculate the useful energy demand projection of industry sector

Key parameters Equations used in MAED model Demand drivers • distribution of fuel Useful energy demand for motor power in industry • GDP growth consumptions by sector in time frame t: industrial subsector = GDP contribution of each type of industry sub-sector × (see Table A.19) ratio of energy uses to GDP (final energy use of motor fuel by industry subsector/GDP of industry sub-sector) • GDP aggregation by sub-sector in industry Useful energy demand for thermal uses in industry (see Table A.21 and sector in time frame t: Table A.22) = GDP contribution of each type of industry sub-sector × ratio of energy uses to GDP (final energy use of gas, • GDP growth (see Table LPG and coal by industry subsector/GDP of industry A.14 and Table A.20) sub-sector)

Useful energy demand for electricity uses in industry sector in time frame t: = GDP contribution of each type of industry sub-sector × ratio of energy uses to GDP (final energy use of electricity by industry subsector/GDP of industry sub- sector)

t = 2013, 2015,….., 2050 in 5-year time intervals

256

Source: MEIH (2017)

Figure A.7: Historical fuel consumptions (1997-2010) in the industrial sector

Source: MEIH (2017)

Figure A. 8: Trend of total GDP and historical fuel consumptions (1980-2008) in the industrial sector

In 2013, gas and electricity were mainly consumed in the industrial sector and the trend of energy consumption had changed over the years. To address energy efficiency and energy conservation in the country’s industrial sector, the Government of Malaysia initiated the Malaysian Industrial Energy Efficiency Improvement Project (MIEEIP) in 1999 to improve the rational use of energy in the industrial sector. The MIEEIP initially focussed on eight energy- intensive industries (wood, rubber, food, ceramics, glass, pulp & paper, iron & steel and cement). During the project implementation, three other sub-sectors were later added (plastics, textile and oleo-chemical). In 2007, the global crisis had affected the manufacturing exports that generated high revenues for the country. Major export commodities such as petroleum, palm oil, rubber and timber had dropped. This drop in the manufacturing exports also influenced the trend of energy consumption of this sector after the year 2007.

257

Table A. 26: GDP contributions by sector (base year 2013) at constant 2010 prices (USD billion)

Sector and Sub-sectors USD billion Manufacturing and Handicrafts Sector

Basic Materials 29.28 Wood and Wood Products Including Furniture a. wood product b. furniture Paper and Paper Products Printing and Publishing a. paper n paper products b. publishing, printing and reproduction of recorded media Chemicals including Petroleum and plastic Products a. refine petroleum products b. chemical product c. plastic product Non-Metallic Mineral Products a. rubber products b. Non-Metallic Mineral Products Basic Metal Industries a. basic metals b. fabricated metal products Oil & gas industries

Machinery & Equipment 20.05 Fabricated Metal Products Machinery and Equipment a. machinery n equipment b. manufacture of office, accounting and computing machinery c. electrical machinery and apparatus d. manufacture of radio, TV and communication equipment & apparatus e. manufacture of medical, precision and optical instrument, watches and clocks f. motor, vehicle and transport equipment

Nondurables goods 8.18 Food, Beverages and Tobacco a. beverages b. food processing c. Tobacco d. vegetable & animal oil & fat Textile, Wearing Apparel & Leather a. Textile and wearing apparel b. Leather and footwear

Miscellaneous 0.4 Other Manufacturing Industries

Sector and Sub-sectors (agriculture, mining and construction) Agricultural, Livestock, Forestry & Fishing 24.10 a. Agricultural Crops b. Livestock c. Forestry d. Fishing Mining and Quarrying 23.22 a. Non-oil & gas b. Quarrying Building and Construction 10.22 Source: DOSM (2015a), DOSM (2015b), DOSM (2016)

258

Table A. 27: GDP by region and kind of economic activity (2013) at constant 2010 prices ($USD billion)

Peninsular Malaysia Sabah Sarawak Total Agriculture 16.39 4.10 3.62 24.10 Mining and Quarrying 13.93 3.48 5.81 23.22 Manufacturing 50.71 1.42 5.80 57.93 Construction 9.07 0.41 0.82 10.22 Services 118.25 8.06 8.06 134.37 Total 208.35 17.21 24.11 249.90 Percentage distribution by region (%) 83% 7% 10%

Table A. 28: GDP by region and kind of economic activity (2013) - percentage (%)

Peninsular Malaysia Sabah Sarawak Total Agriculture 68% 17% 15% 100% Mining and Quarrying 60% 15% 25% 100% Manufacturing 87% 2% 10% 100% Construction 88% 4% 8% 100% Services 88% 6% 6% 100%

Table A. 29: GDP (agriculture) by region (2013) at constant 2010 prices ($USD billion)

Peninsular Malaysia Sabah Sarawak Total Agricultural Crops 11.73 2.93 2.59 17.26 Livestock 1.63 0.41 0.36 2.40 Forestry 1.26 0.32 0.28 1.86 Fishing 1.76 0.44 0.39 2.58 Total (Agricultural, Livestock, Forestry & Fishing) 16.39 4.10 3.62 24.10

Table A. 30: GDP (mining and quarrying) by region (2013) at constant 2010 prices ($USD billion)

Peninsular Malaysia Sabah Sarawak Total Oil & gas 13.15 3.29 5.48 21.91 Quarrying 0.79 0.20 0.33 1.31 Total (Mining and Quarrying) 13.93 3.48 5.81 23.22

Table A. 31: GDP (manufacturing) by region (2013) at constant 2010 prices ($USD billion)

Peninsular Sabah Sarawak Total Malaysia Basic Materials 25.60 0.59 2.93 29.28 Machinery & Equipment 17.53 0.40 2.01 20.05 Nondurables 7.15 0.16 0.82 8.18 Total (Manufacturing and Handicrafts) 50.71 1.16 5.80 57.99

Table A. 32: GDP (commercial) by region (2013) at constant 2010 prices ($USD billion)

259

A.4 Transportation Sector Information on number of vehicles, average mileage travelled by passengers and vehicle, passenger per vehicle obtained from national statistics (Table A. 33–Table A. 38) to populate the MAED model. GDP per-capita growth is used to drive the increase of car ownership and GDP growth is used to drive the freight activity in future.

Table A. 33: Total number of vehicles in Malaysia by region (base year 2013)

Motorcycle Motorcar Bus Taxi Hire & Drive Goods vehicle Others Total

Malaysia 11087878 10535575 62784 99921 53954 1116167 862977 23819256 Peninsular Malaysia 10105026 9295632 53014 93115 48886 909744 670970 21176387 Sabah 306184 556699 6765 4448 3602 118000 96248 1091946 Sarawak 676668 683244 3005 2358 1466 88423 95759 1550923 Source: MOT (2013), MAA (2017)

Table A. 34: New registered vehicles by type and fuel (base year 2013)

Type of Vehicles Fuel Petrol Diesel NGV Electric Petrol & Electric Petrol and NGV Motorcycle 600173 14 0 145 0 0 Cars 573314 2035 2 16 16851 93 Bus 45 150 5 0 0 11 Taxi 1801 0 6 0 12 3056 Goods Vehicle 3805 9904 48 0 0 33 Source: MOT (2013)

Table A. 35: Average distance travelled in a year per person in Malaysia (base year 2013)

Distance travelled Unit 2013 Person km/prsn/day 12.0 Source: SPAD (2012), Shariff (2012), MOT (2013)

Table A. 36: Average annual passenger travel in Malaysia: Train (base year 2013)

Average load factor Average annual passenger (pass/veh) activity 109 pass-km Train Diesel 750 1.08 Train Electric 470 0.31 Source: SPAD (2012), Shariff (2012), MOT (2013), NAP (2014; pg. 7)

Table A. 37: Average annual passenger mileage in Malaysia: Road vehicle (base year 2013)

