Renewable Energy 108 (2017) 321e335

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Renewable Energy
journal homepage: www.elsevier.com/locate/renene

Costs and beneﬁts of large-scale deployment of wind turbines andsolar PV in Mongolia for international power exports
Takashi Otsuki
Asia Paciﬁc Energy Research Centre (APERC), The Institute of Energy Economics, Japan (IEEJ), Inui Bldg.-Kachidoki 11F, 1-13-1 Kachidoki, Chuo-ku, Tokyo
104-0054 Japana r t i c l e i n f o

a b s t r a c t

Article history:
Received 5 October 2016
Received in revised form
28 December 2016
Accepted 7 February 2017
Available online 13 February 2017

Power grid interconnection has gained attention in Northeast Asia (NEA) as a means to effectively utilize
the abundant renewable resources in Mongolia. This paper quantiﬁes the potential economic andenvironmental beneﬁts of deploying massive wind turbines and solar PV in Mongolia for power exports.
The author uses an NEA-wide multi-region power system model formulated as a linear programming
problem. The analysis considers power systems characteristics, such as the seasonal and daily electricload curves of the NEA regions.
The simulation results show that the large-scale Mongolian renewables contribute to signiﬁcant CO2
reductions in NEA. China, in particular, beneﬁts from a signiﬁcant reduction in coal-ﬁred generation.
However, huge investments would be required for the massive renewables and cross-boundary transmission facilities, pushing up electricity supply cost. The relevant planning organizations need to carefully consider these environmental opportunities and economic barriers before implementation. This
paper also investigates the economic impacts of transmission route circuity due to avoiding transmission
through the Democratic People's Republic of Korea (DPRK). Our results imply modest effects of thecircuity on the total system cost; availability of routes through the DPRK would not signiﬁcantly increase
the beneﬁts to the NEA system of integrating massive renewables in Mongolia.
© 2017 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).

Keywords:
Grid interconnections
Electricity trade
Renewable energy
Northeast Asia Super Grid
Linear programming
Energy systems modeling

1. Introduction
International power grid interconnection becomes potentiallyattractive to decarbonize the electricity sector in Northeast Asia1(NEA), especially after several recent regional events, including
the Fukushima nuclear disaster in Japan, the power shortage in
Korea, and increased concern about air pollution in China. These
three major electricity consuming countries, together accounting
for 31% of world electricity demand in 2013 [1], have recently
reafﬁrmed the importance of lower-carbon and more resilient energy systems. Their governments and/or industries are currently
discussing alternative energy supply options, including imports
from foreign countries [2e4]. At the same time, other neighboring
NEA countries with abundant renewable resources, in particular,
Mongolia, have shown strong interest in developing renewables

E-mail address: takashi.otsuki@aperc.ieej.or.jp.
This paper deﬁnes Northeast Asia as the following six countries: China, theDemocratic People's Republic of Korea (DPRK), Japan, the Republic of Korea (Korea),
Mongolia and Russia.
1and exporting electricity to attract new investment [5]. According
to Elliiott et al. [6] and Energy Charter et al. [7], estimated wind andsolar photovoltaics (PV) potential in Mongolia reach 1100 GW2 and1500 GW, respectively. Thus, several multilateral interconnection
schemes have been proposed in NEA, with a focus on renewableenergy developments in Mongolia for international power exports[7]. Also, relevant organizations, including GEIDCO initiated by
State Grid Corporation of China, have started discussion and planning from various perspectives, such as economic, environmental,
legal and institutional [3,4,8].
This research aims to examine the economic viability of developing the abundant wind and solar resources in Mongolia for international power exports. There have previously been various
studies examining renewable energy integration and transmission
expansion in each individual country in NEA3; however, few papers
so far have quantiﬁed the costs and beneﬁts of the Mongolian

2Good-to-Excellent wind resource estimated in Elliott et al. [6].
For example, see Refs. [47] and [48] for China [28], and [49] for Japan [50], and[51] for Korea and [52] for Russia.
3

http://dx.doi.org/10.1016/j.renene.2017.02.018
0960-1481/© 2017 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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T. Otsuki / Renewable Energy 108 (2017) 321e335

renewables for power trade. There are several studies which have
performed modeling and analyses of international power trade in
NEA [7,9e13]. Yet, these studies, except for Energy Charter et al. [7]and Otsuki et al. [13], did not consider wind and solar resources in
Mongolia. As for Energy Charter et al. [7] and Otsuki et al. [13], thescenarios discussed in their analyses limited the Mongolian renewables to 100 GW (50 GW wind and 50 GW solar PV), despite thehuge estimated potential.
This study utilizes an NEA-wide multi regional power systemmodel, formulated as a linear programming problem. This model
considers power system characteristics, including seasonal anddaily electric load curves of each region and output proﬁles of renewables in Mongolia. Compared with the author's previous study[13], the novelty of this paper includes the two points as follows: Quantiﬁcation of the costs and beneﬁts of large-scale (such as2000 GW-scale) deployments of Mongolian renewables for international exports, and Expansion of the modeled grids in China, from two in our previous study (CH-N and CH-NE) to the all markets in this paper(Fig. 2). This enhancement allows us to consider China's large
market and to discuss the optimal grid integration of massive
Mongolian renewables in a more comprehensive andconvincing manner.
Although uncertainties exist regarding the degree of futureenergy cooperation that can be achieved in NEA, the author believes that this paper can contribute to stakeholders' and policy
makers' discussions by quantitatively providing ﬁndings and implications from an economic perspective.
This paper proceeds as follows: Section 2 gives an overview ofthe multi-region power system model; Section 3 presents thesimulation results; and Section 4 summarizes major conclusions
and implications, and then proposes a future research agenda.
2. Methods
2.1. Overview of the multi-region power system modelThis paper uses a multi-region power system model developedby Otsuki et al. [13].4 This is a linear programming model, whichaims to minimize a single-year total system cost, consisting of theannualized initial cost, operation and maintenance (O&M) cost, fuelcost and carbon cost for the whole of NEA under various technicaland political constraints. Hence the NEA economies are assumed tocooperate fully to achieve the regional optimization. Although thedegree of future energy cooperation in NEA is uncertain, this paper
assumes the most ideal situation in order to quantify the maximum
beneﬁts potentially obtainable from developing renewables in
Mongolia for power exports. Fig. 1 is a schematic diagram of thismodel. An additional enhancement to the model used in this study
compared to our previous work is the inclusion of battery storage
technology. Detailed mathematical descriptions and model validation are provided in Otsuki et al. [13]. The assumed discount rate
to annualize initial investments is 5%; carbon cost considers only
direct emissions from fuel combustion.
Fig. 2 illustrates the modeled nodes, and lines between nodes
indicate the assumed transmission routes. Two transmission routes
through the DPRK (dotted lines in Fig. 2) are not considered in
Sections 3.1e3.5.1; instead, we perform a sensitivity analysis withthese routes included in Section 3.5.2 as the routes through the4
For its modeling work, Otsuki, et al. [13] refers to the detailed approach in
Komiyama et al. [28], Schaber, et al. [36] and Steinke et al. [46].

