ALTERNATIVES FOR POWER GENERATION IN THE GREATER MEKONG SUBREGION
Volume 1: Power Sector Vision for the Greater Mekong Subregion
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5 April 2016
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Disclaimer
This report has been prepared by Intelligent Energy Systems Pty Ltd (IES) and Mekong Economics (MKE) in relation to provision of services to World Wide Fund for Nature (WWF). This report is supplied in good faith and reflects the knowledge, expertise and experience of IES and MKE. In conducting the research and analysis for this report IES and MKE have endeavoured to use what it considers is the best information available at the date of publication. IES and MKE make no representations or warranties as to the accuracy of the assumptions or estimates on which the forecasts and calculations are based. IES and MKE make no representation or warranty that any calculation, projection, assumption or estimate contained in this report should or will be achieved. The reliance that the Recipient places upon the calculations and projections in this report is a matter for the Recipient’s own commercial judgement and IES accepts no responsibility whatsoever for any loss occasioned by any person acting or refraining from action as a result of reliance on this report.
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Executive Summary
Introduction Intelligent Energy Systems Pty Ltd (“IES”) and Mekong Economics (“MKE”) have been retained by World Wild Fund for Nature Greater Mekong Programme Office (“WWF-GMPO”) to undertake a project called “Produce a comprehensive report outlining alternatives for power generation in the Greater Mekong Sub-region”. This is to develop scenarios for the countries of the Greater Mekong Sub-region (GMS) that are as consistent as possible with the WWF’s Global Energy Vision to the Power Sectors of all Greater Mekong Subregion countries. The objectives of WWF’s vision are: (i) contribute to reduction of global greenhouse emissions (cut by >80% of 1990 levels by 2050); (ii) reduce dependency on unsustainable hydro and nuclear; (iii) enhance energy access; (iv) take advantage of new technologies and solutions; (v) enhance power sector planning frameworks for the region: multi-stakeholder participatory process; and (vi) develop enhancements for energy policy frameworks. The purpose of this report is to provide a summary of the 5 detailed country-level descriptions of three scenarios for the Greater Mekong Subregion provided in the separate country reports, as well as an overview of regional implications of such a transition to a sustainable power sector. The three scenarios were
• Business as Usual (BAU) power generation development path which is based on current power planning practices, current policy objectives;
• Sustainable Energy Sector (SES) scenario, where measures are taken to maximally deploy renewable energy1 and energy efficiency measures to achieve a near-100% renewable energy power sector; and
• Advanced Sustainable Energy Sector (ASES) scenario, which assumes a more rapid advancement and deployment of new and renewable technologies as compared to the SES. The scenarios were based on public data, independent assessments of resource potentials, information obtained from published reports and power system modelling of the GMS region for the period 2015 to 2050.
Greater Mekong Subregion The Greater Mekong Subregion (GMS) is defined to be a set of countries located around the Mekong River basin in Southeast Asia. In 1992, the Asian Development Bank (ADB) defined the six states of King of Cambodia (“Cambodia”), Lao People’s Democratic Republic (“Lao PDR”), Union of the Republic of Myanmar (“Myanmar”),
1 Proposed but not committed fossil fuel based projects are not developed. Committed and existing fossil fuel based projects are retired at the end of their lifetime and not replaced with other fossil fuel projects. A least cost combination of renewable energy generation is developed to meet demand.
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Kingdom of Thailand (“Thailand”), Socialist Republic of Viet Nam (“Viet Nam”) and the Yunnan Province2 of the People’s Republic of China (PRC) as an economic zone. However, for the purpose of this project, we refer to the Greater Mekong Subregion (GMS) to consist of five countries: Cambodia, Lao PDR, Myanmar, Thailand and (5) Socialist Republic of Viet Nam (“Viet Nam”). The GMS countries are illustrated as Figure 1.
Figure 1 The GMS and its location within Asia
Mandalay VIETNAM Hanoi Luang Prabang MYANMAR
LAO PDR Chiang Rai Chiang Mai Vientiane
Vientiane Yangon
THAILAND
Bangkok Angkor CAMBODIA Siem Reap Phnom Penh Ho Chi Minh City
Greater Mekong Subregion Power Sectors Cambodia, Lao PDR, Myanmar, Thailand and Viet Nam, with a combined GDP of 662 US billion and population of 232 million in 2014 form one of the fastest growing regions in the world. Over the last decade the GMS region has experienced significant economic growth. This is evidenced in Figure 2 which shows historical
2 Note that often the GMS is sometimes also defined to include the Guangxi Zhuang Autonomous region – see Asian Development Bank (ADB), “Greater Mekong Subregion Economic Cooperation Program”, November 2014, available: http://www.adb.org/sites/default/files/publication/29387/gms-ecp-overview.pdf. However, the scope of this study was to consider Cambodia, Lao PDR, Myanmar, Thailand and Viet Nam and treatment of these five countries as a region.
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FINAL average Real GDP growth rates of the GMS countries compared to those of the world. The high growth rates are attributable to the countries within the GMS taking measures to transform their economies to be more open, diversified and market-oriented as compared to the past. This has enabled a steady flow of foreign investment. Efforts have also been taken to remove trade barriers in the GMS member countries and this has stimulated economic activity and enhanced the region’s overall ability to become integrated into the world economy.
Figure 2 Average Real GDP growth rates (2000-14) for GMS countries and the world
10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0% Cambodia Lao PDR Myanmar Thailand Viet Nam World
Economic growth has been accompanied with high levels of electricity growth. As illustrated in Table 1, the final electricity consumption and electricity peak demand have experienced very high growth rates in most of the GMS countries, a trend that the governments of the GMS countries expect to be sustained for at least the next 5 years to a decade.
Table 1 GMS Country Electricity Demand and Growth Rates (2014) Country Electricity Consumption Peak Demand TWh CAGR3, % MW CAGR4, % Cambodia 4.2 19.4% 687 16.0% Lao PDR 3.4 14.5% 748 12.5% Myanmar 9.6 15.7% 2,235 16.2% Thailand 168.2 4.4% 26,942 2.9% Viet Nam 142.3 12.7% 22,100 10.2% Source: Compiled by Consultant from various sources
3 The Compound Annual Growth Rate (CAGR) is for the last ten years for Cambodia, Lao PDR, and Viet Nam, last five years for Myanmar and twelve years for Thailand. 4 Last five years for Cambodia, Myanmar, and Thailand, ten years for Lao PDR and Viet Nam.
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Figure 3 shows the GMS breakdown of consumption by the sectors. Industry almost accounts for half of electricity use in the region at 47%, followed by the residential and commercial sectors at 29% and 23% respectively. The composition of sector consumption across the region has remained relatively stable with residential energy increasing 1% displacing the industrial sector between 2005 to 2014 as a result of increasing electrification rates and per capita consumption levels in the region. Figure 4 compares the countries’ sectoral composition of power consumption. It indicates that the industrial sector is the largest aggregate electricity consumer in Viet Nam (54%), Thailand (43%) and Myanmar (45%); whereas for Cambodia and Lao PDR, the residential sector accounts for the largest part on total consumption (47% and 35%). The proportion of commercial electricity consumption in Viet Nam at 10% is significantly lower compared to other countries. Table 2 provides information on installed capacity by fuel type for each GMS country and Figure 5 compares the capacity mix between the countries.
Figure 3 GMS Historical Energy Demand (TWh) by Sector: 2005-14
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Source: IEA (Demand includes transmission and distribution losses), 2014 based on IES estimates
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Figure 4 Electricity Consumption Breakdown by Sector (2014)
100% 90% 24% 32% 80% 35% 35% 47% 70% 60% 20% 10% 33% 50% 31% 40% 29% 30% 45% 54% 43% 20% 33% 10% 21% 0% 3% 1% 3% 1% 0.2% Cambodia Lao PDR Myanmar Viet Nam Thailand
Agriculture Industry Commercial Residenjal
Table 2 Installed Capacity (MW) by Fuel Type (2014)
Generation Type Cambodi Lao PDR Myanmar Thailand Viet Nam a Coal 268 - 120 6,538 10,405 Gas - - 1,325 21,888 6,825 Large Hydro 929 3058 3,011 3,444 13,050 Fuel Oil/Diesel 291 - 87 9 1,738 RE Sources 23 - 40 2,789* 1034 Solar - - - 464 - Wind - - - 209 52 Small Hydro - - 33 14 800* Biomass 23 - 5 1,851 180* Biogas - - 2* 251 2* Total (MW) 1,511 3,058 4,583 34,668 33,052 Source: Compiled by Consultant from various sources, * = estimated.
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Figure 5 Installed Capacity Mix by Fuel Technology (2014)
100% 3% 8% 5% 19% 10% 80% 39% 66% 60% 61% 61% 100% 40% 21%
20% 29% 31% 18% 21% 0% Cambodia Lao PDR Myanmar Thailand Vietnam
Coal Gas Hydro (Large Scale) Oil / Diesel Renewable
Hydro power is dominant in all systems except Thailand; Lao PDR’s installed capacity is entirely based on hydro power while in Myanmar, Cambodia and Viet Nam the shares are around 66%, 61% and 39% respectively. Power stations running on natural gas make up a significant share of installed capacity for Thailand at more than 60%; natural gas is also significant in Myanmar (29%) and Viet Nam (21%). Coal based generation is seen to be a significant part of Viet Nam’ and Thailand’s installed capacity mix accounting for 31% and 19% respectively. Shares of generating capacity for renewable energy sources (excluding large hydro) remain low across the GMS. Thailand is leading in developing renewable energy (RE) plants, having around 8% of the total installed capacity. In the other countries, the proportion of renewable capacity is 3%. Table 3 summarises the electrification rates overall and also for urban and rural areas. The table shows that Viet Nam and Thailand have very high electrification rates compared to the other countries in the GMS - the result of concentrated investments in transmission and distribution grids in the past to target high rates of electricity access. The others countries are lagging with Myanmar and Cambodia both have very low rural electrification rates. In this study, we explore two different ways of enhancing access to electricity: one is connection to a central grid, the other is deployment of mini and meso grids.
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Table 3 Electrification rates in GMS countries (2014) Population Urban Rural without Access Electrification Country Electrification Electrification to Electricity Rate5 (%) Rate (%) Rate (%) (millions) Cambodia 9.2 39% 90% 24% Lao PDR 0.8 89% 98% 83% Myanmar 38.1 26% 40% 20% Thailand 0.2 100% 100% 99% Viet Nam 1.9 98% 100% 97%
Power Development Plans in the GMS Countries Each of the power sectors is unique and each faces its own set of challenges. The key features of current power development plans for each country are summarised in Table 4. Table 4 Approach to Power Planning in each GMS Country
Country Features of Current Plans Renewable Energy Plan Energy Efficiency Plan Cambodia Most planned generation capacity Renewable Energy Action National Energy Efficiency in the near term6 is based on coal Plan in Place to promote Policy has target to and hydro projects with natural gas renewable energy but no reduce demand by 20% in development in the longer term. targets. 2035 vs. BAU demand. Lao PDR Most planned generation capacity Renewable Energy Energy efficiency is in an is based on hydro and one coal Development Strategy early stage in Lao PDR. project. Many planned hydro (2011) which promotes the Some efforts have been projects are geared towards export deployment of small hydro, taken in rural to neighbouring countries. solar, wind, biomass, electrification projects to biogas, solid waste and consider demand side geothermal. management measures. Myanmar MOEP’s publicly available plan Myanmar does not Apart from broad suggests hydro being dominant in currently have in place a directives to promote the generation mix, followed by comprehensive and energy efficiency and coal, gas and renewables. The targeted policy for conservation, Myanmar National Electrification Plan has a renewable energy. does not have a concrete target of 100% central grid policy framework for electrification by 2030. Power promoting energy development plans continue to efficiency. evolve in Myanmar with the optimal generation mix being strongly debated. Thailand PDP2015 suggests a technology Thailand’s Alternative Thailand’s energy capacity mix by 2036 consisting of Energy Development Plan efficiency development around 30-40% natural gas, 20% 2015 (AEDP2015) targets plan targets to reduce renewable energy, 20-25% coal, 15- some 19.6 GW of energy intensity by 25% 20% hydro, and up to 5 % nuclear renewables (waste, in 2030 compared to power. The total new required biomass, biogas, hydro, 2005 levels, or
5 Electrification rate is based on the proportion of population with access to electricity. 6 Next 10 years.
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Country Features of Current Plans Renewable Energy Plan Energy Efficiency Plan installed capacity from 2015 to wind, solar and energy equivalently, a 20% 2036 is some 57 GW. crops) by 20367. reduction against a BAU demand outlook. Viet Nam The most updated PDP7 (2016 New RE targets have been In 2006, the Prime version) plans129,500 MW of total included into the last Minister approved the EE installed capacity by 2030 updated PDP7. Renewable national target to save 5% (compared to 146,800 MW in the sources (small hydro, wind, - 8% total electricity original, 2011 version of PDP7). The solar and biomass) would consumption by 2015 capacity mix is expected to consist account for a 21% share in against a BAU outlook. of 42.6% coal, 16.9% hydropower, the capacity mix and a The EE target has not 14.7% natural gas, 21% RE, 3.6% 10.7% share in the been updated, but nuclear and 1.2% imports. generation mix by 2030 generally 8% - 10% savings have been expected by 2020.
Summary of Development Options Table 5 summarises key findings of a detailed review of development options relevant to renewable energy and fossil fuel for each of the GMS countries. This forms the basis of the assumptions that were used in the power system modelling conducted for each scenario. It should be noted that the renewable energy potential numbers were drawn from multiple sources and informed by analysis of IRENA Global Atlas data as well as our own analyses of potential.
7 The AEDP2015 is to promote usage of alternative energy replacing fossil fuel such as oil and natural gas and at the same time reducing Thailand’s dependency on energy imports.
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Table 5 Summary of Power Sector Development Options for each GMS Country (MW)
Comments on Development Potential Resource GMS Total Cambodia Lao PDR Myanmar Viet Nam Thailand Potential Large Hydro A total installed capacity of 24,105 More than 30,000 of 10,000 MW total, of 23,000 MW total, of 15,155 MW of MW (2014), 46,000 total of which which 13,833 developed which 929 developed which 3,058 which 5,541 MW potential for 3,011 developed (2014) (2014). Plans for further (2014) developed (2014) developed (2014). 124,155 MW in hydro development total Small Hydro 27,265 700 2,000 231 24,334 - Pump 18,807 - - - 8,000 10,807 Storage Solar PV Very Good Significant 8,812 Significant 119,863 Significant Solar CSP Moderate to Good Has potential Has potential Significant Significant in the South Moderate Wind At least 110,000 At least 500 27,104 26,962 26,673 30,000 Onshore MW Wind Significant Offshore (Thailand & Viet Has potential - Has potential Significant 7,000 Nam) Biomass 37,952 2,392 1,271 6,899 10,358 17,032 Biogas 14,757 1,591 1,146 4,741 5,771 1,507 Geothermal 859 - 59 400 400 - Ocean 13,950 - - 1,150 12,800 - Low coal reserves Approximately Domestic Over 2,500 million Approximately 900 Approximately 400 Significant, currently around Northern 1,200 million tons Coal tons million tons of coal million tons of coal producing 45 mt per year Cambodia of coal Imported Required under Possible Unlikely Possible Yes Yes Coal BAU generation
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Comments on Development Potential Resource GMS Total Cambodia Lao PDR Myanmar Viet Nam Thailand Potential development Estimated at 140 billion 283 Bcm, or estimated 617 Bcm – a number of Domestic cubic metres, not No confirmed Over 1,000 Bcm to be 10 trillion cubic offshore gas and oil fields 284 Bcm Natural Gas currently being reserves feet could be developed produced Potential at Son My, Binh Already exists, LNG / Currently imports from Possible but dependent Oil and gas is Thuan Province for 3.5 importing 11 Bcm Natural Gas Thailand, Viet Nam and on gas demand and imported mtpa expanding to 6 via LNG or pipelines Imports Singapore economics mtpa. from Myanmar Development in Yes as part of Nuclear Unlikely in the near Unlikely in the near Unlikely in the near Yes as part of power Viet Nam and power Power future future future development plan Thailand development plan Sources: Refer to Appendix F
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Power Sector Vision Scenarios The three development scenarios (BAU, SES and ASES) are conceptually illustrated in Figure 6.
Figure 6 GMS Power Sector Vision Scenarios
BAU Scenario
SES Scenario (Existing Technologies)
Advanced SES
2015-30 2030-50
The BAU scenario is characterised by electricity industry developments consistent with the current state of planning within the GMS countries and reflective of growth rates in electricity demand consistent with an IES view of base development, existing renewable energy targets, where relevant, aspirational targets for electrification rates, and energy efficiency gains that are largely consistent with the policies seen in the region. In contrast, the SES seeks to transition electricity demand towards the best practice benchmarks of other developed countries in terms of energy efficiency, maximise the renewable energy development, cease the development of fossil fuel resources, and make sustainable and prudent use of undeveloped conventional hydro resources. Where relevant, it leverages advances in off-grid technologies to provide access to electricity to remote communities. The SES takes advantage of existing, technically proven and commercially viable renewable energy technologies. Finally the ASES assumes that the power sector is able to more rapidly transition towards a 100% renewable energy technology mix under an assumption that renewable energy is deployed more than in the SES scenario with renewable energy technology costs declining more rapidly compared to BAU and SES scenarios.
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Business as Usual (BAU) Scenario The BAU demand forecasts were developed to grow in line with historical consumption trends and projected GDP growth rates in a way similar to what is often done in government plans. Electric vehicle uptake in region was assumed to reach 20% across all cars and motorcycles by 20508. Overall the GMS’s total on-grid electricity demand (including transmission and distribution losses9) was forecast to increase at a rate of 4.5% pa over the 35-year period to 2050 with the region going through a period of industrialisation and high GDP growth of 7% pa starting in 2015 and generally slowing across the region by 2035. The industrial sector is forecast to grow the fastest at 4.8 % followed by the commercial sector at 4.6%, residential sector at 3.3% and agriculture at 2.8% per annum as the GDP shifts towards commerce/services and industry with increases in residential per capita electricity consumption. The transport sector is forecast to hit 70 GWh by 2050 as the number of cars and uptake of electric cars and motorbikes increase to 20% uptake. GMS electricity demand is forecast to reach 1,685 TWh by 2050. This is illustrated in Figure 7.
Figure 7 GMS Projected Electricity Demand (2015-2050, BAU)
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0 2018 2028 2038 2048 2010 2012 2014 2016 2020 2022 2024 2026 2030 2032 2034 2036 2040 2042 2044 2046 2050
Agriculture Industry Commercial Residen_al Transport
The BAU installed capacity (MW) for GMS is charted in Figure 8 by installed capacity and Figure 9 shows the capacity shares for selected years. Installed capacity
8 The uptake rates were different for each country – please refer to the country reports for the details. 9 Note that unless otherwise stated, all other demand charts and statistics include transmission and distribution losses.
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FINAL increases from 77 GW in 2014 to 352 MW in 2050 with coal generation accounting for the largest share, or 29% of total installed capacity. Coal-fired capacity increases from 20 GW in 2015 with the recent commissioning of several coal plants to 104 GW in 2050, especially in Viet Nam. Large-scale hydro becomes the second most dominant generation type growing to 69 GW by 2050 driven by hydro resource exploitation along the Mekong River and tributaries. Renewable technologies, mainly solar PV and wind, grow to 29% of capacity while gas generation declines from 43% in 2015 to 18% by 2050. Nuclear also features in the capacity mix with 11 GW built in Viet Nam and Thailand. Figure 10 plots the BAU scenario generation mix10 over time and Figure 11 shows the corresponding percentage shares. Coal- fired generation in line with capacity increases to account for 46% of generation in the GMS with gas falling to 17% by 2050. The large-scale hydro generation share increases in the earlier years then maintains its share around 17% and renewable energy generation (excluding large-scale hydro) increases to 16% mainly driven by renewable developments in Thailand. Most of the renewable generation comes from solar PV and wind. Figure 12 shows the generation mix in each GMS country for the BAU for 2015, 2030 and 2050 with an indication of power flows across the various borders. Table 6 summarises the renewable generation share. The BAU assumes generation development consistent with the current state of planning within the GMS countries and is characterized by generation developments on a country by country basis leading to minimal flows (below 10,000 GWh) traded across borders. The current systems are largely dominated by large-hydro in Myanmar, Cambodia and Lao PDR and gas and coal in Thailand and Viet Nam. By 2050, other renewable technologies are developed to meet country-specific BAU renewable energy generation targets (between 10% and 20%) but is still largely dominated by growth in fossil fuel generation. Lao PDR remains largely dependent on large hydro whereas the Myanmar and Cambodia systems shift towards fossil fuels by 2050. Flow from Lao PDR to Thailand, and Viet Nam to Cambodia grow to 374 MW and 247 MW on average and by 2050, Myanmar and Lao PDR are exporting 822 MW and 655 MW into Thailand with flows into Cambodia from Viet Nam growing to 636 MW. Flows into Thailand and Cambodia displace some of the gas generation in those countries as most of the flows are driven by generation cost differences between the grids.
10 Unless otherwise stated, all generation charts and statistics in this report are presented on an “as generated” basis, meaning that generation to cover generator’s auxiliary consumption accounted for.
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Figure 8 GMS Installed Capacity (BAU, MW)
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Capacity MW 150,000
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Coal Hydro Gas Wind Diesel/FO Nuclear Bio Solar Hydro ROR
Figure 9 GMS Installed Capacity (BAU, %)
100% 8% 12% 90% 14% 14% 2% 80% 6% 43% 8% 70% 9% 58% 34% 27% 22% 60% 18%
50%
Capacity Mix 40% 31% 27% 22% 21% 20%
30% 25% 20% 29% 25% 28% 28% 10% 23% 14% 0% 2010 2015 2020 2030 2040 2050
Coal Hydro Gas Wind Diesel/FO Nuclear Bio Solar Hydro ROR
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Figure 10 GMS Generation Mix (BAU, GWh)
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Coal Hydro Gas Wind Diesel/FO Nuclear Bio Solar Hydro ROR
Figure 11 GMS Generation Mix (BAU, %)
100% 3% 5% 5% 5% 90% 4% 5% 4% 5% 80% 4% 47% 37% 70% 31% 24% 17% 61%
60% 16% 50% 17% 18% 24% 40% 27% Genera_on Mix
30% 18% 46% 42% 20% 38% 33% 25% 10% 19%
0% 2010 2015 2020 2030 2040 2050 Coal Hydro Gas Wind Diesel/FO Nuclear Bio Solar Hydro ROR
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Table 6 BAU Renewable Generation Shares by Country
Year Cambodia Lao PDR Myanmar Thailand Viet Nam 2015 87% 83% 61% 13% 40% 2020 53% 83% 65% 20% 33% 2030 52% 75% 57% 28% 25% 2040 47% 72% 47% 33% 26% 2050 44% 74% 41% 37% 24%
Sustainable Energy Sector (SES) Scenario Demand in the SES scenario assumes a transition towards energy efficiency benchmarks taken from the industrial sector of Hong Kong11 and of Singapore for the commercial sector by year 2050. For the residential sector, it was assumed that urban residential demand per electrified capita grows to approximately 60% of the level in the BAU. Demand-response measures were assumed to be phased in from 2021 with some 15% of demand being flexible12 by 2050. Central grid-electrification rates in Cambodia and Myanmar in the SES were slower but the scenario foresees the deployment of off-grid solutions that achieve nearly the same level of electricity access for those countries. The off-grid networks that are developed, before the central grids in Myanmar and Cambodia are built out, become interconnected to the national system over the longer-term. Electric vehicle uptake is the same as in the BAU. Figure 13 plots GMS’s forecast energy consumption from 2015 to 2050 with the BAU energy trajectory charted as a comparison. The significant savings are due to additional energy efficiency assumptions relating to the various sectors achieving energy intensity benchmarks of comparable developed countries in Asia as described above. The SES demand grows at a slower rate of 3.5% pa over the period to 2050 with the commercial sector growing at 3.5% pa, industry growing at 3.9% pa and the residential sector and agricultural sectors growing at 1.6% pa. The uptake of electric transport options occurs from 2025 onwards and grows to 70 TWh accounting for 6% of total demand by 2050, or 20% of all cars and motorbikes. The off-grid component is not visible as it accounts for a low percentage of total electricity demand.
11 Based on our analysis of comparators in Asia, Hong Kong had the lowest energy to GDP intensity for industrial sector while Singapore had the lowest for the commercial sector. Thailand, Myanmar, Lao PDR and Cambodia’s industry intensity was trended towards levels commensurate with Hong Kong. Viet Nam’s industrial intensity was trended towards Korea (2014) by 2035 and continues the trajectory to 2050. 12 Flexible demand is demand that can be rescheduled at short notice and would be implemented by a variety of smart grid and demand response technologies. Five percent is allocated to storage technologies and the other ten percent based on changes in demand consumption throughout the day.
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Figure 13 GMS Projected Electricity Demand (2015-2050, SES)
1,800 1,600 1,400 1,200 1,000 800 600
Energy (inc losses, TWh) 400 200 0 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050
Agriculture Industry Commercial Residenaal Transport Offgrid Demand
BAU The SES assumes no new coal and gas entry beyond what is understood to be committed already. A modest amount of large scale hydro, 4,700 MW in total, was deployed in Lao PDR and Myanmar above and beyond what is understood to be committed hydro developments in these countries13. Supply was developed based on a least cost combination of renewable generation sources limited by estimates of potential rates of deployment and judgments on when technologies would be available for implementation to deliver a power system with the same level of reliability as the BAU. Technologies used include: solar photovoltaics, biomass, biogas, CSP with storage, onshore and offshore wind, utility scale batteries, geothermal and ocean energy. Transmission limits between regions were upgraded as required to support power sector development in the GMS as an integrated whole, and the transmission plan allowed to be different compared to the transmission plan of the BAU. Figure 14 plots the installed capacity developments under the SES and Figure 15 the corresponding percentage shares. Committed and existing plants are assumed to come online as per the BAU but aren’t replaced when retired. Planned and proposed thermal and large-scale hydro developments are not built and all other generation requirements are instead met by renewable technologies14. Coal and gas fired-generation in the earlier years is very similar to the BAU due to committed
13 This is important to all countries because the GMS is modelled as an interconnected region. 14 Myanmar and Lao PDR has an additional 4,700 MW of large-scale hydro to support renewable developments.
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FINAL projects. Over time, coal, gas and large hydro capacity shares drop to 3%, 4% and 8% respectively by 2050 from a combined 97% share in 2015. Demand in the SES is predominantly met by renewables with 375 GW required to meet 2050 electricity demand dominated by investment in solar PV (159 GW) supported by 62 GW discharge equivalent of battery storage, onshore wind (62 GW), CSP (32 GW) and biomass (26 GW). Smaller amounts of hydro run of river, ocean energy, and geothermal are also developed in the SES. By 2050, there is 444 GW of installed capacity which includes 1 GW of off-grid technologies which is integrated back into the grid as the central grids are built out. The projected generation mix of the GMS is shown in Figure 16 and Figure 17. In the earlier years to 2020 the generation mix is similar to the BAU case as committed new entry is commissioned. Coal, gas and large-scale hydro generation increase from 353 TWh in 2015 to 468 TWh in 2030 before declining to 303 TWh as coal and gas units are retired and not replaced over time. The generation share of these conventional technologies decrease from 99% in 2015 to 25% in 2050, or 14% if large hydro is not included. Timing of renewable energy developments is based on the maturity of the technologies and judgments of when it could be readily deployed. Solar PV backed up by battery storage (to provide off-peak generation from solar PV) generates 287 TWh by 2050 followed by bioenergy generation (mainly biomass) of 234 TWh with wind and CSP contributing 172 TWh and 153 TWh respectively. Figure 18 shows the evolution of the SES which assumes greater deployment of renewable technologies and higher energy efficiency measures relative to the BAU. Table 7 summarises the renewable generation share. The SES has the GMS shifting away from fossil fuels and by 2030 57% the generation mix is non-fossil fuel based growing to 86% in 2050. Generation resources are optimised across the region with significant renewable generation developed in Myanmar and Lao PDR over and above their demand requirements to support the regional shift away from fossil fuels. By 2050, solar PV and CSP are generating 36% of the region’s electricity followed by biomass at 19% and wind at 14%. The SES has much greater flows going between each of the GMS countries given optimised generation and transmission developments at the regional level with significant amounts of power (above 20 TWh) exported into Thailand and Viet Nam from Myanmar and Lao PDR respectively. Myanmar is a major exporter in the SES with flows going into Thailand increasing to 3,000 MW and 5,300 MW in 2030 and 2050 respectively. Thailand also imports power from Lao PDR and exports a portion of it into Cambodia. There are significant net flows from Lao PDR to Viet Nam with 7,400 MW on average by 2050.
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Figure 14 GMS Installed Capacity by Type (SES, MW)
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Figure 15 GMS Installed Capacity by Type (SES, %)
100% 3% 8% 13% 90% 14% 6% 80% 5% 29% 7% 43% 70% 58% 34% 30% 60% 7% 36% 50% 14% 8%
Capacity Mix 40% 31% 13% 26% 7% 16% 30% 25% 17% 20% 18% 7%
11% 4% 10% 23% 22% 14% 12% 8% 6% 3% 0% 2010 2015 2020 2030 2040 2050
Offgrid Coal Hydro Gas Wind Diesel/FO Bio Solar CSP Hydro ROR Baeery Geothermal Ocean Pump Storage
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Figure 16 GMS Generation Mix (SES, GWh)
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Bio Solar CSP Hydro ROR Geothermal Ocean
Figure 17 GMS Generation Mix (SES, %)
100% 6% 3% 4% 90% 9% 3% 14% 13% 80% 19% 47% 12% 70% 32% 24% 61% 8% 60% 19%
50% 17% 19% 26% 12% 40% 27% Generaaon Mix 18% 8% 14% 30% 18% 13% 6% 20% 25% 29% 25% 11% 10% 19% 16% 8% 0% 2010 2015 2020 2030 2040 2050 Offgrid Coal Hydro Gas Wind Diesel/FO Bio Solar CSP Hydro ROR Geothermal Ocean
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Figure 18 GMS Power Sector Development under the SES Scenario
2015 SES (2030) SES (2050)
Resource Flows Coal, Diesel, Fuel Oil, Nuclear Below 10,000 GWh Gas 10,001 - 20,000 GWh Large Hydro Above 20,000 GWh Wind Solar, Battery, CSP Biomass and Biogas Other Renewables
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Table 7 SES Renewable Generation Shares by Country
Year Cambodia Lao PDR Myanmar Thailand Viet Nam 2015 87% 83% 61% 13% 40% 2020 68% 92% 78% 28% 39% 2030 63% 91% 92% 51% 52% 2040 78% 95% 98% 75% 68% 2050 87% 98% 100% 84% 81%
Advanced Sustainable Energy Sector (ASES) Scenario The ASES demand assumptions were implemented as a sensitivity analysis to the SES demand with the key differences as follows: an additional 10% energy efficiency applied to the SES demands (excluding transport), flexible demand assumed to reach 25%15 by 2050 and uptake of electric vehicles doubled compared to BAU and SES scenarios by 2050. Figure 19 shows the electricity consumption forecast for the GMS from 2015 to 2050 with the BAU and SES energy trajectory charted using dashed lines for comparison. The SES energy savings against the BAU are due to allowing GMS’s energy demand to transition towards energy intensity benchmarks of comparable developed countries in Asia. The ASES demand grows at a slower rate of 3.4% pa over the period from 2015 to 2050 with the commercial sector at 3.3% pa, industry growing at 3.7% pa and residential sector growing at 1.5% pa. Demand from the transport sector in the ASES is doubled and grows to 140 TWh, 12% of total demand by 2050. Total electricity demand increases to 1,156 TWh by 2050. Off-grid demand grows to almost 7 TWh as off-grid technologies are deployed in place of building out the central grids in Myanmar and Cambodia.
15 Of this 25%, 7.5% is assumed to be enabled through storage technology and the remaining portion based on demand responses to peak system conditions.
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Figure 19 GMS Projected Electricity Demand (2015-2050, ASES)
1,800
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600 Energy (inc losses, TWh) 400
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0 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050
Agriculture Industry Commercial Residen_al Transport Offgrid Demand BAU SES ASES supply assumptions were also implemented as a sensitivity to those in the SES, with the following being the key differences: (1) allow rates of renewable energy deployment to be more rapid compared to the BAU and SES, (2) technology cost reductions were accelerated for renewable energy technologies, (3) implement a more rapid programme of retirements for fossil fuel based power stations, and (4) electricity policy targets of 70% renewable generation by 2030, 90% by 2040 and 100% by 2050 for the GMS put in place. It was assumed that technical / operational issues with power system operation and control for a very high level of renewable energy are addressed16. Figure 20 shows the projected installed capacity mix for the ASES and the corresponding percentage shares. The ASES has coal plant retiring earlier than in the SES under a 100% renewable generation target across the region. Total installed capacity increases to 530 GW which is considerably higher than the installed capacity in the SES (444 GW) due to the retirement of coal and gas units and replacement with lower capacity factor technologies. Solar PV accounts for 36% of total installed capacity, or 190 GW, supported by 108 GW equivalent of battery
16 In particular: (1) sufficient real-time monitoring for both supply and demand side of the industry, (2) appropriate forecasting for solar and wind and centralised real-time control systems in place to manage a more distributed supply side, storages and flexible demand resources, and (3) power systems designed to be able to manage voltage, frequency and stability issues that may arise from having a power system that is dominated by asynchronous technologies.
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FINAL storage for generation deferral. Onshore wind accounts for 79 GW with 15 GW of offshore wind developed in Viet Nam and Myanmar. Biomass and CSP contributes 35 GW each. The ASES has 6 GW of biogas and allows for up to 4 GW of ocean/marine energy technologies as part of diversifying the renewable energy mix. Off-grid technologies are also deployed in Myanmar and Cambodia with 5 GW of installed solar PV and battery storage. ASES grid generation is plotted in Figure 22. The GMS generation mix in the earlier years to 2020 is similar to the BAU case as committed new generation projects are commissioned and this has largely been kept the same. Of the renewable technologies, by 2050, solar PV combined with battery storage contributes the highest generation share of 343 TWh or 29%, significantly higher than onshore wind and biomass generation with a share of 16% and 17% respectively. As gas plants are retired in Thailand (and not replaced) from 2020 and coal units across the region are retired starting from 2030, bioenergy, CSP and solar PV with battery technologies fill the baseload role in the power system. By 2030 more than 70% of the generation is from renewables (including large-scale hydro), and by 2040 this share increases past 90% reaching 100% by 2050. Figure 24 shows the generation development in the ASES which has in place a 90% and 100% renewable generation target by 2040 and 2050 respectively with higher energy efficiency measures than the SES. Table 8 summarises the renewable generation share. The ASES follows a similar path as the SES with retirement of all fossil fuel power plants to meet the 100% renewable generation target. Significant amounts of solar PV and CSP are developed over this period accounting for 43% of total generation in the region by 2050. Wind and bio generation also play a significant role accounting for 20% of the generation mix each. Myanmar is a major exporter in the ASES with flows going into Thailand doubling from 3,700 MW to 7,500 MW from 2030 to 2050 as Myanmar’s renewable resources are developed to support the region’s 100% renewable generation target. Thailand also imports a significant amount of power from Lao PDR as it retires all of its gas and coal-fired generators, which provided a lot of the base load power in the BAU and SES. The other major importer is Viet Nam with almost 8,000 MW of power flowing into the north from Lao PDR; Viet Nam’s significant demand growth relative to its renewable resources available requires it to import up to 15% of its power needs by 2050.
