Technical Assistance Consultant’s Reportp. aues transmission grid stabilization 57 vii. altai soum (off-grid hybrid re) 61 q. background 61 r. condition of existing electricity supply

Technical Assistance Consultant’s Report

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. (For project preparatory technical assistance: All the views expressed herein may not be incorporated into the proposed project’s design.

Project Number: 50088-001 December 2018

Mongolia: Upscaling Renewable Energy Sector Project (Financed by the Strategic Climate Fund)

Prepared by

Integration Environment & Energy Gmbh, Germany in association with German ProfEC, Germany and Mon-Energy Consult LLC, Mongolia

Ulaanbaatar, Mongolia

For the Ministry of Energy

TA-9224 MON

SCALING UP RENEWABLE ENERGY (PHASE II)

Final Report

Executive Summary

Prepared for

Asian Development Bank

by

INTEGRATION environment & energy GmbH

in association with

Mon-Energy & Profec

December 2018

TA-9224 MON: Scaling Up Renewable Energy in Mongolia – Phase II Final Report

i

ABBREVIATIONS

ADB – Asian Development Bank AuES – Altai-Uliastai Energy System CES – Central Energy System CHP – Combined Heat & Power CIF – Climate Investment Fund CO2 – Carbon Dioxide EA – Executing Agency EARF – Environmental Assessment Review Framework EES – Eastern Energy System EIA – Environmental Impact assessment EIRR – Economic Internal Rate of Return ERC – Electricity Regulatory Commission FIRR – Financial Internal Rate of Return FiT – Feed-In-Tariff FSR – Feasibility Study GCF – Green Climate Fund GDP – Gross Domestic Product GSHP – Ground Source Heat Pump HOB – Heat Only Boiler IEE – Initial Environmental Examination JFJCM – Japan Fund for Joint Crediting Mechanism MDB – Multi-lateral Development Bank MoE – Ministry of Energy MoF – Ministry of Finance NPTG – National Power Transmission Grid Company NOx – Nitrogen Oxides PM – Particulate Matter SOx – Sulphur Dioxides SREP – Scaling Up Renewable Energy SOJSC – State-Owned Joint Stock Company TA – Technical Assistance TOR – Terms of Reference UB – Ulaanbaatar WACC – Weighted Average Cost of Capital WES – Western Energy System

NOTE

In this report, “$” refers to US dollars.


ii

CONTENTS

II. INTRODUCTION 4

A. SCOPE OF PROJECT 4

B. FINANCIAL / ECONOMIC SUMMARY 5

C. OUTPUT-BASED RESULTS FRAMEWORK 7

III. MACRO-ECONOMIC INDICATORS 8

A. HOUSEHOLDS & ELECTRICITY CONSUMPTION 8

B. REGIONAL GROSS DOMESTIC PRODUCT (GDP) 9

C. ELECTRICITY STATISTICS 10

IV. ELECTRICITY DEMAND FORECASTS WES & AUES 12

D. WES DEMAND FORECAST 12

E. AuES DEMAND FORECAST 17

F. DEMAND PROFILE FOR WES & AuES 22

G. IMPACT OF STORAGE BATTERIES 22

V. ON-GRID RENEWABLE ENERGY IN WES & AUES 23

H. SOLAR PV 23

I. WIND 29

J. STORAGE BATTERIES 36

VI. ELECTRICITY SYSTEM INTEGRATION (ON-GRID) 41

K. INTRODUCTION 41

L. INTERMITTENCY 41

M. WESTERN ENERGY SYSTEM EXPANSION 44

N. WES TRANSMISSION GRID STABILIZATION 48

O. ALTAI ULIASTAI ENERGY SYSTEM EXPANSION 53

P. AuES TRANSMISSION GRID STABILIZATION 57

VII. ALTAI SOUM (OFF-GRID HYBRID RE) 61

Q. BACKGROUND 61

R. CONDITION OF EXISTING ELECTRICITY SUPPLY SYSTEM 61

D. LEVELIZED COST OF ENERGY 72

E. WIND FARM SITING 73

VIII. GROUND SOURCE HEAT PUMPS 74

S. BACKGROUND 74

T. GSHP STUDY 74

U. GSHP TECHNOLOGIES 75

V. INVESTMENT 77

IX. FINANCE / ECONOMICS 80

W. INTRODUCTION 80

X. HISTORICAL FINANCIAL PERFORMANCE AND CORPORATE PROJECTIONS FOR

IMPLEMENTING AGENCIES 80

Y. FINANCIAL MANAGEMENT ASSESSMENT AND RELATED RISKS 83

Z. FINANCIAL ANALYSIS OF THE PROJECT 83

AA. ECONOMIC ANALYSES OF THE PROJECT 86


iii

X. SOCIAL SAFEGUARDS 91

BB. POVERTY & SOCIAL ANALYSES 91

CC. LAND ACQUISITION AND RESETTLEMENT 93

XI. ENVIRONMENT 94

APPENDICES

Appendix A: Solar PV Detailed Technical Report

Appendix B: Wind Detailed Technical Report

Appendix C: Storage Batteries Detailed Technical Report

Appendix D: On-Grid System Integration

Appendix E: Altai Soum Off-Grid Hybrid RE Technical Report

Appendix F: Ground Source Heat Pumps Detailed Technical Report

Appendix G: Finance / Economics Report

Appendix H: Poverty & Social Analysis Report

Appendix I: Initial Environmental Examination (IEE) Report

Appendix J: EARF Report

Appendix K: CVRA Report


4

II. INTRODUCTION

A. SCOPE OF PROJECT

1. The Project aims to develop the renewable based distributed energy system in a remote and less developed region of western Mongolia, that is the first-of-its-kind renewable energy application in Mongolia, and entails perceived risks on the system performance, cost of energy and its impact upon end-user affordability, and grid stability.

2. The Project will help develop 41.5 MW of distributed renewable energy systems in the Western and Altai-Uliastai regions, in a time and geographic slicing manner. The project supports the development, demonstration and gradual roll out of the distributed renewable energy system in the targeted load centres in batches (the first stage 2018-2021 and the second stage 2021-2023).

3. The successful completion of the project will see the establishment of an institutional platform that encourages private sector investment in distributed renewable energy to sustainably expand clean and affordable electricity supply in remote and less developed regions of Mongolia, and to decarbonize the energy sector in the country.

• Component A. Distributed renewable energy system development. The component will be implemented with two batches to develop a total of 41.0 MW of renewable energy throughout the project implementation period from 2018 to 2023. The first batch (2018-2021) will develop a total of 25.5 MW of distributed renewable energy systems in Umnugovi, Uvs province, Altai, Govi Altai province, Uliastai, Zavkhan province, and Altai-Soum, Govi-Altai province. The second batch (2021-2023) will see construction of another 16.0 MW of distributed renewable energy capacity in Telmen, Zavkhan province, and Moron, Khovsgol province.

• Component B. Shallow ground heat pump demonstration. The component will also install around 100 kW of unit heat capacity of shallow ground heat pump in the public buildings in selected five townships of the targeted region to supply air pollutant free space heating which would cover 10,000 square meter of floor area in total. Decentralized coal based individual heating system is common in the targeted regions and is a major source of air pollution. The component demonstrates the performance of shallow-ground heat pump systems and develops experience in design, installation, operation and maintenance of a shallow ground heat pump system for future scaling-up.

4. Mongolia is currently highly debt distressed mainly due to sluggish mineral export and slowed-down economic growth and is now under the three years of the Extended Financing Facility under the International Monetary Fund. To forge ahead and scale up renewable energy in remote and less developed regions of Mongolia, while the government has now to downsize both borrowing and expenditure, means that matching grant funding is critical for the success of the programme.


5

B. FINANCIAL / ECONOMIC SUMMARY

5. The base costs of the sub-Projects are summarised in the following table:

Table II-1: Base Cost Table

Component

Sub-component MUSD

TOTAL 49.03

Component 1: Distributed Renewable Energy System

First Batch

Umnugovi 10 MW Wind 13.71 Govi Altai 10 MW PV 9.71 Uliastai 5 MW PV + Battery 7.95 Altai Soum Hybrid 0.92 Second Batch

Muren 10 MW PV 9.38 Salkhit Khutul 5 MW Wind 6.36

Component 2: Shallow Ground Heat Pump Demonstration Shallow Ground Heat Pumps 1.00

Source: ADB Consultant

6. Financial cost-benefit analysis of the sub-projects and the whole project was carried out both without grant financing and with grant financing. Summary of the project financial analysis results is given in the following tables.

Table II-2: Summary of Financial Cost-Benefit Analysis by sub-Project, no Grants

WACC (%) FIRR (%)

FNPV

(million MNT) FBCR

Umnugovi 10 MW Wind 2.91 % 9.93 % 42 490 2.13

Govi Altai 10 MW PV 2.81 % 7.67 % 18 973 1.68

Uliastai 5 MW PV + Batt 3.03 % 6.42 % 10 456 1.45

Altai Soum Hybrid (PV & Wind) 2.01 % 1.31 % -227 0.91

Muren 10 MW PV 2.01 % -3.27 % -14 401 0.46

Salkhit Khutul 6 MW Wind 2.01 % 12.15 % 28 633 2.63

SGHP 3.49 % 4.13 % 128 1.05

Whole Project 2.59 % 7.10 % 88 491 1.64


Table II-3: Summary of Financial Cost-Benefit Analysis by sub-Project, with Grants

WACC (%) FIRR (%) FNPV

(million MNT) FBCR

Umnugovi 10 MW Wind 2.91 % 20.27 % 62 666 4.56

Govi Altai 10 MW PV 2.81 % 14.25 % 30 592 2.88


Altai Soum Hybrid (PV & Wind) (*) 2.01 % 1.31 % -227 0.91

Muren 10 MW PV (*) 2.01 % -3.27 % -14 401 0.46

Salkhit Khutul 6 MW Wind (*) 2.01 % 12.15 % 28 633 2.63


6

WACC (%) FIRR (%)

FNPV

(million MNT) FBCR

SGHP 3.49 % 53.26 % 2 428 5.92

Whole Project 2.59 % 12.10 % 136 684 2.51

Source: ADB Consultant (*) No grants are assumed for these sub-projects

7. As it can be seen in the above tables, under the given assumptions, even without grants most of the sub-projects will have positive FNPV and FIRR higher than their WACC. Among the sub-components, only the Muren PV and the Altai Soum Hybrid have negative profitability. It is worth to notice that none of these two sub-projects is supposed to receive grant financing, thus they would continue to be infeasible even after the injection of the grants to the project. However, the overall profitability of the project is quite good even despite the poor performance of the Muren PV and the Altai Soum Hybrid sub-projects.

8. The following tables show results of the analysis (EIRR and ENPV) including effects of the grants.

Table II-4: Summary of Economic Cost-Benefit Analysis by sub-Project, no Grants

ECOK (%) EIRR (%)

ENPV

(million MNT) EBCR

Umnugovi 10 MW Wind 9.00 % 16.40 % 31 271 1.82

Govi Altai 10 MW PV 9.00 % 14.90 % 17 571 1.64


Altai Soum Hybrid 9.00 % 18.31 % 2 737 2.06

Muren 10 MW PV 9.00 % 15.46 % 17 008 1.70


SGHP 9.00 % 24.33 % 2 660 2.06

Whole Project 9.00 % 16.34 % 108 473 1.81


Table II-5: Summary of Economic Cost-Benefit Analysis by sub-Project, with Grants

ECOK (%) EIRR (%)

ENPV

(million MNT) EBCR

Umnugovi 10 MW Wind 9.00 % 29.34 % 50 657 3.73

Govi Altai 10 MW PV 9.00 % 23.31 % 28 806 2.79


Altai Soum Hybrid (*) 9.00 % 18.31 % 2 737 2.06

Muren 10 MW PV (*) 9.00 % 15.46 % 17 008 1.70


SGHP 9.00 % 177.37 % 4 749 12.32

Whole Project 9.00 % 23.60 % 154 644 2.77


9. As it can be seen in the above tables, under the given assumptions, even without grants all sub-projects will have positive ENPV and EIRR higher than the ECOK.


7

C. OUTPUT-BASED RESULTS FRAMEWORK

10. An output-based results frame has been prepared covering selected core indicators and other relevant indicators.

Table II-6: Output-Based Results Framework

Core indicators

Expected tonnes of carbon dioxide equivalent (t CO2 eq) to be reduced or avoided (Mitigation only)

Annual 88,016 tons (upon the project completion)

Lifetime 2,200,402 tons (for 25 years of the project life)

• Expected total number of direct

and indirect beneficiaries,

disaggregated by gender

(reduced vulnerability or

increased resilience);

• Number of beneficiaries relative

to total population,

disaggregated by gender

(adaptation only)

Total 304,000 population of which 140,000 populations are female. (upon the project completion)

Percentage (%)

46% (Total population in the project targeted regions.)

Other relevant indicators

Renewable energy Capacity (MW) Total 41.5 MW

Number of Households with access to low-emission energy Total 77,179 households



8

III. MACRO-ECONOMIC INDICATORS

A. HOUSEHOLDS & ELECTRICITY CONSUMPTION

11. The recent statistics concerning the population, household count, total electricity consumption and electricity consumption of the household sector is presented in the following table:

Table III-1: Population & Household Statistics

Population ‘000’s

Households ‘000’s

Total Electricity Consumption

mln. kWh

From which Households Consumed mln. kWh

2015 2016 2015 2016 2015 2016 2015 2016

Mongolia 2,990 3, 064 859 870 5,284 5,446 1,278 1,321

Ulaanbaatar (capital) 1,346 1,381 376 381 2,083 2,121 773 811

Aimags in the Western Region

Khuvsgul 128 129 38 38 35 35 17 17

Bayan-Ulgii 98 99 23 24 33 33 20 21

Govi-Altai 56 57 16 16 27 29 12 13

Zavkhan 70 71 21 21 21 23 12 13

Uvs 80 81 21 22 32 32 17 17

Khovd 83 85 22 22 39 38 20 20

Source: Energy Regulatory Commission (Mongolia)

12. Table III-2 shows electricity consumption metrics as specific consumption per capita, total per household and household energy per household. A wide spread can be seen in the specific consumption, ranging from 274 to 508 kWh per capita in 2016, a difference of +85%.

Table III-2: Electricity Consumption Metrics

kWh/per person

Total kWh/per household

Household kWh/per household

2015 2016 2015 2016 2015 2016

Mongolia 1,767 1,778 6,150 6,261 1,487 1,519

Ulaanbaatar (capital) 1,548 1,536 5,533 5,570 2,053 2,129

Aimags in the Western Region

Khuvsgul 272 274 923 932 451 452

Bayan-Ulgii 334 335 1,416 1,417 860 874

Govi-Altai 479 508 1,665 1,778 752 778

Zavkhan 304 326 1,023 1,096 580 598

Uvs 403 396 1,528 1,498 814 789

Khovd 468 450 1,793 1,753 933 913

Source: Energy Regulatory Commission (Mongolia)


9

B. REGIONAL GROSS DOMESTIC PRODUCT (GDP)

Figure III-3: Western Region GDP 2003 - 2015

Source: National Statistics Office of Mongolia

Figure III-4: WES GDP – Electricity Sales1 Regression 2003 - 2015

Source: TA Consultant, 1 Non-Residential Sales only, real GDP


10

Figure III-5: AuES GDP – Electricity Sales1 Regression 2003 - 2015

Source: TA Consultant, 1 Non-Residential Sales only, real GDP

C. ELECTRICITY STATISTICS

Table III-6: WES Electricity Consumption 2001 - 2008

Indicators unit 2001 2002 2003 2004 2005 2006 2007 2008

Total power purchase / Import+Hydro plant/

mln kWh 37.69 38.20 39.20 40.38 43.69 48.97 53.75 62.48

Transmission losses mln kWh 4.86 5.20 5.01 5.18 5.32 5.65 5.45 7.47

% 0.13 0.14 0.13 12.82 12.19 11.54 10.15 11.96

Distribution mln kWh 32.83 33.00 34.19 35.20 38.36 43.32 48.29 55.00

Distribution & supply losses % 40.20 40.20 40.20 40.35 42.54 40.55 36.47 35.31

Sales mln kWh 33.00 34.19 35.20 38.36 43.32 48.29 55.00 62.90

Peak demand MW 12.3 13.9 13.4 17.1 18.7 16.4 20.7 18.1 Load Factor (based on AuES) % 31 28 30 26 26 34 30 40



11

Table III-7: WES Electricity Consumption 2009 - 2016

Indicators unit 2009 2010 2011 2012 2013 2014 2015 2016


mln kWh 71.81 78.37 117.53 138.93 171.66 145.10 143.10 146.45


% 12.41 11.81 10.70 9.34 10.20 10.78 9.88 9.40



Sales mln kWh 62.90 69.12 104.95 125.96 154.15 129.46 128.96 132.69


Table III-8: AuES Electricity Consumption 2001 - 2008

Indicators unit 2001 2002 2003 2004 2005 2006 2007 2008


mln kWh 9.71 9.81 9.97 8.98 7.98 8.47 8.81 11.05


% 3.50 3.50 3.40 3.20 4.89 2.86 2.66 1.68



Sales mln kWh 43.21 47.26 42.68 43.17 25.63 10.75 14.30 10.66


Table III-9: AuES Electricity Consumption 2009 - 2016

Indicators unit 2009 2010 2011 2012 2013 2014 2015 2016


mln kWh 16.05 18.63 27.99 34.68 52.75 63.56 63.30 68.83


% 3.50 3.80 2.28 3.61 3.50 2.73 1.83 1.57



Sales mln kWh 25.00 25.12 27.16 24.17 22.19 24.68 23.51 23.84




12

IV. ELECTRICITY DEMAND FORECASTS WES & AuES

D. WES DEMAND FORECAST

1. GDP Regression on Electricity Consumption

13. A demand forecast was developed taking into account the historical relationship between GDP and electricity consumption. The correlation factor between GDP and electricity consumption is reasonably high at 0.79 and is suitable for forecasting purposes. The electricity demand forecast is given below as Figure IV-3 and Table IV-5.

Figure IV-1: Western Region GDP 2003 - 2015


Figure IV-2: WES GDP – Electricity Sales Regression 2003 - 2015

Source: TA Consultant

y = 0,1169x - 28,026R² = 0,7896

0,00

20,00

40,00

60,00

80,00

100,00

120,00

- 200,0 400,0 600,0 800,0 1.000,0 1.200,0 1.400,0

Sal

es G

Wh

WES GDP const 2005 mln MNT


13

Figure IV-3: Western Region Electricity Demand Forecast


2. RE Farm Planting

14. The size of the renewable plants (from energy perspective) and their planting schedule was determined in relation to the soum and area load forecasts for the WES and the summer and winter peaks and valleys in the demand profile.

