Energy Policy 89 (2016) 311–329

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Energy Policy
journal homepage: www.elsevier.com/locate/enpol

Electric power grid interconnections in Northeast Asia:
A quantitative analysis of opportunities and challenges
Takashi Otsuki a,n, Aishah Binti Mohd Isa b,1, Ralph D. Samuelson c,1aAsia 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, Japanb
College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysiac
Ministry of Transport, Level 6 SAS House, 89 The Terrace, Wellington 6011, New Zealand

H I G H L I G H T S

 We developed a multi-region power system model of Northeast Asia (NEA).
 The model considers renewable energy in the Gobi Desert and eastern Russia.
 Expanding renewables for export brings CO2 reductions and fuel cost savings in NEA.
 Economic beneﬁts due to reduced total costs are modest.

art ic l e i nf o

a b s t r a c t

Article history:
Received 25 March 2015
Received in revised form
4 October 2015
Accepted 21 November 2015

Power grid interconnection has gained attention in Northeast Asia (NEA) as a means to build an economically efﬁcient power system and to effectively utilize renewable energy, such as wind and solar
resources in the Gobi Desert and hydro resources in Eastern Russia.
In order to quantify the potential economic and environmental beneﬁts from connecting power grids
and developing renewables in NEA, we build an NEA-wide multi-region power system model using
linear programming techniques. Our analysis considers power system characteristics, such as the seasonal and daily electric load curves of the various NEA economies.
Compared to a “no grid extension” scenario, increased access to renewables contributes signiﬁcantly
to emissions reductions and fuel cost savings. However, the results imply modest beneﬁts in lowering
total cost because of the large initial investments needed in developing the renewables and the transmission lines. These limited total cost savings are likely to pose an implementation challenge for NEA
grid interconnections. Our results also suggest that grid interconnections become more economicallyattractive in higher fuel price or lower initial cost situations. The relevant planning organizations shouldcarefully consider the initial cost and future fuel price trends when considering how to interconnect
power girds in an economical manner.
& 2015 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.

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

1. Introduction
Over the past two decades, electric power grid interconnections have gained attention in Northeast Asia (NEA), an area that
we deﬁne as four Asia Paciﬁc Economic Cooperation (APEC)
economies—China, Japan, the Republic of Korea (Korea), and Russia—and two non-APEC economies—Mongolia and the Democratic
People’s Republic of Korea (DPRK). Various interconnectionnCorresponding author.
E-mail address: takashi.otsuki@aperc.ieej.or.jp (T. Otsuki).
1
Formerly Asia Paciﬁc Energy Research Centre (APERC).

http://dx.doi.org/10.1016/j.enpol.2015.11.021
0301-4215/& 2015 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.

schemes have been proposed for NEA (Streets, 2003; Yun andZhang, 2006; Hippel et al., 2011). Yet, while technically feasible,
these cooperative proposals have been hampered by factors suchas existing national policies of energy self-sufﬁciency and thesometimes-volatile diplomatic and political situation in the region.
Thus, the only existing cross-border power cooperation projects
are small in scale, linking Russia to Mongolia, Russia to China, andChina to the DPRK.
However, several recent regional events, including the Fukushima nuclear disaster in Japan, the power shortage and rolling
blackouts in Korea, and increased concern regarding air pollution
in China, have made power grid interconnections potentially more

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T. Otsuki et al. / Energy Policy 89 (2016) 311–329

attractive. Several organizations have proposed multilateral power
grid interconnection concepts in NEA, i.e., Asia Super Grid (ASG)
and Gobitec, with a focus on developing the abundant renewable
resources in the Gobi Desert and Eastern Russia and on building amore resilient and economically efﬁcient power system (KEPCO,
2014; Energy Charter et al., 2014; Graaf and Sovacool, 2014). The
wind and PV potential in Mongolia has been estimated at 1100 GW
and 1500 GW, respectively (Elliott et al., 2001; Energy Charter
et al., 2014), and economically feasible hydropower potential in
Eastern Russia is estimated at 690 TWh/yr (estimated by European
Bank for Reconstruction and Development, see IEA (2003)).
There have been some previous economic analyses on connecting power grids in various parts of the world: Southern Africa
is the focus of Bowen et al. (1999), Europe of Lilliestam and Ellenbeck (2011) and Schaber et al. (2012) and Southeast Asia of
Chang and Li (2013) and Matsuo et al. (2015). Among those studies, Schaber et al. (2012) conducted a detailed analysis on theimpacts of grid interconnections on regional renewable energy
utilization. They employed a Europe-wide power system modelwith a detailed temporal resolution (hourly time slice for six representative weeks), which appropriately reproduce the actual
power generation, electricity prices and cross-border powertransportation.
The economics of power grid interconnection in the NEA region
have also been investigated. Cost–beneﬁt analyses of grid interconnection scenarios in NEA were performed by Hippel (2001),
Podkovalnikov (2002), Lee et al. (2005), Chung and Kim (2007),
Energy Charter et al. (2014) and Skoltech (2015). Analyses ofpower system reliability were conducted by Choi et al. (2006) andYoon (2007). Yet, to our knowledge, few studies have focused on
the whole of NEA and analyzed the impacts of grid interconnections with a focus on renewables both in the Gobi Desert andEastern Russia considering power system characteristics (e.g. load
curves, generation dispatch). Except for Energy Charter et al.
(2014) and Skoltech (2015), the studies listed above covered only apart of NEA (three to four out of the six economies) and did notconsider renewable energy in the Gobi Desert. Skoltech (2015) also
does not take into account renewables in the Gobi Desert. As forEnergy Charter et al. (2014), they proposed to install 50 GW ofwind and 50 GW of solar photovoltaics (PV) in the Gobi Desert,
and estimated the supply costs to other NEA economies. However,their cost assessment did not consider regional power systemcharacteristics, such as the load curves of the importing economies
and the seasonal and diurnal output variation of the solar andwind power from the Gobi Desert.
Thus, we developed a multi-region power system model, which
covers the whole of NEA, in order to quantitatively evaluate thepotential beneﬁts of, and barriers to, power grid interconnectionand expansion of renewable energy for export. The model seeks tominimize overall system cost, considering seasonal and daily
characteristics of electric load of each region and the output patterns of renewables in the Gobi Desert. This model can determine
cost-optimal grid expansion and cross-boundary power ﬂows, aswell as generation dispatch. Also, nodal marginal pricing gives us
some implications for regional electricity prices. We believe that
our analysis contributes to understanding of the costs and beneﬁts
of grid interconnection and large-scale renewable energy utilization in NEA from a systems viewpoint.
This paper proceeds as follows: Section 2 gives an overview ofthe multi-region power system model and the scenario assumptions; Section 3 presents the simulation results and discusses the
economic feasibility of grid interconnections, as well as a sensitivity analysis on initial cost and energy prices; and Section 4summarizes major conclusions and implications, and then proposes a future research agenda.