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Figure 20 GMS Installed Capacity by Type (ASES, MW)
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100,000
0 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 Offgrid Coal Hydro Gas Wind Bio Solar CSP Baeery Hydro ROR Geothermal Ocean Pump Storage
Figure 21 GMS Installed Capacity by Type (ASES, %)
100% 3% 17% 90% 17% 20% 80% 3% 43% 6% 35% 5% 70% 58% 7%
60% 23% 8% 36% 50% 36% 15% 40% 31% Capacity Mix 28% 8% 30% 6% 25% 8% 20% 16% 18% 18% 10% 23% 21% 14% 9% 11% 7% 0% 2010 2015 2020 2030 2040 2050 Offgrid Coal Hydro Gas Wind Bio Solar CSP Hydro ROR Baeery Pump Storage Geothermal Ocean
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Figure 22 GMS Generation Mix (ASES, GWh)
1,400,000
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800,000
600,000 Genera_on (GWh) 400,000
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0 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050
Offgrid Coal Hydro Gas Wind Bio Solar CSP Hydro ROR Geothermal Ocean
Figure 23 GMS Generation Mix (ASES, %)
100% 8% 4% 3% 4% 90% 4% 10% 4% 14% 19% 80% 47% 28% 70% 61% 31% 17% 29% 60%
11% 50% 20% 6% 20% 40% 27% 26% Genera_on Mix
30% 19% 18% 18% 19% 20% 28% 25% 14% 10% 19% 21% 12% 4% 0% 2010 2015 2020 2030 2040 2050 Offgrid Coal Hydro Gas Wind Bio Solar CSP Hydro ROR Geothermal Ocean
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Figure 24 GMS Power Sector Development under the ASES Scenario
2015 ASES (2030) ASES (2050)
Resource Flows Coal, Diesel, Fuel Oil, Nuclear Below 10,000 GWh Gas 10,001 - 20,000 GWh Large Hydro Above 20,000 GWh Wind Solar, Battery, CSP Biomass and Biogas Other Renewables
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Table 8 ASES Renewable Electricity generation Shares by Country
Year Cambodia Lao PDR Myanmar Thailand Viet Nam 2015 87% 83% 61% 13% 40% 2020 72% 86% 80% 33% 40% 2030 77% 92% 89% 76% 64% 2040 90% 97% 100% 92% 95% 2050 100% 100% 100% 100% 100%
Investment Requirements Figure 25 shows the cumulative investment in generation CAPEX and energy efficiency in millions of Real 2014 USD. The earlier observation of the SES and ASES having lower demand owing to energy efficiency gains is also valid here. The figure shows the BAU requiring the least capital investment by the end of the modelling horizon primarily driven by the lower CAPEX costs of traditional coal technologies, which provide base-load support i.e. the CAPEX cost taking into account capacity factors is far lower for coal than solar PV with battery as an example. The SES and ASES include investment in energy efficiency measures and greater investments in CSP, biogas and battery storage to defer generation post-2035 with the ASES requiring more investment because of higher replacement requirements for retired coal and gas plants. Figure 26 shows cumulative investment by technology type at 2030 and 2050 for all three scenarios. The BAU directs most investment (65%) to coal and hydro projects, while in the SES and ASES investments are spread over a wider range of technologies: 50% is directed to solar17 and battery system technologies across the SES and ASES, with other significant investments in energy efficiency measures (17% SES and 18% ASES), wind (12% in SES and ASES) and less than 1% in off-grid supply in both the SES and ASES. Clearly, compared to the BAU, the SES and ASES will require investments across a more diverse range of technologies and also technologies that are of a smaller scale and more distributed rather than a smaller number of large scale developments as per the BAU. This highlights the importance to the SES and ASES of having investment frameworks for energy infrastructure that can accommodate a larger number of smaller investments.
17 PV and CSP technologies.
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Figure 25 GMS Cumulative Investment (Real 2014 USD)
1,000,000 900,000 800,000 700,000 600,000 500,000 400,000 300,000 200,000 Cumula[ve Investment ($m's) 100,000 0 2015 2017 2019 2021 2025 2027 2029 2031 2035 2037 2039 2041 2045 2047 2049 2023 2033 2043
BAU SES ASES
Figure 26 GMS Cumulative Investment at 2030 and 2050 (Real 2014 USD)
1,000 900 800 700 600 500 400 300 200 Cumula[ve Investment ($bn's) 100 0 BAU (2030) SES (2030) ASES (2030) BAU (2050) SES (2050) ASES (2050)
Hydro Wind Coal Gas Diesel/FO Nuclear Bio Solar CSP Badery Geothermal Ocean Grid Electrifica[on Offgrid Energy Efficiency
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Figure 27 and Table 9 present the net present value of the power system costs in the GMS by component using an 8% and 15% discount rate over the period from 2015 to 2050 grouped according to fuel costs, capital costs, fixed operation and maintenance costs, variable operation costs, grid electrification costs, energy efficiency costs and deployment of off-grid generation solutions. The BAU is comprised of a higher percentage of fuel costs, whereas the ASES has the highest percentage relating to capital costs. The total NPV difference between the BAU and ASES is approximately $192 billion under an 8% discount rate.
Figure 27 NPV of System Costs (Real 2014 USD) for period 2015 to 2050
900,000 800,000 700,000 600,000 500,000 400,000 NPV ($m's) 300,000 200,000 100,000 0 BAU @ 8% SES @ 8% ASES @ 8% BAU @ 15% SES @ 15% ASES @ 15% Fuel Cost Capital Cost FOM VOM Grid Electrifica[on Energy Efficiency Offgrid
Table 9 NPV of System Costs (Real 2014 USD) for period 2015 to 2050
BAU @ SES @ ASES @ BAU @ SES @ ASES @ NPV 8% 8% 8% 15% 15% 15% Fuel Cost 462,919 288,682 219,927 208,384 150,668 126,589 Capital Cost 322,100 321,220 347,175 142,637 143,706 149,783 FOM 31,035 32,394 35,582 14,222 14,552 15,153 VOM 34,841 30,264 29,199 15,414 13,902 13,371 Grid Electrification 4,601 3,386 1,825 1,902 1,341 807 Energy Efficiency 0 22,111 28,028 0 6,587 8,715 Off-Grid 0 856 2,071 0 355 648 Total 855,495 698,913 663,807 382,560 331,111 315,066
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Cost of Electricity Based on the outcomes of modelling the BAU, SES and ASES scenarios, we also examined the following issues in relation to electricity costs: (1) levelised cost of electricity, (2) investment requirements, (3) sensitivity of electricity prices to fuel price shocks, and (4) the implications of a price on carbon equivalent emissions for electricity prices. Based on this analysis we draw the following conclusions:
• The BAU requires lower levels of capital investment than the SES and ASES, and in relation to generation costs, the SES and ASES across the modelling period deliver a lower overall generation cost;
• The comparison of the LCOE (only includes generation costs) is shown in Figure 28, noting that Thailand and Viet Nam drive most of the fluctuations. The LCOE for the BAU starts to increase as fuel costs increase back to long-term averages before declining to $92/MWh as a result of the deployment of lower capital costs associated with its slow transition to renewable energy generation. The LCOE of the ASES and SES increase initially as renewable developments are deployed earlier but declines towards to $88/MWh in 2035 as renewable technologies decrease in costs before edging up to $91/MWh due to higher cost renewable technologies. This LCOE analysis only compares central grid connected electricity production and it does not include the cost of externalities18.Under the SES and ASES significant benefits are gained in the form of avoided fuel costs and this contributes to achieving a lower overall dollar cost for the GMS. The observation is made that the composition of LCOE under the SES and ASES is largely driven by investment costs, hence exposure to fuel shocks is significantly reduced; and
• The LCOE under the SES and ASES is also largely insensitive to a carbon price, as could be reasonably anticipated for a power system that is entirely dominated by renewable energy.
18 A detailed study on the cost of externalities is presented in the following reference: Buonocore, J., Luckow, P., Norris, G., Spengler, J., Biewald, B., Fisher, J., and Levy, J. (2016) ‘Health and climate benefits of different energy- efficiency and renewable energy choices’, Nature Climate Change, 6, pp. 100–105.
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Figure 28 GMS LCOE for Generation
110 105 100 95 90 85 80 LCOE ($/MWh) 75 70 65 60 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
BAU SES ASES
Carbon Emissions Figure 29 and Figure 30 show the carbon intensity of GMS’s power system and the total per annum carbon emissions respectively. The intensity trajectory moves up in the BAU as more coal enters the system then maintains its level around 0.45 t- CO2e/MWh as renewable technologies are also developed. The intensity in the SES drops to 0.10 t-CO2e/MWh by 2050 and the ASES is 100% carbon emissions free. In terms of total carbon emissions, the shift towards the SES and ASES saves up to 659 and 771 mt-CO2e, respectively, or the equivalent to a 85% and 100% saving from the BAU.
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Figure 29 GMS Carbon Intensity Comparison
0.50 0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10
Carbon Intensity (t-CO2e/MWh) 0.05 0.00 2015 2017 2019 2021 2025 2027 2029 2031 2035 2037 2039 2041 2045 2047 2049 2023 2033 2043
BAU SES ASES
Figure 30 GMS Carbon Emissions Comparison
900 800 700 600 500 400 300 200
Emissions (mt-CO2e per annum) 100 0 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
BAU SES
ASES Avoided Emissions (SES)
Avoided Emissions (ASES)
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Implications for Jobs Creation The SES and ASES scenarios both result in quite different technology mixes compared to the BAU. Each has quite different implications for the workforce that would be required to support each scenario. Based on analysis of the required jobs we estimate that19:
• The BAU from 2015 to 2050 would be accompanied by the creation of some 13 million job years20 (20% manufacturing, 46% construction, 22% operations and maintenance, and 12% fuel supply);
• The SES would involve the creation of some 21 million job years (25% in manufacturing, 56% in construction, 18% in operations and maintenance and 0.8% in fuel supply); and
• The ASES would involve the creation of 28 million job years (24% in manufacturing, 53% in construction, 23% in operations and maintenance and less than 0.1% in fuel supply).
Barriers for the SES and ASES Scenarios The GMS has abundant renewable energy resources. However, there are a number of social, economic, financial, technical and institutional barriers for the SES and ASES which potentially deter new investment in renewable energy and the implementation of energy efficiency measures. Social barriers
• A lack of public awareness and understanding on the importance of renewable energy and energy efficiency in addressing environmental concerns. This is due to insufficient information from relevant government agencies on the benefits and potentials of renewable energy and energy savings. This may also relate to the broader education levels and programs in some of the GMS countries.
• A lack of effective and considered measures relating to adverse social and environmental impacts of large scale renewable projects such as hydropower. Economic and financial barriers
• The main economic barrier in the GMS is the high investment costs of renewable technologies, which are significantly higher than conventional generation technologies at present.
• In all of the GMS countries, project developers have experienced difficulties in securing finance to invest in renewable energy projects.
19 Based on the employment factors presented in Appendix C. 20 A job year is one job for one person for one year. We use this measure to make comparisons easier across each scenario as the number of jobs created fluctuates from year to year.
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• Fossil fuel price subsidies, particularly in Myanmar, Viet Nam and Thailand, represent another significant barrier in new investment in renewable energy. Subsidies also discourage energy conservation and energy efficiency measures. Technical barriers
• Limited knowledge on renewable energy technology. There is a shortage of technical, operational and maintenance expertise within the government and the local private sector which limits development opportunities. This is due to a lack of training organisations and facilities leading to a lack of qualified experts and skilled technicians.
• Inadequate transmission and distribution networks to support an increase in renewable energy projects, particularly in remote areas.
• Insufficient research and development effort in the renewable energy sector in the GMS countries. This includes a lack of detailed studies on the impact of high renewable penetration on the operation of power grids and conventional power plants.
• A lack of measurements, reporting and verification system to follow up on the outcomes of energy saving programs. This makes it difficult to assess the effectiveness of the programs. Policy and institutional barriers
• A lack of sufficient supporting schemes, strategies and plans to promote renewable energy and energy efficiency, particularly in Cambodia, Lao PDR and Myanmar.
• Although Thailand and, to some extent, Viet Nam have put in place policies and supporting schemes to promote renewable energy, there is still a lack of coordination between different governmental agencies which are responsible for policy decision-making resulting in uncoordinated and incoherent policies.
• There are also significant uncertainties over future policies and regulatory frameworks which represent risks to potential investors.
• Difficulties and long waiting times in obtaining licences and connecting renewable plants to the grid due to a lack of well-defined operational and technical standards.
Recommendations The following are key recommendations that potentially reduce the barriers to the SES and ASES in the GMS. Overcoming social barriers
• Disseminate information on the benefits of renewable energy and energy efficiency through effective communication methods and educational programs.
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• Conduct detailed assessments of the impacts of renewable energy projects and measures to alleviate social and environmental impacts and make the results publicly available. Overcoming economic and financial barriers
• Develop energy policies and schemes to increase the cost competitiveness of renewable technologies. The aim is to create an environment that is conducive for investment in renewable energy technologies.
• Conduct detailed assessments of renewable energy potential to enable prospective investors to understand the potential, identify the best opportunities and subsequently take steps to explore investment and deployment.
• Consider removing or replacing fossil fuel subsidies with other supporting schemes. Overcoming technical barriers
• Knowledge transfer and capacity building in the renewable energy technologies and energy efficiency for policy makers and staff working in the energy industry to ensure the human capacity is being developed to support a national power system that has a high share of generation from renewable energy. As we have shown the SES and ASES will require a large number of skilled workers to support a technology mix with a significant share of renewable energy.
• Investments in ICT systems to allow for greater real-time monitoring, control and forecasting of the national power system, including SCADA/EMS, and smart- grid technology and renewable energy forecasting systems and tools. This will enable efficient real-time dispatch and control of all resources in the system which will facilitate high levels of renewable energy as well as cross-border power trading.
• Encourage cross-border power trade in the region, as this works to the advantage of exploiting scattered renewable energy resource potentials and diversity in electricity demand.
• Take measures to improve power planning in the region to explicitly account for project externalities and risks and consider scenarios with high penetration of renewable energy and energy efficiency, as well as plans for tighter power system integration within the region. Overcoming policy and institutional barriers
• Formation of more comprehensive energy policies to create an environment that is appropriate for investment in renewable energy technologies and encourage energy efficiency. Investor confidence in renewable energy investment will be enhanced by having a transparent regulatory framework that provides certainty to investors and appropriately considers the ramifications of high levels of renewable energy in the generation mix.
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• Implement regulatory frameworks and well-defined technical codes to streamline procedures for providing licenses and avoiding delay in grid connection.
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Acronyms
AD Anaerobic Digestion ADB Asian Development Bank AEDP Alternative Energy Development Plan (Thailand) AGL Above Ground Level ASEAN Association of Southeast Asian Nations ASES Advanced Sustainable Energy Sector BAU Business As Usual BCM / Bcm Billion Cubic Metres BNEF Bloomberg New Energy Finance BOT Build – Operate – Transfer BP British Petroleum BTU / Btu British Thermal Unit CAGR Compound Annual Growth Rate CAPEX Capital Expenditure CCGT Combined Cycle Gas Turbine CCS Carbon Capture and Storage CENER National Renewable Energy Centre CIEMOT Centro de Investigaciones Energeticas Medioambientales y Tecnológicas COD Commercial Operations Date CSP Concentrated Solar Panel DEDE Department of Alternative Energy Development and Efficiency (Thailand) DNI Direct Normal Irradiation DTU Technical University of Denmark EAC Electricity Authority of Cambodia EDC Electricité du Cambodge EDL Electricité du Laos
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EE Energy Efficiency EEZ Exclusive Economic Zones EGAT Electricity Generation Authority of Thailand EIA Energy Information Administration EPPO Energy Policy and Planning Office (Thailand) ERAV Electricity Regulatory Authority of Viet Nam ERC Energy Regulatory Commission (Thailand) EVN Electricity of Viet Nam FOB Free on Board FOM Fixed Operating and Maintenance GDP Gross Domestic Product GHI Global Horizontal Irradiance GIS Geographical Information System GMS Greater Mekong Subregion GSP Gas Subcooled Process GT Gas Turbine HV High Voltage IAEA International Atomic Energy Agency ICT Information and Communication Technology IDAE Instituto para la Diversificación y Ahorro de la Energía IEA International Energy Agency IES Intelligent Energy Systems Pty Ltd IMF International Monetary Fund INIR Intergraded Nuclear Infrastructure Review IPP Independent Power Producer IRENA International Renewable Energy Agency JICA Japan International Cooperation Agency JV Joint Venture LCOE Overall Levelised Cost of Electricity LNG Liquefied Natural Gas
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LPG Liquefied Petroleum Gas MEPE Myanmar Electric Power Enterprise MKE Mekong Economics MMcf Million Cubic Feet MMcfd Million Cubic Feet per Day MOEP Ministry of Electric Power (Myanmar) MOGE Myanmar Oil and Gas Enterprise MOIT Ministry of Industry and Trade (Viet Nam) MOM Ministry of Mines (Myanmar) MOST Ministry of Science and Technology MOU Memorandum of Understanding MTPA Million Tonnes Per Annum MV Medium Voltage NASA National Aeronautics and Space Administration (the United States) NEDO New Energy and Industrial Technology Development Organisation (Japan) NOAA National Oceanic and Atmospheric Administration (the United States) NGV Natural Gas Vehicle NPP Nuclear Power Plant NPV Net Present Value NREL National Renewable Energy Laboratory (the United States) OECD Organisation for Economic Co-operation and Development OPEC Organisation of the Petroleum Exporting Countries OPEX Operational Expenditure PDP Power Development Plan PDR People’s Democratic Republic (of Laos) PEA Provincial Electricity Authority (Thailand) PRC People’s Republic of China PTT Petroleum Group of Thailand PTTEP PTT Exploration and Production
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PV Photovoltaic PVN Petroleum of Vietnam / PetroVietnam RE Renewable Energy REVN Renewable Energy of Viet Nam Joint Stock Company ROR Run of River RPR Reserves to Production Ratio SCADA/EMS Supervisory Control and Data Acquisition/Energy Management System SES Sustainable Energy Sector SWERA Solar and Wind Energy Resource Assessment SWH Solar Water Heating TCF / Tcf Trillion Cubic Feet UN United Nations USD United States Dollar VOM Variable Operating and Maintenance WEO World Energy Outlook WWF World Wide Fund for Nature WWF- WWF – Greater Mekong Programme Office GMPO
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Table of Contents
Executive Summary iii Introduction iii Greater Mekong Subregion iii Greater Mekong Subregion Power Sectors iv Power Development Plans in the GMS Countries ix Summary of Development Options x Power Sector Vision Scenarios xiii Business as Usual (BAU) Scenario xiv Sustainable Energy Sector (SES) Scenario xix Advanced Sustainable Energy Sector (ASES) Scenario xxv Investment Requirements xxxi Cost of Electricity xxxiv Carbon Emissions xxxv Implications for Jobs Creation xxxvii Barriers for the SES and ASES Scenarios xxxvii Recommendations xxxviii 1 Introduction 48 1.1 Greater Mekong Subregion 48 1.2 Structure of this Report 49 2 Greater Mekong Subregion Countries: Economic Conditions and Power Sectors 51 2.1 Economic Growth 51 2.2 Population 55 2.3 Supply and Demand Trends 55 2.4 Cambodia’s Power Sector 61 2.5 Lao PDR’s Power Sector 64 2.6 Myanmar’s Power Sector 66 2.7 Thailand’s Power Sector 68 2.8 Viet Nam’s Power Sector 71 2.9 Summary 75 3 Electricity Supply Options 77 3.1 Solar Power 77 3.2 Onshore and Offshore Wind Power 80 3.3 Power Generation Potential from Biomass 85 3.4 Power Generation Potential from Biogas 87 3.5 Hydro Power 87 3.6 Geothermal Energy 93 3.7 Ocean Energy 94 3.8 Coal Resources 94 3.9 Imported Coal 96 3.10 Offshore Natural Gas Resources 96 3.11 Liquefied Natural Gas 101 3.12 Nuclear Power 102 3.13 Power Planning in the GMS 103 3.14 Summary of Developments for GMS Power Sectors 106
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4 Power Sector Vision Scenarios 110 4.1 Scenarios 110 4.2 Technology Cost Assumptions 113 4.3 Fuel Pricing Outlook 115 4.4 Real GDP Growth Outlook 117 4.5 Population Growth 118 4.6 Committed Generation Projects in BAU, SES and ASES Scenarios 118 4.7 Transmission System, Imports and Exports 118 4.8 Power Imports and Exports 120 4.9 Technical-Economic Power System Modelling 123 5 Business as Usual Scenario 125 5.1 Business as Usual Scenario 125 5.2 Projected Demand Growth 125 5.3 Projected Installed Capacity 127 5.4 Projected Generation Mix 130 5.5 Evolution of GMS Power Systems under BAU Scenario 133 5.6 Projected Generation Fleet Structure 135 5.7 Reserve Margin and Generation Trends 137 5.8 Electrification and Off-Grid Supply 139 6 Sustainable Energy Sector Scenario 140 6.1 Sustainable Energy Sector Scenario 140 6.2 Projected Demand Growth 140 6.3 Projected Installed Capacity 142 6.4 Projected Generation Mix 145 6.5 Evolution of GMS Power Systems under SES Scenario 148 6.6 Projected Generation Fleet Structure 150 6.7 Reserve Margin and Generation Trends 152 6.8 Electrification and Off-Grid 154 7 Advanced Sustainable Energy Sector Scenario 155 7.1 Advanced Sustainable Energy Sector Scenario 155 7.2 Projected Demand Growth 155 7.3 Projected Installed Capacity 157 7.4 Projected Generation Mix 160 7.5 Evolution of GMS Power Systems under ASES Scenario 163 7.6 Projected Generation Fleet Structure 165 7.7 Reserve Margin and Generation Trends 166 7.8 Electrification and Off-Grid 168 8 Analysis of Scenarios 169 8.1 Energy and Peak Demand 169 8.2 Energy intensity 172 8.3 Generation Mix Comparison 172 8.4 Renewable Energy Integration 174 8.5 Carbon Emissions 175 8.6 Coal Power Developments 177 8.7 Hydro Power Developments 178 8.8 Analysis of Bioenergy 179 9 Economic Implications 181 9.1 Overall Levelised Cost of Electricity (LCOE) 181
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9.2 Annual System Cost 182 9.3 Cumulative Capital Investment 185 9.4 Operating Costs, Amortised Capital Costs and Energy Efficiency Costs 188 9.5 Off-grid Cost Comparison 188 9.6 Fuel Price Sensitivity 189 9.7 Impact of a Carbon Price 190 9.8 Renewable Technology Cost Sensitivity 191 9.9 Jobs Creation 192 10 Conclusions 195 10.1 Comparison of Scenarios 195 10.2 Economic Implications 196 10.3 Barriers for the SES and ASES in GMS 197 10.4 Recommendations 199 Appendix A Technology Costs 201 Appendix B Fuel Prices 205 Appendix C Methodology for Jobs Creation 206 Appendix D Committed Power Projects 208 Appendix E Hydro Power Development 213 Appendix F Sources of Information for Renewable Energy Potential 218 Appendix G Economic Indicators 221 Appendix H GMS Transition Statistics 225
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1 Introduction
Intelligent Energy Systems Pty Ltd (“IES”) and Mekong Economics (“MKE”) have been retained by WWF – Greater Mekong Programme Office (“WWF-GMPO”) to undertake a project called “Produce a comprehensive report outlining alternatives for power generation in the Greater Mekong Sub-region”. This is to develop scenarios for the countries of the Greater Mekong Sub-region (GMS) that are as consistent as possible with the WWF’s Global Energy Vision to the Power Sectors of all Greater Mekong Subregion countries. The objectives of WWF’s vision are: (i) contribute to reduction of global greenhouse emissions (cut by >80% of 1990 levels by 2050); (ii) reduce dependency on unsustainable hydro and nuclear; (iii) enhance energy access; (iv) take advantage of new technologies and solutions; (v) enhance power sector planning frameworks for the region: multi-stakeholder participatory process; and (vi) develop enhancements for energy policy frameworks. The purpose of this report is to provide a summary of the 5 detailed country-level descriptions of three scenarios for the Greater Mekong Subregion provided in the separate country reports, as well as an overview of regional implications of such a transition to a sustainable power sector:
• Business as Usual (BAU) power generation development path which is based on current power planning practices, current policy objectives;
• Sustainable Energy Sector (SES) scenario, where measures are taken to maximally deploy renewable energy 21 and energy efficiency measures to achieve a near-100% renewable energy power sector; and
• Advanced Sustainable Energy Sector (ASES) scenario, which assumes a more rapid advancement and deployment of new and renewable technologies as compared to the SES. The scenarios were based on public data, independent assessments of resource potentials, information obtained from published reports and power system modelling of the GMS region for the period 2015 to 2050. The purpose of this report is to provide a detailed overview of the main features of these scenarios at the regional level and to set out the implications of these scenarios for each GMS country.
1.1 Greater Mekong Subregion For the purpose of this project, the Greater Mekong Subregion (GMS) consists of the following five countries surrounding the Mekong River22 basin: Kingdom of
21 Proposed but not committed fossil fuel based projects are not developed. Committed and existing fossil fuel based projects are retired at the end of their lifetime and not replaced with other fossil fuel projects. A least cost combination of renewable energy generation is developed to meet demand.
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Cambodia (“Cambodia”); Lao People’s Democratic Republic (“Lao PDR”); Union of Myanmar (“Myanmar”); Kingdom of Thailand (“Thailand”); and Socialist Republic of Viet Nam (“Viet Nam”). An illustration of the GMS within this context and as it will be analysed in this project is provided in Figure 31. All references to the GMS will henceforth correspond to the five countries listed above.
Figure 31 GMS Countries and their Location in Asia
Mandalay VIETNAM Hanoi Luang Prabang MYANMAR
LAO PDR Chiang Rai Chiang Mai Vientiane
Vientiane Yangon
THAILAND
Bangkok Angkor CAMBODIA Siem Reap Phnom Penh Ho Chi Minh City
1.2 Structure of this Report This report is part of a set of reports that collectively provide details of the power sector vision scenarios for each GMS country. The full set of reports is as follows:
• Volume 1: Greater Mekong Subregion Power Sector Vision;
• Volume 2: Kingdom of Cambodia;
22 Note that often the GMS is defined to include Yunnan Province and/or the Guangxi Zhuang Autonomous region – see Asian Development Bank (ADB), “Greater Mekong Subregion Economic Cooperation Program”, November 2014, available: http://www.adb.org/sites/default/files/publication/29387/gms-ecp-overview.pdf. However, the scope of this study was on the power sectors of: Cambodia, Lao PDR, Myanmar, Thailand and Viet Nam and treatment of these five countries as a region that we henceforth refer to as the GMS in this report.
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• Volume 3: Lao People’s Democratic Republic;
• Volume 4: Republic of the Union of Myanmar;
• Volume 5: Kingdom of Thailand; and • Volume 6: Socialist Republic of Viet Nam; and
• Volume 7: Assumptions Book. This report has been organised in the following way:
• Section 2 provides a summary of the status of each country’s power sector;
• Section 3 covers the various resource supply options available to each country; • Section 4 sets out the scenarios and underlying assumptions;
• Section 5 sets out the key results for the Business as Usual Scenario; • Section 6 sets out the key results for the Sustainable Energy Scenario;
• Section 7 sets out the key results for the Advanced Sustainable Energy Scenario;
• Section 8 provides comparative analysis of the two scenarios based on the computation of a number of simple metrics that facilitate comparison;
• Section 9 provides analysis into the cost of electricity under the two scenarios; and
• Section 10 provides the main conclusions from the modelling. The following appendices are included:
• Appendix A summarises the technology cost assumptions; • Appendix B summarises the fuel price assumptions;
• Appendix C sets out information used to estimate jobs creation potential;
• Appendix D provides a summary of the generation projects assumed to be committed in the modelling;
• Appendix E lists hydro power developments;
• Appendix F lists sources of information that were used to develop assumptions for renewable energy potential in the region;
• Appendix G provides some tables of economic indicators for the countries for reference; and • Appendix H sets out some additional statistics and charts. Note that unless otherwise stated, all currency in the report is Real 2014 United States Dollars (USD) and all projections presented in this report start from the year 2015.
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2 Greater Mekong Subregion Countries: Economic Conditions and Power Sectors
Cambodia, Lao PDR, Myanmar, Thailand and Viet Nam, with a combined gross domestic product (GDP) of 662 US billion and population of 232 million in 2014, collectively form one of the fastest growing regions in the world. Each country’s power sector status is unique and each faces its own set of challenges. This section provides a brief summary of each GMS country’s situation and the current status of their power sector development plans23.
2.1 Economic Growth The Association of South East Asian Nations (ASEAN) is the second-fastest growing economy in Asia, second to PRC24. Figure 32 shows the GDPs of major economies in the Asia-Pacific region, with a breakdown of ASEAN’s GDP (in 2013), to illustrate the contribution of each member country. In 2013, the GMS made up around 27% of ASEAN’s total GDP, with the other major contributors being Indonesia (36%), Malaysia (13%), Singapore (12%), and the Philippines (11%). Over the past decade, the GMS region has experienced significant economic growth. This is evidenced by Figure 33 and Figure 34. The former shows that Thailand and Viet Nam are major contributors to the GMS GDP, while the latter shows that the average rates of GDP growth of the GMS countries has exceeded or matched25 the annual average GDP growth rate of the world. Figure 35 illustrates the historical Real GDP growth rates of the GMS countries to illustrate the long- term trend for the last 15 years. The high growth rates are attributable to the countries within the GMS taking measures to transform their economies to be more open, diversified and market- oriented as compared to the past. This has enabled a steady flow of foreign investment. Efforts have also been taken to remove trade barriers in the GMS member countries which have stimulated economic activity on a localised level and enhanced the region’s overall ability to become integrated into the world economy. Unsurprisingly, the economic growth experienced in the region has resulted in increases in the GDP per capita, as illustrated in Figure 36. In all instances increases are observed. However, Figure 36 also shows that the GMS countries, on a GDP per
23 More detailed information on each country is provided in the country reports that accompany this regional summary report. 24 East-West Center, “ASEAN Matters for America”, available: http://www.asiamattersforamerica.org/asean/data/gdppercapita. 25 Note that average growth in Real GDP in Thailand is the lowest of the countries in the region which has in the recent past experienced a low growth rate owing to the country being affected by political instability that has in turn seen a reduction in investment, tourism and lower economic activity across the country generally.
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Figure 32 GDP Comparison – ASEAN and ASEAN GDP breakdown (2013)
Source: East-West Center
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Figure 33 Real GDP (in Real USD 2014) for the GMS
$700
$600
$500
$400
$300
$200
Real GDP (Real 2014 USD Billions) $100
$- 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Cambodia Lao PDR Myanmar Thailand Viet Nam
Data Source: IMF WEO October 2014
Figure 34 Average Real GDP growth rates (2000-14) for GMS countries and the world
12%
10%
8%
6%
4%
2%
0% Cambodia Lao PDR Myanmar Thailand Viet Nam World
Data Source: IMF WEO October 2014
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Figure 35 Real GDP growth rates for the GMS countries
16%
14%
12%
10%
8%
6%
4%
2% Annual Growth Rate (%) 0%
-2%
-4%
Cambodia Lao PDR Myanmar Thailand Viet Nam
Data Source: IMF WEO October 2014
Figure 36 Real GDP per capita (in Real 2014 USD) of GMS countries for selected years
$12,000
$10,000
$8,000
$6,000
$4,000
$2,000
$- Cambodia Lao PDR Myanmar Thailand Viet Nam World
2000 2010 2014
Data Source: IMF WEO October 2014
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2.2 Population The population of the GMS is around 233 million people and has been growing at an average rate of 0.9% for the last 5 years. Figure 37 shows the population trend for the period 2000-14 and Table 10 provides population statistics for the GMS countries for selected years.
Table 10 Population statistics (number of people in millions) for selected years Country 2000 2005 2010 2011 2012 2013 2014 Cambodia 12.2 13.4 14.4 14.6 14.9 15.1 15.3 Lao PDR 5.4 5.8 6.4 6.5 6.6 6.8 6.9 Myanmar 46.4 48.0 49.7 50.1 50.5 51.0 51.4 Thailand 61.9 65.1 67.3 67.6 67.9 68.2 68.6 Viet Nam 77.6 82.4 86.9 87.8 88.8 89.7 90.6 GMS Total 203.5 214.7 224.7 226.6 228.7 230.8 232.8 Data Source: IMF WEO October 2014
Figure 37 GMS population by GMS country (2000-14)
250
200
150
100 People in Millions 50
0
Cambodia Lao PDR Myanmar Thailand Viet Nam
Data Source: IMF WEO October 2014
2.3 Supply and Demand Trends Electricity demand across the entire region has grown from 189 TWh in 2005 to 337 TWh by 2014, at an annual average rate of 6.6%. The significant growth can be attributed to the high growth in Viet Nam, accounting for only 27% of the GMS electricity consumption in 2005 increasing to 40% by 2014. Thailand’s share of electricity consumption in the region decreases from 69% to 54% over this period.
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Viet Nam and Thailand make up most of the demand in the region due to their relatively developed economies and high electrification rates. The change in country composition of total electricity demand in the region is charted in Figure 38 and Figure 39.
Figure 38 GMS Electricity Demand by Country (GWh, 2005)
Source: IEA (Demand includes transmission and distribution losses)
Figure 39 GMS Electricity Demand by Country (GWh, 2014)
4,211; 1% 4,364; 1% 11,746; 4%
136,161; 40%
181,221; 54%
Vietnam Thailand Cambodia Lao PDR Myanmar
Source: IEA (Demand includes transmission and distribution losses)
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Table 11 shows final electricity consumption and electricity peak demand for 2014 with corresponding rates of growth for each GMS country. Most of the GMS countries have experienced substantial growth, and this is expected to continue in the next decades.
Table 11 GMS Country Electricity Demand and Growth Rates (2014) Country Electricity Consumption Peak Demand TWh CAGR26, % MW CAGR27, % Cambodia 4.15 19.4% 687 16.0% Lao PDR 3.38 14.5% 748 12.5% Myanmar 9.57 15.7% 2,235 16.2% Thailand 168.20 4.4% 26,942 2.9% Viet Nam 142.25 12.7% 22,100 10.2% Source: Compiled by Consultant from various sources Figure 40 presents the GMS breakdown of consumption by the sectors. Industry almost accounts for half of electricity use in the region at 47%, followed by the residential and commercial sectors at 29% and 23% respectively. The composition of sector consumption across the region has remained relatively stable with residential energy increasing 1% displacing the industrial sector as a result of increasing electrification rates and per capita consumption levels in the region. Figure 41 compares the countries’ sectoral composition of power energy consumption (for 2014 data). It indicates that the industrial sector is the largest aggregate electricity consumer in Viet Nam (54%), Thailand (43%) and Myanmar (45%); whereas for Cambodia and Lao PDR, the residential sector accounts for the largest part on total consumption (47% and 35%). The proportion of commercial electricity consumption in Viet Nam at 10% is significantly less than that of the other countries (at 20% and above).
26 The Compound Annual Growth Rate (CAGR) is for the last ten years for Cambodia, Lao PDR, and Viet Nam, last five years for Myanmar and twelve years for Thailand. 27 Last five years for Cambodia, Myanmar, and Thailand, ten years for Lao PDR and Viet Nam.
