IN
DEGREE PROJECT ENERGY AND ENVIRONMENT, SECOND CYCLE, 30 CREDITS
STOCKHOLM SWEDEN 2020,
Techno-economic analysis of implementing energy-efficiency and alternative fuels in Indonesia using OSeMOSYS
KUSHAGRA GUPTA
KTH ROYAL INSTITUTE OF TECHNOLOGY
SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
TRITA TRITA-ITM-EX 2020:455
www.kth.se
Master of Science Thesis
Title Techno-economic analysis of implementing energy-
efficiency and alternative fuels in Indonesia using OSeMOSYS.
Title (på svenska) Teknokonomisk analys av implementering av energieffektivitet och alternativa bränslen i Indonesien med OSeMOSYS
Author Kushagra Gupta
Department Department of Energy Technology, School of Industrial Engineering and Management
Supervisor Dr. Francesco Gardumi
Examiner Prof. Viktoria Martin
TRITA TRITA-ITM-EX 2020:455
ABSTRACT
Indonesia’s energy demand has been growing rapidly driven by increasing population, urbanization, and rapid economic growth. With increasing energy demand, the emissions associated with the energy sector continue to increase. With the gradual increase in demand and dominant share of fossil fuels in the energy mix, implementing the energy efficiency measures is crucial for Indonesia to achieve its energy and climate goals. From the policy perspective, National Energy plan of Indonesia aims to achieve higher levels of energy efficiency to reduce the overall energy intensity. Indonesia also has commitments to reduce greenhouse gas emissions and achieve SDG targets.
This report reviews the current status of energy demand and energy efficiency in Indonesia and evaluates the potential of implementing energy efficiency measures and fuel switching options to achieve future low carbon energy future. Long term energy model of Indonesia is modelled using the open-source modelling tool OSeMOSYS. Different scenarios have been developed to investigate the outcome of implementing energy efficiency and fuel switching measures in the Residential, Commercial, and Transportation sectors. The results are presented in terms of reduction in total final energy use, greenhouse gas emissions, and local air pollution. Cost- Benefit analysis of the applied measures present their financial feasibility.
With the deployment of efficient appliances, up to 30% electricity savings can be achieved in the residential and commercial sector. Vehicle electrification can contribute towards reduction in annual energy use by 48% by the end of modelling period. Measures in the residential and commercial sector directly contribute towards emission reductions. Vehicle electrification does not show proportionate reduction in emissions compared to energy use reduction due to high carbon intensity of the electricity grid. However, significant reduction in local air pollutants can be achieved. Cost benefit analysis shows that deployment of efficient appliances is financially feasible with maximum 2 years of payback period. On the other hand, successful deployment of electric vehicles will require tangible support from government due to its high price premium compared to conventional vehicles. Energy efficiency measures and fuel switching also contribute substantially to achieving Sustainable Development Goal 7.3.
In conclusion, this study presents a set of technically and economically feasible energy system development options for Indonesia. From the modelling perspective, this study identifies ways to implement demand side management measures in the energy supply modelling system OSeMOSYS.
Key words: Energy System Model, OSeMOSYS, Open Source, Indonesia, Sustainable Development Goals, Energy Policies, Energy Efficiency, Fuel Switching, Emission.
Sammanfattning
Indonesiens energibehov har ökat snabbt drivet av ökande befolkning, urbanisering och snabb ekonomisk tillväxt. Med ökande energibehov fortsätter utsläppen i energisektorn att öka. Med den gradvisa ökningen i efterfrågan och den dominerande andelen fossila bränslen i energimixen är genomförandet av energieffektivitetsåtgärderna avgörande för att Indonesien ska uppnå sina energi- och klimatmål. Ur politiskt perspektiv syftar Indonesiens nationella energiplan till att uppnå högre nivåer av energieffektivitet för att minska den totala energiintensiteten. Indonesien har också åtaganden att minska utsläppen av växthusgaser och uppnå SDG-mål.
Denna rapport granskar den aktuella statusen för efterfrågan på energi och energieffektivitet i Indonesien och utvärderar potentialen för att genomföra energieffektivitetsåtgärder och alternativ för bränsleomkoppling för att uppnå framtida energiförbrukning med låg koldioxid.
Indonesiens långsiktiga energimodell modelleras med hjälp av open-source- modelleringsverktyget OSeMOSYS. Olika scenarier har utvecklats för att undersöka resultatet av genomförande av energieffektivitet och bränsleomkopplingsåtgärder inom bostads-, kommersiellt och transportsektorn. Resultaten presenteras i termer av minskning av den totala slutliga energiförbrukningen, växthusgasutsläpp och lokal luftföroreningar. Kostnads- nyttoanalys av de tillämpade åtgärderna utgör deras ekonomiska genomförbarhet.
Med användning av effektiva apparater kan upp till 30% elbesparing uppnås i bostads- och affärssektorn. Fordonselektrifiering kan bidra till minskning av den årliga energiförbrukningen med 48% i slutet av modelleringsperioden. Åtgärder inom bostads- och kommersiell sektor bidrar direkt till utsläppsminskningar. Fordonselektrifiering visar inte proportionell minskning av utsläpp jämfört med energiförbrukningen på grund av hög kolintensitet i elnätet. Emellertid kan en betydande minskning av lokala luftföroreningar uppnås. Kostnads för delningsanalys visar att distribution av effektiva apparater är ekonomiskt möjlig med maximalt 2 års återbetalningsperiod. Å andra sidan kommer framgångsrik distribution av elfordon att kräva konkret stöd från regeringen på grund av dess höga prispremie jämfört med konventionella fordon. Energi effektivitetsåtgärder och bränsleomkoppling bidrar också väsentligt till att uppnå mål för hållbar utveckling 7.3.
Sammanfattningsvis presenterar denna studie en uppsättning tekniska och ekonomiskt genomförbara energisystemutvecklingsalternativ för Indonesien. Från modellerings perspektivet identifierar denna studie sätt att implementera hanteringsåtgärder på efterfrågesidan i modelleringssystemet för energiförsörjning OSeMOSYS.
Key words: Energi System Modell, OSeMOSYS, Öppen källa, Indonesien, Hållbara Utvecklingsmål, Energi Politik, Energi Effektivitet, Bränsle Omkoppling, Utsläpp.
Acknowledgment
I would like to thank all the people who supported me for their guidance, support, and motivation during my masters thesis work. First, I would like to thank my supervisor at KTH- Division of Energy Systems Dr Francesco Gardumi for providing me with the opportunity to work on the thesis topic of my interest and for his guidance and support throughout the thesis work.
I would also like to thank the whole team of KTH-dES who have contributed to my learning experience in the field during my studies, which made me capable of doing this thesis.
I would like to thank my parents, sister, and my close friends for always motivating and supporting me during this time.
