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48030-001: Strategy for Northeast Asia Power System Interconnection · 2020-02-14 · on regional...
Transcript of 48030-001: Strategy for Northeast Asia Power System Interconnection · 2020-02-14 · on regional...
Technical Assistance Consultant’s R eport Project Number: 48030-001 February 2020
Mongolia: S trategy for Northeast Asia Power S ystem Interconnection (Cofinanced by the C limate Change Fund, the People’s R epublic of China R egional Cooperation and Poverty R eduction Fund, and the R epublic of Korea e-Asia and Knowledge Partnership Fund)
Prepared by
E lectricite de France
Paris, France
For the Ministry of E nergy, Mongolia
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.
TA 9001-MON: S trategy for Northeast Asia Power S ystem Interconnections
E DF R eferences: C IS T – DCO – PhL – 18 - 207
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.
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FOREWORD
The project Team would like to thank:
- The Ministry of Energy of Mongolia for easing direct discussions with the National Dispatching Center, TRANSCO and Public Entities in Mongolia
- The ADB’s Country Coordinators of Mongolia, People’s Republic of China, Republic of Korea, Japan for their support:
Mongolia: Mr. Byambasaikhan
PRC: Ms. Geng Dan (Danna)
ROK: Mr. Jung-Hwan Kim
Japan: Mr. Omatsu Ryo and Mr. Shota Ichimura
Here is a reminder of the place of the Module 2 in the Project organization:
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TABLE OF CONTENTS
EXECUTIVE SUMMARY ......................................................................................................................... 7
1 METHODOLOGY ............................................................................................................................ 8
1.1 Objectives of the study ............................................................................................................. 8
1.2 Methodology of the study ......................................................................................................... 8
1.3 Model used ................................................................................................................................. 9
1.4 List of inputs and outputs of the model ................................................................................ 11
1.5 Review of the benefits and costs of power system evolutions .......................................... 12
2 INPUT DATA USED IN THE STUDY ............................................................................................ 13
3 STUDY RESULTS ......................................................................................................................... 18
3.1 SCENARIOS 2036 .................................................................................................................... 18
3.1.1 Impact of interconnection and RES in Mongolia on the NAPSI region generation mix......... 19
3.1.2 Results on profitability ........................................................................................................... 20
3.1.3 Impact of interconnection and RES in Mongolia on the NAPSI region CO2 emissions ........ 22
3.1.4 Use of the interconnection lines ............................................................................................ 23
3.1.5 Variants with RES in Korea/Japan instead of Mongolia ........................................................ 24
4 SCENARIOS 2020 AND 2026 ...................................................................................................... 26
5 CONCLUSIONS ............................................................................................................................ 27
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LIST OF TABLES
Table 1. Demand and generation dataset (outside coal, gas and fuel oil, which are optimized
by the model) ...................................................................................................................... 14
Table 2. Global economic hypotheses on commodities ....................................................... 15
Table 3. Variable generation costs dataset (input of power plants) ...................................... 15
Table 4. Annualized fixed generation costs dataset (include CAPEX and OPEX) ................ 16
Table 5. Cost assessments for interconnection overhead lines/submarine cables (annualized
costs, including CAPEX, OPEX and losses) ........................................................................ 17
Table 6. Comparison of CAPEX and load factors in 2036 for the renewables in Mongolia, Korea
and Japan ........................................................................................................................... 25
LIST OF FIGURES
Figure 1 The five steps of a Cost-Benefit Analysis ................................................................. 9
Figure 2 Main features of the optimization model used ........................................................ 10
Figure 3 Objective function of the optimization model .......................................................... 10
Figure 4 Constraints to be respected by the optimization model .......................................... 11
Figure 5 Notation used in the description of the optimization model .................................... 11