Annual vehicle mileage Average load factor (1000km/vehicle) (pass/veh) Car 28 3 Bus 22 60 Motorbike 28 2 Source: SPAD (2012), Shariff (2012), MOT (2013), NAP (2014; pg. 7)

Table A. 38: Average annual freight mileage in Malaysia: Truck (base year 2013)

Annual vehicle mileage Average load factor Annual (1000km/vehicle) (ton/veh) freight activity 109ton-km Truck 34.3 10.8 167.4 Source: Kontena Malaysia (2009)

260

Table A. 39: Car ownership projection (2013-2050) – per 1000 people in Malaysia

Car ownership 2013 2020 2040 2050 REF scenario 251 299 591 950 High scenario 251 309 710 1266 Low scenario 251 289 513 592 Note: GDP per capita is used as a reference to drive the increase of car ownership

Source: EPU (2013) Figure A. 9: New Registration of Private Motor Vehicles (2000-2013)

SPAD (2012; pg. 16) shows the growth in travel demand. Travel by car, bus, rail and air had tripled since 1991 – from 13 million trips per day to around 40 million in 2010. The statistics showed that the increase in travel demand was mainly due to trips by cars. The growth in car travel demand is correlated to the increasing number of cars owned. The analysis also projected that the travel demand would triple by 2030 compared to 2010. The travel demand is anticipated to increase due to the continuous improvement and expansion of transportation facilities, especially public transport services and road infrastructure parallel with economic and demographic development. Figure A. 9 shows the increase of sales of new private cars in Malaysia.

According to the IEA (2012), passenger vehicle travel will continue to grow rapidly in non-OECD countries although the travel demand may have reached a saturation point in OECD countries, for example, in the UK, the US, France and Japan. The study discussed that these developed countries had a steady growth in passenger vehicle demand over many decades due to population and economic growth. However, the travel demand may have already achieved a saturation point probably because of the limits on vehicle travel per person have been reached. Moreover, there were also differences in the travel demand patterns in these countries: for example, about 9000 km per vehicle per year in Japan compared to about 14,000 km per vehicle per year in Europe and 19,000 km per vehicle per year in US.

261

Therefore, taking into account the evolution of travel demand in other countries in relation to population, economic growth and household income, the trend of travel demand in Malaysia is also assumed to increase in the future. The average distance travelled and annual vehicle mileage are anticipated to increase as the population, urbanisation level and economic activities are forecasted to increase along with the improvements in transportation infrastructure. Table A. 35 shows the average distance travelled per person in a year as input in the MAED model for the base year 2013, prepared based on national statistics (SPAD 2012, Shariff 2012, MOT 2013). Table A. 37 shows the average vehicle travel per person included in the MAED model (2013).

Dargay et al. (2007) analysed the relationship between car ownership and demographic factors worldwide, focusing on urbanisation and population density between the years 1960–2030. The authors concluded that non-OECD countries including Malaysia will experience vehicle ownership growth as rapid as their economic growth, based on the historical economic trends of OECD countries. The study projected that vehicle-ownership for the country will to increase up to 677 vehicles per 1000 people in 2030 as compared to 240 vehicles per 1000 people in 2002 with a growth rate of 3.8% (Dargay, Gately et al. 2007; pg. 161). With rapid growth forecasted for Malaysia beyond 2030, the vehicle-ownership growth rate is anticipated to further increase, which at the same time will impact the projection of fuel consumption in the transportation sector (see Table A.39 on car ownership projection). Another similar study also analysed car ownership in relation to GDP per capita and concluded that car-ownership in Malaysia is projected to reach almost 800 cars per 1000 people with log GDP per capita forecasted above 10 by 2050 (Chamon, Mauro et al. 2008, pg. 247).

Table A. 40: Freight transportation activity

Mode Fuel Total in Fleet Operating Annual Annual Average Annual freight Fraction of in freight vehicle vehicle load freight operating mileage activity factor activity Unit 103 fraction 103 103km/veh 106veh-km ton/veh 109ton-km Boat Diesel 0.06 Big truck Diesel 571.86 0.87 497.52 66 32836.03 10.8 354.63 Medium truck Diesel 83.25 0.87 72.42 10 724.24 5.5 3.98 Total freight transportation activity 358.68 Source: MOT (2013)

262

Source: MEIH (2017)

Figure A. 10: Historical fuel consumptions (1997-2010) in the transportation sector

Source: MEIH (2017)

Figure A. 11: Trend of total energy demand in transportation and total GDP growth

The structure of the transportation sector in the MAED model is divided into three parts: intracity passenger transport, intercity passenger transport and freight transport, as depicted in Figure A. 12. Average distance travelled, total number of vehicles, load factors and total population variables are some of the key inputs used to calculate the energy demand.

Average Distance Travelled Intercity/Intracity Car Ownership Passenger Transportation Car-kilometres Transportation Sector Energy Demand Total freight-kilometres Freight Transportation

Modal Split freight transportation

Figure A. 12: Summary of transportation structure in the MAED model

263

Summary of important industry sector variables in the MAED model illustrated in Table A. 41.

Table A. 41: Transportation sector variables in the MAED model

Variables Unit Total distance travelled (intracity/intercity) total passenger-kilometres Average intracity/intercity distance travelled km/person/year per person per year Population in cities millions Total population millions Energy consumption of intracity/intercity passenger ktoe transportation Total freight-kilometres ton-kilometres Energy consumption of freight transportation ktoe

Table A. 42: Key input assumptions for transportation structure in MAED model

Variables Assumptions Average distance travelled To prepare the total intracity/intercity passenger activity for the base year, 2013: a. assumptions and data on the total number of vehicles by mode, fuel and share of vehicles are obtained from national statistics (EPU 2013, MOT 2013, MAA 2017). b. annual vehicle mileage is assumed based on the following studies: • A survey conducted in 2012 to look at urban public transportation and travelling demands in the Greater KL region (SPAD 2012). • A study on private vehicle ownership and transportation planning (Shariff 2012). Average passenger per vehicle (intracity or Assumed based on a survey conducted by SPAD (2012; intercity passenger transportation) pages 3–11) Total freight-kilometres Assumptions for freight transportation such as modal Modal split of freight transportation split of freight transportation and total freight- kilometres are prepared based on statistics from MOT (2013), SPAD (2012) and MAA (2014) Note: The IEA-ETSAP E-TechDS is an Energy Technology Data Source offers consistent sets of data on energy demand and supply technologies. Data and figures obtained from summary table by vehicle mode. Available at: https://iea-etsap.org/index.php/energy- technology-data

264

Table A. 43: Summary of key parameters needed in MAED model, demand drivers and equations to calculate the useful energy demand projection of transportation sector

Key parameters Equations used in MAED model Demand Drivers • Total population (see Table Car passenger (passenger-km) in time • Population A.2) frame t: • GDP/capita = total population × car ownership × average • Total number of vehicle (see annual distance travelled by each car × Table A.33) average passenger per vehicle

• Car ownership (see Table Average annual distance travelled by each A.39) car = average passenger-kilometre by car / See Table A.37 for details on: number of operating cars / Average Load • Annual vehicle mileage factor • Passenger/vehicle (load factor) Bus passenger travel (passenger-km) in • Car-kilometre time frame t: = Total number of bus × annual mileage bus Note: GDP per capita is used as a reference travelled × average passenger per vehicle to drive the increase of car ownership Motorbike passenger travel (passenger- km) in time frame t: = total number of operating motorbikes × annual mileage motorbike travelled × average passenger per vehicle

t = 2013, 2015,….., 2050 in 5-year time intervals See Table A.40 for details on: Freight (Truck) (tonne-km) in time frame t: • GDP = Total number of trucks × average annual • Total number of trucks mileage per vehicle × • Annual vehicle mileage average tonne per vehicle • tonne/vehicle (load factor) t = 2013, 2015,….., 2050 in 5-year time intervals Note: GDP is used as a reference to drive the increase of freight transportation demand

265

A.5 Assumptions and inputs to OSeMOSYS model The sub-module structures for household, commercial, industry and transportation sectors to develop the OSeMOSYS model (detailed in Section 3.2.1.1-Section 3.2.1.4) and Table A. 44 – Table A. 56 details the data of technologies captured in the OSeMOSYS.