Contents
ABSTRACT ... 2
Contents ... 5
List of Figures ... 6
List of Tables ... 7
List of Abbreviations ... 8
1. Introduction ... 9
2. Model and Methods ... 12
2.1 OSeMOSYS and MoManI Interface ... 12
2.2 Model Structure ... 12
2.3 Demand Projections ... 14
2.4 Technology Data ... 17
2.5 Annual emissions ... 20
2.6 Cost-Benefit Analysis ... 20
2.7 SDG Indicator ... 21
3. Scenario Description ... 22
4. Results ... 25
4.1 Final Energy Use ... 25
4.2 Energy Supply, Electricity Mix and Fuel Share ... 28
4.3 Annual Emissions ... 30
4.4 Costs ... 33
4.5. Cost-Benefit Analysis ... 33
4.6. SDG Indicators ... 36
5. Discussion ... 38
6. Conclusion and Future work ... 40
References ... 42
Appendices ... 45
List of Figures
Figure 1: Final Energy Use by Sector, 2018 (MEMR 2019) ... 9
Figure 2: Reference Energy System - Indonesia ... 13
Figure 3: Appliance Energy Use Calculation ... 15
Figure 4: Vehicle Energy Use Calculation ... 16
Figure 5: BAU Electricity Use ... 25
Figure 6: BAU Cooking and Water Heating Energy Use ... 25
Figure 7: Intermediate Electricity Use ... 26
Figure 8: Intermediate Cooking and Water Heating Energy Use ... 26
Figure 9: Ambitious Electricity Use ... 26
Figure 10: Ambitious Cooking and Water Heating Energy Use ... 26
Figure 11: Electricity Usage Comparison ... 27
Figure 12: Cooking and Water Heating Energy Usage Comparison ... 27
Figure 13: BAU Transport Energy Use ... 27
Figure 14: Intermediate Transport Energy Use ... 27
Figure 15: Ambitious Transport Energy Use ... 27
Figure 16: Transport Energy Use comparison ... 27
Figure 17: Fuel Utilization - Three Scenarios ... 28
Figure 18: Energy Supply ... 29
Figure 19: Electricity Mix ... 29
Figure 20: Base Year Fuel Mix ... 30
Figure 21: BAU Fuel Mix ... 30
Figure 22: Intermediate Scenario Fuel Mix ... 30
Figure 23: Ambitious Scenario Fuel Mix ... 30
Figure 24: BAU CO2 Emissions ... 31
Figure 25: Electricity Generation – CO2 emission intensity ... 31
Figure 26: Emissions - Efficient Appliances ... 31
Figure 27: Emissions - Vehicle Alternate Fuel ... 31
Figure 28: Sensitivity Analysis Emissions... 32
Figure 29: CO Emissions ... 32
Figure 30: NOx Emissions ... 32
Figure 31: PM Emissions... 32
Figure 32: Investments – Efficient Appliances ... 33
Figure 33: Investments – Vehicle Alternate Fuel ... 33
Figure 34: Energy Systems' Costs ... 34
Figure 35: Sensitivity Analysis Electric Cars Payback Period ... 36
Figure 36: Energy Intensity ... 37
Figure 37: Energy Intensity Improvement Rate ... 37
List of Tables
Table 1: Demographics ... 14
Table 2: Energy Demand Forecast ... 14
Table 3:Appliances... 16
Table 4: Transport Stocks ... 17
Table 5: AC Technologies ... 18
Table 6: Refrigerator technologies ... 18
Table 7:Lighting options ... 18
Table 8: TV Technologies ... 19
Table 9: Stove efficiencies ... 19
Table 10: Vehicles' Data ... 19
Table 11: Fuel Pricing ... 21
Table 12: Cost Analysis Air Conditioners ... 34
Table 13: Cost Analysis Refrigerators ... 35
Table 14: Cost Analysis Electric Cars ... 35
Table 15: Cost Analysis Electric Motorcycles ... 36
List of Abbreviations
AC ……….. Air Conditioner
ASEAN ……….. Association of Southeast Asian Nations BAU ………... Business as usual
BEV ………... Battery Electric Vehicle CO ………... Carbon Monoxide
CO2 ………... Carbon Dioxide
CRT ………...
CWH ……….
EER ………...
Cathode Ray Tube
Cooking and Water Heating Energy Efficiency Ratio GDP ………... Gross Development Product GHG ……….. Green House Gas
ICV ……….... Internal Combustion Vehicle KM ………...
KTH ………...
KWH ……….
Kilometre
Kungliga Tekniska Hogskolan Kilo Watt Hours
LCD ...……… Liquid Crystal Display LED ...……… Light Emitting Diode LNG……… Liquified Natural Gas LPG……….... Liquified Petroleum Gas MT………... Million Tons
NOx……….... Nitrogen Oxides PJ………... Pita Joules PM………...
RES………
RT ………..
Particulate Matter
Reference Energy System Refrigerating Tons
SDG……….... Sustainable Development Goals TV………... Television
USD……….... United States Dollar
1. Introduction
Indonesia is the world’s largest island nation located in Southeast Asia. It is the world’s 4th most populous country with an estimated population of 265 Million inhabitants in 2018 (MEMR 2019). In terms of energy use, Indonesia is the largest energy consumer in the ASEAN (Association of Southeast Asian Nations) accounting for 36% of the final energy use.
Indonesia’s electricity consumption has been increasing rapidly over the past decades and is expected to continue increasing owing to driven by increasing population, rapid urbanization, and robust economic growth (Michael A. McNeil 2019)
In 2018, total primary energy production in Indonesia was 17233 Pita Joules (PJ), with 64% of the energy being exported mainly in the form of coal and LNG. Total final energy utilization was 7274 PJ with the highest share of oil followed by biofuels and waste at 41% and 32%
respectively. Electricity supply mix is heavily dominated by coal and natural gas at 58% and 22% respectively with a negligible share of renewables (NEC 2019). Sectoral demand in Indonesia is dominated by the transport sector followed by industries and residential sector as presented in Figure 1.
Figure 1: Final Energy Use by Sector, 2018 (MEMR 2019)
Indonesia is a resource-rich country, playing a significant role in the global energy economy.
It is the world’s largest coal exporter (IEA 2015). It also has large natural gas reserves and substantial oil fields. However, with high exports of oil coupled with rapidly increasing oil demand, Indonesia became the net importer of oil resources in 2006 (Gunningham 2012). With a gradual increase in energy demand and diminishing oil and natural gas resources, energy security has been becoming a concerning issue for the Indonesian government.
In 2018, 98% of Indonesian inhabitants had access to electricity while only 68% had access to clean cooking fuels (IEA 2019a). Although access to electricity is continually increasing, however, some remote rural locations still have lower access. The Papua and NTT region have 61% and 60% access to electricity, respectively. Besides, many households have unreliable power access with fewer hours of power availability during the day (Michael A.
McNeil 2019).
