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Project Number: 46389-001 June 2017
Republic of the Union of Myanmar: Institutional Strengthing of National Energy Management Committee in Energy Policy and Planning
(Financed by the Japan Fund for Poverty Reduction and the
Technical Assistance Special Fund)
FINAL REPORT
Prepared by TA 8356-MYA Individual Consultant, Bruce P. Hamilton, ADICA, LLC
For the Oil & Gas Planning Department of the Ministry of Electricity & Energy
This consultant’s report does not necessarily reflect the views of ADB or the Government concerned, and
ADB and the Government cannot be held liable for its contents. All the views expressed herein may not be
incorporated into the proprosed project’s design.
Asian Development Bank
Technical Assistance Consultant’s Report
Report for ADB Issue Number 1
Contract No. 128594-S52841 Date 06/16/2017
ASIAN DEVELOPMENT BANK
TA-8356 MYA: Institutional Strengthening of National Energy
Management Committee in Energy Policy and Planning
Final Report on
CONSULTANCY TO BUILD ENHANCED INSTITUTIONAL CAPACITY WITHIN
THE MYANMAR MINISTRY OF ELECTRICITY AND ENERGY TO PERFORM
POWER SYSTEM PLANNING USING WASP-IV AND GTMAX
Prepared by
BRUCE P. HAMILTON
ADICA, LLC
Submitted on
JUNE 16, 2017
Report for ADB Issue Number 1 Contract No. 128594-S52841 Date 06/16/2017
Report for ADB Issue Number 1 Contract No. 128594-S52841 Date 06/16/2017
CONTENTS
1 INTRODUCTION ..................................................................................................................... 3
1.1 OBJECTIVE OF THE ASSIGNMENT .............................................................................................. 3
1.2 PROJECT TASKS .................................................................................................................... 3
2 PROJECT ACCOMPLISHMENTS ................................................................................................ 4
3 CONCLUSIONS AND RECOMMENDATIONS ............................................................................ 11
APPENDIX A UPDATED NATIONAL POWER EXPANSION PLANNING REPORT
APPENDIX B DRAFT OP ED ON ADB CAPACITY BUILDING SUPPORT
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1 INTRODUCTION
In support of Asian Development Bank (ADB) TA-8356 MYA: Institutional Strengthening of National Energy Management Committee in Energy Policy and Planning, in 2015, ADICA, LLC applied the Wien Automation System Planning (WASP) model in an effort to prepare a National Power Expansion Plan (NPEP) for Myanmar.
WASP is an optimization model for examining medium- to long-term development options for electrical generating systems. The International Atomic Energy Agency (IAEA) distributes this model, which is one of the most frequently used programs for expansion planning of electrical generating systems. The latest version of the model, called WASP IV, is designed to find the economically optimal generation expansion policy for an electric utility system.
While tools like WASP are useful for long-term generation planning, they do not take into consideration network constraints; capture locational variations in electricity prices and demand; optimize unit dispatch and trading opportunities on an hourly basis; or adequately represent hydro power plant operations. A market model performs this type of analysis.
Argonne National Laboratory developed the Generation and Transmission Maximization (GTMax) model to simulate complex electricity market and operational issues. When analyzing regional or national generation and transmission systems, GTMax determines the optimal dispatching of hydro power cascades, scheduling of thermal power generation, and economic trade of energy among utility companies and other market participants.
1.1 Objective of the Assignment
This consultancy is focused on building enhanced institutional capacity within the Ministry of Electricity and Energy (MOEE) to perform power system planning using WASP-IV and GTMax in Myanmar and update the NPEP to reflect current understanding related to energy policies, price and quantity of fuel, hydro power development, and opportunities for power exchange with neighboring systems.
1.2 Project Tasks
The following key tasks were identified in the Consultant’s Terms of Reference:
Task 1 Conduct Advanced Training on Use of WASP-IV;
Task 2 Update the Myanmar WASP-IV Case;
Task 3 Update the Myanmar GTMax Case;
Task 4 Conduct Introductory Training on the Use of GTMax,
Task 5 Assist MOEE in Preparing an Updated NPEP;
Task 6 Draft Op-ed on Capacity Building Support for Power System Planning in Myanmar;
Task 7 Provide Technical Support for NPEP Workshop 1 – MOEE;
Task 8 Provide Technical Support for NPEP Workshop 2 – National Stakeholders and Development Partners; and
Task 9 Prepare NPEP Briefing Booklet.
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Upon successful completion of Tasks 1 through 7 by the ADB consultant, in consultation with the local counterpart, MOEE, and ADB project management, it was decided to cancel Tasks 8 and 9.
2 Project Accomplishments
Task 1 Conduct Advanced Training on Use of WASP-IV: One ADB Consultant, Bruce Hamilton visited Nay Pyi Taw, Myanmar, during 5-8 July 2016 to organize advanced training on the use of WASP-IV and collaborate with national stakeholders and ADB project personnel to agree on the approach and timeline for preparing an updated NPEP for the country. Twenty-six (26) MOEE staff participated in this training, including professionals from:
Department of Electric Power Planning (DEPP)
Department of Power Transmission and System Control (DPTSC)
Department of Hydropower Implementation (DHPI)
Electricity Supply Enterprise (ESE)
Myanmar Oil & Gas Enterprise (MOGE)
Yangon Electricity Supply Corporation (YESC)
Mandalay Electricity Supply Corporation (MESC)
Participants learned how to apply WASP-IV for determining a power generating system expansion plan that meets demand at minimum cost while satisfying user-specified constraints for electricity system reliability, environmental protection and fuel availability. The course participants and ADB Consultant discussed and shared information on current energy policies, NPEP study assumptions, and necessary input data (e.g., updated demand forecast, characterization of existing power system, hydro cascades, first year of availability for candidate power plants, domestic natural gas supply, import potential, etc.). It was decided that the updated NPEP shall:
1. Have a study period from 2015 through 2035;
2. Use the medium demand forecast defined in the Energy Master Plan;
3. Use updated assumptions on the timing of candidate hydropower plant additions and
amount of domestic natural gas available for electricity generation; and
4. Consider the potential for cross-border power trade with neighboring systems.
The course participants were also guided through development of an improved WASP
Reference Case using current country specific data provided by professionals from various
departments within the Ministry.
Task 2 Update the Myanmar WASP-IV Case: The Consultant collaborated with local
planners at the MOEE to enhance the WASP-IV Case for Myanmar and report on the resulting
optimal generation expansion plan to meet electricity requirements through 2035.
Task 3 Update the Myanmar GTMax Case: The Consultant collaborated with local
planners at the MOEE to enhance the GTMax Case for Myanmar and evaluate the optimal
hourly dispatch of power plants in reference years 2017, 2020 and 2025, with due
consideration of hydro cascades, transmission constraints and opportunities for cross-board
power exchange.
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Task 4 Conduct Introductory Training on the Use of GTMax: Two experts in the use of
GTMax, Bruce Hamilton and Tom Veselka, visited Nay Pyi Taw, Myanmar, during 17 to 28
October 2016 to provide training on the use of GTMax for power system planning professionals
at the MOEE. Course participants were trained on basic model principles and formulations,
data entry, model execution and interpretation of model results. Thirty (30) MOEE staff
participated in this training, which transferred knowledge on how the GTMax market model
could be used at the Ministry for:
1. Optimizing hydro cascades and generation dispatch, with due consideration of
transmission constraints and locational variations in electricity generation, prices and
demand;
2. Identifying opportunities for mutually beneficial power transactions with neighboring
systems; and
3. Prioritizing investments in transmission interconnection lines.
Through presentations, model demonstrations and hands-on work sessions, during the first week of training, participants learned how to construct a GTMax electricity system network, run the model, and interpret optimization results. The Consultants also presented lectures on how to model real-world situations, such as hydro cascades, transmission constraints, power purchase agreements, and proposed interconnections with neighboring systems.
During the second week of training, course participants were guided through development of an improved GTMax Case for Myanmar using current country specific data. A screen capture of this case is provided in Figure 1. The Consultants also prepared a customized reporting tool for use by MOEE professionals to present GTMax results. Sample graphs from the GTMax Reporting Tool are illustrated in Figures 2, 3 and 4.
On the final day of the course, the Consultants and MOEE course participants presented the updated GTMax Case for Myanmar and initial NPEP study assumptions for consideration of MOEE DG Daw Mi Mi Kaing.
The ADB Consultant complemented MOEE DG on the strong analytical capabilities and access to power system information demonstrated by the participants in the current course and recommended to establish a Coordinated Planning Team and Centralized Data Repository within MOEE. The DG indicated that this recommendation shall be discussed with the Deputy Minister.
MOEE DG agreed with the proposal for the ADB Consultant and MOEE planning team to complete updating of the WASP and GTMax analyses before end of 2016, and present study findings in the form of an updated National Power Expansion Plan.
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Figure 1 GTMax System Topology for Myanmar
Figure 2 Hourly Power Transfer (MWh) from Central Region to Yangon
Figure 3 Hourly Generation (MWh) from Upper Yeywa Hydro Power Plant
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Figure 4 National Energy Balance and Regional Power Flows
Task 5 Assist MOEE in Preparing an Updated NPEP: After transferring planning tools
and know-how on their use to local energy planning professionals, the Consultant provided
support to a team of energy planning professionals within MOEE in the application of these
tools to analyze a number of scenarios for future development of the Myanmar power system.
Three of the analyzed scenarios are described below.
A “Least Cost” scenario evaluated all power
system expansion candidates (i.e., hydropower,
fossil-fired, renewable energy and imports) in the
identification of a least cost generation expansion
plan. In the Least Cost scenario, hydropower and
gas-fired generation continue to play a dominant
role in meeting electrical needs of the country. With
the assumed diminishing capital cost of solar
power, 2,400 MW of new solar is projected to be
added to the system by 2035. Imported electricity
is shown to be competitive depending on the established cost of energy and interconnection
requirements. In the final years of the study, when the identified list of economic hydropower
candidates is exhausted, coal is shown to be an economic option for base-load generation.
A “No Coal” scenario uses the same assumptions
as the Least Cost scenario, but does not consider
new coal-fired power plants as a candidate for
system expansion. Compared with the Least Cost
scenario, the No Coal scenario results in a 0.3%
increase in total system cost, a 14% reduction in
CO2 emissions and increases the renewable
energy share in the 2035 capacity mix to 21%.
Figure 5 Least Cost Scenario Results
Figure 6 No Coal Scenario Results
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A “Delayed Hydro” scenario uses the same
assumptions as the Least Cost scenario, except
that the commissioning date for new hydropower
plants are delayed by three years. As compared
with the Least Cost scenario, the Delayed Hydro
scenario results in a US$ 2.69 billion increase in
total system cost, earlier entry of new coal-fired
power plants and a 53% increase in CO2
emissions.
The updated NPEP presents a structured approach for comparing scenarios based on their
relative success in achieving national goals for a sustainable, reliable and competitive
electricity supply. WASP model results for each evaluated scenario are listed in Table 1.
Table 1 Comparison of Scenarios Analyzed in Updated NPEP
For each of the evaluated scenarios, the optimal generation expansion plan identified using
WASP includes a substantial amount of new hydropower in the Shan and Kayar regions of the
country and affordable imported electricity from China and Lao PDR. However, as illustrated
in Figure 8, the GTMax market model analysis for 2025 identified that transmission constraints
within Myanmar would result in a significant level of hydro spillage and eliminate the potential
for electricity imports.
A sensitivity analysis for 2025, Figure 9, further indicates that strengthening of the grid corridor
of Shan-Mandalay-Bago and Shan-Kayar-Bago by 1000MW each will facilitate the delivery of
hydro energy from Shan and Kaya to Yangon, as well as provide significant import
opportunities from China and Lao PDR. This produces an 87% decrease in spilled energy and
a considerable decrease in system operation costs.
Figure 7 Delayed Hydro Scenario Results
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Figure 8 2025 Base Case – Annual Energy Exchanges
Figure 9 2025 Sensitivity Analysis– Annual Energy Exchanges
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Task 6 Draft Op-ed on Capacity Building Support for Power System Planning in
Myanmar: The Consultant drafted an Op-ed describing this ADB effort to strengthen
institutional capacity for charting a sustainable energy future in Myanmar. The draft text
included a discussion of energy challenges and opportunities in Myanmar, noted the
importance of national power system planning, and summarized results of analyzed scenarios
for future development of the Myanmar power system.
The article also noted that planning is a regular and recurrent exercise. It sheds light on
economic, reliability and sustainability aspects of possible future development pathways to
support decision making on the optimal strategy for the country.
Task 7 Provide Technical Support for NPEP Workshop 1 – MOEE: The ADB Consultant
visited Nay Pyi Taw, Myanmar, during March 27-31, 2017, to provide technical assistance to
the newly established MOEE Energy Planning Team in presenting NPEP results and
discussing draft findings with Ministry officials. During this visit, the Consultant met and
collaborated with ADB and MOEE staff to:
1. Review results of the completed power system planning study;
2. Prepare a series of slides summarizing the energy planning process, key inputs to the
study, model results, and primary observations;
3. Help the MOEE Energy Planning Team rehearse their presentation of the finalized slide
deck to Ministry officials; and
4. Attend a workshop organized for MOEE officials, where members of the MOEE Energy
Planning Team presented and discussed results of their power system planning study
and provide technical assistance as needed in responding to questions.
Over 30 senior officials (DDG, Chief Engineers, Directors) and staff of MOEE attended this workshop.
Figure 10 Workshop Participants
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3 CONCLUSIONS AND RECOMMENDATIONS
The Consultant believes the ADB’s capacity building project has produced impressive results. In the process of this project, MOEE formed an internal team of energy planners and for the first time this MOEE Energy Planning Team presented results of their power sector planning study for consideration of Ministry officials.
The slide at right, which was presented by the local team at the MOEE Workshop, illustrates the potential role the Energy Planning Team could have in the context of national energy planning.
While presenting an overview of the national power system planning study, local planners demonstrated deep knowledge of the Myanmar power system, understanding of modeling tools applied in the study, and proficiency in presenting study findings and responding to questions from Ministry officials.
The scenarios analyzed by the MOEE Energy Planning Team provide improved understanding of issues and challenges facing the Myanmar power sector. These insights are anticipated to help the Government in charting an economically optimal and sustainable development path, which ultimately contributes to improved quality of life for the people of Myanmar.
While the Energy Planning Team was established within the MOEE, the Consultant recommends that additional support is required to build a critical mass of capability and experience required for this team to provide lasting support for national planning. Suggested priority issues warranting further capacity building support, include:
a) Publication of Biannual MOEE NPEP: With ADB Consultant support, the MOEE EnergyPlanning Team developed a draft report on the updated NPEP. MOEE should be encouragedto publish an official NPEP and task the Energy Planning Team with preparing regular updates(e.g., every 2 years). In such a case, the national team may require limited consultant supportto finalize the first official MOEE NPEP report.
b) Cross-Border Energy Trade: Myanmar and Lao PDR have signed an MOU agreeing toevaluated opportunities for cross-border energy trade between the two countries. Bothcountries would benefit from capacity building assistance on the conduct of coordinated marketanalyses and regional transmission planning studies to evaluate potential interconnections,build consensus on mutually beneficial opportunities for energy trade and identify infrastructureimprovements required at both ends of the interconnection to assure that the connection willoperate in a safe and reliable manner to produce the intended benefits.
c) Renewable Energy Integration in Myanmar: The newly formed MOEE Energy PlanningTeam can benefit from capacity building assistance in the application of analytical tools toevaluate a range of critical issues related to renewable energy (RE) integration. The team hasbeen trained on the use of a long-term planning tool to evaluate the role of RE in providingadequate capacity to meet peak load situations, while contributing to national goals foraffordable and clean supply of electricity. The team has also been introduced to the use of a
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short-term market model to optimize the scheduling of hydro resources and thermal generation while considering RE variability, and evaluate opportunities for beneficial power exchange with neighboring systems. Additional training and support is needed on the use of a detailed load flow model (like PSSe) to evaluated short-term effects of RE on balancing the system and maintaining reliable electricity supply at an operational time scale of seconds to hours.