2. Methods
2.1. Overview of the multi-region power system model
We developed a multi-region power system model using linear
programming techniques. Fig. 1 is a schematic diagram of thismodel. The model aims to minimize a single-year overall systemcost, consisting of the annualized initial cost, operation andmaintenance (O&M) cost, fuel cost and carbon cost for the whole
NEA under various technical and political constraints. Hence, theNEA economies are assumed to cooperate fully to achieve the regional optimization. A detailed mathematical description of themodel is provided in Appendix A.1. Validation of the model isdiscussed in Appendix A.2.
A capital recovery factor is used to annualize initial investments
in generation, storage and cross-boundary transmission facilities.
The assumed discount rate is 3% and lifetime assumptions are
discussed in Sections 2.3.2 and 2.3.5. O&M cost includes both ﬁxed
and variable O&M cost. Fixed O&M cost, which is incurred even if
the plant is not operated (i.e., landowner cost), is assumed to be inproportion to capacity, while variable O&M cost (i.e., consumables)
varies with generated electricity. Carbon cost in this study considers only direct emissions from fuel combustion.
The cost of generation includes initial cost, ﬁxed and variable
O&M cost, fuel cost and carbon cost. The cost of cross-boundarytransmission lines includes initial cost and ﬁxed O&M cost. Power
trade is selected by the model if its beneﬁt (usually the savings in
generation cost) is larger than the cost of cross-boundary transmission lines.
This model is formulated in a consistent way in General Algebraic Modeling System (GAMS) software. There are 75,000 equations or constraints and 38,000 endogenous variables. For ourmodeling work, we referred to the detailed modeling approach in
Schaber et al. (2012), Komiyama and Fujii (2014) and Komiyama
et al. (2015), but due to data availability we selected the temporaland geographical resolution explained below.
Regarding the temporal resolution, the model considers thehourly load curves of typical days for ﬁve seasons (Summer-peak,
Summer-average, Winter-peak, Winter-average, and Intermediate)
in order to model the diversity of seasonal and daily load variation
among the regions. Thus, in each node, one calendar year is decomposed into 120 time segments ( ¼24 h per day  1 representative day per season  5 seasons per year).
As for the geographical resolution, we divide NEA into ten
nodes (Fig. 2), represented by seven city nodes (round markers)
and three supply nodes (triangle markers). City nodes have electricity demand and power supply facilities, while supply nodes
have only power supply facilities to export electricity. Endogenous
capacity additions are allowed in both types of nodes. Five of thecity nodes correspond to power grids or power service areas:
North China grid (CH-N); China Northeast grid (CH-NE); Japan
Hokkaido area (JP-H); Korea (KR), and Russia Far East power• Wind• Photovoltaics (PV)• Nuclear• Coal-fired• Gas-fired• Oil-fired• Hydro• Additional hydro 1• Additional hydro 2
Electricity imports
From other nodes

Electricity

Suppression control

Electricity

Electricity

Power gridElectricity load
(exogenous variable)

Electricity

Pumped hydroElectricity exports
To other nodes

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

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313

1070kmRussia-Siberia(RU-SI)

3050kmRussia-Sakhalin(RU-SK)

Russia-Far East
(RU-FE)

870km

1130km

1400kmJapan-Hokkaido(JP-H)

China-Northeast(CH-NE)

Gobi Desert area
(GD)

1700km1340km1030km

900km
750km

China-North(CH-N)

1200km

480km

Korea
(KR)

Japan-East(JP-E)

1200kmCity Node

Japan-West(JP-W)

Supply NodeFig. 2. Regional division and assumed transmission distances.

Table 1Deﬁnition of scenarios.
Scenario

Base

NoNewRE

RuHyd

Gobitec

Fossil fuel ﬁred capacity

Cost optimized for all scenarios. For coal-ﬁred plants, we impose upper bounds based on the projected capacity in APERC (2013),
reﬂecting environmental concerns.

Wind/PV Capacity

Projected capacity for 2030 for the Base, NoNewRE and RuHyd scenarios (APERC, 2013)

Gobi desert (GD): 50GW solar, 50 GW wind
(Energy Charter, et al., 2014).
Other nodes: same as Base scenario

Hydro Capacity

Projected capacity for 2030 (APERC, 2013)Same as Base scenario

Interconnection

Current capacity

Carbon price

30$/t-CO2 for all scenarios

Endogenous additions allowedin Russia nodes forexport (see Section 2.3).
Other nodes: same as Base scenario

Gobitec+RuHyd

Same as RuHyd scenario

Cost optimized for the last four scenarios

system (RU-FE). The Tohoku and Tokyo areas in Japan are aggregated as JP-E, and the western parts of Japan as JP-W. Supply
nodes consist of the Gobi Desert area in Mongolia (GD), the Russia
Siberia area (RU-SI) and the Russia Sakhalin area (RU-SK), whichhave abundant energy supply potential (wind and solar in GD,
hydro in RU-SI and coal and gas in RU-SK). Assumptions for thesenodes are discussed in Section 2.3.
We modeled electricity transmission as a transport problem.
Kirchhoff’s ﬁrst law (conservation of current) is considered in eachnode of the network, but the second law (voltage law) is not incorporated. This simpliﬁed approach allows us to keep the optimization problem linear and to optimize grid extensions, generation expansion and their operations simultaneously (Schaber et al.,2012). Distances between nodes in Fig. 2 were based on airline
distances between representative cities in each region plus 20% toallow for expected circuity (Google, 2015; Energy Charter et al.,2014). Regarding the transmission between KR and RU-FE, there
are signiﬁcant diplomatic challenges involved in arranging transit
across the DPRK. However, we take this route into account as the
Korea Energy Master Plan explicitly mentions that the connection“should be a prospective mid- to long-term governmental project”
if it is proﬁtable and overall conditions, including inter-Korean
relations, improve (MOTIE, 2014).

2.2. Scenario setup
We examine the ﬁve scenarios in Table 1. The simulated year inthis study is 2030. The Base scenario assumes no grid expansion
from the existing transmission line capacity. In the NoNewRE
scenario, new interconnections are allowed based on total systemcost optimization, but renewable capacity is ﬁxed to the Basescenario assumptions at all nodes. The RuHyd scenario endogenously allows additional hydro power development in the
Russian nodes. As hydro power development for Russian domestic
supply is already considered in the initial capacity settings (APERC,
2013), we assume that the additional hydro plants are used only
for exports to the foreign nodes in this study. The Gobitec scenario
attempts to quantify the costs and beneﬁts of the “Gobitec/ASG”
concept proposed by Energy Charter et al. (2014). They targeted
50 GW wind and 50 GW solar PV2 in GD (the Gobi Desert) by 2030,
although we believe sensitivity analyses on the capacity needs tobe considered in future work. The last scenario (Gobitec þRuHyd)2
To our knowledge, they do not mention the reason for their choice of thislevel of capacity, yet there seems to be a consideration of energy security risks(Mano, 2014). The land area of 50 GW solar PV capacity would account for approximately 0.1% of the Gobi Desert, assuming that total Gobi Desert area is
1,300,000 km2 and the land area required for solar PV cells is 20 km2/GW (Eurus
Energy, 2012).