Figure 6 The four main scenarios used in the study ............................................................ 13
Figure 7 Generation mix (in energy) compared to current and other projected pie charts .... 18
Figure 8 Generation mix and deviations (in energy) in the NAPSI region for the 2036 scenarios
............................................................................................................................................ 19
Figure 9 Results of the Cost-Benefits Analyses: annual gains in the 2036 scenarios .......... 20
Figure 10 Impact of interconnection and RES in Mongolia on the CO2 emissions of the power
sector in the NAPSI region .................................................................................................. 22
Figure 11 Diagrams of the load flow distribution on the various interconnection lines, showing
the direction and the magnitude of the flows ........................................................................ 23
Figure 12 Cost-Benefit Analyses of different variants of location for installing renewables
Mongolia, Korea, and Japan ................................................................................................ 24
Figure 13 Cost-Benefit Analyses of the 2020 and 2026 scenarios compared to the 2036
moderate interconnection scenario ...................................................................................... 26
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PHYSICAL UNITS AND CONVERSION FACTORS
bbl barrel (1t = 7.3 bbl)
cal calorie (1 cal = 4.1868 J)
Gcal Giga calorie
GWh Gigawatt-hour
h hour
km kilometer
km² square kilometer
kW kilo Watt
kWp kilo Watt peak (solar PV)
kWh kilo Watt hour (1 kWh = 3.6 MJ)
MBtu Million British Thermal Units (= 1 055 MJ = 252 kCal)
one cubic foot of natural gas produces approximately 1,000 BTU
MJ Million Joule (= 0,948.10–3 MBtu = 238.8 kcal)
MW Mega Watt
m meter
m3/d cubic meter per day
mm millimeter
mm3 million cubic meter
Nm3 Normal cubic meter, i.e. measured under normal conditions, i.e. 0°C and 1013 mbar
(1 Nm3 = 1.057 m3 measured under standard conditions, i.e. 15°C and 1013 mbar)
pu per unit
sqm Square meter
t ton
toe tons of oil equivalent
tcf ton cubic feet
°C Degrees Celsius
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ABBREVIATIONS AND ACRONYMS
ADB Asian Development Bank
BNEF Bloomberg New Energy Finance
BTB Back To Back
CAPEX Capital Expenditure
CBA Cost-Benefit Analysis
CCGT Combined Cycle Gas Turbine
CEPRI China Electric Power Research Institute
CHP Combined Heat Power
EENS Expected of Energy Not Supplied
ERC Energy Regulatory Commission
ESRI Environmental Systems Research Institute
GDP Gross Domestic Product
GHI Global horizontal irradiation
GIS Geographical Information System
GTI Global Tilted Irradiation/Irradiance
HPP Hydro Power Plant
HV High Voltage
HVAC High Voltage Alternative Current
HVDC High Voltage Direct Current
IEA International Energy Agency
IEC International Electrotechnical Commission
IHS Markit Provider of business and finance information for major industries
IRENA International Renewable Energy Agency
LCoE Levelized Cost of Electricity
MCDA Multi-criteria decision Analysis
MoE Ministry of Energy (Mongolia)
MNT Mongolian currency
NDC National Dispatching Center (Mongolia)
NEA North East Asia
NREC National Renewable Energy Corporation (Mongolia)
NREL National Renewable Energy Laboratory of the USA
NTPG National Power Transmission Grid (Mongolia)
NWP Numerical Weather Prediction
O&M Operation and Maintenance
OCGT Open Cycle Gas Turbine
OPEX Operational expenditure
PRC People’s Republic of China
PV Photovoltaic
RES Renewable Energy Source
TL Transmission Line
TPP Thermal Power Plant
UA Unit of Account
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UNESCAP United Nations Economic and Social Commission for Asia and the Pacific
USD United States Dollar
VSC Voltage Source Converter
WACC Weighted Average Cost of Capital
WLC Weighted linear combination
In this report, "$" refers to US dollars.
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EXECUTIVE SUMMARY
The first objective of this module is to assess the economic feasibility of a massive Renewable
Energy Resources (RES) development in the Mongolian Power System to be largely exported
on regional energy markets.
The second objective is to determine the impact of these potential evolutions on the level of
CO2 emissions of the electric sector in the NAPSI region.
The main findings and key messages are the following:
- Interconnection between North East Asia countries is beneficial, already in the pre-
sent situation and with the existing generation fleets
- Interconnection lines are all used in both directions, allowing countries to export or import according to hours and situations
- Development of renewable generation in Mongolia will bring additional benefits in the mid-term (2036), due to drastic cost reduction of renewables in Mongolia in the forth-coming decades
- Generating RES in Mongolia brings higher profit than producing the same amount of renewable energy in Korea or Japan
- Beyond financial profits, these future evolutions will bring other valuable benefits (re-duction of CO2 emissions, contribution to the achievement of the clean energy objec-tives for the different countries, job creations, opportunities for adaptation of national networks).