With increasing imports of oil resources, diminishing resources, and lack of adequate access to electricity Indonesia is facing issues regarding energy security, energy poverty. To cater to the increasing electricity demand, the government considers coal power generation to be the quickest and cheapest way to provide electricity access to the population of Indonesia
36%
4% 16%
42%
2%
Industry Residential Commercial Transport Others
(Gunningham 2012). On the other hand, Indonesia has committed to unconditional reductions of 29% in its Greenhouse Gas (GHG) emissions compared to business as usual scenario until 2030. However, with a growing share of fossil fuels in the electricity generation mix the emissions are expected to increase over the next decade.
Under these circumstances, the Indonesian government faces the energy trilemma concerning energy security, energy poverty and climate change mitigation. On one hand, the government needs to provide a reliable and adequate supply of energy. At the same time, there are added pressures from International bodies to take adequate measures to mitigate climate change. From a sustainable development point of view, Indonesia also has the responsibility to contribute to achieving Sustainable Development Goals (SDG). In this context, energy efficiency and alternative fuels with higher fuel economy will not only contribute to ensuring energy security and eradicating energy poverty but also play a significant role in achieving the SDG 7 targets.
SDG 7 has 3 main targets: 7.1. to ensure universal access to affordable, reliable, and modern energy services; 7.2. increase substantially the share of renewable energy in the global energy mix; 7.3. double the global rate of improvement in Energy Efficiency (UN 2015).
Energy efficiency in Indonesia is also closely linked to its National Energy Policy which aims to achieve the security of domestic energy supply. Under the National Energy Policy, Indonesia’s National Master Plan for Energy Conservation (RIKEN) sets a goal of decreasing energy intensity by 1% annually until 2025. To reach this goal, energy savings potentials have been identified as follows: industry (15-30%), commercial buildings (25%), and households (10-30%) (NEC 2017). From a global perspective, the final energy intensity improved by 1.3%
in 2018, the lowest improvement rate since 2010 (IEA 2019). Indonesia being a large contributor to the global energy demand, energy efficiency in Indonesia presents a huge potential to contribute to improving global energy intensity and play a vital role in achieving SDG 7.3 target.
The transport sector in Indonesia is the highest contributor to Indonesia’s final energy demand at 42% share in total final energy use in 2018. About 96% of energy usage comes from petroleum products. Energy demand in the transport sector is dominated by road fleet transport catering to more than 90% passenger and freight transport (NEC 2019). Demand in the transport sector has been increasing rapidly with a large share of the population moving to the cities thus increasing the consumption of oil resources. It possesses a great threat to the energy security of the country’s energy system. Transport sector is also a major contributor to national GHG emissions. Apart from the GHG emissions, Indonesia is also facing problems associated with air pollution. Indonesia has the world’s fourth-highest mortality rate associated with air pollution (Zhenying Shao 2020). Thus, fuel switching presents great potential to achieve energy security in the transport sector, reduce GHG emissions, and air pollution. With alternative fuels with higher fuel economy, energy demand can significantly decrease contributing further to the SDG 7.3 targets. From the policy’s perspective Indonesia’s National Energy Plan aims to achieve a 30% share of biodiesel and 20% share of bioethanol in the transportation fuel mix by 2025 (NEC 2017), however, there are no targets to achieve a significant share of electric vehicles in the sector.
Thesis Scope and Objective
The scope of this thesis is to build upon the already developed energy model of Indonesia using OSeMOSYS (KTH-dES 2020) to analyze the potential reduction of implementing energy efficiency and fuel switching measures on the Final Energy Use and GHG emissions from the Residential, Commercial and Transport sectors of the energy system. This report also aims to evaluate the impact of applied measures on the Sustainable Development Goal 7.3.
The objectives of the thesis are:
• Identify the current trends and potential market penetration of energy-efficient appliances and alternative fuel vehicles
• Model the newly selected technologies and analyze their impact on total final energy use and GHG emissions and perform a cost-benefit analysis.
• Define indicators for SDG 7.3 and evaluate the impact of applied measures on the SDG.
• Provide policy recommendations for implementing the applied measures.
2. Model and Methods
This project aims to investigate the impact of implementing energy efficiency and fuel switching measures in the Indonesian energy system. To achieve the desired objective, a Long- term energy model has been developed using Open Source Energy Modelling System (OSeMOSYS). Residential, commercial, and transportation Sectors are considered in the study.
For this study, the model for Electricity Supply and Fuel Extraction has been taken from already developed model at KTH division of Energy Systems (KTH-dES, 2020)
2.1 OSeMOSYS and MoManI Interface
Open Source Energy Modelling System (OSeMOSYS) is an open-source and freely available bottom-up modelling framework for the full-fledged system optimization model for long-run energy planning. OSeMOSYS model has been widely used in scientific studies, academic teaching, and capacity building for energy planners. It is the open-source tool featured by the Optimus community. It is a linear program, seeking the energy mix which meets the energy demands and minimizes the total system cost based on the defined constraints of the model (Francesco Gardumi. et al. 2018).
OSeMOSYS is designed to be easily operated, updated, and modified to suit the needs of any analysis. It requires a less significant learning curve and time commitment to build and operate compared to other energy modelling tools available. Being an open-source system, OSeMOSYS does not require any upfront financial investment, thus making it the optimal tool for conducting the study (Mark Howells et al. 2011). The Model Management Infrastructure (MoManI) is an open-source interface for OSeMOSYS.
2.2 Model Structure
The long-term energy system model has been built upon the already developed energy supply model to understand the energy demand reductions, feasibility, cost structures, investments, and GHG emission mitigation under different scenarios. With the focus on 2030 policies, particularly the SDGs and to analyse the impact of applied measures for an additional 5 years, the modelling period has been taken from 2018-2035. The discount rate for cost analysis is assumed at 8% based on the already developed model. It is based on the discount rate proposed by the participants of the modelling workshop held by KTH and UN ESCAP (United Nations Economic and Social Commission for Asia and the Pacific) in Jakarta in November 2019. The monetary unit used in this study is the United States Dollar (USD).
In the model, demand-side management of Residential (public, private households and residential complexes), Commercial (markets, hotels, restaurants, financial institutions, government agencies, schools, hospitals, etc), and Transport Sectors (passenger and freight transport in all economic sectors) has been considered. With the aim of a detailed analysis of the energy demand and limited time frame, Industry and Other end-use (Agriculture, Fishery) sectors are not considered in this study. Hence, it is assumed that the model for energy supply will not be affected by the Industry and Other Sectors.
To simplify the model and to understand and design the Indonesia energy system, a schematic representation of the energy system is drawn, called the Reference Energy System (RES). RES provides an overview of the overall model structure as shown in Figure 2.
