technical assistance consultant’s report€¦ · for the oil & gas planning department of the...

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

Report for ADB Page | 3 Issue Number 1 Contract No. 128594-S52841 Date 06/16/2017

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.


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.


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.


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


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


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


Figure 8 2025 Base Case – Annual Energy Exchanges

Figure 9 2025 Sensitivity Analysis– Annual Energy Exchanges


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


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


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

This page intentionally left blank.

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.

i

APPENDIX A - 3

ADB Myanmar


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

ii

APPENDIX A - 4

ADB Myanmar


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

iii

APPENDIX A - 5

ADB Myanmar


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.

4

APPENDIX A - 6

ADB Myanmar


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.

5

APPENDIX A - 7

ADB Myanmar


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.

6

APPENDIX A - 8

ADB Myanmar


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.

7

APPENDIX A - 9

ADB Myanmar


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


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

http://www.adica.com/generation-investment-study.html

http://www.adb.org/projects/documents/central-asia-regional-economic-cooperation-power-sector-regional-master-plan-tacr

http://www.adb.org/projects/documents/central-asia-regional-economic-cooperation-power-sector-regional-master-plan-tacr

http://www.adica.com/us-utility.html

http://nebula.wsimg.com/33bc46dd9f17d3c4aefd333040f9f318?AccessKeyId=1A0D9A575B761BCFC58F&disposition=0&alloworigin=1

http://nebula.wsimg.com/33bc46dd9f17d3c4aefd333040f9f318?AccessKeyId=1A0D9A575B761BCFC58F&disposition=0&alloworigin=1

http://www.adica.com/armenia-georgia.html

ADB Myanmar


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


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


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


Table 3: Fossil-fired power plants (existing, committed additions and new) up to 2025

13

APPENDIX A - 15

ADB Myanmar


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


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


3 Ba Luchaung


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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%


Imports 921 3%

Total 26,997


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


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





29

APPENDIX A - 31

ADB Myanmar



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


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


Coal Oil Gas


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


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


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


31

APPENDIX A - 33

ADB Myanmar


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


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


MW %

Gas 5,590 22%

Coal 3,120 12%

Hydro 12,506 48%


Imports 0 0%

Total 25,876


32

APPENDIX A - 34

ADB Myanmar


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


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






33

APPENDIX A - 35

ADB Myanmar



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


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


Coal Oil Gas


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

APPENDIX A - 36

ADB Myanmar


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


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


35

APPENDIX A - 37

ADB Myanmar


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


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


MW %

Gas 5,990 24%

Coal 2,620 11%

Hydro 12,506 50%


Imports 921 4%

Total 24,797


36

APPENDIX A - 38

ADB Myanmar


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


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



37

APPENDIX A - 39

ADB Myanmar



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


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


Coal Oil Gas


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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ADB Myanmar


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


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


39

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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

40

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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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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

42

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Figure 17: Zonal model of Myanmar – 2017

Figure 18: Zonal model of Myanmar - 2025

43

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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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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).

45

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• 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.

46

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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

47

APPENDIX A - 49

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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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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

49

APPENDIX A - 51

ADB Myanmar


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

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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

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APPENDIX A - 53

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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.

52

APPENDIX A - 54

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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

APPENDIX A - 55

ADB Myanmar


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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APPENDIX A - 56

ADB Myanmar


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

55

APPENDIX A - 57

ADB Myanmar


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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APPENDIX A - 58

ADB Myanmar


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 A - 59

APPENDIX B

DRAFT OP ED ON ADB CAPACITY BUILDING SUPPORT

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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

APPENDIX B - 1

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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

APPENDIX B - 2

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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.

APPENDIX B - 3

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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

ADICA, LLC

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