APPENDIX A
UPDATED NATIONAL POWER EXPANSION PLANNING REPORT
Project Number: TA No. 8356-MYA
UPDATED NATIONAL
POWER EXPANSION PLAN A study conducted in cooperation with the Asian Development Bank
and Myanmar Ministry of Electricity and Energy
Prepared by
15 January 2017
APPENDIX A- 1
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APPENDIX A - 2
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
AUTHOR
Bruce P. Hamilton
ACKNOWLEDGMENTS
The author wishes to acknowledge the participation of management and staff of The Republic of
the Union of Myanmar Ministry of Electricity and Energy, together with members of the Asian
Development Bank study teams for their help, suggestions, and cooperation toward preparing
this document.
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ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
TABLE OF CONTENTS
Author ...................................................................................................................................................... i
Acknowledgments .................................................................................................................................... i
Table of Contents .................................................................................................................................... ii
I. INTRODUCTION .............................................................................................................................. 4
Background ........................................................................................................................................ 4
Objectives .......................................................................................................................................... 5
II. MODELING APPROACH .................................................................................................................. 6
Description of the WASP Model ........................................................................................................ 6
Description of the GTMax Model ....................................................................................................... 7
Integrated Analysis with WASP and GTMax .................................................................................... 10
III. STUDY PARAMETERS .................................................................................................................... 10
Study Period ..................................................................................................................................... 10
Discount Rate ................................................................................................................................... 10
Reserve Margin ................................................................................................................................ 10
Cost of Energy Not Served ............................................................................................................... 10
Loss of Load Probability ................................................................................................................... 11
Demand Forecast ............................................................................................................................. 11
Existing Generating System ............................................................................................................. 12
IV. CANDIDATE PLANTS FOR FUTURE SYSTEM EXPANSION .............................................................. 17
Thermal Power Plants ...................................................................................................................... 17
Hydro Power Plants.......................................................................................................................... 18
Renewable Generation Options ....................................................................................................... 18
Import Opportunities ....................................................................................................................... 20
Preliminary Screening of Generation Options ................................................................................. 21
V. ALTERNATIVE POWER SYSTEM EXPANSION SCENARIOS ............................................................. 22
Scenario 1 − Least Cost .................................................................................................................... 22
Scenario 2 − No Coal ........................................................................................................................ 28
Scenario 3− No Imports ................................................................................................................... 32
Scenario 4− Delayed Hydro .............................................................................................................. 36
Comparing Alternative Scenarios..................................................................................................... 40
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ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
VI. SENSITIVITY ANALYSIS .................................................................................................................. 40
Effects of Natural Gas Price on Least Cost Plan ............................................................................... 40
Effects of Environmental Considerations......................................................................................... 41
VII. INTEGRATED ANALYSIS OF GENERATION AND TRANSMISSION .................................................. 41
GTMax Input Data and Modeling Assumptions ............................................................................... 41
Model Topology and Network Constraints ...................................................................................... 42
2017 Base Case Scenario .................................................................................................................. 44
2017 Sensitivity Analysis on Impact of PPAs .................................................................................... 46
2025 Base Case Scenario .................................................................................................................. 49
2025 Sensitivity Analysis on Impact of Further Grid Reinforcements ............................................. 52
VIII. OBSERVATIONS AND RECOMMENDATIONS ................................................................................ 54
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APPENDIX A - 5
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
I. INTRODUCTION
BACKGROUND
1. In support of Asian Development Bank (ADB) TA-8356 MYA: Institutional Strengthening of
National Energy Management Committee in Energy Policy and Planning, in 2015, ADICA, LLC worked
closely with Myanmar Government Agencies and Development Partners as well as other ADB experts
engaged under the Energy Master Plan (EMP) study and in using the Wien Automation System
Planning (WASP IV) model to prepare a National Power Expansion Plan (NPEP) for Myanmar.
2. WASP is an optimization model for examining medium- to long-term development options for
electrical generating systems. The International Atomic Energy Agency (IAEA) distributes this model,
which is the public domain's most frequently used program for expansion planning of electrical
generating systems. The latest version of the model, called WASP IV, is designed to find the
economically optimal generation expansion policy for an electric utility system.
3. While tools like WASP are useful for long-term generation planning, they do not take into
consideration network constraints; capture locational variations in electricity prices and demand;
optimize unit dispatch and trading opportunities on an hourly basis; or adequately represent hydro
power plant operations. This type of analysis is performed with a market model – like the Generation
and Transmission Maximization (GTMax) model. GTMax was developed by Argonne National
Laboratory to simulate complex electricity market and operational issues. When analyzing regional or
national generation and transmission systems, GTMax determines the optimal dispatching of hydro
power cascades, scheduling of thermal power generation, and economic trade of energy among utility
companies and other market participants.
4. As a follow up to the initial NPEP development effort, this consultancy is focused on building
enhanced institutional capacity within the Ministry of Electricity and Energy (MOEE) to perform power
system planning using WASP-IV and GTMax in Myanmar and update the NPEP to reflect current
understanding related to energy policies, price and quantity of fuel, hydro power development, and
opportunities for power exchange with neighboring systems.
5. Key tasks identified in the Terms of Reference for this assignment, include to:
Task 1 Conduct Advanced Training on Use of WASP-IV,
Task 2 Update the Myanmar WASP-IV Case,
Task 3 Update the Myanmar GTMax Case,
Task 4 Conduct Introductory Training on the Use of GTMax, and
Task 5 Assist MOEE in Preparing an Updated NPEP.
6. Task 1 was completed, during 5-8 July 2016, when Mr. Bruce Hamilton (the “Consultant”)
organized training on the use of the WASP-IV model for 28 professionals of the MOEE.
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ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
7. Task 4 was completed, during 17-28 October 2016, when Mr. Bruce Hamilton and Mr. Thomas
Veselka trained 24 professionals of the MOEE on the use GTMax to:
• Represent network constraints and capture locational variations in electricity
generation-prices-demand;
• Optimize hydro cascades and generation dispatch;
• Identify opportunities for mutually beneficial daily and seasonal power
transactions with neighboring systems; and
• Prioritize investments in transmission interconnection lines.
8. During the above-mentioned training events, the Consultant transferred updated WASP and
GTMax reference cases for Myanmar, prepared under project Tasks 2 and 3, for continued use at the
MOEE and collaborated with MOEE management and staff to enhance these cases with improved
input data and guidance on study assumptions and policy options to be evaluated. MOEE staff
participating in this effort include professionals from:
• DEPP- Department of Electric Power Planning
• DPTSC- Department of Power Transmission and System Control
• DHPI- Department of Hydropower Implementation
• EPGE- Electric Power generation Enterprise
• MOGE- Myanmar Oil & Gas Enterprise
• YESC- Yangon Electricity Supply Corporation
• MESC- Mandalay Electricity Supply Corporation
OBJECTIVES
9. This report is produced as a work product for project Task 5, which calls on the Consultant to
collaborate with local planners at the M0EE to finalize the WASP-IV and GTMax analyses and utilize
model results to prepare an updated NPEP for the country. This report describes initial assumptions
and results for the analysis carried out with the goal to support informed decision making through the
evaluation of alternative scenarios for power system expansion to meet electricity requirements in
Myanmar over the 21-year period beginning 2015.
10. It is important to note that, in developing an updated NPEP for the country, the WASP-IV and
GTMax models are being applied in an integrated manner to evaluated least cost generation options
and opportunities for power exchange with neighbouring systems.
11. Initial WASP-IV results are used to define the future power system for further analysis in
GTMax. The GTMax model is then be used to optimize system operations and evaluate opportunities
for power exchange with neighbouring systems in future years. GTMax results on the optimal timing
and amount of cross-border trading and hydro power operations are then transferred to WASP-IV for
use in reoptimizing the power system expansion plan taking into consideration opportunities for
power exchange.
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ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
II. MODELING APPROACH
DESCRIPTION OF THE WASP MODEL
12. WASP is an optimization model for examining medium- to long-term development options for
electrical generating systems. The International Atomic Energy Agency (IAEA) distributes and
maintains this model, which is one of the most frequently used programs for expansion planning of
electrical generating systems.
13. The latest version of the model, called WASP IV, is designed to find the economically optimal
generation expansion policy for an electric utility system. It utilizes probabilistic estimation of system
production costs, unserved energy cost, and reliability, a linear programming technique for
determining optimum dispatch policy satisfying exogenous constraints on environmental emissions,
fuel availability and electricity generation by groups of plants, and the dynamic programming method
of optimization for comparing the costs of alternative system expansion policies.
14. WASP IV permits finding the optimal expansion plan for a power generating system over a
period of up to thirty years, within constraints given by the planner. The optimum solution is evaluated
in terms of minimum discounted total costs. Each possible sequence of power unit additions that
meets the specified constraints is evaluated by means of a cost function (i.e., the “objective function”)
represented by the following equation:
T
j j,t j,t j,t j,tj,t j,t
t 1
B [ I - S L F M O ]=
= + + + +∑
Where: � is the depreciable capital investment costs� is the salvage value of investment costs
is the non-depreciable capital investment costs� is the fuel costs
is the non-fuel operation and maintenance costs� is the cost of the energy-not-served
15. WASP IV comprises the following eight modules.
16. LOADSY (Load System Description): Processes information describing the peak loads and load
duration curves for up to 30 years. The objective of LOADSY is to prepare all the demand information
needed by subsequent modules.
17. FIXSYS (Fixed System Description): Processes information describing the existing generating
system. This includes performance and cost characteristics of all generating units in the system at the
start of the study period and a list of retirements and "fixed" additions to the system. Fixed additions
are power plants already committed and not subject to change.
18. VARSYS (Variable System Description): Processes information describing the various
generating units to be considered as candidates for expanding the generating system.
19. CONGEN (Configuration Generator): Calculates all possible year-to-year combinations of
expansion candidate additions that satisfy certain input constraints and that, in combination with the
existing system, can adequately meet the electricity demand.
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ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
20. MERSIM (Merge and Simulate): Considers all configurations put forward by CONGEN and uses
probabilistic simulation of system operation to calculate the associated production costs, unserved
energy, and system reliability for each configuration. The module also calculates plant loading orders
and maintenance schedules.
21. DYNPRO (Dynamic Programming Optimization): Determines the optimum expansion plan as
based on previously derived operating costs along with input information on capital cost, economic
parameters, unserved energy cost, and system reliability constraints.
22. REMERSIM (Re-MERSIM): Simulates the configurations contained in the optimized solution.
By providing a detailed output of the simulation, REMERSIM allows the user to analyze particular
components of the production-cost calculation, such as unit-by-unit capacity factors and fuel
requirements for each season and hydroelectric condition.
23. REPROBAT (Report Writer of WASP): Writes a report summarizing the results for the optimum
power system expansion plan.
DESCRIPTION OF THE GTMAX MODEL
24. GTMax allows the simulation of a complex electricity market and operational issues, both for
competitive and regulated environments. The model simulates the dispatch of electric generating
units and economic trade of energy among utility companies and other market participants. Linear
and mixed integer programming techniques are applied to solve the problem of simultaneously
optimizing hourly operations across all components of the analyzed generation and transmission
system. This is done while taking into account the power system topology, hydro cascades, transfer
capabilities with neighboring systems, chronological hourly loads, and differences in the electricity
generation costs in each of the modeled systems.
25. With GTMax, planners can maximize the value of the electric system taking into account the
utility’s own energy and transmission resources, together with firm contracts, independent power
producer (IPP) agreements, and bulk power transaction opportunities with interconnected systems in
the region. Both zonal and nodal modeling of transmission network is supported, with the ability to
simulate power flows on a contractual or DC load flow basis.
26. On the demand-side of the power equation, the GTMax model includes an hourly
representation of residential, commercial, and industrial electricity demands at user-specified load
centers. Demands that are served by the power sector are a function of electricity price. When the
locational market price in the network reaches or exceeds a user-specified level, GTMax can simulate
a demand-side-management program for that region to reduce system loads.
27. GTMax simultaneously optimizes transactions to minimize overall system operating costs and
calculates market prices for electricity sales/purchases in different areas (market hubs) of the power
network based on transmission network capacity constraints. Model output includes: the units to be
dispatched, how much power should be generated on an hourly basis, energy exchanges/power flows
between different areas/nodes, when to buy and sell power on the spot market, the cost of alternative
power plant operations, the incremental value of water, and the value of demand-side management
programs. Figure 1 provides an illustration of GTMax model inputs and results.
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ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
Figure 1: GTMax Model Inputs and Results
28. The GTMax graphical user interface is used to prepare a customized topology of the system
being analyzed and provide detailed information about link and node network components. An
example of a hypothetical power system representation is shown in Figure 2 and example of the
GTMax case for Myanmar in Figure 3.
Figure 2: Sample GTMax Power System Topology
8
APPENDIX A - 10
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
Figure 3: Sample GTMax Representation of Myanmar Power System
29. GTMax has been applied in a variety of projects focused on national power system planning,
strengthening of regional transmission interconnections and power exchange, including:
• Generation Investment Study for Southeast Europe1
• CAREC Power Sector Regional Master Plan2
• Market Analysis and Trading in the Western Region of USA3
• Regional Power Trade Opportunities from Transmission Interconnections in Africa4
• Strengthen Energy Security and Regional Integration in Armenia5
1 Study for European Community and World Bank, 2004. [Online]. Available at:
http://www.adica.com/generation-investment-study.html
2 Study for Asian Development Bank, 2012 (Online) Available at:
http://www.adb.org/projects/documents/central-asia-regional-economic-cooperation-power-sector-regional-
master-plan-tacr
3 Analysis Conducted by Western Area Power Administration, 2015 (Online) Available at
http://www.adica.com/us-utility.html 4 Study for World Bank, 2008 (Online) Available at:
http://nebula.wsimg.com/33bc46dd9f17d3c4aefd333040f9f318?AccessKeyId=1A0D9A575B761BCFC58F&disp
osition=0&alloworigin=1 5 Study for USAID, 2011 (Online) Available at: http://www.adica.com/armenia-georgia.html
9
APPENDIX A - 11
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
INTEGRATED ANALYSIS WITH WASP AND GTMAX
30. At the start of the integrated analysis, WASP is used to develop an initial least cost expansion
plan (LCP). The initial LCP is used to define the power system configurations in future years.
31. Next, GTMax is used to simulate system operations for future year configurations, optimize
the dispatch of generating units, compute nodal market prices, and determine the optimal amount
and schedule of hourly energy exchanges with neighboring systems.
32. GTMax model results are then used to enhance the representation of power exchange
opportunities in WASP, which is run to create a final LCP.
III. STUDY PARAMETERS
33. Input data and assumptions for the updated NPEP prepared by the Consultant and energy
planning professionals from MOEE (“national power system planning team”) are described below.
STUDY PERIOD
34. The updated WASP-IV Case for Myanmar spans a period of 21 years from 2015 through 2035.
DISCOUNT RATE
35. A discount rate of 8% is applied in the present worth discounting of costs.
RESERVE MARGIN
36. Reserve Margin and Loss of Load Probability (LOLP) are common approaches for introducing
reliability into system planning. The Asia Pacific Energy Research Centre (APERC) reports while
Peninsular Malaysia and Singapore require a 30 percent reserve margin, other areas in the region
define reliable service as maintaining an LOLP no greater than 1 day per year.6
37. System reserve margin is a reliability criteria used in WASP. When simulating operations in
each year, WASP identifies the “critical period” as the period of the year for which the difference
between corresponding available generating capacity and peak demand has the smallest value. For a
configuration of unit additions to satisfy the reserve margin constraint, installed capacity in the critical
period must lie between the given min. and max. reserve margins above the peak demand in the
critical period of the year. A minimum reserve margin of 15% is applied in this study.
COST OF ENERGY NOT SERVED
38. Energy not Served (ENS) is the amount of energy required by the system, which cannot be
supplied by the generating equipment existing in the system. WASP IV computes ENS in GWh.