RES has been broken down into five different levels. All the natural resources that are nationally available and extracted or are imported from another country are represented under
the Resources section. Extraction technologies are used to process fuels and transport them to the next level. In the Primary level, the processed fuels are ready to be fed into the power plants for electricity generation. In case of biofuel production, biomass residues are fed into the biofuel production unit in the Primary level. Electricity generated from the power plants is fed into the secondary level for transmission and distribution. From the secondary level, the electricity is distributed to the final level i.e. consumer demand. Electricity generated from decentralized power plants is directly fed into the distribution system to the final level.
Reference Energy System
Figure 2: Reference Energy System - Indonesia
In this study, technologies based on energy efficient appliances are also used in the secondary level that are assumed to supply electricity corresponding to the electricity savings from efficient appliances. This approach has been used fo the deployment of efficient technologies because the final energy demand can not be varied in the model, however total final electricity use varies with the deployment of efficient appliances.
In case of utilization of processed fuels to fulfil the demands of oil products for transportation and fuels for cooking, fuels are directly fed into the final level.
To implement demand side measures, final energy demand has been further broken down into energy end-use for residential, commercial and transportation sectors.
2.3 Demand Projections
To evaluate the impact of energy-efficient technologies on Final Energy Use and GHG emissions, demand projections are made for residential, commercial and transportation sectors.
Demand calculations and future projections are based on demographic data, market stock, sales growth and technology data. Demographic data for Indonesia is presented in Table 1. Overall electricity demand and projected growth for residential and commercial sector is evaluated based on historical data, calculated as average annual growth over the last 10 years. Growth in Residential Cooking and Water Heating (CWH) demand is assumed to be the same as population growth. Base year demand and projected growth rate is shown in Table 2. Total final heating demand for Residential CWH is calculated based on fuel consumption and efficiency of stoves as shown in Demand Calculation.
Table 1: Demographics
2018 Reference Growth Rate (%) Reference
Population 265,015,000 (MEMR 2019) 0.7 (NEC 2019)
Households 67,945,000 (MEMR 2019)
Household Size 3.9
Urban Population Share
55 % (BPS 2020)
GDP (Trillion USD) 1.042 (MEMR 2019) 5.6 (NEC 2019)
Table 2: Energy Demand Forecast
Unit 2018 Reference Growth
Rate (%)
Reference Residential
Electricity Demand
PJ 385 (MEMR 2019) 7.46 (MEMR
2019) Commercial
Electricity Demand
PJ 223 (MEMR 2019) 6.86 (MEMR
2019)
Residential CWH PJ 243 (MEMR 2019) 0.7 Author’s
assumption Liquified Petroleum
Gas
PJ 364 (MEMR 2019)
Biomass PJ 135 (MEMR 2019)
Kerosene PJ 18 (MEMR 2019)
Residential and commercial electricity demand has been further broken down into demand for electricity end-use. Residential end-use appliance electricity demand has been calculated using bottom-up modelling approach. Appliance stocks, projected sales and current technology in use are used to calculate end-use electricity demand. Unlike the residential sector, there is a lack of available data for energy end-use in the commercial sector. End-use electricity demand for commercial sector is evaluated based on their share in the total electricity usage.
Transport sector has been further broken down into different types of vehicles. Energy demand for transport sector is evaluated using bottom-up modelling approach considering the vehicle stocks and average yearly fuel consumption.
Demand Calculation
Residential and Commercial Sector
Electricity demand for the different appliances is calculated based on their average power input and yearly operating hours as described in
Figure 3 and presented in equation (1). In case of air conditioners, power imput is calculated using equation (2). Electricity Demand for the two sectors has been modelled as Specified Annual Demand to specify the demand profiles for different appliances. The same demand profile has been assumed for all appliances.
In the residential sector, a set of appliances called Other Appliances (Cooking equipment, Fans, Laundry, Water pumps, etc) has been included in the final residential demand. It has been done to have an overall residential electricity demand.
𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 (𝑃𝐽) −𝑆 ∗ 𝑃 ∗ 𝑂𝐻 ∗ 365 ∗ 10(−6) 277.778
(1)
where S is annual appliance stock, P is the power input (Kilo Watt), OH is daily operating hours.
In the case of air conditioners, power input is calculated using the equation (2). 𝑃 = (𝑇 ∗ 3.504)/( 𝐸𝐸𝑅
3.41214)
(2)
where T is the AC tonnage, EER is the energy efficiency ratio.
Final heating demand required for residential cooking and water heating is calculated using the annual fuel consumption and stove efficiency as presented in equation (3).
𝐴𝑛𝑛𝑢𝑎𝑙 ℎ𝑒𝑎𝑡𝑖𝑛𝑔 𝑑𝑒𝑚𝑎𝑛𝑑 (𝑃𝐽) =(𝐹𝐶 ∗ 𝑆𝐸)
(3)
where FC is the fuel consumption (PJ) and SE is stove efficiency.
Figure 3: Appliance Energy Use Calculation
Transport Sector
Energy demand for different modes of transport has been calculated using their annual vehicle stock. Average annual kilometres traveled and average fuel economy for different vehicle types have been assumed based on available data and total yearly energy utilization has been calculated (Figure 4). The calculation for annual energy use is presented in equation (4). In the energy model, transport sector demand has been modelled in terms of passenger kilometers and tonne kilometers under the Accumulated Annual Demand parameter. In this sector, demand profiles are not considered.
𝐴𝑛𝑛𝑢𝑎𝑙 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 (𝑃𝐽) = 𝑆 ∗ (𝐴𝐾𝑀
𝑉𝐹𝐸) ∗ 𝐸𝐷 ∗ 10−9
(4)
where S is annual vehicle stock, AKM is annual vehicle kilometres, VFE is vehicle fuel economy (Km/Litregasoline, eq), ED is gasoline fuel density (34.2 MJ/Litre).
Appliance Stock
Residential electricity demand is distributed into Air-Conditioners, Refrigerators, Lighting, Entertainment, Fan, Cooking (kettles, rice cookers), laundry. In the Commercial sector, Air- Conditioners, Refrigerators, Lighting dominate the total electricity usage (Michael A. McNeil 2019). In this study, the major electricity consuming equipment have been considered as specified in Table 3. The calculation for stock projection and sales potential for different appliances considered is presented in Appendix I.
Table 3:Appliances
Residential Sector Air-Conditioners, Refrigerators, Lighting, Televisions Commercial Sector Air-Conditioners, Refrigerators, Lighting
Residential Air-Conditioners
To estimate the stock of air conditioners, historical stock and sales data has been used. In 2015, the estimated stock of air conditioners was 13.5 Million (Dietram Oppelt et al. 2017). Sales data has been considered for the year 2015 to 2017 to calculate the average sales growth at 9.5% (Virginie Letschert 2020).
Domestic Refrigerators
Domestic refrigerators had an approximate stock of 33 Million units in 2015 and are expected to grow at the compounded annual growth rate of 9.4% (Dietram Oppelt et al. 2017). In this study, the stock growth has been taken at 9.4 % from 2015 until 2030. After 2030, when significant adoption rates are achieved stock growth has been assumed to be equal to the population growth rate at 0.7%.