15. The summer day time demand at Khovd is important for sizing of the PV farm at Mayngad. The winter night time demand to the south of Umnugovi is important for sizing of the wind farm at Umnugovi. The relevant demand projections are given below as Table IV-6 to Table IV-8, and the load profile as Figure IV-17.

16. It can be seen from Table IV-8 that the summer day time average demand at Mayngad (Khovd) is 7.8 MW in 2020. A 10 MW PV farm is of a suitable size albeit curtailment may be required in the early years of operation to avoid reverse power flow towards the north. It can be seen from Table IV-6 that the winter night time average demand to the south of Umnugovi is 12.0 MW in 2020. A 10 MW wind farm is of a suitable size albeit curtailment will be needed at other times to 1) cap the total RE penetration to 30% of total sent-out energy, and 2) ensure that reverse power flow towards the north does not occur. In practice some reverse power flow may be tolerated but for planning purpose it has been assumed that this will not happen.

17. In recognition that RE penetration should be limited to 30%, and that capacity factors should remain high, the following planting schedule is proposed:

Table IV-4: WES Planting Schedule

2018 2019 2020 2021 2022 2023

Umnugovi 10 MW Wind

- phase 1 3 MW X - phase 2 3 MW X - phase 3 2 MW X - phase 4 2 MW X

Battery X


0,0

10,0

20,0

30,0

40,0

50,0

60,0

0,0

50,0

100,0

150,0

200,0

250,0

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

WE

S S

ent O

ut (

MW

)

WE

S S

ent-

Out

(G

Wh)

Non-residential GWh Res GWh Transmission Losses GWh Total WES Sent Out MW


14

Table IV-5: WES Electricity Demand Forecast

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Total WES Sent Out Total WES Sent Out MW 34.6 36.1 38.3 40.0 41.7 43.5 45.2 47.0 48.9 50.9 52.9

Total WES Sent Out GWh 151.5 158.3 167.8 175.1 182.7 190.6 198.1 206.0 214.3 222.9 231.8

Transmission Losses GWh 12.1 12.7 13.4 14.0 14.6 15.2 15.8 16.5 17.1 17.8 18.5

Transmission Losses 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8%

WES Sales Totals

WES Sales MW 31.8 33.2 35.2 36.8 38.4 40.0 41.6 43.3 45.0 46.8 48.7

WES Sales GWh 139.4 145.6 154.4 161.1 168.0 175.3 182.3 189.5 197.1 205.0 213.3

WES Residential

Res GWh 125.4 129.6 135.9 140.1 144.5 149.0 154.9 161.1 167.6 174.3 181.3

Res growth -4% 3% 5% 3% 3% 3% 4% 4% 4% 4% 4%

% Res growth on Total WES sales 90% 89% 88% 87% 86% 85% 85% 85% 85% 85% 85%

Household Electricity Connections 96,491 99,693 104,504 107,788 111,167 114,643 119,168 123,912 128,888 134,063 139,445

kWh per household 1,300 1,300 1,300 1,300 1,300 1,300 1,300 1,300 1,300 1,300 1,300

WES Non-Residential

Non-residential GWh 13.9 16.0 18.5 20.9 23.5 26.3 27.3 28.4 29.6 30.8 32.0



15

Table IV-6: WES Aimag and Soum Forecasts – Winter Peak Demand (20:00 hrs)

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Ulaangom 110/35/10 8.2 8.6 9.1 9.5 9.9 10.3 10.7 11.1 11.6 12.0 12.5

Malchin 110/35/10 1.7 1.7 1.8 1.9 2.0 2.1 2.2 2.2 2.3 2.4 2.5

Umnugobi 110/35/10 1.0 1.0 1.1 1.1 1.1 1.2 1.2 1.3 1.3 1.4 1.5

Altantsugts110/10 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5

Mayngad 110/35/6 9.6 10.0 10.6 11.0 11.5 12.0 12.5 13.0 13.5 14.1 14.6

Mankhan110/35/10 1.0 1.1 1.1 1.2 1.2 1.3 1.3 1.4 1.4 1.5 1.6

AuES 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7

Bayan-Ulgii110/35/10 12.4 13.0 13.8 14.4 15.0 15.7 16.3 16.9 17.6 18.3 19.0

Total 34.6 36.1 38.3 40.0 41.7 43.5 45.2 47.0 48.9 50.9 52.9

South of Umnugovi 12.0 12.5 13.3 13.9 14.5 15.1 15.7 16.3 17.0 17.6 18.3

Table IV-7: WES Aimag and Soum Forecasts – Summer Valley Demand (04:00 hrs)

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Ulaangom 110/35/10 1.5 1.5 1.6 1.7 1.8 1.9 1.9 2.0 2.1 2.2 2.3

Malchin 110/35/10 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.5

Umnugobi 110/35/10 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3

Altantsugts110/10 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Mayngad 110/35/6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.3 2.4 2.5 2.6

Mankhan110/35/10 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3

AuES 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Bayan-Ulgii110/35/10 2.2 2.3 2.5 2.6 2.7 2.8 2.9 3.0 3.2 3.3 3.4

Total 6.2 6.5 6.9 7.2 7.5 7.8 8.1 8.5 8.8 9.2 9.5




16

Table IV-8: WES Aimag and Soum Forecasts – Summer Day Time Average Demand (12:00 noon)

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Ulaangom 110/35/10 6.7 7.0 7.4 7.8 8.1 8.4 8.8 9.1 9.5 9.9 10.3

Malchin 110/35/10 1.4 1.4 1.5 1.6 1.6 1.7 1.8 1.8 1.9 2.0 2.1

Umnugobi 110/35/10 0.8 0.8 0.9 0.9 0.9 1.0 1.0 1.1 1.1 1.1 1.2

Altantsugts110/10 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

Mayngad 110/35/6 7.8 8.2 8.7 9.1 9.5 9.9 10.3 10.7 11.1 11.5 12.0

Mankhan110/35/10 0.8 0.9 0.9 1.0 1.0 1.0 1.1 1.1 1.2 1.2 1.3

AuES 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.6

Bayan-Ulgii110/35/10 10.2 10.7 11.3 11.8 12.3 12.8 13.3 13.9 14.4 15.0 15.6

Total 28.4 29.6 31.4 32.8 34.2 35.7 37.1 38.6 40.1 41.7 43.4




17

E. AuES DEMAND FORECAST

1. GDP Regression on Electricity Consumption

18. A demand forecast was developed taking into account the historical relationship between GDP and electricity consumption.

Figure IV-9: Western Region GDP 2003 - 2015


Figure IV-10: AuES GDP – Electricity Sales Regression 2003 - 2015


y = 0,0325x - 10,628R² = 0,9758

0,00

10,00

20,00

30,00

40,00

50,00

60,00

- 200,0 400,0 600,0 800,0 1.000,0 1.200,0 1.400,0 1.600,0 1.800,0 2.000,0

Sal

es G

Wh

AuES GDP const 2005 mln MNT, real


18

Figure IV-11: AuES Electricity Demand Forecast (excluding Muren)


2. Renewable Energy Farm Planting

19. The size of the renewable plants and their planting schedule was determined in relation to the town and area load forecasts for the AuES and the summer and winter peaks and valleys in the demand profile.

20. The summer day time demand is important for sizing of the PV farms at Govi Altai, Uliastai and Muren. The relevant demand projections are given below as Table IV-14 to Table IV-16, and the load profile as Figure IV-17.

21. It can be seen from Table IV-16 that the summer day time average demand in 2020 at Govi Altai is 14.7 MW – a 10 MW PV farm is of a suitable size. Uliastai is 8.3 MW – a 5 MW PV farm is of a suitable size. Muren is 26.6 MW – a 10 MW PV farm is of a suitable size.

22. It can be seen from Table IV-14 that the winter night time average demand at Telmen is forecast at 5 MW in 2021 (this assumes significant development in anticipation of new wind power). A 5 MW wind farm is of a suitable size albeit curtailment will be needed.

23. In recognition that RE penetration should be limited to 30%, and that capacity factors should remain high, the following planting schedule is proposed:

Table IV-12: AuES Planting Schedule

2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Govi Altai 10 MW PV

- phase 1 5 MW X

- phase 2 2.5 MW X

- phase 3 2.5 MW X

Uliastai 5 MW PV

- phase 1 3 MW X

- phase 2 1 MW X

- phase 3 1 MW X

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

0,0

50,0

100,0

150,0

200,0

250,0

300,0

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

AuE

S S

ent O

ut (

MW

)

AuE

S S

ent-

Out

(G

Wh)

Non-residential GWh Res GWh Transmission Losses GWh Total AuES Sent Out MW


19

2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Battery X

Muren 10 MW PV

- phase 1 4 MW X

- phase 2 2 MW X

- phase 3 2 MW X

- phase 4 2 MW X

Salkhit Khutul 5 MW Wind

- phase 1 2 MW X

- phase 2 2 MW X

- phase 3 1 MW X



20

Table IV-13: AuES Electricity Demand Forecast

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Total AuES Sent Out Total AuES Sent Out MW 24.0 29.8 35.6 41.4 47.2 52.9 55.4 57.8 60.2 62.7 65.1

Total AuES Sent Out GWh 105.1 130.5 155.8 181.2 206.5 231.9 242.5 253.2 263.8 274.5 285.1

Transmission Losses GWh 21.0 24.8 28.0 30.8 33.0 34.8 36.4 38.0 39.6 41.2 42.8

Transmission Losses 20% 19% 18% 17% 16% 15% 15% 15% 15% 15% 15%

AuES Sales Totals

AuES Sales MW 19.2 24.1 29.2 34.3 39.6 45.0 47.1 49.1 51.2 53.3 55.3

AuES Sales GWh 84.1 105.7 127.8 150.4 173.5 197.1 206.1 215.2 224.2 233.3 242.3

AuES Residential

Res GWh 75.7 94.1 112.4 130.8 149.2 167.5 175.2 182.9 190.6 198.3 206.0

Res growth 5% 24% 20% 16% 14% 12% 5% 4% 4% 4% 4%

% Res growth on Total AuES sales 90% 89% 88% 87% 86% 85% 85% 85% 85% 85% 85%

Household Electricity Connections 58,220 72,352 86,495 100,637 114,767 128,873 134,789 140,704 146,620 152,535 158,451

kWh per household 1,300 1,300 1,300 1,300 1,300 1,300 1,300 1,300 1,300 1,300 1,300

AuES Non-Residential

Non-residential GWh 8.4 11.6 15.3 19.5 24.3 29.6 30.9 32.3 33.6 35.0 36.4



21

Table IV-14: AuES Aimag and Soum Forecasts – Winter Peak Demand (20:00 hrs)

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Muren 26.6 28.7 30.8 32.9 35.0 37.1 38.8 40.5 42.2 43.9 45.6

Telmen 1.0 5.0 9.0 12.9 16.9 20.9 21.9 22.8 23.8 24.7 25.7

Uliastai 8.3 9.0 9.6 10.3 10.9 11.6 12.1 12.6 13.2 13.7 14.2

Yesonbulag 14.7 15.9 17.0 18.2 19.3 20.5 21.4 22.4 23.3 24.2 25.2

Total 50.6 58.5 66.4 74.2 82.1 90.0 94.1 98.3 102.4 106.5 110.7

Table IV-15: AuES Aimag and Soum Forecasts Summer Valley Demand (04:00 hrs)

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Muren 4.8 5.0 5.2 5.4 5.6 5.8 6.1 6.3 6.5 6.7 7.0

Telmen 0.2 0.8 1.4 2.0 2.7 3.3 3.4 3.5 3.7 3.8 3.9

Uliastai 1.5 1.6 1.6 1.7 1.8 1.8 1.9 2.0 2.0 2.1 2.2

Yesonbulag 2.6 2.8 2.9 3.0 3.1 3.2 3.4 3.5 3.6 3.7 3.9

Total 9.1 10.1 11.1 12.2 13.2 14.2 14.7 15.3 15.8 16.4 16.9

Table IV-16: AuES Aimag and Soum Forecasts Summer Day Time Average Demand (12:00 noon)

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Muren 21.8 23.7 25.6 27.5 29.3 31.2 32.7 34.2 35.6 37.1 38.6

Telmen 0.8 4.2 7.5 10.9 14.2 17.6 18.4 19.3 20.1 20.9 21.8

Uliastai 6.8 7.4 8.0 8.6 9.2 9.7 10.2 10.7 11.1 11.6 12.0

Yesonbulag 12.1 13.1 14.1 15.2 16.2 17.3 18.1 18.9 19.7 20.5 21.3

Total 41.5 48.3 55.2 62.1 68.9 75.8 79.4 83.0 86.6 90.2 93.7



22

F. DEMAND PROFILE FOR WES & AuES

24. A load profile was developed to support the analyses in this report. The profile was based on a general profile for the Khovd Aimag. The general profile was developed based on data provided by the WES SOJSC and the demand recorded at the Durgun HPP.

Figure IV-17: Load Profile


G. IMPACT OF STORAGE BATTERIES

25. The potential impact of storage batteries on day-time commercial / light industrial suppressed electricity demand is discussed in detail in Appendix C.

0,00

5,00

10,00

15,00

20,00

25,00

30,00

35,00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

MW

e

Hours (Day)

Sep Dec


23

V. ON-GRID RENEWABLE ENERGY IN WES & AuES

H. SOLAR PV

26. A solar PV farm study was undertaken in the Western Energy System (WES) and the Altai Uliastai Energy System (AuES). The purpose was to investigate solar PV power generation technologies, identify an appropriate PV technology (size and type) for these regions, and to establish the economic viability of solar PV power facilities in selected locations. During the investigation, it was noted that the government has committed to develop a 10 MW solar PV farm at Mayngad, near Khovd Aimag centre, in the WES province of Khovd.

27. Mongolia is located around 48°, northern latitude, outside of the world`s “solar belt”. It enjoys a solar irradiation similar to that of southern Europe, where it is noted that solar power generation has increased significantly during the last decade. In fact, Mongolia´s solar potential is higher than southern Europe, since the pronounced continental climate leads to a high “clear sky” direct radiation ratio with about 300 clear days yearly. There is an annual variation from around 3 kWh / m²-d in winter to above 6 kWh/m²d in summer. On annual basis, a 10 MW installation in Mongolia of 0.25 km² is estimated to yield a typical electricity output of 15.3 GWh. In Mongolia, the overnight cost of grid-connected solar PV farms is estimated to range from $ 16,000 per kW for a 0.5 MW installed capacity, to $ 1,280 for 5 MW and $ 970 for 10 MW. This compares to a cost of $ 1,000 per kW for a 10 MW capacity farm built in southern Europe.

28. The study identified three sites suitable for grid-connected solar PV farms. The target sites for evaluation were those sites with high solar insolation but it was determined that insolation levels are relatively high throughout Mongolia. The selected locations are in the vicinity of the towns of Govi Altai (Yesonbulag) and Uliastai in the AuES and Muren in Khuvsgul Aimag (Muren is supplied by the same 110 kV line that supplies the AuES). As the AuES is also connected to the CES, PV has the advantage that the higher summer production will reduce the demand (and the corresponding CO2 exhaust) from the central heating power plants which are not operating in a co-generation mode and are therefore inefficient at burning coal.

29. An analysis of the town loads has determined that the optimal size for the wind farms is 10 MW at Govi Altai, 5 MW at Uliastai and 10 MW at Muren. Together these solar PV farms will produce enough electricity to supply the needs of approximately 13,200 Mongolian homes. It is expected that the strengthening of day time power supplies with PV will motivate commercial activity with a positive impact on regional GDP, particularly when coupled with battery storage (see below).

30. The total estimated project cost, without government assistance, will amount to $30 million (including land and infrastructure). If engineering design, planning and environmental work was to commence in 2018, it is envisaged that the first plant could be commissioned in 2020.


24

Figure V-1: Proposed Grid-Connected Solar PV Farms in AuES


31. The potential solar PV farm annual yields were assessed for the selected locations based on insolation data obtained from the PVGIS-SARAH online database by the CM SAF consortium. This data base offers insolation data averaged 20 years for the specified locations. When converted to power output, the estimated yields computed as follows:

Figure V-2: Govi-Altai Solar PV Daily Profiles by Season (MW)


32. It can be seen that the long-term average Govi Altai 10 MW solar PV daily patterns in each month are highly consistent throughout the day, with the highest power output occurring in the summer months. Overall the analysis indicates an annual capacity factor of 17.6%.

http://www.cmsaf.eu/


25

Figure V-3: Uliastai Solar PV Daily Profiles by Season (MW)


33. It can be seen that the long-term average Uliastai solar 5 MW PV daily patterns in each month are highly consistent throughout the day, with the highest power output occurring in the summer months. Overall the analysis indicates an annual capacity factor of 18.0%.

Figure V-4: Muren Solar PV Daily Profiles by Season (MW)


34. It can be seen that the long-term average Muren 10 MW solar PV daily patterns in each month are highly consistent throughout the day, with the highest power output occurring in the summer months. Overall the analysis indicates an annual capacity factor of 18.5%.

-

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

MW

Daily Hours

Jan Feb Mar Apr May Jun

Jul Aug Sep Oct Nov Dec


26

35. The salient costs of each solar PV farm have been estimated for Mongolian conditions as follows:

Table V-5: Cost Estimates for Solar PV

$ '000 Govi Altai 10

MW

Uliastai PV 5

MW

Muren PV 10

MW

Overnight Costs

PV Generator 4,675 2,500 4,675

Balance of Plant 4,386 2,335 4,386

Construction 644 390 644

Total 9,705 6,399 9,705

$ per kW 971 1,280 971

Operating Costs

per Annum 247 200 247

As % of Capex 2.5% 3.1% 2.5%


36. The Levelized Cost of Energy (LCoE) based on the above costs and a capacity factor of 20%, assuming a solar PV farm plant life of 25 years, and a 6% discount rate, is given in the following chart:

Figure V-6: Levelized Cost of Energy of Solar PV



27

37. The annualized cost per kW varies with capacity factor as follows:

Figure V-7: Annualized Cost per kW of Solar PV with CF


38. Solar radiation, as a power source, has the advantage that it is not bound to a specific location as is the case for hydropower and wind power. The locations in Mongolia have been selected based on the availability of suitable land, soil, and accessibility. The proposed locations have the advantage that they are near to consumers and to the grid infrastructure and substations needed for power evacuation.

39. PV generation plants are fast to install, easy to expand and simple to maintain. This makes them an ideal power source for the remote and difficult to access rural sites of Mongolia. The PV farm design concept layout comprises of solar panel fields, the wiring and connectors, and the inverters producing AC power which is then up-linked to the mains lines.

40. The soil in the selected locations allows sturdy ground mounting by ramming poles into the ground, which is a fast an efficient fixation. The solar panels are mounted on these poles, high enough to be clear from snow compilation in winter. They are tilted at a fixed angle which is selected to emphasize maximum production towards the load peaks after noon and in the winter season.