The report starts with the description of methodology followed, then the input data of the study
are presented, and finally the results are shown and explained.
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1 METHODOLOGY
1.1 OBJECTIVES OF THE STUDY
The first objective of this module is to assess the economic feasibility of a massive Renewable
Energy Resources (RES) development in the Mongolian Power System to be largely exported
on regional energy markets. The second objective is to determine the impact of these potential
evolutions on the level of CO2 emissions of the electric sector in the NAPSI region.
1.2 METHODOLOGY OF THE STUDY
The methodology adopted is based on the principles of Cost-Benefit Analysis (CBA).
This approach allows to assess the societal value of a project, for large geographical and
functional perimeters. Therefore, it is well suited to help institutional actors to carry out strategic
arbitrages on power system development (generation + network).
The main features of a CBA are the following:
- CBA assesses societal value (instead of private values)
- CBA is based on a comparison of factual and counterfactual scenarios and allows to explicitly identify alternative scenario’s impacts on power system processes, in order to avoid risks of double counting of benefits or CO2 emissions
- Sensitivity analyses allow to cope with long term scenarios uncertainties.
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Figure 1 The five steps of a Cost-Benefit Analysis
1.3 MODEL USED
The model used has a twofold purpose:
- It is a generation expansion planning tool that optimizes the share of each available asset type. The optimization is done with respect to the system fundamentals, e.g. cost structure of generation technologies and level of power demand. The time horizon can be multiannual so that long-run trade-off can be made.
- The model also provides hourly optimization of the dispatch of thermal generation in order to satisfy the residual demand (i.e. demand - RES intermittent generation) all over the years represented in the study.
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Figure 2 Main features of the optimization model used
The main modelling characteristics are the following:
- The model can take into account several balancing areas linked by transmission lines dimensioned by their commercial exchange capacity.
- The temporal resolution is hourly, either on all days along the year, or only on repre-sentative days. The model embeds a clustering module that can select the representa-tive days among a large amount of data according to their statistical representation.
- In each balancing area, power supply is made of dispatchable generation units with an aggregated view by technology, and must-run generation units (wind and solar power, run-of-river hydro, CHP) that can be modelled either by a generation time series or by capacity factor time series associated with generating capacity.
The next figures present successively the objective function of the model (i.e. the criteria used
to optimize the system), the constraints to be respected, and the notations.
Figure 3 Objective function of the optimization model
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Figure 4 Constraints to be respected by the optimization model
Figure 5 Notation used in the description of the optimization model
1.4 LIST OF INPUTS AND OUTPUTS OF THE MODEL
The input dataset mainly consists of the following items:
- Hourly power demand time series within each area represented
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- Hourly generation or capacity factor time series for must-run assets
- Description of generation units (variable, fixed O&M and investment costs, initial ca-pacity, CO2 intensity, availability factor)
- Capacities of transmission lines
- Value of loss load
- CO2 value (if any, this feature being not used in the simulations presented here, all results corresponding to cases where the CO2 price was taken equal to zero).
The outputs include the following results:
- Optimal portfolio: within each area
- Hourly generation by technology and use of transmission lines
- Global system costs, marginal costs, revenues of generation units CO2 emissions.
1.5 REVIEW OF THE BENEFITS AND COSTS OF POWER SYSTEM EVOLU-TIONS
The development of renewable generation capacities in Mongolia combined with the creation
of new interconnection links in the region give birth to new energy exchanges between the
countries/areas concerned.
On the one hand, these energy exchanges bring potential benefits that can be decomposed
as follows:
- Benefits linked to the better use of the conventional generation fleets (pooling effects on investment and operational costs)
- Benefits linked to the substitutions between conventional generation units located in the various countries (switching effects on investment and operational costs, e.g. be-tween coal and gas units)
- Benefits linked to the substitution between conventional generation and renewable generation (which may have a significant impact in terms of CO2 emission reduction).
On the other hand, the associated costs to be considered are the development costs of the
renewable energy sources in Mongolia, and the development costs of the interconnection in-
frastructures.
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2 INPUT DATA USED IN THE STUDY
The main study criteria and the fundamental data of the systems have been specified in the
Module 3 report.
The analyses object of the Module 2 are carried out on four main scenarios combining various
time horizon (2020, 2026, 2036), various levels of renewable capacity in Mongolia (0.3GW,
5GW, 10GW, 100GW), and various configurations of interconnection between NAPSI coun-
tries/areas. These scenarios are the same as those used in Module 4 (generation resources).