Residential Lighting
Lighting stock calculation has been done using the contribution of lighting to final electricity use in Indonesia. Due to its stagnant demand, its share in final electricity use is expected to decrease from 21% in 2016 to 13.5% in 2026 (Ambarita 2018). The share is further projected
Figure 4: Vehicle Energy Use Calculation
until 2035. Based on the annual residential electricity usage, the stocks for lighting equipment is calculated.
Televisions
Televisions are considered the cheapest and most desirable appliance. Even in developing countries, it is common to have more than one television in households. It is estimated that each electrified household has at least one tv and it exceeds 100% at higher incomes (Michael A. McNeil 2010). Based on the above information, this study assumes a diffusion rate of 1.5 televisions for each urban household and 1 television per household for rural households.
Commercial Appliances
In this study, the stocks of commercial appliances have been estimated using previously conducted study on the share of commercial appliances in the total final electricity use in the sector. (Virginie Letschert 2020).
Transport Sector
Transport sector in Indonesia is dominated by passenger and freight road transport covering more than 90% energy demand of the sector (Marciano 2020). In this study, the four modes of transportation considered with their 2018 stocks and annual growth rate are presented in Table 4 (BPS 2020a).
Table 4: Transport Stocks
Mode of Transport 2018 Stocks 2035 Stocks Annual Growth Rate Passenger Cars 16,440,987 50,952,729 6.88%
Motorcycles 120,101,047 356,542,931 6.61%
Buses 2,538,182 3,225,701 1.42%
Trucks 7,778,544 19,896,468 5.68%
Market penetration
Market penetration for different appliances/vehicles is important to identify the potential for appliance replacement with an energy-efficient appliance. For each appliance, units retiring after the base year are calculated based on the base year stocks and appliance lifetime. The number of units retiring is then subtracted from the residual capacity of the appliance for subsequent years. The market potential is calculated using the annual stock requirements and residual capacity for the specified year. Every newly sold equipment is also retired after its lifetime.
The stock analysis considers the phase-in of new equipment driven by the sales development and the phase-out of old equipment considering standardized assumptions for the lifetime of the equipment. The market potential for different equipment is presented in Appendix I.
2.4 Technology Data
In the energy model, different sets of technologies have been chosen for appliances and vehicles based on their efficiency standards. The selection of baseline technologies is based on their current penetration levels in the Indonesian market. For future scenarios, technologies with higher energy efficiency which are available in Indonesia, or best practices being implemented across other nations have been considered. In case of air-conditioners and refirgerators, technologies with higher energy efficiency are named as moderate and high
efficiency equipments. Data for baseline and higher efficiency equipments will be combined in different ways in different scenarios as detailed in 3. Scenario Description.
In this section, the main parameters are presented to give an overview. Detailed information about all the parameters along with their sources is available in Appendix II.
Air-Conditioners
For this study, Split-Type air conditioners with 0.75 refrigeration tons (RT), equivalent to 9000 Btu/Hr have been considered due to its high market penetration, constituting more than 80%
of Indonesia’s market (Virginie Letschert 2020). Parameters for AC technologies are presented in Table 5.
Table 5: AC Technologies
Type Capacity
(RT)
Energy Efficiency Ratio (EER)
Cost per unit (USD)
Baseline AC 0.75 11.05 210
Moderate Efficiency AC 15.43 259
High Efficiency AC 20.79 304
Refrigerators
The average size of refrigerators used in developing countries lies between 180-250 Litres (U4E 2018). For this study, double door refrigerators with 210 Litres average capacity has been considered (Hakimul Batih 2016). Table 6 shows data for different refrigerator technologies.
Table 6: Refrigerator technologies Type Capacity (Litres) Power Input
(Watt)
Cost per unit (USD)
Baseline Refrigerator 210 125 265
Moderate Efficiency Refrigerator
97 295
High Efficiency Refrigerator
68 327
Lighting
Based on the survey conducted by ASEAN Shine in 2015, most lamps found in Indonesia are Compact Fluoroscent Light (CFL) lamps. The average wattage of lamps used is 14 W (Ambarita 2018). The two lighting options considered as shown in Table 7 are CFL and Light Emitting Diode (LED) lamps (GIB, 2020).
Table 7:Lighting options
Type Capacity (Lumens) Power Input (Watt) Cost per unit (USD)
CFL 800 14 2
LED 10 8
Television
In a survey conducted in Indonesia in 2013, a majority of televisions owned by respondents were Cathode Ray Tube (CRT) with an average size of 21 Inches (Muhammad Ery Wijaya
2013). In recent times, there has been a large scale transition from CRT to Liquid Crystal Display (LCD), LCD to Light Emitting Diode (LED) along with the projected increase in average screen size for the TV (Won Young Park et al. 2011). In this model, three types of TVs are considered (Table 8).
Table 8: TV Technologies
Type Screen Size (inch) Power Input (Watt) Cost per unit (USD)
CRT 21 86 125
LCD 32 97 300
LED 32 60 339
Residential Cooking and Water Heating (CWH)
In the base year, fnal energy use associated with Liquified Petroleum Gas (LPG), Biomass, and Kerosene has been considered as the cooking demand. For this sector. stove energy efficiency for the different stoves is considered (Francesco Fuso Nerini 2015) as shown in Table 9.
Table 9: Stove efficiencies Stoves Efficiency (%)
LPG 55
Biomass 25
Kerosene 55
Electric Stoves 100 Solar Heaters 100
Passenger and Freight Vehicle
Motorcycles are the most dominant mode of passenger transport in Indonesia followed by cars and buses. Railways and air transport contribute to less than 10% of the total transport energy hence not considered in this study. In freight transport, the share of road transport is more than 90% thus only trucks are considered (Marciano 2020). In the global scenario, electric vehicle is considered the most suitable replacement for fossil-fuel-based transport. For the implementation of alternate fuel vehicles, internal combustion vehicles (ICV) and battery electric vehicles (BEV) are considered. Due to limited data on the capacities of vehicles, average fuel economies of vehicles in Indonesia have been used to define the vehicle capacity.
Data for vehicles used is presented in Table 10.
Table 10: Vehicles' Data Transport
Mode
Type of
Vehicle
Fuel Used Fuel Economy (Km/L- gasoline eq.)
Cost per unit 2017 (USD)
Passenger Car ICV Gasoline 12.4 22,000
BEV Electricity 49.2 33,300
Motorcycle ICV Gasoline 30.9 1,500
BEV Electricity 115.2 2,276
Bus ICV Diesel 6.4 67,000
BEV Electricity 25.4 156,544
Trucks ICV Diesel 6 47,000
BEV Electricity 24 71,180
For electric vehicles with given prices of 2017 and 2035, a declining price trend has been considered due to the expected decrease price in vehicle parts, specifically batteries (Shigeru Kiteru 2017). The declining purchasing prices for electric vehicles is presented in Appendix II.