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T. Otsuki et al. / Energy Policy 89 (2016) 311–329

Summer-peakWinter-peakFig. 3. Estimated electric load curve at city nodes in the summer-peak and winter-peak seasons (ratio to peak load at each node).

considers both the Gobitec and the RuHyd assumptions. The assumed carbon price for all scenarios is 30$/t-CO2. Further explanations of the assumptions, including the limits for fossil fuelcapacity additions and additional hydro potentials in Russia, aregiven in Section 2.3.
Kunstýř and Mano (2013) investigated the security risks forJapan of importing electricity from Russia. They concluded that thepower trade would not pose substantial security risks for Japan
with the appropriate measures, such as ensuring a capacity buffer
at the current level. In general, each service area needs to beprepared for cross-boundary transmission interruptions. Therefore
we limit the share of net transmission inﬂows at each city node to
be less than the reserve margin level as described in Eq. (A.26) inAppendix A.1. Note that Eq. (A.26) does not distinguish between
transmission inﬂows from foreign and domestic sources, sinceeither one can fail. This is especially true in the case of Japan,
where the two domestic links represented in the model aretechnically similar to import links. The JP-H to JP-E link is an undersea DC connection. The JP-W to JP-E link is a back-to-back ACto-DC-to-AC converter, similar to an undersea connection, due tothe differing frequencies between JP-E (50 Hz) and JP-W (60 Hz).
2.3. Input data assumptions
2.3.1. Electricity demand and load curves
Our analysis considers the seasonal and diurnal characteristics
of electric load in each node. We estimated electricity demand in
2030 from each node’s historical data (JEPIC, 2013; Government ofJapan, 2014) and the projected growth rate from APERC (2013). We
also constructed daily load curves for the ﬁve seasons fromavailable historical load curve information and load factor data
(JEPIC, 2006; SO UPS, 2014; Government of Japan, 2014; Nagayama, 2014; JEPIC, 2014). Fig. 3 depicts the estimated daily load
curves in the summer-peak and winter-peak seasons.3 Note that
all the curves are plotted in Japan Standard Time. Time differencesamong the modeled city nodes are a maximum two hours.4 Japanand Korea are located in the same time zone. Compared to these
two regions, China is one hour behind, while the Russia Far East
area (Vladivostok time) is one hour ahead. The peak load season3

Due to data availability, the same load curve is assumed for CH-N and CH-NE.
None of the modeled city nodes in China, Japan, Korea and Russia use daylight saving time (DST) as of September 2015. Mongolia does use DST, but the windand solar output curves in Figs. 6 and 7, as well as all of our other calculations for
the Gobi Desert (GD), are in Mongolia Standard Time, which is the same as the timeat the China nodes.
4Fig. 4. Initial capacity settings of generation and storage facilities at city nodes.

varies by node; for example, the peak load season is summer in JPE and winter in RU-FE.
2.3.2. Generation and storage facilities
The model is allowed to endogenously add fossil fuel-ﬁred
generation capacity in the all scenarios. Capacities for nuclear,solar PV, wind and pumped hydro are given exogenously in eachscenario. Hydro plant capacities are exogenous variables, except
for RU-SI and RU-FE in the Ruhyd and Gobitec þRuHyd scenarios.
Assumptions for additional hydro potential for these nodes are
discussed in Section 2.3.4. Fig. 4 depicts initial capacity settings forgeneration and storage facilities. The initial capacity of fossil fuelﬁred plants and pumped hydro are based on existing capacities in2011 (JEPIC, 2014; Hippel et al., 2011). For coal-ﬁred plants, we
impose upper bounds based on the projected capacity in APERC(2013), reﬂecting environmental concerns. The initial capacity forrenewables, except for GD (the Gobi Desert area), is estimatedbased on the projected capacity for 2030 in APERC (2013) as well
as renewable energy potential information (McElroy et al., 2009;
Energy and Environment Council, 2011). For renewables in GD in
the Gobitec and the Gobitec þRuHyd scenarios, we assumed 50 GW
of PV and 50 GW of wind turbines (Energy Charter et al., 2014). For
nuclear, we estimated capacity in 2030 based on available information (JAIF, 2013, 2014; MOTIE, 2014). Nuclear capacity in Japan assumes no new additions by 2030 and a 40-year lifetime forexisting plants. We assumed that all remaining nuclear plants arerestarted by the year 2030.
Tables 2–6 show the assumptions for generation and storage
facilities. We set the assumptions in a consistent way, comparing

T. Otsuki et al. / Energy Policy 89 (2016) 311–329

315

Table 2
Assumptions for generation and storage facilities.

Life time [year]
Carbon content [t-CO2/toe]Own-use rate [%]Maximum ramp-up/down rate [%/h]Capacity credit [%]Minimum output level [%]Cycle efﬁciency (storage) [%]Self-discharge rate (storage) [%/h]Maximum kWh ratio to kW (storage)

Nuclear

Coal

Gas

Oil

Hydro

Wind

PV

Pumped

40
0
4
0
90
100




40
3.80
6
30
90
30




40
2.07
3
50
90
20




40
2.86
5
100
90
15




60
0


40





20
0


25





20
0


15





60
0


85

75
0.01
8

Table 3
Cost assumptions for North China (CH-N), China Northeast (CH-NE) and the Gobi Desert area (GD).

Initial cost [$/kW]
Fixed O&M cost [$/kW/yr]
Variable O&M cost [$/kWh]Maximum availability
Conversion efﬁciency

Nuclear

Coal

Gas

Oil

Hydro

Wind

PV

Pumped

2600
65
0.001
0.8
1.00

750
15
0.002
0.7
0.40

700
14
0.004
0.9
0.50

800
16
0.002
0.9
0.37

2500
30
0
0.4


1300
1100
33
17
0
0
Estimated output proﬁle



2500
30
0
0.8


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

Initial cost [$/kW]
Fixed O&M cost [$/kW/yr]
Variable O&M cost [$/kWh]Maximum availability
Conversion efﬁciency

Nuclear

Coal

Gas

Oil

Hydro

Wind

PV

4000
104
0.002
0.7
1.00

2400
48
0.005
0.75
0.42

1150
23
0.006
0.7
0.50

1900
39
0.004
0.9
0.37

6000
70
0
0.4


1700
2500
43
31
0
0
Estimated output proﬁle



6000
70
0
0.8


Nuclear

Coal

Gas

Oil

Hydro

Wind

Pumped

3300
86
0.002
0.95
1.00

1500
30
0.003
0.9
0.40

800
16
0.005
0.9
0.50

1900
39
0.004
0.9
0.37

2500
30
0
0.4


1600
2250
40
34
0
0
Estimated output proﬁle



2500
30
0
0.8


PumpedTable 5
Cost assumptions for Korea (KR).

Initial cost [$/kW]
Fixed O&M cost [$/kW/yr]
Variable O&M cost [$/kWh]Maximum availability
Conversion efﬁciency

PV

Table 6
Cost assumptions for Russia nodes (RU-FE, RU-SI, and RU-SK).