Figure 6 The four main scenarios used in the study
Considering the current LCOEs of Mongolian wind and PV, and their perspectives of reduction
along the coming two decades, we have chosen to share the amount of renewable capacity
considered in Mongolia haft on wind and half on PV for all the scenarios considered.
Hence for 10GW capacity in Mongolia, and taken into account the respective power factors of
wind and PV in 2036 for Mongolia (Cf. Module 4), that gives 5GW of wind (generating 21TWh
annually), and 5GW of PV (generating 9TWh annually). Of course for 100GW total capacity
developed in Mongolia, the wind/solar capacities and generations are ten times greater.
A coherent data set on demand levels and structure of the generation fleet has been consti-
tuted. For the purpose of the study, the generation fleet is divided in two parts; one part is
fixed and considered as input of the model (nuclear, hydro, wind and solar), the rest
(conventional generation) is optimized by the model, and constitutes outputs of the
simulations.
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Table 1. Demand and generation dataset
(outside coal, gas and fuel oil, which are optimized by the model)
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A coherent dataset on economic parameters has been constituted, based on International En-
ergy Agency data and projections (World Energy Outlook 2017, New Policies Scenario), com-
pleted by direct information from ADB Country Coordinators and expertise of the consulting
team.
Table 2. Global economic hypotheses on commodities
The costs of fuels for conventional generation at the input of the power plants have been cal-
culated from the global economic hypotheses on commodities, and the transportation costs
between mines/gas fields/exporting-importing ports, making use of the “netback method” that
allows to build coherent values between countries/areas importing or exporting energy either
under the form of raw materials, or electricity. These costs are consistent with assessments
elaborated by IHS Markit (for cost of fuels in the Chinese provinces), Russian official agencies
(for cost of fuels in the Russian federal districts), and Mongolian government (for cost of coal
in Mongolia).
Table 3. Variable generation costs dataset (input of power plants)
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The fixed costs of generation units have been taken according to IEA data projections (Power
generation assumptions in the New Policies and 450 Scenarios in the World Energy Outlook
2016), completed by direct information from ADB Country Coordinators and expertise of the
consulting team.
Table 4. Annualized fixed generation costs dataset (include CAPEX and OPEX)
As far as interconnection infrastructures are concerned, assessments have been done in the
framework of Module 5 (networks) and used in this study. The following table sums up the
values retained for the 2036 scenario (moderate interconnection). The costs presented are
annualized costs and include CAPEX, OPEX and losses. They are given in the form of a range
of values to take into account the high uncertainties unavoidably associated with such an ex-
ercise of costing projection for this type of infrastructures.
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Table 5. Cost assessments for interconnection overhead lines/submarine cables
(annualized costs, including CAPEX, OPEX and losses)
The costs relative to the infrastructures to be deployed for the other scenarios (2026, 2036
with large interconnection) can be deduced from these figures, using down-scaling or up-scal-
ing factors based on the transmission capacities considered.
Annualized Estimated Cost
Node 1 Node 2 Length and type
Transmission
Capacity
(GW)
Range (M$/year)
Mongolia (UB) Mongolia (Gobi) 430 km (OHL) 2 49 - 86
Mongolia (UB) Russia-Siberia 520 km (OHL) 2 52 - 91
Mongolia (Gobi) China-East 520 km (OHL) 6 165 - 297
China-East Rep. of Korea20 km (OHL) +
500 km (cable)3 138 - 258
China-East Russia-Far East 750 km (OHL) 2 58 - 106
Rep. of Korea Japan20 km (OHL) +
230 km (cable)2 62 - 112
Rep. of Korea Russia-Far East50 km (OHL) +
1400 km (cable)2 84 - 156
Russia-Siberia Russia-Far East 3500 km (OHL) 2 142 - 272
Russia-Far East Japan2200 km (OHL) +
200 km (cable)2 125 - 238
2036 scenario (moderate interconnection)
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3 STUDY RESULTS
We choose to present results beginning by the scenario 2036, because the logic we adopt is
to deal first with the long-term fundamentals that will guide decisions makers in defining their
targets of energy policy and their priority in terms of long-term investments. The results of the
interim scenarios 2020 and 2026, presented afterwards, will be helpful to determine the best
ways and rhythms to reach the target and manage the transition periods.