Fig. 1. Schematic diagram of the multi-region power system model.

DPRK stand out for presenting signiﬁcant diplomatic challenges.
Regarding temporal resolution, this study selected the same temporal resolution as our previous study [13], which considers thehourly load curves of typical days for ﬁve seasons (summer-peak,summer-average, winter-peak, winter-average, and intermediate).
2.2. Case settings
The author simulated a total of twenty one cases: a Base caseand twenty Mongolian renewables cases (Mon100 GW, Mon200 GW,…, Mon2000 GW) as shown in Table 1. The simulated year in thisstudy is 2030. The Base case assumes no grid expansion from theinitial transmission capacity assumptions. The Mon100 GWMon2000 GW cases attempt to quantify the costs and beneﬁts oflarge-scale deployment of the Mongolian renewables. Each of thesecases adds 100 GW of Mongolian renewable generation capacity tothe previous one, split equally between wind and solar PV. For
example, the Mon100 GW case assumes 50 GW of wind turbinesand 50 GW of solar PV; the Mon200 GW case assumes 100 GW ofwind turbines and 100 GW of solar PV, and so on; the Mon2000 GW
case assumes 1000 GW of wind turbines and 1000 GW of solar PV.
As this paper employs an optimization model, the results in Section
3 present a “best-case” outcome under each level of Mongolian
renewable capacity.
Transmission line capacity and transmitted power are cost
optimized in the Mon100 GW-Mon2000 GW cases. Otsuki et al. [13]limited the net transmission inﬂows at each of the city nodes toreﬂect likely concern about transmission interruptions (Equation(A.26) in Ref. [13]); since this study is seeking to quantify the
maximum beneﬁts potentially obtainable from developing renewables in Mongolia, there is no such constraint here. The model
also determines fossil fuel-ﬁred capacity and generation. However,for coal-ﬁred capacity, we impose upper bounds based on thebusiness-as-usual scenario in APERC [14], reﬂecting each country's
environmental concerns about expanding coal-ﬁred plants. The
assumed carbon price for all cases is 30 USD/tCO2. Further explanations of the assumptions are given in Section 2.3.
In the Mon2000 GW case, 1000 GW of wind turbines and1000 GW of solar PV are about 90% of Mongolia's estimated windpotential and about two-thirds of Mongolia's estimated solar PV
potential. The land area required would be approximately
0.16 million km2, which is equivalent to 10% of the land area ofMongolia. Assumptions for this estimate are as follows: the landarea of Mongolia is 1.56 million km2; the land area required forwind turbines and solar PV cells are about 140 km2/GW [6] and
20 km2/GW [15], respectively. This vast land use would bring somesigniﬁcant social, economic and environmental impacts in
Mongolia, including changes in land-use, industrial structure andtraditional nomadic lifestyles. Comprehensive examination of thesesocio-economic and environmental impacts would be needed infuture studies.

T. Otsuki / Renewable Energy 108 (2017) 321e335

323

Fig. 2. Regional division and assumed transmission routes. Note: The two transmission routes through the DPRK (dotted lines) are considered in Section 3.5.2. City nodes haveelectricity demand as well as generation and storage facilities, while the supply node has only generation and/or storage facilities to export to neighboring nodes.

Table 1Case settings.
Base case
Wind and PV capacity at
Mongolia (MN) node
Cross-boundary transmission
capacity
Other capacity

Carbon priceMongolian Renewables Cases (Mon100 GW case, Mon200 GW case, …, Mon2000 GW case)

Not
50 GW each for the Mon100 GW case, 100 GW each for the Mon200 GW case, …, 1000 GW each for the Mon2000 GW case.
considered.
No new
Economically optimized with no limit on capacity additions. For the supply node (MN), total transmission capacity isadditionsassumed to match the renewable capacity there (explained as constraint (A.28) in Ref. [13]).
City nodes: Fossil fuel-ﬁred and battery capacity are economically optimized, with limits for coal-ﬁred capacity based on the projected
capacity for 2030 from APERC [14]. Renewables and pumped storage capacity is given based on the projection in APERC [14].
MN: Battery installation is allowed. Other technologies are not included.
30 USD/tCO2 for all cases.

2.3. Input data assumptions
2.3.1. Electricity demand and load curves
We estimated electricity demand from the projections in APERC[14]. As APERC [14] shows each of these demands on a countrywide basis, we disaggregated them into nodal demand by referring to historic data for each node [16]. Daily load curves for the ﬁveseasons (Fig. 3) in the modeled China nodes are from JEPIC [17], andthose in the other nodes are estimated in Otsuki et al. [13].

2.3.2. Generation and storage facilities
Fig. 4 depicts initial capacity settings for the generation andstorage facilities. The model is allowed to endogenously add fossil
fuel-ﬁred generation and battery capacity at the city nodes. At the
supply node (MN), power generation capacity is given exogenously
for all cases as shown in Table 1.
The initial capacity of fossil fuel-ﬁred plants is based on existing
capacity [18e21]. For coal-ﬁred plants, we impose the upperbounds as mentioned in Section 2.2. Capacities for renewables(except for Mongolia), nuclear and pumped hydro are given

Fig. 3. Assumed electric load curves at city nodes in the summer-peak and winter-peak seasons (ratio to peak load at each node).