2.5 Annual emissions
Annual carbon dioxide (CO2) emissions are calculated using the emission activity ratio associated with the different fuels being used in the model for energy supply. Emission activity ratio of different fuels is presented in Appendix III.
With the aim of evaluating the emission reduction potential of implementing energy-efficient technologies and fuel switching options, carbon intensity (gram-CO2/kWh) of the electricity grid is assumed same for the three scenarios, i.e. same as carbon intensity for Business As Usual. This approach is followed to eliminate the impact of changing electricity generation mix on the emission reduction from energy-efficient technologies and fuel switching options.
Also, assumption of 80% reduction in CO2 emissions (excluding land use change) associated with biofuels compared to conventional fuels is made (SONI SISBUDI HARSONO et al., 2011).
Grid emission intensity and emission reduction by biofuels can depend on different variables.
Hence a sensitivity analysis is conducted to identify the potential impact of change in the two factors on the annual emissions. For this analysis, impact of varying the two parameters by - 20% to +20% for the two parameters on percentage change in CO2 emissions in the ambitious scenario compared to business as usual for the modelling period 2018-2035.
The annual emission of local air pollutants (Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Particulate Matters (PM)) is evaluated for the transport sector. Emissions are calculated by associating pollutant emissions per unit activity with the vehicles as per their current emission standards in Indonesia. Emission per unit activity of local air pollutants is presented in Appendix III.
2.6 Cost-Benefit Analysis
In this study, two different approaches have been considered to analyse the cost and benefits of the different energy savings measures being implemented.
Analysis from Governments’ perspective
With the rapid growth of energy demand in Indonesia, the Government needs to make additional investments in the energy sector to ensure an adequate supply of electricity and other fuels for its citizens. With increasing penetration of efficient technologies, the demand for electricity and fossil fuels will change. It will significantly impact the investments and operating costs in the energy sector. This section provides a comparison between changing system costs for different rates of efficient technology deployment
For the analysis, three output variables of OSeMOSYS are considered:
• Capital Investments – Provides the annual investments in energy supply infrastructure.
• Annual Fixed Operating Costs – Provides the annual operating costs for the power plants considered.
• Annual Variable Operating Costs – Provide the annual costs associated with the consumption of fuels.
Analysis from Consumers’ perspective
From a consumers’ perspective, it is important to understand the overall cost of implementing energy-saving measures. While there is an additional initial cost for premium products, on the other hand, there are savings in terms of reduced energy use over the lifetime of the product.
Thus, it is important to understand the lifetime cost to the customer for adopting energy efficiency measures. In this analysis, the overall lifetime cost of equipment has been estimated based on capital investments and fuel pricing. The outcome of this analysis is presented in terms of life-time savings and payback period. This analysis is important to understand the feasibility of implementing the measures from a consumers’ perspective.
For this analysis, fuel pricing considered as shown in Table 11. The fuel prices are assumed to be constant during the modelling period.
Table 11: Fuel Pricing
Pricing Reference Electricity Pricing (USD/kWh) 0.105 (GPP 2020) Gasoline Pricing (USD/Litre) 0.651 (GPP 2020)
2.7 SDG Indicator
SDG 7.3 targets to double the global rate of improvement in energy efficiency. Two indicators are defined to evaluate the contribution of energy-saving measures being applied in this study.
The two indicators considered are:
• Final Energy Use per unit GDP –Final energy used, evaluated in the results section is used to evaluate this indicator. The energy intensity is presented as pita joules of energy demand per billion dollars GDP.
• Annual energy intensity improvement – Using the energy demand per unit GDP, annual improvement in the energy intensity has been evaluated.
The two indicators described above provide a comparison of energy demand growth and intensity improvement for the three scenarios. These indicators depict the impact of energy- saving measures and portray how these measures can help achieve the SDG 7.3 target of global improvements in energy efficiency from a demand-side perspective.
3. Scenario Description
The main aim of modelling the energy system for any region is to present the policymakers with different pathways to achieve the desired outcomes in the long term. In this study, three scenarios have been developed, deploying energy system development options using different penetration rate to investigate the change in total final energy use, capital investments and annual emissions. The data presented in section 2. Model and Methods is fixed for all scenarios. The scenarios are outlined as follows:
Business as Usual Scenario
In this scenario, the current energy demands are projected into the future considering the current expectation of growth. In the different sectors, the following equipment is used to fulfil the sectoral demands.
Residential and Commercial Sector
Air Conditioners • Low efficiency Air Conditioners are used to fulfill the space cooling demands.
Refrigerators • Low efficiency Refrigerators are used to fulfill the refrigeration demands
Lighting • CFL Light bulbs are used to fulfill the lighting demand.
Televisions • CRT TVs are used to fulfill the demand for current stock, and the demand for new sales is fulfilled by LCD Televisions
Residential Cooking and Water Heating
• Kerosene and Biomass stoves have been reduced to zero by 2030.
LPG stoves are used to fulfill the required cooking demand.
Transport Sector
Passenger Cars • Gasoline Cars with Euro 4 emission standards are used to fulfill the demand for passenger cars.
Motorcycles • Gasoline motorcycles with Euro 3 emission standards are used to fulfill the demand for motorcycles.
Buses • Diesel Buses with Euro III standards are used to fulfill the demand for buses.
Trucks • Diesel Trucks with Euro III standards are used to fulfil the demand for buses.
Intermediate Deployment Scenario
In the intermediate scenario, moderate diffusion of energy-efficient technologies has been implemented from the year 2020. These measures are implemented to reduce total final energy use and annual emissions.
Residential and Commercial Sector
Air Conditioners • Moderate efficiency air conditioner deployment starting in 2020 and reaching 100% of the annual sales by 2030.
• High efficiency air conditioner deployment starting in 2031 and reaching 50% of the annual sales by 2035.
• All the retired new AC’s within the modelled period are replaced by high efficiency AC.
Refrigerators • Moderate efficiency refrigerators deployment starting in 2020 and reaching 100% of the annual sales by 2030.
• High efficiency refrigerators deployment starting in 2031 and reaching 50% of the annual sales by 2035.
• All the retired new refrigerators within the modelled period are replaced by high efficiency refrigerators.
Lighting • CFL light bulbs are replaced by LED Lamps reaching 100%
penetration by 2035.
Televisions • LCD Televisions are replaced by LED Televisions reaching 100%
penetration by 2035.
Residential Cooking and Water Heating
• Electric Stoves and Solar Powered heating fulfilling 15% each of the final demand for cooking and water heating.
Transport Sector
Passenger Cars • Electric vehicle penetration, reaching 60% of the market for passenger cars by 2035.
Motorcycles • Electric vehicle penetration, reaching 60% of the market for motorcycles by 2030 and gradually reaching 80% by 2035.