39. The planner can specify a cost of unserved energy (CUE) in US$/kWh representing the average
loss to the economy due to unsupplied electrical energy. Approaches for estimating CUE include the
production loss method – relating the value of lost production to the loss of power supply, the
captive generation method – estimating the extra cost incurred by consumers that must rely on
alternative or back-up power generation, and the willingness to pay method – determining a value
based on surveys of consumer’s willingness to pay for a reliable and uninterrupted electricity supply.
6 Electric Power Grid Interconnections in the APEC Region, APERC, 2004
10
APPENDIX A - 12
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
40. In the absence of reference evaluations of estimated outage costs to consumers in Myanmar,
the ADB consultant chose to remain consistent with earlier national studies in applying a 1.0 US$/kWh
CUE in this study. In comparison, a survey of the production loss for twelve major industries in
Bangladesh reports the associated average cost of unplanned outages at 0.83 US$/kWh7.
LOSS OF LOAD PROBABILITY
41. LOLP is defined as the percentage of time during which the system load exceeds the available
generating capacity of the system. For example, a cumulative failure duration of one (1) day per year
has a corresponding LOLP of 0.274%.
42. As noted in a recent ADB study report, the security and reliability requirements in Lao PDR
specify a maximum cumulative failure duration for the generating system of 5.5 days/year, while
planning criteria in Thailand call for LOLP not more than 24 hours per year.8 The planning criteria
adopted by the Korea Power Exchange (KPX) calls for a maximum LOLP of 12 hours per year.9
43. This study specifies a maximum system LOLP of 24 hours per year to be met beginning in 2025.
DEMAND FORECAST
44. As part of the EMP10, IES/MMI prepared an
electricity demand forecast using a “bottom-up” approach
for agriculture, industry, transport and household power
and energy demand. The report examines energy trends
by region and by customer class and aggregates the
results, including system losses, to provide one
consolidated electricity demand forecast for the country.
The EMP postulates three demand forecasts with the
following associated average growth rate (AGR): high
(11.7%), medium (9.6%) and low (7.6%).
45. In a separate National Electricity Master Plan
(NEMP)11 study for Myanmar, Newjec utilized a top-down
approach to forecast the rate of electric power demand
growth. An assumed elasticity of 1.4 was applied to the
projected rate of GDP growth in producing a high demand
forecast with an AGR of 12.38%. Upon review of past
studies, historical statistics and current national electrification goals, the national power system
planning team agreed on the demand forecast provided in Table 1. This forecast uses historical values
for Peak Load in 2015 and 2016, the NEMC high growth forecast values from 2017 through 2020 during
7 Energy Strategy Approach Paper Annexes, Sustainable Development Network, WBG, Oct 2009 8 Final Technical Report on Harmonization Study for ASEAN Power Grid, ADB TA 7893 REG, Sep 2013 9 The 5thBasic Plan for Long-term Electricity Supply and Demand (2010~2024), KPX, 2010 10 Myanmar Energy Master Plan, December 2015, ADB TA 8356 11 The Project for Formulation of the National Electricity Master Plan in the Republic of the Union of Myanmar,
Newjec Final Report (for former MOEP), JICA, Dec 2014
Table 1: Annual Peak Load Forecast
11
APPENDIX A - 13
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
which time large strides are expected to be taken toward national electrification, and EMP medium
growth forecast values from 2021 through 2035
46. In order to capture the variability in system load characteristics across time and space, the
power system planning team aggregated 2015 hourly electricity consumption data for each region.
47. Transmission and distribution losses were accounted for in the computation of hourly system
loads and the PRELOAD program used to read in the 8760 values of hourly system load for 2015 and
create representative period load duration curves and peak load ratios required as input to WASP. The
computed period peak load ratios are displayed in Table 2.
Table 2: Period Peak Load Ratios
EXISTING GENERATING SYSTEM
48. In October 2016, generating capacity in Myanmar totaled 4,764 MW of which 2,820 MW is
from hydro, 1,824 MW is gas fired, and 120 MW from coal fired plants.
49. Historical generation statistics for the past ten (10) years were used to characterize expected
monthly generation for each existing hydropower facility and to create generation profiles for
candidate run-of-river and reservoir storage facilities. A graph of average monthly generation for the
Baluchaung I hydropower plant (HPP) is provided in Figure 4.
Figure 4: Average monthly generation for Baluchaung I HPP
50. Details on existing and committed additions of fossil-fired generation facilities are listed in
Table 3. Similar information on hydropower facilities is presented in Table 4.
Period 1 2 3 4 5 6 7 8 9 10 11 12
Peak Load Ratio 0.86 0.89 0.94 0.93 0.99 0.94 0.93 0.94 0.95 0.99 1.00 0.99
12
APPENDIX A - 14
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
Table 3: Fossil-fired power plants (existing, committed additions and new) up to 2025
13
APPENDIX A - 15
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
Source: Ministry of Electricity and Energy, December 2016.
Lease NIHC Yangon HFO Unit 1 barge 300
Committed Addition
Lease for 5-years from April 2017, with PPA agreement for CF of
90% from November to June and 50% from July to October
Lease K-power Yangon HFO Unit 1 barge 300
Committed Addition
Lease for 5-years from April 2017, with PPA agreement for CF of
90% from November to June and 50% from July to October
Diesel engines Thanintharyi HFO Unit 1 DE 101.3 Committed Addition in 2016
Thaton New Mon Natural gas Unit 1 GT 118.9 Committed Addition in 2018
Unit 1 GT 71.5
Unit 2 GT 71.5
Unit 3 GST 82
Unit 1 GT 75
Unit 2 GST 31
Unit 1 100
Unit 2 100
Shwe Taung Bago Natural gas GT 70 Committed Addition in 2020
Kyauk Phyu Rakhine Natural gas GT 50 Committed Addition in 2020
GTCC NEW 2022 Yangon Natural gas Unit 1 GTCC 250 New in 2022
GE NEW 2025 Yangon Natural gas Unit 1 GE 50 New in 2025
Committed Addition in 2021
PPA agreement for CF of 95% of production from January to July
Thateta (UREC) Yangon Natural gasCommitted Addition in 2018
PPA agreement for CF of 82% from January to July
Kanbauk Thanintharyi Natural gas CC
Myin Gyan Sembcorp Mandalay Natural gasCommitted Addition in 2018
PPA agreement for CF of 96% from January to July
14
APPENDIX A - 16
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
15
Table 4: Hydropower plants (existing and committed additions) up to 2025
No. Name of Hydro
Power Station Category
Commissioning
year Region
Installed
capacity
[MW]
Annual
generation
[GWh]
1 Ba Luchaung
No.1 RoR 1992 Kayah State 28 200
2 Ba Luchang
No.2 RoR 1974 Kayah State 168 1190
3 Ba Luchaung
No.3 RoR 2015 Kayah State 52 334
4 Chipwi Nge Storage 2015 Kachin 99 599
5 Dapein No.1 RoR 2011 Kachin 19 [221]* 86 [984]
6 Kabaung Storage 2008 Bago 30 120
7 Keng Taung RoR 2008 Shan 54 378
8 Kinda Daily Storage 1985 Mandalay 56 165
9 Kun Storage 2012 Bago 60 190
10 Kyeeon
Kyeewa Daily Storage 2012 Magway 74 330
11 Lower
Paunglaung Storage 2005 Mandalay 280 911
12 Mone Storage 2004 Magway 75 330
13 Nancho RoR 2014 Mandalay 40 152
14 Phyuu Chaung Storage 2015 Bago 40 120
15 Sedawgyi Daily Storage 1989 Mandalay 25 134
16 Shwegyin Storage 2011 Bago 75 262
17 Shweli No.1 RoR 2008 Shan 400 [200] 2681 [1241]
18 Thapanzeik Daily Storage 2002 Sagain 30 117
19 Thaukyegat II Storage 2013 Bago 120 604
20 Upper
Paunglaung Storage 2015 Mandalay 140 454
21 Yenwe Storage 2007 Bago 25 123
22 Yeywa Storage 2010 Mandalay 790 3550
23 Zaung Tu Storage 2000 Bago 20 76
24 Zawgyi No1 RoR 1995 Shan 18 35
25 Zawgyi No2 Daily Storage 1998 Shan 12 30
26 Myogyi Storage 2016 Shan 30 136
27 Upper Yeywa RoR 2020 Shan (N) 280 1409
28 Upper
Baluchaung RoR 2020 Kayah 30 135
29 Upper Bu Storage 2020 Magway 150 399
30 Upper
Kengtawng Storage 2021 Shan (S) 53 231
31 Deedoke RoR 2021 Mandalay 66 338
32 Shweli 3 Storage 2022 Shan (N) 1050 3400
33 Dapein 2 Storage 2023 Kachin 140 642
34 Shweli 2 Storage 2023 Shan (N) 520 2814
35 Tha-Htay Storage 2024 Rakhine St 111 543
36 Manipura Storage 2025 Sagaing 400 1887
APPENDIX A - 17
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
16
37 Hpak Nam Storage 2023 Kayah 103 557
38 Hpe San Storage 2023 Kayah 48 265
39 Lower Nam
Pawn Storage 2023 Kayah 147 618
40 Upper Haw
Kham Storage 2023 Kayah 139 755
41 Upper Nam
Pawn Storage 2023 Kayah 140 782
42 Middle
Paunglaung Storage 2024 Mandalay 100 500
43 Namtu Storage 2024 Shan 100 500
44 Nam Lin Storage 2024 Shan 36 156
45 Belin Storage 2024 Mon 280 1612
46 Bawgata Storage 2024 Bago 160 500
47 Nam Paw Storage 2025 Shan (N) 20 85
48 Nam Lang Storage 2025 Shan (S) 210 840
49 Nam Hsim Storage 2025 Shan (N) 30 108
50 Middle Yeywa Storage 2025 Shan 700 3253
51 Mantong Storage 2025 Shan (N) 225 992
Note: Data [in brackets] indicates power supplied to People’s Republic of China and is NOT included in total.
Source: Ministry of Electric and Energy, December 2016.
51. Existing gas-fired plants depend on domestic supply from the Yadana, Zawtika, and Shwe gas
fields. As noted in Table 5, assuming 60% of natural gas supply is available for electricity generation,
203 MMcfd (million cubic feet per day) of gas is expected to be allocated to the power sector in 2017.
52. Gas supply for electricity generation is expected to more than double with implementation
of a Floating Storage & Regasification Unit (FSRU) and associated infrastructure in 2020.
Table 5: Natural Gas Supply for Electricity through 2030
Sources: MOGE Master Plan 2017 to 2030, and eGen Natural Gas Study12
53. This study uses fuel characteristics listed in Table 6, which are consistent with the previous
NPEP, with exception that the price of natural gas is reduced from 11.19 $/MMbtu to
10.94 $/MMbtu.13
12 eGen, Study on Economic Cost of Natural Gas for Myanmar Domestic Market, World Bank, June 2016
(“eGen-16-06”) at 80. 13 Fuel price is based on value of 8.1 $ per MMbtu for imported LNG (source: eGen-16-06 at 67) plus 35%
(source: consultant estimate) for domestic transportation.
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Yadana 225 225 225 225 206 276 276 276 183 156 131 95 68 23 2 0 0 0
Zawtika - 60 100 100 75 75 75 75 75 75 0 0 0 0 0 0 0 0
Shwe - 20 80 100 58 58 58 58 58 58 58 58 58 58 58 58 58 58
Imported Natural Gas 500 500 500 500 500 500 500 500 500 500 500
Gas Supply 225 305 405 425 339 409 409 909 816 789 689 653 626 581 560 558 558 558
Gas for Electricity 135 183 243 255 203 245 245 545 490 473 413 392 376 349 336 335 335 335
Gas Supply 164 241 337 357 278 329 329 811 744 724 633 607 588 555 540 538 538 538
Gas for Electricity 98 144 202 214 167 197 197 487 446 434 380 364 353 333 324 323 323 323
Supply (MMcfd)
Demand (MMcfd)
Demand (bcfd)
APPENDIX A - 18
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
Table 6: Thermal Fuel Types
IV. CANDIDATE PLANTS FOR FUTURE SYSTEM EXPANSION
54. Given the abundant energy resources in Myanmar, the updated NPEP analysis considers a
range of options, including: generation from hydro, fossil fuel based thermal, wind and solar power in
Myanmar and imported electricity from neighboring countries. A large number of factors including
cost of development, operation and maintenance costs, technical operational characteristics, impact
on system reliability, and environmental effects were evaluated in order to consider the suitability of
these candidates for system expansion.
THERMAL POWER PLANTS
55. Operational characteristics for candidate thermal power plants in the updated WASP-IV Case
for Myanmar are displayed in Table 7 and associated capital cost assumptions in Table 8.
Table 7: Candidate Thermal Power Plant – Operational Characteristics
56. Assumptions related to candidate thermal power plants are consistent with the previous
NPEP, developed as part of the EMP study, with the exception of an updated fuel price for natural gas
of 10.94 $/MMbtu (i.e., 4336 US cents per MMkcal).
Table 8: Candidate Thermal Power Plant – Capital Cost
Fuel Cost
($/mmbtu)
domestic 1.93
imported 4.26
2 YADA Yadana GAS 10.94 6474 (kcal/m3)
3 NGAS NATURAL GAS 10.94 8581 (kcal/m3)
4 HSD High Spead Diesel 19.40 10146 (kcal/m3)
5 SOLAR Solar Power
6 WIND Wind Power
5000 (kcal/kg)
Heat Value DescriptionNameType
COAL COAL1
Min.
Operationg
Level
Max
Generating
Capacity
Heat Rate Fuel CostSpinning
Reserve
Forced
Outage
Schedule
MaintenanceFixed O&M
Variable
O&M
(MW) (MW) (kcal/kWh) (c/106 kcal) (%) (%) (Day)
($/kW-
month)($/MWh)
GTCC 125 250 1700 4336 0 7 37 2.3 1.0
COAL 250 500 2000 1690 0 7 32 2.5 2.0
37
1.9
2.0
2.0
1.9
Thermal
Plant Type
GE
GT 25 50 2765
50 188625
4336
4336 6 7
0 7 37
17
APPENDIX A - 19
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
Figure 5: Areas with High Solar Potential Figure 6: Areas with Significant
Wind Potential
HYDRO POWER PLANTS
57. The national power system planning team identify a list of thirty (30) candidate HPPs, with a
total installed capacity 6946 MW, for the updated WASP-IV Case for Myanmar. Each candidate HPP
was further characterized by the associated installed capacity, first possible year of operation, average
annual energy, and typical monthly operations.
58. As was the case with the 2015 NPEP, due to limited availability of information on the
estimated cost of HPP candidates, an average value of $2,000 US$/MW applies to all HPP candidates
in the updated WASP-IV Case for Myanmar.
RENEWABLE GENERATION OPTIONS
59. With estimated reserves of 365 TWh/year from wind and 52,000 TWh/year from solar14 and
the strong emphasis renewable energy receives in the National Energy Policy Myanmar, this study
investigated the viability of large-scale renewable energy projects by evaluating wind and solar energy
candidate projects in the context of the least-cost generation expansion plan.
60. Under the EMP project, ADB consultants analyzed wind speed and solar irradiation estimates
in order to understand geographical dispersion of RE potential in the country. As illustrated in Figure
5 and Figure 6, the analysis suggests that: (i) solar is better located with respect to the transmission
system and distance to major load centers, and (ii) wind potential is generally in less favorable
locations further away from existing transmission.
61. ADB undertook an assessment, study and roadmap (ASR) on renewable energy potential in
Myanmar.15 This effort developed estimates of full-load hours of generation for solar PV and wind
14 Source: MOE (2013), ADB (2012) and Japan Electric Power Information Center (2012) documents. 15 H.-W. Boehnke, ASR Report, TA-8356 Myanmar 2014
18
APPENDIX A - 20
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
energy converters at different sites throughout the country. ASR results were used to estimate annual
forced outage rates for renewable energy candidates in the NPEP analysis.
Table 9: Estimated Annual Outage Rate for Solar PV in Myanmar
62. Based on the outage rate estimates for a variety of sites listed in Table 9, the NPEP analysis
assumes an average annual forced outage rate of 81.3% for solar PV candidates.