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T. Otsuki / Renewable Energy 108 (2017) 321e335

Fig. 4. Initial capacity settings for city nodes.

exogenously from the projections in APERC [14]. As APERC [14]shows the country-wide capacity, we disaggregate it into nodal
capacity for the modeled nodes in China and Japan, referring to
renewable potential and actual capacity [22,23] as well as futurenuclear and pumped hydro construction information [21,24]. The
initial capacity of battery storage is assumed to be zero at all nodes.
Tables 2e7 show the economic and technical assumptions forgeneration and storage facilities. Cost assumptions (except forannual ﬁxed O&M cost for solar PV in MN), maximum availability,
and output proﬁles of solar PV and wind power in Mongolia (MN)
are based on Otsuki et al. [13]. As for the annual ﬁxed O&M cost forsolar PV in MN, this study considers the cost of cleaning sand dust,
which was estimated from the cleaning costs in a desert area given
in SASIA [25] and the performance of cleaning equipment given in
MIRAI [26]. The carbon content of each fuel type is estimated fromIEA [1,27], and other technical assumptions for generation technologies are from IEA [1], Komiyama, et al. [28] and METI [29]. Datafor pumped hydro and battery storage relies on Komiyama et al.
[28]. The author assumes the use of sodium-sulfur (NaS) batteries.
Speciﬁc fossil fuel prices in 2030 are estimated by increasing
actual 2014 prices at the same rate of increase as the IEA's projected
future energy prices [30]. The main references for the actual 2014
prices are as follows: coal price in China from Qinhuangdao coal
network [31]; gas price in China from Zhang [32]; coal, gas and oil
prices in Japan from IEEJ [33]; coal and gas prices in Korea fromKESIS [20]; and coal price in Russia from JOGMEC [34]. The oil pricein Japan is also applied to other countries, and China's gas price isapplied to Russia. Please note that all references to costs in thisstudy are expressed in real 2014 USD.
2.3.3. Transmission lines
HVDC overhead line technology is assumed for overland transmission, and HVDC cable technology for undersea transmission.
AC-DC conversion stations are installed at the each end of theinterconnection. We estimated transmission line cost referring toresearch and technical papers [35,36] as well as from actual project
information [37e39]. We assume 4.2 Million USD/km (M USD/km)
for HVDC overhead lines (rated power: 3 GW), 7.2 M USD/km for
HVDC undersea cable (rated power: 3 GW) and 300 M USD/GW/
station for AC-DC conversion stations. Fig. 2 shows the assumed
transmission routes. The linear programming model requires thatinterconnection costs be expressed as cost per unit of capacity, so
we calculated the initial cost of each transmission route [USD/kW]
as summarized in Table 8. Assumed lifetime, transmission losses,
AC-DC conversion losses and annual ﬁxed O&M cost are 40 years,
5%/1000 km, 1.5%/station, and 0.3% in a ratio to initial cost for all
line types, respectively [35,36,40]. After our previous study [13], we
re-estimated cost assumptions for transmission lines and AC-DC
converter stations, referring to updated information in several
relevant countries (e.g., METI [39]). These updates resulted inhigher cost assumptions for several transmission routes; forexample, transmission costs between JP-W and KR and between JPE and JP-W are assumed to be 2084 USD/kW and 1268 USD/kW,
respectively, in this study, whereas they were 1840 USD/kW and
672 USD/kW in the previous study.
We set the initial capacity assumptions for transmission lines(see the transmission capacity in Fig. 9a) referring to available information about existing capacity (for example, METI [41] and SGCC[42]) and transmitted electricity [23]. No capacity additions areallowed in the Base case, with no limit on capacity additions in theother cases.
3. Results and discussion
3.1. Power generation mix and capacity
Fig. 5a shows the projected power generation mix and capacity
in the NEA region under each of the cases. Renewable power in
Mongolia (MN) has a signiﬁcant potential to decarbonize the NEA
power system, although massive capacity needs to be installed. In
the Base case, fossil generation dominates the generation mix witha share of 70% in NEA (Fig. 5a), and renewables (hydro, wind andPV) account for 20%. The renewables' share grows in the latter
cases, reaching 47% in the Mon2000 GW case. The massive renewables installation in MN signiﬁcantly changes the NEA capacity mix
as well (Fig. 5b); for example, total NEA generation capacity increases by 66%, from the Base case (2789 GW) to the Mon2000 GW
case (4621 GW) due to the relatively lower capacity factor of variable renewables (wind and PV). As discussed later in Section 3.4,
these large-scale deployments would bring several challenges for
NEA, especially in terms of costs and ﬁnancing.
Several organizations have proposed the “Gobitec” concept,
targeting 100 GW renewables in MN (50 GW wind and 50 GW solar;
see Energy Charter et al. [7]). Yet, our results imply that a 100 GW
installation (the Mon100 GW case) would have a relatively small

Table 2
Assumptions for power plants and storage facilities (for all nodes).

Life time [year]
Capacity credit [%]Own consumption rate [%]Maximum ramp-up/ramp-down rate [%/h]Share of DSS operation [%]Minimum output level [%]Cycle efﬁciency (storage) [%]Self-discharge rate (storage) [/hour]
Maximum discharge duration (storage) [hour]

Nuclear

Coal

Gas

Oil

Hydro

Wind

PV

Pumped hydroBattery

40
85
4
0
0
100
e
e
e

40
85
6
30
0
30
e
e
e

40
85
3
50
40
20
e
e
e

40
85
4
100
70
30
e
e
e

60
40
e
e
e
e
e
e
e

20
10
e
e
e
e
e
e
e

20
10
e
e
e
e
e
e
e

60
85
e
e
e
e
75
0.0001
8

15
85
e
e
e
e
90
0.001
6

T. Otsuki / Renewable Energy 108 (2017) 321e335

325

Table 3
Cost assumptions for the China nodes (CH-NE, CH-N, CH-E, CH-C, CH-NW, and CH-S).