Table A. 44: Energy efficiency of household technology

Household Technology Efficiency (%) Electric Stove 75.2% LPG Stove 48.1% GLS 54.9% CFL 58.0% LED 61.0% Existing Air-conditioning 39.0% New Air-conditioning 44.0% Electrical Appliances 68.0% Source: IEA-ETSAP E-TechDS

Table A. 45: Energy efficiency of commercial technology

Commercial Technology Efficiency (%) Electric Stove 75.0% LPG Stove 66.0% Electrical Machine 83.0% Oil Machine 76.0% Electric Air-conditioning 77.0% Gas Air-conditioning 70.0% Source: IEA-ETSAP E-TechDS

Table A. 46: Energy efficiency of industry technology

Industrial Technology (Boilers) Gas 86.0% Oil 89.0% Efficiency (%) LPG 88.0% Coal 90.0% Electricity 85.0% Source: IEA-ETSAP E-TechDS

Table A. 47: Energy efficiency of transport technology

Transport Technology Unit Car Gasoline l/ km 0.072 Car Electric MJ/km 0.98 Car Diesel l/km 0.057 Car Gas l/km 2.6 Bus Diesel l/100 km 55 Bus Gas l/100 km 44 Truck Diesel l/100 km 40.6 Truck Gas l/100 km 35.8 Motorbike Gasoline l/100 km 4.86 Motorbike Electric MJ/km 0.28 Train diesel MJ/seat-km 0.136 Train electric MJ/seat-km 0.119 Source: IEA-ETSAP E-TechDS Note: Assuming that the cars are mainly medium sized cars e.g. city cars or medium family cars.

266

Table A. 48: Renewable energy targets by 2050 (MW)

Cum Biomass Cum Biogas Cum Mini- Cum Solar PV Cum Solid Cum Total RE Hydro Waste 2011 110 20 60 7 20 217 2015 77.3 100 290 55 200 975 2020 800 240 490 175 360 2065 2025 1190 350 490 399 380 2809 2030 1340 410 490 854 390 3484 2035 1340 410 490 1677 400 4317 2040 1340 410 490 3079 410 5729 2045 1340 410 490 5374 420 8034 2050 1340 410 490 8874 430 11544 Source: KeTTHA (2009)

Table A. 49: Installed capacity of commissioned renewable installations (MW)

Year Biogas Biomass Small Hydro Solar PV Geothermal Total 2012 5.16 52.30 11.70 31.56 0 100.72 2013 6.58 0 0 106.90 0 113.48 2014 1.10 12.50 0 65.02 0 78.62 2015 7.40 19.00 6.60 22.32 0 55.32 2016 0 0 0 0.03 0 0.03 Cumulative 20.24 83.80 18.30 225.83 0 348.17 Source: SEDA (2016)

Table A. 50: List of existing power plants in Malaysia (MW)

No OSEMOSYS Name Power Plant Name Ownership Input fuel Unit Year of availability 1 OCGT_PD power PD power IPP Gas 4 (109.1 × 4) 1995- Jan 2016 2 OCGT_Powertek Powertek IPP Gas 4 (108.5 × 4) 1995- Jan 2016 3 OCGT_Putrajaya1 Putrajaya1 TNB Gas 2 (110 × 2) 1990 - Aug 2025 4 OCGT_Putrajaya2 Putrajaya2 TNB Gas 3 (135 × 3) 1991-Aug 2015 OCGT 5 OCGT_Connought Bridge Connought Bridge TNB Gas 4 (130 × 4) 1998-Aug 2014 6 OCGT_Oil Tewa langkawi Oil/Gas 2 (34.0 × 2) 1997-Aug 2018 7 OCGT_Kapar Kapar IPP Gas 2 (110.0 × 2) 1987-Jul 2019 8 OCGT_Pasir Gudang Sultan Iskandar Pasir Gudang TNB Gas 2 (110.0 × 2) 1989- Aug 2016 9 CCGT_Connought_Bridge Connought Bridge (CBPS) TNB Gas 1(308.0 × 1) 1983-Sep 2014 10 CCGT_Genting Genting/Kuala Langat IPP Gas 1(704.0 × 1) +(38&20)OCGT 1985-Feb 2026 11 CCGT_Segari Segari IPP Gas 2(651.5 × 2) 1986-Jun 2027 12 CCGT_Pasir_Gudang Sultan Iskandar Pasir Gudang TNB Gas 1(269.0 × 1) 1987-Aug 2022 P 13 CCGT_Pahlawan Pahlawan IPP Gas 1(322.0 × 1) 1990-Aug 2020 e 14 CCGT_GB3 Lumut/Segari (GB3) IPP Gas 1(640.0 × 1) 1995-Dec 2022 n 15 CCGT_Panglima Panglima IPP Gas 1(720.0 × 1) 1996-Feb 2023 i CCGT 16 CCGT_Prai Prai/MPSS IPP Gas 1(322.0 × 1) 1997 - Jun 2024 n 17 CCGT_Gelugor Gelugor TNB Gas 1(330.0 × 1) 1997-Aug 2024 s 18 CCGT_TTPC Teknologi Tenaga Perlis Consortium IPP Gas 1(650.0 × 1) Aug 2024 u 19 CCGT_Tengku_Jaafar1 Tengku Jaafar (PD1) TNB Gas 1(714.0 × 1) 2001-Aug 2028 l 20 CCGT_Tengku_Jaafar2 Tengku Jaafar (PD2) TNB Gas 1(708.0 × 1) 2008-Jan 2030 a 21 CCGT_YTL_Paka YTL Paka IPP Gas 2(390.0 × 2) end Sept 2015 r 22 CCGT_YTL_Pasir_Gudang YTL Pasir Gudang IPP Gas 1(390.0 × 1) end Sept 2015

23 CCGT_SI_Paka Sultan Ismail Paka TNB Gas 4(284 × 4) 1986-end Aug 2017 M GAS 24 Thermal_Kapar Kapar KEV IPP Oil/Gas 2(300.0 × 2) 1999-Jul 2029 a THERMA 25 Thermal_Pasir_Gudang Sultan Iskandar Pasir Gudang TNB Oil/Gas 1(120.0 × 1) Dec 2012 l 26 Coal_Janamanjung Janamanjung/Sultan Azlan Shah, Manjung IPP Coal 3(700.0 × 3) 2003-Aug 2031 a 27 Coal_TanjungBin Tanjung Bin IPP Coal 3(700.0 × 3) 2003-Sept 2031 y COAL 28 Coal_Kapar Kapar IPP Coal 2(300.0 × 2) 2(500.0 × 2) 2001-July 2029 s 29 Coal_Jimah Jimah IPP Coal 2(700.0 × 2) 2009-Dec 2033 i 30 Hyd_Stor_Pergau Pergau Hydro Aug 2037 a 31 Hyd_Stor_Temenggor Temenggor Hydro Aug 2022 32 Hyd_Stor_Kenyir Kenyir Hydro Aug 2025 Bersia S.J Sg Perak Aug 2022 Kenerong Scheme (NEB 33 Hyd_ROR_Perak Chenderoh 2013) HYDRO Sg Piah Upper Sg Piah Lower Jor S.J Sg Cameron Aug 2027 35 Hyd_ROR_Cameron Wor Highlands Scheme Minor (mini hydro) (NEB 2013) 36 Hyd_ROR_Kenerong Kenerong Upper Kenerong Lower