Buses • Electric bus penetration, reaching 60% of the market for the bus by 2030 and eventually reaching 100% by 2035.
Trucks • Electric truck penetration reaching 15% of the market share by 2035.
Public Transportation • 7.5% shift from passenger cars and motorcycles towards public bus transportation
Ambitious Deployment Scenario
In this scenario, a higher rate of energy efficiency measures has been deployed. Also, higher shares of high efficiency equipment have been considered. This scenario is being implemented to study the feasibility of rapid deployment of energy-saving measures and its impact on the Indonesia Energy System
Residential and Commercial Sector
Air Conditioners • Moderate efficiency air conditioner deployment starting in 2020 and reaching 100% of the annual sales by 2025.
• High efficiency air conditioner deployment starting in 2026 and reaching % of the annual sales by 2035.
• All the retired new AC’s within the modelled period are replaced by high efficiency AC.
Refrigerators • Moderate efficiency refrigerators deployment starting in 2020 and reaching 100% of the annual sales by 2025.
• High efficiency refrigerators deployment starting in 2026 and reaching 100% of the annual sales by 2035.
• All the retired new refrigerators within the modelled period are replaced by high efficiency refrigerators.
Lighting • CFL light bulbs are replaced by LED Lamps reaching 100%
penetration by 2030.
Televisions • LCD Televisions are replaced by LED Televisions reaching 100%
penetration by 2030.
Residential Cooking and Water Heating
• Electric Stoves and Solar Powered heating fulfilling 30% each of the final demand for cooking and water heating.
Transport Sector
Passenger Cars • Electric vehicle penetration, reaching 60% of the market for passenger cars by 2030 and gradually reaching 80% by 2035.
Motorcycles • Electric vehicle penetration, reaching 60% of the market for motorcycles by 2025 and gradually reaching 100% by 2035.
Buses • Electric bus penetration, reaching 60% of the market for the bus by 2025 and eventually reaching 100% by 2030.
Trucks • Electric truck penetration reaching 30% of the market share by 2035.
Public Transportation • 15% shift from passenger cars and motorcycles towards public bus transportation
As mandated by the Indonesia government, the following mix of biofuels has been considered in vehicular fuel for the Intermediate and Ambitious Scenario (NEC 2017).
Fuel Mix 2020 2025
Bioethanol in Gasoline 10% 20%
Biodiesel in Diesel 30% 30%
Deployment of Alternative Solutions
Two different approaches have been considered for the deployment of energy-saving measures in the model.
• For electricity saving options, the demand for electricity has been assumed to be the same across different scenarios. For electricity-savings calculations, technologies with electricity supply corresponding to the difference in final energy use between baseline and efficient equipment have been deployed to fulfill the final electricity demand. These technologies are presented as Energy-Efficient Appliances in the secondary level of the RES as shown in Figure 2.
• In residential cooking and water heating, direct replacement based on energy use is done for the efficient stove.
For fuel-switching options, in the transport sector, demand has been set in terms of passenger kilometres and tonne kilometres. Different technologies specified in 2.4 Technology Data are used to feed the transport sector demand and accordingly their fuel consumption is calculated.
The cumulative difference in fuel consumption per unit service provides a metric for decreased energy intensity of the transportation services.
4. Results
The results obtained by modelling the three scenarios in OSeMOSYS for the modelling period 2018 to 2035 are presented in this section.
4.1 Total Final Energy Use
Total final energy utilization results for the three scenarios, divided into residential, commercial, and transportation sectors are evaluated in this section.
Residential and Commercial Sector
Figure 5 shows demand projection in the business as usual scenario. The growth in electricity utilization is mainly due to population growth and economic development leading to higher appliance ownership. Demand increased from 608 PJ in 2018 to 1998 PJ in 2035. The electricity usage in Indonesia is dominated by air conditioners followed by Refrigerators.
Electricity use by AC accounted for 28% share in 2018 and reaching 42% by 2035. Residential cooking demand for Indonesia was 517 PJ in 2018 and reaches 499 PJ in 2035 as seen in Figure 6. While the growth in heating demand for residential cooking follows the population growth trend, diminishing use of inefficient cooking fuels biomass and kerosene reduces the total final energy use.
Figure 5: BAU Electricity Use Figure 6: BAU Cooking and Water Heating Energy Use
Figure 7 shows the electricity use for the intermediate scenario. It reaches 1564 PJ in 2035. With the deployment of energy efficiency measures, maximum savings are achieved in the AC followed by refrigerators and lighting. Over the modelling period, total cumulative electricity savings in the residential and commercial sector accounts for 1357 PJ and 881 PJ, respectively.
In the residential CWH, energy usage for 2035 decreased by 13.5% following the moderate deployment of electric stoves and solar water heaters as seen in Figure 8.
Figure 7: Intermediate Electricity Use Figure 8: Intermediate Cooking and Water Heating Energy Use
Figure 10 shows the electricity usage for the ambitious scenario. Total final energy use grows from 608 PJ to 1389 PJ by 2035. This scenario achieves net electricity savings of 609 PJ in 2035 compared to BAU. Following the similar trends as of intermediate scenario, AC and refrigerators show the maximum potential for electricity savings with energy-efficient equipment deployment. Over the modelling period, net electricity savings achieved in this scenario amounts to 2216 PJ in the residential sector and 1478 PJ in the commercial sector.
Residential cooking energy usage in this scenario reduced to 364 PJ in 2035 as compared to 499 PJ in the baseline scenario as shown in Figure 10.
Figure 9: Ambitious Electricity Use Figure 10: Ambitious Cooking and Water Heating Energy Use
Comparison of the results for the intermediate and ambitious scenarios to BAU in Figure 11 and
Figure 12 show a significant potential of energy savings in the residential and commercial sector; with a 22% annual reduction in the total final energy use in intermediate and 30%
reduction in the ambitious scenario for 2035. In the cooking sector, reduction in final energy usage of 13% and 27% can be achieved in the intermediate and ambitious scenarios, respectively for 2035.
Figure 11: Electricity Usage Comparison Figure 12: Cooking and Water Heating Energy Usage Comparison
Transport Sector
The transport sector has the highest share in the final energy use in Indonesia thus play a major role in the overall energy system development. Total final energy use for business as usual, intermediate, and ambitious scenarios are presented in Figure 13, Figure 14, and Figure 15
respectively. The overall comparison of energy usage under the three scenarios is shown in
Figure 16. Figure 17 represents the fuel share for the three scenarios.
In the business as usual scenario, the annual energy usage in the transport sector increases to 6354 PJ in 2035 compared to 2365 PJ in the base year. Motorcycles account for maximum energy use with a 39% share in the final energy demand followed by trucks and cars. In terms of fuels consumed, gasoline accounts for a 60% share in 2018 and reaching 68% share in 2035.