Initial cost [$/kW]
Fixed O&M cost [$/kW/yr]
Variable O&M cost [$/kWh]Maximum availability
Conversion efﬁciency

Nuclear

Coal

Gas

Oil

Hydro

Wind

2800
73
0.001
0.8
1.00

2200
44
0.005
0.7
0.40

1000
20
0.005
0.9
0.50

1200
24
0.003
0.9
0.37

2500
30
0
0.4


1500
2000
38
30
0
0
Estimated output proﬁle

multiple sources from international organizations, governments,
industries as well as research institutes in order to ensure thevalidity. Speciﬁcally, the initial cost data for 2030 are estimatedfrom IEA and individual economy analyses and projections (IEA,
2010, 2014b; METI, 2015; Skoltech, 2015). The future initial cost offossil fuel-ﬁred plants and wind power remain similar to thecurrent level, while solar PV shows drastic cost reductions.5 Annual ﬁxed O&M cost [$/kW/yr] are estimated based on EIA (2013),

PV

2500
30
0
0.8


IEA (2014b) and METI (2015). For variable O&M cost (except fuelcost), in Japan we assumed $2/MWh for nuclear, $5/MWh for coalﬁred, $6/MWh for gas-ﬁred, and $4/MWh for oil-ﬁred (EIA, 2013;

5
IEA (2014b) shows the projected initial cost in 2020 and 2035, and, for example, the initial cost of large scale solar PV in China is projected to decrease byapproximately 40% by 2035 compared to the 2012 level.

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T. Otsuki et al. / Energy Policy 89 (2016) 311–329

Table 7
Fuel price assumption in 2030.

China/Gobi
RussiaJapan
Korea

Coal [$/t]Gas [$/MMBtu]

Oil [$/bbl]

110.5
99.8
140.1
137.3

9.3

121.5

14.4
13.5

METI, 2015). In other regions we adjusted the variable O&M costusing the ratio of initial cost between Japan and each region. The
carbon content of each fuel type is derived from EDMC (2014).
Assumptions for own-use of electricity at generating plants, rampup/down rate, minimum output level and conversion efﬁciency aretaken from IEA (2014a, 2014c) and METI (2015). Data for storage
relies mainly on Komiyama et al. (2015).
In considering the maximum availability for nuclear, fossil fuelﬁred and hydro generation, we estimated the values using historical capacity and generation data (JEPIC, 2013; EDMC, 2014;
KESIS, 2015). We conﬁrmed that the model reproduces results
similar to the actual power system (see Appendix A.2). As for wind
and PV, hourly output proﬁles are given exogenously. We estimated those output proﬁles for GD (the Gobi Desert) as explainedin Section 2.3.3. Because of the limited meteorological information
available for some economies, the daily output proﬁles for otherareas rely on the following simple assumptions: the output proﬁlefor wind is kept ﬂat at all times in all seasons assuming 20% capacity factor, and, for PV, hourly output proﬁles in each season are
estimated from the estimated or observed proﬁles in Shiraki et al.
(2011) and Zhao et al. (2009). Fossil fuel price assumptions in 2030
in Table 7 have been determined from the best available projections (IEA, 2010; Shinoda, 2013; MUFJ, 2013; Morita, 2013; Ling,
2013; KESIS, 2015). We based these estimates on historical CIF
prices for energy importing economies, historical FOB prices forexporting economies, and future import price trends from IEA
WEO (2013).
2.3.3. Power output of wind and PV in the Gobi Desert area (GD)
2.3.3.1. Estimated wind output. The seasonal proﬁle of the regionwide wind output in GD is estimated by using long-term windobservation data (Elliott et al., 2001). We used a similar estimation
approach to Komiyama et al. (2015). Fig. 5 shows a ﬂow chart forthis calculation. Elliott et al. (2001) reports average hourly windspeed data in a day in each month at various sites. We selected ﬁve
observation sites (Sainshand, Mandalgovi, Center Tuvshin Sukh,
Tumurbaatar, and Center Manlai Ummu station), and estimatedthe weighted average wind speed of the Gobi Desert area. Next, weestimated the equivalent wind speed at the hub height of the windturbines using the power law, assuming that the measured windWind speed data from
Elliott, et al. (2001)

Weighted average windspeed in Gobi Desert

Height of observation
point

Equivalent average windspeed at hub height

Hub height of windturbineWind output of
unit capacity

Power lawparameter

Performance curve ofwind turbine

Fig. 5. Flow chart for the calculation of wind power output in the Gobi Desert area.

Fig. 6. Assumed daily output proﬁle of wind in the Gobi Desert area (GD).

Fig. 7. Assumed daily output proﬁle of PV in the Gobi Desert area (GD).

speed is at 10 m and that the n-value in the power law is eight.
(For discussion of the power law, see Peterson and Hennessey, Jr.
(1979).) Then, we calculated the hourly wind turbine output proﬁle (Fig. 6) based on the average hourly wind speed at hub height
and a typical power curve.
Wind speed data in January is assumed for the winter season, in
July for the summer season. Wind speed in the intermediate season
is estimated by averaging the data for April and October. The assumption for the hub height of the wind turbine is 80 m, while thecut-in wind speed, rated speed, and cut-out wind speed are 5 m/s,
12.5 m/s and 25 m/s, respectively. The estimated average wind capacity factor is 26%. The data in Elliott et al. (2001) show the highwind speeds observed in the intermediate season (around April andOctober) in the Gobi area, and that trend is reﬂected in Fig. 6.
2.3.3.2. Estimated PV output. We estimated the average solar PV
output proﬁle (Fig. 7) by referring to Battushig et al. (2003) andAdiyabat et al. (2006). Battushig et al. (2003) reported the averagehourly maximum power of PV modules in October, November,
December, March, and April in the Gobi Desert area. PV output
proﬁles depend on various climatic conditions at the sites, such as
sunshine duration, air mass and ambient temperature. Because oflimited information about detailed solar irradiation in the area, weestimated output proﬁles by assuming that the observed output
shape in April in Battushig et al. (2003) is the representative
diurnal variation in the intermediate and the summer season in
the Gobi Desert area and that the shape in December represents
winter season. We then calculated the PV output proﬁle in eachseason based on the shapes and the observed daily average PV
energy output [Wh/day] in each season in Sainshand city in
Mongolia (Adiyabat et al., 2006).

T. Otsuki et al. / Energy Policy 89 (2016) 311–329

317

conversion stations are installed at the each end of the connection.
For the connection between JP-E (50 Hz) and JP-W (60 Hz), we
assume BTB (Back-to-Back) facilities for frequency conversion.
We estimated transmission line cost by drawing from Bahrman
and Johnson (2007), Schaber et al. (2012) and Matsuo et al. (2015).
We assume substation/switching station costs of $240M/station
and AC–DC conversion station costs of $480M/station, all for stations of 3 GW of capacity. We also assume $1.2M/km for overheadline (rated power: 3 GW) and $7.2M/km for HVDC submarine cable(rated power: 3 GW). Assumed transmission distances are asshown in Fig. 2. The linear programming model requires that interconnection capacity costs be expressed as cost per unit of capacity, so we calculated the initial cost for each transmission route[$/kW] as shown in Table 9. Assumed lifetime, transmission losses
and annual ﬁxed O&M cost are 40 years, 5%/1000 km and 0.3% in aratio to initial cost for all line types, respectively (Bahrman andJohnson, 2007; Matsuo et al., 2015).
Fig. 8 shows the initial capacity assumptions (LMI in Eq. (A.11)
in Appendix A.1) for transmission lines. We set initial capacity
referring to existing capacity and planned additions announced
by power utilities or transmission companies (TEPCO, 2014;
HEPCO, 2014; SGCC, 2014). No capacity additions are allowed in
the Base scenario (LMA ¼LMI), and we do not constrain capacity
additions in the other scenarios ( LMA = ∞). Instead, theconstraint on the net imports to each city node (Eq. (A.26) inAppendix A.1) indirectly regulates the maximum transmission
capacity level.