41. The inverters must meet the challenge to set the maximum power point of any generator group at any instance, depending on irradiation and temperature. The power conditioning equipment monitors and maintains grid quality, sets the power factor and the required power limitations, and communicates with the local or remote-control system. Inverters in Mongolia are required to operate across an extreme temperature range and must be housed securely in temperature-conditioned housing. The latest models allow outdoor distribution with increased efficiency.

42. The Sun is a reliable but intermittent and diffuse source of energy. Intermittency operates on different timescales. There is strong and predictable daily and seasonal variation and availability. There is potential for short term impacts due to passing clouds wherein variability can be of the order of seconds. This short-term variability can be greatly reduced by using independent strings of inverters rather than clustered inverters. Moreover, short-term variability can also be reduced by spreading PV panels across a wider field. In Mongolia, there is ample land available for this purpose.

43. Battery storage has been considered to smooth short-term variations in PV output over periods of 1 to 5 minutes, but also to provide balancing capacity during the middle of the day when commercial activities require a steady supply capacity. This is the subject of further discussion in Section I below.


28

Figure V-8: Layout PV Location Altai-Govi (N 46.387 / E 96.211)


Figure V-9: Layout PV Location Uliastai (N 47.682 / E 96.557)


20°


29

Figure V-10: Layout PV Location Muren (N49.631 / E 100.198)


44. A detailed technical report concerning the solar PV farms is attached to this summary report as Appendix A.

I. WIND

45. A wind farm study was undertaken in the Western Energy System (WES) and the AuES. The purpose was to investigate wind power generation technologies, identify an appropriate wind technology (size and type) for these regions, and to establish the economic viability of wind power facilities in selected locations. In Mongolia, the overnight cost of grid-connected wind farms is estimated to be $ 1,300 per kW, based on wind turbines in the range of 500 kW to 1.8 MW.

46. The study identified two sites suitable for grid-connected wind farms. Using a meso-scales data analysis technique, the target areas chosen for initial evaluation were those areas with potential wind speeds above 8 metres per second. The locations are in the vicinity of the towns of Umnugovi in WES and Salkhit Khutul (near Telmen) in AuES.

47. An analysis of the town loads determined that the optimal size for the wind farms was 10 MW and 5 MW at Umnugovi and Salkhit Khutul respectively. Together these wind farms will produce enough electricity to supply the needs of approximately 80,000 Mongolian homes, at a total estimated project cost, without government assistance, of $23 million (including land and infrastructure). If engineering design, planning and environmental work was to commence in 2018, it is envisaged that the first plant could be commissioned in 2020.


30

Figure V-11: Proposed Grid-Connected Wind Farms in Western Mongolia


48. The prospective wind farm annual yields were assessed, for selected point locations in the Umnugovi soum and the Salkhit Khutul, using meso-scale data analysis (80 m height above ground). When converted to power output, the estimated yields and annual capacity factors were estimated as shown in Figure V-12 and Figure V-13.

Figure V-12: Umnugovi Wind Daily Profiles by Season (MW)


49. It can be seen that the Umnugovi wind patterns in each month are highly consistent throughout the day, with the highest wind speeds occurring in the winter months. Overall the analysis indicates an excellent and exceptionally high annual capacity factor of 63%.


31

Figure V-13: Salkhit Khutul Wind Daily Profiles by Season (MW)


50. It can be seen that the Salkhit Khutul wind patterns in each month are highly consistent throughout the day, with the highest wind speeds again occurring in the winter months. Overall the analysis again indicates an excellent very high annual capacity factor of 55%.

51. Within the areas identified by the meso-scale analysis, the most promising sites were chosen during the initial assessment in July 2017. The results of the assessment were given in the “Energy Assessment Report MN_Vfinal - Wind Resource Estimate for Two Study Sites in Mongolia”. However, near-surface measurements were performed during site visits and it was found that some sites may lead to more or less energy yield than suggested by the meso-scale data analysis – the final estimates of yield can only be confirmed by ground measurement survey. The final selection of the wind farm sites will depend on the trade-off between yield and grid connection and other costs.

52. The salient costs (capital and O&M) of each wind farm have been estimated for Mongolian conditions as given in Table V-14.

Table V-14: Cost Estimates for Wind

$ '000 Umnugovi 10 MW Salkhit Khutul 5 MW

Overnight Costs

Wind Turbines 11,333 5,667

Balance of Plant 1,336 491

Construction 543 242

Total 13,213 6,399

$ per kW 1,321 1,280

Operating Costs

per Annum 51 51

As % Capex 0.4% 0.8%


53. The Levelized Cost of Energy (LCoE) based on the above costs and a capacity factor of 30% for 5 MW and 40% for 10 MW, a wind farm plant life of 25 years, and a 6% discount rate, is given in the following chart:


32

Figure V-15: Levelized Cost of Energy of Wind



Figure V-16: Annualized Cost per kW of Wind with CF


55. To increase the overall availability and flexibility of power generation, it is recommended to avoid the installation of large capacity wind turbines, e.g. 4 x 2.5 MW in Umnugovi Soum, but instead to install smaller WT units. This approach will deliver a higher overall availability and the flexibility to shut down individual generators in periods of low electricity consumption. The available capacity is generally higher if several smaller units are installed, albeit the investment will be slightly higher. The recommended size of the turbines, for the Umnugovi Soum, would be from 500 kW to 1.8 MW. In this size range however, there are only a few turbines currently available in the market. Three manufacturers can offer wind turbines of the appropriate size and technology:


33

Table V-17: Wind Turbine Selection

Manufacturer Comments

GE Offer an old wind turbine concept (GE 1.6-82.5xle); it is kept in the portfolio only for markets in Africa, Asia and Latin America. Not sold to industrialised countries.

Vestas Offering an old wind turbine concept (Vestas V52-850); kept in the portfolio only for comparable markets in Africa, Asia and Latin America. Not sold to industrialised countries.

Wind Technik Nord

Offering recent wind turbine concepts in their normal production portfolio (500-600kW) and a rugged, robust 200kW wind turbine type. Turbines by default also are sold to industrialised countries. Wind turbines equipped with inverter technology for off-grid and hybrid system applications.


56. Taking into account for the Wind Technik Nord turbine (WTN 500kW) different distances to the sub-station in Umnugovi, the following energy yields are estimated based on the meso-scale data that were computed.

Table V-18: Yields with Distance from Umnugovi 110 kV Substation

1km (scenario 1) 5km (scenario 2) 10km (scenario 3)

Installed capacity [MW] 10 MW 10 MW 10 MW

Gross AEP of the wind farm

(not including any losses) 27,750 MWh/year 28,106 MWh/year 33,770 MWh/year

Net AEP of the wind farm

(including internal wake

and shading losses only)

27,201 MWh/year 27,788 MWh/year 33,598 MWh/year

Further losses:

1 Availability 2.0 % 2.0 % 2.0 %

2 Electrical efficiency 1.0 % 1.0 % 1.0 %

3 Turbine Performance 2.0 % 2.0 % 2.0 %

4 Site Conditions 1.0 % 1.0 % 1.0 %

5 Electrical losses 1.0 % 1.5 % 2.5 %

Total losses 7.0 % 7.5 % 8.5 %

Net energy production of

the wind farm (including

appliance of afore

mentioned losses)

25,297 MWh/year 25,704 MWh/year 30,742 MWh/year

Net wind farm Capacity

Factor [%] 29% % 29% % 35% %

Full load hours of the wind

farm 2,530 h/year 2,570 h/year 3,074 h/year



34

57. Following the assumptions given, the standard uncertainty of the predicted annual energy production for the planned wind farm for the Umnugovi site follows:

Table V-19: Uncertainty of Estimated Wind Yield

Uncertainty component Contribution [%]

Meteorological data 18.7

Modelling of wind resource 7.2

Modelling of wake losses 3.5

Turbine data 5.3

Energy Losses 3.0

Resulting overall uncertainty 21.3


58. As a point of reference, a “bankable” energy yield assessment based on a near-surface wind measurement survey is 8-10%. The uncertainty of 21.3% indicates that ground survey is required.

59. Wind farm layouts will vary according to location from the substation, revealed by the meso-scale data analyses. The figures that follow indicate the wind farm layouts for distances of 1 km, 5 km and 10 km (based on a wind farm fitted with WTN 500-48 WTG’s).

Figure V-20: Layout 1 – 1 km from Umnugovi 110 kV Substation



35






36

60. The environmental assessment has resulted in a recommendation to adopt an 11.5 km buffer between the wind farm and Uvsiin Khar Us Lake, while avoiding hilly areas which could also be good raptor habitat. Accordingly, the site selected is shown in Figure V-23. The location is at Umnugovi (N 49° 0'32.26"/ E 91°43'19.07"). This location should be considered as indicative and to be confirmed with the MoE. Notwithstanding wind measurements, and the need to maintain the environmental buffer, the final location, orientation and siting of the wind turbines will be made according to the results of ground survey measurements.

Figure V-23: Layout 4 – 7.5 km from Umnugovi 110 kV Substation


61. On completion of a ground survey, a trade-off analysis of yield versus costs associated with distance to the substation, will determine the best location –notwithstanding other considerations such as ease of operation and maintenance, and site security.

62. Wind power, at the selected sites in the western region of Mongolia, is a reliable but intermittent source of energy. Very short-term variability of the order of seconds to minutes is reduced due to the inertia of the wind turbines. However, short term variation over 5 minutes to one hour can be expected to be of the order of 15% to 20% in either direction. The source of regulating balancing reserve in the both the Western Energy System is Russian hydropower. As the Russian capacity reserve is limited it is necessary to compute the spinning reserve requirement with intermittent generation. A detailed technical report is attached to this summary report as Appendix B.

J. STORAGE BATTERIES

63. A storage battery study has been undertaken with consideration of the need to reduce the impact of the variability of intermittent generation and 2) to reduce high-carbon imports by charging batteries with low cost energy and substituting high-carbon imported energy with discharged batter energy.

64. After due consideration of the target sites for solar PV and wind farms (described above), and the committed solar PV 10 MW at Mayngad in WES, it was determined that the batteries would


37

be beneficial in smoothing the day time supply output of the solar PV farms during the hours from 09:00 to 15:00. In this way, the supply available for day time use by commercial and light industrial enterprises would be maintained at a flat output as shown in Figure V-24 for a typical day in the summer months in Uliastai. Moreover, the discharge of batteries during the day time hour will reduce import from Russia at a time when coal-fired power is operating, thereby reducing CO2.

Figure V-24: Harmonizing of Solar PV and Battery Output


65. Charging of the battery at Umnugovi will be by night-time wind that would otherwise be curtailed. It is envisaged that one wind turbine will be largely dedicated for this purpose.

66. The charging of the battery at Uliastai will be by the Uliastai 2 MW SHPP in the early morning hours in the summer. In winter when the SHPP is closed due to freezing the battery will be charged from the 110 kV line from the CES via Bulgan.

67. With regard to battery technology, the most common battery chemistry is that of Li-ion followed closely by NaS (Sodium Sulphur). The study has identified the needs for power conversion and energy storage of the batteries by considering the solar PV output to 2030, i.e. for a 10-year period. This is appropriate as battery life is currently considered to be 12 years, after which the power convertor and storage cells must be replaced. The requirements can be reviewed towards the end of the battery’s useful life.


38

Figure V-25: General Schematic of a Cell-Based Battery Energy Storage System

Source: Li-Ion Storage Battery manufacturer

Table V-26: Li-Ion Storage Battery Manufacturers

Technology Manufacturer

NCM

Enerdel

Hitachi

LeClanche

LG Chem

Panasonic

PBES

Samsung

XALT

Electronova

LiFePO4

A123

BYD

K2 Energy

Microvast

Saft

Sony

Thundersky

XO Genesis

LTO Altainano


39

Technology Manufacturer

LaClanche

Microvast

Toshiba

XALT


68. The sizing and cost estimates are given by the following table:

Table V-27: Cost Estimates for Li-Ion Storage Batteries

$ '000 Umnugovi Battery Uliastai Battery

Power Conversion (kW) 2,000 1,200

Energy Storage (MWh) 6,900 3,700

Overnight Costs

Power Conversion 1,050 630

Storage Cells 2,674 1,434

Balance of Plant 180 108

Construction 1,035 555

Total 4,939 2,727

$ per kW for Power Conversion 615 615

$ per kWh for Storage 0.39 0.39

Operating Costs

per Annum 12 7

As % on Capex 0.24% 0.26%



40

69. The Levelized Cost of Energy (LCoE) based on the above costs and a capacity factor of 7%, a battery plant life of 12 years, and a 6% discount rate, is given in the following chart:

Figure V-28: Levelized Cost of Battery Storage in Western Mongolia



Figure V-29: Annualized Cost per kW of Battery with CF


71. A detailed technical report is attached to this summary report as Appendix C.


41

VI. ELECTRICITY SYSTEM INTEGRATION (ON-GRID)

K. INTRODUCTION

72. The introduction of intermittent energy into the WES and AuES transmission system requires careful consideration of several factors:

• The harmonization of renewable energy supply sources for optimal efficiency, notably medium-hydropower sources such as Durgun and Taishir HPP’s, also wind and solar PV sources

• The need to curtail intermittent power generation to ensure that transmission grid security is maintained and the spill of energy to Russia is avoided

• The maintenance of grid stability under varying supply and demand conditions (small-signal voltage stability)

• The avoidance of significant reverse power flow (towards the Russian supply source)

• Intermittency and the impact on the need for spinning reserve balancing capacity

73. These issues were considered in detail as part of long-term expansion planning and operations planning. The introduction of intermittent generation requires a detailed consideration of the prospective operating regime.

L. INTERMITTENCY

74. The intermittency of renewable energy can result in an increase in the variability of the net demand seen by the generator that is providing frequency control (through active power injection) and voltage regulation (through reactive power injection). Intermittency manifests on different timescales, from seconds to minutes to hours. Each timescale is considered in turn.

1. Hourly time scales

75. The variation of the output of intermittent renewable energy will increase the variability of the demand for electricity as seen by Russia. On an hourly timescale demand is variable; when the variability of renewable energy is added to the variability of demand, the effect will be to increase the variability of the net demand seen by Russia.

76. The degree to which the demand and mean hour renewable energy output are correlated will tend to reduce the variability seen by Russia. The higher the correlation the lower the variability. In the case of the WES it is beneficial that the correlation is high. The following chart is an example of the output of the variation for 10 MW solar PV and 6 MW wind in year 2020.


42

Figure VI-1: WES Correlation of Demand & Intermittent Generation

Source: WES SOSJC, TA Consultant

77. The TA Consultant has modelled the hourly variability for each of the expansion cases considered, using a Monte Carlo analyses based on probability distributions that characterize the demand, the output of a solar PV farm and a wind farm. The demand distribution is based on a normal distribution whereas the intermittent energy distribution is based on Weibull distributions. The PV farm is modelled using distributed inverters (not a clustered inverter) meaning that the farm has been divided into two blocks with independent output. The following chart is an example of the output of the variation for 10 MW solar PV and 6 MW wind, in hour 13, month of September and year 2020.

Figure VI-2: Variability of Net Demand


R² = 0,8874

-

2,0

4,0

6,0

8,0

10,0

12,0

14,0

16,0

18,0

- 2,0 4,0 6,0 8,0 10,0 12,0 14,0

Load

(M

W)

PV + Wind Production (MW)

-25,0

-20,0

-15,0

-10,0

-5,0

0,0

5,0

10,0

15,0

20,0

25,0

1

366

731

1097

1462

1827

2193

2558

2923

3289

3654

4019

4384

4750

5115

5480

5846

6211

6576

6941

7307

7672

8037

8403

Sca

ttere

d P

V +

Win

d +

Dem

and

Hou

rly

Var

iatio

n

Net Demand after RE Load alone RE Alone


43

78. It can be seen from the chart that the variability of the net demand seen by Russia increases due to the presence of the intermittent generation. However, when compared to the variability of load alone this increase is small. The average demand in the hour is 14 MW with standard deviation of 5 MW. The increase in variability due to RE, as seen by Russia, is small.

79. The variability has been modelled for the preferred expansion cases and used to determine the spinning reserve that must be supplied, to the WES by Russia, and to the AuES from Bulgan / CES. The TA Consultant considers that it will be necessary to inform the Russian operators of the required system reserve capacity to ensure that the variability can be followed by the Russian plant(s) that regulates the supply to Mongolia.

2. Medium time scales (5 minutes to 10 minutes)

80. In principle, the Russian supply to WES, and the CES supply to AuES, should be able to respond equally well to 5-minute variations as hourly variations. However, it is recommended that the PV plants are built in blocks with physical separation of blocks to minimize the impact of passing cloud, particularly cumulus cloud, to minimize the variability of PV farm output.

3. Short time scales (seconds to 5 minutes)

81. Short term variation is not considered to be a concern if power inverters are of the ‘scattered’ type. In a report by NREL “A Statistical Characterization of Solar Photovoltaic Power Variability at Small Timescales”, the authors stated the following

82. “Understanding the correlation of changes in output is more critical for determining the regulation reserves necessary to maintain system stability than the correlation of instantaneous power output. Although power output levels for individual generating units were significantly correlated at all timescales within the regulation timeframe, correlation of changes in power output was weak for all timescales shorter than 5 min. The steady increase in correlation with increasing timescale can be explained by the lack of correlation of solar irradiance at very short timescales, and is evidence of smoothing of variability because of geographic distribution of individual generating units within a single PV plant. The lack of strong correlation at short timescales indicates that the impact on the levels of regulation reserve required to accommodate variability could be less than previously theorized.”

83. The implication of the NREL research is that dispersion of the ‘generators’ within a single PV farm, including the inverters, will reduce the variations in instantaneous power output to an acceptable level. Variation greater than 5 minutes is of primary concern to the NREL researchers and this can be addressed through the provision of storage batteries, as described in the preceding sub-section, or by providing sufficient balancing reserve capacity at all times to cater for rise and fall and demand and intermittent generation at an acceptable level of risk.


44

Figure VI-3: NREL - Standard Deviations of Ramp Distributions (Various Timescales &

Geographic Arrangements)

Source: NREL

M. WESTERN ENERGY SYSTEM EXPANSION

84. Expansion cases were selected after taking into account the available primary energy resources, the need for Russian inter-connection for load following, the maximum penetration of renewable energy in weak transmission networks, and the potential to curtail renewable energy plants using modern control technologies. In all cases where intermittent generation has been investigated, the maximum penetration of renewable energy sources (wind and solar PV) has been limited to 30%. The following cases were examined:

Table VI-4: WES Supply Expansion Cases

Case Description

1 Base Case – Russia + Durgun

2 Case 1 + Small Coal + 10 MW PV (Mayngad)

3 Case 2 + 10 MW Wind (Umnugovi)

4 Case 3 + Battery (Umnugovi)


0

10

20

30

40

50

60

One

sec

ond

Five

sec

onds

Ten

seco

nds

Fifte

en s

econ

ds

Thir

ty s

econ

ds

One

min

ute

Five

min

utes

Sta

ndar

d de

viat

ion

/ Out

put C

apac

ity

Entire Plant Scattered Inverters

Clustered Inverters Single Inverter


45

85. The most attractive alternative, from the perspective of the system Levelized Cost of Energy, is Case 3. This case includes intermittent generation – a 10 MW wind farm at Umnugovi and a 10 MW solar PV farm at Mayngad. The system LCoE of Case 3 is significantly lower than for Case 1 – business as usual, and Case 2 – Case 1 with a small coal plant and a 10 MW solar PV farm at Mayngad. A storage battery at Umnugovi increases the LCoE by 3.3%.