267

No OSEMOSYS Name Power Plant Name Ownership Input fuel Unit Year of availability 37 S_CCGT_Ranhill_I Teluk Salut/Ranhill Powertron IPP Gas 1(190.0 × 1) 1998-Oct 2029 38 S_CCGT_Ranhill_II Rugading/Ranhill Powertron II IPP Gas 1(199.0 × 1) 2010-Sept 2032 CCGT 39 S_CCGT_Separang Sepanggar Bay Power Corporation IPP Gas 1(105.0 × 1) 2006-May 2029 40 S_CCGT_Patau Patau-Patau SESB Gas 1(104.5 × 1) 1992 41 S_Diesel_ARL ARL Power IPP Diesel (MFO) 1(47.5 × 1) 1996-Oct 2016 42 S_Diesel_Melawa Melawa SESB Diesel 1(33.0 × 1) 1992- 43 S_Diesel_Tawau Tawau Power Station SESB Diesel 1(45.3 × 1) 1984 Batu Sapi: 31MW(1991) Labuk: 3.5MW (1997) 44 S_Diesel_SJSandakan SESB Diesel 88.5 Gantisan: 34MW (1996) Batu Sapi Rehab: 20MW (2013) 45 S_Diesel_SHarbour Sutera Harbour IPP Diesel 1(36.0 × 1) 1998-Dec 2013 DIESEL 46 S_Diesel_Lahad Lahad Datu SESB Diesel 1(16.1 × 1) 1997-Dec 2014 S Kudat: 2.5MW (1987/1996) a Kota Belud: 0.7MW (1994) 47 S_Diesel_KKKS b Kota Marudu: 0.7MW (1996) a Semporna: 5.1MW (1995/1996) SESB Diesel 9 end in Dec 2014 h 48 S_Diesel_Kubota Kubota SESB Diesel 1(64.0 × 1) 2013-Dec 2023 49 S_MFO_Serudong Serudong Power IPP MFO 1(36.0 × 1) 1996-Dec 2018 50 S_MFO_Stratavest Libaran/Stratavest IPP MFO 1(60.0 × 1) 1998-Dec 2019 51 S_MFO_Sandakan Sandakan Power Corporation IPP MFO 1(32.0 × 1) 1999-Dec 2011 HYDRO 52 S_Hyd_Tenom Tenom Pangi (ROR) SESB Hydro 1(66.0 × 1) 1984 ESAJADI Sg Kadamaian:2MW (2009) ESAJADI Sg Pangapuyan: 4.5MW (2011) ESAJADI- 53 S_MiniHydro Melangkap & Sayap: 1.5MW (1990/91) Hydro 10.2 end in Dec 2028 IPPs Merotai: 1.1MW RE Bombalai: 1.1MW TSH Bioenergy: 10MW (2004) Kina Biopower: 10MW (2004) 54 S_Biomass IPP Biomass 33 end in Dec 2028 Seguntor Bioenergy: 10MW (2004) Teck Guan: 3MW (2011)

No OSEMOSYS Name Power Plant Name Ownership Input fuel Unit Year of availability OCGT 55 Sw_OCGT_SWPower SARAWAK POWER GENERATION SEB Gas 1(317.0 × 1) Assume all PP end S 56 Sw_OCGT_Kidu TG. KIDURONG POWER STATION SEB Gas 1(192.0 × 1) in 2030 based on a 57 Sw_OCGT_Miri MIRI POWER STATION SEB Gas 1(79.0 × 1) RECODA pg 44 r DIESEL 58 Sw_MFO_TAR Tun Abdul Rahman Power Station SEB MFO/Diesel 1(114.0 × 1) ends in 2009 a COAL 59 Sw_Coal_Mukah Mukah Power Station SEB Gas 1(270.0 × 1) w 60 Sw_Coal_Sejingkat SEJINGKAT (TERMASUK PPLS) SEB Gas 1(210.0 × 1) a HYDRO 61 Sw_Hyd_Stor_Bakun Bakun HEP IPP Gas 1(2400.0 × 1) k 62 Sw_Hyd_ROR_AI Batang AI Power Station SEB Gas 1(104.0 × 1) Source: RECODA (2011), RECODA (2012), NEB (2013), EC (2014), EC (2014a) and MEIH (2017)

Table A. 51: List of future power plants in Malaysia (MW)

Type No OSEMOSYS Name Power Plant Name Unit 1 PM_new_CCGT New CCGT 1st June2018: (1000MW) 2021: (2,000MW) 2 CCGT_Connought_Bridge CBPS Redevelopment 1st Sept 2015: 384.7MW CCGT CCGT_Prai TNB Prai 1st Jan 2016: 1,071.43 MW 3 P Pengerang Co-Generation 1st June 2017: 400MW e 5 PM_NewCoal_SC TNB Janamanjung (Unit 4) Supercritical 31 March 2015: 1010MW M n PM_NewCoal_All Tg Bin Energy 1st March 2016: 1000MW a COAL i 6 Manjung IV 1st Oct 2017: 1000MW l n Jimah East Power U1: 15th Nov 2018 :1000MW U2: 15th May 2019: 1000MW a s 9 PM_NewHydroStor1 Hulu Terengganu (storage) 250MW U1:16th Sept & U2:17th Dec 2015 y u s 10 PM_NewHydroStor3 Ulu Jelai (storage) 372MW U1:13th Dec 15 & U2:14th Mar 16 l i 11 PM_NewHydroStor2 Tekai (storage) 156MW: Dec 2020 a a HYDRO r 12 PM_NewHydroStor2 Telom (storage) 132MW: Dec 2022 13 PM_NewHydroStor2 Lebir (storage) 274MW: Mar 2025 14 PM_NewHydro_ROR Hulu Terengganu (Tembat) (ROR) 15MW U1: 15th Nov 16 &U2: 15th Dec 16 15 PM_NewHydro_ROR Additional Chenderoh (ROR) 12MW: Oct 2018 16 Nuclear Power

268

Type No OSEMOSYS Name Power Plant Name Unit 1 SPR Energy (M) Sdn. Bhd 100MW (2014) Kimanis Power Sdn. Bhd. 285MW (2014) Sepanggar Bay Power Corporation Additional Capacity Extension5 MW(2015) Ranhill Powertron II Additional Capacity Extension 9MW (2017) CCGT & OCGT 2(60.0 × 2) (2017) S_New_Gas OCGT Eastern Sabah Power Consortium 300MW(2017) CCGT (60.0 × 2)GT + 60MW ST (2019) OCGT 2017 convert to CCGT additional of 60MW ST (2020) CCGT 50MW (2021) CCGT 50MW (2022) S 2 S.J. Kubota, Tawau (Relocation) 64MW (July 2013) a S.J. Batu Sapi (Rehabilitation) 20MW (March 2013) b Melawa GTM relocation 18MW(2015) a DIESEL S_New_Diesel (17.0 × 2)MW (2016) h (17.0 × 5)MW (2017) New Engine (17.0 × 5)MW (2018) IPP Serudong Extension 36MW(2015) 3 S.J. Tenom Pangi (Upgrade) 8MW (2015) S_NewHyd_Upadas Upper Padas HEP 180MW (2023) HYDRO Sarawak Import 100MW (2023) 4 SREP Afie Power 8.9MW (2015) SREP Cash Horse 10MW (2014) Renewable RE SREP Kalansa 5MW (2015) SREP Eco-Biomass 5MW (2014) 5 S_Geothermal_Tawau 30MW (2016)

Type No OSEMOSYS Name Power Plant Name Unit 1 Miri Lutong Gas 250MW () CCGT & Sw_New_Gas Samalaju Gas 700.0-900.0MW () OCGT Tanjung Kidurong Gas 350.0-450.0MW () 2 2018 start (assuming based on 4 years construction period IEA-ETSAP, RECODA Balingian 600MW () stated the construction COAL Sw_New_Coal start in 2014) S Mukah West 600MW () a Merit Pila 300MW () r 3 Murum HEP 944MW (2014) a Baleh HEP 1180MW() w Pelagus HEP 465MW() a Belaga HEP 220MW() k Linau HEP 182MW () Belepeh HEP 110MW() HYDRO Sw_New_Hydro Baram 1 HEP 1180MW() Baram 3 HEP 300MW() Limbang 1 HEP 45MW () Limbang 2 HEP 130MW () Trusan HEP 200MW() Lawas HEP 47MW () Kota 2 HEP 10MW() Source: RECODA (2011), RECODA (2012), NEB (2013), EC (2014), EC (2014a) and MEIH (2017)

The electrification rate in 2013 for Malaysia is at 96.86% (Peninsular Malaysia, 99.72%; Sabah, 92.94%; and Sarawak, 88.01%) and the country is planning to increase electricity access to rural areas, especially in Sabah and Sarawak in the future (EPU 2010).