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Figure 40 GMS Historical Energy Demand (TWh) by Sector: 2005-14
350
300
250
200
150
100 Energy (TWh, inc losses) 50
0 2005 2010 2014
Agriculture Industry Commercial Residen[al
Source: IEA (Demand includes transmission and distribution losses), 2014 based on IES estimates
Figure 41 Electricity Consumption Breakdown by Sector (2014)
100% 90% 24% 32% 80% 35% 35% 47% 70% 60% 20% 10% 33% 50% 31% 40% 29% 30% 45% 54% 43% 20% 33% 10% 21% 0% 3% 1% 3% 1% 0.2% Cambodia Lao PDR Myanmar Viet Nam Thailand
Agriculture Industry Commercial Residen[al
Table 12 provides information on installed capacity by fuel type for each GMS country and Figure 42 compares the capacity mix between the countries. Hydro
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Table 12 Installed Capacity by Fuel Type (2014)
Generation Type Cambodia Lao PDR Myanmar Thailand Viet Nam Coal 268 - 120 6,538 10,405 Gas - - 1,325 21,888 6,825 Large Hydro 929 3,058 3,011 3,444 13,050 Fuel Oil/Diesel 291 - 87 9 1,738 RE Sources 23 - 40 2,789* 1034 Solar - - - 464 - Wind - - - 209 52 Small Hydro - - 33 14 800* Biomass 23 - 5 1,851 180* Biogas - - 2* 251 2* Total (MW) 1,511 3,058 4,583 34,668 33,052 Source: Compiled by Consultant from various sources
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Figure 42 Installed Capacity Mix by Fuel Technology (2014)
100% 3% 8% 5% 19% 10% 80% 39% 66% 60%
61% 61% 100% 40% 21%
20% 29% 31% 18% 21%
0% Cambodia Lao PDR Myanmar Thailand Vietnam
Coal Gas Hydro (Large Scale) Oil / Diesel Renewable
Table 13 summarises the electrification rates for the overall and also for urban and rural areas. Viet Nam and Thailand have very high electrification rates compared to the other countries in the GMS as a result of focused plans to extend transmission and distribution systems to remote areas to increase electricity access rates. Myanmar and Cambodia’s electrification rates are lagging with very low electrification rates particularly in rural areas. Table 13 Electrification rates in GMS countries (2014) Country Population Urban Rural without Access Electrification Electrification Electrification to Electricity Rate28 (%) Rate (%) Rate (%) (millions) Cambodia 9.2 39% 90% 24% Lao PDR 0.8 89% 98% 83% Myanmar 38.1 26% 40% 20% Thailand 0.2 100% 100% 99% Viet Nam 1.9 98% 100% 97%
28 Electrification rate is based on the proportion of population with access to electricity.
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2.4 Cambodia’s Power Sector Figure 43 graphs the electricity supplied to Cambodia (that is generated within the country as well as imported from neighbouring countries) and the electricity that has been sold to end users in Cambodia. Annual average growth rates from 2004 to 2014 are also plotted on the chart. Over the period shown, national electricity demand in Cambodia has increased nearly six-fold, from some 704 GWh to 4,144 GWh, with a compound annual growth rate (CAGR) of 19.4%, which is quite high for a power system. Such rapidly growing demand has been attributed to: (1) Cambodia’s economic growth as measured by annual GDP growth rates which have been in range from 7% to 8%, (2) urban population growth, and (3) increased electrification rates. Some 70% of Cambodia’s national demand is concentrated in Phnom Penh. Demand is expected to continue to rise in line with a general policy direction of increasing access to electricity with access being provided to rural areas and also the expansion of the transmission system in order to reduce delivered electricity costs. The residential sector has traditionally consumed the highest proportion of total electricity consumption in the country. This is illustrated in Figure 44, where it can be seen that for 2012 the residential share of electricity consumption was some 50% of the total, while consumption attributable to the commercial and services sectors made up some 28% with the industrial sector making up the remaining 18%.
Figure 43 Electricity Demand Trends (2004-14)
6,000 30%
5,000 25%
4,000 20%
3,000 15% GWh
2,000 10%
1,000 5%
0 0% 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Electricity Supplied Electricity Sold Annual Growth, %
Source: EAC Statistics (2015)
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Figure 44 Electricity Demand Shares by Category (2012)
Others 4%
Industrial 18%
Residen[al Commercial 50% & Services 28%
Source: IEA (2014)
Figure 45 shows Cambodia’s annual electricity generation which has increased from around 700 GWh in 2004 to 3,000 GWh in 2014. The share of generation by fuel type is plotted for 2014 in Figure 46. As was earlier observed for the capacity mix, the generation mix reflects the dominance of hydropower in Cambodia’s power system, accounting for 61% of the total generation mix. This was followed by coal- based generation at 28%, diesel and heavy fuel oil at 11% and biomass making up the remainder at 0.5%. It should be noted that total domestic generation is lower than the total electricity supplied due to reliance on power imports from neighbouring countries.
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Figure 45 Total Electricity Generation (2000-2014)
3,500
3,000
2,500
2,000 GWh 1,500
1,000
500
0 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Figure 46 Generation Mix Proportion by Fuel Type (2014)
Biomass 0.5%
Coal 28.2%
Hydro 60.5%
Diesel/HFO 10.7%
Source: EAC Statistics (2015)
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2.5 Lao PDR’s Power Sector Figure 47 shows Lao PDR’s total final electricity consumption and the annual growth rates from 1996 to 2014. It indicates domestic demand has been growing rapidly; in particular, annual electricity consumption increased at an average rate of 15% per annum from 1011 GWh in 2005 to 3,791 GWh in 2014. Electricity consumption has traditionally been dominated by residential consumption, which made up 42% in 2010 dropping to 38% in 2014 (Figure 48). Industry consumption as at 2014 accounted for 41% of total electricity consumption. This trend is expected to continue with additional industrial load to come online over the next few years as part of the Government’s industrial development plans. By 2014, the power system had a peak demand of 743 MW, which has been growing 12% per annum over a 10- year period, and nearly doubled since 2008. Lao PDR’s main load centre is the Vientiane capital city, with other locations of significant demand in Vientiane province, Savannakhet, Khammouane and Champasak provinces. The government of Lao PDR has been promoting the creation of industrial zones throughout the country.
Figure 47 Electricity Demand Growth (1996-2014)
4,000 40%
3,500 35%
3,000 30%
2,500 25%
2,000 20% Rate (% pa) Energy (GWh) 1,500 15%
1,000 10%
500 5%
0 0% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Electricity Consump[on Growth Rate (pa)
Source: Electricity Statistics 2013, Electricite Du Laos, 2014
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Figure 48 Electricity Demand Shares by Category (2014)
Edu. And Sport Business 0.2%
Industry Residen[al 41.3% 37.6%
Commercial Int. Organisa[on 14.0% 0.3% Irriga[on Govt Office Entertainment 0.9% 5.4% 0.3%
Figure 49 shows annual statistics generation, import and export of electricity from 1991 to 2012. It indicates that while it had significantly increased its own generation supply (which was entirely from hydropower), Lao PDR also had to import more electricity to meet the domestic demand.
Figure 49 Generation, Imports and Exports (1991-2014)
2,500
2,000
1,500
1,000 Energy (GWh)
500
0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Genera[on Import Export
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2.6 Myanmar’s Power Sector Figure 50 shows Myanmar’s final electricity consumption by sector until 2013/14. Electricity consumption has increased significantly in the last five years at an annual average growth rate of 15.7%. Figure 51 shows that residential (domestic), industrial, and commercial sectors were the major end users of electricity, with their shares in the 2013/14 total final consumption being 31%, 22% and 13% respectively. Industrial demand has been observed to have annual average growth rate in excess of 15% over the last 5 years, with commercial and residential sectors experiencing annual growth rates in excess of 10%.
Figure 50 Electricity Demand by Category (2000-14)
Sources: Ministry of Electric Power (MOEP)
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Figure 51 Electricity Demand Shares by Category (2014)
Sources: Ministry of Electric Power (MOEP)
Figure 52 shows generation by technology type for the period 2000 to 2014, illustrating how the industry has become more heavily dependent on hydropower with its contribution being around 72% of total electricity supplied. Figure 53 plots the shares by generation fuel types for 2013/14: a total of 12,202 GWh was generated, of which 8,778 GWh (71.9%) was from hydropower, 2,794 GWh (22.9%) from gas-fired turbines and 433 GWh (3.6%) from steam associated with heat from the gas-fired generators.
Figure 52 Generation by Technology (2000-2014)
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Sources: Ministry of Electric Power (MOEP)
Figure 53 Generation Shares (2013)
Sources: Ministry of Electric Power (MOEP)
2.7 Thailand’s Power Sector Figure 54 shows Thailand’s final electricity consumption by the end use categories from 2002 to 2014. Over this period, electricity consumption increased from 100.1 TWh to 168.2 TWh, with a CAGR of 4.44%. The industrial sector makes up the largest portion, consuming some 73.8 TWh, or 43.8% of the total consumption in 2014. This is followed by the residential sector (23.1%), commercial sector (18.6%) and small general services (11.2%). The changes in electricity demand composition shown in Figure 55 indicate that the industry share in the total consumption has been slightly decreasing as opposed to gradual increases in percentage for consumption by the other sectors.
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Figure 54 Electricity Demand by Category (2002-14)
Source: EPPO Statistics (2015)
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Figure 55 Electricity Demand Shares by Category for Selected Years
Source: EPPO Statistics (2015) Figure 56 shows generation by fuel type over the last 15 years, illustrating how natural gas increasingly dominates Thailand’s fuel mix. In 2014, the total production of electricity was 180,945 GWh, of which 120,315 GWh or 66.5% was generated from natural gas. The next major type of fuel is coal, which accounted for 120,314 GWh or 20.8% of the 2014 generation mix. The contribution of imports and other fuel sources has become more significant, increasing from 3,461 GWh (3.5%) in 2010 to 16,252 GWh (9.0%) in 2014. Generation proportions of all fuel types in 2014 are shown in Figure 57.
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Figure 56 Generation by Fuel Type (2000-2014)
200,000 180,000 160,000 140,000 120,000 100,000 GWh 80,000 60,000 40,000 20,000 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Gas Coal Oil Hydro Imports & Others
Figure 57 Generation Mix Proportion by Fuel Type (2014)
Imports & Others 9.0%
Hydro 2.9%
Oil 0.9%
Coal Gas 20.8% 66.5%
Source: EPPO Statistics (2015)
2.8 Viet Nam’s Power Sector Figure 58 shows peak demand on a national level and total electricity demand. Over the past 10 years, national energy demand has had a compound annual
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Figure 58 Peak Demand and Energy Production (2000-14)
Source: ERAV The composition of electricity consumption is illustrated for selected years in Figure 59 to facilitate comparison and for the period 2010-13 in Figure 60. These show that industrial and residential customers in aggregate make up the most dominant consumers of electricity in Viet Nam and that in the last 3 years the breakdown between industrial, residential, and the other categories has remained almost unchanged.
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Figure 59 Electricity Consumption Composition for Selected Years (1995, 2000, 2005 and 2010-13)
Source: ERAV
Figure 60 Electricity Consumption Breakdown by Customer Category (2000-13)
Source: ERAV
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Figure 61 shows generation by fuel type over the last 14 years in Viet Nam illustrating the significant contribution of gas, hydro and coal in satisfying the electricity demand. As shown in Figure 62, a total of 142.25 TWh was generated by these three main fuels in 2014, with the shares of 38.0%, 30.9% and 25.6% respectively.
Figure 61 Generation by Fuel Type (2001-2014)
Source: ERAV/ EVN
Figure 62 Generation by Fuel Type (2014)
Source: ERAV/EVN
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2.9 Summary The GMS, which as defined in this report consists of Cambodia, Lao PDR, Myanmar, Thailand and Viet Nam, has one of the fastest growing economies in the world. With a combined population of 232 million, the region has experienced significant economic growth over the past decade. In particular, for the last five-year period from 2009 to 2014, the GMS region’s total GDP increased from 527 billion to 662 billion in Real 2014 US$, resulting in an average annual growth rate of 4.7%. At the country level, Thailand and Viet Nam are the GMS two major economies, contributing nearly 86% the total regional GDP (2014 data). On the other hand, Cambodia, Lao PDR and Myanmar have achieved relatively higher economic growth rates, averaging at 7.0%, 7.9% and 7.1% per annum respectively, compared to 3.6% and 5.8 % of Thailand and Viet Nam over the 2009 – 2014 period. In accompanying the economic growth, there has been substantial growth in electricity demand, which increased from 189 TWh in 2005 to 320 TWh in 2013, at an annual average rate of 6.75% across the entire region. Viet Nam and Thailand make up most of the demand in the region due to its more developed economies and high electrification rates. By 2014, end-use electricity consumption and its compound annual grow rate was 4.15 TWh and 19.4% for Cambodia, 3.38 TWh and 14.5% for Lao PDR, 9.57 TWh and 15.7% for Myanmar, 168.20 TWh and 4.4% for Thailand, and 142.25 TWh and 12.7% for Viet Nam. By 2014 the GMS countries had in total 76 GW of installed capacity, of which 1.5 GW is for Cambodia, 3.1 GW for Lao PDR, 4.6 GW for Myanmar, 33.9 GW for Thailand and 33.1 for Viet Nam. The region’s overall capacity mix was 23.5% by coal-fired, 37.7% from natural gas, 30.9% from large hydro power, 2.8% from fuel oil and diesel, and 5.1% from renewable energy sources (with biomass and small hydro being the two main RE types). Countrywide, hydro power is dominant in all systems except Thailand: Lao PDR’s installed capacity is nearly entirely based on hydro power while in Myanmar, Cambodia and Viet Nam the shares are around 66%, 59% and 40% respectively. Natural gas power stations make up a significant share of installed capacity for Thailand at more than 60%, Myanmar (29%) and Viet Nam (21%). Coal based generation is seen to be a significant part of Viet Nam’ and Thailand’s installed capacity mix accounting for 32% and 21% respectively. Shares of renewable energy generating capacity remain low across the GMS, with Thailand having around 8% of the total installed capacity from renewable technologies while the share is only 3% or less in the other countries. Electrification rates differ quite significantly across the GMS, with Viet Nam and Thailand (see Table 13) having much higher electrification rates compared to the other countries in the GMS (39% for Cambodia, 89% for Lao PDR and 26% for Myanmar in 2014). Myanmar and Cambodia’s lowest electrifications rates are featured by the countries’ rural electrification rates (20% and 24%) being very low and far lower than urban rates.
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The summarised country economic and electricity statistics have formed the basis for our baseline year of 2014, from which the projections presented in this report were developed. Further detail is provided in the country reports, please refer to Volumes 2 to 6.
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3 Electricity Supply Options
This chapter summarises the main development options covering both renewable energy and fossil fuels for each GMS country. It provides general findings and quantitative indicators about potential of each fuel. For more detailed assessments of development options for each GMS country, please refer to the country reports. The main sources of information that were used to formulate the overall renewable energy potentials in each country are listed in Appendix F. It should be noted that in a number of cases we undertook supplementary analysis to make inferences about renewable energy potential for situations where the information was not as complete as we would like.
3.1 Solar Power In Figure 63 “3TIER’s Global Solar Dataset provides average annual GHI at a 3km spatial resolution. Average values are based on more than 10 years of hourly GHI data and derived from actual, half-hourly, high-resolution visible satellite imagery observations via the broadband visible wavelength channel at a 2 arc minute resolution. 3TIER processed this information using on a combination of in-house research and algorithms published in peer-reviewed scientific literature”. This shows that across the GMS solar potential is generally quite good and this is supported by estimates of the solar potential made by many sources.
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Figure 63 Global Horizontal Irradiance (GHI) W/m2/day
3TIER’s Global Solar Dataset 3km with units in W/m² The main observations for each GMS Country in relation to solar potential are:
• Cambodia: Cambodia is considered to have high solar energy potential, which has been estimated to be at least 8,074 MW29 according to the latest ADB study30 entitled “Renewable Energy Developments and Potential in the Greater Mekong Subregion” (2015). An earlier study on renewable energy options for Cambodia’s rural electrification had also indicated that significant parts of the country have average direct normal irradiation (DNI) levels in excess of 5 kWh per square meter per day. Despite these favourable conditions for solar energy development both for DNI and GHI (Global Horizontal Irradiance) based technologies, the current installed capacity in Cambodia for solar photovoltaics remains at a very low level of less than 2 MW. The SWERA data collected by NASA Atmosphere Science Data Centre has indicated that the period from November through to April exhibits excellent solar conditions and that these would be suitable for photovoltaics and likely would be able to support
29 Represents the technical potential taking into account water bodies, protected areas, or areas unsuitable for PV development because of slope and elevation. IES estimates the actual potential to be significantly higher. 30 Source: http://www.adb.org/publications/renewable-energy-developments-and-potential-gms, accessed: 10 February 2016.
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Concentrated Solar Power (CSP) technology. Area with the greatest potential for solar are located in the north-eastern region of the country.
• Lao PDR: According to the same ADB study in 2015, Lao PDR has a potential of 8,812 MW of combined peak solar capacity, which far exceeds the earlier estimates31. Lao PDR has GHI levels ranging between 1,200 and 1,800 kWh/m2 pa and average DNI levels around 1,350 kWh/m2 per annum32, however, the hotter regions in Lao PDR have DNI levels between 1,600 to 1,800 kWh/m2 pa which can accommodate CSP technology. According to SWERA data collected by NASA Atmosphere Science Data Centre, the months from November through to March exhibit excellent solar conditions. This data also indicates that the greatest potential for solar lies in the central region of the country, covering the main load centre of Vientiane.
• Myanmar: Myanmar has high solar radiation levels especially in the Central Dry Zone Area. Potential available solar energy of Myanmar is estimated to be around 52,000 TWh per year35. However, similar to wind energy, solar energy in Myanmar is in the research and development stage. Solar energy is being introduced in a limited manner in some rural areas, through photovoltaic cells to generate electricity for charging batteries and to pump water for irrigation. As an initial step to demonstrate photovoltaic power systems for remote villages, some equipment has been installed under a technical cooperation program with other developing countries. Stand-alone PV systems are being used for rural electrification in areas that cannot be connected to the national grid, with notable initiatives in schools and universities. The SWERA data collected by NASA Atmosphere Science Data Centre shows that the period from October through to May exhibit excellent solar conditions. This also indicates that the greatest potential for solar lies in the central region of the country, where largescale integration of solar resources is possible.
• Thailand: Located in the tropics, Thailand has high potential for solar energy. The annual average of total daily solar radiation in Thailand is 5.06 kWh/m2 or 18.2 MJ/ m2. Most of the country receives the maximum solar radiation during April / May, ranging from 5.56 to 6.67 kWh/m2 per day. The North-eastern and central regions are among those locations that have greater solar power potential. SWERA data for Thailand has indicated that the period from November through to April exhibits the best solar conditions. The greatest potential for solar lies in the central and eastern region of the country, where largescale integration of solar resources is possible. According to the 2015 Alternative Energy Development Plan (AEDP), Thailand had 1,299 MW of solar power production capacity installed at the end of 2014. The plan has set a target of 6,000 MW for solar photovoltaics by 2036.
31 IES estimates the actual potential to be significantly higher. 32 Source: ADB, “Renewable Energy Developments and Potential in the Greater Mekong Subregion”, 2015.
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• Viet Nam: Viet Nam is considered to have very high solar potential. A number of studies have been conducted to assess the potential, the most recent and detailed of which was a study entitled: “Maps of Solar Resource and Potential in Vietnam”, published in January 2015. This was undertaken by the MOIT and a Spanish Consortium consisting of Centro de Investigaciones Energeticas Medioambientales y Tecnológicas (CIEMOT), National Renewable Energy Centre (CENER) and Instituto para la Diversificación y Ahorro de la Energía (IDAE). This broadly shows that based on GHI and DNI measurements there is substantial potential for solar photovoltaic deployment throughout the country, with the greatest potential identified in the southeast, central highlands, Mekong River Delta, all coastal areas and the northeast. The study also concludes that based on DNI measurements, there is substantial potential for CSP based technologies, with the greatest potential in the central regions, highlands and southeast of the country. SWERA data for Viet Nam show that the months from November through to April provide excellent solar conditions. Main solar locations lie in the south central and southern regions of the country. According to the latest Prime Minister’s Decision No. 2068/QD-TTg dated 25 November 2015, approving the development strategy of renewable energy of Viet Nam by 2030 with a vision to 2050, total electricity production from solar power would increase from 10 million kWh in 2015 to 1.4 billion kWh in 2020 (0.5% share), about 35.4 billion kWh in 2030 (6%) and about 210 billion kWh in 2050 (20%).
3.2 Onshore and Offshore Wind Power The GMS is regarded to have moderate to good wind potential. The geographical dispersion of wind resources in the GMS is summarised by the following:
• Figure 64 is “3TIER’s Global Wind Dataset which provides average annual wind speed at 80 meters above ground. Average values are based on over 10 years of hourly data created through advanced computer model simulations. 3TIER created this dataset using a combination of statistical methods and physics- based numerical weather prediction models, which create realistic wind fields throughout the world by simulating the interaction between the entire atmosphere and the Earth’s surface.”; and
• Figure 65 is NREL’s offshore wind speed measurements for 2006, 2008, 2009. This was based on “GIS data for offshore wind speed (meters/second). Specified to Exclusive Economic Zones (EEZ). Wind resource based on NOAA blended sea winds and monthly wind speed at 30km resolution, using a 0.11 wind sheer to extrapolate 10m - 90m. Annual average >= 10 months of data, no nulls.” Units: m/s at 90m above ground level (AGL).
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Figure 64 Onshore Wind Speeds (m/s)
Source: 3TIER’s Global Wind Dataset 5km onshore wind speed at 80m height units in m/s.
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Figure 65 Offshore Wind Speeds AGL (m/s)
Source: National Renewable Energy Laboratory (NREL) The following summarises the wind potential in each of the countries: Cambodia: Cambodia does not have vast wind resources. On average the wind speeds across the country are under 3m/s. The technical potential represents an upper limit and shows 1,380 MW categorised at or above good wind speeds33. Nevertheless, some parts of Cambodia may present opportunities for wind developments34. These wind resource areas are generally in the southern part of the great lake Tonle Sap, the mountainous districts in the southwest and the coastal regions (Sihanoukville, Kampot, Kep and Koh Kong regions) and have an annual average wind speed of 5m/s or greater. Although the potential in Cambodia is small relative to the other GMS countries wind may be viable given Cambodia’s relatively low energy levels and technical maturity of wind technology. Wind pilot projects, in part financed by the government of Belgium and the European Commission, are currently in place in the country. As reported by NASA Atmosphere Science Data Centre for the locations that have the highest average wind speeds throughout the year, a number of locations in
33 Study was based on global winds and were not supported by ground measurements 34 Blue Circle, wind developer, has identified 500 MW of wind developments that are technically feasible by 2020.
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Cambodia record high wind speeds during the period of November to February. The locations with the best wind potential are along the country’s eastern region and south-west coastal area. 3TIER’s Global Wind Dataset has also provided average annual wind speed largely consistent with the NASA’s lower resolution information, with greatest potential found in the northeast of the country and a belt of wind potential from the south coast towards the west border of Thailand. This also shows that there does not appear to be high offshore wind potential.
• Lao PDR: Lao PDR has a wind potential estimated at approximately 26,000 square kilometres with wind speeds between 7-9 m/s. The resource mapping in Wind Energy Resource Atlas of Southeast Asia shows approximately 2,800 MW at ‘very good’ and ‘excellent’ wind speeds. According to the 2015 ADB study “Renewable Energy Developments and Potential in the Greater Mekong Subregion”, Lao PDR has a theoretical wind energy potential of 455 GW and a potential production capacity of about 1,112 TWh/yr. To get these estimates, the land area suitable for wind power result was multiplied by the average amount of wind power capacity that can be installed in a given area (assumed to be 10 MW/km2). However, the technical wind energy potential would be much less due to the limitations of the overall power generation and transmission grid systems. 3TIER’s Global Wind Dataset has also provided average annual wind speed at 80 meters above ground level. This has located regions of high potential along the border with Viet Nam and in the south of the country, as well as localised areas of 6 to 7 m/s potential in the north.
• Myanmar: Myanmar has significant potential for wind energy, with reports suggesting some 365 TWh per year35 could be produced. However, the industry is currently underdeveloped. Due to the initial high cost of wind energy, its development is mostly at the experimental and research phase. The evaluation of wind energy resources using modern systems has been conducted since 1998, led by the Myanmar Scientific and Technological Research Department and the Department of Meteorology and Hydrology. Judging from existing data, the western part of the country appears to have the best potential for harnessing wind power. As reported by NASA Atmosphere Science Data Centre for the locations that have the highest average wind speeds throughout the year. May to September and November to December are the periods with highest wind speeds recorded. Regions with the greatest solar power potential are located along the coastline of the country. There are also some locations with good potential within the central region and in the north. In general, an issue for wind generation in
35 MOEP; http://www.asiatradehub.com/burma/energy6.asp
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Myanmar is the distance of the locations with the greatest potential from demand centres. At present, there are three wind turbines operational in Myanmar, including the 1.2 kW turbine installed at the Technological University in Shwetharlyoug Mountain (Kyaukse) Township, another 1.2 kW turbine at the Government Technical High School (Ahmar) in Ayeyarwaddy region, and a 3 kW wind project at Dattaw Mountain in Kyaukse Township. Others are appearing and have been reported.
• Thailand: Thailand has an annual average wind speed of 4-5 meters per-second at an elevation of 90 meters above sea level. Higher wind speeds of 6-7 meters per second can be found in mountain ranges in the south and the northeast during the period of the monsoons. There is potential for utilisation of wind turbines for power generation throughout the country, particularly along the sea shores and on islands either in the Gulf of Thailand or Andaman Sea. Low- speed wind turbines can start rotating at wind speeds of 2.5 meters per- second and generate a full load of electricity at 9 meters per-second. Wind speed in Thailand is mainly influenced by the northeast monsoon, the southwest monsoon and local topography. The total onshore wind potential in Thailand is estimated at up to 30,000 MW and 7,000 MW for offshore wind around the Gulf of Thailand36. As reported by NASA Atmosphere Science Data Centre for the locations that have the highest average wind speeds throughout the year, the periods of June to August and November to December have been recorded with highest wind speeds. The main wind locations are located along the country’s southern and central regions which are close to the metropolitan load centre. There are also some locations with significant wind potential further to the north. The DTU Global Wind Atlas dataset has also provided quite consistent assessments, with wind potential existing to both the east and west coastlines of the Thailand’s peninsular in the south. The Thai government supports investors with special incentives for investing in wind energy. In addition, the Department of Alternative Energy Development and Efficiency (DEDE) has initiated the Demonstration Project on (Micro) Wind Power Generation at a Community Level, since 2007, by supporting the installation of micro wind turbine sets for one kilowatt power generation. The targeted areas are 60 communities nationwide. This effort is intended to promote production of wind turbines and increased use of wind energy in the future. Wind Energy Holding Co., Ltd, a wind project developer, has already finished installing windfarm projects called “West Huay Bong 3” and “West Huay
36 Wind Energy Resource Atlas of Southeast Asia (TrueWind Solutions, 2001), Renewable Energy Developments in the Greater Mekong Subregion (ADB, 2015), Offshore wind power potential of the Gulf of Thailand (Waewsak, Landry, Gagnon, 2015)
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Bong 2”. Both wind farm projects, located in Nakhon Ratchasima, have capacity of 103.5 MW each and started commercial operation since November 2012 and February 2013, respectively. Additionally, the company has a long-term investment plan for wind farms with a total installed capacity of 1,000 MW by 2017. Under the 2015 AEDP, Thailand targets some 3,002 MW of wind farm installed capacity by 2036.
• Viet Nam: Viet Nam is considered to have quite good wind energy potential. However, like many other developing countries, the potential of wind power in Viet Nam has not yet been quantified in detail. According to the World Bank study (2011), a total of 10,000 MW of wind capacity could be theoretically exploited at surfaces with 80 m height and with wind speeds over 6 m/s. The study identified that Binh Thuan province has the greatest wind potential being measured. According to the data collected by NASA Atmosphere Science Data Centre, many locations in Viet Nam have been recorded with reasonable wind speeds throughout the year except for April, May and September. Geographically, locations with strong wind are located along the country’s south central and central coastal areas Different reports have indicated that since 2007 Viet Nam has planned up to 50 wind power projects. However, many of these projects have not progressed due to various difficulties and barriers. Vietnam had 83.2 MW of wind power capacity added in January 2016, as a result of the Bac Lieu wind farm expansion from 16 MW to 99.2 MW. The total wind installed capacity as at Jan 2016 is 135.2 MW. A limited amount of data is reported by Institute of Energy in relation to offshore wind resources at a height of 10m for 11 islands and at a height of 60m for two islands. The information is limited, but it appears that Viet Nam has potential for offshore wind with a little under half the sites having been tested being rated as “good” or better for offshore. According to the latest development strategy of renewable energy of Viet Nam approved by the Prime Minister, total electricity production from wind power would increase from 180 million kWh in 2015 to about 2.5 billion kWh in 2020 (1% share), approximately 16 billion kWh in 2030 (2.7%) and about 53 billion kWh in 2050 (5%).
3.3 Power Generation Potential from Biomass In relation to biomass potential the following summarises the main prospects by GMS country:
• Cambodia: Cambodia has significant biomass resources such as that from natural forests, plantation forests, rice husks and palm trees but this has
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dropped significantly as a result of logging and clearing of forestland. Biomass can be used for power requirements or converted into other fuels. The 2015 ADB study estimated Cambodia’s theoretical biomass energy generation potential at 15,025 GWh/year. Biomass-based energy generation in Cambodia has gained momentum during the last 2-3 years applying biomass gasification technology both for captive consumption as well as electricity generation and supply companies although energy conversion efficiency is low and applicable to mainly small scale projects. Several larger scale projects are planned at various sugar cane and palm oil plantations. There are also various other smaller biomass pilot projects at rice mills, ice factories, brick factories and garment factories, of around 40 projects with capacities between 150 kW and 700 kW.
• Lao PDR: Lao PDR has vast forest coverage around 100,000 square kilometres or about 45% of its land. In addition, a large amount of agricultural residues representing significant energy potential can be harvested. Projections of biomass potential based on the ADB study “Renewable Energy Developments and Potential in the Greater Mekong Subregion” suggest an energy potential of around 17,000 GWh per year or up to 2,300 MW is achievable for Lao PDR by 2050.
• Myanmar: Approximately two-thirds of primary energy in Myanmar is supplied by biomass including fuelwood, charcoal, agriculture residue, and animal waste. Fuelwood accounts for more than 90% of biomass-sourced energy, most of which is harvested from natural forests and used in both urban and rural areas. Charcoal, which accounts for 4% - 6% of total fuelwood consumption, is mainly used in urban areas. The annual consumption of fuelwood per household is estimated to be about 2.5 cubic tons (4.5 m3) for rural households and 1.4 cubic tons (2.5 m3) for urban residents37. According to MOEP, use of biomass for off- grid electricity production is currently not significant, with only 5 MW of capacity currently installed. The 2015 ADB study (Renewable Energy Developments and Potential in the Greater Mekong Subregion) suggests total theoretical energy potential from agricultural residues at around 60,000 GWh per annum38. Projections we have made suggest that around 48,000 GWh/year of generation from biomass would be possible by 205039.
• Thailand: Thailand has a huge agricultural output, such as rice, sugarcane, rubber sheets, palm oil and cassava. Part of the harvest is exported each year, generating billions of baht revenues for the country. In processing these agricultural products, a large amount of residues is generated which can be exploited as a feedstock to generate electricity. As of end 2014, Thailand is estimated to have some 400 MW of biomass power production capacity
37 ADB Myanmar Energy Sector Initial Assessment (2012) 38 Rice husks, rice straw, corn cob, cassava stalk, bagasse, sugarcane trash, and oil palm and coconut residues. 39 Based on improved efficiency and collection rates over time, as well as a growing agricultural sector.
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installed. The AEDP2015 has put in place a target for biomass power capacity of 5,570 MW by 2036.
• Viet Nam: As an agricultural country, Viet Nam has significant potentials for power generation from biomass. Typical forms of biomass include wood energy, crop waste and residues. Sustainable exploitation capacity of biomass for energy production in Viet Nam is estimated at about 150 million tons per year40, with overall power generation potential of around 11-15 GW from biomass. According to the latest development strategy of renewable energy of Viet Nam, total biomass electricity production is targeted to increase from 0.6 billion kWh in 2015 to nearly 7.8 billion kWh in 2020 (3% share), approximately 37 billion kWh in 2030 (6.3%) and 85 billion kWh in 2050 (8.1%).
3.4 Power Generation Potential from Biogas In addition to biomass / solid waste potential, there is also the potential to generate electricity from biogas. Biogas potential based on the ADB study “Renewable Energy Developments and Potential in the Greater Mekong Subregion” estimates Cambodia to have a technical potential primarily based on livestock manure of around 13,590,766 kWh/day. Lao PDR’s biogas energy technical potential from livestock manure has been estimated at around 8,540 MWh per day. Over the past 10 years, in Myanmar, around 150 community-based biogas digesters (plants) have been built, mostly in the central region (Mandalay, Sagaing, and Magway divisions) and in the Northern Shan State. The digesters vary in capacity (from 25 to 100 cubic meters) and electricity output ranges from 5–25 kW. While the combined output of these digesters is modest, it has been enough to serve some 172 villages with four hours of electricity per day. In Thailand, biogas power likewise has high potential in Thailand due to the abundant availability of industrial waste and livestock manure. According to AEDP2015, the installed capacity of Thai biogas sources was 312 MW at end of 2014 and has set a target of 600 MW by 2036. This could be supplemented by some 500 MW of installed capacity that would be based on power generation from municipal waste. Finally, Viet Nam is considered to have reasonable potential for power generation from biogas sources with typical forms being animal waste, urban waste and other organic waste. Some 4-5 GW of generation from biomass has been estimated.
3.5 Hydro Power All GMS countries have hydro power potential, and many have significant amounts of untapped potential. We summarise the hydro power potential of each country in each subsection that follows.
40 http://ievn.com.vn/tin-tuc/Tong-quan-ve-hien-trang-va-xu-huong-cua-thi-truong-nang-luong-tai-tao-cua-Viet- Nam-5-999.aspx
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3.5.1 Cambodia’s Hydro Power Potential Cambodia has an estimated hydro potential of 10,000 MW, with currently less than 10% developed. Approximately 50% of these resources are located in the Mekong River Basin, 40% on tributaries of the Mekong River, and the remaining 10% in the south-western coastal areas. Hydro has been a focus of recent developments with previous studies highlighting 42 potential hydropower projects, with a total installed capacity of 1,825 MW, being capable of generating around 9,000 GWh/year of electricity. By the end of 2014, approximately 930 MW of hydropower installed capacity had been in operation, 800 MW was under construction and another 198 MW being considered for feasibility.
3.5.2 Lao PDR’s Hydro Power Potential Hydropower is the most abundant energy resource in Lao PDR. There is an estimated potential of 23,000 MW along the Mekong River and its sub-basins. By 2014, around 3,200 MW has been developed and is supplying domestic demand and other neighbouring countries. Figure 66 summarises the information about capacity of the existing, committed and considered projects in Lao PDR. There is currently 6,000 MW of committed projects in the pipeline with 75% of it planned for export. For implementation of this plan, the Lao Government has opened up development opportunities to the neighbouring governments (Thailand, Lao PDR, and Viet Nam) and foreign companies. The country’s small hydropower potential is also substantial, estimated to be around 2,000 MW. The development of small hydropower (capacity up to 15 MW) could also play an important role in meeting the country’s objectives of increasing rural electrification coverage from the current level of 70% to 90% in 2020. There are 75 smaller hydro projects as at the end of 2013 at various stages. There are also approximately 60,000 micro units installed in Lao PDR servicing 90,000 households.
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Figure 66 Lao PDR Hydro Projects: Existing, Committed and Considered
Exis[ng (Export - Vietnam) 250
Exis[ng (Export - Thailand) 2,335
Exis[ng (Lao PDR) 611
Commided (2020) 2611.8
Considered on Mekong (Poten[al Vietnam Export) 1,288
Considered on Mekong (Poten[al Thailand Export) 3,632
Other Considered (Poten[al Vietnam Export) 1,635
Other Considered (Poten[al Thailand Export) 2,426
- 1,000 2,000 3,000 4,000 MW
Source: Compiled by Consultant
3.5.3 Myanmar’s Hydro Power Potential Hydropower is by far the dominant source of electricity in Myanmar, accounting for around 70% of both the capacity mix and annual production. Various studies have reported Myanmar has huge hydropower potential, estimated to be at 108 GW, from its four main river basins: Ayeyarwaddy, Chindwin, Thanlwin and Sittaung. Myanmar Electric Power Enterprise (MEPE), under the MOEP, has so far identified more than 300 locations suitable for hydropower development, with a combined potential capacity of about 46,000 MW. Among these locations there are as many as 92 potential sites for construction of medium to large power plants, each having capacity greater than 10 MW. These hydro sites have been grouped into 60 potential hydro projects including 10 projects that are in various stages of development. Similarly, as many as 210 small and medium size sites each have less than 10 MW potential. A total potential installed capacity of 231 MW has been identified. The majority of hydropower potential is located on the eastern side of the country in Kayin State (17 GW potential), Shan State (7 GW potential) and Kayah State (3.9 GW potential). At the present, just over 4,000 MW of hydro power capacity has been developed, representing just a small portion of the estimated potential of 46 GW for the country. Until 2030 and beyond, thirty-six projects have been formed to realise the
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FINAL untapped hydro power resources, most of them would be built under a JV/BOT basis by foreign investors and only small portions of the projects would be funded by Ministry of Electric Power and domestic entrepreneurs. Small hydropower projects for border area development: Over the past 5 years, some 26 micro and 9 mini-hydropower power projects have been developed by MEPE, with installed capacity ranging from 24 to 5,000 kW. These projects have included border areas, aimed at improving the social and economic conditions of poor rural households and remote communities. These mini-hydropower projects also facilitate cottage industries and enhance agricultural productivity through improved irrigation.