Figure 13: BAU Transport Energy Use Figure 14: Intermediate Transport Energy Use
Figure 15: Ambitious Transport Energy Use Figure 16: Transport Energy Use comparison
In the intermediate scenario with moderate deployment of electric vehicles and a shift towards public transportation, the average fuel economy increases. Total final energy use in 2035 decreases by 33.5% compared to BAU reaching 4221 PJ. The fuel mix in this scenario becomes more diverse with the introduction of electric vehicles and biofuels. In 2035, however, gasoline and diesel are still dominant, 16% of the final energy usage is associated with electricity and 21% with biofuels.
For the ambitious scenario, total final energy use in the transport sector decreases significantly compared to BAU reaching 3317 PJ in 2035. The fuel mix is dominated by diesel and electricity accounting for 31% and 27% of the final energy use followed by gasoline with a 23% share.
The share of biofuels is 19%.
With the deployment of electric vehicles having a significantly higher average fuel economy compared to conventional vehicles huge fall in the total final energy use can be achieved. Move towards public transportation also helps reduce energy demand. With the same requirement for passenger kilometres and tonne kilometres, total final energy use reduction of 33% and 48% in the intermediate and ambitious scenarios can be achieved compared to business as usual scenario.
Figure 17: Fuel Utilization - Three Scenarios
4.2 Energy Supply, Electricity Mix and Fuel Share
Energy Supply
The energy supply over the modelling period increased due to the increasing final energy use.
However, with the implementation of efficient appliances and electric vehicles growth in energy supply decreased significantly. Energy supply fell by 29% and 42% in the intermediate and ambitious scenarios, respectively as seen in Figure 18. In the ambitious scenario, the growth in energy supply starts to straighten by the end of the modelling period.
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Gasoline Bioethanol Diesel Biodiesel Electricity Gasoline Bioethanol Diesel Biodiesel Electricity Gasoline Bioethanol Diesel Biodiesel Electricity
Business-as-usual Intermediate Scenario Ambitious Scenario
PJ
2018 2025 2030 2035
Figure 18: Energy Supply Electricity Mix
Electricity supply mix in Indonesia has been extracted from the previously developed model considered in this study (KTH-dES, 2020). Being the cheapest and the most easily available resource, coal fired power plants dominate the electricity supply mix in Indonesia in the base year. Same trend continues for the modelling period, as government in Indonesia considers Coal being the cheapest and quickest solution for fulfilling the rapidly growing electricity demands. The share of renewable sources is significantly low, reaching 20% by the end of the modelling period due to no additional constraints applied in the energy supply model. The electricity generation mix for the business-as-usual model is presented in Figure 19.
Figure 19: Electricity Mix Fuel Share
Figure 20 with the base year fuel mix shows that gasoline and diesel dominate the final energy supply needs due to high energy needs in the transport sector. Electricity is being used to fulfill the residential and commercial sector demands, while LPG is being used to fulfill the residential cooking demand. In the business as usual scenario, the share of fuels in the final energy use follows the same trend over the modelling period except biomass and kerosene as shown in Figure 21.
0 2000 4000 6000 8000 10000
2018 2021 2024 2027 2030 2033
PJ
Business-as-usual Intermediate Scenario Ambitious Scenario
0 500 1000 1500 2000
2018 2021 2024 2027 2030 2033
Biomass Coal Geothermal Hydro
Natural Gas Oil Solar Wind
Figure 20: Base Year Fuel Mix Figure 21: BAU Fuel Mix
Figure 22 and Figure 23 represent the fuel share for the intermediate and ambitious scenarios. In the intermediate scenario, the share of electricity increases significantly with an increase in the number of electric vehicles and the share of gasoline decreases. With the introduction of biofuels in the vehicle fuel mix share of biofuels significantly increases and share of gasoline further falls in the fuel mix.
In the ambitious scenario, electricity dominates the fuel mix with a 47% share while the gasoline share falls to 15% as compared to 49% in the BAU Scenario.
Apart from reduction in total final energy use, 15% shift of passenger kilometres towards buses will reduce 7.6 million cars and 53.5 million motorcycles on the road by just adding 0.82 million buses.
Figure 22: Intermediate Scenario Fuel Mix Figure 23: Ambitious Scenario Fuel Mix
4.3 Annual Emissions
CO2 Emissions
Annual GHG emissions are a very crucial indicator to evaluate the climate change mitigation potential of implementing energy system development measures.
In this section, GHG emission for the intermediate and ambitious scenarios are compared with the business as usual scenario for two sets of energy system development options: Efficient appliances and cooking stoves, and vehicle electrification, public transportation shift and biofuel mix.
In the base year, the GHG emissions associated with the three sectors studied are 368 Million Tons(MT) of CO2. Electricity production for the residential and commercial sector and fuel use for transport sector equally contribute to the GHG emissions with 45% and 48% emissions, respectively. Under the business as usual scenario, the emissions increase by more than 140%
reaching 887 MT in the year 2035 while the share remains the same as presented in Figure 24.
2018
ElectricityGasoline Diesel LPG Biomass Kerosene Biofuels
2035
Electricity Gasoline Diesel LPG Biofuels
2035
Electricity Gasoline Diesel LPG Biofuels
2035
Electricity Gasoline Diesel LPG Biofuels
For the evaluation of emission reduction potential, carbon intensity (gm-CO2/kWh) of the electricity grid is assumed same for the three scenarios, i.e. same as carbon intensity for BAU.
Figure 25 presents the carbon intensity of the grid considered for emission calculation.
Figure 24: BAU CO2 Emissions Figure 25: Electricity Generation – CO2 emission intensity
With the implementation of efficiency measures in the residential and commercial sectors, the emissions have reduced substantially as shown in Figure 26. However, between the intermediate and ambitious scenarios, the emissions reduction is comparatively lower. Annual CO2
emissions reduced by 21% in the intermediate scenario and 29% in the ambitious scenario compared to BAU in 2035. Net CO2 savings for the modelling period reached a maximum of 626 MT for ambitious scenario
Figure 27 shows the emission reduction associated with the deployment of alternative fuel for vehicles. The main factors associated with change in annual emissions is associated with vehicle electrification and biofuel mix in transportation fuels. With the assumption of same grid CO2 intensity as BAU in all scenarios and emission activity ratio for biofuels noticeable reductions in annual emission can be achieved. Annual emission reduction of 29% and 36% in 2035 can be achieved for intermediate and ambitious scenarios respectively. Sudden break in the continuity is observed due to Indonesia government’s biofuel mandate for the year 2020 and 2025.
Figure 26: Emissions - Efficient Appliances Figure 27: Emissions - Vehicle Alternate Fuel
Impact of change in grid emission intensity and change in emission reductions for biofuels equally impact the net CO2 emissions for ambitious scenario. With the base assumptions, it is more likely to achieve improvement in grid emission intensity with higher share of clean electricity. However, as the base value of emission reduction due to biofuels compared to