269

Table A. 52: Key data and figures for power and end-use technologies

Supercritical coal-based power plants

Data projection 2010 2020 2030 Efficiency 46% 50% 50% Investment cost USD/kW 2200 2000 1800 Technical lifetime, year 40 40 40

Natural gas-based power plants (CCGT)

Data projection 2010 2020 2030 Efficiency 52% 64% 64% Investment cost USD/kW 1100 1000 900 Technical lifetime, year 30 30 30

Nuclear power plants (LWR/EPR)

Data projection 2020 2030 2050 Efficiency 40% 50% 60% Investment cost USD/kW 2500 2500 2000 Technical lifetime, year 60 60 60

Hydropower plants

Data projection 2010 2020 2030 Investment cost USD/kW 4000 3600 3600 Technical lifetime, year 100 100 100

Solar power plants

Data projection 2010 2020 2030 Investment cost USD/kW 4500 2400 2400 Technical lifetime, year 25 25 25

Geothermal power plants

Data projection 2010 2020 2030 Investment cost USD/kW 4000 3500 3100 Technical lifetime, year 20 20 20

Biomass power plants

Data projection 2010 2020 2030 Investment cost USD/kW 4500 3700 3300 Technical lifetime, year 20 20 20

270

Table A. 53: Life span and cost of end-use technologies

Life-span (years) Average Cost (USD) Domestic cooking hobs (gas) 19 303/unit Domestic cooking induction hobs 19 917/unit (electricity) Commercial cooking hobs (gas) 12 3338/unit Commercial cooking solid plate hobs 12 3282/unit (electricity) Air conditioners 14 498/kW Lighting (GLS) 1 5/unit Lighting (CFL) 2 10/unit Industrial Boilers 25 60000/unit Medium Cars (gasoline) 12 18833/unit Medium Hybrid Electric Cars 12 23433/unit Medium Hybrid Gas Cars 12 20562/unit Motorcycles 10 11780/unit Trucks 10 22966/unit Buses 15 33948/unit Source: IEA-ETSAP https://iea-etsap.org/index.php/energy-technology-data Table A. 54: Fossil-fuel prices (oil, gas and coal)

Prices 2016 2025 2030 2035 2040 Subsidised gas price to power sector (USD/mmbtu) 3.32 3.68 6.95 6.95 6.95 Subsidised gas price to non-power sector (USD/mmbtu) 3.41 4.14 7.41 7.41 7.41 Market gas price to power and non-power sector (USD/mmbtu) 5.80 9.40 9.70 10.00 10.20 Oil price to non-power sector (USD/barrel) 41.0 83.0 94.0 103.0 111.0 Coal price to power and non-power sectors (USD/tonne) 80.0 87.0 89.0 90.0 91.0 Source: IEA-ETSAP https://iea-etsap.org/index.php/energy-technology-data; EC (2019) Notes: mmBtu = million British thermal units

Table A. 55: Capacity credit for technologies

Technology Capacity credit (%) (Ultra)supercritical plants (U)SCPC 100 Combined-cycle gas turbine (CCGT) 100 Nuclear 100 Biomass 93 Photovoltaic Solar Power 20 Geothermal 95 Hydropower 100 Source: IEA-ETSAP https://iea-etsap.org/index.php/energy-technology-data; IRENA (2017)

Table A. 56: Discount Rate

Discount Rate 5%

271

Appendix B: Appendix to Chapter 5

B.1 Hybrid model systematic scenarios results

The following section shows the electricity production and installed capacity of three scenarios: Reference (REF) supply pathway, high supply pathway and low supply pathway.

Table B. 1: Reference (REF) supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of REF scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 267 615 1041 1510 Gas 234 278 148 178 526 Oil 20 0 0 0 0 Nuclear 0 0 0 0 0 Hydro 54 136 199 273 390 Solar 1 2 26 25 70 Biomass 2 5 11 13 13 Other renewables 0 3 11 17 13

Total installed capacity (GW) of REF scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 9 21 36 51 Gas 13 13 6 12 18 Oil 2 1 0 0 0 Nuclear 0 0 0 0 0 Hydro 4 6 7 9 13 Solar 0 0 5 5 15 Biomass 0 0 0 1 1 Other RE 0 0 0 1 1

Table B. 2: High supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of high scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 246 619 1057 1588 Gas 234 353 279 369 1158 Oil 20 0 0 0 0 Nuclear 0 0 0 50 384 Hydro 54 136 219 337 567 Solar 1 2 26 50 80 Biomass 2 6 41 44 14 Other renewables 0 3 11 17 14

Total installed capacity (GW) of high scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 10 21 36 51 Gas 13 15 11 13 47 Oil 2 1 0 0 0 Nuclear 0 0 0 2 13 Hydro 4 6 8 11 19 Solar 0 0 5 11 17 Biomass 0 0 2 2 1 Other RE 0 0 0 1 1

272

Table B. 3: Low supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of low scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 267 615 1041 1510 Gas 234 278 148 178 526 Oil 20 0 0 0 0 Nuclear 0 0 0 0 0 Hydro 54 136 199 273 390 Solar 1 2 26 25 70 Biomass 2 5 11 13 13 Other renewables 0 3 11 17 13

Total installed capacity (GW) of low scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 7.9 8.6 21.2 28.3 32.4 Gas 13.0 12.0 3.7 8.1 6.0 Oil 1.8 1.3 0.1 0.1 0.1 Nuclear 0.0 0.0 0.0 0.0 0.0 Hydro 3.6 5.5 7.0 7.7 8.0 Solar 0.2 0.3 5.5 5.3 0.0 Biomass 0.1 0.3 0.4 0.3 0.5 Other RE 0.0 0.1 0.4 0.4 0.2

B.2 Deep decarbonisation scenarios

The following section shows the electricity production and installed capacity of 20%, 40%, 60% and 80% decarbonisation scenarios under three umbrella scenarios: Reference (REF) supply pathway, high supply pathway and low supply pathway.