2.3.4. Additional hydro power potential in Russia Far East and Siberia nodes
The assumptions for the additional hydro potential in Russia
are based on the estimates by the European Bank for Reconstruction and Development (EBRD) (from IEA (2003)). According to IEA (2003), “economically feasible hydro power capability” in East Siberia and in the Far East is 350 TWh/yr and
294 TWh/yr, respectively. These regions account for 81% of thetotal potential in Russia. However, hydro resources are widelydistributed in the Far East region, which consists of the Far East (orfederal) power grid area, the autonomous power grid area and thenon-electriﬁed area (IEA, 2003). Similarly, hydro potential in East
Siberia is widely distributed from the south border region to thenorthern part of the region. Only limited parts of the Far East andEast Siberia regions have been connected to the federal grid (Popel, 2012). Therefore, we made a simple assumption that one-thirdof the economical hydro potential of the Far East and Siberia isaccessible in practice.
We estimated additional hydro potential on a GW basis using
the assumed “accessible potential” (TWh-basis), an assumed capacity factor (40%) and subtracting-off existing capacity (already
exploited resources). We equally divided the total additional potential into two categories (Add-Hyd1 and Add-Hyd2) as summarized in Table 8. Initial cost of hydro power potential depends
on its geographical location, and in general, undeveloped resources are more expensive than already exploited resources.
Thus, this study assumes a higher initial cost for the additionalhydro resources compared to already exploited resources. AddHyd1 is assumed to be more expensive than the average cost of
already exploited resources (Table 6) by 25% and Add-Hyd2 isassumed to be more expensive by 50%.

2.3.6. Operating reserve margin
As our study focuses on hourly dispatch, we assumed operating
reserve margins in Eq. (A.25) in Appendix A.1 as follows: 10% for
China, Japan and Korea and 15% for Russia referring to generalcriteria and/or historical data (Electric utility course (2008) and2.3.5. Transmission lines
HVAC overhead line technology is assumed to be used foroverland interconnections, except for the KR  RU-FE connection,
where HVDC technology is proposed in governmental publications(MOTIE, 2014). For undersea connections, HVDC cable technology
is assumed. Regarding HVAC substation/switching stations, we
assume that one substation/switching station needs to be installed
for every 150 km of HVAC overhead transmission line to ensure
grid stability. For HVDC connections, we assume AC–DC
Table 8Assumptions for additional hydro plants.
Additional hydro potential [GW]

Add-hyd1
Add-hyd2

RU-FE

RU-SI

12
12

5
5

Initial cost [$/kW]

3125
3750
Fig. 8. Initial capacity settings of transmission lines.

Table 9Initial cost assumptions for each interconnection route [$/kW]a.

CH-N
CH-NE
JP-H
JP-E
JP-W
KR
RU-FE
RU-SI
RU-SK
GD
a

CH-N

CH-NE

JP-H

JP-E

JP-W

KR

RU-FE

RU-SI

RU-SK

GD

–
700
–
–
–
1992
–
–
–
840

700
–
–
–
–
–
1360
–
–
1256

–
–
–
1392
–
–
–
–
1288
–

–
–
1392
–
672
–
–
–
–
–

–
–
–
672
–
1840
–
–
–
–

1992
–
–
–
1840
–
1560
–
–
–

–
1360
–
–
–
1560
–
–
1548
2900

–
–
–
–
–
–
–
–
–
1092

–
–
1288
–
–
–
1548
–
–
–

840
1256
–
–
–
–
2900
1092
–
–

“–” Indicates that interconnections are not allowed in this study.

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T. Otsuki et al. / Energy Policy 89 (2016) 311–329

Changes from the Base scenario
Fig. 9. Power generation in NEA region and changes from the Base scenario.

FEPC (2010) for Japan, KPX (2015) for Korea and SO UPS (2014b)
for Russia. As for the China nodes, we assumed a level similar to
Japan and Korea, due to limited data availability).

3. Results and discussion
3.1. Power generation mix and capacity
Fig. 9 displays the power generation (NEA total) for each scenario and changes from the Base scenario.
Fig. 9(a) indicates that coal-ﬁred generation remains thedominant source in this region (59–63%) even under the Gobitec þRuHyd scenario. In the Base scenario, coal-ﬁred electricity
accounts for 61% of total generation, and it increases to 63% in the
NoNewRE scenario. This is because grid interconnection allows
high electricity cost regions (like Japan and Korea) to accesscheaper coal electricity from China and Russia. The NoNewRE
scenario in Fig. 9(b) shows coal-ﬁred generation in the China
nodes and Russia Sakhalin (RU-SK) replace gas-ﬁred generation in
Japan (JP-H, JP-E and JP-W) and Korea (KR). This result implies thatcost optimal grid interconnections without expanding renewableenergy potentially increases coal-ﬁred generation in China andRussia. This situation might be undesirable especially for China,
which is suffering from severe air pollution.
Deployments of renewables in Eastern Russia and the Gobi
Desert contribute to an environmentally-friendly generation mix
in NEA. Renewables account for 11% of power generation both in
the Base and the NoNewRE scenario, and increase to 13%, 16% and
18% in the last three scenarios, respectively. In the RuHyd scenario,
additional hydro generation in Eastern Russia, not coal-ﬁred generation in CH-N, mainly replaces gas-ﬁred generation in JP-W and
KR (Fig. 9(b)). Yet, China still exports coal-ﬁred generation as Japanand Korea have room for further imports. In the last two scenarios,
gas-ﬁred generation in Japan and Korea as well as coal-ﬁred
generation in CH-N are replaced by renewable electricity from GD(the Gobi Desert) or Eastern Russia. The gap between the incremental generation and the generation decreases elsewhere represent cross-boundary transmission losses. Our results indicatethat 15% and 11% of the transmitted electricity is lost through long
distance transmission in the last two scenarios, respectively.
Fig. 10(a)–(e) show the power generation mix and net imports
share by region. Electricity trade is very limited in the Basescenario. The net imports share is not more than 0.6%. In the
NoNewRE scenario, the share of net imports increases in Japan
Hokkaido (JP-H) and the eastern parts of Japan (JP-E) to 7% and8.5% of annual demand, respectively. These nodes import mainly
from the newly installed fossil fuel-ﬁred coal plants in Sakhalinwith a capacity of 5 GW. As noted in Section 2.2, the model limits
net transmission inﬂows to each city node to the reserve margin at
all times, reﬂecting a likely concern over secure electricity supply(see Eq. (A.26) in Appendix A.1). These reserve margins are assumed to be 10% for China, Japan and Korea and 15% for Russia. In
JP-W and KR, the net imports almost reaches this upper bound on
average. The main exporter to these nodes is China (see also
Section 3.3).
In the RuHyd scenario, large scale hydro developments in RU-FE
allow KR, JP-W and JP-E to import hydroelectricity from Russia
instead of fossil fuel-ﬁred electricity from China or Russia. The net
imports share in these importing nodes remains similar in level to
the NoNewRE scenario. In the Gobitec scenario, the ‘Gobi electricity’ increases the net imports in CH-NE to 2%, which results in
reducing coal-ﬁred generation in the China nodes. In the Gobitecþ RuHyd scenario, the annual imports of the two China nodes
amount 78 TWh/yr (net imports share: 3% in both nodes). The net
imports share almost reaches the upper bound not only in JP-W
and KR but also in JP-H and JP-E.
3.2. CO2 emissions
Fig. 11 displays direct CO2 emissions in NEA from fossil fuelcombustion. Larger coal-ﬁred generation in the NoNewRE scenario
results in higher emissions by 66Mt-CO2 (þ 2.3%) compared to theBase scenario. This result implies that interconnection without
renewable resource expansion could increase CO2 emissions in
NEA. In the RuHyd scenario, additional hydro developments inEastern Russia slightly reduce the emissions by 0.4 Mt-CO2( 0.01%). In the Gobitec and Gobitec þRuHyd scenarios, Gobi
electricity or Gobi electricity þhydro in Eastern Russia contributeto emissions reductions of about 85 Mt-CO2 ( 3.1%) and 148 MtCO2 (  5.3%), respectively. These last two scenarios result in larger
reductions as the massive renewable deployments replace carbonintensive coal-ﬁred electricity in China (see Fig. 9(b)). The limited
beneﬁt to emissions in the RuHyd scenario is because the emissions from exported coal-ﬁred generation in China (Fig. 9(b))
partly offset the reductions from hydro in Russia.