63. While the WASP IV model was originally designed to analyze conventional thermal and
hydroelectric generation options, planners have employed a number of special unit representations
to analyze renewables. The most common approach is to represent renewable generation candidates
as thermal power plants, which enables the planner to: (i) analyze viability of solar and wind
generation in an expansion plan without having to specify a predefined scenario, (ii) produce an
accurately accounting of annual renewable generation (through specification of planned maintenance
and force outage rate) and cost (through specification of capital cost and fixed O&M), and (iii) evaluate
the impact of renewables on system reliability. Others have commented on the merits of this type of
approach to modeling renewable energy resources in long-term planning models, including the
following quote from the referenced National Renewable Energy Laboratory (NREL) publication:
If time-of-day power delivery information is not available, modeling a time-dependent
resource as a generating unit with constant capability and an appropriate forced
outage rate may yield a reasonable approximation. The benefit of modeling the
resource as a generating unit is that many utility planning models [such as WASP] have
probabilistic algorithms for addressing generating unit unavailability attributable to
random equipment failures. This feature could be used to reflect the uncertainty
associated with renewable power delivery. In some models, [like WASP] unit
unavailability is specified by a forced outage rate - the percentage of time that a unit
is expected to be unavailable. Other models (notably those of a chronological nature)
allow a user to model a unit's availability by specifying probability distributions for the
time between outages and the time it may take to restore the unit to service. In
renewable resource modeling, any of these availability features could be used to
represent the renewable generation that would be curtailed because of equipment
failure (usually a minor factor) or lack of wind or sunshine (the major factor that limits
wind and solar resource generation).16
64. For the updated NPEP analysis, renewable energy options are represented with the
operational characteristics listed in Table 10.
16 RCG/Hagler, Baily, Inc., Modeling Renewable Energy Resources in Integrated Resource Planning, NREL,
1994
Location Myitkyina Mandalay Magwey Sittwey Yangon Dawei
G kWh/m²d 4.507 5.048 5.138 4.736 4.694 4.844
E kWh/kWp 1532 1716 1746 1610 1596 1647
Outage Rate (%) 82.5 80.4 80.1 81.6 81.8 81.2
19
APPENDIX A - 21
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
20
Table 10: Candidate Renewables – Operational Characteristics
65. When simulating system operation for a configuration of unit additions that includes a 50 MW
solar PV candidate with a forced outage rate of 81.3%, the WASP IV model reflects that the PV
candidate operates only 18.7% of the time. For the remainder of time, when the solar PV unit is not
generating, the full system load must be satisfied by other units or result in increased cost of unserved
energy and a higher loss of load probability.
66. Capital cost assumptions for candidate renewables are listed in Table 11. As the cost of solar
PV continues to decline due to learning curve and mass production effects, with reference to the Black
& Veatch generation technology report,17 this study applied a scaling factor to reduce the cost of PV
by 5.5% in 2020, and another 5.4% in 2025.
Table 11: Candidate Renewables – Capital Cost
IMPORT OPPORTUNITIES
67. The national power system planning team identified opportunities for potential imports from
China, Lao PDR and Thailand to be considered as expansion candidates in the 2016 NPEP. For example,
an existing 220 kV connection serves to transfer 200 MW from Shweli 1 to China. With low investment,
this can be converted to a 230 kv line connected to the Myanmar Grid and supply an additional 400
MW to the Myanmar system. The 2016 NPEP considers this potential import of 400 MW from China
at a price of 6 US cents per kWh beginning in 2018.
68. A second candidate for imports from China, is for Myanmar to purchase electricity from the
200 MW from Shweli 1 that is currently transferred to China. This study considers the potential import
of 200 MW from China via Shweli 1 at a price of 6 US cents per kWh beginning in 2019.
69. Another candidate for imports from China, is for Myanmar to purchase electricity from the
221 MW from Dapein 1 hydropower plant that is currently transferred to China. This study considers
the potential import of 221 MW from China via Dapein 1 at a price of 6 US cents per kWh beginning
in 2022. The WASP model was used in an iterative manner to identify the break-even capital cost of
connecting Dapein 1 to the Myanmar Grid that results in this source of imports being selected as part
of the LCP. The break-event cost was estimated to be 565 US$/kW (US$125 million).
17 Black & Veatch, Cost and Performance Data for Power Generation Technologies, NREL, 2012
Min.
Operationg
Level
Max
Generating
Capacity
Heat Rate Fuel CostSpinning
Reserve
Forced
Outage
Scheduled
Maintenance
Maintenance
Class Size
Fixed
O&M
Variable
O&M
(MW) (MW) (kcal/kWh)(c/million
kcal)(%) (%) (Day) (MW)
($/kW-
month)($/MWh)
SOLAR 1 50 0 0 5 0 81.3 10 50 2.0 0.0
WIND 1 100 0 0 6 0 71.4 10 50 3.3 0.0
RenewablesFuel
Type
Capital Cost Plant LifeConstruction
Time
(2013 US$/kW) years years
SOLAR 1,800 20 2
WIND 1,782 20 2
Renewables
APPENDIX A - 22
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
70. This study considers potential imports of 100 MW from Lao PDR at a price of 6 US cents per
kWh in 2020. Under these terms, the GTMax analysis evaluated the optimal level of imports from Lao
PDR to be at a utilization rate of 45% and the WASP analysis determined a break-even capital cost of
1030 US$/kW (US$100 million). This break-even cost more than doubles with an assumed utilization
rate of 60%.
71. Additional imports considered in this study are two proposed interconnections with Thailand
providing 100 MW each at a price of 35 US cent/kWh and no capital cost to be paid by Myanmar.
PRELIMINARY SCREENING OF GENERATION OPTIONS
72. A preliminary screening exercise was performed to chart the economic competitiveness of
expansion candidates as a function of their technology utilization. This approach is used to develop
initial insights into the relative competitiveness of generation options over a range of technical and
cost assumptions before carrying out the expansion planning study.
73. The screening curve diagram in Figure 7 shows the levelized generation cost expressed in
US$/kW-yr calculated at different capacity factors for all candidates using a discount rate of 8% and
technical and cost parameters as described above (i.e., for the 2016 updated NPEP analysis).
74. As an initial indication of economic competitiveness of expansion candidates, the diagram
points to hydro candidates being most competitive, while solar appears more economic than wind. In
comparing dispatchable thermal power plants defined as candidates for system expansion in the
NPEP, Gas Turbine is economic when dispatched to operate at a low capacity factor, GTCC has an
advantage for intermediate load and Coal for base load generation (Figure 7).
Figure 7: Screening Curves for Expansion Candidates – Based on 2016 Updated NPEP Assumptions
21
APPENDIX A - 23
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
75. For comparison, the screening curve in Figure 8 illustrates the economic competitiveness of
expansion candidates based on technical and cost parameters defined in the 2015 NPEP.
Figure 8: Screening Curves for Expansion Candidates – Based on 2015 NPEP Assumptions
V. ALTERNATIVE POWER SYSTEM EXPANSION SCENARIOS
76. To inform decision making on power system development, the national power system
planning team analyzed a range of possible development scenarios, including:
Scenario 1: Least Cost − considers all available power system expansion candidates in the
identification of a least cost power system expansion plan.
Scenario 2: No Coal − same assumptions as Least Cost scenario, but does not consider new
coal-fired power plants as a candidate for system expansion.
Scenario 3: No Imports − same assumptions as Least Cost scenario, but does not consider
imported electricity as a candidate for system expansion.
Scenario 4: Delayed Hydro − same assumptions as Least Cost scenario, except that the
commissioning date for new hydropower plants are delayed by three years.
SCENARIO 1 − LEAST COST
77. This section presents model results for the least cost power system expansion plan developed
with the updated WASP-IV Case for Myanmar.
78. The capacity mix associated with the Myanmar power sector in 2015 is provided in Table 12
and resulting capacity mix in 2035 for the least cost scenario is provided in Table 13.
22
APPENDIX A - 24
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
Table 12: Actual Capacity Mix for Myanmar Power System in 2015
Table 13: Least Cost Scenario – Capacity Mix in 2035
79. The schedule of capacity additions for the least cost expansion plan is provided in Table 14.
MW %
Gas 1,737 38%
Coal 120 3%
Hydro 2,730 60%
Renewables 0 0%
Imports 0 0%
Total 4,587
Plant TypeInstalled Capacity in 2015
MW %
Gas 5,940 24%
Coal 2,620 10%
Hydro 12,506 50%
Renewables 3,010 12%
Imports 921 4%
Total 24,997
Installed Capacity in 2035Plant Type
23
APPENDIX A - 25
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
Table 14: Least Cost Scenario – Power Expansion Plan
80. In the Least Coast Scenario, near-term electricity requirements are met by hydro and gas-fired power plants that are currently under
construction, plus the leased barge facilities and new sources of imports from China and Lao PDR.
81. All 6,946 MW of candidate hydropower facilities is added over the study period and 3,500 MW of new gas-fired generation is brought online
from 2022 through 2035.
82. In addition, 2,400 MW of solar power is added to the system from 2028 to 2035, and 2,500 MW from coal power plants added in the last six
years of the study.
Existing Plants 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
Gas 1737 1920 1920 2398 2398 2573 2673 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440
Coal 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120
Hydro 2730 2760 2760 2760 2760 3220 3339 4389 5049 5160 5560 5560 5560 5560 5560 5560 5560 5560 5560 5560 5560
Solar 180 180 550 550 550 550 550 550 550 550 550 550 550 550 550 550 550 550
Wind 30 30 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
HFO 600 600 600 600 600
Annual Fixed Capacity (MW): 4587 4800 5400 6088 6088 7123 7342 7559 8219 8330 8730 8730 8730 8730 8730 8730 8730 8730 8730 8730 8730
Candidate Plants
Gas 500 50 300 100 250 250 350 500 250 250 250 250 200
Coal 500 500 500 500 500
Hydro 577 676 1185 1528 1340 1345 295
Solar 50 550 200 200 50 600 150 600
Wind
Imports 400 200 100 221
Total Capacity Additions: 0 0 0 400 600 700 700 1421 2048 3024 4309 6087 7677 9422 10472 11717 12417 13217 14067 14967 16267
Total Installed Capacity 4587 4800 5400 6488 6688 7823 8042 8980 10267 11354 13039 14817 16407 18152 19202 20447 21147 21947 22797 23697 24997
Renewable Capacity (MW) 0 0 0 210 210 610 610 610 610 610 610 610 610 660 1210 1410 1610 1660 2260 2410 3010
Renewable % of Total Capacity 0.0% 0.0% 0.0% 3.2% 3.1% 7.8% 7.6% 6.8% 5.9% 5.4% 4.7% 4.1% 3.7% 3.6% 6.3% 6.9% 7.6% 7.6% 9.9% 10.2% 12.0%
24
APPENDIX A - 26
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
Figure 9: Least Cost Scenario – Cumulative Capacity (MW) by Plant Type
83. The cumulative capacity (i.e., existing system plus new additions) by plant type for the least
cost expansion strategy is displayed in Figure 9.
84. Annual generation by plant fuel type is reported in Table 15.
Table 15: Least Cost Scenario – Annual Generation by Plant Type
85. Hydropower generation falls from 60% of the total to around 40% during 2019 to 2021, then
increases as new hydropower facilities come online.
86. Heavy Fuel Oil consumed by the leased barge plants satisfies 12 to 19% of the annual
generation requirements during the 5-year term of PPA from 2017 through 2022.
Total
GWh % GWh % GWh % GWh % GWh % GWh % GWh % GWh
2015 9,439 60% 5,932 37% 488 3% 0 0% 0 0% 0 0% 0 0% 15,859
2016 9,573 54% 7,494 42% 724 4% 0 0% 0 0% 0 0% 0 0% 17,791
2017 9,573 48% 6,419 32% 255 1% 3,748 19% 0 0% 0 0% 0 0% 19,995
2018 9,573 43% 7,223 32% 1 0% 3,748 17% 287 1% 73 0% 1,566 7% 22,471
2019 9,573 38% 8,684 34% 297 1% 3,748 15% 287 1% 73 0% 2,598 10% 25,260
2020 11,517 41% 9,005 32% 115 0% 3,748 13% 872 3% 146 1% 2,982 11% 28,385
2021 12,087 39% 10,881 35% 452 1% 3,748 12% 875 3% 146 0% 2,982 10% 31,171
2022 15,487 45% 12,952 38% 727 2% 0 0% 876 3% 146 0% 4,026 12% 34,214
2023 21,918 58% 10,023 27% 515 1% 0 0% 853 2% 145 0% 4,026 11% 37,480
2024 25,667 63% 9,883 24% 493 1% 0 0% 815 2% 140 0% 4,026 10% 41,024
2025 32,252 72% 7,466 17% 346 1% 0 0% 673 1% 126 0% 4,026 9% 44,889
2026 37,361 76% 6,795 14% 301 1% 0 0% 562 1% 96 0% 4,026 8% 49,141
2027 42,522 79% 6,312 12% 292 1% 0 0% 540 1% 92 0% 4,026 7% 53,784
2028 47,664 81% 6,302 11% 291 0% 0 0% 550 1% 90 0% 4,026 7% 58,923
2029 49,628 77% 9,515 15% 375 1% 0 0% 954 1% 95 0% 4,026 6% 64,593
2030 52,208 74% 10,600 15% 2,308 3% 0 0% 1,339 2% 106 0% 4,026 6% 70,587
2031 52,906 71% 11,304 15% 4,611 6% 0 0% 1,713 2% 126 0% 4,026 5% 74,686
2032 53,307 67% 12,480 16% 7,246 9% 0 0% 1,897 2% 130 0% 4,026 5% 79,086
2033 53,641 64% 15,014 18% 8,055 10% 0 0% 2,908 3% 135 0% 4,026 5% 83,779
2034 53,867 61% 16,100 18% 11,474 13% 0 0% 3,240 4% 138 0% 4,026 5% 88,845
2035 54,057 57% 17,205 18% 14,689 16% 0 0% 4,189 4% 140 0% 4,026 4% 94,306
YearSolar ImportsHydro Gas Coal HFO Wind
25
APPENDIX A - 27
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
87. Imported electricity from China and Lao PDR increase from 7% of the annual total generation
requirement in 2018 to 12% in 2022. As this study, did not analyze the potential for additional imports
from Lao PDR and China, total imports remain at 4,026 GWh per year through 2035.
88. When the list of hydropower candidates and import options are exhausted, in 2030, the least
cost expansion plan includes an increasing amount of coal-fired generation.
89. To meet increased demand for electricity over the period 2015 through 2035, fuel
consumption in the power sector is expected to increase as elaborated in (Table 16). For comparison,
the gas supply limit for electricity generation (from Table 5) is also listed. Cells highlighted in yellow
draw attention to years in which the fuel requirement exceeds gas supply for electricity.
Table 16: Least Cost Scenario – Fuel Requirements and Comparison with Gas Supply for Electricity
90. Carbon dioxide (CO2) emission factors were developed for each power plant based on
characteristics of the generating technology and fuel consumed. The computed emission factors were
combined with values of annual generation by plant type reported by WASP to estimate annual CO2
emissions (Figure 10). Total CO2 emissions over the study period amount to 147 million tonnes.
Coal HFO Gas
ktonne ktonne bbtud bbtud
2015 248 0 137 202
2016 369 0 175 214
2017 130 912 146 167
2018 1 912 157 197
2019 151 912 192 197
2020 58 912 202 487
2021 230 912 254 446
2022 370 0 294 434
2023 262 0 226 380
2024 251 0 220 364
2025 176 0 166 353
2026 153 0 149 333
2027 148 0 138 324
2028 148 0 137 323
2029 191 0 203 323
2030 976 0 228 323
2031 1904 0 244 323
2032 2964 0 269 323
2033 3297 0 321 323
2034 4670 0 342 323
2035 5959 0 365 323
Gas Supply for
Electricity
Fuel Requirements for Electricity
Year
26
APPENDIX A - 28
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
27
Figure 10: Least Cost Scenario – Annual CO2 Emissions from Electricity Generation
91. The optimum solution for each scenario is evaluated in terms of minimum discounted total
system costs. For the least cost scenario, the annual and cumulative discounted costs associated with
the optimum solution are provided in Table 17.