Table VI-5: Summary of LCoE Results


86. The screening process further considered the alternatives using a broad set of prioritization criteria - financial, energy independence, environment and social measures – as follows:

Table VI-6: Raw Scores by Alternative

Priority Measure Case 1 Case 2 Case 3 Case 4

Financial Discounted Cashflow (@ 6%) 234.03 234.97 199.24 205.86

Energy

Independence

% Import / Total Sent-Out

Energy 20% 19% 18% 18%

Environment CO2 production (metric tons) 6,532,501 6,767,275 5,856,549 5,836,312

Social Benefits Incremental annual local

O&M cost $m 0.000 2.221 2.244 2.244

* assumes that WES has 10 MW solar PV and 6 MW wind

Table VI-7: Normalized Scores by Alternative



Energy Independence % Import / Total Sent-Out Energy 0.90 0.93 1.00 1.00

Environment CO2 production (metric tons) 0.89 0.86 1.00 1.00

Social Benefits Incremental annual local O&M cost $m 0.00 0.99 1.00 1.00

Table VI-8: Prioritization Weights

Priority Measure Weight

Financial Discounted Cashflow (@ 6%) 40%

Energy Independence % Import / Total Sent-Out Energy 30%

Environment CO2 production (metric tons) 20%

Social Benefits Local O&M cost 10%


Case 1 Case 2 Case 3 Case 4

LCoE BaU CES

Case 1 +

Small Coal +

10 MW PV

Case 2 + 10

MW Wind

Case 3 +

Storage

Battery

w/o CO2 60.70 57.53 48.78 50.40

with CO2 89.30 84.50 72.11 73.61

Rank 4 3 1 2


46






Social Benefits Local O&M cost 0.00 0.10 0.10 0.10

78.9 89.0 99.9 98.7

4 3 1 2


87. The WES dispatch trajectory is equal to the sent-out energy demand, comprising energy sales and distribution losses. The trajectory is given by the following chart:

Figure VI-10: Energy Supply Projection (Sent-Out MWhe)


Figure VI-11: Capacity (MW)



47

88. The daily dispatch profiles in 2020 and 2030 are given by the following charts:

Figure VI-12: Daily Dispatch Profiles (2020)




0

10

20

30

40

50

60

701 10 19 28 37 46 55 64 73 82 91 100

109

118

127

136

145

154

163

172

181

190

199

208

217

226

235

244

253

262

271

280

MW

Coal Durgun Solar PV Wind Russia

-

10,0

20,0

30,0

40,0

50,0

60,0

70,0

1 10 19 28 37 46 55 64 73 82 91 100

109

118

127

136

145

154

163

172

181

190

199

208

217

226

235

244

253

262

271

280

MW

Durgun Solar PV Wind Russia


48

89. The balancing capacity need (spinning reserve) and unloadable generation is estimated as follows for Case 3:

Table VI-14: Case 3 – WES System Reserve Margin (MW)

MW 2015 2020 2025 2030

Maximum probable drop of intermittent generation 0.0 5.5 5.5 5.5

Maximum probable generation loss 3.4 3.4 3.4 3.4

Maximum probable load rise 5.0 5.8 7.4 8.6

Maximum probable load drop 5.0 5.8 7.4 8.6

Maximum probable increase of intermittent generation 0.0 1.1 1.1 1.1

Largest single commitment 3.4 3.4 3.4 3.4

Minimum probable operating level 21.2 24.2 31.7 37.8

SP - spinning reserve 8.4 14.7 16.3 17.5

UG - unloadable generation 8.4 10.3 11.9 13.1

System Demand 23.6 34.6 43.5 50.9


90. The computation in Table VI-14 is based on an assumption of 500 kW wind turbines at Umnugovi. The reserve balancing capacity requirement (spinning reserve) increases mainly due to the Mayngad 10 MW solar PV farm (assuming the farm is arranged with 2 x 5 MW inverter strings).

91. It is understood that Russia offers a spinning reserve of around 10 MW but the balancing computation suggests that 15 MW will be needed once the 10 MW solar PV farm is commissioned. This requirement can be relaxed by 2 MW if a battery is installed at Umnugovi.

N. WES TRANSMISSION GRID STABILIZATION

92. Grid stabilization – Transmission Network Security; 110 kV substation bus voltages must remain within limits under critical loading conditions. The 110 kV bus voltages are determined by load flow.

93. For Case 3, the critical loading conditions are the substation bus voltages under minimum and maximum loading conditions. These conditions occur in the early hours of summer and winter evenings respectively. WES Case 3 sees the introduction of a wind farm at Umnugovi which is expected to operate in both of these time periods. The following wind generation is predicted on average in the critical hours of 2020 and 2030; also shown is the load to the south of Umnugovi in these critical hours, and the net load after subtracting the wind farm output:

Table VI-15: Critical Umnugovi Wind Output MW

MW Summer

Low Wind

Summer

Low Load

South of

Umnugovi

Net

Summer

Low Load

Winter

Peak

Wind

Winter

Peak

Load

South of

Umnugovi

Net

Winter

Peak

Load

2020 0.0 2.2 2.2 2.5 12.0 9.5

2030 2.0 3.3 1.3 8.3 18.3 10.0



49

94. The net loads in the critical hours can be seen to be lower than the raw loads. This means that voltage drop will be reduced for the same level of power delivered to the area of south of Umnugovi. Moreover, transmission losses will be reduced.

95. It also means that in the summer low period the current operating practice of using reactors to reduce voltage rise will need to continue. If wind is allowed to operate freely then by 2030 the net load will reduce and higher reactive compensation will be required. The alternative is to direct the wind farm output to battery charging or simply to curtail as necessary in the 04:00 hour of the summer months. It is recommended not to install more reactive compensation; once the operating regime is established and bus voltage behaviour understood under the new Mayngad PV / Umnugovi wind operating regime.

96. Grid stabilization – Small Signal Disturbances; the voltage-power and reactive power-voltage stability under the impact of small signal disturbances. These relationships are typically described as ‘nose’ curves due to their characteristic shape. At a critical level of power transfer, a small signal disturbance can potentially result in a voltage collapse as the voltage ‘turns over’ at the nose point.

Figure VI-16: V – P Stability Curve for Undervoltage at Umnugovi


Figure VI-17: Q – V Stability Curve for Undervoltage at Umnugovi


0,8000

0,8500

0,9000

0,9500

1,0000

1,0500

1,1000

1,50

1,60

1,70

1,80

1,90

2,00

2,10

2,20

2,30

2,40

2,50

2,60

2,70

2,80

2,90

3,00

3,10

3,20

3,30

3,40

3,50

3,60

3,70

V p

er u

nit

P per unit

-6,0

-5,0

-4,0

-3,0

-2,0

-1,0

0,0

1,0

2,0

3,0

4,0

1,05

1,04

1,03

1,03

1,02

1,02

1,01

1,01

1,00

0,99

0,99

0,98

0,98

0,97

0,96

0,96

0,95

0,94

0,94

0,93

0,92

0,92

0,91

Q (

MV

Ar)

V per unit


50

Figure VI-18: V-P Stability Curve for Overvoltage at Umnugovi


97. The stability curves show that the transmission network will operate securely for small signal disturbances. In fact, because the Umnugovi wind farm reduces voltage drop it increases the stability margins which is favorable for small signal stability.

98. Grid stabilization – Large Signal Disturbances (a result of line and equipment faults). The response to large signal disturbances is a matter of protection relay response. Protection relays should trip circuit breakers to isolate a faulted equipment to avoid a situation where voltage may oscillate in an un-damped manner occasioning over- or under-voltage tripping and a potential ‘system-black’ event.

99. The Umnugovi wind farm will have inertia and therefore voltage ride-through capability. Protection operation cycle times will fall well within the ride-through capability. Consequently, the risk of the Umnugovi wind farm causing a system voltage collapse is considered negligible, moreover, a battery at Umnugovi will also support ride through.

100. A detailed technical report is attached to this summary report as Appendix D.

0,8000

0,9000

1,0000

1,1000

1,2000

1,3000

1,4000

0,0 0,2 0,4 0,6 0,8 1,1 1,3 1,5 1,7 1,9 2,1 2,3 2,5 2,7 2,9 3,2 3,4 3,6 3,8 4,0

V p

er u

nit

P per unit


51

Figure VI-19: Western Energy System (2020)

Durgun HPP

3 x 4.3 MW

Umnugovi

1.0 MW

Khovd

Mayngad

9.6 MW

Ulgii

12.4 MW

Russia

Chandgani

Ulaangom

8.2 MW

Altantsugts

0.3 MWMalchin

1.7 MW

Mankhan

1.0 MW

3.0 MW

10 MW


52

Figure VI-20: WES – Optimal Expansion (2030)

Durgun HPP

3 x 4.3 MW

Umnugovi

1.5 MW

Khovd

Mayngad

14.6 MW

Ulgii

19.0 MW

Russia

Chandgani

Ulaangom

12.5 MW

Altantsugts

0.5 MWMalchin

2.5 MW

Mankhan

1.6 MW

10 MW

10 MW

Erdeneburen

HPP – 64 MW

Small Coal -

60 MW


53

O. ALTAI ULIASTAI ENERGY SYSTEM EXPANSION

102. Expansion cases were selected after taking into account the available primary energy resources, the need for Russian inter-connection for load following, the maximum penetration of renewable energy in weak transmission networks, and the potential to curtail renewable energy plants using modern control technologies. In all cases where intermittent generation has been investigated, the maximum penetration of renewable energy sources (wind and solar PV) has been limited to 30%. The following cases were examined:

Table VI-21: AuES Supply Expansion Cases

Case Description

1 Base Case – CES Import via Bulgan, Taishir SHPP,

Uliastai SHPP

2 Case 1 + Small Coal

3 Case 2 + 25 MW PV + 5 MW Wind

4 Case 3 + Battery (Uliastai)


103. The most attractive alternative, from the perspective of the system Levelized Cost of Energy, is Case 3. This case includes intermittent generation – a 10 MW solar PV farm at Govi Alta, a 5 MW solar PV farm at Uliastai, a 10 MW solar PV farm at Muren and a 5 MW wind farm at Salkhit Khutul (near Telmen). The system LCoE of Case 3 is significantly lower than for Case 1 – business as usual, and Case 2 – Case 1 with a small coal plant and no intermittent generation.



104. The screening process further considered the alternatives using a broad set of prioritization criteria - financial, energy independence, environment and social measures – as follows:

Table VI-23: Raw Scores by Alternative



Energy

Independence

% Import / Total Sent-Out

Energy 95% 50% 56% 56%

Environment CO2 production (metric tons) 9,536,219 14,228,734 12,952,273 12,959,296

Social Benefits Incremental annual local

O&M cost $m 0.000 3.947 2.449 2.467


Case 1 Case 2 Case 3 Case 4

LCoE BaU CES

Case 1 + Small

Coal + 10 MW

PV

Case 2 + 10

MW Wind

Case 3 +

Storage

Battery

w/o CO2 101.23 73.17 72.30 72.61

with CO2 131.78 104.18 99.40 100.87

Rank 4 3 1 2


54

Table VI-24: Normalized Scores by Alternative





Social Benefits Incremental annual local O&M cost $m 0.00 0.62 1.00 0.99


Table VI-25: Prioritization Weights

Priority Measure Weight

Financial Discounted Cashflow (@ 6%) 40%

Energy Independence % Import / Total Sent-Out Energy 30%

Environment CO2 production (metric tons) 20%

Social Benefits Local O&M cost 10%







Social Benefits Local O&M cost 0.00 0.06 0.10 0.10

77.8 91.9 93.6 93.3

4 3 1 2


105. The AuES dispatch trajectory is equal to the sent-out energy demand, comprising energy sales and distribution losses. The trajectory is given by the following chart:

Figure VI-27: Energy Supply Projection (Sent-Out MWhe)



55

Figure VI-28: Capacity (MW)


106. The daily dispatch profiles in 2020 and 2030 are given by the following charts:



-

10,0

20,0

30,0

40,0

50,0

60,0

1 10 19 28 37 46 55 64 73 82 91 100

109

118

127

136

145

154

163

172

181

190

199

208

217

226

235

244

253

262

271

280

MW

Taishir Solar PV Muren 10 MW Solar PV Uliastai 5 MW

Wind 5 MW Telmen Yesonbulag Solar PV 10 MW CES Import

CES Export


56



107. The balancing capacity need (spinning reserve) and unloadable generation is estimated as follows for Case 3:

Table VI-31: Case 3 – AuES System Reserve Balancing (MW)

MW 2015 2020 2025 2030

Maximum probable drop of intermittent generation 0.0 5.0 8.5 8.5

Maximum probable generation loss 3.4 3.4 3.4 3.4

Maximum probable load rise 5.7 8.4 15.0 18.5

Maximum probable load drop 5.7 8.4 15.0 18.5

Maximum probable increase of intermittent generation 0.0 1.0 1.7 1.7

Largest single commitment 3.0 3.0 3.0 12.5

Minimum probable operating level 25.3 38.2 70.3 78.1

SP - spinning reserve 9.1 16.8 26.9 30.4

UG - unloadable generation 8.7 12.4 19.7 32.7

System Demand 23.6 50.6 90.0 110.7


108. The reserve balancing capacity requirement (spinning reserve) increases mainly due to the solar PV farms (assuming an arrangement of ~3 MW inverter strings). The computation in Table VI-31 is based on an assumption of 500 kW wind turbines at Salkhit Khutul (by 2025).

109. The spinning reserve requirement almost doubles from 2015 to 2020, and doubles again to 2030. The AuES Bulgan 110 kV transmission is connected to the CES and the CES is a very large system in MW terms and can meet the balancing capacity requirement. The balancing requirement can be relaxed by 2 MW if a battery is installed at Uliastai, and by a further 1 – 2 MW in the summer months if the Taishir and Uliastai SHPP’s are available.

-

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

1 10 19 28 37 46 55 64 73 82 91 100

109

118

127

136

145

154

163

172

181

190

199

208

217

226

235

244

253

262

271

280

MW

Taishir Solar PV Muren 10 MW Solar PV Uliastai 5 MW

Wind 5 MW Telmen Yesonbulag Solar PV 10 MW CES Import

CES Export


57

P. AuES TRANSMISSION GRID STABILIZATION

110. Grid stabilization – Transmission Network Security; 110 kV substation bus voltages must remain within limits under critical loading conditions. The 110 kV bus voltages are determined by load flow.

111. For Case 3, the critical loading conditions are the substation bus voltages under minimum and maximum loading conditions. These conditions occur in the early hours of summer and winter evenings respectively.

112. AuES Case 3 sees the introduction of solar PV farms at Govi Altai, Uliastai and Muren. Also a wind farm at Salkhit Khutul. The solar PV operates during the day and so the minimum loading in summer is a critical condition. The Salkhit wind farm operates throughout the day, notably in the evening. Therefore, the critical loading at Telmen is the winter evening load.

113. The following RE farm generation is predicted on average in the critical hours of 2020 and 2030; also shown are the matching town loads in these critical hours, and the net town loads after subtracting their adjacent RE farm outputs:

Table VI-32: Critical PV Output MW

2020 2030

MW

Summer

Low PV

Output

Summer

Low Load

Net

Summer

Low Load

Summer

Low PV

Output

Summer

Low Load

Net

Summer

Low Load

Govi Altai 0.4 4.8 4.4 0.8 3.9 3.1

Uliastai 0.2 1.5 1.3 0.4 2.2 1.6

Muren 0.0 2.6 2.6 0.8 7.0 6.1

Total 0.6 8.9 8.3 2.0 13.1 11.1


Table VI-33: Critical Salkhit Khutul Wind Output MW

MW

Winter

Peak

Wind

Winter

Peak

Load

Telmen

Net

Winter

Peak

Load

2020 0.0 0.2 0.2

2030 0.0 3.9 3.9


114. The net loads in the critical hours can be seen to be the same or slightly lower than the raw consumer loads. The difference is small and could easily be swamped by the release of suppressed demand as voltage will improve. It is noted that transmission losses will reduce with reduced transmission line flows.

115. The net loads indicate that the Bulgan 110 kV transmission line will be no less stable than is currently the case. If a condition was to arise where operating limits found that curtailment should be practiced to ensure a higher line flow during times of low load, this could be easily applied. The curtailed energy could be at least partly directed to battery charging at Uliastai.


58

116. Grid stabilization – Small Signal Disturbances; the voltage-power and reactive power-voltage stability under the impact of small signal disturbances. These relationships are typically described as ‘nose’ curves due to their characteristic shape. At a critical level of power transfer, a small signal disturbance can potentially result in a voltage collapse as the voltage ‘turns over’ at the nose point.

117. Following on from the findings in relation to Umnugovi, the active and reactive power stability margins will not be worsened by reducing the magnitude of the net town loads when solar PV or wind power is operating. The voltage regulation will improve whenever the RE farms are operating and so also the stability margins.

118. Grid stabilization – Large Signal Disturbances (a result of line and equipment faults). The response to large signal disturbances is a matter of protection relay response. Protection relays should trip circuit breakers to isolate a faulted equipment to avoid a situation where voltage may oscillate in an un-damped manner occasioning over- or under-voltage tripping and a potential ‘system-black’ event.

119. The 110 kV transmission line from Bulgan is a very long radial line with no opportunity to switch around a faulted line section. Therefore, the result of large signal disturbances will invariably be that the transmission line will be lost and a restoration process required to return the transmission system to a normal operating state.

120. A detailed technical report is attached to this summary report as Appendix D.


59

Figure VI-34: AuES System (2020)

Taishir

HPP

to

Kharkorin,

etc

Govi Altai

(Yesonbulag)

14.7 MW

ErdenetBulgan

9.8 MW

120

km

254

km44

km

Muren

26.6 MW

Uliastai

8.3 MW

200

km

44

km

Mogoin

Gol

Telmen

1.0 MW

160

km

2 MW

HPP


60

Figure VI-35: AuES – Optimal Expansion (2030)

Taishir

HPP

to

Kharkorin,

etc

Govi Altai

(Yesonbulag)

25.2 MW

ErdenetBulgan

17.5 MW

120

km

254

km44

km

Muren

45.6 MW

Uliastai

14.2 MW

200

km

44

km

Mogoin

Gol

Telmen

25.7 MW

160

km

Salkhit

Khutul

2 MW

HPP5 MW

10 MW

10 MW

5 MW


61

VII. ALTAI SOUM (OFF-GRID HYBRID RE)

Q. BACKGROUND

121. The Altai soum is supplied by an off-grid electricity supply system comprising a 200 kW PV station, and three diesel generator sets of total capacity of 220 kW. The soum centre is geographically isolated from the northern part of AuES because of a mountain range.