Initial cost [USD/kW]
Fuel cost [USD/kWh]Fixed O&M cost [USD/kW/yr]
Variable O&M cost [USD/kWh]Maximum availability [%]Carbon content of fuel [gCO2/kWh]
Efﬁciency [%]Nuclear

Coal

Gas

Oil

Hydro

Wind

PV

Pumped hydroBattery

2600
0.014
68
0.001
80
0
100

750
0.019
15
0.002
70
329
38

700
0.040
14
0.003
90
200
53

800
0.088
16
0.003
90
249
41

2500
0
30
0
40
0
e

1300
0
33
0
20
0
e

1500
0
23
0
Estimated proﬁle
0
e

2500
0
30
0
85
0
e

1440
0
14
0.04
85
0
e

Table 4
Cost assumptions for the Japan nodes (JP-H, JP-E, and JP-W).

Initial cost [USD/kW]
Fuel cost [USD/kWh]Fixed O&M cost [USD/kW/yr]
Variable O&M cost [USD/kWh]Maximum availability [%]Carbon content of fuel [gCO2/kWh]
Efﬁciency [%]Nuclear

Coal

Gas

Oil

Hydro

Wind

PV

Pumped hydroBattery

4000
0.014
104
0.002
70
0
100

2400
0.021
48
0.005
75
339
42

1150
0.056
23
0.006
70
205
51

1900
0.088
39
0.004
90
257
41

6000
0
72
0
40
0
e

1700
0
43
0
20
0
e

2500
0
38
0
Estimated proﬁle
0
e

6000
0
72
0
85
0
e

1440
0
14
0.04
85
0
e

Table 5
Cost assumptions for the Korea node (KR).

Initial cost [USD/kW]
Fuel cost [USD/kWh]Fixed O&M cost [USD/kW/yr]
Variable O&M cost [USD/kWh]Maximum availability [%]Carbon content of fuel [gCO2/kWh]
Efﬁciency [%]Nuclear

Coal

Gas

Oil

Hydro

Wind

PV

Pumped hydroBattery

3300
0.014
86
0.002
95
0
100

1500
0.021
30
0.003
90
328
38

800
0.056
16
0.004
90
202
53

1900
0.088
39
0.004
90
249
41

2500
0
30
0
40
0
e

1600
0
40
0
20
0
e

2250
0
34
0
Estimated proﬁle
0
e

2500
0
30
0
85
0
e

1440
0
14
0.04
85
0
e

Table 6
Cost assumptions for the Russia Far East node (RU-FE).

Initial cost [USD/kW]
Fuel cost [USD/kWh]Fixed O&M cost [USD/kW/yr]
Variable O&M cost [USD/kWh]Maximum availability [%]Carbon content of fuel [gCO2/kWh]
Efﬁciency [%]Nuclear

Coal

Gas

Oil

Hydro

Wind

PV

Pumped hydroBattery

2800
0.014
73
0.001
80
0
100

2200
0.018
44
0.005
70
287
31

1000
0.040
20
0.005
90
198
36

1200
0.088
24
0.003
90
220
41

2500
0
30
0
40
0
e

1500
0
38
0
20
0
e

2000
0
30
0
Estimated proﬁle
0
e

2500
0
30
0
85
0
e

1440
0
14
0.04
85
0
e

Table 7
Cost assumptions for the Mongolia node (MN).
Wind
Initial cost [USD/kW]
Fixed O&M cost [USD/kW/yr]
Variable O&M cost [USD/kWh]Maximum availability [%]

PV

1300
1500
33
28
0
0
Estimated proﬁleBattery
1440
14
0.04
85

Note: This table shows only the technologies considered in MN in this study.

impact on the whole NEA system. The share of fossil fuel-ﬁred
generation does decline in this case, but only by two percentagepoints due to the large market size of NEA countries, especiallyChina.5 Hence, further deploymentsdmuch more than the proposal in the Gobitec conceptdneed to be considered by the relevant
planning organizations to reap large environmental beneﬁts.

5

See also modest emissions reductions described in Fig. 10.

Balancing electricity supply and demand becomes more of achallenge as variable renewable generation capacity grows.
Generated electricity that exceeds either customer demand or theability of the system to transmit would be dealt with various
measures, including electricity storage or suppression. Suppression
control (also called “curtailment”) is a reduction in the output of
power from what the generator would otherwise produce given theavailable resource [43]. In this study, the cost-optimization model
selects suppression control if it is more economic compared to thecosts for additional storage or additional transmission facilities toutilize the excess generation.
Demand for electricity storage and suppression control ismodest up to the Mon500 GW case. The NEA power system integrates the Mongolian renewables mainly by ﬂexible operation offossil fuel-ﬁred plants in these cases, resulting in smaller coal-ﬁred
generation (Fig. 5a). Fig. 6 shows an example of power generation
proﬁles in China-North (CH-N) and MN. In the Mon500 GW case,
CH-N integrates the renewable power from MN by the ramping up/
down of coal and gas-ﬁred plants during the daytime.

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T. Otsuki / Renewable Energy 108 (2017) 321e335

Table 8Initial cost assumptions for each interconnection route [USD/kW].

CH-NE
CH-N
CH-E
CH-C
CH-NW
CH-S
JP-H
JP-E
JP-W
KR
RU-FE
MN

CH-NE

CH-N

CH-E

CH-C

CH-NW

CH-S

JP-H

JP-E

JP-W

KR

RU-FE

MN

e
1644
e
e
e
e
e
e
e
(1533)
2549
2466

1644
e
2354
3023
2521
e
e
e
e
2805
e
1853

e
2354
e
3023
3440
2605
e
e
e
e
e
e

e
3023
3023
e
1853
2438
e
e
e
e
e
e

e
2521
3440
1853
e
3774
e
e
e
e
e
e

e
e
2605
2438
3774
e
e
e
e
e
e
e

e
e
e
e
e
e
e
2084
e
e
4093
e

e
e
e
e
e
e
2084
e
1268
e
e
e

e
e
e
e
e
e
e
1268
e
2513
e
e

(1533)
2805
e
e
e
e
e
e
2513
e
(2967)
e

2549
e
e
e
e
e
4093
e
e
(2967)
e
e

2466
1853
e
e
e
e
e
e
e
e
e
e

Note: “e” indicates that interconnections are not allowed in this study. See Fig. 2 for the node abbreviations. Initial costs for the two transmission routes through the DPRK (seethe note in Fig. 2) are shown with brackets.