Table 17: Least Cost Scenario – System Costs
Investment Salvage Operating ENS Total Cumulative
2015 0 0 637146 0 637,146 637,146 0
2016 0 0 737474 0 737,474 1,374,620 0
2017 0 0 922922 0 922,922 2,297,542 0
2018 0 0 1011416 0 1,011,416 3,308,958 0
2019 0 0 1098456 0 1,098,456 4,407,414 0
2020 76225 19645 1118401 0 1,174,981 5,582,395 0
2021 0 0 1186344 6 1,186,350 6,768,745 0.002
2022 380970 93163 999105 152 1,287,064 8,055,809 0.032
2023 784516 275846 776602 64 1,285,336 9,341,145 0.015
2024 977301 362032 707515 46 1,322,830 10,663,975 0.012
2025 1380444 572067 545714 17 1,354,108 12,018,083 0.005
2026 1448103 644937 472167 6 1,275,339 13,293,422 0.002
2027 1409039 681875 422579 3 1,149,746 14,443,168 0.002
2028 1282722 669382 395123 1 1,008,464 15,451,632 0.001
2029 382157 186997 471284 42 666,486 16,118,118 0.013
2030 790786 458441 500482 114 832,941 16,951,059 0.027
2031 442078 274850 516554 270 684,052 17,635,111 0.056
2032 438538 302130 538936 270 675,614 18,310,725 0.056
2033 197068 146704 563766 1092 615,222 18,925,947 0.197
2034 394167 327898 576026 1277 643,572 19,569,519 0.229
2035 414743 378000 584478 1527 622,748 20,192,267 0.273
Present Worth Cost of 2015 ( K$ )LOLPYear
APPENDIX A - 29
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
SCENARIO 2 − NO COAL
92. This section presents model results for the No Coal Scenario. The capacity mix associated with
the resulting Myanmar power sector in 2035 is provided in Table 18
Table 18: No Coal Scenario – Capacity Mix in 2035
93. The cumulative capacity by plant type for the optimum expansion strategy is displayed in
Figure 11.
Figure 11: No Coal Scenario – Cumulative Capacity (MW) by Plant Type
94. The schedule of capacity additions for the optimum expansion strategy for the No Coal
Scenario is provided in Table 19.
MW %
Gas 7,890 29%
Coal 120 0%
Hydro 12,506 46%
Renewables 5,560 21%
Imports 921 3%
Total 26,997
Plant TypeInstalled Capacity in 2035
28
APPENDIX A - 30
ADB Myanmar
Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
Table 19: No Coal Scenario – Power Expansion Plan
95. As with the Least Cost Scenario, in the No Coal Scenario:
a) Near-term electricity requirements are met by hydro and gas-fired power plants that are currently under construction, plus the
leased barge facilities and new sources of imports from China and Lao PDR; and
b) All 6,946 MW of candidate hydropower facilities is added over the study period.
96. The major difference from the optimum solution in the Least Cost Scenario is that 2,500 MW from new coal-fired power plants is replaced
by an additional 1,950 MW of gas-and 2,550 MW of solar.
Existing Plants 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
Gas 1737 1920 1920 2398 2398 2573 2673 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440
Coal 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120
Hydro 2730 2760 2760 2760 2760 3220 3339 4389 5049 5160 5560 5560 5560 5560 5560 5560 5560 5560 5560 5560 5560
Solar 180 180 550 550 550 550 550 550 550 550 550 550 550 550 550 550 550 550
Wind 30 30 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
HFO 600 600 600 600 600
Annual Fixed Capacity (MW): 4587 4800 5400 6088 6088 7123 7342 7559 8219 8330 8730 8730 8730 8730 8730 8730 8730 8730 8730 8730 8730
Candidate Plants
Gas 0 0 0 0 0 0 0 500 50 300 100 250 250 400 500 500 500 750 250 750 350
Coal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hydro 0 0 0 0 0 0 0 0 577 676 1185 1528 1340 1345 0 295 0 0 0 0 0
Solar 0 0 0 0 0 0 0 0 0 0 0 0 0 0 550 450 200 50 600 600 2500
Wind 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Imports 0 0 0 400 200 100 0 221 0 0 0 0 0 0 0 0 0 0 0 0 0
Capacity Additions (MW): 0 0 0 400 200 100 0 721 627 976 1285 1778 1590 1745 1050 1245 700 800 850 1350 2850
Total Capacity Additions: 0 0 0 400 600 700 700 1421 2048 3024 4309 6087 7677 9422 10472 11717 12417 13217 14067 15417 18267
Total Installed Capacity 4587 4800 5400 6488 6688 7823 8042 8980 10267 11354 13039 14817 16407 18152 19202 20447 21147 21947 22797 24147 26997
Renewable Capacity (MW) 0 0 0 210 210 610 610 610 610 610 610 610 610 610 1160 1610 1810 1860 2460 3060 5560
Renewable % of Total Capacity 0.0% 0.0% 0.0% 3.2% 3.1% 7.8% 7.6% 6.8% 5.9% 5.4% 4.7% 4.1% 3.7% 3.4% 6.0% 7.9% 8.6% 8.5% 10.8% 12.7% 20.6%
29
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ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
97. Annual generation by plant fuel type is reported in Table 20.
Table 20: No Coal Scenario – Annual Generation by Plant Type
98. Annual generation from hydro and oil, and amount of imported electricity is nearly identical in
the No Coal and Least Cost scenarios.
99. The primary difference is that the No Coal Scenario has higher generation from gas-fired and
solar power plants − nearly doubling the level of annual generation in 2035.
100. The No Coal Scenario results in higher natural gas fuel requirements. As noted in Table 21,
fuel requirements exceed gas supply for electricity in years 2032 through 2035.
Table 21: No Coal Scenario – Fuel Requirements and Comparison with Gas Supply for Electricity
Total
GWh % GWh % GWh % GWh % GWh % GWh % GWh % GWh
2015 9,439 60% 5,932 37% 488 3% 0 0% 0 0% 0 0% 0 0% 15,859
2016 9,573 54% 7,494 42% 724 4% 0 0% 0 0% 0 0% 0 0% 17,791
2017 9,573 48% 6,419 32% 255 1% 3,748 19% 0 0% 0 0% 0 0% 19,995
2018 9,573 43% 7,223 32% 1 0% 3,748 17% 287 1% 73 0% 1,566 7% 22,471
2019 9,573 38% 8,684 34% 297 1% 3,748 15% 287 1% 73 0% 2,598 10% 25,260
2020 11,517 41% 9,005 32% 115 0% 3,748 13% 872 3% 146 1% 2,982 11% 28,385
2021 12,087 39% 10,881 35% 452 1% 3,748 12% 875 3% 146 0% 2,982 10% 31,171
2022 15,487 45% 12,952 38% 727 2% 0 0% 876 3% 146 0% 4,026 12% 34,214
2023 21,918 58% 10,023 27% 515 1% 0 0% 853 2% 145 0% 4,026 11% 37,480
2024 25,667 63% 9,883 24% 493 1% 0 0% 815 2% 140 0% 4,026 10% 41,024
2025 32,252 72% 7,466 17% 346 1% 0 0% 673 1% 126 0% 4,026 9% 44,889
2026 37,361 76% 6,795 14% 301 1% 0 0% 562 1% 96 0% 4,026 8% 49,141
2027 42,522 79% 6,312 12% 292 1% 0 0% 540 1% 92 0% 4,026 7% 53,784
2028 47,664 81% 6,326 11% 291 0% 0 0% 525 1% 90 0% 4,026 7% 58,922
2029 49,628 77% 9,546 15% 376 1% 0 0% 922 1% 96 0% 4,026 6% 64,594
2030 52,208 74% 12,262 17% 479 1% 0 0% 1,507 2% 106 0% 4,026 6% 70,588
2031 52,906 71% 15,177 20% 547 1% 0 0% 1,903 3% 126 0% 4,026 5% 74,685
2032 53,307 67% 18,918 24% 596 1% 0 0% 2,107 3% 130 0% 4,026 5% 79,084
2033 53,641 64% 22,150 26% 666 1% 0 0% 3,160 4% 135 0% 4,026 5% 83,778
2034 53,867 61% 25,991 29% 732 1% 0 0% 4,091 5% 138 0% 4,026 5% 88,845
2035 54,057 57% 27,756 29% 733 1% 0 0% 7,595 8% 140 0% 4,026 4% 94,307
YearSolar ImportsHydro Gas Coal HFO Wind
Coal Oil Gas
ktonne ktonne bbtud bbtud
2015 248 0 137 202
2016 369 0 175 214
2017 130 912 146 167
2018 1 912 157 197
2019 151 912 192 197
2020 58 912 202 487
2021 230 912 254 446
2022 370 0 294 434
2023 262 0 226 380
2024 251 0 220 364
2025 176 0 166 353
2026 153 0 149 333
2027 148 0 138 324
2028 148 0 137 323
2029 191 0 204 323
2030 244 0 259 323
2031 278 0 316 323
2032 303 0 389 323
2033 339 0 454 323
2034 372 0 526 323
2035 373 0 560 323
Year
Fuel Requirements for Electricity Gas Supply
Limit for
Electricity
30
APPENDIX A - 32
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
101. The No Coal Scenario results in a 14% reduction in CO2 emissions as compared with the Least
Cost Scenario. Total CO2 emissions over the study period amount to 127 million tonnes (Figure 12).
Figure 12: No Coal Scenario – Annual CO2 Emissions from Electricity Generation
102. The annual and cumulative discounted costs associated with the optimum solution for the No
Coal Scenario are provided in Table 22. The total discount cost (capital, fuel and O&M) over the study
period amounts to $20.258 billion, which is $66 million (0.3%) higher than the Least Cost Plan.
Table 22: No Coal Scenario – System Costs
Investment Salvage Operating ENS Total Cumulative
2015 0 0 637146 0 637,146 637,146 0
2016 0 0 737474 0 737,474 1,374,620 0
2017 0 0 922922 0 922,922 2,297,542 0
2018 0 0 1011416 0 1,011,416 3,308,958 0
2019 0 0 1098456 0 1,098,456 4,407,414 0
2020 76225 19645 1118401 0 1,174,981 5,582,395 0
2021 0 0 1186344 6 1,186,350 6,768,745 0.002
2022 380970 93163 999105 152 1,287,064 8,055,809 0.032
2023 784516 275846 776602 64 1,285,336 9,341,145 0.015
2024 977301 362032 707515 46 1,322,830 10,663,975 0.012
2025 1380444 572067 545714 17 1,354,108 12,018,083 0.005
2026 1448103 644937 472167 6 1,275,339 13,293,422 0.002
2027 1409039 681875 422579 3 1,149,746 14,443,168 0.002
2028 1274400 666545 395750 0 1,003,605 15,446,773 0.001
2029 382157 186997 472114 32 667,306 16,114,079 0.01
2030 539019 312603 519068 193 745,677 16,859,756 0.045
2031 208420 128588 557411 377 637,620 17,497,376 0.081
2032 222188 152907 601714 329 671,324 18,168,700 0.073
2033 197068 146704 629079 1440 680,883 18,849,583 0.266
2034 295647 244567 656810 1201 709,091 19,558,674 0.231
2035 493911 447866 652317 1326 699,688 20,258,362 0.256
Present Worth Cost of 2015 ( K$ )LOLPYear
31
APPENDIX A - 33
ADB Myanmar
Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
SCENARIO 3− NO IMPORTS
103. This section presents model results for the No Imports Scenario. The capacity mix associated
with the resulting Myanmar power sector in 2035 is provided in Table 23
Table 23: No Imports Scenario – Capacity Mix in 2035
104. The cumulative capacity by plant type for the optimum expansion strategy is displayed in
Figure 13.
Figure 13: No Imports Scenario – Cumulative Capacity (MW) by Plant Type
105. The schedule of capacity additions for the least cost expansion plan for the No Imports
Scenario is provided in Table 24.
MW %
Gas 5,590 22%
Coal 3,120 12%
Hydro 12,506 48%
Renewables 4,660 18%
Imports 0 0%
Total 25,876
Plant TypeInstalled Capacity in 2035
32
APPENDIX A - 34
ADB Myanmar
Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
Table 24: No Imports Scenario – Power Expansion Plan
106. Notable actions taken to replace imports in the No Imports Scenario, include:
a) New fossil-fired power plants are added earlier with the first gas plant in 2019 and first coal plant in 2022.
b) The total amount of new coal plants increases by 500 MW and amount of new solar power increases by 1,650.
c) The amount of new gas plants reduces by 350 MW.
Existing Plants 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
Gas 1737 1920 1920 2398 2398 2573 2673 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440
Coal 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120
Hydro 2730 2760 2760 2760 2760 3220 3339 4389 5049 5160 5560 5560 5560 5560 5560 5560 5560 5560 5560 5560 5560
Solar 180 180 550 550 550 550 550 550 550 550 550 550 550 550 550 550 550 550
Wind 30 30 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
HFO 600 600 600 600 600
Annual Fixed Capacity (MW): 4587 4800 5400 6088 6088 7123 7342 7559 8219 8330 8730 8730 8730 8730 8730 8730 8730 8730 8730 8730 8730
Candidate Plants
Gas 0 0 0 0 250 0 250 0 0 250 100 250 250 350 500 250 0 0 250 50 400
Coal 0 0 0 0 0 0 0 500 0 0 0 0 0 0 0 500 500 500 500 500 0
Hydro 0 0 0 0 0 0 0 0 577 676 1185 1528 1340 1345 0 295 0 0 0 0 0
Solar 0 0 0 0 0 0 0 0 0 0 0 0 0 50 500 250 200 300 100 350 2300
Wind 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Imports 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Capacity Additions (MW): 0 0 0 0 250 0 250 500 577 926 1285 1778 1590 1745 1000 1295 700 800 850 900 2700
Total Capacity Additions: 0 0 0 0 250 250 500 1000 1577 2503 3788 5566 7156 8901 9901 11196 11896 12696 13546 14446 17146
Total Installed Capacity 4587 4800 5400 6088 6338 7373 7842 8559 9796 10833 12518 14296 15886 17631 18631 19926 20626 21426 22276 23176 25876
Renewable Capacity (MW) 0 0 0 210 210 610 610 610 610 610 610 610 610 660 1160 1410 1610 1910 2010 2360 4660
Renewable % of Total Capacity 0.0% 0.0% 0.0% 3.4% 3.3% 8.3% 7.8% 7.1% 6.2% 5.6% 4.9% 4.3% 3.8% 3.7% 6.2% 7.1% 7.8% 8.9% 9.0% 10.2% 18.0%
33
APPENDIX A - 35
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Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
107. Annual generation by plant fuel type is reported in Table 25.
Table 25: No Imports Scenario – Annual Generation by Plant Type
108. Annual generation from hydro and oil, and amount of imported electricity is nearly identical
in the No Imports and Least Cost scenarios.
109. The primary difference is that the No Imports Scenario has higher generation from coal-fired
and solar power plants.
110. Due to the increased amount of coal-fired generation in the No Imports Scenario, natural gas
requirements decrease slightly. As noted in Table 26, fuel requirements exceed gas supply for
electricity only in the last year of the study.