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319

Fig. 10. Power generation mix and net imports by node. Hydro (total) indicates a sum of hydro, additional hydro-1 and additional hydro-2.

The CO2 reduction in the Gobitec scenario that we calculate(85 Mt-CO2) is lower than that from Energy Charter et al. (2014)(187 Mt-CO2). (Please note that Energy Charter et al. (2014) mentioned a 187 Gt-CO2 reduction, but we suspect the units were
misstated.) The CO2 reduction differences are partly because of
two factors. First, in Energy Charter et al. (2014), the assumptions
for utilization factor for both PV and wind are 30%, which aremuch higher than our estimates (20% for PV and 26% for wind).
Second, the two models use a substantially different dispatch logic
for Gobi electricity. Energy Charter et al. (2014) assumed that 80%
of Gobi electricity is sent to coal-intensive (high CO2 emissions perkWh) China, and they use average emissions factors in each importing region to estimate CO2 emission reductions. On the other
hand, our model determines the share of Gobi electricity at each

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T. Otsuki et al. / Energy Policy 89 (2016) 311–329

from Russia rather than China. Instead, China consumes more of
the Gobi Desert electricity, resulting in lower fossil fuel-ﬁred
generation in the China nodes.
Electric utilities and transmission companies in Russia, in cooperation with organizations in neighboring economies, have been
exploring the possibilities of cross-border grid interconnection(Smirnov, 2012; Inter RAO, 2013). Our results indicate that exporting fossil fuel-ﬁred electricity from Sakhalin to Japan could bean economic option. By contrast, the scale of connections from RUFE is relatively small in the NoNewRE and Gobitec scenarios, and it
greatly expands under the RuHyd and Gobitec þRuHyd scenarios inorder to export additional hydro power. The results imply that
additional hydro power can stimulate opportunities for electricity
trade between the ‘mainland’ Russia Far East and other regions.
3.4. Costs and beneﬁts

Fig. 11. CO2 emissions in NEA region.

importing node based on total system cost minimization. Thus, as
shown in Fig. 9(b), the increase in Gobi electricity results in reducing high cost gas-ﬁred generation (with relatively low CO2
emissions per kWh) in Japan and Korea.
3.3. Cross-boundary electricity ﬂow
Fig. 12 indicates cross-boundary electricity ﬂows [TWh/yr] andtransmission capacity [GW]. The Base scenario shows an international power trade only between Russia Far East (RU-FE) andNortheast China (CH-NE).
In the NoNewRE scenario, China and Russia export to Japan and
Korea because of low-cost electricity. Korea becomes a net importing economy as well as playing the role of a transit (“bridge”)
economy between China/Russia and Japan. The economy imports
131 TWh/yr from China, and exports to 66 TWh/yr to Japan.
Transmission line capacity of the China–Korea and Korea–Japan
interconnections are 21 GW and 11 GW, respectively, which areequivalent to 17% and 9% to the total installed capacity of Korea.
The ﬁgure also shows the newly added interconnections from
Russia Sakhalin to the eastern parts of Japan via Hokkaido (JP-H).
By contrast, the transmission capacity from the ‘mainland’ Russia
Far East region (RU-FE) to Japan and Korea is relatively small
compared to the aforementioned connections. The limited transmission scale is probably because of the higher transmission costassociated with the longer transmission distances.
On the other hand, in the RuHyd scenario, RU-FE becomes amajor exporter to Japan and Korea. The majority of the additionalhydroelectricity in RU-FE is transmitted to KR (51 TWh/yr). Also,
instead of fossil fuel-ﬁred electricity, Russia exports hydroelectricity to Japan (31 TWh/yr), of which 80% are consumed in JP-E
rather than JP-H due to the larger scale of the JP-E electricity
market. The Gobitec scenario shows the large-scale cross-borderelectricity ﬂow from the Gobi Desert area to Korea and Japan.
Transmission lines with a capacity of 100 GW are installed from
GD (the Gobi Desert) to match the capacity of the variable renewables there. Yet the utilization rate of these connections, i.e.,
22% in the GD–CH-N connection, is relatively low because of theintermittency of the transmitted power. In the Gobitec þRuHyd
scenario, transmitted electricity from China to Korea decreases
compared to the Gobitec scenario because Korea imports power