Fig. 5. Power generation and installed capacity in Northeast Asia.

Beyond the Mon500 GW case, various integration measures, notonly ﬂexible operation of fossil fuel-ﬁred generation, but also
suppression control and electricity storage, are dynamically combined to accommodate renewable power from Mongolia (Figs. 5
and 6). Suppressed wind and PV amounts to 325 TWh/yr in the
Mon1000 GW case and reaches 850 TWh/yr in the Mon2000 GW
case, which respectively means that 16% and 21% of renewables
generation in MN is discarded from the grid. Fig. 6b implies thatsuppression control would play an important role in balancing
supply and demand especially during the daytime in order tomanage the PV surges in MN (see the Mon1000 GW andMon2000 GW cases).
Battery capacity grows from the Mon1400 GW case, and reaches
about 110 GW (mainly in CH-N) in the last case. Similarly to suppression control, battery storage in CH-N is utilized to absorb the
surplus electricity during the daytime, when solar PV surges in MN,
and discharge during the night to supply the peak load (See the lastcase in Fig. 6a). However, the model selects suppression control,
rather than larger-scale battery storage or transmission to othernodes, as mentioned in the previous paragraph. The main reasonsfor this are the high costs of battery storage and transmission lines.
We conﬁrmed this point by conducting a sensitivity analysis on thecosts (see Appendix).
Fig. 7 describes the power generation mix and net imports'
share by node in the selected cases. Power trade is limited in theBase case; the net imports' share is not more than 1% in all nodes
except for China-East (CH-E). The imports of CH-E are mainly from

China-Central (CH-C), where abundant hydro resources are available to meet the demand in the coastal electricity-consuming area.
With the massive Mongolian renewables, China-North (CH-N) andKorea (KR) become the two major nodes that increase their share of
net imports. This share grows to 29% (630 TWh/yr) in CH-N and 19%(130 TWh/yr) in KR in the Mon500 GW case and it grows to 75%(1,612 TWh/yr) in CH-N and 54% (358 TWh/yr) in KR in the
Mon2000 GW case. The large-scale Mongolian renewables
contribute to a massive reduction in fossil fuel generation, especially in these two countries. In China, coal-ﬁred generation dropsby 36%, from 6,370 TWh/yr in the Base case to 4,100 TWh/yr in the
Mon2000 GW case, leading to signiﬁcant CO2 emission reductions(Section 3.2). Reduced coal-ﬁred generation would also be desirable
in China to relieve air pollution. The imported power in KR significantly replaces both gas-ﬁred and coal-ﬁred generation.
Other nodes, such as CH-NE, JP-W and RU-FE, also increaseimports 21e44% as shown in Fig. 7ced. In contrast, the net importsof JP-E are modest, only about 5%, even with 2000 GW of renewables in MN. The modest imports in JP-E are slightly different fromwhat was reported in our previous study.6 One of the reasons is thehigher transmission costs assumed in this study (See Section 2.3.3
and Appendix). Interconnection costs need to be lower for JP-E toactively participate in the international electricity trade in NEA.

6
In Otsuki et al. [13], the net imports' share in JP-E reaches almost 10% with
100 GW of Mongolian renewables.

T. Otsuki / Renewable Energy 108 (2017) 321e335

327

Fig. 6. Power generation proﬁle in the winter-peak season in CH-N and MN in the selected cases.

3.2. CO2 emissions
Fig. 8 displays annual CO2 emissions from fossil fuel combustion.
The Mongolian renewables contribute to signiﬁcant emissions reductions in NEA. Total NEA power generation emissions decreaseby 10% (679 MtCO2) with 400 GW of Mongolian renewables, 30%(2,127 MtCO2) with 1600 GW and 36% (2,473 MtCO2) with
2000 GW. Our results imply a potential global-scale beneﬁt from
the Mongolian renewables. According to IEA's current policy scenario [30], global energy-related emissions in 2030 are projected tobe 39,153 MtCO2, and electricity sector emissions to be 17,114
MtCO2; therefore, the absolute reductions in the last case would be
about 6% of total global emissions and more than 14% of electricity
sector emissions.7 Renewable resources in Mongolia therefore havea large potential role to play in mitigating climate change.
Among the modeled nodes, China and Korea are most7

CO2 emissions calculated in this paper are approximately in line with IEA's
current policy scenario [30]. For example, emissions in 2030 in China are 6,028
MtCO2 in the Base case and 6,036 MtCO2 in IEA [30], and are 457 MtCO2 and 440
MtCO2 in Japan, respectively. As for Korea and Russia Far East, the author could not
conﬁrm as neither one is not shown in IEA [30].

signiﬁcantly beneﬁted from an environmental perspective, which
can be a strong incentive for these countries to import Mongolian
renewables. China shows the largest CO2 emission reductions inabsolute terms, for example, by 2,095 MtCO2 (35%) in the
Mon2000 GW case, as the Mongolian renewables signiﬁcantly
replace carbon-intensive coal-ﬁred generation in CH-N and CH-NE(Fig. 7d). Korea's CO2 reductions reach about 90% in the
Mon2000 GW case because imported power largely replaces both
gas and coal-ﬁred generation. In contrast, the results show relatively small effects for Japan, with only 13% reductions even in the
Mon2000 GW case. Emission reductions in JP-W reach 20% in the
Mon2000 GW case, yet the environmental beneﬁts in the country as
a whole become modest due to limited electricity imports in otherJapan nodes (JP-H and JP-E).
3.3. Cross-boundary electricity transmission
Fig. 9 illustrates the cross-boundary electricity transmission[TWh/yr] and transmission capacity [GW]. The massive Mongolian
renewables stimulate increased cross-boundary electricity transmission. The Base case shows relatively modest electricity transmission except between CH-C and CH-E, and the power trade grows

328

T. Otsuki / Renewable Energy 108 (2017) 321e335

Fig. 7. Power generation mix and net imports by node in selected cases.

the assumed annual demand in CH-N (2,155 TWh/yr). As for KR, theestimated inﬂow from CH-N is 519 TWh/yr, equivalent to threequarters of KR's annual demand. As for the transmission lines
from MN, the variability of the renewable generation results inrelatively low utilization rates of the lines; for example, 16% on the
MN-CH-N connection in the Mon2000 GW case.