3.1 SCENARIOS 2036
First of all, we present below the generation mix that results from the optimization model, com-
pared to current and other projected pie charts.
Figure 7 Generation mix (in energy) compared to current and other projected pie charts
We recall that the model is set to optimize only the fossil fuel fleet (coal, gas, fuel oil), the other
technologies (renewable and nuclear) being considered as fixed by the model.
In the mix simulated for 2036 (left hand side), coal occupies a large place (around 40%), gas
a small place (4%), and fuel oil a very limited place (less than 1%).
For comparison purpose, the current mix (in energy) of NAPSI region in 2016 (in the middle of
the figure) is composed of 5% nuclear, 58% coal, 10% gas, 3% fuel oil, 18% hydro, 3% wind
and 2% solar.
We can also compare the simulated pie chart for 2036 with the one (right hand side) reconsti-
tuted from IEA projected data for 2040 (World Energy Outlook 2017, New Policies scenario),
in which nuclear would stand for 11%, coal 38%, gas 11%, fuel oil nearly 0%, hydro 16%, wind
12%, PV 10%, and other renewables 3%. The main differences between these two pie charts
(the 2036 simulated one and the 2040 IEA one) are likely to have two causes. The first is
related to the hypotheses on renewable energies (supposedly more developed in our dataset).
Total
10800 TWh
Total
7800 TWh
Total
10900 TWh
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The second is the carbon pricing policy (not applied in our simulations), applied for the IEA in
China with a price of 35$/tCO2 and in Korea with a price of 48$/t. This last fact must certainly
explain the discrepancies observed on the development of gas and coal.
3.1.1 IMPACT OF INTERCONNECTION AND RES IN MONGOLIA ON THE NAPSI REGION GENERA-
TION MIX
Figure 8 Generation mix and deviations (in energy) in the NAPSI region for the 2036 scenarios
On the right hand side of the diagram, the positive (respectively negative) deviations observed
correspond to an increase (respectively a decrease) of energy generated by the concerned
technology, compared to the situation where the countries are isolated. The main changes
concern wind, solar, coal and gas technologies.
The first bar (Interco + 10GW RES) shows that moderate interconnection associated with the
implementation of 10GW renewables in Mongolia (generating around 30 TWh), allows to de-
crease coal generation by 8 TWh, and gas generation by 22 TWh, compared to the reference
case (all countries isolated).
The second bar (Large Interco +100GW RES) shows that high capacity interconnection asso-
ciated with the implementation of 100GW renewables in Mongolia (generating around 300
TWh), allows to decrease to a high extent the use of coal (by around 170 TWh) and gas (by
around 130 TWh).
Hence we see that in these two cases, the combined effect of interconnection and RES in
Mongolia is a substitution of coal and gas by this renewable energy.
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3.1.2 RESULTS ON PROFITABILITY
Figure 9 Results of the Cost-Benefits Analyses: annual gains in the 2036 scenarios
The figure must be read as follows:
- For each situation simulated, the model calculates the global system costs (sum of all fixed and variable costs of the optimized part of the generation mix), and compares them to the global system costs obtained in “country isolated” situation. The cost dif-ference between this factual and counterfactual scenarios gives the gross gain brought by the modifications made in-between. Therefore, the gross gain corresponds to the collective benefits calculated by the optimization model, including the savings in terms of investment expenses and operation expenses, but excluding the investments linked to interconnection infrastructures and renewables in Mongolia.
- Then we subtract the investments linked to interconnection infrastructures and renew-ables in Mongolia, this gives the net gain. Therefore, the net gain corresponds to the final profits including all expenses and revenues. The net gain is given with a range, to take into account uncertainties, particularly on interconnection infrastructure costs.
- As we reason with annualized costs and variable costs on a yearly basis, all gross and net gains are annual values
- As the gain values are not the same order of magnitude between the two cases pre-sented on the figure, we have adopted two different scales, the left hand side one being five times enlarged than the right hand side one.
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The results presented on Figure 9 can be interpreted as follows
- Starting from the left hand side, the first set of dots and bar (Interco) shows the sole effect of the implementation of interconnection (moderate interconnection). The annual gross gain is around 4Bn$, and the annual net gain (integrating the cost of intercon-nection) is around 3Bn$. In other words, the implementation of the sole intercon-
nection is financially profitable (annual net collective benefits of around 3Bn$).