Table B. 4: 20% decarbonisation of REF supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 20% decarbonisation of REF supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 262 615 916 1162 Gas 234 282 125 213 498 Oil 20 0 0 0 0 Nuclear 0 0 0 0 384 Hydro 54 136 199 277 784 Solar 1 2 26 50 80 Biomass 2 5 35 51 28 Other renewables 0 3 11 17 14

Total installed capacity (GW) of 20% decarbonisation of REF supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 9 21 32 41 Gas 13 13 6 13 17 Oil 2 1 0 0 0 Nuclear 0 0 0 0 13 Hydro 4 6 7 9 26 Solar 0 0 5 11 17 Biomass 0 0 1 2 1 Other RE 0 0 0 1 1

273

Table B. 5: 40% decarbonisation of REF supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 40% decarbonisation of REF supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 263 559 562 89 Gas 234 281 174 366 1554 Oil 20 0 0 0 0 Nuclear 0 0 0 192 384 Hydro 54 136 199 357 784 Solar 1 2 26 60 80 Biomass 2 5 41 50 22 Other renewables 0 3 11 17 15

Total installed capacity (GW) of 40% decarbonisation of REF supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 12 19 20 21 Gas 13 13 7 15 51 Oil 2 1 0 0 0 Nuclear 0 0 0 6 13 Hydro 4 6 7 18 26 Solar 0 0 5 13 17 Biomass 0 0 2 2 1 Other RE 0 0 0 1 1

Table B. 6: 60% decarbonisation of REF supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 60% decarbonisation of REF supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 256 517 345 0 Gas 234 288 228 389 155 Oil 20 0 0 0 0 Nuclear 0 0 0 240 600 Hydro 54 136 199 555 1077 Solar 1 2 26 181 645 Biomass 2 5 11 52 274 Other renewables 0 3 18 34 249

Total installed capacity (GW) of 60% decarbonisation of REF supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 12 18 12 8 Gas 13 13 9 16 13 Oil 2 1 0 0 0 Nuclear 0 0 0 8 20 Hydro 4 6 7 19 26 Solar 0 0 5 38 137 Biomass 0 0 0 2 11 Other RE 0 0 1 1 10

274

Table B. 7: 80% decarbonisation of REF supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 80% decarbonisation of REF supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 183 374 0 0 Gas 234 361 364 569 0 Oil 20 0 0 0 0 Nuclear 0 0 0 240 592 Hydro 54 136 195 550 988 Solar 1 2 30 350 1213 Biomass 2 5 11 52 2041 Other renewables 0 3 18 34 1099

Total installed capacity (GW) of 80% decarbonisation of REF supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 11 13 7 4 Gas 13 15 14 23 17 Oil 2 1 0 0 0 Nuclear 0 0 0 8 20 Hydro 4 6 7 19 26 Solar 0 0 6 74 257 Biomass 0 0 0 2 81 Other RE 0 0 1 1 45

Table B. 8 :20% decarbonisation of high supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 20% decarbonisation of high supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 247 621 1009 778 Gas 234 350 308 366 2135 Oil 20 0 0 0 0 Nuclear 0 0 0 192 384 Hydro 54 136 219 449 784 Solar 1 2 26 63 80 Biomass 2 6 11 52 52 Other renewables 0 3 18 18 18

Total installed capacity (GW) of 20% decarbonisation of high supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 12 21 35 34 Gas 13 15 12 17 72 Oil 2 1 0 0 0 Nuclear 0 0 0 6 13 Hydro 4 6 8 22 26 Solar 0 0 5 13 17 Biomass 0 0 0 2 2 Other RE 0 0 1 1 1

275

Table B. 9: 40% decarbonisation of high supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 40% decarbonisation of high supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 247 622 573 48 Gas 234 350 297 662 1847 Oil 20 0 0 0 0 Nuclear 0 0 0 192 384 Hydro 54 136 219 722 784 Solar 1 2 26 181 645 Biomass 2 6 11 52 274 Other renewables 0 3 18 34 249

Total installed capacity (GW) of 40% decarbonisation of high supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 12 21 20 15 Gas 13 15 11 28 62 Oil 2 1 0 0 0 Nuclear 0 0 0 6 13 Hydro 4 6 8 24 26 Solar 0 0 5 38 137 Biomass 0 0 0 2 11 Other RE 0 0 1 1 10

Table B. 10: 60% decarbonisation of high supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 60% decarbonisation of high supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 246 557 100 88 Gas 234 351 353 920 0 Oil 20 0 0 0 0 Nuclear 0 0 0 240 600 Hydro 54 136 220 722 784 Solar 1 2 26 346 1305 Biomass 2 6 11 52 1283 Other renewables 0 3 18 34 1024

Total installed capacity (GW) of 60% decarbonisation of high supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 12 19 13 11 Gas 13 15 13 31 27 Oil 2 1 0 0 0 Nuclear 0 0 0 8 20 Hydro 4 6 8 24 26 Solar 0 0 5 73 277 Biomass 0 0 0 2 51 Other RE 0 0 1 1 42

276

Table B. 11: 80% decarbonisation of high supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 80% decarbonisation of high supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 214 449 0 3 Gas 234 382 371 517 7 Oil 20 0 0 0 0 Nuclear 0 0 0 240 370 Hydro 54 136 186 784 784 Solar 1 3 63 380 1305 Biomass 2 6 41 274 4566 Other renewables 0 3 18 249 2193

Total installed capacity (GW) of 80% decarbonisation of high supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 12 15 10 5 Gas 13 16 14 35 28 Oil 2 1 0 0 0 Nuclear 0 0 0 8 12 Hydro 4 5 7 26 26 Solar 0 1 13 81 277 Biomass 0 0 2 11 181 Other RE 0 0 1 10 90

Table B. 12: 20% decarbonisation of low supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 20% decarbonisation of low supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 259 598 635 617 Gas 234 263 48 78 0 Oil 20 0 0 0 0 Nuclear 0 0 0 21 346 Hydro 54 136 190 228 236 Solar 1 2 26 50 46 Biomass 2 5 41 40 22 Other renewables 0 3 9 16 13

Total installed capacity (GW) of 20% decarbonisation of low supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 9 21 22 21 Gas 13 12 4 8 5 Oil 2 1 0 0 0 Nuclear 0 0 0 1 12 Hydro 4 6 7 8 8 Solar 0 0 5 11 10 Biomass 0 0 2 2 1 Other RE 0 0 0 1 1

277

Table B. 13: 40% decarbonisation of low supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 40% decarbonisation of low supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 255 476 306 32 Gas 234 267 167 251 491 Oil 20 0 0 0 0 Nuclear 0 0 0 192 384 Hydro 54 136 190 345 465 Solar 1 2 26 50 80 Biomass 2 5 41 49 22 Other renewables 0 3 9 17 14

Total installed capacity (GW) of 40% decarbonisation of low supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 12 17 11 6 Gas 13 12 7 13 16 Oil 2 1 0 0 0 Nuclear 0 0 0 6 13 Hydro 4 6 7 13 16 Solar 0 0 5 11 17 Biomass 0 0 2 2 1 Other RE 0 0 0 1 1

Table B. 14: 60% decarbonisation of low supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 60% decarbonisation of low supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 200 366 66 0 Gas 234 322 274 366 0 Oil 20 0 0 0 0 Nuclear 0 0 0 192 384 Hydro 54 136 190 419 465 Solar 1 2 26 181 645 Biomass 2 5 41 52 22 Other renewables 0 3 9 17 14

Total installed capacity (GW) of 60% decarbonisation of low supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 11 13 7 4 Gas 13 14 11 13 9 Oil 2 1 0 0 0 Nuclear 0 0 0 6 13 Hydro 4 6 7 14 16 Solar 0 0 5 38 137 Biomass 0 0 2 2 1 Other RE 0 0 0 1 1

278

Table B. 15: 80% decarbonisation of low supply scenario-Electricity production (PJ) and Total installed capacity (GW)

Electricity production (PJ) of 80% decarbonisation of low supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 211 169 337 0 0 Gas 234 354 322 247 0 Oil 20 0 0 0 0 Nuclear 0 0 0 192 384 Hydro 54 136 191 419 1028 Solar 1 2 26 346 1117 Biomass 2 5 11 52 274 Other renewables 0 3 18 34 248

Total installed capacity (GW) of 80% decarbonisation of low supply scenario (2013-2050) 2013 2020 2030 2040 2050 Coal 8 10 12 6 4 Gas 13 15 12 15 10 Oil 2 1 0 0 0 Nuclear 0 0 0 6 13 Hydro 4 6 7 14 25 Solar 0 0 5 73 237 Biomass 0 0 0 2 11 Other RE 0 0 1 1 10

279

B.2 : Modelling time slices the power sector in OSeMOSYS

Based on the electricity load profile in Malaysia, the OSeMOSYS framework is developed with a timeslice resolution aggregated at two seasonal (northwest and southwest monsoons) and three daily levels (level one: 8 am to 1 pm; level two: 1 pm to 6pm; level three: 6pm to 8am). The number of timeslices are decided based on an analysis of the electricity load profile for the year 2013 as shown in Figure 3.11. The results on REF supply pathways, 20%, 40%, 60% and 80% decarbonisation of the REF scenario are presented below to show that solar technology that will not generate at night.