3.4. Costs

Fig. 8. Annual CO2 emissions from power generation in the modeled city nodes,
Northeast Asia.

with the deployment of the renewables in MN, especially in theroute from Mongolia (MN) to China-North (CH-N), Korea (KR) andJapan-West (JP-W) and from MN to CH-N and China-East (CH-E).
Vast amounts of electricity are estimated to ﬂow into the major
importing nodes, such as CH-N and KR. In the Mon2000 GW case,transmission from MN to CH-N reach 2,667 TWh/yr, which exceeds

Fig. 10 depicts yearly total system cost in NEA. The costs shown
include a carbon cost of 30 USD/tCO2 for all cases. The annual initialcosts are estimated by multiplying total investments by a capital
recovery factor assuming a discount rate of 5%.
Massive deployments of Mongolian renewables and crossboundary transmission lines signiﬁcantly change the cost structure to be more capital intensive, pushing up the annual systemcost (Fig. 10). In the Base Case, the largest cost component is fuelcosts, which are about half of total system cost, while initial costs
become the largest cost component in the Mon2000 GW case, accounting for 55% of total system cost. The Mongolian renewables
contribute to savings in fuel and carbon costs. However, the incremental capital and O&M costs are estimated to be larger than
the savings, resulting in a higher annual system cost by about 30%d
1,391 Billion USD/yr (B USD/yr) in the Mon2000 GW case comparedto 1,053 B USD/yr in the Base case. These annual system costs areT. Otsuki / Renewable Energy 108 (2017) 321e335

329

Fig. 9. Cross-boundary electricity transmission in the selected cases. Note: Each ﬂow indicates the electricity delivered to the importing node.

Fig. 11. Additional investments from the Base case.

Fig. 10. Annual system cost and average generation cost in Northeast Asia.

equivalent to 121 USD/MWh and 95 USD/MWh, respectively, inaverage generation cost in NEA.
Fig. 11 shows additional investments in the selected cases. Huge
investments are necessary for Mongolian renewables and grid interconnections. Compared to the Base case, additional investments
amount to 333B USD in the Mon100 GW case, rising to 3,616B USD
in the Mon1000 GW and 7,527B USD in the Mon2000 GW cases.
Although massive Mongolian renewables contribute to reducing
electricity supply facilities in importing nodes (e.g., a savings of a
total of 105B USD in NEA in the Mon1000 GW case), the savings aremodest compared to the investments required for renewables in
MN and the cross-boundary transmission facilities.
The incremental overall costs (Fig. 10) and huge investments(Fig. 11) may pose signiﬁcant challenges in terms of ﬁnancing andimplementation. The cost increase implies that the Mongolian renewables concept may not be competitive under a market mechanism unless strong environmental policies are implemented, as
discussed later in Section 3.5.1.

3.5. Sensitivity analyses
The results in Sections 3.1e3.4 are based on a number of330

T. Otsuki / Renewable Energy 108 (2017) 321e335

assumptions (Section 2.3), where uncertainties exist. Therefore,
this section performs sensitivity analyses with a focus on the two
factorsdcost assumptions and transmission routes through the
DPRKdto investigate their effects on the economics of gridinterconnections.
3.5.1. Cost assumptions
Fig. 10 suggested that the economic beneﬁts of the large-scale
Mongolian renewables depend mainly on fuel and carbon cost savings, while the higher capital costs for the renewables and transmission facilities make the concept less attractive. However, there
exist uncertainties in these cost factors; energy prices have shown
their volatile nature in the last decade; carbon prices depend heavily
on future environmental policies and regulations; and capital costs
vary depending on site-speciﬁc characteristics. Therefore, in thissection, we conduct a sensitivity analysis on these three factors. The
author additionally conducted 1,232 calculations in total: three casesettings (the Base, Mon100 GW, Mon500 GW and Mon2000 GW)  four
carbon prices (No carbon price, 100 USD/tCO2, 200 USD/tCO2, 300
USD/tCO2)  eleven fossil fuel prices (50% compared to theassumption in Section 2.3, 40%, 30%, 20%, 10%, 0%(¼“Reference”), þ10%, þ20%, þ30%, þ40%, þ50%)  seven capitalcosts of the Mongolian renewables and transmission facilities(30%, 20%, 10%, 0% (¼“Reference”), þ10%, þ20%, þ30%). Other
assumptions are the same as shown in Section 2.3.
Fig. 12 illustrates the net economic beneﬁts of the three selected
Mongolian renewables cases under each carbon price, fossil fuelprice and capital cost setting (positive values, shown in blue,
indicate the total system cost reductions compared to the Base). The
results illustrate the improved economics of the Mongolian renewables cases under higher carbon prices, higher fuel prices orlower capital costs, as each of these factors enhances the relative
cost-competitiveness of renewables in Mongolia.
However, the results also suggest that strong emission reduction
policies, such as high carbon prices, would be important for
implementation. For example, under the reference (“Ref.”) fuelprices and capital costs, carbon prices of 100 USD/tCO2 would not
be enough for the three renewables cases to be economicallyattractive; nor a carbon price of 200 USD/tCO2 for the Mon2000 GW
case. The Mon100 GW and Mon500 GW cases barely show net
beneﬁts with 200 USD/tCO2, but these beneﬁts become negativewith only a 10% fuel cost drop and a 10% capital cost increase (see200 USD/tCO2 in Fig. 12aeb). These levels of carbon price are higher
than the IEA's assumed carbon price for 2030 in their 450 Scenario,8also known as the “2  C Scenario”, which is targeted by the Paris
Agreement at the 21st Conference of the Parties of the UNFCCC [44].
IEA assumes higher carbon prices in the longer-term (see the pricefor 2040 in footnote 8); yet, those prices are still in the range of
100e140 USD/tCO2, much lower than 200 USD/tCO2. Massive
deployment of Mongolian renewables could be an option for a“beyond 2  C” policy for the period up to around 2040, according toour analysis. The relevant planning organizations will need tocarefully consider long-term energy market and environmental
policy trends (such as fuel prices, capital costs and carbon prices) inorder to assure that implementation will be beneﬁcial.
3.5.2. The transmission routes through the DPRKThe results in Sections 3.1e3.5.1 do not include the two transmission routes through the DPRK (the dotted lines in Fig. 2) due tolarge diplomatic uncertainties. The DPRK routes are expected to