Fig. 13 depicts yearly total system cost and its changes from theBase scenario. The costs shown include a carbon cost of $30/t-CO2(see Table 1). The total system cost declines by $2.2B/yr, $5.0B/yr,$1.4B/yr, and $3.4B/yr from Base in the NoNewRE, RuHyd, Gobitec,
and Gobitec þRuHyd scenarios, respectively. These values areequivalent to 0.4–1.3% reductions; therefore, the impacts of interconnection on total system cost appear to be modest. The annualized share of initial cost of cross-boundary transmission tototal system cost is relatively small, e.g., 2.1% in the Gobitecþ RuHyd scenario.
However, grid interconnection affects some components of thetotal system cost more signiﬁcantly. As shown in the last three
scenarios in Fig. 13(b), while deployment of renewables in Gobi
and Eastern Russia and transmission lines have signiﬁcant initialcosts, renewable resource expansion contributes to fuel cost reductions in the NEA region of about 6% (  $11B/yr), 9% (  $16B/yr)
and 11% (  $20B/yr) in the last three scenarios, respectively. These
results imply two points as follows: ﬁrst, the beneﬁts of renewableelectricity trade mainly depend on fuel cost reductions; second,
the main costs which make the power trade and renewable expansion less attractive are the initial costs of renewables andtransmission lines.
Large fuel cost savings are estimated in the importing regions,especially in the last scenario: $3B/yr, $11B/yr and $6B/yr in the
China nodes, Japan and Korea, respectively, which are equivalent
to 5%, 15% and 23% reductions. Nevertheless, these results rely on
the future cost assumptions in Section 2.3. In order to investigate
the impact of future cost uncertainties on the economics of gridinterconnection, we perform a sensitivity analysis on the assumptions for fuel cost and the initial cost of transmission lines
and renewable energy in Section 3.6.
Given the time differences among the NEA regions, we werecurious whether any of the cost reductions were due to diversiﬁcation of daily peak loading times. To test this hypothesis, we recalculated the NoNewRE scenario with the time difference adjustments turned-off (see Eq. (A.7) in Appendix A.1). The indicatedcost reductions from diversiﬁcation of peak loading times were
only $1.4M/yr, which is insigniﬁcant given total cost reductions of$2.2B/yr in the NoNewRE scenario compared to the Base. Becauseof the small time difference (one hour) between the main exporter(China) and importers (Japan and Korea), peak-hours still occur
more or less simultaneously in each region, resulting in themodest effects of time differences. This result implies that the
beneﬁts of interconnections are mainly due to other factors, suchas low-cost electricity in the exporting regions, as well as seasonalload curve variations among the regions.

T. Otsuki et al. / Energy Policy 89 (2016) 311–329

321

Base

NoNewE

RuHyd

Gobitec

Gobitec+RuHyd
Fig. 12. Annual electricity ﬂow in NEA.

3.5. Marginal generation costs
The dual solution to Eq. (A.7) in Appendix A.1 indicates themarginal costs of electricity generation, which are determined by
the variable cost of generation, storage, and transmission. The
marginal costs are important indicators of the electricity pricelevel (IES, 2004; Schaber et al., 2012). Fig. 14 shows the power
generation proﬁle and marginal generation costs in the winterpeak season in JP-H in the Base and Gobitec þ RuHyd scenarios as
an example. A comparison of the two ﬁgures indicates that power
imports reduce gas-ﬁred generation and contribute to a loweringof prices, especially during the daytime (around 9:00–11:00 and322

T. Otsuki et al. / Energy Policy 89 (2016) 311–329

Fig. 13. Yearly total system cost of the NEA region and changes from the Base scenario.

Fig. 14. Power generation proﬁle and marginal generation costs in the winter peak season in the Japan-Hokkaido node (JP-H).

13:00–20:00). For example, at around 14:00, the marginal costs
drop from $0.18/kWh in the Base scenario to $0.12/kWh in the
Gobitec þRuHyd scenario.
Fig. 15 presents the average marginal generation costs in thecity nodes for the ﬁve scenarios. For the China nodes and the Japannodes (except JP-H), the ﬁgure shows weighted average values.
The average marginal costs varies from economy to economy in
NEA; it ranges from 0.075$/kWh in the China nodes to 0.107$/kWh
in Korea and 0.137$/kWh in Japan (except JP-H) in the Base scenario. Grid integration lowers the average marginal costs mainly inelectricity importing regions. In particular, the Japan Hokkaido
area (JP-H) and Korea (KR) show relatively large reductions as thepower imports signiﬁcantly decrease or almost replace highmarginal cost generation, such as oil-ﬁred and gas-ﬁred generation, as shown in Figs. 10 and 14. In the NoNewRE, the averagemarginal costs drops by 0.014$/kWh in JP-H and by 0.003$/kWh in
Korea, which are equivalent to 11% and 3% reductions, respectively.
As for the last three scenarios, the average marginal costs in Korea
remain at a similar level to the NoNewRE scenario, while the costsin JP-H decrease in the Gobitec þRuHyd scenario due to the increase in power imports (see Fig. 10(d) and (e)).
The Japan nodes, except JP-H, show small changes in averagemarginal generation costs as the share of high marginal cost
generation is relatively large compared to JP-H, and the limited
imports (which must be kept less than the reserve margin) are notenough to eliminate them (see Fig. 16). After the nuclear power
plant accident in Japan, electric utilities across the economy raised
residential electricity prices by 13–37% (from March 2011 to July2014) primarily because of additional fuel costs (NHK, 2014), andproposals for grid interconnection received increasing attention in
Japan as one of the potentially effective measures for lowering
prices. Our results indicate that parts of Japan would locally enjoy
lowered prices from grid interconnections. Yet, in Japan as awhole, even with the accelerated renewable developments, theprice reductions would be relatively small compared to the increases after the nuclear accident, at least as long as imports areconstrained due to energy security concerns.

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323

Table 10
Case settings of sensitivity analysis (50 cases total).
Installed capacity settings of generation,
storage and transmission facilities
Initial cost changes for renewables (GobiDesert and Russia) and transmission
lines
Fossil fuel price changes from Table 7

Fixed to the Base or Gobitec þRuHyd scenario result
 20%,  10%, 0% (¼ “Ref.”),þ 10%, þ20%
 20%,  10%, 0% (¼ “Ref.”),þ 10%, þ20%

Fig. 15. Average marginal generation costs in the city nodes.

3.6. Sensitivity analysis of energy prices and initial costs
Fig. 13 suggested that the beneﬁts achieved by interconnecting
power grids and promoting trade in renewable electricity depend
mainly on fuel cost savings, and that higher initial cost of renewables and transmission lines makes regional interconnections
less attractive. In this section, we conduct a sensitivity analysis onthese two factors to investigate their impacts on the economics ofgrid interconnection. We calculated ﬁfty cases total as shown in
Table 10: two installed capacity settings (generation, storage andtransmission capacity are ﬁxed to the Base or Gobitec þRuHyd
scenario result)  ﬁve fossil fuel prices (  20%,  10%, 0% ( ¼“Ref.”),þ10%, þ20% from Table 7)  ﬁve initial costs of renewable energy
in the Gobi Desert and Russia and all transmission lines ( 20%,
10%, 0% (¼“Ref.”), þ10%, þ20%). Other assumptions are thesame as shown in Tables 2–6 (Section 2.3).
Fig. 17 illustrates the economic beneﬁts of the Gobi þRuHyd
scenario in each case (total system cost reductions compared toFig. 17. Economic beneﬁts of the Gobi þRuHyd scenario in each case.

Base). The results show improved economic viability of grid interconnections under lower initial cost or higher fossil fuel prices.
For example, the beneﬁt increases to $12B/yr in the þ20% fossil
fuel price and  20% initial cost case (this beneﬁt is approximately
equivalent to a 3% total cost saving). On the other hand, the results
also suggest the beneﬁt would shrink or become negative with 10–
20% lower fuel prices and higher initial costs. In Sections 3.1–3.5,
we assumed estimated initial costs of renewables and transmission lines in 2030 based on IEA (2014b), Bahrman and Johnson

Fig. 16. Power generation proﬁle and marginal generation costs in the summer peak season in Japan West node (JP-W).