- The second set of dots and bar (Interco + 10GW RES) shows that interconnection associated with the implementation of 10GW renewables in Mongolia, generates a higher annual gross gain (around 5.1Bn$), and a higher annual net gain (3.2Bn$) than the interconnection alone. In other words, renewables in Mongolia bring additional
benefits to the implementation of the interconnection, increasing the annual col-
lective net benefits to a level of approx. 3.08Bn$.
- The third set of dots and bar (Large Interco +10GW RES) shows the outcome of in-creasing largely the interconnection capacity between NAPSI countries, conserving 10GW RES in Mongolia. This results in a large increase of both annual gross gain (18.2Bn$) and annual net gain (9.3Bn$). In other words, the perspective of increas-
ing largely the interconnection capacities brings extended net collective bene-
fits.
- The fourth set of dots and bar (Large Interco +100GW RES) shows that high capacity interconnection associated with the implementation of 100GW renewables in Mongolia, allows to reach high level of annual gross gain (27.6Bn$) and annual net gain (10.7Bn$). In other words combining high capacity interconnection implementa-
tion and development of large amounts of renewables in Mongolia is collectively
highly profitable.
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3.1.3 IMPACT OF INTERCONNECTION AND RES IN MONGOLIA ON THE NAPSI REGION CO2 EMIS-
SIONS
Figure 10 Impact of interconnection and RES in Mongolia on the CO2 emissions
of the power sector in the NAPSI region
The CO2 emissions of the power sector is an output of the model, and results from the effective
operation of the fossil fuel fired plants in the various scenarios simulated.
The results presented on the figure can be interpreted as follows
- The implementation of the interconnection (moderate interconnection) cumulated to the development of 10GW RES in Mongolia (generating around 30TWh) leads to a global
CO2 emission saving of 17Mt (comparison of the light green bar in the middle with the black one at the left hand side). In other words, the substitution emission factor of this capacity of 10GW of Mongolian renewables is 0.57tCO2/MWh, sign that renewa-bles replace approximately coal for a quarter, and gas for the remaining three quarters. Note that this distribution between coal and gas substituted is coherent with the gener-ation variation between these two cases observed on Figure 8
- The dark green bar on the right shows that high capacity interconnection associated with the implementation of 100GW renewables in Mongolia, allows to reach a high level of CO2 emission savings. The 300TWh of renewables reduce CO2 emissions
by 210Mt, which gives a substitution emission factor (for this 100GW capacity of Mongolian renewables) of 0.70tCO2/MWh. This is the sign that renewables replace approximately coal for one half, and gas for the other half (this is also coherent with what is shown on Figure 8)
- We observe that the substitution emission factor depends on the amount of renewa-ble energy injected in the system. This is mainly due to the fact that, as presented in §1.5, changes introduced in the power system have several superimposed impacts, such as pooling effects and switching effects between technologies, which may result when cumulated in an apparent non-linear behavior.
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3.1.4 USE OF THE INTERCONNECTION LINES
Let us analyze now the load flows running through the various cross border lines, on the 2036
scenario with moderate interconnection and 10GW RES in Mongolia.
Figure 11 Diagrams of the load flow distribution on the various interconnection lines,
showing the direction and the magnitude of the flows
Figure 11 shows for each line the load flow distribution, in the form of its annual load flow
duration curve. These curves are deduced from chronological simulation results, by ranking
them from the highest to the lowest values. These curves allow to observe globally the sharing
of hours between the two load flow directions, and in terms of load flow magnitude.
We observe that most of the lines are used in both directions, depending on the situations
occurring along the year. That means that the interconnection lines are not purely used as
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export or import means, but have a shared function that contributes to bring a collective value
to the whole system.
Moreover, the fact that most of the lines are saturated at the maximum or minimum load a large part of the time is a good indicator that the capacities chosen in this scenario (2036 /
moderate interconnection) do not lead to overinvestments.
3.1.5 VARIANTS WITH RES IN KOREA/JAPAN INSTEAD OF MONGOLIA
These variants allow to assess the profitability of renewables developed in Korea or Japan,
and to compare it to the profitability of the same amount of renewable energy developed in
Mongolia.
In order to be consistent, we have compared situations where the additional renewable energy
is made equal, and not the additional renewable installed capacity. There is a certain differ-
ence, because of the power factors are not the same in the various countries, due to natural
resource quality discrepancies.