8
IEA's carbon price assumptions for the 450 Scenario are as follows: 75 USD/tCO2
in 2030 and 125 USD/tCO2 in 2040 for China and Russia, and 100 USD/tCO2 in 2030
and 140 USD/tCO2 in 2040 for Japan and Korea [30].

improve the economics of interconnections in NEA; for example,
the route between CH-NE and KR would allow connecting China
and Korea via overhead lines, not via high cost submarine cables. To
quantify the economic impacts of the routes' uncertainties, we
additionally simulated the Mon2000 GW case considering the DPRK
routes. Fig. 13 illustrates the impacts of the route in terms of crossboundary electricity transmission, generated electricity and systemcosts. Fig. 13bec shows the changes from the Mon2000 GW case in
Sections 3.1e3.4, which does not include the DPRK routes. Our
results imply the following two points.
First, the DPRK routes make it more attractive for Japan toimport electricity from Mongolia. The net imports of Japan expand
to 311 TWh/yr with the route between CH-NE and KR (Fig. 13b),from 146 TWh without the route (Fig. 9d), due to the improved
economics of transmission. The incremental imports allow Japan toreplace high cost generation, such as gas-ﬁred generation; instead,
CH-NE decreases net imports from MN, resulting in the larger coalﬁred generation there (Fig. 13b) and higher CO2 emissions (indicated as increasing carbon costs in Fig. 13c). In contrast to Japan,
impacts on Korea are relatively small, as the economy has alreadyexpanded its imports and replaced fossil fuel generation even in thecases without the routes.
Second, from an economic perspective, our results imply that
there are relatively modest impacts from the availability of the
DPRK routes for NEA (Fig. 13c). Total cost in NEA drops by 6B USD/yr, yet these reductions are limited compared to the annual systemcost (1,391B USD/yr in the Mon2000 GW case; see Fig. 10). The fuelcosts saved in JP-E and JP-W are partly offset by the initial costs for
additional transmission facilities (for example, between KR and JPW) and carbon penalties for the incremental coal-ﬁred generationin CH-NE.
Various discussions (for example, WEC [45]) have pointed thatthe uncertainty of the DPRK routes would be one of the biggest
issues for designing and implementing grid interconnections in
NEA. Our results suggest the DPRK routes would be economicallyimportant to encourage Japan's involvement. However, the results
also imply that the routes do not signiﬁcantly increase the beneﬁts
to the NEA system of integrating massive renewables in Mongolia.
Even if the DPRK routes are available, the NEA power system would
still face increased costs, similar to the level discussed in Section
3.4. Relevant planning organizations need to note that the costburdens for renewables in Mongolia and cross-boundary transmission network would still remain as a major barrier toimplementation.
4. Conclusions and policy implications
This study quantitatively examined the economic viability ofdeveloping the abundant renewable energy in Mongolia for electricity exports. This paper employs a single-year multi-region power system model, which is formulated as a linear program andaims to minimize overall system cost of NEA. We examined twentyone cases, ranging from the Base case to the Mon2000 GW case,which assumes 1000 GW of wind turbine capacity and 1000 GW ofsolar PV capacity in Mongolia, as shown in Table 1. Major results ofthe selected scenarios are summarized in Table 9. These simulation
results provide the following interesting ﬁndings regarding theenvironmental opportunities and economic challenges.
First, from an environmental perspective, renewable energy inMongolia has a signiﬁcant potential to decarbonize the NEA powersystem. Renewables' share grows from 20% in the Base case to 47%
in the Mon2000 GW case, resulting in the CO2 emissions reductions
from power generation of 36%. In particular, China and Korea aresigniﬁcantly beneﬁted; for example, CO2 emissions are reduced bymore than 35% (2,095 MtCO2) in the China nodes as the Mongolian

T. Otsuki / Renewable Energy 108 (2017) 321e335

331

Fig. 12. Economic beneﬁts of the selected Mongolian renewables cases under various carbon price, fossil fuel and capital cost settings.

renewables replace carbon intensive coal-ﬁred generation. On theother hand, the results show relatively modest effects in Japan, with
only a 13% reduction even in the Mon2000 GW case. This is due tolimited electricity imports in JP-H (Hokaido) and JP-E (EasternJapan). We conﬁrmed that high transmission costs are one of thereasons for the limited imports to Japan (see Appendix). Lowertransmission costs would be needed for Japan to enjoy theenvironmental beneﬁts of Mongolian renewables.
Second, in contrast to the environmental merits, the results
indicate economic challenges for implementation. Massive
deployment of Mongolian renewables and cross-boundary transmission lines pushes up the annual system cost by 32%, from1,053B USD/yr (95 USD/MWh) in the Base case to 1,391B USD/yr(121 USD/MWh) in the Mon2000 GW case. Massive renewables

332

T. Otsuki / Renewable Energy 108 (2017) 321e335

Fig. 13. Effects of the DPRK routes in the Mon2000 GW case. Note: “Losses” in Fig. 13b includes losses associated with transmission and electricity storage.

Table 9
Summary of results of the selected cases.