3.5.4 Thailand’s Hydro Power Potential The potential of hydropower in Thailand is estimated at 15,155 MW41. Hydropower has been developed for power generation since 1964 with the construction of several large hydropower projects throughout the country. As of December 2014, hydro installed capacity was 3,444 MW, accounting for 10% the total system capacity. It is noted that the annual volume of electricity generation from hydro has not changed much since decades ago. In 2014, the hydropower generated 5,163 GWh, accounting for less than 3% of the total generation of 180,945 GWh, compared to around 20% back in 1986 42 . The environmental externalities associated with exploiting hydro beyond the current 3.5 GW of large scale hydro already developed is regarded to be unsustainable and there is strong resistance to further developments. The government has therefore focused on and promoted small hydro power projects. The government has been sponsoring development projects of small hydro power plants for a new planned capacity of 350 MW. The DEDE and the Provincial Electricity Authority (PEA) are the main institutions involved with mini- and micro- hydro power plants. DEDE has also installed many village-level hydropower plants, and there is considerable potential for village-scale small hydro in east and central Thailand. According to the 2012 Alternative and Renewable Energy Development Plan (AEDP2012), Thailand planned to increase small hydropower capacity from 102 MW in 2012 to 1,608 MW by 202143. Nevertheless, the latest AEDP2015 has reduced this target to 376 MW for 2036.
3.5.5 Viet Nam’s Hydro Power Potential Viet Nam has high potential for hydro power. The country has some 2,360 rivers and streams that exceed 10km. The main river systems are illustrated in Figure 67 The Red River system in the north comprises the Da and Lo-Gam-Chay river basins,
41 Greenline Energy: http://www.greenlineenergy.com.au/index-4.html 42 EPPO 2015 Statistics: http://www.eppo.go.th/info/5electricity_stat.htm 43 Thailand Alternative Energy Industry: http://www.slideshare.net/boinyc/thailands-alternative-energy-industry
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FINAL the Mekong river delta is in the south. In the central region, there are many river basins, including the Ma River, Ca River in the north central area, Vu Gia – Thu Bon River in the central area, Sesan River and Srepok Rivers are in the central highlands and the Ba River is in the coastal area. The Dong Nai River basin is in the south.
Figure 67 Illustration of the Main River Systems in Viet Nam
Lo-Gam-Chay River Basin
Da River Basin
Ma-Chu River Basin
Ca River Basin
Huong River Basin Vu Gia – Thu Bon River Basin
Sesan River Ba River Basin Basin
Srepok River Basin Dong Nai River Basin
Source: Consultant In 2013, hydro power accounted for 47.5% of the country’s total 30,473 MW installed generating capacity. In 2014, hydropower production was 59,479 million kWh, accounting for 41.41% total electricity supply. Currently, Son La hydropower plant is the largest power plant with 2,400 MW installed capacity. According to the latest development strategy of renewable energy of Viet Nam, electricity production from hydropower sources would increase from approximately 56 billion kWh in 2015 to nearly 90 billion kWh in 2020 and to approximately 96 billion kWh from 2030.
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Large Hydro Around 38% of Viet Nam’s electricity is currently generated by a range of large reservoirs and in some cases cascaded hydropower stations that are located throughout the country. The largest reservoirs, Hoa Binh and Son La, are located in in the north west of the country, although there are significant storages located in the central and south regions as well. Viet Nam is able to gain the benefits of diversity in hydrological conditions across many separate river systems with notable diversity in inflows across north, central and south regions. However, Viet Nam has largely exploited all of the large scale hydro considered to be economically feasible; further development beyond what has been exploited to date and what is under construction now is not considered an option. Small, mini, and micro hydro Viet Nam has untapped small scale hydro potential. In recent years, there has been a lot of small hydropower development in Viet Nam with the number of projects going from about 141 in 2006 (167 MW) to about 156 (622 MW) by 2009, and some 226 projects (1,635 MW) by 2014. Some 1,943 MW of capacity is now under construction, and some 236 projects (with total capacity of 2,019 MW) under study. However, concerns have been raised on small hydro projects in the country based on considerations of the low levels of efficiency achieved from some projects relative to the environmental externalities. Recent revisions of the hydroelectric planning have recognised this issue and indicated that there have been 424 projects eliminated corresponding to reduction of around 34% of the projects that had previously been planned. Pumped storage hydro Viet Nam does not presently have any pumped storage hydro plant in operation. However, feasibility studies have been carried out and show that pumped storage power plants may be feasible with the south and central regions offering the most favourable geographical conditions. Pumped storage hydro plants do feature in government plans for the electricity industry. The National Master Plan for power development for the 2011-2020 period with the vision to 2030 has included five pumped storage hydro plants to be constructed between 2019 and 2030. These projects include Bac Ai 1 (4 x 300 MW), Dong Phu Yen (4 x 300 MW), Don Duong (4 x 300 MW), Ninh Son (4 x 300 MW) and Pumped Storage Hydro plant in the North (3 x 300 MW). According to the latest Prime Minister’s Decision No. 2068/QD-TTg dated 25 November 2015, approving the development strategy of renewable energy of Viet Nam by 2030 with a vision to 2050, pump storage hydro installed capacity should target 2,400 MW by 2030 to 8,000 MW by 2050.
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3.6 Geothermal Energy Some GMS countries have modest levels of geothermal energy potential. Based on research collected from a variety of sources:
• Lao PDR: Based on proceedings to the 2014 World Geothermal Congress, 11 geothermal resources have been identified in Lao PDR in a study of the country’s northern mountainous areas. The sources are believed to be of the low temperature type and unlikely to support power projects on a large scale. The Asian Development Bank (ADB) has however reported that some 59 MW of geothermal generation capacity could be developed in Lao PDR.
• Myanmar: Geothermal energy is considered to be reasonable in Myanmar, with potential for commercial development. Ninety-three geothermal locations have been identified throughout the country. Forty-three of these sites are being tested by the Myanmar Oil and Gas Enterprise (MOGE) and MEPE, in cooperation with the Electric Power Development of Japan and Union Oil Company of California and Caithness Resources of the United States. Areas identified with considerable geothermal potential include Kachin, Shan, and Kayah states, Kayin, Kayah, Mon, Taninthayi, and also the southern part of Rakhine.
• Thailand: There are approximately 64 geothermal locations in Thailand, but major ones are in the north of the country, especially the geyser field at Fang District in Chiangmai Province. Survey on the potential of geothermal energy development at Fang District commenced in 1978, with technical assistance and experts from France later in 1981. Currently, EGAT is operating a 300 kW binary cycle geothermal power plant at Fang District, generating electricity at about 1.2 million kWh per year, which helps reduce oil and coal consumption for power generation. Thailand’s AEDP2012 set a target of 1 MW of geothermal and 2 MW of tidal capacity built by 2021. Nevertheless, this target has been removed from AEDP2015.
• Viet Nam: Presently there are no geothermal power plants in Viet Nam. However, based on surveys and studies carried out over the last few decades on geothermal energy resources, the country is estimated to have the geothermal potential in between 300 MW and 400 MW with the following areas / regions being identified as the prime candidates: - ORMAT in coordination with EVN undertook a pre-feasibility study and it is understood that the findings led to them applying in April 2012 for a license to build 5 geothermal energy plants in Le Thuy (Quang Binh), Mo Duc, Nghia Thang (Quang Ngai), Hoi Van (Binh Dinh) and Tui Bong (Khanh Hoa) with total capacity of the generators in the range from 150 to 200 MW; - Viet Nam Geothermal Energy Corp is also reportedly working with Ormat as the major technical partner for two projects in Mo Duc and Tu Nghia
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district, Quang Ngai province with a designed capacity each being 18.7 MW; and - In 2013, Quang Tri Province granted an investment certificate and construction permit for a geothermal energy plant with a capacity of 25 MW at Dakrong and according to press, the project’s price tag has been stated as US$46.3 million. There has been no potential for geothermal energy reported for Cambodia.
3.7 Ocean Energy There are currently no available studies suggesting any significant ocean/marine potential in Cambodia, Lao PDR or Thailand. However, ocean energy options have been identified for Myanmar and Viet Nam44:
• Myanmar: Myanmar has a vast coastline that is 2,832 km long. There is potential for tidal and ocean current energy given the strong currents and tides along the coast. The first tidal power plant was commissioned in 2007 in Kambalar village. It has a 3 kW turbine and provides electricity to 220 village households. The country is estimated to have wave energy potential between 5 to 10 kW/m45.
• Viet Nam: Viet Nam’s 3,200 km coastline and thousands of islands present significant potential for wave and tidal-based energy technologies. The country is estimated to have a tidal energy potential of around 1,753 GWh per year and wave energy potential between 40 – 411 kW/m located around Binh Thuan and central Viet Nam. The government has included ocean energy as part of its Viet Nam Marine Strategy to 202046.
3.8 Coal Resources Coal deposits of varying grades are scattered throughout the GMS:
• Cambodia: Estimates of coal reserves in Cambodia are low. Coal reserves are known to exist in the Stung Treng province located in northern Cambodia and as of early 2013, 14 exploration licenses have been issued to companies for local coal exploration. Cambodia’s first 120 MW coal-fired power plant commenced operation in February 2014 in Steung Hav District, Sihanoukville Province.
• Lao PDR: Lao PDR has coal reserves estimated at approximately 900 million tons, comprising mostly of lignite, and anthracite to a much smaller extent at various sites. Main lignite basins lie in Hong Sa, Viengphoukha and Khangphaniang. Located in the north western region, Hong Sa is the largest known reserve of lignite, with 400-700 million tons being reserved for power
44 Based on “Ocean renewable energy in Southeast Asia: A review” by Quirapas, Lin, Abundo, Brahim, Santos, 2014. 45 kW per metre of coastline. 46 Refer to: http://english.vietnamnet.vn/fms/special-reports/144832/vietnam-and-the-marine-strategy.html
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generation. The country’s first coal-fired power plant - 1,878 MW Hong Sa Lignite Power Project - would be completed by 2016 with 1,473 MW already sold to EGAT. Anthracite (and bituminous) can be found at various sites, including Saravan and Phong Saly provinces, with the total proven resource at approximately 100 million tons. Currently 130,000 tons of production is used for local factories and export purposes and the government has a plan to support a 500 MW coal unit depending on further exploration success.
• Myanmar: Coal has historically been of minor significance in Myanmar although the country possesses reasonable reserves of coal, it is generally of low quality. There are some 500 occurrences and over 200 deposits, of which around 34 are considered to warrant some attention in terms of exploitation. According to Ministry of Mines (MOM) data, Myanmar’s combined coal reserves have been proven to be some 405.89 million tons in various categories. Significant deposits have been identified in Magway, Tanintharyi, Shan State and Ayeyarwady regions. Most of Myanmar’s coal resources were formed during the Tertiary period and are of lignite to sub-bituminous grade. Coal found in Shan State tends to be of lower quality (sub-bituminous). Closer analysis of Myanmar’s domestic coal reserves, taking into account factors such as deposit size, and the calorific value suggests that exploitation of domestic coal for power generation would only be feasible on a small scale (fluidised bed for example). This implies that future coal power plants if developed, would depend on coal imports.
• Thailand: According to BP Statistics, Thailand proven coal reserves at end of 2013 were estimated at 1,239 million tons, consisting of lignite and sub- bituminous grades of coal. The country’s major coal sites include the Mae Moh basin operated by the Electricity Generating Authority of Thailand (EGAT), the Krabi basin, the Saba Yoi and Sin Pun basins in the southern area, and the Wiang Haeng, Ngao and Mae Than basins in the north. Based on EPPO statistics, the production of domestic lignite over the 2003-2014 period was stable at around 18 million tons per year, whereas the coal imports have substantially increased, from 7 million tons in 2003 to 20.9 million in 2014, surpassing the domestic supply. Most of the domestic lignite supply (17.1 out of 18 million tons in 2014) is produced by EGAT owned and operated Mae Moh Mine, which then is fully consumed by EGAT coal fired power plants. More than 25 million tons of coal was used for electricity generation in 2014. This accounted for around two thirds of the total consumption.
• Viet Nam: Viet Nam is a country with relatively abundant coal reserves. By January 2011, the results of investigations indicated that Viet Nam has coal reserves of around 48.7 billion tons, of which, some 39.35 billion tons, lie beneath the Red River basin in an area of size 2,000 km2. The Northeast of Viet Nam possesses the second largest coal deposit with reserves estimated to be around 8.83 billion tons. The Northeast is the largest mining area in the country because it is currently not feasible to exploit coal from the Red River basin, as
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deposits lie some 150-2,500 meters underground necessitating large investment and modern mining technologies that are currently not available in Viet Nam. In addition, the Red River deposit has complicated hydrogeological features and is located in a populous area. As such coal production is mainly carried out in the Northeast of the country. As of 2012, Viet Nam is the 17th largest coal producer in the world. Coal production in Viet Nam has increased rapidly from 11.6 Mt/y in 2001 to 44.5 Mt/y in 2011. The large increase in production is due mainly to increases in coal exports, although domestic consumption has also increased significantly from 2009, driven in part by the commissioning of coal plants in the north. Coal reserves from Viet Nam have almost entirely been produced in the form of anthracite, sourced from Vinacomin (Viet Nam National Coal Mineral Industries Holding Corporation Limited) mines and used in industry, the electricity sector, and sold as exports.
3.9 Imported Coal Generally, it is recognised within the GMS that for large scale coal generation, there would be a need for imported coal and the development of facilities to support coal import. Indonesia and Australia are the two most feasible countries to import coal from due to their close proximity, coal quality, level of coal reserves and stage of development in terms of transportation and coal handling facilities.
3.10 Offshore Natural Gas Resources
3.10.1 Cambodia’s Natural Gas Reserves Cambodia currently imports all of its oil and natural gas from Singapore, Thailand and Viet Nam. There is an estimated 14 trillion cubic feet of gas reserves in Cambodia including its offshore basins47. In 2005 it was announced gas was found in one well (Block A). To date no gas (and oil) production has commenced due to the uncertain legal framework and insufficient service capacity and infrastructure to support the processing. The government however continues pushing towards oil and gas production, hoping for it to happen sooner rather than later to reduce energy reliance on other countries.
3.10.2 Lao PDR’s Natural Gas Reserves Lao PDR has no confirmed reserves of oil or gas, however, the Government has issued two exploration concessions in central and southern Lao (Salamander Energy Group and Petro Viet Nam respectively). Significant work remains to be done to determine the results. Based on this the likelihood of indigenous oil or gas reserves making an impact to the electricity sector development in the next 10-20 years is low. Consequently, Lao PDR imports petroleum products from other countries, with these products being used
47 Shared with Thailand and contingent on territorial negotiations, http://www.upi.com/Business_News/Energy- Industry/2012/09/27/Cambodia-gears-for-offshore-drilling/UPI-86021348765641/, accessed June 2015.
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3.10.3 Myanmar’s Natural Gas Reserves According to ADB Myanmar Energy Sector Initial Assessment (2012), Myanmar’s natural gas reserves have been estimated to be 11.8 trillion cubic feet (Tcf). Offshore gas discoveries have been significant. Two major offshore gas fields, Yadana (5.7 Tcf) and Yetagun (3.16 Tcf), were discovered in the 1990s in the Gulf of Moattama. The two fields have been supplying natural gas to Thailand since 2000, at a rate of about 755 million cubic feet per day (MMcfd) from the Yadana field and 424 MMcfd from the Yetagun field. In 2004, Daewoo International Corporation discovered the new Shwe gas field, off the coast of Sittwe, with estimated reserves of about 5 Tcf. Production from the Shwe field was commenced in 2013, for export to the PRC, through an overland pipeline from Myanmar to Kunming, Yunnan Province. The pipeline will have capacity of about 500 MMcfd, with a possible expansion to 1,200 MMcfd. The BP statistics in 2014, on the other hand, estimated Myanmar’s proved reserves of natural gas to be at some 283.2 billion cubic metres (Bcm) or 10,0 Tcf, representing around 52% of the total proved natural gas reserves of the GMS. Figure 68 plots proved natural gas reserves for the GMS countries and the reserves to production ratio (RPR)48 in years. For Myanmar, the number is relatively low because a number of fields with proven reserves have already been put into production.
48 The RPR is the proved reserves divided by the amount of reserves produced each year and thus a rough measure of how many years until the resource is depleted. Further information: http://en.wikipedia.org/wiki/Reserves-to- production_ratio.
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Figure 68 Proved Gas Reserves for Myanmar, Thailand and Viet Nam
Source: BP Statistics 2014 Myanmar’s oil and gas industry involves the 100 per cent state-owned Myanmar Oil and Gas Enterprise (MOGE), foreign-invested companies and joint ventures between international and domestic firms. MOGE is responsible for natural gas exploration, domestic supply, pipelines construction, and coordination of the production sharing contracts with foreign companies. Since the year 2000, offshore production has become a key component of Myanmar’s gas sector. Total production in 2012/13 was 453,000 MMcf, more than 90% of which was from the offshore Yadana (57%) and Yetagun (34%) fields; the remainder was from the MOGE-operated onshore fields. Production in Shwe and Zawtika (scheduled to begin in 2014), is anticipated to bring Myanmar's total gas output to roughly 2,200 MMcfd by 2015. Around 80% of natural gas produced in Myanmar is for exports. As of 2012/13, the export volume was 362,000 MMcf and most of it was for Thailand; however, production from Shwe from July 2013 means that PRC has also become a significant export destination for Myanmar’s gas. Myanmar’s electricity sector accounts for around 60% of natural gas domestic consumption. Other major gas users are the government-owned factories (20%), fertiliser plants (7.9%), a compressed natural gas facility (7.2%), and Liquefied Petroleum Gas (LPG) production (0.9%). In absolute terms, the amount of natural gas used for power generation has increased nearly two-fold over the period 2001 – 2013, from 29,066 MMcf to 57,333 MMcf per year.
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3.10.4 Thailand’s Natural Gas Reserves Thailand is estimated to have some 285 Bcm (10.1 Tcf) of proved reserves, or around 6.8% of the total proved natural gas reserves of the GMS. Thailand has a low RPR number, meaning that the majority of fields with proven reserves have already been put into production. Upstream oil and gas activities are dominated by PTT Exploration and Production (PTTEP), a subsidiary of PTT Public Company Limited (PTT). The PTT Group has business areas across supply procurement, transportation, distribution, gas processing, investment in natural gas vehicle (NGV) service stations, and investments in related businesses through the Group’s subsidiaries. Eighty-five per cent of Thailand’s petroleum reserves are located in the Gulf of Thailand, which is characterised by clusters of small wells in shallow water and over 300 platforms According to EPPO statistics, Thailand’s total natural gas production in was 42.1 billion cubic metres in 2013, which was nearly twice as much the 2003 production volume of 21.5 Bcm. Despite increases in production, Thailand is relying on gas imports from Myanmar to meet the domestic demand. In 2014, it imported 10.6 Bcm of natural gas in LNG purchases and via pipelines from Yadana, Yetakun and Zawtika gas fields in Myanmar. Current imported volumes account for around 20% of the total natural gas supply. It is evident that future gas demand growth will have to be met by increased gas imports, and particularly LNG, as domestic supplies progressively deplete49. The total consumption in 2014 was some 48.4 Bcm, of which 28.5 Bcm was used for electricity generation. Although gas consumption by the power sector has increased one third in volume over the period from 2003 to 2014, its share in the total consumption declined, from 77% in 2003 to 59% in 2014. This indicates that use of natural gas by the other sectors including industry, gas subcooled process (GSP) and NGV has been growing at faster rates.
3.10.5 Viet Nam’s Natural Gas Reserves Viet Nam is a coastal country with several hundred thousand square kilometres of continental shelf in which seven tertiary basins have been identified. Gas reserves have been found in five of the seven offshore basins50: Song Hong, Phu Khanh, Nam Con Son, Cuu Long and Malay-Tho Chu. These are shown in Figure 69.
49 Enerdata, 2014: http://www.enerdata.net/enerdatauk/press-and-publication/energy-news-001/thailand-natural- gas-conundrum_29249.html 50 “BCC Contract Signed for Billion Gas Pipeline Project,” PetroVietnam, March 11, 2010, http://english.pvn.vn/?portal=news&page=detail&category_id=11&id=3278.
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Figure 69 Gas Reserves (on the left) and Offshore Gas Fields and Pipelines51 (2015)
In summary the status of upstream fields that are in production is as follows:
• Cuu Long, which is in production and is an oil-prone basin that is in decline;
• Nam Con Son, which is in production and which is a gas-prone basin that is also in decline; and
• Malay-Tho Chu, which transports natural gas to Ca Mau from Block PM3 CAA and the Cai Nuoc field; an offshore area administered jointly with Malaysia. The following are potential offshore reserves that could be exploited in the country:
• Block B – in the Malay Basin under the operatorship of Petroleum of Viet Nam (PVN) with Mitsui and PTT as partners, with reserves estimated to be > 4 TCf; and
• Cai Voi Xanh – in the Song Hong Basin off the central coast, operated by ExxonMobil, has been identified to have reserves of 5 TCf. Figure 69 shows on the right the offshore gas fields and pipeline infrastructure in Viet Nam. This shows the existing infrastructure for gas in Viet Nam as well as some previously planned developments that were under consideration but now deferred (dashed line). Gas production is observed to have ramped up since 2003 and again in 2007 coinciding with the commissioning of the Nam Con Son Gas Project and the Ca Mau pipeline developments. Around 85% of natural gas consumption is attributable to power generation, 10% for fertilizer production, and the rest provided to low pressure gas networks or as LPG to industrial consumers. Current gas supply is only
51 Note that the diagram is not to scale and is intended to be of a conceptual nature. Source: IES.
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satisfying 60% of the demand for gas for Viet Nam’s power demand, 30% of the demand for fertilizer feed stocks and 60% of the demand for LPG52.
3.11 Liquefied Natural Gas Liquefied natural gas (LNG) import facilities have only been developed in Thailand. However, feasibility studies have been undertaken in Cambodia, Myanmar and Viet Nam. Comments on each country are:
• Cambodia: The economics underpinning an LNG terminal need to break even with the benefits associated with developing their offshore reserves and given the present global outlook for fuel prices, the development of an LNG terminal in Cambodia seems unlikely to materialise within the next decade.
• Myanmar: While rich in offshore natural gas potential, Myanmar has entered into gas sales agreements (GSAs) that require the majority of its proven natural gas reserves to be exported to neighbouring countries. In the case of Myanmar, the feasibility of LNG import terminals have been studied as well but at a time when global oil prices made the concept infeasible. The rationale for studying an LNG terminal is predicated on the existing GSAs only allowing a fraction of the natural gas reserves to be directed to domestic uses and a scenario of significant increase in demand for natural gas as expected under the present economic outlook for Myanmar. This combined with an outlook of low international natural gas prices may make sense.
• Thailand: The Map Ta Phut LNG facility in the eastern province of Thailand has been operating only at a partial output as domestic demand is being met primarily by imported supplies. According to PTT, its imports of LNG reached around 2 million tonnes over the last year and it is planning to more than double this volume for 2015, partly to help replace potential declines in pipeline imports from Myanmar. Current LNG suppliers for Thailand include Qatar Liquefied Gas Company Limited; it is also reported to be in talks with other suppliers from Mozambique, United States, Australia and Russia to secure additional long term supply contracts. The existing import terminal has a capacity of 5 million tonnes a year, and PTT is constructing a second LNG terminal at the same location and of the same capacity, with completion expected in 2017. In preparation for falling imports from Myanmar and declining domestic output from the Gulf of Thailand, PTT is also considering a plan to build an LNG receiving terminal adjacent to a gas pipeline linked to gas fields in Myanmar. PTT’s argument has been that such a site on the coast of Myanmar would offer a more convenient delivery point for LNG from Middle East suppliers.
52 Around half of Vietnam’s LPG demand is satisfied by domestic production, with the remainder imported from China, Australia, United Arab Emirates and others.
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• Viet Nam: PetroVietnam is working on the development of a major LNG-to- power complex at Son My, Binh Thuan Province, on the coast to the east of Ho Chi Minh City. Son My is conceived of as a significant onshore LNG terminal, with two phases of LNG-fired power development, each of 2,000 MW, and an initial import capacity of 3 million tonnes per annum (MTPA), and a planned expansion to 6 MTPA. PetroVietnam has memoranda of understanding with GDF Suez (now called Engie) on Son My-1 (2,000 MW) and with Shell related to the LNG terminal; additionally, there are LNG master sales agreements with Shell and Gazprom. The development of Son My terminal, however, has been delayed and it seems unlikely that Viet Nam would develop an LNG import terminal before 2020. A smaller LNG terminal on the Thi Vai river, closer to Ho Chi Minh City, has also been deferred.
3.12 Nuclear Power Nuclear Power has featured in the power development plans of both Thailand and Viet Nam for the last decade with the objective to address energy security concerns. In the case of Thailand:
• Nuclear power was included in the Thailand’s Power Development Plan 2007- 2021 (PDP 2007). This planned to have 2,000 MW of nuclear capacity in operation by 2020 and another 2,000 MW the following year. The PDP has been revised a number of times due to the change in the electricity demand, all revised PDPs have considered nuclear power53.
• Thailand had carried out the self- evaluation on Intergraded Nuclear Infrastructure Review (INIR) and submitted a report to the International Atomic Energy Agency (IAEA) in October 2010. IAEA experts conducted a mission to Thailand during December 2010 to conclude that “Thailand can make a knowledgeable decision on the introduction of nuclear power”.
• According to the PDP2010 – Revision 3, the first nuclear power plant (NPP) project was postponed for 6 years until 2026 to promote greater public understanding of NPP and fill major gaps identified by INIR mission. A pre- project phase was underway for activities such as preparation of laws and regulations, technical and safety reviews, site selection reviews, public communication, education and participation, and human resource development.
• Nevertheless, the latest PDP2015 has nuclear power generators occurring at later periods of time, with the first unit in 2035 and the second in 2036. In the case of Viet Nam:
53 IAEA, 2013: https://www.iaea.org/NuclearPower/Downloadable/Meetings/2014/2014-03-17-03-21-WS- INIG/DAY3/COUNTRY/Thailand_v1.pdf
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• In January 2006, the Prime Minister of Viet Nam signed decision No.01-2006- QD-TTg on the approval of the strategy to apply nuclear energy for peaceful purposes by 2020. The intent is to build and develop a nuclear technology industry. The strategy in place envisaged the commencement of the first nuclear power plant project in Viet Nam by 2020.
• In 2009, the National Assembly decided the first nuclear power plant of 2,000 MW capacity would be built in the Ninh Thuan province. The investigation and construction work has since then begun but the expected commencement of the plant’s operation was pushed back until 2024 due to additional unforeseen work components and tightened safety requirements as part of the fallout from the Fukushima crisis. The second plant, Ninh Thuan 2, has been scheduled to be constructed in the same location and operating from 2025.
• According to the recently revised Power Development Plant 7 (updated in March 2016), expected operation of the 1,200 MW first nuclear power generating unit (Ninh Thuan 1, first phase) has been further delayed to 2028.
3.13 Power Planning in the GMS Each power sector status is unique and each faces its own set of challenges. The key features of current power development plans for each country are summarised in Table 14. Table 14 Approach to Power Planning in each GMS Country
Country Features of Current Plans Renewable Energy Plan Energy Efficiency Plan Cambodia Most planned generation Renewable Energy Action Plan National Energy Efficiency capacity in the near term54 is in Place to promote renewable Policy has target to based on coal and hydro energy but no targets. reduce demand by 20% in projects with natural gas 2035 vs. BAU demand. development in the longer term. Lao PDR Most planned generation Renewable Energy Energy efficiency is in an capacity is based on hydro and Development Strategy (2011) early stage in Lao PDR. one coal project. Many which promotes the Some efforts have been planned hydro projects are deployment of small hydro, taken in rural geared towards export to solar, wind, biomass, biogas, electrification projects to neighbouring countries. solid waste and geothermal. consider demand side management measures. Myanmar MOEP’s publicly available plan Myanmar does not currently Apart from broad suggests hydro being dominant have in place a comprehensive directives to promote in the generation mix, followed and targeted policy for energy efficiency and by coal, gas and renewables. renewable energy. conservation, Myanmar The National Electrification does not have a concrete Plan has a target of 100% policy framework for central grid electrification by promoting energy 2030. Power development efficiency. plans continue to evolve in
54 Next 10 years.
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Country Features of Current Plans Renewable Energy Plan Energy Efficiency Plan Myanmar with the optimal generation mix being strongly debated. Thailand PDP2015 suggests a technology AEDP2015 targets some 19.6 EEP targets to reduce capacity mix by 2036 consisting GW of renewables (waste, energy intensity by 25% of around 30-40% natural gas, biomass, biogas, hydro, wind, in 2030 compared to 20% renewable energy, 20-25% solar and energy crops) by 2005 levels, or coal, 15-20% hydro, and up to 5 2036. equivalently, a 20% % nuclear power. The total reduction against a BAU new installed capacity from demand outlook. 2015 to 36 required is some 57 GW. Viet Nam The most updated PDP7 (2016 New RE targets have been In 2006, the Prime version) plans a 129,500 MW of included into the last updated Minister approved the EE total installed capacity by 2030 PDP7. Renewable sources national target to save 5% (compared to 146,800 MW in (small hydro, wind, solar and - 8% total electricity the original, 2011 version of biomass) would account for a consumption by 2015. PDP7). The capacity mix is 21% share in the capacity mix The EE target has not expected to consist of 42.6% and a 10.7% share in the been updated, but coal, 16.9% hydropower, 14.7% generation mix by 2030 generally 8% - 10% natural gas, 21% RE, 3.6% savings have been Nuclear and 1.2% imports. expected by 2020.
3.13.1 Cambodia’s Power Development Plans The Royal Government of Cambodia sets targets for the energy sector in the National Strategic Development Plan (NSDP) which sets priorities on increasing electricity supply capacity and reducing electricity tariffs to an appropriate level, while strengthening the institutions to manage the energy industry. One of the key focus areas has been to enhance access to electricity, and so an electrification master plan was established around the following three principles: (1) develop electricity generation capacity including hydropower and coal or gas, (2) leverage power imports from neighbouring countries to enhance access to provinces near the Cambodian borders, and (3) continue investments and enhancements to the national transmission system. Most of Cambodia’s committed generation capacity is currently coal and hydro projects. Cambodia has in place a Renewable Energy Action Plan (REAP) to promote renewable energy. However, there are no specific targets in place. There is also a National Energy Efficiency Policy which has a target to reduce future national energy demand by 20% to 2035 against a business as usual projection and to reduce CO2 emissions in 2035 by 3 million tons. In the longer-term, it is expected that offshore gas reserves that have been identified could be developed.
3.13.2 Lao PDR’s Power Development Plans Energy policy in Lao PDR is focused on making energy supplies affordable and reliable while also ensuring the exploitation of energy resources is done in an environmentally-friendly, efficient and sustainable manner. Key policies for Lao PDR are: (1) maintain and expand generation capabilities that will deliver
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FINAL affordable, reliable and sustainable electricity supply to promote socioeconomic development, (2) promote cross-border trade (exports) to generate additional revenue used to further reduce poverty, (3) develop policy, legal and regulatory frameworks to promote private investments and/or partnerships and (4) ensure accountability and transparency in power market developments in relation to sustainable outcomes and enhancing technical knowledge. Lao PDR’s socioeconomic policy also pushes for further industrialisation and higher electrification rates. The former has resulted in focused effort on developing special economic zones which will have implications for electricity demand and transmission development. The latter has resulted in a government electrification target of 90% by 2020, which is nearly achieved. The vast majority of Lao PDR’s generation development is based on hydro projects geared towards export55.
3.13.3 Myanmar’s Power Development Plans Myanmar's power system is currently dominated by hydro (around 70%) with gas- based generation making up most of the rest. Within the GMS, Myanmar has the highest population without access to electricity and increased economic activity over the last 5 years is straining existing infrastructure which is in great need of investment. In 2014, a World Bank study proposed a target to achieve 100% central grid electrification by 2030. MOEP, who is responsible for planning, developed a 15 year power development plan56 where demand was forecast to increase at double digit rates to 2030 and generation expanded to achieve a technology mix of around 81% hydro, 9% coal, and the rest natural gas and renewables (wind, solar and geothermal). However, since this plan was developed in 2014, there have been ongoing debates around what constitutes the most appropriate generation expansion plan to satisfy high demand growth, particularly given constraints on the amount of natural gas that is available for domestic markets57, ensuring sustainable hydro development and opposition to coal. Power sector planning in Myanmar continues to evolve, particularly in light of enhanced understanding of the country’s renewable energy potential.
3.13.4 Thailand’s Power Development Plans Thailand’s power development plan of 2015 (PDP2015) was proposed to the National Energy Policy Council (NEPC) on 14 May and subsequently approved on 15 May 2015. It is based on the following three principles: (1) energy security to support economic and social developments and to diversify the fuel mix to not be too reliant on natural gas, (2) ensure that electricity prices are cost-reflective in
55 Hong Sa coal project is the only exception. 56 http://www.ifc.org/wps/wcm/connect/46f9da00471bab5caff4ef57143498e5/1.4.Min+Khang.pdf?MOD=AJPERES. 57 While Myanmar has significant proven reserves of natural gas the majority is for export to neighbouring countries under long term gas supply agreements, which entitle Myanmar to a fraction of the gas for domestic use.
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order to ensure efficient investment and consumption patterns, and (3) reduce negative impacts on the environment and aim to reduce carbon emissions by promoting renewable energy and energy efficiency. The latest PDP suggests some 57.4 GW of new capacity by 2036 and is characterised by a capacity mix based on 30% to 40% natural gas (vs. 64% as of 2014), renewable energy in the range 15% to 20% (vs. 8% as of 2014), coal around 20% to 25% (vs. 20% as of 2014) with an unspecified portion based on carbon capture and sequestration technology, hydro 15% to 20% and up to 5% nuclear. Complementing the PDP2015 are two other plans: (1) the Alternative Energy Development Plan 2015 (AEDP2015) which targets a total of 19,635 MW of renewables (based on waste, biomass, biogas, hydro, wind, solar and energy crops) by 2036; and (2) the Energy Efficiency Development Plan (EEP) which targets to reduce energy intensity by 25% in 2030 compared to 2005 levels, or equivalently, a 20% reduction against a BAU demand outlook.
3.13.5 Viet Nam’s Power Development Plans Viet Nam's electricity consumption has had annual growth rates in the range of 10% to 15% over the last decade. This has placed pressure on the government to ensure adequate levels of infrastructure are being pursued. EVN and other state-owned corporations involved in electricity generation have not been financially capable to build all the required additional capacity, and this has created a heavy focus to date on least (direct) cost planning coupled with desire of government to diversify investment participations in ensuring energy security. Planning has revolved mainly around domestic coal, imported coal and development of offshore gas reserves in the short term while in the longer term nuclear energy is considered a viable option. Plans for RE have generally been at a modest level within the 2011 Power Development Plant 7 (PDP7), having targeted only a 6% share for RE generation by 2030. More recently the government has made commitments to raise the RE share in the system generation mix to 6.5% by 2020 and 10.7% by 2030. These new targets for RE have been factored into the revised version of the PDP7, which was approved by the Prime Minister in March 2016.