Hence, the various renewable additional capacities that are equivalent to the 10GW in Mon-
golia (5GW wind capacity producing annually 21TWh + 5GW PV capacity producing 9TWh
annually) are the following:
- For the Republic of Korea : an addition of 13.9GW of RES (7.3GW of wind producing
annually approx. 20TWh, and 6.6GW of PV producing annually approx. 10TWh)
- For Japan : an addition of 12.6GW of RES (7.3GW of wind producing annually approx.
20TWh, and 5.3GW of PV producing annually approx. 10TWh).
Figure 12 Cost-Benefit Analyses of different variants of location for installing renewables
Mongolia, Korea, and Japan
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Of course, the left hand side of the diagram (corresponding to renewables installed in Mongo-
lia) is identical to what we have already seen (Cf. Figure 9).
In comparison we observe that producing the same amount of renewable energy in the Re-public of Korea or in Japan brings lower benefits. In other words, generating RES in Mongolia
brings higher profit than producing the same amount of renewable energy in Korea or
Japan.
The explanation comes from the consideration of the quality of wind and solar resources (char-
acterized by their power factors) and the perspectives of CAPEX levels in Mongolia in 2036,
compared to those of Korea and Japan. These last points are illustrated by Table 6.
Table 6. Comparison of CAPEX and load factors in 2036
for the renewables in Mongolia, Korea and Japan
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4 SCENARIOS 2020 AND 2026
Figure 13 sums up the results obtained when simulating the 2020 and 2026 scenarios, and
comparing them to the 2036 scenario (moderate interconnection), case we have already ana-
lyzed (Cf. Figure 9).
As the gain values are not the same order of magnitude between the various cases presented on the figure, we have adopted two different scales, the left hand side one being twenty times
enlarged than the right hand side one.
Figure 13 Cost-Benefit Analyses of the 2020 and 2026 scenarios
compared to the 2036 moderate interconnection scenario
The comparison leads to formulate the following statements:
- Trades through the existing cross border lines bring already benefits: the net gain
for the 2020 case with interconnection (in the left hand side of the diagram) is positive
(around 200M$/year)
- The development of interconnection is economically justified, and allows to in-
crease the social welfare in the NAPSI region: the net gain for the 2026 case with
interconnection (in the middle of the diagram) is largely positive (around 2.5Bn$/year)
and significantly larger than the 2020 net gain
- Renewables in Mongolia exported through the interconnection will be directly
profitable only in the mid-term: the net gains corresponding to Interco + RES are
above the net gains of the interconnection alone only for the 2036 scenario, and not in
the 2020 and 2026 scenarios.
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5 CONCLUSIONS
Interconnection between North East Asia countries is beneficial, already in the present situa-
tion and with the existing generation fleets.
Interconnection lines are all used in both directions, allowing countries to export or import ac-
cording to hours and situations.
Development of renewable generation in Mongolia will bring additional benefits in the mid-term
(2036), due to drastic cost reduction of renewables in Mongolia in the forthcoming decades.
Generating RES in Mongolia brings higher profit than producing the same amount of renewa-
ble energy in Korea or Japan.
Beyond financial profits, these future evolutions will bring other valuable benefits (reduction of
CO2 emissions, contribution to the achievement of the clean energy objectives for the different
countries, job creations, opportunities for adaptation of national networks).
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MINISTRY OF ENERGY, GOVERNMENT OF MONGOLIA
Government Building 14, Khan-Uul District
Chinggis Avenue, 3-r Khoroo
Ulaanbaatar, 17060 Mongolia
Contact: Mr. Chimeddorj Demchigjav
General Director of Energy Policy Department
ASIAN DEVELOPMENT BANK
6 ADB Avenue
Mandaluyong City, 1550
Metro Manila, Philippines
Contact: Mr. Teruhisha Oi
Project Manager, Energy Division (EAEN), East Asia Department (EARD)
Consultant: EDF
EDF CIST, Immeuble Spallis, 2 rue Michel Faraday
93282 Saint-Denis Cedex
France
Contact: Mr. Philippe Lienhart
Strategy Innovation New Business Manager EDF CIST
Strategy for NAPSI Technical Assistance to Mongolia Team Leader
Deliverable: Module 2 Report on Market and Power Assessment
Date: 8 March 2018