Figure B. 1: Electricity generation by time slices (2040-2050) (REF supply pathways)

Figure B. 2: Electricity generation by time slices (2040-2050) (20% decarbonisation of the REF scenario)

Figure B. 3: Electricity generation by time slices (2040-2050) (40% decarbonisation of the REF scenario)

280

Figure B. 4: Electricity generation by time slices (2040-2050) (60% decarbonisation of the REF scenario)

Figure B. 5: Electricity generation by time slices (2040-2050) (80% decarbonisation of the REF scenario)

Table B.16: Distribution of near-optimal results benchmarking REF scenario over the modelled period: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%)

Total CO2 of the system Cumulative electricity generation over (mton) modelled period (PJ) REF30%_NOL4 8267 56406 REF25%_NOL4 8509 53557 REF20%_NOL4 8768 52892 REF15%_NOL4 9081 47324 REF30%_NOL3 9325 51078 REF30%_NOL2 9435 52350 REF25%_NOL3 9552 50950 REF10%_NOL4 9577 47108 REF25%_NOL2 9612 52104 REF20%_NOL3 9794 50676 REF20%_NOL2 9833 51646 REF15%_NOL3 10049 50235 REF15%_NOL2 10074 51142 REF5%_NOL4 10205 47787 REF10%_NOL3 10323 49620 REF10%_NOL2 10333 50233 REF30%_NOL1 10574 51601 REF5%_NOL3 10629 46153 REF5%_NOL2 10629 46479 REF1%_NOL4 10794 47683 REF25%_NOL1 10798 51128 REF1%_NOL2 10942 46943 REF1%_NOL3 10942 47213 REF20%_NOL1 11038 50473 REF15%_NOL1 11295 49686 REF10%_NOL1 11588 47127 REF5%_NOL1 12019 47153 REF1%_NOL1 12722 46769

281

Figure B.6: Electricity generation pathways of REF near-optimal decarbonisation scenarios: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%)

Table B.17: Distribution of near-optimal results benchmarking REF scenario over the modelled period: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%)

Total CO2 of the system Cumulative electricity generation over (mton) modelled period (PJ) REF30%_NOL4 8267 41180 REF25%_NOL4 8509 41024 REF20%_NOL4 8768 41052 REF15%_NOL4 9081 41084 REF30%_NOL3 9325 41180 REF30%_NOL2 9435 41297 REF25%_NOL3 9552 41180 REF10%_NOL4 9577 41094 REF25%_NOL2 9612 41372 REF20%_NOL3 9794 41190 REF20%_NOL2 9833 41401 REF15%_NOL3 10049 41436 REF15%_NOL2 10074 41425 REF5%_NOL4 10205 41425 REF10%_NOL3 10323 42194 REF10%_NOL2 10333 41459 REF30%_NOL1 10574 40939 REF5%_NOL3 10629 42580 REF5%_NOL2 10629 41835 REF1%_NOL4 10794 42482 REF25%_NOL1 10798 40939 REF1%_NOL2 10942 42773 REF1%_NOL3 10942 42785 REF20%_NOL1 11038 41103 REF15%_NOL1 11295 41124 REF10%_NOL1 11588 41136 REF5%_NOL1 12019 41860 REF1%_NOL1 12722 42828

282

Figure B.7 : Electricity generation pathways of REF near-optimal decarbonisation scenarios: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%)

Table B.18: Distribution of near-optimal results benchmarking High scenario over the modelled period: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%)

Total CO2 of the system Cumulative electricity generation over (mton) modelled period (PJ) REF30%_NOL4 8267 75866 REF25%_NOL4 8509 72507 REF20%_NOL4 8768 69074 REF15%_NOL4 9081 65349 REF30%_NOL3 9325 63855 REF30%_NOL2 9435 69160 REF25%_NOL3 9552 63540 REF10%_NOL4 9577 65341 REF25%_NOL2 9612 68970 REF20%_NOL3 9794 63140 REF20%_NOL2 9833 68821 REF15%_NOL3 10049 62401 REF15%_NOL2 10074 68443 REF5%_NOL4 10205 64967 REF10%_NOL3 10323 61971 REF10%_NOL2 10333 67537 REF30%_NOL1 10574 68716 REF5%_NOL3 10629 60729 REF5%_NOL2 10629 66205 REF1%_NOL4 10794 65749 REF25%_NOL1 10798 68565 REF1%_NOL2 10942 63644 REF1%_NOL3 10942 59794 REF20%_NOL1 11038 68223 REF15%_NOL1 11295 67690 REF10%_NOL1 11588 66117 REF5%_NOL1 12019 63177 REF1%_NOL1 12722 60013

283

Figure B.8: Electricity generation pathways of High near-optimal decarbonisation scenarios:

minimise CO2 of system (slack values of 5%, 10%, 20% and 30%)

Table B.19: Distribution of near-optimal results benchmarking High scenario over the modelled period: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%)

Total CO2 of the system Cumulative electricity generation over (mton) modelled period (PJ) REF30%_NOL4 8267 55918 REF25%_NOL4 8509 55925 REF20%_NOL4 8768 55931 REF15%_NOL4 9081 56212 REF30%_NOL3 9325 56113 REF30%_NOL2 9435 54691 REF25%_NOL3 9552 56135 REF10%_NOL4 9577 56744 REF25%_NOL2 9612 54691 REF20%_NOL3 9794 56227 REF20%_NOL2 9833 54900 REF15%_NOL3 10049 56273 REF15%_NOL2 10074 55044 REF5%_NOL4 10205 57133 REF10%_NOL3 10323 56355 REF10%_NOL2 10333 55172 REF30%_NOL1 10574 54720 REF5%_NOL3 10629 56525 REF5%_NOL2 10629 55269 REF1%_NOL4 10794 57593 REF25%_NOL1 10798 55001 REF1%_NOL2 10942 55916 REF1%_NOL3 10942 57593 REF20%_NOL1 11038 55004 REF15%_NOL1 11295 55150 REF10%_NOL1 11588 55188 REF5%_NOL1 12019 55767 REF1%_NOL1 12722 56831

284

Figure B.9: Electricity generation pathways of High near-optimal decarbonisation scenarios: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%)

Table B.20: Distribution of near-optimal results benchmarking Low scenario over the modelled period: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%)

Total CO2 of the system Cumulative electricity generation over (mton) modelled period (PJ) Low30%_NOL4 6943 40711 Low25%_NOL4 7130 38289 Low20%_NOL4 7330 35772 Low30%_NOL3 7542 36086 Low30%_NOL2 7543 35788 Low15%_NOL4 7551 33902 Low25%_NOL2 7657 35269 Low25%_NOL3 7657 35269 Low20%_NOL2 7784 35001 Low20%_NOL3 7784 35001 Low10%_NOL4 7837 32768 Low15%_NOL2 7930 34610 Low15%_NOL3 7930 34610 Low10%_NOL2 8108 34328 Low10%_NOL3 8108 34328 Low5%_NOL4 8274 32298 Low5%_NOL2 8359 32416 Low5%_NOL3 8359 32416 Low30%_NOL1 8490 34444 Low25%_NOL1 8672 34276 Low1%_NOL4 8740 32224 Low1%_NOL2 8761 32229 Low1%_NOL3 8761 32229 Low20%_NOL1 8867 34125 Low15%_NOL1 9075 33983 Low10%_NOL1 9378 32252 Low5%_NOL1 9937 32216 Low1%_NOL1 10557 32163