122. The PV station is no longer operating at design capacity because the battery bank has reached the end of its useful life. Accordingly, it has been proposed to rehabilitate the PV station and to introduce wind energy to accommodate increased demand.

R. CONDITION OF EXISTING ELECTRICITY SUPPLY SYSTEM

1. Altai Soum Power Plant

123. The existing solar PV-station was installed in 2009 financed by the World Bank. The installed capacity of the station was 202.5 kWp capacity, comprising 1 500 PV modules and related battery bank and control devices. As a result of failures only 800 PV modules are in operation today (700 are dis-connected).

124. The original battery system comprised 4 battery arrays, with each array comprising 120 batteries of 2V and 2500 Ah for a total storage capacity of 10,000 Ah. All of the batteries were installed in 2009 and are life-expired, mainly due to a poor practice of deep discharging the batteries. The battery capacity (Ah) is sufficient to produce the power of the associated PV module for a period of 8 hours at a discharge level of 50%.

Figure VII-1: Altai Soum 200 kW PV Farm


1. Solar PV Resources

125. The solar resources in the area are favorable with an average irradiation of 6.1 kWh/m²d. By selecting a southerly inclination of 48° the annual profile can be lifted in the winter period e.g. for January from 2.3 to 5.6 kWh/m²d.

126. The annual profile of monthly specific irradiation [kWh/m²d] at Altai Soum for horizontal and 48° tilt (source PVGIS (c) European Communities, 2001-2016) are given by the following figure


62

Figure VII-2: Annual profile of monthly specific irradiation


127. The load data presented above has been used to determine load and supply profiles shown in the following table and figure

Table VII-3: Demand & Solar PV production

Month

Irradiation

GI 48°

kWh/m²d

Temp.

°C

Load

kWh/d

Production

kWh/d

Jan 5,53 -11,9 2490,3 1375,8

Feb 4,83 -14 2486,7 1201,8

Mar 7,00 -0,6 2125,8 1740,5

Apr 6,47 8,5 1686,6 1607,9

May 6,97 14,7 1746,9 1732,2

Jun 6,03 20,4 1688,4 1500,1

Jul 6,40 23,5 1534,5 1591,3

Aug 6,77 21,6 1606,5 1682,5

Sep 6,43 14,2 1791,9 1599,6

Oct 6,43 6,4 2212,2 1599,6

Nov 4,93 -4,9 2221,2 1226,6



63

Figure VII-4: Alta Soum Matching of Supply & Demand


128. Table VII-3 and Figure VII-4 show that the previous power system design was correct, since the PV component mostly covers the load during summer. In the daytime it feeds directly to the grid, and the surplus is charged to the storage batteries which later support the night peak. At low summer load the peak from 17.00 to 24.00 amounts to 426 kWh, at high summer to 586 kWh. A storage capacity of about 500 kWh provides sufficient balance. At night the maximum deficit of 1 213 kWh/d could be met with wind. In the event of no wind or available battery energy, back-up diesel generation would be required. For a recommended operation time of 8 hours, the minimum available capacity of diesel generation should be 150 kW.

2. Meso-Scale Wind Data Analysis

129. To estimate the theoretical potential at Altai Soum, the following values have been calculated for a representative point with promising wind resource at 80m height above ground level within each of the two study areas, using the closest four meso-scale points:

• Average daily profile of Power Density in Watt per hour per month

• Average daily profile of Energy Production MWh for each hour per month split in day and night time periods

• Hourly Weibull distribution (Parameters A & k)

• Average plant capacity factor per hour considering a standard Vestas V110 2MW wind turbine

• Average plant capacity factor per year and month considering a standard Vestas V110 2MW wind turbine

130. The representative point had the following coordinates:

Table VII-5: Study Area Coordinates

Name East North

Altai Soum 94°55’32,48“E 44°37’4,79“N

0

500

1000

1500

2000

2500

3000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

kWh

Load kWh/M Production kWh/d


64

Figure VII-6: Study Area

131. The following results were derived from the calculations:

Average daily profile of Power Density per hour per month

132. The following table represents for each month the daily profile of Average Power Density per hour in W/m2

Table VII-7: Average Wind Power Density per hour (without turbine)

Hour Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0 111.7 160.4 267.5 337.7 265.0 239.8 204.2 246.2 221.4 183.5 169.3 133.8

1 112.2 164.9 253.6 334.4 288.2 231.6 201.7 263.6 221.4 186.4 174.0 131.7

2 110.8 164.0 244.8 337.2 279.5 233.4 198.1 267.9 224.2 185.7 188.5 137.1

3 103.9 159.3 228.9 330.2 254.6 222.5 180.4 258.5 222.2 180.6 190.0 138.0

4 101.4 155.6 198.2 323.1 233.6 211.2 189.5 248.0 213.8 171.8 195.0 136.6

5 107.4 148.5 191.9 307.9 220.9 199.4 178.2 236.8 205.5 158.9 195.9 136.8

6 113.7 139.6 195.3 292.6 192.7 179.1 165.7 214.9 197.3 151.5 189.2 129.6

7 110.5 139.3 206.5 253.1 167.8 171.5 138.3 176.8 175.0 145.8 189.7 131.0

8 114.1 139.9 201.6 226.3 191.6 212.3 144.9 160.7 150.4 137.0 183.5 129.5

9 113.5 120.4 205.3 262.2 250.8 261.7 172.5 194.3 159.2 137.1 178.3 122.3

10 104.9 106.9 222.4 290.1 309.2 284.2 196.5 219.7 188.0 160.6 179.3 111.0

11 97.4 112.1 246.7 313.9 336.9 284.2 204.8 223.9 205.0 193.7 187.4 106.9

12 106.8 124.7 269.1 356.6 349.8 303.4 216.8 219.7 203.1 216.5 203.4 120.4


65


13 108.9 142.6 287.9 376.6 362.1 329.9 238.1 223.3 200.4 245.8 215.8 131.2

14 108.0 154.2 300.2 390.3 391.6 332.6 257.1 234.2 208.4 253.5 220.3 146.3

15 109.0 164.2 296.0 410.5 433.1 336.9 263.3 244.1 213.2 258.4 224.8 144.8

16 115.3 159.8 276.0 440.2 469.9 348.2 279.7 262.7 220.2 258.4 230.4 143.5

17 117.1 154.7 268.8 462.2 498.9 368.3 283.0 268.2 210.5 255.8 223.7 131.3

18 114.2 147.4 272.8 447.7 524.7 380.6 283.6 257.7 210.1 239.9 201.0 124.0

19 122.4 138.7 267.7 412.1 509.4 366.5 294.5 259.0 207.9 214.2 201.2 116.2

20 122.3 147.7 256.2 362.2 459.5 340.3 305.8 265.6 189.8 195.9 191.4 117.7

21 121.1 158.4 272.6 329.4 357.5 304.2 279.5 242.5 182.1 191.3 186.5 120.3

22 117.4 161.6 279.6 329.5 305.3 267.4 222.0 218.3 184.6 192.3 183.6 129.0

23 114.3 164.7 276.5 346.3 286.1 248.8 215.0 215.2 195.8 188.8 176.4 134.0

Average daily profile of Energy Production per hour per month split in day and night

133. The average hourly power density in W/m2 for hours during the day time period and for hours during the night time period are depicted in the following table:

Table VII-8: Typical Wind Power Density per square meter (without turbine) split in day- and night-time


6:00am - 9:59pm 112.5 140.7 252.8 351.6 362.8 300.2 232.8 229.2 195.0 203.5 200.4 126.6

10:00pm - 5:59am 109.9 159.9 242.6 330.8 266.7 231.8 198.6 244.3 211.1 181.0 184.1 134.6

Hourly Weibull distribution (Parameters A & k)

134. The following tables represent for each month the average hourly Weibull parameters A and k:

Table VII-9: Average Weibull Scale Parameter A [m/s]


0 4.5267 5.4684 6.3354 7.1308 7.0750 6.7827 6.4958 6.8966 5.9833 5.7858 5.8571 4.9042

1 4.4731 5.4584 6.2775 7.0685 7.2116 6.5640 6.2496 6.9356 6.0641 5.7547 5.8234 4.9013

2 4.5118 5.4759 6.1737 7.1504 7.0422 6.2887 6.0894 6.8603 6.1529 5.7527 5.9122 4.9874

3 4.4912 5.4468 6.0411 6.9713 6.7950 6.0124 5.9892 6.7237 6.1391 5.7023 5.8969 5.0290

4 4.4625 5.3508 5.7334 6.8284 6.5818 5.9149 6.0618 6.5331 6.1217 5.5598 5.8794 4.9966

5 4.4987 5.2452 5.5766 6.6884 6.4740 5.7721 5.9624 6.4358 5.9868 5.4497 5.8594 5.0175

6 4.5231 5.1165 5.5497 6.6475 6.1896 5.5107 5.6727 6.2105 5.8360 5.4375 5.7872 4.9633

7 4.4676 5.0106 5.5104 6.4099 5.8717 5.5479 5.3265 5.8421 5.7479 5.3172 5.8321 4.9417

8 4.4683 4.9297 5.3785 5.9778 6.2829 6.4497 5.8723 5.8630 5.4115 5.1636 5.8178 4.8570

9 4.4373 4.6531 5.4683 6.5199 7.1289 7.3144 6.5720 6.5510 5.6487 4.9805 5.6938 4.7422

10 4.2417 4.4330 5.8345 7.0914 7.7705 7.7614 6.9687 7.0027 6.2767 5.4480 5.6464 4.5851

11 4.1377 4.5940 6.2837 7.4749 8.0771 7.7708 7.0740 7.0258 6.5346 6.0764 5.8401 4.4270

12 4.5763 4.9113 6.5979 7.7968 8.2194 7.9339 7.1595 6.9191 6.5441 6.4307 6.0643 4.5773

13 4.8127 5.2034 6.6891 7.9999 8.3958 8.1901 7.3207 6.9372 6.5390 6.6124 6.1937 4.7836


66


14 4.8642 5.3472 6.6832 8.1413 8.6174 8.2432 7.4723 7.0004 6.5165 6.7107 6.2105 4.8978

15 4.8729 5.4195 6.6888 8.2929 8.8666 8.2843 7.6171 7.1189 6.5412 6.7915 6.2422 4.9333

16 5.0172 5.4531 6.6441 8.4257 9.1923 8.2815 7.7964 7.2464 6.5547 6.7969 6.3501 5.0152

17 5.1290 5.5279 6.7109 8.6454 9.3414 8.4578 7.9247 7.2243 6.5003 6.9080 6.2706 4.9545

18 4.9259 5.4970 6.9822 8.5820 9.4769 8.4580 7.9495 7.1916 6.7446 6.9157 6.1202 4.7440

19 4.8996 5.3154 6.8965 8.4774 9.4793 8.3383 8.0224 7.3739 6.8128 6.4293 6.1101 4.7521

20 4.9219 5.4287 6.5684 7.8776 9.1204 8.3149 8.2187 7.4697 6.3317 6.0843 6.0707 4.7959

21 4.9233 5.5683 6.3824 7.3318 8.1530 7.8257 7.7011 7.0409 5.9447 5.9397 6.0162 4.8781

22 4.8036 5.6228 6.3455 7.2119 7.6158 7.1232 6.8658 6.6455 5.7923 5.9662 5.9505 4.8797

23 4.6237 5.5762 6.3329 7.2420 7.3822 6.9277 6.7607 6.5760 5.8404 5.9064 5.8671 4.9040

Table VII-10: Average Weibull Shape Parameter k [-]


0 1.4212 1.6252 1.5342 1.5953 1.8332 1.7927 1.8008 1.7788 1.4923 1.5897 1.7801 1.4735

1 1.3976 1.6043 1.5573 1.5975 1.8153 1.7198 1.6741 1.7034 1.5192 1.5644 1.7035 1.4799

2 1.4360 1.6097 1.5306 1.6242 1.7606 1.5735 1.6312 1.6348 1.5483 1.5803 1.6851 1.4722

3 1.4843 1.5794 1.5263 1.5725 1.7306 1.5013 1.6668 1.6253 1.5473 1.5723 1.6678 1.5044

4 1.5000 1.5470 1.5080 1.5450 1.7228 1.5054 1.6435 1.5797 1.5545 1.5445 1.6428 1.5035

5 1.4732 1.5488 1.4530 1.5170 1.7442 1.4843 1.6537 1.5801 1.5084 1.5594 1.6481 1.5061

6 1.4265 1.5377 1.4548 1.5425 1.7554 1.4735 1.5889 1.5789 1.4810 1.6064 1.6451 1.5009

7 1.4154 1.4775 1.4160 1.5720 1.7317 1.5675 1.6073 1.5969 1.5895 1.5839 1.6549 1.4779

8 1.4065 1.4527 1.4100 1.4987 1.8142 1.8218 1.9217 1.7314 1.5834 1.5559 1.6611 1.4307

9 1.3967 1.4487 1.4314 1.6328 1.9800 2.0586 2.2330 1.9558 1.6785 1.4807 1.6391 1.3874

10 1.3504 1.4540 1.5110 1.8212 2.0810 2.2495 2.3463 2.0884 1.8852 1.5893 1.6009 1.3974

11 1.3467 1.5035 1.6325 1.9226 2.1659 2.2228 2.3329 2.0659 1.9437 1.7587 1.6823 1.3855

12 1.5172 1.5781 1.6933 1.8985 2.1786 2.1820 2.2529 1.9953 1.9565 1.8276 1.7303 1.4026

13 1.6340 1.6213 1.6744 1.9218 2.2216 2.2109 2.1647 1.9720 1.9667 1.7640 1.7368 1.4614

14 1.6627 1.6288 1.6310 1.9160 2.2006 2.1996 2.1081 1.9348 1.8724 1.7631 1.6992 1.4506

15 1.6579 1.5934 1.6298 1.9079 2.1274 2.2263 2.1975 1.9452 1.8583 1.7608 1.6752 1.4878

16 1.6993 1.6043 1.6452 1.8729 2.2010 2.1245 2.2068 1.9107 1.8099 1.7382 1.6978 1.5242

17 1.7548 1.6734 1.6884 1.9308 2.1544 2.1365 2.3191 1.8656 1.8196 1.8246 1.6841 1.5389

18 1.6167 1.7128 1.8144 1.9428 2.1531 2.0690 2.3229 1.9001 1.9893 1.9396 1.7168 1.4662

19 1.5159 1.6499 1.7903 2.0355 2.2581 2.0716 2.3018 2.0267 2.0703 1.7545 1.7086 1.4941

20 1.5330 1.6398 1.6824 1.8760 2.2390 2.2533 2.4481 2.0566 1.8236 1.6616 1.7616 1.4829

21 1.5387 1.6548 1.5407 1.7003 2.0493 2.1080 2.1349 1.9333 1.6484 1.6007 1.7482 1.5211

22 1.5222 1.6945 1.4927 1.6472 1.9595 1.8384 1.9127 1.8275 1.5862 1.6254 1.7173 1.4578

23 1.4561 1.6499 1.4940 1.6137 1.9229 1.8345 1.9043 1.7823 1.5477 1.6261 1.7115 1.4551

Average plant capacity factor per hour considering a standard V110 2MW WT

135. Based on the power curve and wind speed availability, the following plant capacity factors are derived hourly average per month:


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Table VII-11: Average monthly Capacity Factors per hour considering a Vestas V110 2 MW turbine


0 17.8% 23.6% 31.9% 38.6% 38.3% 35.3% 32.7% 37.2% 29.6% 28.0% 27.0% 20.4%

1 17.6% 23.5% 31.6% 37.4% 38.6% 33.0% 31.0% 37.6% 30.6% 27.6% 27.2% 21.0%

2 17.6% 24.0% 31.1% 37.9% 37.3% 31.4% 29.2% 37.8% 30.9% 27.4% 27.7% 22.1%

3 17.2% 24.8% 30.2% 36.3% 35.7% 29.1% 28.6% 36.3% 31.2% 27.4% 27.9% 21.8%

4 16.7% 24.4% 27.7% 35.1% 34.0% 28.4% 29.7% 34.5% 31.4% 26.4% 28.0% 21.4%

5 17.4% 23.0% 27.2% 34.4% 33.0% 27.5% 29.1% 33.4% 31.3% 24.9% 27.5% 21.9%

6 17.9% 22.1% 26.6% 34.5% 30.3% 25.1% 26.8% 31.3% 30.1% 24.7% 26.7% 21.9%

7 17.4% 21.5% 25.7% 32.9% 27.0% 23.8% 23.1% 28.2% 28.0% 23.7% 26.8% 21.8%

8 17.5% 21.2% 23.5% 29.3% 30.9% 30.2% 25.6% 28.0% 24.4% 22.9% 26.8% 21.7%

9 17.2% 18.7% 24.4% 32.2% 39.0% 38.2% 31.4% 32.9% 25.4% 21.5% 25.3% 21.4%

10 16.0% 16.6% 27.2% 35.5% 43.7% 42.7% 35.4% 37.6% 29.6% 24.3% 25.0% 20.0%

11 15.3% 17.7% 29.8% 39.2% 45.7% 43.9% 37.2% 37.9% 31.4% 28.5% 26.3% 18.4%

12 17.4% 20.2% 32.3% 42.0% 47.3% 46.3% 38.5% 36.6% 31.6% 31.3% 28.3% 18.9%

13 18.7% 22.0% 33.1% 44.1% 49.8% 49.1% 40.4% 36.9% 31.6% 32.5% 29.4% 19.8%

14 19.4% 22.8% 33.7% 46.8% 51.2% 50.9% 42.0% 37.3% 32.1% 33.9% 29.9% 20.5%

15 20.0% 23.8% 34.5% 48.5% 53.6% 49.9% 43.2% 38.5% 32.4% 35.2% 30.6% 20.6%

16 20.8% 24.8% 34.7% 49.1% 55.7% 49.3% 45.4% 39.3% 32.8% 36.1% 31.8% 20.7%

17 21.5% 25.3% 35.1% 50.2% 57.3% 50.6% 46.7% 38.8% 32.5% 37.0% 31.3% 20.5%

18 20.8% 25.0% 37.2% 50.7% 58.1% 50.5% 47.6% 39.1% 34.6% 37.0% 29.9% 19.2%

19 21.3% 23.6% 36.5% 50.6% 58.4% 50.1% 48.0% 41.0% 35.2% 33.7% 29.9% 19.4%

20 21.3% 24.5% 33.5% 45.1% 55.4% 49.9% 49.4% 41.9% 32.0% 30.6% 29.0% 20.3%

21 21.0% 25.7% 31.9% 40.5% 47.5% 44.9% 44.4% 37.3% 29.0% 29.6% 28.5% 20.5%

22 19.6% 25.4% 32.5% 39.6% 43.3% 38.6% 37.0% 34.3% 27.1% 29.2% 28.2% 20.8%

23 18.4% 24.8% 32.2% 39.3% 40.7% 36.9% 35.5% 34.5% 27.9% 28.5% 27.8% 20.6%

Average plant capacity factor per year and month considering a standard V110 2MW WT

136. The resulting cumulated energy production in MWh per month is:

Table VII-12: Average Production Profile in MWh considering a Vestas V110 2MW turbine

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

per year

276.32 318.35 461.16 581.93 652.12 573.3 544.36 538.34 439.6 435.16 405.94 307.34 5533.9

137. The average capacity factor per month is:

Table VII-13: Average Capacity Factors considering a Vestas V110 2MW turbine

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

Capacity Factor


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18.60% 22.90% 31.00% 40.40% 43.80% 39.80% 36.60% 36.20% 30.50% 29.20% 28.20% 20.70% 32.60%

138. In winter when the sun is low and the load is high, PV will only cover around 50% of the demand. According to the estimated wind production, a 500 kW wind capacity can augment the supply to cover an energy deficit of up to 1 213 kWh, by direct feed-in and additional charge to storage.