3.14 Summary of Developments for GMS Power Sectors This section has provided a summary of the key issues relevant to the development of renewable energy options and fossil fuel options for each of the GMS countries. The purpose was to provide balanced consideration of the main options that face planners for each GMS country. Table 15summarises the key findings for this section and this in turn forms the basis of the assumptions that were used in the power system modelling conducted for each scenario. It should be noted that the renewable energy potential numbers were drawn from multiple sources and informed by analysis of IRENA Global Atlas data as well as our own analyses of potential. The key sources are summarises in Appendix F.
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Comments on Development Potential Resource GMS Total Cambodia Lao PDR Myanmar Viet Nam Thailand Potential Large Hydro A total installed capacity of 24,105 More than 30,000 of 10,000 MW total, of 23,000 MW total, of 15,155 MW of MW (2014), 46,000 total of which which 13,833 developed which 929 developed which 3,058 which 5,541 MW potential for 3,011 developed (2014) (2014). Plans for further (2014) developed (2014) developed (2014). 124,155 MW in hydro development total Small Hydro 27,265 700 2,000 231 24,334 - Pump 18,807 - - - 8,000 10,807 Storage Solar PV Very Good Significant Good Significant Significant Significant Solar CSP Moderate to Good Has potential Has potential Significant Significant in the South Moderate Wind At least 110,000 At least 500 27,104 26,962 26,673 30,000 Onshore MW Wind Significant Offshore (Thailand & Viet Has potential - Has potential Significant 7,000 Nam) Biomass 37,952 2,392 1,271 6,899 10,358 17,032 Biogas 14,757 1,591 1,146 4,741 5,771 1,507 Geothermal 859 - 59 400 400 - Ocean 13,950 - - 1,150 12,800 - Low coal reserves Approximately Domestic Over 2,500 million Approximately 900 Approximately 400 Significant, currently around Northern 1,200 million tons Coal tons million tons of coal million tons of coal producing 45 mt per year Cambodia of coal
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FINAL Comments on Development Potential Resource GMS Total Cambodia Lao PDR Myanmar Viet Nam Thailand Potential Required under Imported BAU generation Possible Unlikely Possible Yes Yes Coal development Estimated at 140 billion 283 Bcm, or estimated 617 Bcm – a number of Domestic cubic metres, not No confirmed Over 1,000 Bcm to be 10 trillion cubic offshore gas and oil fields 284 Bcm Natural Gas currently being reserves feet could be developed produced Potential at Son My, Binh Already exists, LNG / Currently imports from Possible but dependent Oil and gas is Thuan Province for 3.5 importing 11 Bcm Natural Gas Thailand, Viet Nam and on gas demand and imported mtpa expanding to 6 via LNG or pipelines Imports Singapore economics mtpa. from Myanmar Development in Yes as part of Nuclear Unlikely in the near Unlikely in the near Unlikely in the near Yes as part of power Viet Nam and power Power future future future development plan Thailand development plan Sources: Refer to Appendix F
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4 Power Sector Vision Scenarios
In this section, we define the three scenarios for the GMS that we have modelled: the Business as Usual (BAU), Sustainable Energy Sector (SES), and Advanced SES (ASES) scenarios. We firstly provide the assumptions that were common to all countries in study: technology costs and fuel prices. We then set assumptions used for the GMS, including an economic outlook, generation projects considered to be committed58 and assumptions around power imports and transmission. Further assumptions that are specific to each scenarios are then provided in sections 5, 6 and 7.
4.1 Scenarios The three development scenarios (BAU, SES and ASES) are conceptually illustrated in Figure 70.
Figure 70 GMS Power Sector Scenarios
BAU Scenario
SES Scenario (Existing Technologies)
Advanced SES
2015-30 2030-50
The BAU scenario is characterised by electricity industry developments consistent with the current state of planning within the GMS countries and reflective of growth rates in electricity demand consistent with an IES view of base development, existing renewable energy targets, where relevant, aspirational targets for
58 That is, construction is already in progress, the project is near to commissioning or it is in an irreversible / advanced state of the planning process.
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Table 16 Summary of BAU, SES and ASES Scenarios Scenario Demand Supply BAU Demand is forecast to grow in Generator new entry follows that of line with historical electricity power development plans for the consumption trends and country including limited levels of projected GDP growth rates in renewable energy but not a maximal a way similar to what is often deployment of renewable entry. done in government plans. Electric vehicle uptake was assumed to reach 20% across all cars and motorcycles by 2050 for the GMS. SES • Assumes a transition • Assumes no further coal and gas towards energy efficiency new entry beyond what is already benchmark for the understood to be committed. industrial sector of Hong • A modest amount of large scale Kong60 and of Singapore for hydro (4,700 MW in total) was the commercial sector by deployed in Lao PDR and Myanmar year 2050. above and beyond what is
59 Note that we summarise the key drivers here. For further details, please refer to the separate IES assumptions document. 60 Based on our analysis of comparators in Asia, Hong Kong had the lowest energy to GDP intensity for industrial sector while Singapore had the lowest for the commercial sector. Thailand, Myanmar, Lao PDR and Cambodia’s industry intensity was trended towards levels commensurate with Hong Kong. Viet Nam’s industrial intensity was trended towards Korea (2014) by 2035 and continues the trajectory to 2050.
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Scenario Demand Supply • For the residential sector, it understood to be committed hydro was assumed that urban developments in these countries62. residential demand per • Supply was developed based on a electrified capita grows to least cost combination of approximately 60% of the renewable generation sources level in the BAU. limited by estimates of potential • Demand-response rates of deployment and measures assumed to be judgments on when technologies phased in from 2021 with would be feasible for some 15% of demand implementation to deliver a power being flexible61 by 2050. system with the same level of • Slower electrification rates reliability as the BAU. for the national grids in • Technologies used include: solar Cambodia and Myanmar photovoltaics, biomass, biogas and compared to the BAU, but municipal waste plants, CSP with deployment of off-grid storage, onshore and offshore solutions that achieve wind, utility scale batteries, similar levels of electricity geothermal and ocean energy. access. • Transmission limits between • Mini-grids (off-grid regions were upgraded as required networks) are assumed to to support power sector connect to the national development in the GMS as an system in the longer-term. integrated whole, and the • Electric vehicle uptake as transmission plan allowed to be per the BAU. different compared to the transmission plan of the BAU. ASES The ASES demand ASES supply assumptions were also assumptions are done as a implemented as a sensitivity to the sensitivity to the SES: SES, with the following the main • An additional 10% energy differences: efficiency applied to the SES • Allow rates of renewable energy demands (excluding deployment to be more rapid transport). compared to the BAU and SES. • Flexible demand assumed to • Technology cost reductions were reach 25% by 2050. accelerated for renewable energy • Uptake of electric vehicles technologies. doubled by 2050. • Implement a more rapid programme of retirements for fossil fuel based
61 Flexible demand is demand that can be rescheduled at short notice and would be implemented by a variety of smart grid and demand response technologies. 62 This is important to all countries because the GMS is modelled as an interconnected region.
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Scenario Demand Supply power stations63. • Energy policy targets of 70% renewable generation by 2030, 90% by 2040 and 100% by 2050 across the region are in place. • Assume that technical / operational issues with power system operation and control for a very high level of renewable energy are addressed64.
4.2 Technology Cost Assumptions Technology capital cost estimates from a variety of sources were collected and normalised to be on a consistent and uniform basis65. Mid-points were taken for each technology that is relevant to the GMS region. The data points collated reflect overnight, turnkey engineering procurement construction (epc) capital costs and are exclusive of fixed operating and maintenance costs, variable operating and maintenance costs and fuel costs. The capital costs by technology assumed in the study are presented in Figure 71 for the BAU and SES scenarios. For the ASES scenario, we assumed that the technology costs of renewable technologies decline more rapidly. These technology cost assumptions are listed in Figure 72. Note that the technology capital costs have not included land costs, transmission equipment costs, nor decommissioning costs and are quoted on a Real USD 2014 basis. Comments on the various technologies are discussed below in relation to the BAU and SES technology costs:
• Conventional thermal technology costs are assumed to decrease at a rate of 0.05% pa citing maturation of the technologies with no significant scope for cost improvement.
• Onshore wind costs were based on the current installed prices seen in PRC and India with future costs decreasing at a rate of 0.6% pa. Future offshore wind costs were developed by applying the current percentage difference between current onshore and offshore capital costs for all future years.
• Large and small-scale hydro costs are assumed to increase over time reflecting easy and more cost-efficient hydro opportunities being developed in the first instance. IRENA reported no cost improvements for hydro over the period from
63 Decommissioned coal and gas plant would be mothballed with some units retained as an additional contingency against drought or other low renewable resource situations. 64 In particular: (1) sufficient real-time monitoring for both supply and demand side of the industry, (2) appropriate forecasting for solar and wind and centralised real-time control systems in place to manage a more distributed supply side, storages and flexible demand resources, and (3) power systems designed to be able to manage voltage, frequency and stability issues that may arise from having a power system that is dominated by asynchronous technologies. 65 We standardised on Real 2014 USD with all technologies costs normalised to reflect turnkey capital costs.
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2010 to 2014. Adjustments are made in the case of Lao PDR and Myanmar where significant hydro resources are developed in the BAU case66.
• Solar PV costs are based on the more mature crystalline silicon technology which accounts for up to 90% of solar PV installations (IRENA, 2015), and forecast to continue to drop (2.3% pa) albeit at a slower pace than in previous years.
• Utility scale battery costs are quoted on a $/kWh basis, and cost projections based on a report by Deutsche Bank (2015) which took into account several forecasts from BNEF, EIA and Navigant.
• Solar thermal (CSP) capital costs are projected to fall at 2.8% pa on the basis of the IRENA 2015 CSP LCOE projections. While globally there are many CSP installations in place, the technology has not taken off and the cost of CSP technology over the past 5 years has not been observed to have fallen as rapidly as solar PV.
• Biomass capital costs are based on costs observed in the Asia region which are significantly less than those observed in OECD countries. Capital costs were assumed to fall at 0.1% pa. Biogas capital costs were based on anaerobic digestion and assumed to decline at the same rate as biomass.
• Ocean energy (wave and tidal) technologies were based on learning rates in the ‘Ocean Energy: Cost of Energy and Cost Reduction Opportunities’ (SI Ocean, 2013) report assuming global installation capacities increase to 20 GW by 205067.
• Capital costs were discounted at 8% pa across all technologies over the project lifetimes. Decommissioning costs were not factored into the study.
• For technologies that run on imported coal and natural gas, we have factored in the additional capital cost of developing import / fuel management infrastructure in the modelling. For reference, Appendix A tabulates the technology cost assumptions that we have used in the modelling, as well as resulting LCOE.
66 Capital costs for large scale hydro projects are assumed to increase to $3,000/kW by 2050 consistent with having the most economically feasible hydro resources developed ahead of less economically feasible resources. 67 Wave and tidal costs were averaged.
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Figure 71 Projected Capital Costs by Technology for BAU and SES
* Battery costs are quoted on a Real 2014 USD $/kWh basis.
Figure 72 Projected Capital Costs by Technology for ASES
* Battery costs are quoted on a Real 2014 USD $/kWh basis.
4.3 Fuel Pricing Outlook IES has developed a global fuel price outlook which is based on short-term contracts traded on global commodity exchanges before reverting towards long-term price global fuel price
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68 The IEA’s 450 scenario is an energy pathway consistent with the goal of limiting global increase in temperature to 2°C by limiting the concentration of greenhouse gases in the atmosphere to 450 parts per million CO2; further information available here: https://www.iea.org/media/weowebsite/energymodel/Methodology_450_Scenario.pdf. 69 Reference: Facts Global Energy / Australian Institute of Energy, F. Fesharaki, “A New World Oil Order Emerging in 2016 and Beyond?”, February 2016, suggest a rebound in prices levels over a 5 to 7-year period as the most “probable” scenario. 70 Including biomass and biogas feedstock prices. 71 The IEA’s current policies scenario assumes no changes in policy from the year of WEO publication.
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Figure 73 IES Base Case Fuel Price Projections to 2050
25
20
15
10
Price ($Real 2014 USD/MMBtu) 5
0 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 Crude Oil Dated Brent Fuel Oil Diesel Oil Imported Coal Asian LNG Uranium
4.4 Real GDP Growth Outlook Real GDP growth is assumed to maintain a 7% pa GDP growth rate, in all countries except Thailand, to 2025 which is slightly higher than the 15-year historical average growth as the region continues to pursue industrialisation. Towards 2050, GDP growth is assumed to decline towards the world average of 1.96%72 pa seen in Figure 74. The trend down is assumed to reflect the economic development cycle towards a developed country status. This assumption is held constant in the BAU, SES and ASES scenarios.
72 1.96% reflects the previous 5-year GDP growth of the top 10 GDP countries in the world excluding Brazil, China and Russia.
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Figure 74 GDP Projections
8.0% 7.0% 6.0% 5.0% 4.0% 3.0%
GDP Growth (real) 2.0% 1.0% 0.0% 2015 2019 2021 2023 2025 2029 2031 2033 2035 2039 2041 2043 2045 2049 2017 2027 2037 2047 CM LAO MY TH VN
4.5 Population Growth Population was assumed to grow in line with the UN Medium Fertility scenario and is held constant across all scenarios73.
4.6 Committed Generation Projects in BAU, SES and ASES Scenarios Committed generation projects are the ones that are under construction or at a stage of development that is sufficiently advanced for decision for the project to come online to not be reversed. Across all scenarios we assumed that projects that were committed would be developed and we have set out a full list for each country in Appendix D74. This was based on information from recent Power Development Plans and ongoing research on the current status of power projects in the GMS.
4.7 Transmission System, Imports and Exports The modelling presented in this report assumes transmission in the GMS becomes more tightly integrated than at present. Given the modelling period is for 35 years, we use a very simple model for the interconnections as illustrated in Figure 75. The figure shows the interconnections within the region as well as to countries outside the region (PRC and Malaysia). Initially not all transmission lines are in place and the power system is modelled as per the status quo. However, over the modelling period the transmission system evolves as needed to provide mutual support
73 UN Department of Economic and Social Affairs, World Population Prospects: The 2012 Revision. 74 The list includes dedicated export projects from Lao PDR such as Xekaman 1. The capacity quoted for these projects has been adjusted to reflect the dedicated export quantity.
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Figure 75 GMS Regional Transmission System Model
PRC
Mandalay VIETNAM Hanoi Luang Prabang MYANMAR VN-N
LAO PDR MM Chiang Mai LAO Vientiane
Vientiane Yangon
THAILAND VN-C
TH
Bangkok Angkor CAMBODIA Siem Reap Phnom CAM Penh Ho Chi Minh VN-S City
MAL
There are some slight differences in the assumptions behind the transmission system enhancements in each scenario as follows:
• In the BAU, it is assumed that transmission developments occur slowly and a tightly integrated regional power system is in place from about 2030, but the power sectors are developed so that there is only a limited level of dependency on imports from neighbouring countries. This is consistent with power sector planning that seeks to not be overly dependent on power imports from neighbouring countries.
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as needed to optimise the use of a geographically disperse set of renewable energy resources. A consequence of this is that some countries become significant exports of power while others take advantage of power imports from neighbouring countries. In particular Myanmar and Lao PDR become major power exporters with the beneficiaries being the other GMS countries.
4.8 Power Imports and Exports
4.8.1 Cambodia Apart from generation plants in Cambodia, the National Grid gets electricity supply from Viet Nam at 230 kV, Thailand at 115 kV and Laos at 22 kV75. In 2013, 56% of Cambodia’s total electricity demand was met by power transfers from Thailand, Lao PDR and Viet Nam. Table 17 summarises the imports split by high voltage (HV) and medium voltage (MV) transmission lines. The interconnectors are for imports into Cambodia. The MME has in place an agreement with the Ministry of Industry of Viet Nam for power purchases from Viet Nam into Cambodia across several transmission points. Supply from Thailand via the Provincial Electricity Authority of Thailand is sold to some of the PECs in areas around the Cambodia and Thailand border. Imports from Laos are through Electricité du Cambodge (EDC) and supplied to the Steung Treng area. Imports have been reduced substantially since then due to new hydro plants coming online in Cambodia.
Table 17 Cambodia: Electricity Generation and Imports (2012-13)
Energy in Proportion Source of Electricity Million kWh of energy in % 2012 2013 for 2013 Generation in Cambodia 1,423 1,770 44.7% Import from Viet Nam at HV 1,220 1,329 32.8% Import from Viet Nam at MV 341 362 8.9% Import from Thailand at HV 392 417 10.3% Import from Thailand at MV 143 163 4.0% Import from Laos at MV 9 11 0.3% Total 3,527 4,052 100.0% Source: Report on Power Sector for the Year 2013, Electricity Authority of Cambodia (2014)
4.8.2 Laos PDR Lao PDR power exports to neighbouring countries are mainly in the form of projects that are dedicated76. In addition to these projects, Lao PDR also exports smaller quantities of power into Thailand and Viet Nam via Thakhek and Champasak respectively (power flows totalling approximately 12 GWh in 2014). Lao PDR has importing arrangements with Thailand, Viet Nam and PRC. Flows from Viet Nam (34 GWh in 2014) and Thailand (1,137 GWh) provide electricity to areas in Lao PDR not
75 Several connections from Vietnam and Thailand are at 22 kV. 76 That is the project is connected to the national grid of the neighbouring country.
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4.8.3 Myanmar Presently Myanmar exports electricity to PRC via Shewli, a dedicated hydro power project via a 600 MW 220 kV double circuit transferring power into Dehong (Yunnan, PRC). Myanmar does not have connections to any other GMS country. Myanmar was identified as one of the main sources of power in the GMS with export potential of more than 5.5 GW by 2028 into Thailand as a substitute for its gas generation as part of the Update of the Regional Indicative Master Plan on Power Interconnection (2010) in ADB’s GMS Roadmap77. This forms the basis of the transmission developments modelled in the BAU, SES and ASES.
4.8.4 Thailand Thailand is connected to the Cambodian and Malaysian power grids and there are a number of projects under development in neighbouring countries that will export most if not all of their power output to Thailand. Figure 76 below plots the historical exports and imports from 1990 to 2014 and shows significant increases in imported electricity from 2010 to more than 12,000 GWh per annum by 2014. Most of the imports are from Lao PDR and Malaysia, whereas exports are mainly to Cambodia and the remote regions of Lao PDR (350 and 1,220 GWh in 2015). We have assumed the construction of the projects listed in Table 18. The capacities shown in the table have been de-rated based on the power purchase agreements that Thailand has with the host country for these projects.
77 Greater Mekong Subregion Power Trade and Interconnection, 2012, ADB.
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Figure 76 Thailand Imports and Exports (GWh)
14,000
12,000
10,000
8,000
6,000
4,000 Imports and Exports (GWh) 2,000
0 1991 1995 1997 1998 1999 2001 2005 2007 2008 2009 2011 1990 1992 1993 1994 1996 2000 2002 2003 2004 2006 2010 2012 2013 2014
Export Import
Table 18 Thailand: Committed Import Projects under development
Capacity No. Unit Country Type COD (MW) Su-ngai Kolok - Rantau- Malaysia (TNB) – Thailand (EGAT) Grid-to- 1 100 2015 Panjang 132 kV Interconnection Grid Lao PDR (power purchased from 2 Hongsa Thermal #1-2 982 Coal 2015 Lao PDR) Lao PDR (power purchased from 3 Hongsa Thermal #3 491 Coal 2016 Lao PDR) Impact Energy Wind Most of the wind farm’s output 4 540 Wind 2019 Farm will be purchased by Thailand
4.8.5 Viet Nam Viet Nam imported 966 GWh in 2006 growing to 5,599 GWh in 2011 and 1,683 GWh in 2015 mainly from PRC and more recently Laos PDR. For Viet Nam, it was assumed that projects in Lao PDR that export power to Viet Nam’s national system do so initially on a dedicated basis but over time they become part of an interconnected GMS power system as the countries have their power systems becoming increasingly integrated. Table 19 shows these projects. The capacities shown in the table have been de-rated based on the power purchase agreements that Viet Nam has with the host country for these projects; that is, they reflect just the power that is transferred to Viet Nam, not the portion that is available to the host country.
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Further assumptions that were made in relation to imports and exports that apply to all scenarios:
• Imports from Malaysia into Thailand at 135 MW and imports from PRC into Lao PDR at 25 MW remaining constant throughout the modelling; and
• Imports from PRC into Viet Nam start at 412 MW but declines steadily to 0 MW by 2025 as Viet Nam reduces its reliance on PRC power flows.
Table 19 Viet Nam Committed Import Projects
Capacity No. Unit Country Type COD (MW)78 1 Xekaman 1 Lao PDR (power purchased from Lao PDR) 232 Hydro 2016 2 Xekaman 3 Lao PDR (power purchased from Lao PDR) 200 Hydro 2015 3 Xekaman 4 Lao PDR (power purchased from Lao PDR) 64 Hydro 2018
4.9 Technical-Economic Power System Modelling Technical and economic modelling of the GMS was done in the PROPHET electricity planning and simulation models79. It develops a least cost generation based plan and was used to simulate the operation of the GMS region as an integrated power system. A brief overview of the various aspects is provided below:
• Planning Module: The Planning Module of Prophet allows for intertemporal constraints such as energy limits to be preserved when simulating the power system and developments. It also develops a least cost set of new entrants to satisfy demand over the 35-year modelling horizon.
• Transmission: The power system was modelled based on the configuration as per Figure 75 with fixed / scheduled flows (red lines) to power systems outside the GMS not being explicitly modelled while power transfers within the GMS countries were optimised as needed to allow supply and demand to balance. This is important with respect to modelling diversity in demand in the different regions and geographical variation in generation patterns from supply-driven renewable energy (solar and wind) and seasonal variation of inflows into the hydro storages.
• Economics: Capital and operating costs relating to generation plants as per the assumptions covered in this report allow the Planning Module to model generation and transmission development in a least cost manner. On top of
78 Capacity figures presented here are pro-rated based on the intended power flows between the countries. 79 Simulation is based on hourly profiles of demand and supply. The hourly generation profiles for solar and wind were developed based on seasonal measurements for each location for each GMS country of DNI (solar) and wind speeds. The hourly profiles were based on generation profiles of real wind and solar farms for similar conditions but adjusted for the expected site conditions.
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this, resource constraints had to be formulated to reflect actual limits such as the maximum renewable resource and development rates available to each country.
• Demand: Demand profiles were constructed from energy and peak demand forecasts for electricity based on regression models that were developed for each sector of the electricity industry (commercial, industrial, residential, agricultural and transport). The monthly and intraday construction of the profiles were performed in Prophet based on historical data and/or external data sources indicating the seasonal profile of demand for each country.
• Flexible demand: was modelled as MW and GWh/month quantities that can be scheduled as necessary to reduce system costs. This means that demand tends to be shifted from periods when supply and demand would otherwise be tight to other times. The technology for rescheduling demand was assumed to be rolled out starting from 2020 in the SES and ASES scenarios.
• Supply: The approach taken for modelling generation supply technologies varied according to the technology type. This is discussed further below: - Conventional thermal plant: is modelled as capacity limited plants, with fuel take or pay contracts applied to generators running on natural gas and where relevant supply constraints put in place – for example, gas supply limits applied to LNG facilities or offshore gas fields. Examples of such plants include coal, biomass, gas, and diesel generators. - Energy limited plants: such as large-scale hydros with reservoirs / storages and CSP have monthly energy limits corresponding to seasonal variations in energy inflows. The equivalent capacity factors are based on external reports for hydro and resource data for CSP (see next point). - Supply-driven generation forms: Seasonal profiles for wind, solar and run of river hydros without reservoirs were developed on an hourly basis. For wind and solar they were derived from monthly resource data collected from a variety of sources including NASA, NREL80 and accessed via the Solar and Wind Energy Resource Atlas (SWERA) Toolkit and IRENA Global Atlas. Resource amounts were matched against actual generation data for known plants to develop equivalent monthly capacity factors at various high resource pockets in each country. Several traces were built from known generation traces to provide diversification benefits. - Pump Storage and battery storage: these are modelled in a similar way to flexible demand in that demand can be shifted with a capacity and energy limit but the scheduled demand is stored for generation later with an appropriate energy conversion efficiency (pumped storages assumed to be 70% and battery storage systems at 85%).
80 DNI and Wind NASA Low Resolution and NREL DI Moderate Resolution data.
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5 Business as Usual Scenario
5.1 Business as Usual Scenario The BAU scenario assumes industry developments consistent with the current state of planning within the GMS countries and reflective of growth rates in electricity demand consistent with an IES view of base development, existing renewable energy targets, where relevant, aspirational targets for electrification rates, and energy efficiency gains that are largely consistent with the policies seen in the region.
5.2 Projected Demand Growth GMS’s on-grid electricity demand (including transmission and distribution losses 81 ) is plotted in Figure 77 as is based on the sum of electricity demand from the five countries. The GMS electricity demand is forecast to increase at a rate of 4.5% pa over the 35-year period to 2050 with the region going through a period of industrialisation and high GDP growth of 7% pa. The industrial sector is forecast to grow the fastest at 4.8 % followed by the commercial sector at 4.6%, residential sector at 3.3% and agriculture at 2.8% per annum as the GDPs shifts towards commerce/services and industry with increases in residential per capita electricity consumption. The transport sector is forecast to hit 70 GWh by 2050 as the number of cars and uptake of electric cars and motorbikes increase to 20% uptake. GMS electricity demand is forecast to reach 1,685 TWh by 2050. Peak demand is plotted below in Figure 78 and shows peak demand growing at 4.3% pa reaching 248 GW by 2050. The load factors in the individual countries trend towards 75% by 2040, and Viet Nam to 80%, driven by additional industrial loads. Key drivers for demand growth and the demand projections are summarised in Table 20.
81 Note that unless otherwise stated, all other demand charts and statistics include transmission and distribution losses.
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Figure 77 GMS Projected Electricity Demand (2015-2050, BAU)
1,800
1,600
1,400
1,200
1,000
800
600 Energy (inc losses, TWh) 400
200
0 2018 2028 2038 2048 2010 2012 2014 2016 2020 2022 2024 2026 2030 2032 2034 2036 2040 2042 2044 2046 2050
Agriculture Industry Commercial Residenqal Transport
Figure 78 GMS Projected Peak Demand (BAU)
300,000
250,000
200,000
150,000
100,000 Peak Demand (MW)
50,000
0 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050
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Table 20 GMS Demand and Demand Drivers (BAU) No. Aspect 2015-30 2030-40 2040-50 1 Demand Growth (pa) 6.8% 6.4% 3.7% 2 GDP Growth (Real, pa) 5.4% 4.7% 2.9% 3 Electrification Rate (Population) 72.7% 89.3% 97.5% 4 Population Growth 0.6% 0.2% 0.0% 5 Per Capita Consumption (kWh) 1,991 3,602 5,164 6 Electricity Elasticity* 2.45 1.81 1.43 7 Electricity Intensity (kWh/USD) 0.331 0.388 0.417 * Electricity elasticity is calculated as electricity demand growth divided by the population growth over the same period
5.3 Projected Installed Capacity The BAU installed capacity (MW) for GMS is plotted in Figure 79 and Figure 80 by capacity shares for selected years: 2010, 2015, 2020, 2030, 2040 and 2050. The former shows installed generation capacity by the main generation type categories. We provide corresponding statistics in Table 21 and Table 22. Installed capacity in 2014 increases from 77 GW to 352 MW with coal generation accounting for the largest share, or 29% of total installed capacity, in 2050. Coal- fired capacity increases from 20 GW in 2015 with the recent commissioning of the several coal plants to 104 GW in 2050. Large-scale hydro becomes the second most dominant generation type growing to 69 GW by 2050 driven by hydro resource exploitation along the Mekong River and tributaries. Renewable technologies, mainly solar PV and wind, grows to 29% of capacity while gas generation declines from 43% in 2015 to 18% by 2050. Nuclear also features in the capacity mix with 11 GW built in Viet Nam and Thailand.
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Figure 79 GMS Installed Capacity (BAU, MW)
400,000
350,000
300,000
250,000
200,000
Capacity MW 150,000
100,000
50,000
0 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050
Coal Hydro Gas Wind Diesel/FO Nuclear Bio Solar Hydro ROR
Figure 80 GMS Installed Capacity Mix Percentages (BAU, %)
100% 8% 12% 14% 14% 90% 2% 80% 6% 43% 8% 9% 70% 58% 34% 27% 60% 22% 18% 50% 40% 20% Capacity Mix 31% 27% 22% 21% 30% 25% 20% 28% 28% 29% 10% 23% 25% 14% 0% 2010 2015 2020 2030 2040 2050
Coal Hydro Gas Wind Diesel/FO Nuclear Bio Solar Hydro ROR
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Table 21 GMS Capacity by Type (BAU, MW)
Resource 2010 2015 2020 2030 2040 2050 Coal 7,931 20,066 28,928 55,642 79,076 103,566 Diesel 727 446 446 596 1,216 3,545 Fuel Oil 1,141 1,334 1,004 72 72 142 Gas 32,370 37,231 39,733 54,832 62,656 62,656 Nuclear 0 0 0 1,200 3,500 10,500 Hydro 13,740 27,381 31,351 45,178 60,913 68,749 Onshore Wind 0 273 2,149 12,084 21,705 29,569 Offshore Wind 0 0 0 10 239 1,026 Biomass 0 335 1,708 4,608 8,778 12,978 Biogas 0 0 0 0 0 0 Solar 0 100 9,612 24,312 39,812 50,412 CSP 0 0 0 0 0 0 Battery 0 0 0 0 0 0 Hydro ROR 0 0 400 3,100 4,900 7,100 Geothermal 0 0 0 0 0 0 Pump Storage 0 0 0 200 583 1,750 Ocean 0 0 0 0 0 0 Off-Grid 0 0 0 0 0 0
Table 22 GMS Capacity Share by Type (BAU, %)
Resource 2010 2015 2020 2030 2040 2050 Coal 14% 23% 25% 28% 28% 29% Diesel 1% 1% 0% 0% 0% 1% Fuel Oil 2% 2% 1% 0% 0% 0% Gas 58% 43% 34% 27% 22% 18% Nuclear 0% 0% 0% 1% 1% 3% Hydro 25% 31% 27% 22% 21% 20% Onshore Wind 0% 0% 2% 6% 8% 8% Offshore Wind 0% 0% 0% 0% 0% 0% Biomass 0% 0% 1% 2% 3% 4% Biogas 0% 0% 0% 0% 0% 0% Solar 0% 0% 8% 12% 14% 14% CSP 0% 0% 0% 0% 0% 0% Battery 0% 0% 0% 0% 0% 0% Hydro ROR 0% 0% 0% 2% 2% 2% Geothermal 0% 0% 0% 0% 0% 0% Pump Storage 0% 0% 0% 0% 0% 0% Ocean 0% 0% 0% 0% 0% 0% Off-Grid 0% 0% 0% 0% 0% 0%
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5.4 Projected Generation Mix Figure 81 plots the generation mix (on an as generated basis82) over time in the BAU case and Figure 82 plots the corresponding percentage shares. Coal-fired generation in line with capacity increases to account for 46% of generation in the GMS with gas falling to 17% by 2050. The large-scale hydro generation share increases in the earlier years then maintains its share around 17% and renewable energy generation (excluding large-scale hydro) increases to 16% mainly driven by renewable developments in Thailand. Most of the renewable generation comes from solar PV and wind.
82 Unless otherwise stated, all generation charts and statistics in this report are presented on an “as generated” basis, meaning that generation to cover generator’s auxiliary consumption accounted for.
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Figure 81 GMS Generation Mix (BAU, GWh)
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0 2018 2028 2038 2048 2010 2012 2014 2016 2020 2022 2024 2026 2030 2032 2034 2036 2040 2042 2044 2046 2050
Coal Hydro Gas Wind Diesel/FO Nuclear Bio Solar Hydro ROR
Figure 82 GMS Generation Mix Percentages (BAU, %)
100% 3% 5% 5% 5% 90% 4% 5% 4% 5% 80% 4% 47% 37% 70% 31% 24% 17% 61% 60% 16% 50% 17% 24% 18% 40% 27% Genera^on Mix 30% 18% 46% 20% 38% 42% 33% 25% 10% 19% 0% 2010 2015 2020 2030 2040 2050 Coal Hydro Gas Wind Diesel/FO Nuclear Bio Solar Hydro ROR
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Table 23 GMS Generation by Type (BAU, GWh)
Generation 2010 2015 2020 2030 2040 2050 Coal 49,296 90,035 163,664 350,590 561,397 769,821 Diesel 928 0 0 59 82 183 Fuel Oil 4,760 0 0 0 0 0 Gas 162,316 165,885 186,585 287,935 321,563 292,121 Nuclear 0 0 0 9,750 28,441 85,165 Hydro 47,631 96,976 120,222 172,976 233,160 263,057 Onshore Wind 0 624 5,019 29,238 52,229 70,887 Offshore Wind 0 0 0 25 575 2,459 Biomass 0 2,059 9,083 30,275 57,834 85,270 Biogas 0 0 0 0 0 0 Solar 0 170 17,318 43,456 71,816 90,787 CSP 0 0 0 0 0 0 Battery 0 0 0 0 0 0 Hydro ROR 0 0 1,551 11,843 18,864 27,190 Geothermal 0 0 0 0 0 0 Pump Storage 0 0 0 204 609 1,979 Ocean 0 0 0 0 0 0 Off-Grid 0 0 0 0 0 0
Table 24 GMS Generation share by Type (BAU, %)
Generation 2010 2015 2020 2030 2040 2050 Coal 19% 25% 33% 37% 42% 46% Diesel 0% 0% 0% 0% 0% 0% Fuel Oil 2% 0% 0% 0% 0% 0% Gas 61% 47% 37% 31% 24% 17% Nuclear 0% 0% 0% 1% 2% 5% Hydro 18% 27% 24% 18% 17% 16% Onshore Wind 0% 0% 1% 3% 4% 4% Offshore Wind 0% 0% 0% 0% 0% 0% Biomass 0% 1% 2% 3% 4% 5% Biogas 0% 0% 0% 0% 0% 0% Solar 0% 0% 3% 5% 5% 5% CSP 0% 0% 0% 0% 0% 0% Battery 0% 0% 0% 0% 0% 0% Hydro ROR 0% 0% 0% 1% 1% 2% Geothermal 0% 0% 0% 0% 0% 0% Pump Storage 0% 0% 0% 0% 0% 0% Ocean 0% 0% 0% 0% 0% 0% Off-Grid 0% 0% 0% 0% 0% 0%
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5.5 Evolution of GMS Power Systems under BAU Scenario Figure 83 shows the generation mix in each GMS country for the BAU for 2015, 2030 and 2050 with an indication of power flows across the various borders. Please refer to Appendix H for the tabulated data. The BAU assumes generation development consistent with the current state of planning within the GMS countries and is characterized by generation developments on a country by country basis leading to minimal flows (below 10,000 GWh) traded across borders. The current systems are largely dominated by large- hydro in Myanmar, Cambodia and Lao PDR and gas and coal in Thailand and Viet Nam. By 2050, other renewable technologies are developed to meet country- specific BAU renewable energy targets (between 10-20%) but the power system is still largely dominated by growth in fossil fuel generation. Lao PDR remains largely dependent on large hydro whereas the Myanmar and Cambodia systems shift towards fossil fuels by 2050. Flow from Lao PDR to Thailand, and Viet Nam to Cambodia grow to 374 MW and 247 MW on average and by 2050, Myanmar and Lao PDR are exporting 822 MW and 655 MW into Thailand with flows into Cambodia from Viet Nam growing to 636 MW. Flows into Thailand and Cambodia displace some of the gas generation in those countries as most of the flows are driven by generation cost differences between the grids.