285

Figure B.10: Electricity generation pathways of Low near-optimal decarbonisation scenarios: minimise CO2 of system (slack values of 5%, 10%, 20% and 30%)

Table B.21: Distribution of near-optimal results benchmarking Low scenario over the modelled period: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%)

Total CO2 of the system Cumulative electricity generation over (mton) modelled period (PJ) Low30%_NOL2 7748 31813 Low30%_NOL3 7821 31817 Low30%_NOL4 7821 31817 Low25%_NOL2 7839 31817 Low15%_NOL4 7847 31819 Low20%_NOL4 7864 31820 Low25%_NOL4 7878 31819 Low25%_NOL3 7885 31822 Low10%_NOL4 7917 31922 Low20%_NOL2 7939 31826 Low20%_NOL3 7939 31826 Low15%_NOL2 8049 31838 Low15%_NOL3 8049 31838 Low10%_NOL2 8193 31859 Low10%_NOL3 8193 31830 Low5%_NOL4 8302 32032 Low5%_NOL2 8399 31992 Low5%_NOL3 8399 31992 Low30%_NOL1 8590 31841 Low25%_NOL1 8752 31857 Low1%_NOL4 8780 32137 Low1%_NOL2 8787 32136 Low1%_NOL3 8787 32136 Low20%_NOL1 8941 31865 Low15%_NOL1 9136 31838 Low10%_NOL1 9403 32030 Low5%_NOL1 9968 32130 Low1%_NOL1 10562 32140

286

Figure B.11: Electricity generation pathways of Low near-optimal decarbonisation scenarios: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%)

Figure B.12: Electricity generation pathways of Low near-optimal decarbonisation scenarios: minimise coal in power sector (slack values of 5%, 10%, 20% and 30%)

287

Table B. 22: Total system cost and emission by near-optimal scenarios (benchmarking REF scenario: minimise CO2 emission of the system) over the modelled period

Near-Optimal Decarbonisation Scenarios (benchmarking REF scenario)

Objective Function: minimise CO2 emission of the system (5% slack value) Scenario REF NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 580 609 609 609 609

Total System CO2 of modelled period (Mton) 17185 15591 13991 14136 13850

Objective Function: minimise CO2 emission of the system (10% slack value) REF NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 580 638 638 638 638

Total System CO2 of modelled period (Mton) 17185 14857 13483 13613 12959

Objective Function: minimise CO2 emission of the system (20% slack value) REF NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 580 696 696 696 696

Total System CO2 of modelled period (Mton) 17185 14050 12831 12845 11562

Objective Function: minimise CO2 emission of the system (30% slack value) REF NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 580 754 754 754 754

Total System CO2 of modelled period (Mton) 17185 13508 12351 12176 10559

288

Table B. 23: Total system cost and emission by near-optimal scenarios (benchmarking REF scenario: minimise coal in power sector) over the modelled period

Near-Optimal Decarbonisation Scenarios (benchmarking REF scenario)

Objective Function: minimise coal in power sector (5% slack value) Scenario REF NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 580 609 609 609 609

Total System CO2 of modelled period (Mton) 17185 15850 14401 14536 14162

Objective Function: minimise coal in power sector (10% slack value) REF NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 580 638 638 638 638

Total System CO2 of modelled period (Mton) 17185 15259 14185 14150 13376

Objective Function: minimise coal in power sector (20% slack value) REF NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 580 696 696 696 696

Total System CO2 of modelled period (Mton) 17185 14851 13793 13477 12653

Objective Function: minimise coal in power sector (30% slack value) REF NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 580 754 754 754 754

Total System CO2 of modelled period (Mton) 17185 14474 13552 12914 12635

289

Table B.24: Total system cost and emission by near-optimal scenarios (benchmarking High scenario: minimise CO2 emission of the system) over the modelled period

Near-Optimal Decarbonisation Scenarios (benchmarking High scenario)

Objective Function: minimise CO2 emission of the system (5% slack value) Scenario High NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 805 846 846 846 846

Total System CO2 of modelled period (Mton) 20798 18919 17532 18242 16820

Objective Function: minimise CO2 emission of the system (10% slack value) High NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 805 886 886 886 886

Total System CO2 of modelled period (Mton) 20798 18383 17089 17613 15696

Objective Function: minimise CO2 emission of the system (20% slack value) High NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 805 966 966 966 966

Total System CO2 of modelled period (Mton) 20798 17582 16374 16554 13917

Objective Function: minimise CO2 emission of the system (30% slack value) High NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 805 1047 1047 1047 1047

Total System CO2 of modelled period (Mton) 20798 16945 15772 15632 12733

290

Table B.25: Total system cost and emission by near-optimal scenarios (benchmarking High scenario: minimise coal in power sector) over the modelled period

Near-Optimal Decarbonisation Scenarios (benchmarking High scenario)

Objective Function: minimise coal in power sector (5% slack value) Scenario High NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 805 846 846 846 846

Total System CO2 of modelled period (Mton) 20798 19319 18243 18500 17293

Objective Function: minimise coal in power sector (10% slack value) High NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 805 886 886 886 886

Total System CO2 of modelled period (Mton) 20798 19070 17972 17923 16222

Objective Function: minimise coal in power sector (20% slack value) High NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 805 966 966 966 966

Total System CO2 of modelled period (Mton) 20798 18541 17464 16962 15239

Objective Function: minimise coal in power sector (30% slack value) High NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 805 1047 1047 1047 1047

Total System CO2 of modelled period (Mton) 20798 18080 17102 16112 15201

291

Table B. 26: Total system cost and emission by near-optimal scenarios (benchmarking Low scenario: minimise CO2 emission of the system) over the modelled period

Near-Optimal Decarbonisation Scenarios (benchmarking Low scenario)

Objective Function: minimise CO2 emission of the system (5% slack value) Scenario Low NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 466 490 490 490 490

Total System CO2 of modelled period (Mton) 13142 9937 10329 10329 8274

Objective Function: minimise CO2 emission of the system (10% slack value) Low NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 466 513 513 513 513

Total System CO2 of modelled period (Mton) 13142 9378 8108 8108 7837

Objective Function: minimise CO2 emission of the system (20% slack value) Low NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 466 560 560 560 560

Total System CO2 of modelled period (Mton) 13142 8867 7784 7784 7330

Objective Function: minimise CO2 emission of the system (30% slack value) Low NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 466 606 606 606 606

Total System CO2 of modelled period (Mton) 13142 8490 7543 7542 6943

292

Table B.27: Total system cost and emission by near-optimal scenarios (benchmarking Low scenario: minimise coal in power sector) over the modelled period

Near-Optimal Decarbonisation Scenarios (benchmarking Low scenario)

Objective Function: minimise coal in power sector (5% slack value) Scenario Low NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 466 490 490 490 490

Total System CO2 of modelled period (Mton) 13142 9968 8399 8399 8302

Objective Function: minimise coal in power sector (10% slack value) Low NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 387 513 513 513 513

Total System CO2 of modelled period (Mton) 13142 9403 8193 8193 7917

Objective Function: minimise coal in power sector (20% slack value) Low NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 387 560 560 560 560

Total System CO2 of modelled period (Mton) 13142 8941 7939 7939 7864

Objective Function: minimise coal in power sector (30% slack value) Low NOL1 NOL2 NOL3 NOL4 Total System Cost of modelled period (USD billion) 387 606 606 606 606

Total System CO2 of modelled period (Mton) 13142 8590 7748 7821 7821

293