3. Diesel Generator Back-Up

139. In case wind and/or PV fail, the back-up Diesel generator can be started automatically to directly feed the grid and charge surplus to the storage group, so it operates on full load at minimum time. For a maximum deficit of 1 213 kWh/d and a recommended operation of 8 h the minimum available capacity of these generators should be 150 kW. An automated control system will run the generators to assure best charge of the storage batteries.

140. The system will achieve best operational efficiency and lowest electricity cost when generator operation is minimum. A design aiming to operate without motor generation during summer will use a storage battery of about 500 kWh to cover the evening peak of the daily load. The back-up generator would cut in only if the storage is down below the depth of discharge (80%) Lower battery size will increase cycling and reduce lifetime. Since the battery has been inoperable for years it will no longer perform, so a completely new battery is strongly recommended for rehabilitation, preferably of the Lithium-Iron-Phosphorous class (LFP).

4. Altay Soum Distribution Network

141. The distribution grid is in poor condition. The soum distribution grid was installed before 1990 and is in need of renovation and replacement of overhead line and poles. The overhead line has aluminum wires with small cross-section of only 8 mm2. It has been estimated that the total cost to re-furbish the distribution grid is $ 38k.

5. Hybrid Generation System Design

142. The design of a hybrid plant must take into consideration the various generation resources available from PV, wind, and gensets, all operating independently and at different voltage levels. Moreover, the operating performance of all of these sources is variable over time.

143. A hybrid generation system designed for such conditions, that is flexible and expandable, is based on an AC power bus design. The various generation components (PV, wind) use discrete, high-efficiency, grid-feeding inverters linked to a common AC bus. Gensets are typically AC as well. The AC power bus is connected to an AC distribution line. The location of the power feed-in can be anywhere along the AC distribution line which acts to stabilize voltage and saves line costs and reduces losses. Grid feeding at low load times goes directly to the load, only the additional power needed at peak time or the power used for battery charging is subject to charging efficiency. Battery charging / discharging is performed by bi-directional storage inverters which also perform the overall charging control. For the current peak conditions, a storage inverter of 250 kVA is recommended. An integrated control will automatically start a genset to charge the batteries to determined levels.

144.


69

Figure VII-14: Altai Soum Distribution Grid Single Line Diagram

145. In this strategy, wind is integrated into the system to form a fully hybrid operation. The hybrid system would operate in a coordinated manner with charging of the batteries determined by a control algorithm devised according to a user-defined operating regime. Diesel generation would act as a standby reserve for emergency night-time use. Diesel generation would be called upon only rarely to operate in the event that the battery had reached a 50% discharge level and no wind was available.


70

Figure VII-15: AC-Bus with flexible power components

146. An example of the AC-bus system can be found online here Sunny Island Cluster

Table VII-16: Rehabilitation components for Altai Soum

Pos Component Capacity Function

1 Photovoltaic array

800 V dc 310 kWpeak Crystalline cells on 48° inclined support

2 PV-grid-feeding

inverter 300 kW Standard 3ph grid-feed inverter

3 Wind w/grid-feeding 500 kVA Standard 3ph grid-feed inverter

4 Motor-Generator 1-3

ph 150 kVA Direct to load, battery charge

5 Storage Inverter 3ph 250 kW Bi-directional grid-feed inverter

6 Storage stacks HV 500 kWh Lithium-Ferro-Phosphor (LFP)

7

Rehabilitated 400 V

ac 3ph distribution

grid

100 km

Total power capacity 1200 kW

147. The storage group is recommended to comprise LFP battery stacks, storage inverters and power management. These components to be housed in a container. An example of a container energy storage system can be found here Container Energy Storage

148. Temperatures in Altai Soum average at a minimum of 25°C and a maximum of 35°C. The container group can be protected by a shade wall or a removable shelter to protect from direct sun and winter storm.

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=2ahUKEwjc57St9YPfAhUKblAKHbTjDUIQFjACegQIBxAC&url=http%3A%2F%2Ffiles.sma.de%2Fdl%2F2485%2FSI_6H8H-AEN131411W.pdf&usg=AOvVaw2lyFK4jJkUqo_q8sL2mWdU

https://www.youtube.com/watch?v=m3rUuB7L_68

https://www.youtube.com/watch?v=m3rUuB7L_68


71

Figure VII-17: Container Energy Storage

149. The system will achieve best operational efficiency and lowest electricity cost when generator operation is minimum. A design aiming to operate without motor generation during summer will use a storage battery of about 500 kWh to cover the evening peak of the daily load. The back-up generator would cut in only if the storage is down below the depth of discharge (80%). Lower battery size will increase cycling and reduce lifetime. Since the existing battery has been inoperable for years it will no longer perform, so a completely new battery is strongly recommended for rehabilitation, preferably of the Lithium-Iron-Phosphorous class (LFP).

6. Rehabilitation & New Facilities Costs

150. The solar PV facility rehabilitation cost is taken to be $585,000. This cost includes battery cell and inverter replacement.

151. The wind farm cost is estimated as follows:

Table VII-18: Cost Estimates for Wind

$ '000 Altai Soum

250 kW

Altai Soum

500 kW

Wind Turbines 283.3 566.7

Balance of Plant 8.1 17.2

Construction 5.0 10.3

Total 296.5 594.2

$ per kW 1,186 1,188

Operating Cost

per Annum 11.6 23.2

As % Capex 4.1% 4.1%


152. The rehabilitation of the distribution grid has been taken to be $ 38,500.

153. No investment is allowed for diesel engine rehabilitation or replacement. It is assumed that one of the existing diesel sets can be rehabilitated.


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D. LEVELIZED COST OF ENERGY

154. The high summer day time load is taken as the criteria for setting the PV station capacity; in such case, low summer day time PV output will need to be curtailed. This will be an appropriate time for charging the battery in the summer. At other times charging of the battery will need to be by excess wind, most often in the early morning hours. The peak winter demand has been taken as the criteria for setting the wind capacity. The capacity factors of different combinations of PV, wind and the battery (discharge basis) have been computed using a dispatch method as follows:

Table VII-19: Capacity Factors for Change in Wind Capacity

PV kW CF PV Wind kW CF Wind

Capacity Factor

Battery

(discharge)

200 12% 250 16% 1.6%

200 12% 500 8% 0.3%

200 12% 750 6% 0.3%

200 12% 1000 4% 0.3%


155. Table VII-19 suggests that a wind capacity below 500 kW will be optimal. The estimated dispatch for a 200 kW PV / 500 kW wind farm is shown in the following figure for 2020. Battery discharge is so small at 0.3% that it does not show against PV and wind production. It can be seen that wind is curtailed during the hours of sunshine.

Figure VII-20: Typical Daily Dispatch by Month (2020)


-

50,0

100,0

150,0

200,0

250,0

300,0

350,0

1 9 17 25 33 41 49 57 65 73 81 89 97 105

113

121

129

137

145

153

161

169

177

185

193

201

209

217

225

233

241

249

257

265

273

281

kW

Altai Soum PV Altai Soum Wind Battery Discharge


73

156. The Levelized Cost of Energy (discount rate 6%) computes at

• 200 kW PV / 250 kW wind hybrid farm – $ 0.12 per kWh (295 MNT per kWh)

• 200 kW PV / 500 kW wind hybrid farm – $ 0.18 per kWh (442 MNT per kWh)

157. Governmental bodies, entities and private business shop’s payment tariff is 150.0 MNT per kWh. The private household electricity payment tariff is 120.0MNT per kWh which is the same as the tariff paid by all AUES private household users.

158. Clearly the refurbishment of the PV station, and establishment of new wind facilities will require grant assistance if the tariff is to be maintained near to the current level.]

E. WIND FARM SITING

159. National specialists were dispatched to the field in September 2017, and two potential wind farm sites were identified:

• The first wind site (at coordinates 44°37’26.5”N and 94°55’36.3”E, 1 450 masl) is the site of an abandoned runway that is now considered too close to a settled area for safe take-off and landing. The site is located to the north-east of the Soum center. The PV–station is 600 m to the south. The expected wind direction is from the west to north direction and in this direction the site is very open to the selected site. The site is in big valley from west to east between the mountains (74-134 km wide) and close to China border (32km). On Chinese side people could see big amount of installed wind turbines.

• The second wind site (at coordinates 44°37'30.6"N and 94°55'59.0"E) is a non-preferred alternative site. The site is located behind a flood protection dam.

Figure VII-21: Prospective Wind Farm Sites

2 Second possible wind farm site 1 First preliminary selected site for wind farm


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VIII. GROUND SOURCE HEAT PUMPS

S. BACKGROUND

160. According to REN21 (Renewables 2017 and 2012 Global Status Reports), worldwide heat energy consumption accounts for more than 50% of final energy consumption and remains largely fossil fuel-based.

161. Ground source heat pumps (GSHP) are expected to play an important role in a global heat de-carbonization effort, using renewable heat stored in the ground. Global ground source heat pump installed capacity has doubled from 2005 to 2015. As of end-2014, the estimated global installed capacity stood at 50 GWth with an annual output of around 327 PJ (91 TWh) of heat. Currently the largest markets for heat pumps are the United States, China and Europe as a whole, where France, Germany, Italy and Sweden were the most significant national markets in 2016.

162. GSHPs are suitable for small domestic applications to large multimegawatt industrial solutions. This study concentrates on solutions for public buildings with a thermal capacity range of 60 to 540 kW.

T. GSHP STUDY

163. A total of 11 sites were investigated in the City of Khovd in Western Mongolia for the potential to utilize ground source heat pumps. The sites were visited, data gathered, and five out of the 11 sites were shortlisted for further technical analysis. The criteria used to shortlist the buildings were as following:

• Building owner’s (organization) land available for installation

• Insulation / weatherization undertaken

• Age / condition of buildings indicating life expectancy more than 10 years

• Amount of coal burned for heating (potential emission savings)

164. Heat demand analysis was undertaken for each of the shortlisted buildings. The results are presented in the Table VIII-1:

Table VIII-1: Heat Demands

Kindergarten

No. 1

Kindergarten

No. 8

Kindergarten

No. 10 School No. 7 WEMO

Max demand kW 256.4 99.0 60.0 538.0 61.0

Annual demand for

space heating MWh 684.7 206.3 134.0 1,132.9 162.3

Annual demand for

DHW MWh 69.3 83.0 41.5 440.0 16.6

Total heat demand MWh 754.0 289.4 175.5 1,572.9 179.5

Load factor (total heat

demand) % 34 33 33 33 34



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Table VIII-2: Annual Heat Load Duration Curves


165. After consultation with the ADB it was confirmed that a building under construction, Kindergarten No. 1, was considered to be the most suitable for a GSHP installation due to its high energy efficiency with insulation and sealed windows. This building becomes the pilot site for GSHP. The results of the pilot will inform the case for installing GSHP’s at the other four buildings identified by the shortlisting process.

U. GSHP TECHNOLOGIES

166. Ground source heat pump system comprise of a heat pump system (heat pump units and ancillary equipment as pumps, heat exchangers, pipes etc.) and ground heat exchanger. The ground heat exchanger component can account for up to half of the total system cost and is the most cumbersome part to repair or replace.

167. Ground source heat exchangers can be divided into two main types – shallow (1.0–2.5 m) horizontal heat exchangers and deep (15–150 m) vertical systems. Moreover, the deep vertical systems divide into closed loop systems, which use a mixture of anti-freeze (e.g. propylene glycol) and water as a heating media, and open loop systems which use natural groundwater as a heating media.

168. The study, undertaken by the TA Consultant, identified that the preferred ground heat exchanger type, in the planned area of installation in Khovd, is the deep vertical heat exchanger due to the fact that ground freezing level in Khovd is deeper than at 3.2 meters (which is deeper than the typical installation depth of horizontal heat exchangers).

169. In the study, deep vertical closed loop and open loop systems were considered further; Figure VIII-3 illustrates these two ground heat exchanger types. The final selection of the ground heat exchanger type, i.e. closed or open, is best made once detailed data has been obtained from from geological drillings at the site.

0

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Kindergarten No. 1 Kindergarten No. 8 Kindergarten No. 10 School No. 7 WEMO


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Figure VIII-3: Ground Heat Exchanger Types


170. Capacity optimization was carried out for GSHP’s and other possible supplementary technologies, including coal and electric boilers, as well as solar thermal collectors. It was identified that GSHP technology is preferred (technically and economically) for base load heat supply capacity in public buildings, whereas coal or electric boilers are suitable for peak and reserve capacity.

171. Illustration of the capacity mix and annual energy supply between the GSHP as a base load heat supply, supplemented with peak boilers, is presented in Figure VIII-4. With the selected capacity mix, the annual energy supply of the GSHP is as high as 86%. Peak boilers cover only 14% of the annual energy demand but 49% of the peak demand which is a significant support in reducing the investment needed for the hybrid GSHP system (because the cost of the coal and electric boilers are much lower compared to a stand-alone GSHP).

Figure VIII-4: Preferred Capacity Mix for GSHP System


CondenserEvaporator

Polyethylene pipe, anti-freeze carrier fluid

Thermally conductive bentonite grout filling

Compresssor

Expansion valve

Heat pump

Ground heat exhanger

Vertical closed loop heat exchanger

CondenserEvaporator

Compressor

Expansion valve

Heat pump

Standing column well

Submersible pump

M

Water table

Groundwater

Injection well

Rock/soil

Open loop standing column well heat exchanger

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Base load production Peak load production


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172. A schematic of the preferred GSHP system configuration is presented in Figure VIII-5.

Figure VIII-5: Schematic of GSHP System


173. It can be seen in the schematic how the GSHP is supplemented by a coal boiler and solar thermal collectors. The coal boiler provides peak and reserve capacity. The maximum supply temperature of a GSHP is typically limited at the level of 65 to 70 °C (depending on temperature in cooling side) with a standard solution in which compressors are connected in parallel, even if, over 80 °C can be achieved by connecting multiple compressors in series for lifting the supply temperature. In any case, the maximum design temperature in the building heating circuit is required only few hours during the year when the outdoor temperature is very low. The required supply temperature stays below 70 °C most of the time meaning that heat pump can supply the required temperature most of the time alone without any supplementary capacity. When the required supply temperature (or capacity) in the building exceeds the maximum supply temperature (or capacity) of the heat pump peak boiler is connected in series with heat pump for lifting the temperature (or capacity) for the required level.

174. Solar thermal collectors can be combined within a GSHP system to produce domestic hot water in summer time, and more importantly, the ground loop can be re-charged by the solar heating system during the summer time which improves the efficiency of the GSHPs and prolongs the geological age of the thermal well.

V. INVESTMENT

175. The investment costs of a GSHP system, operating under Mongolian conditions, is given as follows:

Table VIII-6: Schematic of GSHP System

Investment cost estimates ($000s) GSHP

100

GSHP

50

Coal

Boilers

Solar

thermal

Material/equipment (exc. VAT) 77 68 37 13

Heat pump

Storage tank

Ground loop circulation pump

Ground source heat exchanger

Heat exchanger – Ground loop / Solar

thermal loop

Bulding internal heating and hot water circuits

Coal boiler

Solar collectors

Solar thermal circulation pump

Heating circulation pump

Heating circulation pump

Ground level

Ground loop

Heating circuit

Solar thermal circuit


78

Investment cost estimates ($000s) GSHP

100

GSHP

50

Coal

Boilers

Solar

thermal

Construction (exc. VAT) 172 93 7 2

Auxiliary cost 45 35 8 3

Total investment costs ($000s) 294 196 52 18

Installed capacity (kW) 132 132 200 29

Total EPC unit cost ($/kW) 2,224 1,488 258 620


176. The indicators “100” and “50” indicate the required drilling depth in meters; drilling depth depend on ground characteristics. 100 meters is a base case for deep closed loop ground heat exchanger, and 50 meters represents a case where groundwater open loop standing column well heat exchanger is possible and the total drilling depth would be only 50 meters.

177. The total EPC unit cost for the GSHP 100 has been estimated at $2,224 per kW and for GSHP 50 at $1,488 per kW (without any supplementary technology). Adding coal boiler and solar collector in the system the total system cost for GSHP 100 is at $1,094 per kW and for GSHP 50 at $801 per kW; this assumes the total capacity of 332 kW (solar thermal capacity has not taken into account as it does not increase the maximum supply capacity during the peak load).

178. A Levelized Cost of Energy comparison has been developed for GSHP’s, coal boilers and electric boilers. The LCoE curves show that GSHP’s would be economic, when compared to coal boilers if the capacity factor was above 60%; the demand capacity factor is only 33%.

Figure VIII-7: Summary Economic / Financial Evaluation


179. Finally, GSHP systems were compared economically and financially against coal-fired boilers, which are the most common form of heating in the existing public buildings, and also against electric boilers.