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Figure 83 BAU Scenario Development: Snapshots for years 2015, 2030 and 2050
2015 BAU (2030) BAU (2050)
Resource Flows Coal, Diesel, Fuel Oil, Nuclear Below 10,000 GWh Gas 10,001 - 20,000 GWh Large Hydro Above 20,000 GWh Wind Solar, Battery, CSP Biomass and Biogas Other Renewables
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5.6 Projected Generation Fleet Structure Figure 84 shows the installed generation capacity by the main categories of generation: thermal, renewable and large-scale hydro, in order to provide greater insight into the basic structure of installed capacity under the BAU. This highlights that GMS’s BAU projection is heavily dominated by coal and gas-fired generation. Figure 85 shows the on-grid composition of generation by major categories of generation: thermal, large hydro and renewable. As could be anticipated generation closely reflects the BAU’s installed capacity mix.
Figure 84 GMS Installed Capacity by Generation Type (BAU, MW)
400,000 350,000 300,000 250,000 200,000 150,000 Capacity, MW 100,000 50,000 0 2015 2017 2019 2021 2025 2027 2029 2031 2035 2037 2039 2041 2045 2047 2049 2023 2033 2043 Fossil Fuel Large Hydro Renewable
Figure 85 GMS Generation Mix by Generation Type (BAU, GWh)
1,800,000 1,600,000 1,400,000 1,200,000 1,000,000 800,000 600,000 GeneraWon, GWh 400,000 200,000 0 2015 2017 2019 2021 2025 2027 2029 2031 2035 2037 2039 2041 2045 2047 2049 2023 2033 2043
Fossil Fuel Large Hydro Renewable
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To facilitate later comparison with the SES, Figure 86 plots installed capacity with capacity being distinguished between the following basic categories: (1) dispatchable capacity, (2) non-dispatchable capacity; and (3) semi-dispatchable capacity 83 . This provides some insight into the operational flexibility of the generation fleet to match demand uncertainty. The dispatchable category relates to generation that can be controlled and dispatched at short notice to ramp up or down, non-dispatchable means that the generation is not able to respond readily to dispatch instructions while the semi-dispatchable category means that the resource can respond within limits, and in particular is capable of being backed off should the need arise to for example, avoid overloading the network or “spill” energy in the event that an over generation situation emerges; solar photovoltaics and windfarms with appropriately installed control systems can be classified in this category. In the BAU, the dispatchable percentage starts at 100% with only coal, gas and hydro; then, as renewables are added to the system, it drops to 75% by 2050.
Figure 86 GMS Installed Capacity by Dispatch Status (BAU, MW)
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83 Wind and solar is classified as semi-dispatchable, geothermal and hydro run-of-river is classified as non- dispatchable and all other technologies are classified as dispatchable.
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5.7 Reserve Margin and Generation Trends Figure 87 plots the reserve margin based on nameplate capacity and annual peak demand. The GMS reserve margin declines to 34%, due to the deferral of non- committed projects and significant committed supply in the short-term relative to demand, then edges back up to 40% through to 2050 as renewable capacity enters the GMS. Levels around 30-40% are expected for thermal dominated power systems as is the case with the BAU. To obtain a better understanding of the broad mix of generation capacity and generation mix, Figure 88 and Figure 89 show shares in installed capacity and in generation grouped by the main categories of generator: thermal, large hydro, renewable energy (RE) and large hydro plus renewable energy. Figure 89 plots the generation shares by several different categories of generation. The thermal generation share declines to 63% and renewable energy including large-scale hydro increases from 30% to 37%. The BAU has large-scale hydro being largely exploited to support the growing power demands in GMS.
Figure 87 GMS Reserve Margin (BAU)
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0% 2015 2017 2019 2021 2025 2027 2029 2031 2035 2037 2039 2041 2045 2047 2049 2023 2033 2043
Reserve Margin Renewable Capacity
Hydro Capacity Fossil Fuel Capacity
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Figure 88 GMS Capacity Shares by Generation Type (BAU)
100% 90% 80% 70% 60% 50% 40% Capacity Share 30% 20% 10% 0% 2015 2017 2019 2021 2025 2027 2029 2031 2035 2037 2039 2041 2045 2047 2049 2023 2033 2043
Fossil Fuel Large Hydro Renewable Renewable + Large Hydro
Figure 89 GMS Generation Shares by Generation Type (BAU)
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0% 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
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5.8 Electrification and Off-Grid Supply GMS’s grid-based electrification rate for its urban and rural population is assumed to reach close to 100% by 2030 in the BAU. Due to the limited impact of off-grid in this scenario it has been decided to only model the central grid-connected power system.
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6 Sustainable Energy Sector Scenario
6.1 Sustainable Energy Sector Scenario The SES seeks to transition electricity demand towards the best practice benchmarks of other developed countries in terms of energy efficiency, maximise the renewable energy development, cease the development of fossil fuel resources, and make sustainable and prudent use of undeveloped conventional hydro resources. The SES takes advantage of existing, technically proven and commercially viable renewable energy technologies.
6.2 Projected Demand Growth Figure 90 plots GMS’s forecast energy consumption from 2015 to 2050 with the BAU energy trajectory charted as a comparison. The significant savings are due to additional energy efficiency assumptions relating to the various sectors achieving energy intensity benchmarks of comparable developed countries in Asia84. The SES demand grows at a slower rate of 3.5% pa over the period to 2050 with the commercial sector growing at 3.5% pa, industry growing at 3.9% pa and the residential sector and agricultural sectors growing at 1.6% pa. The uptake of electric transport options occurs from 2025 onwards and grows to 70 TWh accounting for 6% of total demand by 2050, or 20% of all cars and motorbikes. Off-grid demand forms part of the overall demand picture as off-grid technologies are deployed in the interim before the central grids in Myanmar and Cambodia are built out. In Figure 91 the firm blue line represents peak demand before any flexible demand side resources have been scheduled85. Flexible demand response is “dispatched” in the model in line with the least cost dispatch of all resources in the power system. The dashed line represents what peak demand became as a consequence of scheduling (“time-shifting”) commercial, industrial and residential loads to minimise system costs. From 2020, the amount of flexible demand was assumed to grow to 10% of total demand across all sectors by 2050, or 15% if storage methods are included. The load factors at the country level in the SES are assumed to reach 80% (compared to 75% under the BAU case) by 2050 and reach 83% at the regional level with demand diversification. Key drivers for demand growth and the demand projections are summarised in Table 25.
84 Thailand, Myanmar, Lao PDR and Cambodia’s industry intensity was trended towards levels commensurate with Hong Kong. Hong Kong had the lowest intensity based on the intensity metric of a basket of comparable countries. Viet Nam’s industrial intensity was trended towards Korea (2014) by 2035 and continues the trajectory to 2050. 85 Flexible demand response is “dispatched” in the model in line with the least cost dispatch of all resources. The solid line represents peak demand as put in the model, while the dashed line represents what peak demand ended up being as a consequence of shifting demand from one period of time to another. This includes scheduling of loads associated with battery storage devices and rescheduling (time-shifting) commercial, industrial and residential loads.
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Figure 90 GMS Projected Electricity Demand (2015-2050, SES)
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0 2018 2028 2038 2048 2010 2012 2014 2016 2020 2022 2024 2026 2030 2032 2034 2036 2040 2042 2044 2046 2050
Agriculture Industry Commercial ResidenWal Transport Offgrid Demand BAU
Figure 91 GMS Projected Electricity Demand (SES)
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0 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050
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Table 25 GMS Demand and Demand Drivers (SES) No. Aspect 2015-30 2030-40 2040-50 1 Demand Growth (pa) 6.3% 5.1% 2.7% 2 GDP Growth (Real, pa) 5.4% 4.7% 2.9% 3 Grid –based electrification Rate 72.1% 84.2% 92.1% (Population) 4 Population Growth 0.57% 0.24% 0.01% 5 Per Capita Consumption (kWh) 1,857 2,977 3,868 6 Electricity Elasticity* 2.29 1.60 1.30 7 Electricity Intensity (kWh/USD) 0.309 0.321 0.313 * Electricity elasticity is calculated as electricity demand growth divided by the population growth over the same period
6.3 Projected Installed Capacity Figure 92 plots the installed capacity developments under the SES and Figure 93 plots the corresponding percentage shares. Table 26 and Table 27 provide the statistical details of the installed capacity and capacity shares by type including the estimated 2010 levels. Committed and existing plants are assumed to come online as per the BAU but aren’t replaced when retired. Planned and proposed thermal and large-scale hydro developments are not built and all other generation requirements are instead met by renewable technologies86. Coal and gas fired-generation in the earlier years is very similar to the BAU due to committed projects. Over time, coal, gas and hydro capacity shares drop to 3%, 4% and 8% respectively by 2050 from a combined 97% share in 2015. Additional demand in the SES is predominantly met by renewables with 375 GW required to meet 2050 electricity demand dominated by investment in solar PV (159 GW) supported by 62 GW discharge equivalent of battery storage, onshore wind (62 GW), CSP (32 GW) and biomass (26 GW). Smaller amounts of hydro run of river, ocean energy, and geothermal are also developed in the SES. By 2050, there is 444 GW of installed grid capacity which includes 1 GW of off-grid technologies which is integrated back into the grid as the central grids are built out.
86 Myanmar and Lao PDR has an additional 4,500 MW of large-scale hydro to support renewable developments.
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Figure 92 GMS Installed Capacity by Type (SES)
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200,000 Capacity MW
100,000
0 2018 2028 2038 2048 2010 2012 2014 2016 2020 2022 2024 2026 2030 2032 2034 2036 2040 2042 2044 2046 2050 Offgrid Coal Hydro Gas Wind Diesel/FO Bio Solar CSP Bakery Hydro ROR Geothermal Ocean Pump Storage
Figure 93 GMS Capacity Shares (SES, %)
100% 3% 13% 8% 90% 14% 6% 80% 5% 29% 7% 43% 70% 58% 34% 30% 60% 7% 36% 50% 14% 8%
Capacity Mix 40% 31% 13% 26% 7% 30% 16% 25% 17% 20% 18% 7% 4% 10% 23% 22% 11% 14% 12% 8% 6% 0% 3% 2010 2015 2020 2030 2040 2050 Offgrid Coal Hydro Gas Wind Diesel/FO Bio Solar CSP Hydro ROR Bakery Geothermal Ocean Pump Storage
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Table 26 GMS Capacity by Type (SES, MW)
Resource 2010 2015 2020 2030 2040 2050 Coal 7,931 20,066 27,288 24,005 20,129 15,051 Diesel 727 446 446 296 5 5 Fuel Oil 1,141 1,334 1,004 72 72 72 Gas 32,370 37,231 37,479 26,842 20,941 17,808 Nuclear 0 0 0 0 0 0 Hydro 13,740 27,381 32,026 35,865 34,917 34,917 Onshore Wind 0 273 6,267 27,822 48,771 62,288 Offshore Wind 0 0 0 144 2,996 11,079 Biomass 0 335 3,110 12,795 21,743 26,382 Biogas 0 0 0 905 5,017 5,898 Solar 0 100 16,520 58,720 109,120 159,220 CSP 0 0 0 6,750 19,500 32,400 Battery 0 0 0 0 26,473 61,793 Hydro ROR 0 0 400 4,900 8,000 11,100 Geothermal 0 0 0 200 750 1,075 Pump Storage 0 0 0 0 900 2,700 Ocean 0 0 0 0 500 1,250 Off-Grid 0 2 107 1,325 1,335 1,348
Table 27 GMS Capacity Share by Type (SES, %)
Resource 2010 2015 2020 2030 2040 2050 Coal 14% 23% 22% 12% 6% 3% Diesel 1% 1% 0% 0% 0% 0% Fuel Oil 2% 2% 1% 0% 0% 0% Gas 58% 43% 30% 13% 7% 4% Nuclear 0% 0% 0% 0% 0% 0% Hydro 25% 31% 26% 18% 11% 8% Onshore Wind 0% 0% 5% 14% 15% 14% Offshore Wind 0% 0% 0% 0% 1% 2% Biomass 0% 0% 2% 6% 7% 6% Biogas 0% 0% 0% 0% 2% 1% Solar 0% 0% 13% 29% 34% 36% CSP 0% 0% 0% 3% 6% 7% Battery 0% 0% 0% 0% 8% 14% Hydro ROR 0% 0% 0% 2% 2% 2% Geothermal 0% 0% 0% 0% 0% 0% Pump Storage 0% 0% 0% 0% 0% 1% Ocean 0% 0% 0% 0% 0% 0% Off-Grid 0% 0% 0% 1% 0% 0%
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6.4 Projected Generation Mix Grid generation is plotted in Figure 94 and Figure 9587. The corresponding statistics for snapshot years are provided in Table 29 and Table 30. GMS’s generation mix in the earlier years to 2020 is similar to the BAU case as committed new entry is commissioned. Coal, gas and large-scale hydro generation increase from 353 TWh in 2015 to 468 TWh in 2030 before declining to 303 TWh as coal and gas units are retired and not replaced over time. The generation share of these conventional technologies decrease from 99% in 2015 to 25% in 2050. Timing of renewable energy developments are based on the maturity of the technology and judgments of when it could be readily deployed. Solar PV backed up by battery storage (to provide off-peak generation) generates 287 TWh by 2050 followed by bioenergy generation (mainly biomass) of 234 TWh with wind and CSP contributing 172 TWh and 153 TWh respectively.
87 Battery storage is not included as storage technologies are generation neutral.
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Figure 94 GMS Generation Mix (SES, GWh)
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0 2018 2028 2038 2048 2010 2012 2014 2016 2020 2022 2024 2026 2030 2032 2034 2036 2040 2042 2044 2046 2050 Offgrid Coal Hydro Gas Wind Diesel/FO Bio Solar CSP Hydro ROR Geothermal Ocean
Figure 95 GMS Generation Share (SES, %)
100% 6% 3% 3% 4% 9% 90% 3% 14% 13% 80% 19% 47% 12% 70% 32% 24% 61% 8% 60% 19% 50% 17% 19% 26% 12% 40% 27% Genera[on Mix 18% 8% 14% 30% 18% 20% 13% 6% 25% 29% 25% 11% 10% 19% 16% 8% 0% 2010 2015 2020 2030 2040 2050 Offgrid Coal Hydro Gas Wind Diesel/FO Bio Solar CSP Hydro ROR Geothermal Ocean
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Table 28 GMS Generation by Fuel (SES, GWh)
Generation 2010 2015 2020 2030 2040 2050 Coal 49,296 90,035 136,603 194,770 160,799 98,889 Diesel 928 0 0 0 10 0 Fuel Oil 4,760 0 0 0 147 0 Gas 162,316 165,885 151,258 136,320 86,809 70,693 Nuclear 0 0 0 0 0 0 Hydro 47,631 96,976 124,419 137,751 131,468 133,996 Onshore Wind 0 624 14,592 65,096 114,147 146,463 Offshore Wind 0 0 0 338 7,011 26,051 Biomass 0 2,059 13,194 89,668 158,486 191,313 Biogas 0 0 0 6,342 36,570 42,773 Solar 0 170 29,941 105,777 197,868 287,322 CSP 0 0 0 24,788 89,295 153,208 Battery 0 0 0 0 0 0 Hydro ROR 0 0 1,668 18,676 30,745 42,430 Geothermal 0 0 0 1,314 4,954 7,087 Pump Storage 0 0 0 0 728 2,720 Ocean 0 0 0 0 1,318 3,285 Off-Grid 0 3 139 1,685 963 971
Table 29 GMS Generation Share by Fuel (SES, %)
Generation 2010 2015 2020 2030 2040 2050 Coal 19% 25% 29% 25% 16% 8% Diesel 0% 0% 0% 0% 0% 0% Fuel Oil 2% 0% 0% 0% 0% 0% Gas 61% 47% 32% 17% 8% 6% Nuclear 0% 0% 0% 0% 0% 0% Hydro 18% 27% 26% 18% 13% 11% Onshore Wind 0% 0% 3% 8% 11% 12% Offshore Wind 0% 0% 0% 0% 1% 2% Biomass 0% 1% 3% 11% 16% 16% Biogas 0% 0% 0% 1% 4% 4% Solar 0% 0% 6% 14% 19% 24% CSP 0% 0% 0% 3% 9% 13% Battery 0% 0% 0% 0% 0% 0% Hydro ROR 0% 0% 0% 2% 3% 4% Geothermal 0% 0% 0% 0% 0% 1% Pump Storage 0% 0% 0% 0% 0% 0% Ocean 0% 0% 0% 0% 0% 0% Off-Grid 0% 0% 0% 0% 0% 0%
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6.5 Evolution of GMS Power Systems under SES Scenario The SES assumes greater deployment of renewable technologies and higher energy efficiency measures relative to the BAU. Figure 109 charts the generation mix in each GMS country (2015, 2030, 2050) with an indication of power flows across the various borders. Please refer to Appendix H for the tabulated data. The SES has the GMS shifting away from fossil fuels and by 2030 57% the generation mix is non-fossil fuel based growing to 86% in 2050. Generation resources are optimised across the region with significant renewable generation developed in Myanmar and Lao PDR over and above their demand requirements to support the regional shift away from fossil fuels. By 2050, solar PV and CSP are generating 36% of the region’s electricity followed by biomass at 19% and wind at 14%. The SES has much greater flows going between each of the GMS countries given optimised generation and transmission developments at the regional level with significant amounts of power (above 20 TWh) exported into Thailand and Viet Nam from Myanmar and Lao PDR respectively. Myanmar is a major exporter in the SES with flows going into Thailand increasing to 3,000 MW and 5,300 MW in 2030 and 2050 respectively. Thailand also imports power from Lao PDR and exports a portion of it into Cambodia. There are significant net flows from Lao PDR to Viet Nam with 7,400 MW on average by 2050.
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Figure 96 SES Scenario Development: Snapshots for years 2015, 2030 and 2050
2015 SES (2030) SES (2050)
Resource Flows Coal, Diesel, Fuel Oil, Nuclear Below 10,000 GWh Gas 10,001 - 20,000 GWh Large Hydro Above 20,000 GWh Wind Solar, Battery, CSP Biomass and Biogas Other Renewables
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Figure 109 ASES Scenario Development: Snapshots for years 2015, 2030 and 2050
2015 ASES (2030) ASES (2050)
Resource Flows Coal, Diesel, Fuel Oil, Nuclear Below 10,000 GWh Gas 10,001 - 20,000 GWh Large Hydro Above 20,000 GWh Wind Solar, Battery, CSP Biomass and Biogas Other Renewables
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6.6 Projected Generation Fleet Structure As for the BAU, to gain insight into the nature of the mix of generation technologies deployed in the SES, we present a number of additional charts. Figure 97 and Figure 98 show installed capacity and generation by type for the SES in the GMS – this is heavily biased towards renewable generation forms. For GMS, a considerable amount of non-renewable energy continues to feature in the generation mix and mainly relates to the committed coal and gas generation projects. Figure 99, shows the dispatchable, semi-dispatchable and non-dispatchable components of installed capacity and it can be seen that semi-dispatchable increases to around 60% of the total system capacity compared to around 23% in the BAU by 2050. Based on operational simulations with this resource mix, it appears to be operationally feasible, although the reliance on generation forms that provide storage and having flexibility in the demand side play important roles. It is clear that short-term renewable energy solar and wind forecasting systems will be important, as will real-time updates on demand that can be controlled. Furthermore, control systems that can allow the dispatch of flexible resources on both supply and demand sides of the industry and across the region will be required.
Figure 97 GMS Installed Capacity by Generation Type (SES)
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Figure 98 GMS Generation Mix by Generation Type (SES)
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Figure 99 GMS Installed Capacity by Dispatch Status (SES)
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6.7 Reserve Margin and Generation Trends Figure 100 plots the reserve margin under the SES. Figure 101 and Figure 102, respectively, show the installed capacity mix and generation mix for different categories of generation in the power system. As more thermal plant is retired, additional renewable capacity is required to support the regional system explaining the reserve margin trajectory. Renewable plant capacity including large-scale hydro reaches 93% or 85% without large-scale hydro. Conventional reserve margin measures are generally not suited to measuring high renewable energy systems in the same context used for thermal-based systems. Renewable technologies generally have much lower capacity factors and require more capacity to meet the same amount of energy produced from thermal-based technologies.
Figure 100 GMS Reserve Margin (SES)
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0% 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
Reserve Margin Renewable Capacity
Hydro Capacity Fossil Fuel Capacity
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Figure 101 GMS Installed Capacity Shares for SES by Generation Type
100% 90% 80% 70% 60% 50% 40% Caacity Share 30% 20% 10% 0% 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
Fossil Fuel Large Hydro Renewable Renewable + Large Hydro
Figure 102 GMS Generation Shares for SES by Generation Type
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40% GeneraXon Share 30%
20%
10%
0% 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
Fossil Fuel Large Hydro Renewable Renewable + Large Hydro
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6.8 Electrification and Off-Grid In the SES, most of the GMS is grid electrified with a much smaller percentage (less than 1%) of total regional demand met by off-grid technologies, more specifically in Myanmar and Cambodia. For more information on off-grid deployment please see the respective country reports.
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7 Advanced Sustainable Energy Sector Scenario
7.1 Advanced Sustainable Energy Sector Scenario The ASES assumes that the power sector is able to more rapidly transition towards a 100% renewable energy technology mix under an assumption that renewable energy is deployed more than in the SES scenario with renewable energy technology costs declining more rapidly compared to BAU and SES scenarios.
7.2 Projected Demand Growth Figure 103 plots GMS’s forecast energy consumption from 2015 to 2050 with the BAU and SES energy trajectory charted with a dashed line for comparison. The SES energy savings against the BAU are due to allowing GMS’s energy demand to transition towards energy intensity benchmarks of comparable developed countries in Asia. The ASES applies an additional 10% energy efficiency against the SES demands, which is partially offset by additional transport demands associated with higher uptake rates (40% uptake). The ASES demand grows at a slower rate of 3.4% pa over the period from 2015 to 2050 with the commercial sector growing at 3.3% pa, industry growing at 3.7% pa and residential sector growing at 1.5% pa. Demand from the transport sector in the ASES is doubled and grows to 140 TWh, 12% of total demand by 2050. Total electricity demand increases to 1,156 TWh by 2050. Off-grid demand grows to almost 7 TWh as off-grid technologies are deployed in place of completely building out the central grids in Myanmar and Cambodia. In Figure 104 the firm blue line represents peak demand before any flexible demand side resources have been scheduled88. Flexible demand response is “dispatched” in the model in line with the least cost dispatch of all resources in the power system. The dashed line represents what peak demand became as a consequence of scheduling (“time-shifting”) commercial, industrial and residential loads to minimise system costs. From 2020, the amount of flexible demand was assumed to grow to 17.5% of total demand across all sectors by 2050, or 25% if storage methods are included. The load factors at the country level in the ASES are assumed to reach 83% at the regional level because of demand diversification. Key drivers for demand growth and the demand projections are summarised in Table 17.
88 Flexible demand response is “dispatched” in the model in line with the least cost dispatch of all resources. The solid line represents peak demand as put in the model, while the dashed line represents what peak demand ended up being as a consequence of shifting demand from one period of time to another. This includes scheduling of loads associated with battery storage devices and rescheduling (time-shifting) commercial, industrial and residential loads.
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Figure 103 GMS Projected Electricity Demand (2015-2050, ASES)
1,800 1,600 1,400 1,200 1,000 800 600
Energy (inc losses, TWh) 400 200 0 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050
Agriculture Industry Commercial ResidenXal Transport Offgrid Demand BAU SES
Figure 104 GMS Projected Electricity Demand (ASES, MW)
180,000
160,000
140,000
120,000
100,000
80,000 Demand
60,000 Demand (post DSM) Peak Demand (MW) 40,000
20,000
0 2018 2028 2038 2048 2010 2012 2014 2016 2020 2022 2024 2026 2030 2032 2034 2036 2040 2042 2044 2046 2050
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Table 30 GMS Demand and Demand Drivers (ASES) No. Aspect 2015-30 2030-40 2040-50 1 Demand Growth (pa) 6.0% 4.9% 2.8% 2 GDP Growth (Real, pa) 5.4% 4.7% 2.9% 3 Grid-based Electrification Rate 71.7% 81.2% 86.0% (Population) 4 Population Growth 0.57% 0.24% 0.01% 5 Per Capita Consumption (kWh) 1,793 2,812 3,692 6 Electricity Elasticity* 2.21 1.57 1.31 7 Electricity Intensity (kWh/USD) 0.298 0.303 0.298 * Electricity elasticity is calculated as electricity demand growth divided by the population growth over the same period
7.3 Projected Installed Capacity Figure 105 plots the installed capacity developments under the ASES and Figure 106plots the corresponding percentage shares. Table 31 and Table 32 provide the statistical details of the installed capacity and capacity shares by type including the 2010 levels. The ASES has coal plants retiring earlier than in the SES under a 100% renewable generation target across the region. Total installed capacity increases to 530 GW which is considerably higher than the installed capacity in the SES (444 GW) due to the retirement of coal and gas units and replacement with lower capacity factor technologies. Solar PV accounts for 36% of total installed capacity, or 190 GW, supported by 108 GW equivalent of battery storage for generation deferral. Onshore wind accounts for 79 GW with 15 GW of offshore wind developed in Viet Nam and Myanmar. Biomass and CSP contribute 35 GW each. The ASES has 6 GW of biogas and allows for up to 4 GW of ocean/marine energy technologies as part of diversifying the renewable energy mix. Off-grid technologies are also deployed in Myanmar and Cambodia with 5 GW of installed solar PV and battery storage.
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Figure 105 GMS Installed Capacity by Type (ASES, MW)
600,000
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400,000
300,000
Capacity MW 200,000
100,000
0 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050
Offgrid Coal Hydro Gas Wind Bio Solar CSP Bajery Hydro ROR Geothermal Ocean Pump Storage
Figure 106 GMS Capacity Shares (ASES, %)
100% 3% 17% 90% 17% 20% 80% 3% 43% 6% 35% 5% 70% 58% 7% 60% 23% 8% 36% 50% 36% 15% 40% 31% Capacity Mix 28% 8% 6% 30% 25% 8% 20% 16% 18% 18% 10% 23% 21% 14% 9% 11% 7% 0% 2010 2015 2020 2030 2040 2050 Offgrid Coal Hydro Gas Wind Bio Solar CSP Hydro ROR Bajery Pump Storage Geothermal Ocean
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Table 31 GMS Capacity by Type (ASES, MW)
Resource 2010 2015 2020 2030 2040 2050 Coal 7,931 20,066 24,871 25,426 8,205 0 Diesel 727 446 446 291 0 0 Fuel Oil 1,141 1,334 1,004 0 0 0 Gas 32,370 37,231 26,615 13,883 5,826 0 Nuclear 0 0 0 0 0 0 Hydro 13,740 27,381 32,026 35,991 35,991 35,991 Onshore Wind 0 273 7,559 34,022 69,024 78,552 Offshore Wind 0 0 0 187 4,885 14,907 Biomass 0 335 3,610 15,760 28,697 35,113 Biogas 0 0 0 940 4,143 5,867 Solar 0 100 19,729 76,737 148,081 190,841 CSP 0 0 0 7,200 20,400 34,500 Battery 0 0 0 3,668 71,430 107,754 Hydro ROR 0 0 400 4,900 8,000 11,100 Geothermal 0 0 0 200 750 1,075 Pump Storage 0 0 0 0 1,500 4,800 Ocean 0 0 0 0 3,375 3,875 Off-Grid 0 2 117 2,407 3,620 5,158
Table 32 GMS Capacity Share by Fuel (ASES, %)
Resource 2010 2015 2020 2030 2040 2050 Coal 14% 23% 21% 11% 2% 0% Diesel 1% 1% 0% 0% 0% 0% Fuel Oil 2% 2% 1% 0% 0% 0% Gas 58% 43% 23% 6% 1% 0% Nuclear 0% 0% 0% 0% 0% 0% Hydro 25% 31% 28% 16% 9% 7% Onshore Wind 0% 0% 6% 15% 17% 15% Offshore Wind 0% 0% 0% 0% 1% 3% Biomass 0% 0% 3% 7% 7% 7% Biogas 0% 0% 0% 0% 1% 1% Solar 0% 0% 17% 35% 36% 36% CSP 0% 0% 0% 3% 5% 7% Battery 0% 0% 0% 2% 17% 20% Hydro ROR 0% 0% 0% 2% 2% 2% Geothermal 0% 0% 0% 0% 0% 0% Pump Storage 0% 0% 0% 0% 0% 1% Ocean 0% 0% 0% 0% 1% 1% Off-Grid 0% 0% 0% 1% 1% 1%
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7.4 Projected Generation Mix ASES grid generation is plotted in Figure 107 and generation shares in Figure 108. The corresponding statistics for snapshot years are provided in Table 33 and Table 34 The GMS generation mix in the earlier years to 2020 is similar to the BAU case as committed new generation projects are commissioned and this has largely been kept the same. Of the renewable technologies, by 2050, solar PV combined with battery storage contributes the highest generation share of 343 TWh or 29%, significantly higher than onshore wind and biomass generation with a share of 16% and 17% respectively. As gas plants are retired in Thailand (and not replaced) from 2020 and coal units across the region are retired starting from 2030, bioenergy, CSP and solar PV with battery technologies fill the baseload role in the power system. By 2030 more than 70% of the generation is from renewables (including large-scale hydro), and by 2040 this share increases past 90% reaching 100% by 2050.
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Figure 107 GMS Generation Mix (ASES, GWh)
1,400,000
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1,000,000
800,000
600,000
GeneraXon (GWh) 400,000
200,000
0 2018 2028 2038 2048 2010 2012 2014 2016 2020 2022 2024 2026 2030 2032 2034 2036 2040 2042 2044 2046 2050
Offgrid Coal Hydro Gas Wind Bio Solar CSP Hydro ROR Geothermal Ocean
Figure 108 GMS Generation Mix (ASES, %)
100% 8% 4% 3% 4% 10% 90% 4% 14% 4% 19% 80% 47% 28% 70% 31% 61% 17% 29% 60% 11% 50% 20% 6% 40% 27% 26% 20% GeneraYon Mix 19% 30% 18% 18% 20% 19% 25% 28% 10% 19% 21% 14% 12% 4% 0% 1% 2010 2015 2020 2030 2040 2050
Offgrid Coal Hydro Gas Wind Bio Solar CSP Hydro ROR Geothermal Ocean
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Table 33 GMS Generation by Type (ASES, GWh)
Generation 2010 2015 2020 2030 2040 2050 Coal 49,296 90,035 125,911 156,993 40,236 0 Diesel 928 0 0 0 0 0 Fuel Oil 4,760 0 0 0 0 0 Gas 162,316 165,885 141,516 43,986 12,266 0 Nuclear 0 0 0 0 0 0 Hydro 47,631 96,976 117,624 137,564 136,162 139,769 Onshore Wind 0 624 17,566 79,633 162,837 185,479 Offshore Wind 0 0 0 438 11,525 35,199 Biomass 0 2,059 17,397 118,372 170,284 199,978 Biogas 0 0 0 7,063 24,582 33,413 Solar 0 170 35,660 137,795 267,207 343,062 CSP 0 0 0 26,690 93,313 163,509 Battery 0 0 0 0 0 0 Hydro ROR 0 0 1,512 18,676 30,707 42,430 Geothermal 0 0 0 1,314 4,954 7,087 Pump Storage 0 0 0 0 1,730 5,438 Ocean 0 0 0 0 8,894 10,184 Off-Grid 0 3 151 3,107 4,672 6,658
Table 34 GMS Generation Share by Type (ASES, %)
Generation 2010 2015 2020 2030 2040 2050 Coal 19% 25% 28% 21% 4% 0% Diesel 0% 0% 0% 0% 0% 0% Fuel Oil 2% 0% 0% 0% 0% 0% Gas 61% 47% 31% 6% 1% 0% Nuclear 0% 0% 0% 0% 0% 0% Hydro 18% 27% 26% 19% 14% 12% Onshore Wind 0% 0% 4% 11% 17% 16% Offshore Wind 0% 0% 0% 0% 1% 3% Biomass 0% 1% 4% 16% 18% 17% Biogas 0% 0% 0% 1% 3% 3% Solar 0% 0% 8% 19% 28% 29% CSP 0% 0% 0% 4% 10% 14% Battery 0% 0% 0% 0% 0% 0% Hydro ROR 0% 0% 0% 3% 3% 4% Geothermal 0% 0% 0% 0% 1% 1% Pump Storage 0% 0% 0% 0% 0% 0% Ocean 0% 0% 0% 0% 1% 1% Off-Grid 0% 0% 0% 0% 0% 1%
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7.5 Evolution of GMS Power Systems under ASES Scenario The ASES has in place a 90% and 100% renewable generation target by 2040 and 2050 respectively with higher energy efficiency measures than the SES. Figure 109 charts the generation mix in each GMS country (2015, 2030, 2050) with an indication of power flows across the various borders. Please refer to Appendix H for the tabulated data. The ASES follows a similar path as the SES with retirement of all fossil fuel power plants to meet the 100% renewable generation target. Significant amounts of solar PV and CSP are developed over this period accounting for 43% of total generation in the region by 2050. Wind and bio generation also play a significant role accounting for 20% of the generation mix each. Myanmar is a major exporter in the ASES with flows going into Thailand doubling from 3,700 MW to 7,500 MW from 2030 to 2050 as Myanmar’s renewable resources are developed to support the region’s 100% renewable generation target. Thailand also imports a significant amount of power from Lao PDR as it retires all of its gas and coal-fired generators, which provided a lot of the base load power in the BAU and SES. The other major importer is Viet Nam with almost 8,000 MW of power flowing into the north from Lao PDR; Viet Nam’s significant demand growth relative to its renewable resources available requires it to import up to 15% of its power needs by 2050.
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7.6 Projected Generation Fleet Structure To gain insight into the nature of the mix of generation technologies deployed in the ASES, we present a number of additional charts. Figure 110 and Figure 111 show the installed capacity by generation type for the SES – this is clearly biased towards renewable generation forms as there are no additional thermal projects built after 2015 and all are retired before 2050. Committed large-scale hydro remains on place in the GMS through to 2050.
Figure 110 GMS Installed Capacity by Type (ASES)
600,000
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400,000
300,000
Capacity, MW 200,000
100,000
0 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 Fossil Fuel Large Hydro Renewable
Figure 111 GMS Generation Mix by Type (ASES)
1,400,000
1,200,000
1,000,000
800,000
600,000
GeneraTon, GWh 400,000
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0 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
Fossil Fuel Large Hydro Renewable
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Figure 112, shows the dispatchable, semi-dispatchable and non-dispatchable components of installed capacity and it can be seen that semi-dispatchable increases to around 67% of the total system capacity compared to around 23% in the BAU by 2050. Based on operational simulations with this resource mix, it appears to be operationally feasible, although the reliance on generation forms that provide storage and having flexibility in the demand side play important roles. It is clear that short-term renewable energy solar and wind forecasting systems will be important, as will real-time updates on demand that can be controlled. Furthermore, control systems that can allow the dispatch of flexible resources on both supply and demand sides of the industry will be required.
Figure 112 GMS Installed Capacity by Dispatch Status (ASES)
450,000
400,000
350,000
300,000
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200,000 Capacity, MW 150,000
100,000
50,000
0 2015 2017 2019 2021 2025 2027 2029 2031 2035 2037 2039 2041 2045 2047 2049 2023 2033 2043
Dispatchable Non-Dispatchable Semi-Dispatchable
7.7 Reserve Margin and Generation Trends Figure 113 plots the reserve margin under the ASES. The ASES reserve margin trends towards 200% as expected with the retirement of conventional thermal coal and gas plants. It is worth noting conventional reserve margin measures are generally not suited to measuring high renewable energy systems in the same context used for thermal-based systems as already explained in Section 6.7. Figure 114 and Figure 115, respectively, show the installed capacity mix and generation mix for different categories of generation in the power system.