System cost
Initial costs
Fuel costs
CO2 emissions
Renewables' share in NEA
Transmission from China to Korea
Transmission from Korea to Japan

Base

Mon500 GW

Mon1000 GW

Mon2000 GW

1,053B USD/yr
265B USD/yr
490B USD/yr
6,855 MtCO2/yr
20%
0 TWh/yr
0 TWh/yr

1,109B USD/yr
383B USD/yr
436B USD/yr
6,057 MtCO2/yr
29%
188 TWh/yr
53 TWh/yr

1,194B USD/yr
506B USD/yr
400B USD/yr
5,440 MtCO2/yr
35%
381 TWh/yr
98 TWh/yr

1,391B USD/yr
772B USD/yr
322B USD/yr
4,382 MtCO2/yr
47%
519 TWh/yr
146 TWh/yrsigniﬁcantly contribute to saving fuel costs; however, the beneﬁts
are offset by the initial costs for the renewables and transmission
lines. The massive deployment of renewables change the cost
structure to be more capital intensive due to the huge investments
required. Additional investments amount to 3,616B USD for1000 GW of Mongolian renewables and 7,527B USD for 2000 GW.
The relevant planning organizations need to carefully consider
how to secure such large investments, taking into account longterm energy prices, capital costs and environmental policy
trends in order to assure that implementation will be beneﬁcial, as
discussed in Section 3.5.1.
Third, the sensitivity analysis implies that the routes throughthe DPRK would be economically important to encourage Japan's
involvement. On the other hand, the results also show that theroutes do not signiﬁcantly increase the beneﬁts to the whole NEA
system of integrating massive renewables in Mongolia. Relevant
planning organizations need to note that, even if the routes
become available, the cost burdens for renewables in Mongolia
and the cross-boundary transmission network would still remain
as a major barrier to implementation from an economic
perspective.
Turning to priorities for future work, we need to further
enhance the model's capabilities and conduct additional analyses
in at least two ways. The ﬁrst would be more detailed modeling ofvariable renewables and energy storage technologies. If detailed
meteorological data (i.e., hourly or more detailed wind speed andsunshine data) is available in each country, future work could
address NEA-wide grid integration issues, not only for the renewables in Mongolia but also in other counties, taking into

account the short-term ﬂuctuations in the variable renewables.
Additional modeling of energy storage, including hydrogen andheat storage technologies, would also allow a more comprehensive assessment of the use of surplus electricity from variable renewables. The second would be to address energy security issues.
To quantify the maximum beneﬁts potentially achievable bydeveloping renewables in Mongolia for power exports, this paper
assumes that the NEA countries fully cooperate for regional optimization. Therefore, emergency situations, such as the disruptionof electricity trade due to technical or political issues, were out of
this research scope. Incorporating energy security aspects into themodel, for example, by using stochastic programming techniques,
would be an important research contribution in order tocomprehensively understand the opportunities and barriers forgrid interconnections.

Acknowledgement
The author would like to thank the following former APERC
researchers for their suggestions and advice: Dr. Ralph D. Samuelson of the New Zealand Ministry of Transport, and Dr. Aishah Binti
Mohd Isa of the Universiti Tenaga Nasional in Malaysia. The author
also expresses his appreciation to his colleagues at APERC, especially Mr. Alexey Kabalinskiy and Dr. Jia Hao, for their assistancewith data collection. The research reported in this paper was
generously supported by Asia Paciﬁc Energy Research Centre(APERC). The views expressed, however, are those of the author andnot those of APERC.

T. Otsuki / Renewable Energy 108 (2017) 321e335

Appendix. Additional analysis of the effects of battery andtransmission costs on suppression control
As illustrated in Figs. 5e7, with a massive renewables installation in Mongolia (MN), the model selects suppression control there,
rather than additional exports or battery storage. To conﬁrm thatthe high cost of cross-boundary transmission and battery storage is
one of the reasons for the suppression, this section performs thesensitivity analysis on these costs under a Mongolian renewables
capacity at 2000 GW. Table A.1 shows the case settings. We assume
cost reductions in transmission facilities (transmission lines, cables
and conversion station) and battery storage.

333

allow Japan and the Central part of China (CH-C) to expand electricity imports, which contributes to less suppression control andmore renewable power exports in MN (see Fig. 9d and Fig. A.2a).
Cost reductions for battery storage also decrease suppression
control by facilitating electricity storage of the surplus electricity
from wind and PV. In the Bat-50% case, the suppression rate in MN
drops to 12% (480 TWh/yr) as shown in Fig. A.1. The additional
battery storage is mainly in China-North (CH-N). Battery capacity in
CH-N grows to 337 GW in the Bat-50% case, from 110 GW in the
Reference case. In contrast with the Chinese nodes, the impacts ofbattery cost on Korea and Japan is relatively modest as the crossboundary transmission is still relatively expensive; the electricity

Table A.1
Case settings regarding the cost of battery systems and transmission facilities.
ReferenceWind and PV capacity
Transmission facility cost reductions
Battery system cost reductions

Tra-25%Tra-50%Bat-25%

Mongolia: 1000 GW each for all cases. Other nodes: same assumptions as Table 1.
0%
25%
50%
0%
0%
0%
0%
25%

Bat-50%0%50%(Note) The Reference case is the same as the Mon2000 GW case in Table 1.

Fig. A.1 describes generated electricity by node in each case.
Suppression control in MN declines when the cross-boundarytransmission becomes less costly. For example, the suppression
rate (the share of suppression in the total generated electricity) in
MN is 21% (849 TWh/yr) in the Reference case, and reduces to 17%(660 TWh/yr) in the Tra-50% case. Lower cost transmission facilities

imports of these countries show a level similar to the Reference case(see Fig. 9d and Fig. A.2b).
These results suggest that the model selects suppression control
partly due to high battery and transmission costs. Reductions intheir costs would allow NEA to accommodate more renewable
power from Mongolia.

Fig. A.1. Generation in NEA in each case.

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T. Otsuki / Renewable Energy 108 (2017) 321e335

Fig. A.2. Cross-boundary electricity transmission in the selected cases.

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