Table 26: No Imports Scenario – Fuel Requirements and Comparison with Gas Supply for Electricity
Total
GWh % GWh % GWh % GWh % GWh % GWh % GWh % GWh
2015 9,439 60% 5,932 37% 488 3% 0 0% 0 0% 0 0% 0 0% 15,859
2016 9,573 54% 7,494 42% 724 4% 0 0% 0 0% 0 0% 0 0% 17,791
2017 9,573 48% 6,419 32% 255 1% 3,748 19% 0 0% 0 0% 0 0% 19,995
2018 9,573 43% 8,641 38% 150 1% 3,748 17% 287 1% 73 0% 0 0% 22,472
2019 9,573 38% 10,838 43% 741 3% 3,748 15% 287 1% 73 0% 0 0% 25,260
2020 11,517 41% 11,402 40% 695 2% 3,748 13% 876 3% 146 1% 0 0% 28,384
2021 12,087 39% 13,594 44% 722 2% 3,748 12% 876 3% 146 0% 0 0% 31,173
2022 15,487 45% 13,606 40% 4,099 12% 0 0% 876 3% 146 0% 0 0% 34,214
2023 21,921 58% 11,211 30% 3,326 9% 0 0% 875 2% 146 0% 0 0% 37,479
2024 25,729 63% 11,205 27% 3,079 8% 0 0% 864 2% 146 0% 0 0% 41,023
2025 32,636 73% 8,999 20% 2,314 5% 0 0% 798 2% 138 0% 0 0% 44,885
2026 38,520 78% 7,963 16% 1,916 4% 0 0% 628 1% 109 0% 0 0% 49,136
2027 43,883 82% 7,492 14% 1,723 3% 0 0% 583 1% 99 0% 0 0% 53,780
2028 49,113 83% 7,435 13% 1,681 3% 0 0% 596 1% 97 0% 0 0% 58,922
2029 50,841 79% 10,268 16% 2,280 4% 0 0% 1,099 2% 105 0% 0 0% 64,593
2030 53,017 75% 11,248 16% 4,661 7% 0 0% 1,533 2% 128 0% 0 0% 70,587
2031 53,380 71% 12,170 16% 7,164 10% 0 0% 1,837 2% 131 0% 0 0% 74,682
2032 53,656 68% 12,925 16% 9,923 13% 0 0% 2,439 3% 137 0% 0 0% 79,080
2033 53,863 64% 13,661 16% 13,393 16% 0 0% 2,723 3% 138 0% 0 0% 83,778
2034 54,045 61% 14,517 16% 16,841 19% 0 0% 3,300 4% 140 0% 0 0% 88,843
2035 54,190 57% 15,952 17% 17,341 18% 0 0% 6,680 7% 142 0% 0 0% 94,305
YearSolar ImportsHydro Gas Coal HFO Wind
Coal Oil Gas
ktonne ktonne bbtud bbtud
2015 248 0 137 202
2016 369 0 175 214
2017 130 912 146 167
2018 76 912 190 197
2019 377 912 237 197
2020 354 912 256 487
2021 367 912 306 446
2022 1732 0 311 434
2023 1410 0 255 380
2024 1306 0 252 364
2025 984 0 202 353
2026 813 0 177 333
2027 730 0 165 324
2028 712 0 163 323
2029 965 0 223 323
2030 1925 0 244 323
2031 2931 0 265 323
2032 4040 0 283 323
2033 5439 0 298 323
2034 6821 0 316 323
2035 7021 0 345 323
Year
Fuel Requirements for ElectricityGas Supply for
Electricity
34
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Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
111. The No Imports Scenario results in a 26% increase in CO2 emissions as compared with the
Least Cost Scenario. Total CO2 emissions amount to 185 million tonnes (Figure 14).
Figure 14: No Imports Scenario – Annual CO2 Emissions from Electricity Generation
112. The annual and cumulative discounted costs associated with the optimum solution for the No
Imports Scenario are provided in Table 27. Total discount cost amounts to $20.853 billion, which is
$661 million (3.3%) higher than the Least Cost Plan.
Table 27: No Imports Scenario – System Costs
Investment Salvage Operating ENS Total Cumulative
2015 0 0 637146 0 637,146 637,146 0
2016 0 0 737474 0 737,474 1,374,620 0
2017 0 0 922922 0 922,922 2,297,542 0
2018 0 0 1068534 0 1,068,534 3,366,076 0
2019 190005 27645 1148896 0 1,311,256 4,677,332 0
2020 0 0 1176677 0 1,176,677 5,854,009 0
2021 162899 32280 1229532 1 1,360,152 7,214,161 0.001
2022 768749 175036 994661 393 1,588,767 8,802,928 0.083
2023 765310 270861 789629 186 1,284,264 10,087,192 0.033
2024 959517 356803 725501 172 1,328,387 11,415,579 0.031
2025 1380444 572066 563574 73 1,372,025 12,787,604 0.016
2026 1448103 644937 469149 30 1,272,345 14,059,949 0.008
2027 1409039 681875 413372 16 1,140,552 15,200,501 0.005
2028 1253031 655675 383099 7 980,462 16,180,963 0.003
2029 363417 178194 456970 92 642,285 16,823,248 0.022
2030 807201 467126 490348 183 830,606 17,653,854 0.039
2031 442078 274849 511215 384 678,828 18,332,682 0.075
2032 431644 295670 528148 764 664,886 18,997,568 0.139
2033 416455 315449 540157 785 641,948 19,639,516 0.144
2034 381158 316498 550932 1487 617,079 20,256,595 0.259
2035 467683 424170 551818 1510 596,841 20,853,436 0.272
Present Worth Cost of 2015 ( K$ )LOLPYear
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Updated National Power Expansion Plan (TA No. 8356-MYA) Draft Report 14 JAN-2017
SCENARIO 4− DELAYED HYDRO
113. This section presents model results for the Delayed Hydro Scenario. The capacity mix
associated with the resulting Myanmar power sector in 2035 is provided in Table 28
Table 28: Delayed Hydro Scenario – Capacity Mix in 2035
114. The cumulative capacity by plant type for the optimum expansion strategy is displayed in
Figure 15.
Figure 15: Delayed Hydro Scenario – Cumulative Capacity (MW) by Plant Type
115. The schedule of capacity additions for the least cost expansion plan for the Delayed Hydro
Scenario is provided in Table 29.
MW %
Gas 5,990 24%
Coal 2,620 11%
Hydro 12,506 50%
Renewables 2,760 11%
Imports 921 4%
Total 24,797
Plant TypeInstalled Capacity in 2035
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
Table 29: Delayed Hydro – Power Expansion Plan
116. While the Delayed Hydro Scenario has nearly an identical capacity mix as the Least Cost Scenario in 2035, the commissioning schedule for
new gas, coal and solar power plants is accelerated.
117. Most notably, 1000 MW of coal-fired power is scheduled to be commissioned in 2022 and another 500 MW in 2023 (as compared with the
first coal-fired power plant schedule to be commissioned in 2030 in the Least Cost Scenario).
Existing Plants 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
Gas 1737 1920 1920 2398 2398 2573 2673 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440 2440
Coal 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120
Hydro 2730 2760 2760 2760 2760 2760 2760 2760 3220 3339 4389 5049 5160 5560 5560 5560 5560 5560 5560 5560 5560
Solar 180 180 550 550 550 550 550 550 550 550 550 550 550 550 550 550 550 550
Wind 30 30 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
HFO 600 600 600 600 600
Annual Fixed Capacity (MW): 4587 4800 5400 6088 6088 6663 6763 5930 6390 6509 7559 8219 8330 8730 8730 8730 8730 8730 8730 8730 8730
Candidate Plants
Gas 0 0 0 0 0 0 250 0 0 500 250 150 250 250 0 500 250 250 300 300 300
Coal 0 0 0 0 0 0 0 1000 500 0 0 0 0 0 0 0 0 0 0 500 500
Hydro 0 0 0 0 0 0 0 0 0 0 0 577 676 1185 1528 1340 1345 0 295 0 0
Solar 0 0 0 0 0 0 0 0 0 0 0 0 250 100 450 50 0 500 400 100 300
Wind 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Imports 0 0 0 400 200 100 0 221 0 0 0 0 0 0 0 0 0 0 0 0 0
Capacity Additions (MW): 0 0 0 400 200 100 250 1221 500 500 250 727 1176 1535 1978 1890 1595 750 995 900 1100
Total Capacity Additions: 0 0 0 400 600 700 950 2171 2671 3171 3421 4148 5324 6859 8837 10727 12322 13072 14067 14967 16067
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
118. Annual generation by plant fuel type is reported in Table 30.
Table 30: Delayed Hydro Scenario – Annual Generation by Plant Type
119. The Delayed Hydro Scenario results in hydro generation as a percent of annual total reducing
from 60% in 2015 to an average of 31% from 2020 to 2025.
120. The reduction in hydro generation is met by increased generation from coal- and gas-fired
power plants.
121. The Delayed Hydro Scenario results in increased coal and natural gas fuel requirements during
2020 through 2033. However, natural gas requirements do not exceed gas supply for electricity until
the last two years of the study (Table 31).
Table 31: Delayed Hydro – Fuel Requirements and Comparison with Gas Supply for Electricity
Total
GWh % GWh % GWh % GWh % GWh % GWh % GWh % GWh
2015 9,439 60% 5,932 37% 488 3% 0 0% 0 0% 0 0% 0 0% 15,859
2016 9,573 54% 7,494 42% 724 4% 0 0% 0 0% 0 0% 0 0% 17,791
2017 9,573 48% 6,419 32% 255 1% 3,748 19% 0 0% 0 0% 0 0% 19,995
2018 9,573 43% 7,223 32% 1 0% 3,748 17% 287 1% 73 0% 1,566 7% 22,471
2019 9,573 38% 8,684 34% 297 1% 3,748 15% 287 1% 73 0% 2,598 10% 25,260
2020 9,573 34% 10,549 37% 510 2% 3,748 13% 876 3% 146 1% 2,982 11% 28,384
2021 9,573 31% 13,139 42% 708 2% 3,748 12% 876 3% 146 0% 2,982 10% 31,172
2022 9,573 28% 12,207 36% 7,385 22% 0 0% 876 3% 146 0% 4,026 12% 34,213
2023 11,517 31% 10,858 29% 10,055 27% 0 0% 876 2% 146 0% 4,026 11% 37,478
2024 12,087 29% 13,130 32% 10,758 26% 0 0% 876 2% 146 0% 4,026 10% 41,023
2025 15,487 35% 13,739 31% 10,610 24% 0 0% 876 2% 146 0% 4,026 9% 44,884
2026 21,921 45% 12,497 25% 9,668 20% 0 0% 876 2% 146 0% 4,026 8% 49,134
2027 25,731 48% 13,072 24% 9,562 18% 0 0% 1,240 2% 146 0% 4,026 7% 53,777
2028 32,878 56% 12,016 20% 8,526 14% 0 0% 1,324 2% 145 0% 4,026 7% 58,915
2029 39,213 61% 11,664 18% 7,690 12% 0 0% 1,855 3% 139 0% 4,026 6% 64,587
2030 45,742 65% 11,694 17% 7,228 10% 0 0% 1,762 2% 134 0% 4,026 6% 70,586
2031 51,697 69% 10,681 14% 6,537 9% 0 0% 1,613 2% 128 0% 4,026 5% 74,682
2032 52,064 66% 13,055 17% 7,500 9% 0 0% 2,308 3% 131 0% 4,026 5% 79,084
2033 53,641 64% 14,925 18% 8,016 10% 0 0% 3,034 4% 135 0% 4,026 5% 83,777
2034 53,867 61% 16,034 18% 11,475 13% 0 0% 3,304 4% 138 0% 4,026 5% 88,844
2035 54,057 57% 17,405 18% 14,831 16% 0 0% 3,847 4% 140 0% 4,026 4% 94,306
YearSolar ImportsHydro Gas Coal HFO Wind
Coal Oil Gas
ktonne ktonne bbtud bbtud
2015 248 0 137 202
2016 369 0 175 214
2017 130 912 146 167
2018 1 912 157 197
2019 151 912 192 197
2020 260 912 243 487
2021 360 912 303 446
2022 3049 0 284 434
2023 4118 0 251 380
2024 4400 0 292 364
2025 4340 0 303 353
2026 3958 0 279 333
2027 3912 0 289 324
2028 3492 0 266 323
2029 3154 0 258 323
2030 2958 0 257 323
2031 2677 0 233 323
2032 3067 0 282 323
2033 3280 0 319 323
2034 4671 0 343 323
2035 6016 0 370 323
Year
Fuel Requirements for ElectricityGas Supply for
Electricity
38
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
122. The Delayed Hydro Scenario results in a 53% increase in CO2 emissions as compared with the
Least Cost Scenario. Total CO2 emissions amount to 224 million tonnes (Figure 16).
Figure 16: Delayed Hydro Scenario – Annual CO2 Emissions from Electricity Generation
123. The total discounted costs associated with the optimum solution for the Delayed Hydro
Scenario are provided in Table 32. Total discount cost amounts to $22.878 billion, which is $2.69 billion
(13.3%) higher than the Least Cost Plan.
Table 32: Delayed Hydro Scenario – System Costs
Investment Salvage Operating ENS Total Cumulative
2015 0 0 637146 0 637,146 637,146 0
2016 0 0 737474 0 737,474 1,374,620 0
2017 0 0 922922 0 922,922 2,297,542 0
2018 0 0 1011416 0 1,011,416 3,308,958 0
2019 0 0 1098456 0 1,098,456 4,407,414 0
2020 76225 19645 1239794 2 1,296,376 5,703,790 0.001
2021 162899 32280 1316929 16 1,447,564 7,151,354 0.006
2022 1616802 374549 1121797 766 2,364,816 9,516,170 0.131
2023 711804 184773 1027541 385 1,554,957 11,071,127 0.071
2024 258629 76045 1051579 473 1,234,636 12,305,763 0.092
2025 119735 39660 996390 421 1,076,886 13,382,649 0.083
2026 653268 291286 872686 180 1,234,848 14,617,497 0.033
2027 883820 409512 828716 292 1,303,316 15,920,813 0.051
2028 1207536 628836 725258 188 1,304,146 17,224,959 0.034
2029 1230201 688990 655945 554 1,197,710 18,422,669 0.092
2030 1216445 746246 605469 304 1,075,972 19,498,641 0.056
2031 980529 656912 527011 148 850,776 20,349,417 0.032
2032 195806 131644 557068 631 621,861 20,971,278 0.118
2033 343075 263966 561833 1426 642,368 21,613,646 0.248
2034 374030 311264 576362 1418 640,546 22,254,192 0.251
2035 396539 361759 587944 1517 624,241 22,878,433 0.271
Present Worth Cost of 2015 ( K$ )LOLPYear
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
COMPARING ALTERNATIVE SCENARIOS
124. This study offers a structured approach for comparing scenarios based on their relative
success in achieving national goals for a sustainable, reliable, and competitive electricity supply.
125. The first step in the approach, is to specify key performance indicators as quantifiable
measures used to evaluate success in meeting performance goals. For example, we could:
a) evaluate sustainability of an expansion scenario in terms of air pollutant emissions over
the study period and amount of renewable energy in the national capacity mix in 2035;
b) evaluate reliability in terms of system LOLP and security of energy supply in 2030; and
c) evaluate competitiveness of an expansion strategy in terms of total discounted system
cost over the study period, and the associated foreign fuel bill.
126. WASP model results for each evaluated scenario are listed in Table 33.
Table 33: Comparison of Scenarios Analyzed in Updated NPEP
127. Linking the comparison of alternative expansion scenarios to key performance indicators
highlights the costs and benefits of each option and provides useful information for decision making
on power system expansion.
128. The following section highlights issues identified through application of the GTMax model to
identify the optimal dispatching of hydro power cascades, scheduling of thermal power generation,
and economic trade of energy with neighboring power systems.
VI. SENSITIVITY ANALYSIS
EFFECTS OF NATURAL GAS PRICE ON LEAST COST PLAN
129. Sensitivity analysis was performed to evaluate the robustness of the identified least cost
power expansion plan and assess the impact on the plan of changes in a number of key factors,
including natural gas price and environmental considerations expressed in the form of a social cost of
carbon.
130. To analyze the effects of natural gas price on the least cost plan, the WASP model was applied
to re-optimize the expansion plan with a range of gas prices. Results show that a 20% reduction in the
GOALS
Reliability
Sustainable
M tonnes
Key Performance
IndicatorsUnits Least Cost No Coal
% 12% 21%
127
Total cost
Foreign fuel bill
147
Renewables in 2035 18%
8.5
20.85
hour / year 7.5 8.5Average LOLP
224
11%
8.7
No Import Delayed Hydro
185
20.25
17.5
CO2 Emissions
Best 2nd best 2nd worst Worst
14.9 21.9
22.88
Competitive
billion $ 20.19
billion $ 16.1
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
assumed price of natural gas does not alter the least cost expansion plan. However, a reduction of
35% has a large impact with no new coal-fired power plants entering the system.