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Figure 113 GMS Reserve Margin (ASES)
250%
200%
150%
100%
50%
0% 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 Reserve Margin Renewable Capacity
Hydro Capacity Fossil Fuel Capacity
Figure 114 GMS Installed Capacity Shares for ASES by Generation Type
100% 90% 80% 70% 60% 50% 40% Capacity Share 30% 20% 10% 0% 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
Fossil Fuel Large Hydro Renewable Renewable + Large Hydro
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Figure 115 GMS Generation Shares for ASES by Generation Type
100% 90% 80% 70% 60% 50% 40%
GeneraTon Share 30% 20% 10% 0% 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
Fossil Fuel Large Hydro Renewable Renewable + Large Hydro
7.8 Electrification and Off-Grid While a quite high share of the population is electrified with off-grid (or micro-grid) technologies in the ASES, this represents a relatively low total electricity consumption compared to the total electricity consumption in the GMS. Less than 1% of total demand ismet by off-grid technologies in Myanmar and Cambodia. For more information on off-grid deployment please see the respective country reports.
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8 Analysis of Scenarios
Section 5, Section 6 and Section 7 presented projections of capacity and generation mix for the BAU, SES and ASES scenarios respectively. In order to understand the implications of the SES and ASES over the BAU, we present a number of simple measures to compare electricity demand, generation mix, renewable energy integration levels, carbon emissions, hydro development and an analysis of biomass and biogas.
8.1 Energy and Peak Demand Figure 116 compares the total electricity consumption of the BAU, SES and ASES with Figure 117 plotting the percentage reduction in electricity consumption of the SES relative to the BAU and ASES relative to the BAU. As can be seen the energy consumption in the SES is lower than the BAU with the main driver being enhancements in energy efficiency in the SES. The reduction in energy in the ASES is partially offset by the doubling of transport demand. Figure 118 compares peak load and shows the same relativities. This is attributable to improvements in load factor (80% in SES and ASES). On top of this the SES and ASES has contributions from flexible and controllable demand that allows reductions in peak demand consumption (not shown here). Figure 119 presents the population electricity access rates based on grid and off-grid access driven by electrification and off-grid assumptions relating to Myanmar and Cambodia. The BAU assumes close to 100% grid electrification by 2030 with the SES following a similar trajectory, albeit delayed, as off-grid technologies are deployed in the interim as the central grid is built out. The ASES also assumes slower grid electrification but stops grid extension once the cost of off-grid technologies (solar and battery storage) reach parity with grid generation costs (which occurs from 2025) and off-grid supply is developed to meet the remaining potential off-grid demand. The SES and ASES reach 100% electricity access by 2032 and 2033 respectively.
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Figure 116 GMS Energy Demand Comparison
1,800,000
1,600,000 BAU SES ASES
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800,000 Energy (GWh) 600,000
400,000
200,000
0 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050
Figure 117 GMS Percentage Reduction in Electricity Demand
35%
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20%
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10%
SES ASES 5%
0% 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
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Figure 118 GMS Peak Demand Comparison
300,000 BAU
250,000 SES
ASES 200,000
150,000
Peak Demand (MW) 100,000
50,000
0
2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050
Figure 119 Grid and Off-grid Electricity Access Rates (%)
100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
BAU SES ASES
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8.2 Energy intensity Figure 120 plots the per capita electricity consumption per annum across the scenarios. Electricity consumption includes all electricity consumption across the country. In the BAU, per capita consumption levels increase at a rate of 3.5% to reach 6,513 kWh pa which reaches Hong Kong and Japan consumption levels currently. In the ASES and SES, it increases more slowly at 2.5% pa and 2.6% pa, respectively, due to higher energy efficiency savings.
Figure 120 GMS Per Capita Consumption Comparison (kWh pa)
12,000
10,000
8,000
6,000
4,000
2,000 Total electricity use (kWh per capita)
0 2015 2017 2019 2021 2025 2027 2029 2031 2035 2037 2039 2041 2045 2047 2049 2023 2033 2043
BAU SES ASES Singapore - 2014 HK - 2014 Japan - 2014 Taiwan - 2014
8.3 Generation Mix Comparison Figure 121 and Figure 122 below show the renewable capacity and generation mix between the three scenarios. Renewable capacity (including large-scale hydro) reaches 48% in the BAU, which is equivalent to a 32% generation share driven by significant large-hydro exploitation. The SES reaches 91% renewable capacity and 86% generation capacity by 2050. The ASES reaches 100% renewable capacity and generation by 2050.
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Figure 121 GMS Renewable Installed Capacity Mix
100% 90% 80% 70% 60% 50% 40% 30% 20% Renewable Capacity Mix (%) 10% 0% 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
BAU (RE) SES (RE) ASES (RE)
BAU (RE + Large Hydro) SES (RE + Large Hydro) ASES (RE + Large Hydro)
Figure 122 GMS Renewable Generation Mix Comparison
100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Renewable GeneraZon Mix (%) 0% 2015 2017 2019 2021 2025 2027 2029 2031 2035 2037 2039 2041 2045 2047 2049 2023 2033 2043
BAU (RE) SES (RE) ASES (RE)
BAU (RE + Large Hydro) SES (RE + Large Hydro) ASES (RE + Large Hydro)
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Table 35 BAU Renewable Energy89 Generation Percentage Summary (%)
Year Cambodia Lao PDR Myanmar Thailand Viet Nam 2015 87% 83% 61% 13% 40% 2020 53% 83% 65% 20% 33% 2030 52% 75% 57% 28% 25% 2040 47% 72% 47% 33% 26% 2050 44% 74% 41% 37% 24%
Table 36 SES Renewable Energy Generation Percentage Summary (%)
Year Cambodia Lao PDR Myanmar Thailand Viet Nam 2015 87% 83% 61% 13% 40% 2020 68% 92% 78% 28% 39% 2030 63% 91% 92% 51% 52% 2040 78% 95% 98% 75% 68% 2050 87% 98% 100% 84% 81%
Table 37 ASES Renewable Energy Generation Percentage Summary (%)
Year Cambodia Lao PDR Myanmar Thailand Viet Nam 2015 87% 83% 61% 13% 40% 2020 72% 86% 80% 33% 40% 2030 77% 92% 89% 76% 64% 2040 90% 97% 100% 92% 95% 2050 100% 100% 100% 100% 100%
8.4 Renewable Energy Integration Figure 123 below plots the GMS in 2030 and 2050 under the SES and ASES against the top 21 countries in 2013 by renewable generation percentage including some additional European countries90. The countries listed here are generally developed or at an advanced development stage with great renewable potential (generally from large hydro) or countries with low generation levels. At a high level, the chart
89 Renewable energy includes large hydro, small hydro, pumped storage hydro, solar PV and CSP, wind, biomass, biogas, ocean energy, geothermal, and off-grid supply (for Cambodia and Myanmar) 90 Includes large hydro. Worldwide electricity production from renewable energy sources, Observer. 2013.
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indicates that SES and ASES renewable developments in the GMS fall within the bounds of percentages achieved currently around the world.
Figure 123 Renewable Generation Percentage (%)
100%
90%
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70%
60%
50%
40%
30% Renewanle Percentage 20%
10%
0% Mali Peru Spain Brazil Latvia Kenya France Austria Iceland Finland Canada Uganda Norway Sweden Ethiopia Uruguay Denmark Germany Colombia Costa Rica Venezuela Cameroon El Salvador Guatemala Switzerland New Zealand GMS 2050 (SES) GMS 2030 (SES) GMS 2030 (ASES) GMS 2050 (ASES) 8.5 Carbon Emissions Figure 124 and Figure 125 show the carbon intensity of GMS’s power system and the total per annum carbon emissions respectively. The intensity trajectory moves up in the BAU as more coal enters the system then maintains its level around 0.45t- CO2e/MWh as renewable technologies are also developed. The intensity in the SES drops to 0.10 t-CO2e/MWh by 2050 and the ASES is 100% carbon emissions free. In terms of total carbon emissions, the shift towards the SES and ASES saves up to 659 and 771 mt-CO2e, respectively, or the equivalent to a 85% and 100% saving from the BAU.
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Figure 124 GMS Carbon Intensity Comparison
0.50 0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10
Carbon Intensity (t-CO2e/MWh) 0.05 0.00 2015 2017 2019 2021 2025 2027 2029 2031 2035 2037 2039 2041 2045 2047 2049 2023 2033 2043
BAU SES ASES
Figure 125 GMS Carbon Emissions Comparison
900 800 700 600 500 400 300 200
Emissions (mt-CO2e per annum) 100 0 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
BAU SES
ASES Avoided Emissions (SES)
Avoided Emissions (ASES)
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8.6 Coal Power Developments Figure 126 plots the dependence on coal in all scenarios. The AES and SES trajectories decline as expected whereas the BAU increases to 46% by 2050 as 104 GW of coal plants is developed to meet increasing demands. Table 38, Table 39 and Table 40 provide a snapshot of the installed coal capacity developments in each of the scenarios.
Figure 126 GMS Coal Share Measure
50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 2010 2015 2020 2030 2040 2050
BAU SES ASES
Table 38 BAU Coal Plant Development (MW)
Year Cambodia Lao PDR Myanmar Thailand Viet Nam 2015 268 405 30 5,758 13,605 2020 1,243 405 0 5,640 21,640 2030 2,093 1,005 1,830 5,276 45,438 2040 4,093 1,605 5,860 8,080 59,438 2050 5,843 1,905 10,300 16,080 69,438
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Table 39 SES Coal Plant Development (MW) Year Cambodia Lao PDR Myanmar Thailand Viet Nam 2015 268 405 30 5,758 13,605 2020 1,243 405 0 5,640 20,000 2030 1,243 405 0 2,567 19,790 2040 1,243 405 0 1,221 17,260 2050 975 405 0 1,221 12,450
Table 40 ASES Coal Plant Development (MW) Year Cambodia Lao PDR Myanmar Thailand Viet Nam 2015 268 405 30 5,758 13,605 2020 508 405 0 5,458 18,500 2030 1,243 405 0 5,098 18,680 2040 975 405 0 2,965 3,860 2050 0 0 0 0 0
8.7 Hydro Power Developments Compared to 2015, in the BAU by 2030 there is approximately 18,000 MW of hydro developed in the GMS (2,100 in Cambodia, 3,900 in Lao, 3,300 in Myanmar, 4,100 in Thailand, and 4,400 in Viet Nam). In contrast, the SES and ASES has 8,500 MW developed between 2015 to 2030 which include approximately 3,500 MW of committed developments and 5,000 MW across Myanmar and Lao PDR to support renewable energy projects. Table 41 and Table 42 provide a snapshot of the installed hydro capacity developments in each of the scenarios.
Table 41 BAU Hydro Development Summary (MW Developed) Year Cambodia Lao PDR Myanmar Thailand Viet Nam 2015 1,634 1,577 3,252 5,743 15,175 2020 1,634 2,257 3,508 6,265 17,688 2030 3,738 5,509 6,544 9,858 19,529 2040 6,268 7,509 9,162 15,465 22,509 2050 7,518 10,009 10,882 17,565 22,775
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Table 42 SES and ASES Hydro Development Summary (MW Developed) Year Cambodia Lao PDR Myanmar Thailand Viet Nam 2015 1,634 1,577 3,252 5,743 15,175 2020 1,634 2,692 3,748 6,265 17,688 2030 1,634 4,192 6,213 6,139 17,688 2040 1,634 4,192 6,213 5,191 17,688 2050 1,634 4,192 6,213 5,191 17,688
Appendix E lists the hydro generation projects and commissioning year under the three scenarios across the GMS.
8.8 Analysis of Bioenergy Figure 127 shows a projection of the biomass available for the GMS (converted to GWh) and the total biomass generation for each scenario for the GMS. The shaded pink area represents the projected total technical biomass resource availability91 while the solid lines show the biomass consumption used by each scenario for the region. The projected available biomass was based on forecast growth rates in the agricultural sectors of each country. It was assumed that no more than 75% of the total projected available biomass resource was used. The remainder of the bioenergy requirements for each scenario was then assumed to be satisfied by biogas technologies. Figure 128 shows a similar chart for the GMS except for biogas. The green shaded area in this chart represents the amount of biogas available (again in units of GWh) and the corresponding generation from biogas in each scenario. This shows that the SES and ASES are dependent on biogas while the BAU is assumed to not deploy this technology. Based on the projections the biomass and biogas resources available to the region can be seen to be sufficient to support the amount of biomass and biogas generation to 2050.
91 Projections of biomass availability developed by IES based on baselines established from information on biomass and biogas potential reported in ‘Renewable Energy Developments and Potential in the Greater Mekong Subregion’, ADB (2015) report.
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Figure 127 Projected Biomass Availability and Consumption in the BAU, SES and ASES scenarios for the GMS as a whole
300,000
250,000
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Figure 128 Projected GMS Biogas Availability and Consumption in the BAU, SES and ASES scenarios for the GMS as a whole
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9 Economic Implications
In this section we consider the economic implications of the three scenarios and examine in particular: (1) the levelised cost of electricity (LCOE) generation for the entire system, (2) investment costs, (3) total operating and capital expenditure including the cost of energy efficiency, (4) off-grid cost comparisons, and (5) implications for job creation. The analysis presented is supported by sensitivity analysis to examine how changes in fuel prices, technology costs and carbon prices may impact projections of the LCOE. It should be noted that the analysis presented in this section is done for the purpose of comparison, and that the prices and costs provided are dependent on the fuel price projections and technology cost assumptions that were used in both scenarios and which have been listed in Appendix A and Appendix B.
9.1 Overall Levelised Cost of Electricity (LCOE) The comparison of the LCOE (only includes generation costs) is shown in Figure 129, noting that Thailand and Viet Nam drives most of the fluctuations, due to their high relative consumption in the region. The LCOE for the BAU starts to increase as fuel costs increase back to long-term averages before declining to $92/MWh as a result of the deployment of lower capital costs associated with its slow transition to renewable energy generation. The ASES and SES LCOE’s remain close due to similar supply mixes with the exception of 2030 to 2040 where committed gas and coal plant still exist in the SES. From 2035 the LCOE edges up slightly as traditional base load technology is replaced with more expensive renewable generation (CSP, battery and biogas generation). This LCOE analysis only compares central grid connected electricity production and it does not include the cost of externalities92.
92 A detailed study on the cost of externalities is presented in the following reference: Buonocore, J., Luckow, P., Norris, G., Spengler, J., Biewald, B., Fisher, J., and Levy, J. (2016) ‘Health and climate benefits of different energy- efficiency and renewable energy choices’, Nature Climate Change, 6, pp. 100–105.
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Figure 129 GMS LCOE for Generation
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9.2 Annual System Cost Figure 130, Figure 131 and Figure 132 plots the annual system cost by component for the BAU, SES and ASES. Grid electrification and off-grid supply applies only to Myanmar and Cambodia and includes the cost of building out the central transmission network, and solar PV and battery technology, respectively. The BAU system costs increase to almost $160 billion a year by 2050 with operational expenditures, mainly fuel costs, accounting for more than 50% of the total cost. The SES and ASES have significantly lower costs by 2050, approximately $120 billion a year, driven by the significant fuel cost savings. The relativities in capital expenditure and operational expenditure relate to the differences in generation mix between the scenarios. Figure 133 and Figure 134 presents the difference in cost components between the BAU and SES and the BAU and ASES, respectively.
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Figure 130 Annual System Cost (BAU)
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Figure 131 Annual System Cost (SES)
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Figure 132 Annual System Cost (ASES)
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Figure 133 Difference in Annual System Cost (BAU against SES)
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Figure 134 Difference in Annual System Cost (BAU against ASES)
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9.3 Cumulative Capital Investment The following section details the investment costs of meeting demand in GMS. Figure 135 shows the cumulative investment in generation CAPEX and energy efficiency in millions of Real 2014 USD, although the earlier observation of the SES and ASES having lower demand owing to energy efficiency gains should be recognised. Figure 135 shows the BAU requiring the least capital investment by the end of the modelling horizon primarily driven by the lower CAPEX costs because of investments into traditional coal technologies, which provide base-load support i.e. the CAPEX cost taking into account capacity factors is far lower for coal than solar PV with battery as an example. The SES and ASES include investment in energy efficiency measures and greater investments in CSP, biogas and battery storage to defer generation post-2035 with the ASES requiring more investment because of higher replacement requirements for retired coal and gas plant. The breakdown of costs by component are presented in Figure 136, Figure 137 and Figure 138.
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Figure 135 GMS Cumulative Investment (Real 2014 USD)
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Figure 136 GMS Cumulative Investment by Type (BAU, Real 2014 USD)
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Figure 137 GMS Cumulative Investment by Type (SES, Real 2014 USD)
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Figure 138 GMS Cumulative Investment by Type (ASES, Real 2014 USD)
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9.4 Operating Costs, Amortised Capital Costs and Energy Efficiency Costs Figure 139 and Table 43 present the net present value of the power system costs in the GMS by component using an 8% and 15% discount rate. The BAU is comprised of a higher percentage of fuel costs, whereas the ASES has the highest percentage relating to capital costs. The total NPV difference between the BAU and ASES is approximately $192 billion under an 8% discount rate.
Figure 139 NPV of System Costs (Real 2014 USD) for period 2015 to 2050
900,000 800,000 700,000 600,000 500,000 400,000 NPV ($m's) 300,000 200,000 100,000 0 BAU @ 8% SES @ 8% ASES @ 8% BAU @ 15% SES @ 15% ASES @ 15% Fuel Cost Capital Cost FOM VOM Grid ElectrificaTon Energy Efficiency Offgrid
Table 43 NPV of System Costs (Real 2014 USD) for period 2015 to 2050
NPV BAU @ 8% SES @ 8% ASES @ 8% BAU @ 15% SES @ 15% ASES @ 15% Fuel Cost 462,919 288,682 219,927 208,384 150,668 126,589 Capital Cost 322,100 321,220 347,175 142,637 143,706 149,783 FOM 31,035 32,394 35,582 14,222 14,552 15,153 VOM 34,841 30,264 29,199 15,414 13,902 13,371 Grid Electrification 4,601 3,386 1,825 1,902 1,341 807 Energy Efficiency 0 22,111 28,028 0 6,587 8,715 Off-Grid 0 856 2,071 0 355 648 Total 855,495 698,913 663,807 382,560 331,111 315,066
9.5 Off-grid Cost Comparison Figure 140 below compares the cost of providing 100% electricity access by 2050 across the three scenarios, for the population that has no access to electricity
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currently. The BAU is assumed to achieve close to 100% central grid based electrification by 2030 and the costs relate to grid electrification and grid generation costs to support the electrified loads93. The ASES assumes a much slower central grid based electrification which ceases around 2030 when off-grid solar and battery storage becomes economic. The ASES line comprises mainly investment costs relating to residential solar PV and battery storage and a small grid electrification cost component. The SES assumes a 100% central grid based electrification target albeit at a slower pace than in the BAU with off-grid demand supplied with solar PV and battery technology in the interim. The differences are mainly driven by the difference in electricity demands per capita between the scenarios.
Figure 140 Grid Electrification and Off-grid Costs
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9.6 Fuel Price Sensitivity Figure 141 plots the LCOE of the BAU, SES and ASES. In addition, it plots the LCOE for a 50% increase to the fuel prices, which reflects the difference between IEA’s crude oil pricing under the 450 Scenario and the Current Policies Scenario ($95/bbl and $150/bbl respectively) and a -50% sensitivity. It can be seen that the LCOE of the BAU rises more (up to $20/MWh) against a fuel price increase compared with smaller increases in the SES and ASES as would be anticipated as a direct consequence of having a higher thermal
93 Myanmar National Electrification Program Roadmap and Investment Prospectus, Castalia Strategic Advisors (2014). Electrification costs were based on Myanmar’s cost estimates of 100% electrification (7.2 million households by 2030) costing $5.8 billion and pro-rated based on Myanmar population figures.
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generation share in the BAU compared to renewable energy in the SES and ASES. The SES increases, and the ASES to a smaller extent, as a consequence of bioenergy generation, but is still less sensitive to fuel price shocks than the BAU.
Figure 141 GMS Fuel Price Sensitivity ($/MWh)
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9.7 Impact of a Carbon Price In a similar way to the previous section, Figure 142 plots the LCOE under the BAU, SES and ASES and the LCOE under a carbon price scenario. The carbon scenario puts a $20/t-CO2 impost throughout the entire modelled period. This is intended to show the sensitivity of the BAU, SES and ASES to carbon prices. In a similar way to the previous section, this shows that the LCOE in the SES and ASES is insensitive to carbon prices by 2050 while for the BAU, it adds an additional $10 Real 2014 USD/MWh to the LCOE because of its coal generation.
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Figure 142 GMS Carbon Sensitivities ($/MWh)
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9.8 Renewable Technology Cost Sensitivity Figure 143 shows the LCOE sensitivity to 20% and 40% decreases in renewable technology costs. As expected the ASES followed by the SES are the most sensitive with potential declines of up to $25/MWh. The results also show that any technology cost drops beyond what was assumed will bring the SES and ASES LCOE well below that in the BAU.
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Figure 143 GMS Renewable Technology Cost Sensitivities ($/MWh)
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9.9 Jobs Creation To assess the implications for Job Creation for each scenario we applied the methodology used by the Climate Institute of Australia. The methodology is summarised in Appendix C. The numbers of jobs created for each of the scenarios are shown in Figure 144, Figure 145 and Figure 146. The job categories shown include: manufacturing, construction, operations and maintenance and fuel supply management. Figure 147 provides a comparison of total jobs created for BAU, SES and ASES. The key observations are:
• Across all scenarios, manufacturing and construction account for most of the jobs with a much smaller share attributable to O&M and fuel supply.
• The BAU job creation profile peaks at around 450,000 jobs compared to SES job creation peaking towards 1 million or more than two times that in the BAU. This is entirely driven by renewable energy developments that require more jobs in the manufacturing and construction phases. See Appendix C for assumptions.
• The ASES job creation peaks at 1.5 million jobs, more than three times that of the BAU driven by even more renewable energy projects required as the region moves towards a 100% renewable generation target by 2050.
• Different skills are required between the scenarios, BAU has people working on conventional coal and hydro, whereas the SES and ASES has people mainly working on solar & battery storage systems.
• Note that the manufacturing and fuel supply jobs shown to be created may not be created within the region if manufacturing of equipment and fuel management (for imported fuels) occurs in other countries.
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Figure 144 Job Creation by Category (BAU)
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Figure 145 Job Creation by Category (SES)
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Figure 146 Job Creation by Category (ASES)
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Figure 147 Total Job Creation Comparison BAU, SES and ASES
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10 Conclusions
10.1 Comparison of Scenarios The following are the key conclusions that have been drawn from the analysis:
• The SES delivers an energy efficiency gain beyond the BAU case of about 30% compared to the BAU. The ASES delivers efficiency gains of 31% after doubling transport electricity demand;
• The SES and ASES are able to achieve a power system that delivers 86% and 100% of generation from renewable energy resources (including large-scale hydro) by 2050. In contrast, only 32% of the generation in the BAU is provided by renewable energy resources by 205094;
• By 2050, the SES and ASES avoid around 569 and 771 million tons of greenhouse gas emissions per year compared to the BAU.
• Based on some simple measures for energy security: - Under the ASES and SES, GMS benefits from a more diverse mix of technologies and is not as dependent on a single source of primary energy as the BAU; for example, the BAU is highly dependent on large-scale hydro and coal, while the SES and ASES diversifies supply across a range of renewable energy technologies with no generation type accounting for more than 25% and 30% of the generation share, respectively; - The ASES and SES achieve a reliable power system through coordination on both the supply and demand side of the industry, with similar energy reserve margins as the BAU. Though as a measure of energy supply storage and flexibility the ASES and SES overall are lower than the BAU driven by higher levels of non and semi-dispatchable generation. The BAU would be more resilient against extreme events but the ASES and SES benefit from a more integrated regional power system through cross-border trading. Modelling has shown that the SES is operationally feasible (even with less directly dispatchable resources in the SES compared to the BAU), but stress testing of the SES scenarios against more significant threats to the operation of the power system would help to understand and develop appropriate mitigation measures if required. A key condition for this scenario to be operationally feasible in practice is real-time monitoring and control systems for all elements of the power system, near real-time and automated dispatch operations, and high quality forecasting systems for solar and wind energy.
94 Large-scale hydro is included
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10.2 Economic Implications
10.2.1 Electricity Costs Based on the outcomes of modelling the BAU, SES and ASES scenarios, we also examined the following issues in relation to electricity costs: (1) levelised cost of electricity, (2) investment requirements, (3) sensitivity of electricity prices to fuel price shocks, and (4) the implications of a price on carbon equivalent emissions for electricity prices. Based on this analysis we draw the following conclusions:
• The BAU requires lower levels of capital investment than the SES and ASES, and in relation to generation costs, the SES and ASES across the modelling period deliver a lower overall generation cost;
• Under the SES and ASES significant benefits are gained in the form of avoided fuel costs and this contributes to achieving a lower overall dollar cost for the GMS. The observation is made that the composition of LCOE under the SES and ASES is largely driven by investment costs, hence exposure to fuel shocks is significantly reduced; and
• The LCOE under the SES and ASES is also largely insensitive to a carbon price, as could be reasonably anticipated for a power system that is entirely dominated by renewable energy.
10.2.2 Investment Implications From 2015 to 2050, the overall investment for each scenario varies significantly: $660 billion in the BAU compared to $835 billion in the SES and $958 billion in the ASES (Real 2014 USD). However, the composition of the investments is quite different. The BAU directs most investment (65%) to coal and hydro projects, while in the SES (and ASES) investments are spread over a wider range of technologies: 50% is directed to solar95 and battery system technologies across the SES and ASES, with other significant investments in energy efficiency measures (17% SES and 18% ASES), wind (12% in SES and ASES) and less than 1% in off-grid supply. Clearly, compared to the BAU, the SES and ASES will require investments across a more diverse range of technologies and also technologies that are of a smaller scale and more distributed rather than a smaller number of large scale developments as per the BAU. This highlights the importance to the SES and ASES of having investment frameworks for energy infrastructure that can accommodate a larger number of smaller investments.
10.2.3 Jobs Creation The SES and ASES scenarios both result in quite different technology mixes compared to the BAU. Each has quite different implications for the workforce that
95 PV and CSP technologies.
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would be required to support each scenario. Based on analysis of the required jobs we estimate that96:
• The BAU from 2015 to 2050 would be accompanied by the creation of some 13 million job years97 (20% manufacturing, 46% construction, 22% operations and maintenance, and 12% fuel supply);
• The SES would involve the creation of some 21 million job years (25% in manufacturing, 56% in construction, 18% in operations and maintenance and 0.8% in fuel supply); and
• The ASES would involve the creation of 28 million job years (24% in manufacturing, 53% in construction, 23% in operations and maintenance and less than 0.1% in fuel supply).
10.3 Barriers for the SES and ASES in GMS The GMS has abundant renewable energy resources. However, non-hydro renewable energy resources, particularly wind and solar energy in this region are currently underexploited due to a number of social, economic, financial, technical and institutional barriers. The following barriers potentially deter new investment in renewable energy and the implementation of energy efficiency measures:
10.3.1 Social barriers
• A lack of public awareness and understanding on the importance of renewable energy and energy efficiency in addressing environmental concerns. This is due to insufficient information from relevant government agencies on the benefits and potentials of renewable energy and energy savings. This may also relate to the broader education levels and programs in some of the GMS countries. The lack of public awareness is also due to inadequate data monitoring and analysis for performance reporting to properly quantify the benefit.
• A lack of effective and considered measures relating to adverse social and environmental impacts of large scale renewable projects such as hydropower.
10.3.2 Economic and financial barriers
• The main economic barrier to promoting renewable energy and energy efficiency in the GMS is their high investment costs, which are significantly higher than conventional generation technologies at present.
• In all of the GMS countries, project developers have experienced difficulties in securing finance to invest in renewable energy projects.
96 Based on the employment factors presented in Appendix C. 97 A job year is one job for one person for one year. We use this measure to make comparisons easier across each scenario as the number of jobs created fluctuates from year to year.
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• Fossil fuel price subsidies, particularly in Myanmar, Viet Nam and Thailand, represent another significant barrier in new investment in renewable energy. Subsidies also discourage energy conservation and energy efficiency measures as the true costs of fossil fuels are not reflected.
10.3.3 Technical barriers
• The overall knowledge on renewable energy technology in the GMS is somewhat limited. There appears to be a shortage of technical, operational and maintenance expertise within the government and the local private sector which limits development opportunities. This is due to a lack of training organisations and facilities leading to a lack of qualified experts and skilled technicians.
• Inadequate transmission and distribution networks to support an increase in renewable energy projects, particularly in remote areas.
• Insufficient research and development effort in the renewable energy sector in the GMS countries. This includes a lack of detailed studies on the impact of high renewable penetration on the operation of power grids and conventional power plants.
• There is a lack of measurements, reporting and verification systems to follow up on the outcomes of energy saving programs. This makes it difficult to assess the effectiveness of the programs.
10.3.4 Policy and institutional barriers
• Due to the high costs of renewable technologies at present, these technologies rely on incentive schemes to compete with conventional technologies. However, there is a lack of sufficient supporting schemes, strategies and plans to promote renewable energy and energy efficiency, particularly in Cambodia, Lao PDR and Myanmar.
• Although Thailand and, to some extent, Viet Nam have put in place policies and supporting schemes to promote renewable energy, there is still a lack of coordination between different governmental agencies which are responsible for policy decision-making resulting in uncoordinated and incoherent policies. This barrier is found in government agencies which usually work in vertical hierarchy of management. There are also significant uncertainties over future policies and regulatory frameworks which represent risks to potential investors.
• Difficulties and long waiting times in obtaining licenses and connecting renewable plants to the grid due to a lack of well-defined operational and technical standards.
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10.4 Recommendations The following are key recommendations that potentially reduce the barriers to the SES and ASES in the GMS.
10.4.1 Overcoming social barriers
• Disseminate information on the benefits of renewable energy and energy efficiency through effective communication methods and educational programs.
• Conduct detailed assessments of the impacts of renewable energy projects and measures to alleviate social and environmental impacts and make the results publicly available.
10.4.2 Overcoming economic and financial barriers
• Develop energy policies and schemes to increase the cost competitiveness of renewable technologies. The aim is to create an environment that is conducive for investment in renewable energy technologies.
• Conduct detailed assessments of renewable energy potential to enable prospective investors to understand the potential, identify the best opportunities and subsequently take steps to explore investment and deployment.
• Consider removing or replacing fossil fuel subsidies with other supporting schemes.
10.4.3 Overcoming technical barriers
• Knowledge transfer and capability building in the renewable energy technologies and energy efficiency for policy makers and staff working in the energy industry to ensure the human capacity is being developed to support a national power system that has a high share of generation from renewable energy. As we have shown the SES and ASES will require a large number of skilled workers to support a technology mix with a significant share of renewable energy.
• Investments in ICT systems to allow for greater real-time monitoring, control and forecasting of the national power system, including SCADA/EMS, and smart- grid technology and renewable energy forecasting systems and tools. This will enable efficient real-time dispatch and control of all resources in the system which will facilitate high levels of renewable energy as well as cross-border power trading.
• The SES and ASES depend on power import and export among the GMS countries therefore it is important to take measures to encourage cross-border power trade in the region, as this works to the advantage of exploiting scattered renewable energy resource potentials and diversity in electricity demand. These measures include:
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- Develop an overarching transmission plan that has been informed by detailed assessments and plans to leverage renewable energy potential in the region and diversity in demand and hydrological conditions; - Enhance technical standards and transmission codes in each country to allow for better interoperation of national power systems; - Develop a framework to encourage energy trade in the region, and in particular towards a model that can support multilateral power trading via a regional power market. • Take measures to improve power planning in the region to: - Explicitly account for project externalities and risks, - Evaluate a more diverse range of scenarios including those with high levels of renewable energy and energy efficiency plans, - Take into consideration overarching plans to have tighter power system integration within the region, and - Carefully evaluate the economics of off-grid against grid connection where this is relevant.
10.4.4 Overcoming policy and institutional barriers
• Formation of more comprehensive energy policies to create an environment that is appropriate for investment in renewable energy technologies and encourage energy efficiency. Investor confidence in renewable energy investment will be enhanced by having a transparent regulatory framework that provides certainty to investors and appropriately considers the ramifications of high levels of renewable energy in the generation mix.
• Implement regulatory frameworks and well-defined technical codes to streamline procedures for providing licenses and avoiding delay in grid connection.
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Appendix A Technology Costs
Table 44 sets out the technology cost assumptions that were used in the modelling presented in this report for the BAU and SES scenarios. Table 45 sets out the technology costs used in the ASES. The technology costs of coal and gas do not include overheads associated with infrastructure to develop facilities for storing / managing fuel supplies. These costs were however accounted for in the modelling. Figure 148 and Figure 149 present the levelised cost of new entry generation based on assumed capacity factors. LCOE levels presented in Section 9 are based on weighted average LCOEs and modelled output and will differ from the LCOEs presented here. The LCOE for battery storage is combined with solar PV technology assuming 75% of generation is stored for off-peak generation.
Table 44 Technology Costs Assumptions for BAU and SES Scenarios Technology Capital Cost (Unit: Real 2014 USD/kW) Technology 2015 2030 2040 2050 Generic Coal 2,492 2,474 2,462 2,450 Coal with CCS 5,756 5,180 4,893 4,605 CCGT 942 935 930 926 GT 778 772 768 764 Wind Onshore 1,450 1,305 1,240 1,175 Wind Offshore 2,900 2,610 2,480 2,349 Hydro Large 2,100 2,200 2,275 2,350 Hydro Small 2,300 2,350 2,400 2,450 Pumped Storage 3,340 3,499 3,618 3,738 PV No Tracking 2,243 1,250 1,050 850 PV with Tracking 2,630 1,466 1,231 997 PV Thin Film 1,523 1,175 1,131 1,086 Battery Storage - Small 600 375 338 300 Battery - Utility Scale 500 225 213 200 Solar Thermal with Storage 8,513 5,500 4,750 4,000 Solar Thermal No Storage 5,226 4,170 3,937 3,703 Biomass 1,800 1,765 1,745 1,725 Geothermal 4,216 4,216 4,216 4,216 Ocean 9,887 8,500 7,188 5,875 Biogas (AD) 4,548 4,460 4,409 4,359 *Battery technology quoted on a $/kWh basis
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Figure 148 Levelised Cost of New Entry (BAU & SES, $/MWh)
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Table 45 Technology Costs Assumptions for ASES Scenario Technology Capital Cost (Unit: Real 2014 USD/kW) Technology 2015 2030 2040 2050 Generic Coal 2,492 2,462 2,450 2,437 Coal with CCS 5,756 4,893 4,605 4,334 CCGT 942 930 926 921 GT 778 768 764 761 Wind Onshore 1,450 1,240 1,175 1,113 Wind Offshore 2,900 2,480 2,349 2,225 Hydro Large 2,100 2,275 2,350 2,427 Hydro Small 2,300 2,400 2,450 2,501 Pumped Storage 3,340 3,618 3,738 3,861 PV No Tracking 2,243 1,050 850 688 PV with Tracking 2,630 1,231 997 807 PV Thin Film 1,523 1,131 1,086 1,043 Battery Storage - Small 600 338 300 267 Battery - Utility Scale 500 213 200 188 Solar Thermal with Storage 8,513 4,750 4,000 3,368 Solar Thermal No Storage 5,226 3,937 3,703 3,483 Biomass 1,800 1,745 1,725 1,705 Geothermal 4,216 4,216 4,216 4,216 Wave 9,887 7,188 5,875 4,802 Biogas (AD) 4,548 4,359 4,309 4,259 *Battery technology quoted on a $/kWh basis
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Figure 149 Levelised Cost of New Entry (ASES, $/MWh)
250
200
150
100
Levelised Cost of GeneraTon ($/MWh) 50
0 2015 2017 2019 2021 2025 2027 2029 2031 2035 2037 2039 2041 2045 2047 2049 2023 2033 2043
Hydro Wind Coal Gas Bio Solar CSP PV + Baoery [75%] Hydro ROR Geothermal Pump Storage
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Appendix B Fuel Prices
Table 46 sets out the Free on board (FOB) fuel price assumptions that were used in the modelling presented in this report. This fuel price set was common to all three scenarios.