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GSHP 100 GSHP 50

Electric boilers Coal boilers

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GSHP 100 GSHP 50

Electric boilers Coal boilers


79

180. A summary of the economic and financial performance (WACC 5%, 20-year calculation period) of GSHP system against the coal-fired and electric boilers is given as follows:

Table VIII-8: Summary Economic / Financial Evaluation

Economic Financial GSHP 100 GSHP 50 GSHP 100 GSHP 50

NPV against coal boilers $000s -86 -18 -228 -156

NPV against electric boilers $000s 198 267 20 92

IRR against coal boilers % 1.5% 3.9% -5.0% -6.3%

IRR against electric boilers % 12.2% 19.5% 5.8% 10.4%

LCoE, GSHP system $/MWh 73.9 66.0 61.0 52.7


181. The comparison shows that coal boilers have low capital costs and low operating costs (coal cost is low at $ 51.9 / MWh). The economic evaluation narrows the net present value gap considerably when the cost of carbon is taken into account. Nevertheless, it is apparent that a grant subsidy may be required to support GSHP’s. A pilot installation in Khovd will reveal more accurate costs and benefits, thereby defining the subsidy gap.

182. A detailed technical report is attached to this summary report as Appendix F.


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IX. FINANCE / ECONOMICS

W. INTRODUCTION

183. The proposed project aims to develop the renewable based distributed energy system in the Mongolia’s Western and Altai-Uliastai regions. The proposed project will have the following components:

- Component A: Distributed renewable energy system development. The component will be implemented with two batches to develop a total of 41.0 MW of renewable energy throughout the project implementation period from 2018 to 2022. The first batch will develop a total of 25.5 MW of distributed renewable energy systems in Umnugovi, Uvs province, Altai, Govi Altai province, Uliastai, Zavkhan province, and Altai-Soum, Govi-Altai province. The second batch will see construction of another 16.0 MW of distributed renewable energy capacity in Telmen, Zavkhan province, and Moron, Khovsgol province.

- Component B: Shallow ground heat pump demonstration. The component will install heat capacity of shallow ground heat pump in pre-selected public buildings in the targeted region to supply air pollutant free space heating.

184. The total project cost is MNT 161.7 billion or $66.1 million. The project will be financed with ADB loan (MNT98.0 billion or $40.47 million), the Japan Fund for Joint Crediting Mechanism (JFJCM) grant (MNT14.7 billion or $6.0 million), SREP grant (MNT35.8 billion or $14.6 million) and contribution of the Government of Mongolia (MNT13.2 billion or $5.21 million). The project will be implemented during 2018–2023 (in 2023, only some minor project administration costs are anticipated).

185. The Ministry of Energy (MOE) is the executing agency of the project. The Western Region Energy System State Owned Corporation (WRES), Altai-Uliastai Energy System State Owned Corporation (AUES), and National Renewable Energy Centre (NREC) are the project implementing agencies.

X. HISTORICAL FINANCIAL PERFORMANCE AND CORPORATE

PROJECTIONS FOR IMPLEMENTING AGENCIES

186. In 2012–2016, gross profit margin of WRES was negative reflecting that sales tariffs could not cover costs of sales, however it was constantly improving (from -120% in 2012 to -34% in 2016). Net profit margin was fluctuating in-between -62% (2013) and -4% (2015), with best results in 2015 (-4%) and -10% (2016) mainly due to significant state subsidies received during these years. The company’s liquidity was good. Due to increase of the owners’ equity, WRES’s formal solvency stayed on high levels (in average the share of long-term liabilities in total capital was 49% over the period of 2012-2016). Despite of such good solvency, the debt service coverage and interest coverage of the company was poor due to significant losses. Selected WRES’s key historical financial ratios are presented together with the company’s corporate projections.

187. In AUES, during 2012 – 2016, sales revenues could not cover costs of sales, and the company was receiving state subsidies. However, the EBITDA margin was negative over the whole observation period, although it improved from -404% in 2012 to -33% in 2016. Net profit margin was also negative, reaching its best value (-37%) in 2016. The company’s liquidity was good. Analysis of the company’s solvency shows that although the long-term debt in the total capital was in average 41%, the interest coverage and debt service ratios were negative due to poor financial results. Selected AUES’s key historical financial ratios are presented together with the company’s corporate projections.

188. During 2012 – 2016, NREC’s operating income was negative. Its EBIT was fluctuating in-between -5% (2013) and -122% (2015). In 2016, EBIT was -56%. Net profit margin was positive


81

but decreasing from 16% in 2012 to 3% in 2014; it was negative in 2015 (-114%) and 2016 (-62%). Unlike the operating profit which was negative during 2012-2016, NREC’s non-operating incomes were positive during 2012-2015; in 2014-2016, a significant share of non-operating income came from rental activities. The company’s liquidity was on a good level during 2012-2015. However, the situation dramatically changed in 2016 when the current ratio achieved only 63%. Although NREC was not attracting any long-term debt financing, its high share of short-term debt financing and the decline in total shareholders’ equity caused by negative retained profits during 2012-2016 raise concerns about the company’s solvency and sustainability. Unless the situation has improved in 20171 NREC will become insolvent very soon. Selected NREC’s key historical financial ratios are presented together with the company’s corporate projections.

189. The corporate financial projections for WRES and AUES were modelled with the following target indicators:

• Net profit margin for the companies to be at least zero in 2023 and 5% in 2030;

• Share of long term debt in total capital not more than 70% (according to the current estimate, during 2018-2030, max share for WRES is 55% (2022) and for AUES 69% (2023))

190. Since the nature of NREC business is not regulated the same way as the one of WRES and AUES, and because under normal conditions revenues from SGHP heat sales will play a minor role in NREC’s business, no particular covenants have been set for NREC.

191. Assumed increase of retail electricity tariffs for WRES’ and AUES’ end-consumers over time is shown in the table below.

Table IX-1: Projected Tariffs for WRES and AUES

MWh = megawatt hour, MNT = Mongolian tugrik

Source: Asian Development Bank estimates.

192. Mongolia’s benchmark for affordability is electricity and heating costs equalling to not more than 5% of the total household income. However, this target is not “hard”, and slight deviations are usually acceptable. It is expected that in WRES, the share of utility expenditures will be in average 5.50% during 2018-2022, thereafter being below 5%. In AUES, the customers’ share of utility expenses in their household income will be in average 5.35% during 2018-2020, being below 5% thereafter. Assuming no distribution charges for the off-grid hybrid PV-Wind facility in Altai Soum, the share of utility expenditures in the local household income will be ca. 6.15% during 2018-2022 (max 7.13% in 2018), and below 5% thereafter. SGHP will be used in public buildings (kindergartens) thus they are not subject of affordability analysis.

1 By the time of preparation of this report, financial statements for 2017 were not available.

2017 2019 2021 2023 2025 2030

WRES

Weighted average electricity tariff MNT/MWh 118 844 134 369 161 699 193 064 228 898 342 222Electricity transmission tariff MNT/MWh 80 000 117 333 154 667 192 000 251 857 380 000Average electricity end-user tariff MNT/MWh 198 844 251 702 316 366 385 064 480 755 722 222

AUES

Weighted average electricity tariff MNT/MWh 49 799 90 231 118 627 134 267 136 660 163 696Electricity distribution tariff MNT/MWh 35 000 69 800 104 600 139 400 199 657 277 000Average electricity end-user tariff MNT/MWh 84 799 160 031 223 227 273 667 336 317 440 696


82

193. The following tables present financial projections for WRES, AUES and NREC until 2030.

Table IX-2: Summary of Financial Projections for WRES

EBIDTA = earnings before interest, depreciation, taxes and amortization, MNT = Mongolian Tugrik.

Source: Asian Development Bank estimates

Table IX-3: Summary of Financial Projections for AUES


Table IX-4: Summary of Financial Projections for NREC


WRES

Year ending December 31 2012 2013 2014 2015 2016 2017 2021 2023 2030

Key Indicators (MNT billion)

Gross profit (5.98) (6.37) (7.24) (4.04) (4.08) (6.47) 10.42 12.35 30.68Operating profit (EBITDA) (7.67) (3.69) (10.81) (8.15) (1.15) (10.04) 3.34 4.19 17.05Net profit (0.89) (3.56) (3.86) (0.44) (1.21) (12.52) (1.87) 0.02 6.69Total assets 81.05 80.17 73.71 82.22 82.00 74.31 150.64 143.61 172.19

Financial Ratios

Gross profit margin (1.20) (1.11) (0.97) (0.37) (0.34) (0.54) 0.29 0.24 0.23Operating profit margin (1.54) (0.64) (1.44) (0.74) (0.10) (0.83) 0.09 0.08 0.13Net profit margin (0.18) (0.62) (0.51) (0.04) (0.10) (1.04) (0.05) 0.00 0.05Current ratio 4.64 2.71 1.39 4.39 5.82 2.87 0.43 1.21 10.93Quick ratio 3.58 2.20 0.91 3.37 4.95 1.16 0.36 1.07 10.78Debt to Total Capital 0.55 0.55 0.49 0.44 0.44 0.46 0.54 0.55 0.36Interest Coverage n/a n/a n/a n/a (33.25) 0.00 2.14 3.39 10.20Debt Service Coverage n/a (220.82) (678.60) (225.98) n/a (29.49) 1.32 1.40 1.99

ProjectedActual

AUES



Gross profit (7.07) (0.98) (8.55) (7.39) (2.99) (0.79) 12.10 18.48 30.92Operating profit (EBITDA) (8.69) (2.38) (11.64) (7.82) (2.27) (2.59) 5.14 12.26 20.67Net profit (1.79) (5.78) (4.18) (3.75) (2.53) (7.79) (4.45) 0.01 2.17Total assets 105.11 99.61 108.76 107.09 104.64 102.70 186.41 194.20 186.52

Financial Ratios

Gross profit margin (3.29) (0.46) (1.91) (1.29) (0.44) (0.30) 0.77 0.81 0.72Operating profit margin (4.04) (1.11) (2.61) (1.37) (0.33) (0.99) 0.33 0.54 0.48Net profit margin (0.83) (2.69) (0.94) (0.66) (0.37) (2.99) (0.28) 0.00 0.05Current ratio 19.68 4.06 4.51 5.20 6.06 0.26 6.10 22.88 26.65Quick ratio 15.30 1.78 2.15 2.31 2.99 0.00 4.11 21.47 26.00Debt to Total Capital 0.41 0.44 0.40 0.41 0.42 0.45 0.67 0.69 0.62Interest Coverage (152.93) n/a n/a n/a n/a 0.00 3.01 4.20 5.92Debt Service Coverage (543.87) (125.85) (32.46) (42.31) n/a (1.50) 1.91 1.52 1.40

ProjectedActual

NREC



Gross profit 0.42 0.47 0.54 0.01 0.15 0.86 2.19 2.54 4.35Operating profit (EBITDA) (0.08) (0.06) (0.02) (0.34) (0.17) 0.36 1.18 1.46 2.52Net profit 0.13 0.01 0.05 (0.32) (0.19) 0.33 1.59 1.19 2.15Total assets 1.08 0.74 0.95 0.71 0.60 0.61 6.94 10.17 22.12

Financial Ratios

Gross profit margin 0.54 0.45 0.35 0.03 0.49 0.98 0.96 0.96 0.96Operating profit margin (0.11) (0.05) (0.01) (1.22) (0.56) 0.42 0.52 0.55 0.56Net profit margin 0.16 0.01 0.03 (1.14) (0.62) 0.38 0.70 0.45 0.48Current ratio 2.64 218.92 3.46 1.36 0.63 11.19 29.22 35.72 58.65Quick ratio 0.99 111.50 2.31 0.63 0.25 7.85 28.34 35.04 58.24Debt to Total Capital 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.08 0.04Interest Coverage n/a n/a (2.01) n/a n/a 0.00 80.33 51.32 85.16Debt Service Coverage (20.50) n/a (2.42) (322.03) n/a 0.00 113.73 18.40 23.61

ProjectedActual


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Y. FINANCIAL MANAGEMENT ASSESSMENT AND RELATED RISKS

194. The financial management capacity assessment (FCMA) proved that MOE, WRES, AUES and NREC have relatively good financial management practices and qualified management and operational staff which will secure successful implementation of the project. The major financial management weakness is related to (i) lack of experience in ADB’s policies and procedures, (ii) lack of segregation of duties and some internal controls; (iii) less independent role of internal auditor, and (iv) absence of procedures for foreign exchange risk hedging and financial management reporting adjustment. These risks can be mitigated in accordance with the risk management and mitigation plan presented in Report on Finance & Economics.

195. Mongolia’s public sector and entities level financial management are relatively robust and present moderate risks from ADB’s perspective. Identified risks in lacking knowledge on ADB procedures, foreign exchange risk management, internal audit, and financial reporting can be mitigated according to the risk management plan presented in the relevant part of Report on Finance & Economics.

Z. FINANCIAL ANALYSIS OF THE PROJECT

196. It is assumed that the sub-projects will start in 2018 and be fully completed in 2022. Capital investments in new RE facilities will be financed mainly with ADB loan. The total estimated ADB loan amounts to $40.47 million (this amount includes CAPEX investment, all contingencies, IDC and commitment fee of which only CAPEX and physical contingency are included in the project financial analysis). The total ADB loan includes $8.44 million to WRES, $31.77 million to AUES and $0.26 million to NREC. It is expected that financial contribution of the Government of Mongolia will cover associated value added tax (10%) costs ($5.21 million in total), SREP will cover $14.60 million for on-grid wind and PV solutions in WRES and AUES and off-grid wind and PV hybrid in AUES, and the Japan Fund for Joint Crediting Mechanism (JFJCM) grant will cover $6.00 million associated with investments in on-grid Uliastai PV and institutional strengthening.

197. The base costs of the sub-projects are summarized in the following table.

Table IX-5: Base Cost Table

Component Sub-component MUSD

TOTAL 49.03

Component 1:

Distributed Renewable Energy System

First Batch

Umnugovi 10 MW Wind 13.71

Govi Altai 10 MW PV 9.71

Uliastai 5 MW PV + Battery 7.95

Altai Soum Hybrid 0.92

Second Batch

Muren 10 MW PV 9.38

Salkhit Khutul 5 MW Wind 6.36

Component 2:

Shallow Ground Heat Pump Demonstration Shallow Ground Heat Pumps 1.00



84

198. Summary of WACC values used for individual sub-projects and the whole project is presented below.

Table IX-6: Project WACC, real terms

Sub-Project After-tax WACC

Umnugovi 10 MW Wind 2.909 % Govi Altai 10 MW PV 2.813 % Uliastai 5 MW PV + Batt 3.034 %

Altai Soum Hybrid (PV & Wind) 2.007 % Muren 10 MW PV 2.007 % Salkhit Khutul 6 MW Wind 2.007 % SGHP 3.491 % Whole project 2.594 %


199. Financial cost-benefit analysis of the sub-projects and the whole project was carried out both without grant financing and with grant financing. Summary of the project financial analysis results is given in the following tables.

Table IX-7: Summary of Financial Cost-Benefit Analysis by sub-Project, no Grants

WACC (%) FIRR (%)

FNPV

(million MNT) FBCR

Umnugovi 10 MW Wind 2.91 % 9.34 % 34 178 1.90

Govi Altai 10 MW PV 2.81 % 6.02 % 11 924 1.43


Altai Soum Hybrid (PV & Wind) 2.01 % 6.49 % 1 540 1.60

Muren 10 MW PV 2.01 % 5.24 % 12 475 1.47


SGHP 3.49 % 5.69 % 604 1.22

Whole Project 2.59 % 7.31 % 90 552.33 1.65


Table IX-8: Summary of Financial Cost-Benefit Analysis by sub-Project, with Grants

WACC (%) FIRR (%) FNPV

(million MNT) FBCR

Umnugovi 10 MW Wind 2.91 % 21.29 % 54 353 4.03

Govi Altai 10 MW PV 2.81 % 13.13 % 24 526 2.61


Altai Soum Hybrid (PV & Wind) (*) 2.01 % 6.49 % 1 540 1.60

Muren 10 MW PV (*) 2.01 % 5.24 % 12 475 1.47


SGHP 3.49 % 84.34 % 2 902 6.76

Whole Project 2.59 % 12.89 % 139 694.41 2.57



85

200. Under given assumptions, even without grants all sub-projects will have positive FNPV and FIRR higher than their WACC. Among the sub-components, only the Uliastai PV plant has a very modest feasibility, while other sub-components as well as the whole project show rather good profitability. Injection of grants makes the Uliastai PV sub-component quite profitable while further improving other sub-projects’ feasibility.

201. The results of the sensitivity analysis of FIRR for the Project and its sub-components are summarized in the tables below.

Table IX-9: Sensitivity Analysis for FIRR, no Grants

Base Case

CAPEX +10%

OPEX +10%

Physical output or

price change

-10%

Delay in operations

2 yrs.

All combined

Umnugovi Wind 9.34 % 8.23 % 9.30 % 8.06 % 7.3 % 4.52 %

Govi Altai PV 6.02 % 5.11 % 5.77 % 4.71 % 4.4 % 1.33 %

Uliastai PV + Batt 3.43 % 2.59 % 3.12 % 2.13 % 2.2 % -0.96 %

Altai Soum Hybrid 6.49 % 5.48 % 6.02 % 4.86 % 4.5 % 0.68 %

Muren PV 5.24 % 4.40 % 5.01 % 4.02 % 3.7 % 0.74 %

Salkhit Khutul Wind 12.38 % 11.15 % 12.32 % 10.94 % 9.6 % 6.54 %

SGHP 5.69 % 4.58 % 5.29 % 3.94 % 3.7 % -0.11 %

Whole project 7.31 % 6.32 % 7.14 % 6.01 % 5.5 % 2.56 %


Table IX-10: Sensitivity Analysis for FIRR, with Grants

Base Case

CAPEX +10%

OPEX +10%

Physical output or

price change

-10%

Delay in operation

s 2 yrs.

All combined

Umnugovi Wind 21.29 % 17.55 % 21.22 % 19.04 % 15.5 % 10.57 %

Govi Altai PV 13.13 % 10.88 % 12.76 % 11.21 % 9.6 % 5.16 %

Uliastai PV + Batt 14.47 % 11.12 % 13.89 % 12.09 % 9.9 % 4.53 %

Altai Soum Hybrid (*) 6.49 % 5.48 % 6.02 % 4.86 % 4.5 % 0.68 %

Muren PV (*) 5.24 % 4.40 % 5.01 % 4.02 % 3.7 % 0.74 %

Salkhit Khutul Wind (*) 12.38 % 11.15 % 12.32 % 10.94 % 9.6 % 6.54 %

SGHP 84.34 % 41.77 % 80.50 % 65.59 % 26.0 % 12.50 %

Whole project 12.89 % 10.89 % 12.65 % 11.13 % 9.6 % 5.61 %


202. Analysis of the sub-projects’ switching values2 confirms that among all sub-components, Uliastai PV is the most sensitive to the change of key input parameters. The second most sensitive sub-component is the SGHP demonstration project in Khovd. Switching values for the sub-projects’ and whole project’s FNPV are shown in the tables below.

2 Switching value is the change in a parameter which brings NPV to zero and thus shifts the project decision from acceptance

to rejection.