131. Sensitivity analysis results also indicate that an increased gas price of 15% results in a larger
amount and earlier entry of new coal in the least cost plan.
EFFECTS OF ENVIRONMENTAL CONSIDERATIONS
132. While this study focused on development of an economically optimal generation expansion
plan that satisfies specified reliability constraints, it is important to value both environmental
protection and economic considerations in the development of an optimum solution.
133. One method of assigning a value to environmental protection, is through use of a carbon
pricing mechanism. WBG’s Carbon Pricing Watch 2015 brief notes the following recent carbon pricing
developments: Beijing and Kazakhstan use a fee of 8 US$/tCO2, Korea 9 US$/tCO2, and France
15 US$/tCO2. Also notable is that, in Canada, the federal government has set a national "floor price"
on carbon that all provinces must levy on emissions -- starting at a minimum of $10 per tonne of
carbon dioxide emissions in 2018, and rising by $10 each year to $50 a tonne by 2022.
134. In this study, model runs were executed for CO2 taxes of $15 and $25 per tonne. At $15 per
tonne, the amount of solar increases and entry of coal is delayed. However, the resulting least cost
plan has the same amount of coal installed in 2035. At $25 per tonne, there is a large increase in solar
power and 1000 MW decrease in the amount of coal − resulting in coal representing 5.9% of total
installed capacity in 2035.
VII. INTEGRATED ANALYSIS OF GENERATION AND
TRANSMISSION
GTMAX INPUT DATA AND MODELING ASSUMPTIONS
135. Complimenting the WASP IV analysis, this GTMax analysis has been performed as a
comprehensive simulation of the Myanmar power system operation, which considers full hourly
chronological representation of generation and load characteristics by region as well as network
constraints between regions throughout the country and the time, amount, location and cost of
opportunities to import electricity from neighboring countries.
136. Detailed GTMax simulations have been conducted for two target years:
2017 – to simulate the current system operation considering short term planning needs
2025 – to simulate the system operation under the projected WASP least cost development
plan, and a forecast of its long-term effects.
137. Input data for creating the GTMax models consists of data collected from the Myanmar power
system planning team as well as outputs from WASP simulations that provided optimal development
plans including the commissioning and decommissioning of facilities.
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
138. In addition, a special focus of the analysis has been placed on evaluating the effects of several
existing power purchase agreements (PPAs) on the dispatch of the generation fleet. The policy of
contracting guaranteed PPAs with power producers can contribute to sustainable generation
development and security of supply. However, if the take or pay provisions of the PPAs is
overestimated and/or overpriced, distortions and suboptimal dispatch of the generation fleet can
result. Suboptimal generation fleet dispatch can lead to hydro spillage as well as an increased use of
the more expensive thermal units. Therefore, for 2017, two sets of simulations are performed: the
first with power plants operation modeled accordingly to the PPAs (where signed PPAs exist), and the
second where the generation fleet dispatch is optimized using techno-economic constraints.
MODEL TOPOLOGY AND NETWORK CONSTRAINTS
139. The following approach is utilized for GTMax model topology and network constraints
definitions:
a) Information on power plant location and demand for each region is used to create a zonal
market model for Myanmar with the power system divided into subzones. Subzones
mainly correspond to the state: Ayeyarwady, Bago (West, East), Chin, Kachin (Chipwinge),
Kachin (Dapein), Kayar, Kayin, Magway, Mandalay, Mon, Rakhine, Sagaing, Shan (North,
South), Shan (East), Tanintharyi and Yangon.
b) The network constraints are specified as NTC (Net Transfer Capacity) values that represent
network restrictions on electricity trade necessary to insure power flows and system
operation within security limits. NTC values have been defined in collaboration with the
MOGE expert team for both 2017 and 2025 and are presented in Figure 17 and Figure 18.
Improvements in NTC values from 2017 to 2025 are shown in RED on Figure 18.
c) Connections with neighboring countries are modeled according to outputs of the WASP
analysis:
• China: 400MW connection available from 2018 with the import price of 60$/MWh
• China/HPPs: full production from Shweli 1 (2019) and Dapein 1 (2022) to Myanmar
with the price of 60$/MWh
• Lao PDR: 100MW connection available from 2020 with the import price of 60$/MWh
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
Figure 17: Zonal model of Myanmar – 2017
Figure 18: Zonal model of Myanmar - 2025
43
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ADB Myanmar
Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
A. GTMAX SIMULATION RESULTS AND ANALYSIS
2017 BASE CASE SCENARIO
Table 34: 2017 Base Case – Monthly Generation
140. In 2017, Myanmar is characterized with a balanced hydro–thermal mix, with 10TWh or 51.4%
of energy produced by thermal power plants, and 9.5TWh or 48.6% of energy produced by hydro
power plants. The highest utilization of the thermal generation fleet is observed from April until June
when thermal generation participation is a 60%
share of total production. The observed level of
thermal dispatch is driven by both a high level of
consumption as well as “must run” obligations of
some of the thermal power plants (defined by
PPAs). Due to this seasonal “must run obligation”,
notable hydro spillages are observed in April and
June, mainly during peak night hours characterized
by low demand.
141. Occurrence of energy not supplied by the
system is identified in part of Kachin, which is in
island operation (Dapein), and in Kayin region
where the network capacities are not sufficient to
supply load peaks higher than 51MW (defined NTC
between Mon and Kayar).
As illustrated in Figure 19, central regions (Shan,
Mandalay, Kayar), with dominant hydro production
have the highest annual surplus of energy that will
be supplied to the south where the largest demand
centers exist.
Analyzing the situations in the south of Myanmar
where there is a lack of hydro potential, all of the
energy in Yangon and Mon will be produced from
thermal power plants. As the largest demand
center, Yangon will face constant energy import
needs and will rely on energy produced in the
central and northeast part of the country. Figure 19: 2017 Base Case – Regional Energy Balances
44
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
142. Analyzing power plant operations in 2017 (Figure 20), the highest production is observed from
Yeywa and Shweli hydro power plants, followed by gas-fired Myanmar lighting and two lease barges
(operating from April 2017). The level of lease barge operation is mainly driven by their power
purchase agreements. Myanmar lighting also operates under a PPA but represents one of the most
efficient power plants in the domestic thermal fleet and therefore is favorable for dispatch.
143. The annual capacity factor of the thermal generation fleet is 42%, which is a result of excess
thermal capacities. Therefore, older and less efficient gas-fired thermal units are not operating for
most of the time.
Figure 20: 2017 Base Case – Top 20 Power Plants by Annual Generation
144. Observed annual variable production cost for 2017 is 54.4 $/MWh (Table 35). The highest
costs, above 60$/MWh, are observed in the period from April until June and are due to the high level
of thermal dispatch. Costs below 50 $/MWh are observed in months when hydro production is higher
compared to thermal production. In October, when the lowest average costs are observed, only two
hydro power plants, Yeywa and Shweli 1, supply 25% of domestic energy demand.
Table 35: 2017 Base Case – Annual Average Production Cost
145. Regarding energy exchanges (Figure 21), the following patterns in 2017 are presented:
• Major energy exchange corridors are from the east (Shan, Kayar) and central (Mandalay)
towards the south (Yangon).
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
• About 50% of energy needed to supply the load of Yangon is transiting through Mandalay
– Bago and Kayar – Bago connections.
• Although the highest level of regional exchange is observed in the central part of the
country, the defined level of NTCs are sufficient to support these exchanges, i.e. there is
no network congestion.
• However, occurrence of network congestion is reported in the south, which indicates the
necessity for better connections between the Mon and Kayin, as well as Mon and
Tanintharyi regions.
Figure 21: 2017 Base Case – Annual Energy Exchanges
2017 SENSITIVITY ANALYSIS ON IMPACT OF PPAS
146. As a sensitivity analysis to the base case scenario, an alternative scenario has been created
in order to measure the effects of PPAs on generation dispatch. In this alternative scenario, power
plant dispatch is not constrained to the “must run” PPA obligation, i.e. optimization is performed on
pure market principles under techno-economic constraints of the system. Table 36 shows the
monthly generation mix for this analysis.
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
Table 36: 2017 Sensitivity Analysis – Monthly Generation Mix
147. The main findings of this sensitivity analysis are:
• On an annual basis, avoided hydro spillage amounts to 116GWh.
• A decreased level of hydro spillage creates increased hydro production from April until
August, mainly in Shan region (110GWh).
• A change in thermal generation (Figure 22) is influenced by both an increase of hydro
generation and a slightly different dispatch pattern without PPA obligations. Overall
thermal generation decreases by the amount of avoided spillages (i.e. increased hydro
generation).
• Lower utilization of two leased barges and Myin Gyan (Aggreko) is observed resulting in
an 813GWh generation decrease.
• However, higher utilization of 14 TPPs is observed, most notably Myanmar Lighting. The
overall generation increase of these TPPs is 697GWh.
• No significant changes in regional exchange patterns are observed, with the energy
corridor from the east and central of the country to the south remaining dominant
• As a consequence of the impacts mentioned above (avoided spillages and moderate
change in thermal dispatch), overall annual system costs decrease by 3% or 29 million of
USD.
Figure 22: Comparison of 2017 Base Case vs PPA Sensitivity Analysis
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
148. This sensitivity analysis for 2017 indicates that a lower level of contracted PPAs (50 to 100
MW), especially from April to August, could create an opportunity for more efficient dispatch and
produce cost savings up to 29 million USD. Figure 23 illustrates monthly GWh of generation for each
producer that operates under a PPA for the base case (Blue) and for the sensitivity case (Red).
Figure 23: 2017 Base Case vs PPA Sensitivity Analysis – Monthly Generation for Thermal Plants with PPAs
48
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
2025 BASE CASE SCENARIO
Table 37: 2025 Base Case – Monthly Generation
149. In 2025, Myanmar is characterized by predominantly hydro generation with 28.1TWh (65% of
energy) produced by hydro power plants, 14.3TWh (33% of energy) by thermal power plants, and
1.1TWh (2% of energy) produced by solar and wind power plants (Table 37 and Figure 23). Compared
to 2017, the decrease of thermal generation is primarily driven by the commission of new hydro
facilities as well as a lower percentage of PPAs.
150. What should be noted in this 2025 case is
a significant amount of hydro spillage (6.9 TWh
annually) throughout the year with the highest
values recorded from July until November. This
result is mainly due to the large number of hydro
facilities and comparatively low transmission
capacities between the Shan region, where 39% of
total hydro generation is located, and neighboring
regions, especially those in the south.
151. The highest utilization of the thermal
generation fleet is observed from April until June
when thermal generation participates with an
average of 47% of total production. The level of
thermal dispatch is driven by a high level of
consumption, “must run” obligations of some of
the thermal power plants defined by PPAs
(although 20% lower than in 2017) and a high level
of spillage in Shan and Kayar which result in a
commitment of thermal units in other regions.
152. With the increase of transmission
capacities between Mon and Kayin as well as a
connection from Dapein (which was previously in
island operation) to the rest of Kachin region, the
occurrence of energy not served in 2025 remains
but is much lower (2.3 GWh). Energy not served
appears in Yangon, but only in May when demand
is high, borders with hydro exporting regions are Figure 24: 2025 Base Case – Regional Energy Balances
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congested and there is not enough local generation to supply demand.
153. New HPP Belin, the only hydro power plant located in Mon region, will place it among the
regions with the highest annual surplus alongside to Shan, Kayar and Kachin, with dominant hydro
production (Figure 25). This energy will be supplied to the south, where the largest demand center is
located.
154. Analyzing power plants operations (Figure 26), the highest production is from Middle Yeywa,
Shweli 1, Yeywa and Manipura hydro power plants, followed by gas-fired GTCC commissioned in 2022
and Myanmar lighting. Myanmar lighting, although operating under a PPA, represents one of the most
efficient power plants in the domestic thermal fleet and therefore it is favorable for dispatch.
Figure 25: 2025 Base Case – Top 20 Power Plants by Annual Generation
155. What should be highlighted here is the fact that these hydro power plants have the potential
to produce an additional 6.9 TWh of energy, which is, in this 2025 base case, represented as water
spillage due to insufficient transmission capacities, both internal and cross-border. The identification
of transmission bottle necks is shown in Figure 27.
156. Annual capacity factor of the thermal generation fleet is at a level of 59%. This is higher than
in 2017 due to water spillage and commitment of new more efficient thermal power plants. Therefore,
older and less efficient gas-fired thermal units will not operate most of the time.
157. Considering the higher hydro and renewable generation, the observed annual variable
production cost for 2025 is 42.1 $/MWh (Table 38), which is lower by 12.3$/MWh compared to 2017.
The highest costs above 50$/MWh are recorded in the period from April until June due to a higher
level of thermal dispatch. However, the costs around 30 $/MWh are observed in months with the
highest hydro production (August-October).
Table 38: 2025 Base Case – Annual Average Production Cost
50
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ADB Myanmar
Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
158. In terms of energy exchanges (Figure 27), the following patterns in 2025 are emphasized:
• Major energy exchange corridors are from the east (Shan, Kayar) and central (Mandalay)
towards the south (Yangon).
• More than 67% of energy needed for supplying the load in Yangon is transiting through
Mandalay – Bago and Kayar with the highest transits through Bago (14.2 TWh) and
Mandalay (10.8 TWh).
• The defined levels of NTCs are not sufficient to support the exchanges going from east
and central Myanmar, where the large amount of hydro energy is being generated, to the
south of Myanmar. Congestion appears on the borders between Shan and Mandalay
region, Kayar and Mandalay, as well as Kayar and Bago 100% of the time and produces
high levels of water spillages in Shan (88%) and Kayar region (12%).
• The occurrence of water spillage in Shan and Kayar results in a higher need for
commitment of thermal units in other regions. Therefore, with a slightly higher level of
thermal capacities, thermal generation is expected to increase by 4.3 TWh, compared to
2017. This is a strong indicator of the necessity for better connectivity between
northeastern and the southern regions of Myanmar by 2025.
Figure 26: 2025 Base Case – Annual Energy Exchanges
51
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ADB Myanmar
Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
2025 SENSITIVITY ANALYSIS ON IMPACT OF FURTHER GRID REINFORCEMENTS
159. Since significant internal network bottlenecks are identified within the base case scenario for
2025, a sensitivity analysis has been performed to assess the benefits of further grid reinforcements
between the hydro dominant east regions (Shan, Kayar) towards the central regions (Mandalay, Bago).
The following assumptions are used in this alternative scenario:
• Additional 1000MW of NTC is added on the Shan – Mandalay – Bago corridor.
• Additional 1000MW of NTC is added on the Shan – Kayar – Bago corridor.
Table 39: 2025 Sensitivity Analysis – Monthly Generation
160. Comparing this alternative case with the 2025 base case scenario, the main findings of the
sensitivity analysis are:
• On an annual basis, hydro spillage decreases by 5.9TWh or 87% of the value observed in
the base case (Figure 28).
• Market congestion between the east (Shan, Kayar) and central regions (Mandalay, Bago)
is fully relieved in the alternative case, which enables evacuation of hydro energy and
utilization of import opportunities with China and Lao PDR (Figure 29).
• However, significant hydro spillage in this alternative case remains during January, and
from July until October. This spillage is not caused by internal network limitations, but
rather the level of contracted PPA capacities as well as the size of the hydro fleet
commissioned in the period from 2017 until 2025.
• Annual import from China is at the annual level of 1558GWh in this alternative scenario,
with an equivalent capacity factor of 45%.
• Annual import from Lao PDR is at the annual level of 373GWh in this alternative scenario,
with an equivalent capacity factor of 43%.
• Considering the increase of imports from China and Lao PDR and the efficient use of hydro
generation in this alternative scenario, thermal generation significantly decreases to the
level of 6.5TWh or a 16% share of total generation
• There is no unserved energy in May in Yangon area in the alternative case. In addition,
Yangon area increased its import by 30% in this alternative case.
• More than 90% of energy produced in the Shan and Kayar region is exported.