Table 46 Fuel Price Assumptions (Real 2014 USD/GJ)
Year Coal Gas Diesel Uranium Fuel Oil Biomass Biogas 2015 2.39 10.08 13.34 0.72 9.13 2.57 1.00 2016 2.51 11.88 15.24 0.76 10.49 2.62 1.00 2017 2.63 12.91 15.28 0.80 11.68 2.67 1.00 2018 2.74 13.72 16.41 0.80 12.43 2.72 1.00 2019 2.86 14.47 17.53 0.80 13.18 2.78 1.00 2020 2.98 15.16 18.64 0.80 13.93 2.83 1.00 2021 3.10 15.81 19.73 0.80 14.65 2.89 1.00 2022 3.21 16.46 20.80 0.80 15.36 2.95 1.00 2023 3.33 17.10 21.86 0.80 16.06 3.01 1.00 2024 3.45 17.72 22.90 0.80 16.76 3.07 1.00 2025 3.56 18.34 23.93 0.80 17.44 3.13 1.00 2026 3.56 18.29 23.86 0.80 17.39 3.19 1.00 2027 3.56 18.24 23.79 0.80 17.34 3.25 1.00 2028 3.56 18.19 23.72 0.80 17.29 3.32 1.00 2029 3.56 18.14 23.65 0.80 17.24 3.39 1.00 2030 3.56 18.09 23.58 0.80 17.19 3.45 1.00 2031 3.56 18.06 23.53 0.80 17.15 3.52 1.00 2032 3.56 18.02 23.49 0.80 17.12 3.59 1.00 2033 3.56 17.99 23.44 0.80 17.08 3.67 1.00 2034 3.56 17.96 23.40 0.80 17.05 3.74 1.00 2035 3.56 17.92 23.35 0.80 17.02 3.81 1.00 2036 3.56 17.89 23.30 0.80 16.98 3.89 1.00 2037 3.56 17.86 23.26 0.80 16.95 3.97 1.00 2038 3.56 17.83 23.21 0.80 16.92 4.05 1.00 2039 3.56 17.79 23.16 0.80 16.88 4.13 1.00 2040 3.56 17.76 23.12 0.80 16.85 4.21 1.00 2041 3.56 17.76 23.12 0.80 16.85 4.29 1.00 2042 3.56 17.76 23.12 0.80 16.85 4.38 1.00 2043 3.56 17.76 23.12 0.80 16.85 4.47 1.00 2044 3.56 17.76 23.12 0.80 16.85 4.56 1.00 2045 3.56 17.76 23.12 0.80 16.85 4.65 1.00 2046 3.56 17.76 23.12 0.80 16.85 4.74 1.00 2047 3.56 17.76 23.12 0.80 16.85 4.84 1.00 2048 3.56 17.76 23.12 0.80 16.85 4.93 1.00 2049 3.56 17.76 23.12 0.80 16.85 5.03 1.00 2050 3.56 17.76 23.12 0.80 16.85 5.13 1.00
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Appendix C Methodology for Jobs Creation
This section briefly summarises the methodology that we adopted for jobs creation. The methodology that we have adopted has been based on an approach developed by the Institute for Sustainable Futures at the University of Technology, Sydney and used by the Climate Institute of Australia98. In essence the jobs created in different economic sectors (manufacturing, construction, operations & maintenance and fuel sourcing and management) can be determined by the following with the information based on the numbers provided in Table 47.
Figure 150 Job Creation Calculations
We have applied this methodology to the results in each scenario discussed in this report in order to make estimates of the jobs creation impacts and allow comparisons to be made99.
98 A description of the methodology can be found in the following reference: The Climate Institute, “Clean Energy Jobs in Regional Australia Methodology”, 2011, available: http://www.climateinstitute.org.au/verve/_resources/cleanenergyjobs_methodology.pdf. 99 The percentage of local manufacturing and local fuel supply is assumed to be 1 to reflect the total job creation potential in total.
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Table 47 Employment Factors for Different Technologies
Annual decline
applied to
employment multiplier
Construction time Construction Manufacturing Operations maintenance & Fuel
Technology 2010- 20 2020-30 years per MW per MW per MW per GWh Black coal 0.5% 0.5% 5 6.2 1.5 0.2 0.04 (include Brown coal 0.5% 0.5% 5 6.2 1.5 0.4 in O&M) Gas 0.5% 0.5% 2 1.4 0.1 0.1 0.04 Hydro 0.2% 0.2% 5 3.0 3.5 0.2 Wind 0.5% 0.5% 2 2.5 12.5 0.2 Bioenergy 0.5% 0.5% 2 2.0 0.1 1.0 Geothermal 1.5% 0.5% 5 3.1 3.3 0.7 Solar thermal 1.5% 1.0% 5 6.0 4.0 0.3 generation SWH 1.0% 1.0% 1 10.9 3.0 0.0 PV 1.0% 1.0% 1 29.0 9.0 0.4
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Appendix D Committed Power Projects
Complete lists of the power projects that were assumed to be committed in the modelling are provided in Table 48, Table 49, Table 50, Table 51 and Table 52 respectively for Cambodia, Lao PDR, Myanmar, Thailand and Viet Nam.
Table 48 Cambodia: Committed New Entry Assumptions
No. Country Capacity (MW)100 Type COD101 1 Russei Chrum Hydroelectric 338 Hydro 2015 2 Stung Tatay Hydroelectric 246 Hydro 2015 3 Stung Atay Hydro plant 120 Hydro 2015 4 C.I.I.D.G Erdos Hongjun Electric Power Co., Ltd #2&3 240 Coal 2016 5 C.I.I.D.G Erdos Hongjun Electric Power Co., Ltd #4 135 Coal 2018 6 Sihanoukville Imported Coal #1 300 Coal 2018 7 Sihanoukville Imported Coal #2 300 Coal 2020 8 Sihanoukville Imported Coal #3 300 Coal 2022 9 Sihanoukville Imported Coal #4 300 Coal 2024
Table 49 Lao PDR: Committed New Entry Assumptions
No. Project Exports To Capacity (MW) Technology COD 1 Nam Ngiep 2 180 Hydro 2015 2 Hong Sa Thailand 405 Coal 2015 3 Nam Ou 2 120 Hydro 2015 4 Nam Ou 5 240 Hydro 2015 5 Nam Ou 6 180 Hydro 2015 6 Nam Kong 2 66 Hydro 2015 7 Xekaman 1 Viet Nam 64 Hydro 2016 8 Nam Sim 8 Hydro 2016 9 Nam Mang 1 64 Hydro 2016 10 Nam Beng 34 Hydro 2016 11 Nam Sane 3A 69 Hydro 2016 12 Nam Sane 3B 45 Hydro 2016 13 Nam Lik 1 61 Hydro 2017 14 Nam Phay 86 Hydro 2018 15 Nam Tha 1 (Nam Pha) 168 Hydro 2018 16 Xekaman 4 Viet Nam 16 Hydro 2018
100 Capacity figures presented here are pro-rated based on the intended power flows between the countries as of the year of commissioning. 101 Commercial Operation Date.
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Table 50 Myanmar: Committed New Entry Assumptions
No. Unit Capacity (MW) Generation Type COD 1 Mawlamyine MPLP(1st) 98 Gas 2015 2 Thaton GT (W-B) 106 Gas 2015 3 Myinchan Aggrego 103 Gas 2015 4 APR Energy 100 Gas 2015 5 V-Power 50 Gas 2015 6 Upper Nam Htwan 3.2 Hydro 2016 7 Mong Wa 60 Hydro 2016 8 Thilawa(1) 25 Gas 2016 9 Shwedaung IPP 70 Gas 2016 10 Kanbauk GEG 6 Gas 2016 11 Thilawa(2) 25 Gas 2017 12 Myinchan IPP 250 Gas 2017 13 Thahtay 111 Hydro 2018 14 Upper Keng Tong 51 Hydro 2018 15 Upper Baluchaung 30.4 Hydro 2018 16 Tharkayta UREC 1st 115 Gas 2018 17 Kanbauk GTCC 200 Gas 2018
Table 51 Thailand: Committed New Entry Assumptions
Capacity Generation No. Project COD102 (MW) Type 1 Gulf JP UT 800 Gas 2015 2 Ratchaburi World Cogeneration Co.Ltd. (project 2) 90 Gas 2015 3 B. Grimm Power 90 Gas 2015 4 Kwae Noi Dam #1-2 30 Hydro 2015 5 Sakae Solar Cell 5 Solar 2015 6 Prakarnchon Dam 10 Hydro 2015 7 Chulabhorn Hydropower 10 Hydro 2015 8 Other Hydro 6.7 Hydro 2015 9 Mae Hydro 12 Hydro 2015 10 Very Small Power Producers (VSPPs) 271 Gas 2016 11 Bang Lang Dam (upgrade) 12 Hydro 2016 12 Sirindhorn Dam Solar Cell 0.3 Solar 2016 13 EGAT Solar Project 10 Solar 2016 14 Other VSPPs 283 Gas 2017
102 Commercial operation date.
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15 Hydropower 5.5 Hydro 2017 16 Lamtakong Phase 2 24 Wind 2017 17 Gulf JP UT Co., Ltd. #1-2 (Jun, Dec) 1600 Gas 2018 18 Other VSPPs 288 Gas 2018 19 Lamtakong Pump Storage #3-4 500 Hydro 2018 20 Maw #4-7 Replacement 600 Coal 2018 21 Tron Dam Hydropower 2.5 Hydro 2018 22 Chulabhorn Dam Hydropower 1.3 Hydro 2018 23 EGAT Biomass 4 Bio 2018 24 EGAT Biogas 5 Bio 2018
Table 52 Viet Nam: Committed New Entry Assumptions
No. Region Project Capacity MW Generation Type COD 1 North Ngoi Phat 72 Hydro 2015 2 North Song Bac 42 Hydro 2015 3 Central Song Bung 4 156 Hydro 2015 4 Central Srepok 4A 64 Hydro 2015 5 North Ba Thuoc 1 60 Hydro 2015 6 North Bac Me 45 Hydro 2015 7 South Dong Nai 5 150 Hydro 2015 8 North Huoi Quang 1 260 Hydro 2015 9 North Lai Chau 1-1 400 Hydro 2015 10 North Nậm Mức 44 Hydro 2015 11 North Nam Na 2 66 Hydro 2015 12 North Nậm Na 3 84 Hydro 2015 13 North Nam Toong 34 Hydro 2015 14 North Ngoi Hut 2 48 Hydro 2015 15 Central Nhan Hac 45 Hydro 2015 16 North Nho Que 32 Hydro 2015 17 North Nho Que 2 48 Hydro 2015 18 Central Xekaman 3 200 Hydro 2015 19 Central Song Bung 2 108 Hydro 2015 20 South Sông Giang 2 37 Hydro 2015 21 Central Song Tranh 3 62 Hydro 2015 22 South Formosa HT 600 Coal 2015 23 North An Khanh 2-1 50 Coal 2015 24 North An Khanh 2-2 50 Coal 2015 25 South Duyen Hai 1-1 600 Coal 2015 26 South Formosa Ha Tinh 1-1 150 Coal 2015
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No. Region Project Capacity MW Generation Type COD 27 South Formosa Ha Tinh 1-2 150 Coal 2015 28 South Formosa Ha Tinh 1-3 100 Coal 2015 29 South Formosa Ha Tinh 1-4 100 Coal 2015 30 South Formosa Ha Tinh 1-5 150 Coal 2015 31 North Mong Duong 1-1 540 Coal 2015 32 North Mong Duong 1-2 540 Coal 2015 33 North Mong Duong 2-1 622 Coal 2015 34 North Mong Duong 2-2 622 Coal 2015 35 Central Nong Son 30 Coal 2015 36 North Thai Binh 2-2 600 Coal 2015 37 North Uong Bi Ext 2 330 Coal 2015 38 Central Dak Mi 2 98 Hydro 2016 39 Central Dak Mi 3 45 Hydro 2016 40 North Huoi Quang 2 260 Hydro 2016 41 North Lai Chau 1-2 800 Hydro 2016 42 Central Xekaman 1 80% 232 Hydro 2016 43 Central Song Tranh 4 48 Hydro 2016 44 North Trung Son 260 Hydro 2016 45 North Yen Son 70 Hydro 2016 46 South Duyen Hai 1-2 600 Coal 2016 47 South Duyen Hai 3-1 600 Coal 2016 48 South Formosa Dong Nai 150 Coal 2016 49 Central Chi Khe 41 Hydro 2017 50 South Da Nhim MR 80 Hydro 2017 51 North Long Tao 42 Hydro 2017 52 Central Xekaman Xanay 26 Hydro 2017 53 South Thac Mo MR 75 Hydro 2017 54 Central Tra Khuc 36 Hydro 2017 55 South Duyen Hai 3-2 600 Coal 2017 56 South Long Son 1-1 75 Coal 2017 57 North Luc Nam 1-1 50 Coal 2017 58 North Thai Binh 1-1 300 Coal 2017 59 North Thai Binh 2-1 600 Coal 2017 60 North A Lin 62 Hydro 2018 61 Central Dak Mi 1 54 Hydro 2018 62 South Hoi Xuan 102 Hydro 2018 63 South La Ngau 36 Hydro 2018 64 Central Xekaman 4 80% 64 Hydro 2018 65 North Song Lo 6 44 Hydro 2018 66 North Song Mien 4 38 Hydro 2018 Intelligent Energy Systems IESREF: 5973 211
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No. Region Project Capacity MW Generation Type COD 67 South Duyen Hai 3-Ext 660 Coal 2018 68 South Long Son 1-2 150 Coal 2018 69 South Long Phu 1-1 600 Coal 2018 70 North Luc Nam 1-2 50 Coal 2018 71 North Thai Binh 1-2 300 Coal 2018 72 North Thai Binh 2-2 600 Coal 2018 73 North Thang Long 1-1 300 Coal 2018 74 South Vinh Tan 4-1 600 Coal 2018 75 South Vinh Tan 4-2 600 Coal 2018
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Appendix E Hydro Power Development
Table 53 lists the hydro generation projects and commissioning year under the three scenarios. Hydro projects are assumed to be refurbished as required to maintain operations throughout the modelling horizon. As discussed earlier, projects such as Xekaman 4 dedicated to exports are split into projects in the domestic and export markets (with capacities adjusted accordingly). Up to 2,500 MW of non-committed large-scale hydro projects in Myanmar and Lao PDR are developed to support renewable energy technologies in the SES and ASES103.
Table 53 Hydro Project Developments
Installed Year Commissioned Country Hydro Project Capacity (MW) BAU SES ASES Russei Chrum Hydroelectric 338 2015 2015 2015 Stung Tatay Hydroelectric 246 2015 2015 2015 Stung Atay Hydro plant 120 2015 2015 2015 Hydro Power Lower Sesan 2 Co., Ltd 400 2023 Stung Cheay areng Hydroelectric Project 108 2025 Cambodia Sesan Hydro 400 2025 Prek Laang Hydroelectric Project 90 2026 Not commissioned in the SES and Stung Sen Hydro 40 2026 ASES Lower Sre Pok 2 66.6 2027 Stung Treng 1000 2027 Sambor Dam 780 2037 Nam Ngiep 2 180 2015 2015 2015 Nam Ou 2 120 2015 2015 2015 Nam Ou 5 240 2015 2015 2015 Nam Ou 6 180 2015 2015 2015 Nam Kong 2 66 2015 2015 2015 Xekaman 1 64.4 2016 2016 2016 Lao PDR Nam Sim 8 2016 2016 2016 Nam Mang 1 64 2016 2016 2016 Nam Beng 34 2016 2016 2016 Nam Sane 3A 69 2016 2016 2016 Nam Sane 3B 45 2016 2016 2016 Nam Lik 1 61 2017 2017 2017
103 The selected large hydro projects for future construction are example hydro projects and do not mean that we have a particular preference for the hydro projects that we bring online as compared to the others.
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Installed Year Commissioned Country Hydro Project Capacity (MW) BAU SES ASES Nam Phay 86 2018 2018 2018 Nam Tha 1 (Nam Pha) 168 2018 2018 2018 Xekaman 4 16 2018 2018 2018 Xayabouly (Mekong) 65 2020 Sepian-Xenamnoy 56 2021 Not Commissioned in Nam Ngiep 1 21 2021 the SES or ASES Nam Pha 130 2021 scenarios Nam Phak 45 2021 Upper Nam Htwan 3.2 2016 2016 2016 Mong Wa 60 2016 2016 2016 Thahtay 111 2018 2018 2018 Upper Keng Tong 51 2018 2018 2018 Upper Baluchaung 30.4 2018 2018 2018 Upper Yeywa 280 2022 Not commissioned Shweli(3) 1050 2026 in SES or ASES Middle Paunglaung 100 2027 2020 2020 Deedoke 66 2028 Not commissioned Dapein-2 140 2028 2020 2020 Upper Thanlwin(kunlong) 1400 2028 Not commissioned Shweli-2 520 2037 2022 2022 Myanmar Middle Yeywa 320 2038 2023 2023 Bawgata 160 2038 2023 2023 Naopha 1200 2038 Not commissioned Mangtong 225 2040 2025 2025 Wan Ta Pin 33 2040 Not commissioned Solue 160 2040 2025 2025 Keng Wang 40 2041 Not commissioned Manipur 380 2048 2026 2026 Gawlan 120 2048 2026 2026 Hkan Kawn 140 2048 2026 2026 Lawngdin 600 2049 Not commissioned Tongxinqiao 340 2050 2026 2026 Nan Tu (Hsipaw) 100 2050 Not commissioned Kwae Noi Dam #1-2 30 2015 2015 2015 Prakarnchon Dam 10 2015 2015 2015 Thailand Chulabhorn Hydropower 10 2015 2015 2015 Other Hydro 6.7 2015 2015 2015 Mae Hydro 12 2015 2015 2015
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Installed Year Commissioned Country Hydro Project Capacity (MW) BAU SES ASES Bang Lang Dam (upgrade) 12 2016 2016 2016 Lamtakong Pump Storage #3-4 500 2018 2018 2018 Tron Dam Hydropower 2.5 2018 2018 2018 Xe-Pian Xe-Namoi 354 2025 Nam Ngiep 1 269 2021 Xayaburi 1220 2026 Hydro Power 18 2027 Pha Dam 14 2028 Lamtakong Dam 1.5 2029 Lam Pao Dam 1 2032 Yasothon Hydropower 4 2032 Pranburi Dam 1.5 2033 Maha Sarakham Hydropower 3 2033 Not Man Phaya Hydropower 2 2034 Commissioned in the SES or ASES Noida Hydropower 2 2034 scenarios Lamtapearn Hydropower 1.2 2034 Village Hydropower 1.5 2035 Chulabhorn Pum Storage 800 2035 Thap Salao Dam 1.5 2035 Sri Nakarin Pump Storage 801 2036 Fai Lam Dome Yai Hydropower 2 2037 Kamalasai Hydropower 1 2037 Samong Dam 1 2037 Dam Hydropower 16 2037 Luang Dam Hydropower 1 2038 Ngoi Phat 72 2015 2015 2015 Song Bung 4 156 2015 2015 2015 Srepok 4A 64 2015 2015 2015 Ba Thuoc 1 60 2015 2015 2015 Bac Me 45 2015 2015 2015 Dong Nai 5 150 2015 2015 2015 Viet Nam Huoi Quang 1 260 2015 2015 2015 Lai Chau 1-1 400 2015 2015 2015 Nậm Mức 44 2015 2015 2015 Nam Na 2 66 2015 2015 2015 Nậm Na 3 84 2015 2015 2015 Nam Toong 34 2015 2015 2015 Ngoi Hut 2 48 2015 2015 2015
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Installed Year Commissioned Country Hydro Project Capacity (MW) BAU SES ASES Nhan Hac 45 2015 2015 2015 Nho Que 32 2015 2015 2015 Nho Que 2 48 2015 2015 2015 Xekaman 3 200 2015 2015 2015 Song Bung 2 108 2015 2015 2015 Sông Giang 2 37 2015 2015 2015 Song Tranh 3 62 2015 2015 2015 Dak Mi 2 98 2016 2016 2016 Dak Mi 3 45 2016 2016 2016 Huoi Quang 2 260 2016 2016 2016 Lai Chau 1-2 800 2016 2016 2016 Xekaman 1 232 2016 2016 2016 Song Tranh 4 48 2016 2016 2016 Trung Son 260 2016 2016 2016 Yen Son 70 2016 2016 2016 Chi Khe 41 2017 2017 2017 Da Nhim MR 80 2017 2017 2017 Long Tao 42 2017 2017 2017 Xekaman Xanay 26 2017 2017 2017 Thac Mo MR 75 2017 2017 2017 Tra Khuc 36 2017 2017 2017 A Lin 62 2018 2018 2018 Dak Mi 1 54 2018 2018 2018 Hoi Xuan 102 2018 2018 2018 La Ngau 36 2018 2018 2018 Xekaman 4 64 2018 2018 2018 Song Lo 6 44 2018 2018 2018 Song Mien 4 38 2018 2018 2018 Bao Lam 46 2044 Pac Ma 140 2024 Thuong Kon Tum 1-1 220 2044 Nam Pan 5 35 2024 Not My Ly 250 2027 Commissioned in Ban Mong 60 2028 the SES or ASES scenarios Tich Nang Bac Ai 1-1 300 2028 Tich Nang Bac Ai 1-2 300 2029 Tich Nang Bac Ai 1-3 300 2030 Tich Nang Dong Phu Yen 1-1 300 2030
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Installed Year Commissioned Country Hydro Project Capacity (MW) BAU SES ASES Pa Ma 80 2032 Tich Nang Bac Ai 1-4 300 2033 Tich Nang Dong Phu Yen 1-2 300 2033 Huoi Tao 180 2034 Tich Nang Dong Phu Yen 1-3 300 2036 Lower Sre Pok 2 155.4 2027 Sambor Dam 1820 2037
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Appendix H GMS Transition Statistics
Table 60 Generation Snapshot Statistics (GWh)
2015 2030 2050 Country / Type Actual BAU SES ASES BAU SES ASES Region Hydro 96,976 172,976 137,751 137,564 263,057 133,996 139,769 Fossil Fuel 90,035 360,398 194,770 156,993 855,169 98,889 0 Gas 165,885 287,935 136,320 43,986 292,121 70,693 0 GMS Wind 624 29,263 65,434 80,071 73,346 172,514 220,678 Solar 170 43,456 130,565 164,485 90,787 440,530 506,571 Bio 2,059 30,275 96,010 125,435 85,270 234,086 233,391 Other RE 0 12,047 21,675 23,097 29,169 56,493 71,796 Hydro 58,491 75,271 68,177 66,334 87,782 68,177 69,443 Fossil Fuel 41,755 280,640 160,405 109,205 565,713 84,675 0 Gas 44,932 97,164 21,564 16,576 97,170 14,783 0 Viet Nam Wind 125 21,605 22,296 28,029 48,256 69,710 99,409 Solar 0 18,985 50,399 62,326 40,885 181,054 216,185 Bio 0 9,557 48,741 57,142 22,697 86,436 95,099 Other RE 0 3,673 6,272 6,272 7,432 18,860 25,038 Hydro 22,137 37,997 23,795 24,146 67,702 20,259 24,146 Fossil Fuel 46,807 42,836 20,841 39,189 146,792 5,622 0 Gas 115,720 177,954 108,582 18,682 183,068 55,910 0 Thailand Wind 500 4,755 24,629 33,533 17,322 58,517 68,386 Solar 170 17,723 47,681 68,308 31,384 136,904 161,172 Bio 2,059 17,564 32,745 53,769 56,984 92,919 105,652 Other RE 0 5,782 6,272 6,272 16,517 18,504 19,799 Hydro 4,211 21,229 16,020 15,786 38,569 15,902 16,408 Fossil Fuel 887 8,219 3,342 3,077 15,585 1,745 0 Gas 0 0 0 0 0 0 0 Lao PDR Wind 0 958 7,117 7,117 1,716 14,707 18,510 Solar 0 392 4,542 4,542 928 23,191 23,191 Bio 0 1,332 3,119 3,118 2,120 16,294 6,764 Other RE 0 385 2,698 2,698 1,541 5,342 5,342 Hydro 8,099 24,075 23,125 25,280 40,036 23,362 23,287 Fossil Fuel 0 13,062 0 0 83,529 0 0 Gas 5,233 12,161 6,174 8,728 9,255 0 0 Myanmar Wind 0 1,808 10,980 10,980 5,641 27,800 32,593 Solar 0 3,733 20,882 21,107 11,547 74,725 78,515 Bio 0 1,349 8,445 8,445 2,663 27,187 15,923 Other RE 0 2,207 6,015 7,089 3,679 11,221 17,419 Hydro 4,038 14,404 6,633 6,018 28,968 6,295 6,485 Fossil Fuel 587 15,642 10,182 5,522 43,551 6,847 0 Gas 0 657 0 0 2,628 0 0 Cambodia Wind 0 137 412 412 411 1,780 1,780 Solar 0 2,623 7,061 8,202 6,043 24,657 27,507 Bio 0 473 2,961 2,961 806 11,250 9,954
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2015 2030 2050 Country / Type Actual BAU SES ASES BAU SES ASES Region Other RE 0 0 417 765 0 2,567 4,199
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Appendix F Sources of Information for Renewable Energy Potential
Table 54 summarises the main sources of information that were used for assessment of renewable energy potential , which were combined with other IES estimates of renewable energy potential.
Table 54 Sources of Information for Renewable Energy Potential
Sources of Information Resource Viet Nam Thailand Myanmar Lao PDR Cambodia Hydro Refer to power sector K. Aroonat and S. Information published by Lao PDR hydropower Various publicly available (Large) status report. Wongwises, “Current status MOEP. potential and policy in the reports. and potential of hydro GMS context (EDL) energy in Thailand: a Review”, Renewable and Sustainable Energy Reviews, Vol. 36, June 2016, pp. 70- 78 Hydro Refer to power sector Lack of data Various publicly The Need for Sustainable Various publicly available (Small) status report. available reports. Renewable Energy in Lao reports. PDR (Vongchanh) Pump Prime Minister’s Decision The Small Hydropower No publicly available No publicly available No publicly available Storage No. 2068/QD-TTg (Nov Project as the Important feasibility studies feasibility studies feasibility studies 2015) capacity target Renewable Energy Resource in Thailand (Chamamahattana, Kongtahworn, Pan-aram, 2005) Solar Prime Minister’s Decision See resource maps. IES See resource maps. IES Renewable Energy See resource maps. IES No. 2068/QD-TTg (Nov analysis of IRENA Global analysis of IRENA Global Developments and analysis of IRENA Global 2015) production target Atlas information. Atlas information. Potential in the Greater Atlas information.
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Sources of Information Resource Viet Nam Thailand Myanmar Lao PDR Cambodia converted to MW Mekong Subregion (ADB, equivalent. IES analysis of 2015). IES analysis of IRENA Global Atlas IRENA Global Atlas information. Study information. conducted by Spanish Consortium for MOIT. Wind Renewable Energy Potential resource above Renewable Energy Potential resource above Power Sector Vision for the Onshore Developments and 6m/s. Wind Energy Developments and 7m/s. Wind Energy Mekong Region (The Blue Potential in the Greater Resource Atlas of Southeast Potential in the Greater Resource Atlas of Circle, 2015) Mekong Subregion (ADB, Asia (TrueWind Solutions, Mekong Subregion (ADB, Southeast Asia (TrueWind 2015) 2001), Renewable Energy 2015) Solutions, 2001) Developments in the Greater Mekong Subregion (ADB, 2015). It is understood that there are difficulties in Thailand in terms of mountainous and remote areas for the locations that have high wind potential, but have assumed that these are not insurmountable in the SES and ASES. Wind See World Bank Group, via Offshore wind power Lack of publicly available Not applicable Lack of publicly available Offshore IRENA resource maps potential of the Gulf of studies studies (Figure 23, Figure 24) Thailand (Waewsak, Landry, Gagnon, 2015) Biomass IES projections based on IES projections based on IES projections based on IES projections based on IES projections based on data from Renewable data from Renewable data from Renewable data from Renewable data from Renewable
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Sources of Information Resource Viet Nam Thailand Myanmar Lao PDR Cambodia Energy Developments and Energy Developments and Energy Developments Energy Developments and Energy Developments and Potential in the Greater Potential in the Greater and Potential in the Potential in the Greater Potential in the Greater Mekong Subregion (ADB, Mekong Subregion (ADB, Greater Mekong Mekong Subregion (ADB, Mekong Subregion (ADB, 2015) 2015) Subregion (ADB, 2015) 2015) 2015) Biogas IES projections based on IES projections based on IES projections based on IES projections based on IES projections based on data from Renewable data from Renewable data from Renewable data from Renewable data from Renewable Energy Developments and Energy Developments and Energy Developments Energy Developments and Energy Developments and Potential in the Greater Potential in the Greater and Potential in the Potential in the Greater Potential in the Greater Mekong Subregion (ADB, Mekong Subregion (ADB, Greater Mekong Mekong Subregion (ADB, Mekong Subregion (ADB, 2015) 2015) Subregion (ADB, 2015) 2015) 2015) Geothermal Refer to power sector Not significant enough, with Refer to discussion in Lao PDR Energy Sector Lack of studies available status report. Geothermal targets Myanmar country Assessment, Strategy, and removed from AEDP2015 report. Road Map (ADB, 2013) Ocean Ocean renewable energy Lack of studies available Ocean renewable energy Not applicable Lack of studies available in Southeast Asia: A in Southeast Asia: A review (2014), based on review (2014), based on 40kW/m wave potential, 5kW/m wave potential, 3200km coastline, 10% 2300km coastline, 10% efficiency efficiency
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Appendix G Economic Indicators
For reference, this appendix sets out a summary of economic indicators for the GMS countries discussed in the body of the report. These are presented for each country in Table 55, Table 56, Table 57, Table 58 and Table 59.
Table 55 Cambodia: Economic Indicators Parameter Unit Source 2000 2005 2010 2011 2012 2013 2014 Real GDP (2014) Real 2014 Riel (Billions) IMF 23,902 37,355 51,669 55,326 59,374 63,783 68,364 Real GDP (2014) Real 2014 USD (Billions) IMF 6 9 13 14 15 16 17 Real GDP Growth (%) % IMF 8.8% 13.3% 6.1% 7.1% 7.3% 7.4% 7.2% Agriculture % ADB -1.2% 15.7% 4.0% 3.1% 4.3% 1.7% -2.5% Industry % ADB 31.2% 12.7% 13.0% 13.4% 10.4% 11.0% 13.0% Services % ADB 8.9% 13.1% 3.3% 5.7% 7.4% 8.7% 11.2% Inflation (Average) Index IMF 83 92 136 144 148 152 159 Inflation (Average) (%) % YoY IMF -0.8% 6.3% 4.0% 5.5% 2.9% 3.0% 4.5% Population People (Millions) IMF 12.2 13.4 14.4 14.6 14.9 15.1 15.3 Population Growth Rate % YoY IMF 1.6% 1.6% 1.7% 1.8% 1.5% 1.5% GDP per Capita Real 2014 USD / Person IMF 483 691 889 936 987 1,045 1,104
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Table 56 Lao PDR: Economic Indicators
Parameter Unit Source 2000 2005 2010 2011 2012 2013 2014 Real GDP (2014) Real 2014 Kip (Billions) IMF 35,463 48,118 70,637 76,317 82,344 88,957 95,511 Real GDP (2014) Real 2014 USD (Billions) IMF 4 6 9 9 10 11 12 Real GDP Growth (%) % IMF 6.3% 6.8% 8.1% 8.0% 7.9% 8.0% 7.4% Agriculture % ADB 4.2% 0.7% 3.2% 2.7% 3.3% 2.9% 6.9% Industry % ADB 9.3% 10.6% 17.5% 14.6% 11.4% 8.9% 5.8% Services % ADB 6.9% 9.9% 7.0% 8.1% 9.2% 7.6% 9.6% Inflation (Average) Index IMF 92 150 191 205 214 228 240 Inflation (Average) (%) % YoY IMF 23.2% 7.2% 6.0% 7.6% 4.3% 6.4% 5.5% Population People (Millions) IMF 5.4 5.8 6.4 6.5 6.6 6.8 6.9 Population Growth Rate % YoY IMF 1.6% 2.0% 2.0% 1.9% 1.9% 1.9% GDP per Capita Real 2014 USD / Person IMF 807 1,018 1,354 1,434 1,519 1,611 1,697
Table 57 Myanmar: Economic Indicators Parameter Unit Source 2000 2005 2010 2011 2012 2013 2014 Real GDP (2014) Real 2014 Kyat (Billions) IMF 17,826 32,648 47,443 50,246 53,914 58,362 63,323 Real GDP (2014) Real 2014 USD (Billions) IMF 18 34 49 52 56 60 65 Real GDP Growth (%) % IMF 13.7% 13.6% 5.3% 5.9% 7.3% 8.3% 8.5% Agriculture % ADB 11.0% 12.1% 4.7% -0.7% 2.0% 4.5% 2.8% Industry % ADB 21.3% 19.9% 18.6% 10.2% 8.0% 8.4% 15.4% Services % ADB 13.4% 13.1% 9.5% 8.6% 12.6% 11.7% 7.6% Inflation (Average) Index IMF 261 797 1627 1672 1720 1818 1938 Inflation (Average) (%) % YoY IMF -1.7% 10.7% 8.2% 2.8% 2.8% 5.7% 6.6% Population People (Millions) IMF 46.4 48.0 49.7 50.1 50.5 51.0 51.4
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Population Growth Rate % YoY IMF 0.6% 0.8% 0.8% 0.9% 0.9% 0.9% GDP per Capita Real 2014 USD / Person IMF 396 701 984 1,034 1,100 1,180 1,270
Table 58 Thailand: Economic Indicators Parameter Unit Source 2000 2005 2010 2011 2012 2013 2014 Real GDP (2014) Real 2014 Baht (Billions) IMF 7,242 9,287 11,063 11,072 11,791 12,131 12,248 Real GDP (2014) Real 2014 USD (Billions) IMF 225 288 344 344 366 377 380 Real GDP Growth (%) % IMF 4.8% 4.6% 7.8% 0.1% 6.5% 2.9% 1.0% Agriculture % ADB 6.8% -0.1% -0.4% 6.2% 1.9% 0.4% -6.4% Industry % ADB 2.6% 5.2% 10.3% -4.2% 7.5% 1.5% 0.5% Services % ADB 5.3% 4.1% 6.8% 3.3% 7.9% 4.3% 2.7% Inflation (Average) Index IMF 75 83 96 100 103 105 107 Inflation (Average) (%) % YoY IMF 1.6% 4.5% 3.3% 3.8% 3.0% 2.2% 2.1% Population People (Millions) IMF 61.9 65.1 67.3 67.6 67.9 68.2 68.6 Population Growth Rate % YoY IMF 0.1% 0.6% 0.5% 0.5% 0.5% 0.5% GDP per Capita Real 2014 USD / Person IMF 3,636 4,429 5,109 5,089 5,395 5,524 5,550
Table 59 Viet Nam: Economic Indicators
Parameter Unit Source 2000 2005 2010 2011 2012 2013 2014 Real GDP (2014) Real 2014 VND (Trillions) IMF 1,673 2,382 3,236 3,438 3,618 3,814 4,024 Real GDP (2014) Real 2014 USD (Billions) IMF 78 111 151 160 169 178 188 Real GDP Growth (%) % IMF 6.8% 7.5% 6.4% 6.2% 5.2% 5.4% 5.5% Agriculture % ADB 4.6% 4.2% 3.3% 4.0% 2.7% 2.6% 4.5% Industry % ADB 10.1% 8.4% 7.2% 6.7% 5.7% 5.4% 6.5% Services % ADB 5.3% 8.6% 7.2% 6.8% 5.9% 6.6% 6.1% Inflation (Average) Index IMF 80 100 167 198 216 231 243 Inflation (Average) (%) % YoY IMF -1.8% 8.4% 9.2% 18.7% 9.1% 6.6% 5.2%
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Population People (Millions) IMF 77.64 82.39 86.93 87.84 88.76 89.69 90.63 Population Growth Rate % YoY IMF 0.4% 1.1% 1.0% 1.0% 1.0% 1.0% GDP per Capita Real 2014 USD / Person IMF 1,006 1,350 1,738 1,827 1,903 1,985 2,073
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