86

Table IX-11: Switching Values for FNPV, no Grants

CAPEX OPEX

Physical output

Delay in operation

s

Price change

Umnugovi Wind 89.6 % 1383.1 % -45.1 % 10 -45.1 %

Govi Altai PV 42.8 % 120.5 % -23.3 % 5 -23.3 %

Uliastai PV + Batt 4.5 % 12.7 % -3.1 % 1 -3.1 %

Altai Soum Hybrid 59.9 % 86.1 % -25.7 % 6 -25.7 %

Muren PV 46.8 % 125.5 % -24.7 % 5 -24.7 %

Salkhit Khutul Wind 171.9 % 1467.5 % -59.9 % 14 -59.9 %

SGHP 21.6 % 53.8 % -12.5 % 3 -12.5 %

Whole project 65.5 % 251.3 % -33.5 % 7 -33.5 %


Table IX-12: Switching Values for FNPV, with Grants

CAPEX OPEX

Physical output

Delay in operation

s

Price change

Umnugovi Wind 142.5 % 2168.9 % -70.5 % 17 -70.5 %

Govi Altai PV 88.0 % 247.2 % -47.6 % 11 -47.6 %

Uliastai PV + Batt 64.8 % 175.5 % -43.1 % 10 -43.1 %

Altai Soum Hybrid (*) 59.9 % 86.1 % -25.7 % 6 -25.7 %

Muren PV (*) 46.8 % 125.5 % -24.7 % 6 -24.7 %

Salkhit Khutul Wind (*) 171.9 % 1467.5 % -59.9 % 14 -59.9 %

SGHP 103.6 % 257.2 % -59.8 % 14 -59.8 %

Whole project 101.0 % 379.0 % -51.1 % 12 -51.1 %


AA. ECONOMIC ANALYSES OF THE PROJECT

1. COST-BENEFIT ANALYSIS

203. Energy volumes generated by new RES are assumed to release local demand which would otherwise stay suppressed (electricity) and to be consumed by a new kindergarten (heat); thus, the RES generation volumes are treated as incremental.

204. Economic internal rate of return (EIRR) was calculated for the sub-projects based on economic cost-benefit stream analysis. According to ADB recommendations3, the economic cost of capital (ECOK) in this analysis is 9%, though it could be lowered to 6% for the social projects including rural electrification.

205. The economic analysis was performed with and without grants expected from Scaling up Renewable Energy Program (SREP) and Japan Fund for Joint Crediting Mechanism (JFJCM). These grants were treated in the way similar to the one applied in financial analysis of the projects described earlier.

3 ADB Guidelines for Economic Analysis, 2017.


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206. The following tables show results of the analysis (EIRR and ENPV) including effects of the grants.

Table IX-13: Summary of Economic Cost-Benefit Analysis by sub-Project, no Grants

ECOK (%) EIRR (%)

ENPV

(million MNT) EBCR

Umnugovi 10 MW Wind 9.00 % 19.31 % 39 292 2.03

Govi Altai 10 MW PV 9.00 % 13.82 % 12 383 1.46

Uliastai 5 MW PV + Batt 9.00 % 9.14 % 264 1.01

Altai Soum Hybrid 9.00 % 26.82 % 3 888 2.51

Muren 10 MW PV 9.00 % 12.47 % 8 279 1.35


SGHP 9.00 % 27.66 % 3 132 2.24

Whole Project 9.00 % 15.82 % 87 441 1.66


Table IX-14: Summary of Economic Cost-Benefit Analysis by sub-Project, with Grants

ECOK (%) EIRR (%)

ENPV

(million MNT) EBCR

Umnugovi 10 MW Wind 9.00 % 37.94 % 58 677 4.10

Govi Altai 10 MW PV 9.00 % 25.07 % 24 535 2.64


Altai Soum Hybrid (*) 9.00 % 26.82 % 3 888 2.51

Muren 10 MW PV (*) 9.00 % 12.47 % 8 279 1.35


SGHP 9.00 % 415.71 % 5 215 12.98

Whole Project 9.00 % 24.90 % 134 414 2.57


207. As it can be seen in the above tables, under given assumptions, even without grants all sub-projects will have positive ENPV and EIRR higher than the ECOK. Among the sub-components, the Uliastai PV plant has a rather low feasibility, while other sub-components as well as the whole project show rather good profitability. Injection of grants makes the Uliastai PV sub-component quite profitable while further improving other sub-projects’ feasibility.

208. The results of the sensitivity analysis of EIRR for the Project and its sub-components are summarized in the tables below.

Table IX-15: Sensitivity Analysis for EIRR, no Grants

Base

Case

CAPEX

+10%

OPEX

+10%

Physical

output or

price change

-10%

Delay in

operation

s 2 yrs.

All

combined

Umnugovi 10 MW Wind 19.31 % 17.58 % 19.27 % 17.36 % 14.6 % 11.55 %

Govi Altai 10 MW PV 13.82 % 12.48 % 13.59 % 12.11 % 10.6 % 7.51 %

Uliastai 5 MW PV + Batt 9.14 % 8.01 % 8.86 % 7.60 % 6.9 % 3.97 %


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Base

Case

CAPEX

+10%

OPEX

+10%

Physical

output or

price change

-10%

Delay in

operation

s 2 yrs.

All

combined

Altai Soum Hybrid 26.82 % 24.10 % 26.38 % 23.39 % 17.5 % 12.69 %

Muren 10 MW PV 12.47 % 11.31 % 12.27 % 10.98 % 9.7 % 6.86 %

Salkhit Khutul 6 MW

Wind

20.71 % 19.01 % 20.66 % 18.78 % 15.7 % 12.50 %

SGHP 27.66 % 24.16 % 27.15 % 23.36 % 17.1 % 12.78 %

Whole project 15.82 % 14.35 % 15.67 % 14.04 % 12.1 % 9.02 %


Table IX-16: Sensitivity Analysis for EIRR, with Grants

Base

Case

CAPEX

+10%

OPEX

+10%

Physical

output or

price change

-10%

Delay in

operations

2 yrs.

All

combined

Umnugovi 10 MW Wind 37.94 % 31.84 % 37.86 % 34.25 % 24.9 % 19.20 %

Govi Altai 10 MW PV 25.07 % 21.31 % 24.68 % 22.25 % 17.4 % 12.58 %

Uliastai 5 MW PV + Batt 26.65 % 20.80 % 25.94 % 23.00 % 17.1 % 11.12 %

Altai Soum Hybrid (*) 26.82 % 24.10 % 26.38 % 23.39 % 17.5 % 12.69 %

Muren 10 MW PV (*) 12.47 % 11.31 % 12.27 % 10.98 % 9.7 % 6.86 %

Salkhit Khutul 6 MW

Wind (*)

20.71 % 19.01 % 20.66 % 18.78 % 15.7 % 12.50 %

SGHP 415.71 % 226.65 % 410.44 % 358.68 % 65.6 % 41.24 %

Whole project 24.90 % 21.52 % 24.66 % 22.19 % 17.5 % 13.09 %


209. Analysis of the sub-projects’ switching values confirms that among all sub-components, Uliastai PV with Battery is the most sensitive to the change of key input parameters. Switching values for the sub-projects’ and total project’s ENPV are shown in the tables below.

Table IX-17: Switching Values for ENPV, no Grants

CAPEX OPEX Physical

output

Delay in

operations

Price

change

Umnugovi Wind 102.5 % 2530.2 % -49.6 % 8 -49.6 %

Govi Altai PV 45.6 % 208.9 % -27.2 % 4 -27.2 %

Uliastai PV + Batt 1.2 % 5.1 % -1.0 % 1 -1.0 %

Altai Soum Hybrid 151.1 % 394.1 % -52.2 % 8 -52.2 %

Muren PV 35.0 % 168.1 % -22.5 % 3 -22.5 %

Salkhit Khutul Wind 126.2 % 1956.6 % -54.2 % 8 -54.2 %

SGHP 124.3 % 437.3 % -49.2 % 8 -49.2 %

Whole project 66.0 % 429.8 % -36.4 % 5 -36.4 %



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Table IX-18: Switching Values for ENPV, with Grants

CAPEX OPEX

Physical

output

Delay in

operations

Price

change

Umnugovi Wind 153.1 % 3778.5 % -74.1 % 14 -74.1 %

Govi Altai PV 90.4 % 414.0 % -54.0 % 9 -54.0 %

Uliastai PV + Batt 61.3 % 261.6 % -49.2 % 8 -49.2 %

Altai Soum Hybrid (*) 151.1 % 394.1 % -52.2 % 8 -52.2 %

Muren 10 PV (*) 35.0 % 168.1 % -22.5 % 3 -22.5 %

Salkhit Khutul Wind (*) 126.2 % 1956.6 % -54.2 % 8 -54.2 %

SGHP 207.0 % 728.3 % -81.9 % 17 -81.9 %

Whole project 101.5 % 660.7 % -55.9 % 9 -55.9 %


2. DISTRIBUTION ANALYSIS

210. The distribution analysis involves reconciliation of economic and financial flows used in the project analysis. The relevant annual cash flows (taxes, difference between economic and financial costs as well as economic and financial prices for energy paid by consumers, and environmental benefits) for 2018-2045 are discounted at ECOK 9%.

211. Major stakeholders of the project are the Government of Mongolia, energy consumers, and regional environment. Since all labor is treated as skillful labor in scarce supply, it means this group of stakeholders neither gains nor loses from this project in the economic scenes since this workforce can easily find work and get competitive salaries and wages disregarding whether this project is implemented or not.

212. Results of the distribution analysis for the whole project are presented in the table below. The major benefit from the project would be gained through reduction of CO2 emissions.

Table IX-19: Distribution of the Project Economic Benefits

PV @ ECOK 9% Million MNT Share in total

economic gain

Government 12 003 11.4 %

of which taxes 22 306 21.2 %

influence of shadow FX -10 303 -9.8 %

Consumers 5 758 5.5 %

Regional / emissions reduction 87 434 83.1 %

Total 105 195 100.0 %


3. RISK ANALYSIS

213. Risk analysis was performed for the economic feasibility Base Study without consideration of SREP and JFJCM grants. Input parameters under uncertainty include energy yield of the new RES, change in CAPEX, change in FX rate and sales price of the energy produced by the RES.

214. Results of the risk analysis are presented in the table below. Under the given assumptions on uncertainty, three sub-projects (Uliastai PV & battery, Altai Soum Hybrid and Muren PV) do not yield an acceptable level of EIRR (higher than ECOK 9%). If the hurdle rate were 6%, Muren PV’s expected EIRR would pass the feasibility test (against the given benchmark of 9%, Muren fails only


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narrowly yielding 8.97%). However, despite the expected EIRRs for the three projects do not pass the economic hurdle rate test, the whole project’s feasibility can be considered as solid, with expected EIRR of 13.03% and expected ENPV of MNT 54,843 million (USD 22.5 million).

Table IX-20: Risk Analysis vs Base Study Results

EIRR ENPV (million MNT)

Base

Study

Risk

Analysis

Difference

(Risk

Analysis-

Base Study)

Base

Study

Risk

Analysis

Difference

(Risk

Analysis-

Base

Study)

Umnugovi Wind 19.31 % 19.62 % 0.31 % 39 292 43 730 4 438

Gobi Altai PV 13.82 % 9.86 % -3.96 % 12 383 2 533 -9 849

Uliastai PV & Batt 9.14 % 5.60 % -3.54 % 264 -6 320 -6 583

Altai Soum Hybrid 26.82 % 3.13 % -23.68 % 3 888 -1 202 -5 089

Muren PV 12.47 % 8.97 % -3.50 % 8 279 216 -8 063

Salkhit Khutul Wind 20.71 % 16.16 % -4.56 % 20 206 12 913 -7 293

SGHP 27.66 % 25.00 % -2.66 % 3 132 2 972 -159

Whole Project 15.82 % 13.03 % -2.80 % 87 442 54 843 -32 599


215. Histograms of EIRR and ENPV of the whole project are presented in the figure below.

Table IX-21: EIRR and ENPV Histograms, Whole Project

216. The most important factor influencing expected EIRR of the whole project is economic electricity sales price for on-grid RES located in the AUES service area. Its adverse effect alone can decrease the EIRR for the whole project for as much as 4.18%. The second largest influence factor is CAPEX of materials and construction. Altogether, in an unfavourable case the sub-projects’ CAPEX may cause additional decrease of 1.18% in the EIRR of the whole project. The third most important factor is adverse development of FX rate which may add up to 0.87% drop in the whole project EIRR when compared to the Base Study.

217. A detailed Finance / Economics Report is attached to this summary report as Appendix G.


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X. SOCIAL SAFEGUARDS

BB. POVERTY & SOCIAL ANALYSES

218. The Project will improve living, health, and environmental conditions for a population of some 304 000 people including 140,000 women by promoting safe and reliable low-emission energy sources. The project will affect 46% of the population of the Project regions or 77.2 thousand households.

219. The Project is bound to secure an increase in incomes, poverty reduction, and improvements of living standards for the population in the area. The expected social and economic results of the proposed activities include the following:

• Throughout the Western region, the Project will reduce the dependence of the population and enterprises from remote Aimags on external and unstable energy supply systems and will contribute to regional development by expanding opportunities to create new jobs in the improved energy infrastructure. Given that the price of alternative energy sources is significantly lower than that of conventional ones, the Project will support the high expectations for sustainable and profitable RE technologies to be replicated.

• The generation of electricity through the use of alternative energy sources will contribute to the solution of the region's environmental problems and will reduce levels of burning products of organic fuel, the use of which is very common nowadays, especially in traditional gers. As a result, this will lead to better health indicators of the population, especially women and children, who spend by far the most time in the conditions of the closed premises heated by coal and fire wood and are exposed to risk of pulmonary diseases.

• Improved electricity supply will also have a positive impact on the health indicators of the population, especially women and children, who have to use unsafe water in the absence of or due to high prices of fuels in the winter period. It will reduce the incidence of diseases, especially acute intestinal infections. The expected decrease in the number of illness cases will allow for not only lower expenses on healthcare for the households, but also higher economic activity of employable members of the households, women especially. It is if great importance for low-income households which fail to bear expenditure on necessary medical assistance.

• Ensuring stable and sufficient heating, as well as hot water supply, can also have a most positive impact on the health of the population as a whole, women and children especially, by sparing them from heavy physical exertion and reducing the risks to health and safety.

• In the structure of family budgets, a significant reduction in the expenses on of purchasing, delivering and storing fuels is expected (up to 10-15% of total household expenditure in winter). First of all, this will concern those households without access to centralized power supply, as well as to heating and hot water supply services and therefore have to resolve these issues on their own. Financial benefits will also be extracted by those households that currently pay for low-quality (inadequate and unreliable) electricity, heating and hot water supply services.

• The project will significantly improve the quality of life for vulnerable groups - the elderly, the disabled, and children. It is of particular importance that the Project will improve the quality of life for women, who bear the main responsibility not only for maintaining the sanitary condition of the dwelling and preparing food, but also for caring for the young, elderly and disabled members of the family. The implementation of the Project will reduce the use of unpaid women’s and child labor. It will enable to reduce unproductive


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labour, giving at least a few days free from housework for women per month, which is bound to stimulate their economic activity.

• Eliminating disruptions in electricity supply will also contribute to the creation of normal conditions for doing homework and will improve access to the Internet for students of schools and other educational institutions.

• By far the biggest benefits upon the realization of the Project are expected for poor households which have to reduce their consumption of energy for various purposes to economize. As a result of the Project implementation, the poor households will be able to increase the consumption of electricity as well as their households’ access to adequate and reliable systems of hot water and home heating supply without substantial rise in spending.

• A direct effect on employment in the Project area will not be substantial. A small number of jobs for qualified personnel will be generated in organizations responsible for the maintenance and repair of the grids and hot water/heating supply network. The proposed construction work will lead to the creation unqualified jobs including the involvement of the unemployed.

• A significant indirect effect for the population is envisaged in terms of widened employment and self-employment opportunities in such sectors of the economy as catering, service and the processing of agricultural produce.

• Given the high interest of the population (including women) in the development and expansion of entrepreneurial activities, including home-based one, stable energy supply can significantly affect the sustainability of small business projects and family businesses.

• Providing an adequate hot water, heating and power supply for social infrastructure (kindergartens, secondary schools, vocational colleges, healthcare institutions, and other social and cultural establishments) and private enterprises in the service and food-catering sectors will also reduce the risk of epidemics and will enable the reallocation of a portion of government budgets to other urgent local development needs.

• Project implementation will increase the capacity of private businesses engaged in production of goods and services, primarily in the sectors of production and processing of food products, public catering, and private medical clinics and hospitals, will enhance the volume, quality and competitiveness of their products, which indirectly promote the increase and stabilization of income of workers employed in these enterprises.

• Implementation of the Project will also contribute to the recovery and enhancement of the local social capital, by reducing the number of conflicts over unstable energy supply and by smoothing over conflict situations. It will increase the level of trust in local authorities, government, and self-governance.

• The Project will enhance the institutional development of maintenance and supply services with regard to electricity, hot water and centralized heating via the rise in the capacity of local suppliers of service in the spheres. An increase in social capital will be promoted via both mobilization of local communities and their wider participation in the realization of the Project (consultations, surveys, participation on technical events under the Project) and trainings on energy saving, sanitation, and protection of the service’ consumers’ rights.

• The project implementation will raise significantly the efficiency of related both budgetary and non-budgetary investment projects implemented in the Project area, especially projects aimed at the development of small business, family entrepreneurship and domestic labour.


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220. A detailed Poverty and Social Analyses Report is attached to this summary report as Appendix H.

CC. LAND ACQUISITION AND RESETTLEMENT

221. Local authorities and communities’ representatives took active part in consultations and site selection and generally approved the proposed subprojects.

222. The sites visit missions of TA consultants and interviews on land acquisition and resettlement with local authorities and the energy system representatives showed that the project is to be classified category C for both involuntary resettlement and indigenous peoples. The project does not entail permanent or temporary land acquisition, land use restriction, demolition of any structure, or relocation of people.

223. All subprojects are either inside the existing substation premises or the government owned land. The project will be implemented in areas with no vulnerable ethnic minority communities; various ethnic groups are mixed and live together and there is no significant difference on lifestyle, socioeconomic status, or vulnerability between individuals of ethnic majority and minorities. Thus, it is not expected to have any impact on ethnic minorities.

224. Land use orders were received for all sub-Projects from the relevant local authorities.


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XI. ENVIRONMENT

225. Environmental assessments have been prepared according to ADB standards and guidelines. The detailed assessments are provided in the form of Appendices to this report

Appendix I – Initial Environmental Examination (Draft)

Appendix J – Environmental and Social Assessment and Review Framework

Appendix K – Climate Risk Vulnerability Assessment (Draft)

Technical Assistance Consultant’s Reportp. aues transmission grid stabilization 57 vii. altai soum (off-grid hybrid re) 61 q. background 61 r. condition of existing electricity supply

Documents