• Mandalay-Shan-Bago-Yangon represents the network corridor that transports more than
10TWh of energy annually.
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
161. By enabling a decrease in hydro spillage, a higher level of import and a moderate change in
thermal dispatch, network reinforcements analyzed in alternative scenario enable an overall annual
system cost decrease of 858.5 million of USD or 47% of costs observed in base case (Figure 28). This is
clear signal that large scale hydro expansion in the east of the county must be followed by transmission
network expansion that would facilitate evacuation of energy towards the largest demand centers.
Figure 27: 2025 Base Case vs Sensitivity Analysis
Figure 28: 2025 Sensitivity Analysis– Annual Energy Exchanges
53
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ADB Myanmar
Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
VIII. OBSERVATIONS AND RECOMMENDATIONS
162. As mentioned in the introduction to this report, the Consultant collaborated with local
planners at the M0EE to apply the WASP-IV and GTMax models in an integrated manner to evaluate
least cost generation options and opportunities for power exchange with neighbouring systems, and
and utilize model results to prepare an updated NPEP for the country.
163. It is important to keep in mind that the role of the energy planner is not to develop “the plan”
to be implemented. Rather, energy planning involves analysis of the energy system with the intent of
providing decision makers information that will enable them to make informed judgments on
strategies needed to meet current and future energy objectives.
164. The WASP model analysis provides useful information for decision making on generation
system expansion in Myanmar, including but not limited to the following observations:
a) The least cost long-term generation
expansion plan designed to meet
national energy demand through
2035 (Figure 30) shows hydropower
and gas-fired generation continuing
to play a dominant role in meeting
electrical needs of the country
through 2030 and imported electricity
being competitive at a purchase
price of $60 per MWh.
b) With the assumed diminishing capital cost of solar power, 2400 MW of new solar is added
from 2028 through 2035.
c) After commissioning of the last candidate hydropower plant in 2028, coal enters the
system as the least cost option for
base-load generation.
d) In the No Coal Scenario, coal is
replaced by gas and solar. As
compared with the Least Cost
Scenario, the No Coal Scenario has a
$66 million (0.3%) increase in total
system cost, produces a 14% reduction
in CO2 emissions, and increases the
renewable energy share in the 2035
capacity mix to 21%.
e) The No Imports Scenario, results in increased and earlier commissioning of new coal-fired
units (Figure 32). As compared with the Least Cost Scenario, the No Imports Scenario has
Figure 29: Least Cost Scenario – Generation Expansion
Figure 30: No Coal Scenario – Generation Expansion
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ADB Myanmar
Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
a $660 million (3.3%) increase in
total system cost, produces a 26%
increase in CO2 emissions and
increases the renewable energy
share in 2035 capacity mix to 18%.
f) The Delayed Hydro Scenario has a
substantial impact on system costs
and environmental emissions.
While this scenario has nearly an
identical capacity mix as the
Least Cost Scenario in 2035, the
commissioning schedule for new
gas, coal and solar power plants is
accelerated (Figure 33). Most
notably, 1000 MW of coal-fired
power is scheduled to be
commissioned in 2022 and another
500 MW in 2023.
g) As compared with the Least Cost
Scenario, the Delayed Hydro
Scenario has a $2.69 billion (13.3%)
increase in total system cost and 53% increase in CO2 emissions.
h) Natural gas demand for electricity generation is not expected to exceed the computed
limit of domestic gas supply allocation for the power sector (Table 5) until 2034 in the
Least Cost Scenario and 2032 in the No Coal Scenario.
i) Sensitivity analysis related to natural gas price assumptions identified that a 20%
reduction in price has little effect on the least cost expansion strategy, but a 15% increase
in price results in a larger amount and earlier entry of new coal in the least cost plan.
j) This study investigated the viability of large-scale renewable energy projects by evaluating
wind and solar energy candidate projects in the context of the least cost generation
expansion plan and identifies substantial potential for solar PV. Contributing factors
include: (i) declining price of PV, (ii) renewable potential for solar being high in locations
close to the grid and major load centres, (iii) Myanmar’s largely hydro based system with
significant spinning reserve capability, and (iv) the strong seasonal variations of solar and
hydro energy potential in the country complement each other over the year.
k) While this study focuses on development of an economically optimal (“least cost”)
generation expansion plan that satisfies specified constraints on system reliability, it is
important to value both environmental protection and economic considerations in
development of an optimum strategy for the country. One method of assigning a value
to environmental protection is through use of a carbon pricing mechanism. Results of
sensitivity analyses point to a price of 15 US$/tCO2 producing an effect of increased solar
Figure 31: No Imports Scenario – Generation Expansion
Figure 32 Delayed Hydro Scenario – Generation Expansion
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ADB Myanmar
Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
and delayed entry of coal. At 25 US$/tCO2, there is a large increase in solar power and
1000 MW decrease in the amount of new coal − resulting in coal representing 5.9% of
total installed capacity in 2035.
165. Main finding of the performed GTMax analysis, as a support to the WASP analysis for
developing a national power expansion plan for Myanmar include:
a) In the 2017 base case, balanced generation mix of almost equal hydro-thermal production
is observed in Myanmar. Thermal power plants dispatch is significantly influenced by the
obligations of existing power purchase agreements.
b) Sensitivity analysis for 2017 indicates that PPA agreements cause annual spillage of
116GWh in periods from April until August. A somewhat lower level of PPA contracted
volumes (50-100 MW) could provide benefits and annual savings in terms of system
operating costs of up to 29 million of USD under defined hydrological conditions.
Therefore, all potential future PPAs should be carefully assessed, both in terms of security
of supply (i.e. usage of PPAs as incentive for generation investment), as well as the impact
on the system operation and production costs.
c) In 2017, important energy transfer corridors have been identified from hydro dominant
areas in the east of the country towards the largest demand center in the south, but there
are no congestions in the central part of the country.
d) In the 2025 base case that includes the commissioning of numerous HPPs, the share of
hydro production to total production increases in comparison with 2017. A significant
level of hydro spillage has been identified (estimated at 6.8 TWh), mainly influenced by
insufficient transmission connections with the hydro dominant regions (Shan, Kayar)
e) Due to the observed network bottlenecks between Shan and Kayar towards Bago and
Mandalay, the opportunity for energy import from China and Lao PDR is not feasible in
the 2025 base case.
f) The sensitivity analysis for 2025 further indicates that strengthening of the grid corridor
of Shan-Mandalay-Bago and Shan-Kayar-Bago by 1000MW each will facilitate the
evacuation of hydro energy from Shan and Kayar, as well as provide significant import
opportunities from China and Lao PDR. This produces an 87% decrease in spilled energy
and a considerable decrease in system operation costs.
g) The sensitivity analysis for 2025 also demonstrates that, even without internal network
congestion, a certain volume of hydro spillage cannot be avoided. The remaining spillage
(approximately 1 TWh) can be further decreased by lowering the volume of contracted
PPAs and/or through a less ambitious hydro development plan.
h) Finally, when the projected generation expansion plan is fully implemented and the
transmission system upgrades are completed by 2025, an additional option for Myanmar
is to export energy to surrounding countries when it is economical to do so.
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Updated WASP-IV Case (TA No. 8356-MYA) Draft Report 14-JAN-2017
166. As planning is a process, the power system expansion plan should be revised annually by the
national power system planning team at MOEE according to updated information and assumptions
related to energy demand, fuel prices and availability, government policies, etc. Suggested priority
issues warranting further consideration in the next update of the national power expansion plan,
include:
a) Hydropower Development: Due to limited availability of information on candidate hydro
plants, the EMP expansion planning study used an average cost of new hydro developed
by Newjec and applied aggregated characteristics of existing HPPs to develop initial
estimates of seasonal operations for new hydro candidates. The ADB consultants agree
with earlier comments by the WBG, that “a proper hydropower development study is
needed to … optimize hydropower development.” We note that a Norwegian effort
intends to upgrade MOEEs hydro data base and recommend that this effort also be
deployed to capture information tailored to represent hydro capital cost and operational
data for the WASP database. In parallel with the data collection effort, we recommend
that MOEE consider complementing the current WASP-based planning with use of
additional models that are able to capture the stochastic representation of hydropower
that is lacking in WASP. For example, WASP is regularly run together with the VALORAGUA
model (and others) for systems with a substantial amount of hydro.
b) Power System Analysis for Renewable Energy Integration in Myanmar: Increased levels of
renewable energy (RE) integration can have short and long-term effects on the power
system. The short-term effects are caused by balancing the system at the operational time
scale (seconds to hours). The long-term effects are related to the contribution RE power
can make to the adequacy of the system and its capability to meet peak load situations
with high reliability. The impacts on the system are also both local and system-wide.
Locally, RE power plants, just like any other power station, interact with the grid voltage
making it necessary to consider issues related to steady state voltage deviations, power
quality and voltage control at or near RE sites. System-wide, RE can provide voltage and
active power control and can reduce transmission and distribution losses when applied as
embedded (distributed) generation. Major issues of RE integration to be analyzed and
addressed, include: (i) new approaches in operation of the power system; (ii) connection
requirements for RE plants to maintain a stable and reliable supply; (iii) extension and
modification of the grid infrastructure; and (iv) influence of RE on system adequacy and
security of supply. Therefore, taking into account both existing and possible future power
system conditions in Myanmar as well as planned development of RE, an RE Integration
Analysis should be performed that will include tasks to: (a) Confirm secure power system
operation is not jeopardized by planned RE capacity; and (b) Identify needed
reinforcements of the national grid in order to enable secure operation of the planned RE
capacity.
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APPENDIX B
DRAFT OP ED ON ADB CAPACITY BUILDING SUPPORT
1/4
Strengthening institutional capacity for charting a sustainable energy future in
Myanmar
In September 2015, world leaders adopted the 2030 Agenda for Sustainable
Development with the goal to end poverty, protect the planet, and ensure prosperity
for all. Access to affordable, reliable and sustainable energy is essential to
achieving this goal. Energy powers the production of goods and services. It is vital
to advancements in health, education, clean water supply, economic development
and climate change mitigation.
As only thirty-five percent of households in Myanmar have access to electricity, the
rest of the population must depend on traditional methods to light homes and cook
meals. Among other means to achieve its Millennium Development Goals (MDGs)
of poverty alleviation, the Government of Myanmar aspires to increase the
electrification rate to 45% by 2020, 60% by 2025, and 80% by 2030.
With increasing electrification and industrialization in the country, electricity demand
is projected to grow at the average annual rates of 12.35% through 2020 and 9.54%
over the following 10 years. Such growth in electricity demand requires detailed
planning and analyses of alternative development paths to enable informed
judgments on optimal strategies for charting a sustainable energy future.
Energy challenges and opportunities in Myanmar
While pursuing access to sustainable energy for all, Myanmar is confronted with
several challenges. Most notably are the current limitation in gas supply for
electricity generation, large seasonal variation in water flows required to operate
existing hydropower plants, and huge financial requirements in energy
infrastructure; as well as the need for tariff reform; legal and regulatory framework
improvement related to renewable energy development, environmental and social
safeguards and mechanisms for enforcement, enhanced policies for energy
efficiency and conservation, institutional capacity building and human resource
development.
National energy decision makers must also take into account opportunities arising
from rapidly changing parameters in the energy sector, including: the steady decline
in price of oil and natural gas, falling cost of solar power, potential for near-term
power purchase and long term energy exchange with neighboring countries,
innovative energy storage and grid modernization technology that are quickly
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becoming marketable, and new sources of support for addressing climate change.
In 2015, Myanmar communicated the country’s “intended nationally determined
contribution” (INDC) to the United Nations Framework Convention for Climate
Change (UNFCCC). Later that year, 195 countries approved the Paris Agreement
providing a comprehensive framework which will guide international efforts to limit
GHG emissions and meet the associated challenges posed by climate change. With
the Paris Agreement entering into force on 4 November 2016, all countries have the
legally binding obligation to make “nationally determined contributions” (NDCs) for
reducing GHG emissions, to pursue domestic measures aimed at achieving them,
and to report regularly on progress made in implementing and achieving their
NDCs. This creates a continued necessity for comprehensive energy systems
analysis as a prerequisite to evaluating a portfolio of technologies and measures for
climate mitigation and informing policy makers on the costs and benefits of a range
of potential pledges under the Paris Agreement. This deeper understanding of
mitigation options could also help in seeking support from others to assist Myanmar
in meeting its ambition for reducing GHG emissions.
Importance of National Power System Planning
To effectively and efficiently address the evolving energy challenges in Myanmar
and maximize the value of opportunities, a coordinated national planning process is
needed which leverages the insights of local professionals, with a clear
understanding of policy directions, access to reliable information, and use of
defendable quantitative methods for evaluating alternative development paths.
Results from this structured approach to national power system planning would
support informed judgments on optimal strategies to meet current and future energy
objectives.
As a key development partner in the energy sector, the Asian Development Bank
(ADB) responded to a request from the Government of the Republic of the Union of
Myanmar for assistance to strengthen its capacity to prepare sector policies and
strategies. Specifically, ADB assisted the Myanmar Ministry of Electricity and
Energy (MOEE) to build local energy planning capability by transferring energy
planning tools, information and know-how for continued use by the government.
Myanmar’s wealth of natural resources
Myanmar possesses abundant energy development potential, particularly from
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hydropower, natural gas, and renewable energy. An ADB study estimates
renewable energy potential of 365 terawatt-hour per year (TWh/year) from wind and
52,000 TWh/year from solar. Hydropower potential is estimated to be more than
100,000 megawatts (MW) of installed capacity. The country currently has 2,760 MW
of operating hydropower and 2,800 MW of committed additions in various stages of
development.
Myanmar is actively engaged in designing and implementing the required policies,
governance, financial and programming instruments to protect the environment and
conserve energy resources in the process of sustainable energy development. The
Government, for example, has made environment one of the seven strategic pillars
of its National Comprehensive Development Plan (2011-30); it has promulgated the
Environmental Conservation Law (2012); and, as noted in its INDC, is resolute in
mainstreaming environment into the national policy and development agenda.
Analyzing scenarios for future development of the Myanmar power system
After transferring planning tools and know-how on their use to local energy planning
professionals, ADB provided consulting support to a team of energy planning
professionals within MOEE in the application of these tools to analyze a number of
scenarios for future development of the Myanmar power system. Three of the
analyzed scenarios are described below.
A “Least Cost” scenario evaluated all power
system expansion candidates (i.e.,
hydropower, fossil-fired, and renewable
energy) in the identification of a least cost
generation expansion plan designed to meet
national energy demand through 2035. As
illustrated in the figure of Least Cost scenario
results, hydropower and gas-fired generation
continuing to play a dominant role in meeting electrical needs of the country. With
the assumed diminishing capital cost of solar power, 2,400 MW of new solar is
projected to be added to the system. Imported electricity is also shown to be
competitive depending on the established cost of energy and interconnection
requirements. In the final years of the study, when the identified list of economic
hydropower candidates is exhausted, coal is shown to be an economic option for
base-load generation.
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A “No Coal” scenario uses the same
assumptions as the Least Cost scenario, but
does not consider new coal-fired power
plants as a candidate for system expansion.
Compared with the Least Cost scenario, the
No Coal scenario results in a 0.3% increase
in total system cost, a 14% reduction in CO2
emissions, and increases the renewable
energy share in the 2035 capacity mix to 21%.
A “Delayed Hydro” scenario use the same
assumptions as the Least Cost scenario,
except that the commissioning date for new
hydropower plants are delayed by three
years. As compared with the Least Cost
scenario, the Delayed Hydro scenario results
in a US$ 2.69 billion increase in total system
cost, earlier entry of new coal-fired power plants, and a 53% increase in CO2
emissions.
Conclusions
Planning is a regular and recurrent exercise. It sheds light on economic, reliability
and sustainability aspects of possible future pathways to support decision making
on the optimal strategy for the country.
The scenarios analyzed by the MOEE energy planning team provide improved
understanding of issues and challenges facing the Myanmar power sector. These
insights are anticipated to help the Government in charting an economically optimal
and sustainable development path, which ultimately contributes to improved quality
of life for the people of Myanmar.
APPENDIX B - 4
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