324

T. Otsuki et al. / Energy Policy 89 (2016) 311–329(2007) and so on. However, these costs, especially for hydro generation and transmission lines, depend on site-speciﬁc characteristics. Also, IEA (2014b) assumes learning rates which reduce thefuture initial costs of renewables, yet uncertainties exist in thesecost reduction trends. Similarly, energy prices have shown theirvolatile nature in the past decade. The relevant planning organizations should carefully assess the actual initial costs and longterm fossil fuel price trends, bearing in mind their signiﬁcance, as
indicated Fig. 17.

4. Conclusions and policy implications
This study quantitatively investigated the economic viability of
power grid interconnections in NEA and renewable energy developments in the Gobi Desert and Eastern Russia with a singleyear multi-region power system model. The model is formulated
as a linear program, which aims to minimize overall system cost.
The model considers nodal electric load characteristics, including
representative hourly load curves, as well as the output proﬁle ofvariable renewables in the Gobi Desert. We validated our model
capability using historical data (see Appendix A.2).
We investigated ﬁve scenarios for the NEA power system of2030: Base, NoNewRE, RuHyd, Gobitec and Gobitec þRuHyd scenarios (see Table 1). Major results in each scenario are summarizedin Table 11. These simulation results lead us to several interestingﬁndings as follows.
First, from an environmental perspective, the Gobitec þRuHyd
scenario shows that access to wind/solar resources in the Gobi
Desert and additional hydro resources in Eastern Russia promotes
an environmentally-friendly generation mix. The total renewable
share in NEA increases from 11% to 18% of which 2% is from Russia
and the remaining 5% is from the Gobi Desert. Deployment ofthese renewables contributes to NEA emission reductions of 5.3%.
By contrast, the NoNewRE scenario shows that cost-optimal gridinterconnections without renewable energy development would
promote low-cost coal generation in China and Russia, resulting in
an increase in CO2 emissions in NEA (þ2.3% from the Base) andpotentially worse air pollution in China. Thus, interconnection
projects should be undertaken in tandem with renewable energy
expansion in order to reap both economic and environmental
beneﬁts.
Second, all grid interconnection scenarios indicate that economic beneﬁts in the form of total cost reductions depend mainlyon the fuel cost saved by shifting to cheaper fossil fuel or to renewables. Expanding renewable energy results in larger fuel costsavings, for example the 4% reductions (  $7B/yr) in the NoNewRE
versus the 11% reductions (  $20B/yr) in the Gobitec þRuHyd.
However, the total system cost reductions appear modest (lessthan 1.3% reduction from the Base) due to the increase in initialcosts and O&M costs. In addition, sensitivity analysis on fuel price
Table 11
Summary of the results of each scenario.

Annual total cost
Annual initial costs
Annual fuel costs
Annual CO2 emissions (Gt)
Share of coal-ﬁred
generation in NEA(%)
Share of renewables
in NEA (%)

Base

NoNewRE RuHyd

Gobitec

GobitecþRuHyd$392 B/yr$79B/yr$187B/yr
2.79 Gt$390B/yr$81B/yr$180B/yr
2.86 Gt$387B/yr$84B/yr$175B/yr
2.79 Gt$390 B/yr$94B/yr$171B/yr
2.71 Gt$388 B/yr$97B/yr$167B/yr
2.65 Gt

61%

63%

62%

60%

59%

and initial costs (see Section 3.6) imply that the beneﬁt potentiallyshrinks or becomes negative with 10–20% lower fuel prices andhigher initial costs. These limited economic beneﬁts are likely to
be a major challenge to implementing grid interconnection in NEA.
The relevant planning organizations should carefully assess theactual initial costs and long-term fossil fuel price trends in order toassure that implementation will be beneﬁcial.
Third, the RuHyd and Gobitec þRuHyd results imply that interconnection opportunities between ‘mainland’ Russia Far East andthe rest of NEA expand with additional hydro development inEastern Russia. Hence, access to additional hydropower will be thedriver of opportunities for grid interconnections between Russia
and other regions.
Turning to priorities for future work, Section 3.6 mentioned theimportance of site-speciﬁc characteristics to the cost of renewables
and transmission lines. Further examination of site-speciﬁc costs,
as well as site-speciﬁc performance of renewables, should be undertaken. For example, if detailed meteorological data wereavailable in each node (i.e., hourly or more detailed data for a fullyear), future work could better characterize the output of intermittent renewables.
Future work should perform sensitivity analysis on the upperbounds for net imports in importing regions and the installedcapacity of renewables in the Gobi Desert. Future work might
examine alternatives to the transmission route through the DPRK
in order to mitigate political risk. And future work might test
constraining the output of gas-ﬁred generation in Japan, Korea andChina in the alternative cases to Base levels, thereby forcing renewables to mainly replace coal-ﬁred generation, for environmental reasons.
Our modeling approach includes some simpliﬁcations thatshould be addressed in future work. The single-year simulation
could be replaced with a multi-year simulation in order to examine the evolution of costs and beneﬁts over time. Perhaps mostimportantly, the current model is deterministic in nature. Incorporating probabilistic representations could improve the robustness of the model in at least two ways. First, incorporating
probabilistic behavior into the representation of intermittent renewables would allow a more detailed examination of NEA-wide
grid integration issues, not only for the Gobi Desert renewables
discussed in this paper, but also for intermittent renewables in theother NEA economies. Second, a probabilistic representation of theperformance of additional system components (transmission lines
and thermal generating facilities) would allow the model to address the impact of NEA grid interconnection on power system
reliability and the costs of maintaining reliability.

Acknowledgment
We would like to sincerely thank Brantley Liddle, formerly
Special Advisor at the Asia Paciﬁc Energy Research Centre (APERC),
for his guidance and informative comments, as well as fouranonymous referees for their helpful suggestions for improvement. The research reported in this paper was generously supported by APERC. The views expressed, however, are those of theauthors and not necessarily those of APERC.

Appendix A
A.1. Mathematical formulation

11%

11%

13%

16%

18%

We describe the equations of the model in order to provide a
detailed understanding of this study. Table A1 shows the

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Table A1
Endogenous variables in the multi-region power system model.

325

CIn = ∑ PA p ⋅PIn, p⋅kpn, p + ∑ STAst ⋅STIn, st⋅kstn, stpst+ ∑ ∑ LIl⋅LIn, n2, l⋅kln, n2, l

TC: total annual cost [$/yr]
xpn, p, s, t : output of generation type p at local time t in season s in node n [kW]n2 < nl(A.2)

den, p, s, t : suppressed output of generation type p (p ¼wind or PV) at local time t
in season s in node n [kW]mpn, p, s : daily maximum output of generation type p in season s in node n [kW]kpn, p : capacity of generation type p in node n [kW]xln, n2, l, s, t : exported power from node n to node n2 via line type l at time t (noden time) in season s [kW]kln, n2, l : total transmission capacity between node n and n2 via line type l [kW]xchn, st , s, t : electricity charge of storage type st at local time t in season s in noden [kW]
xdcn, st , s, t : electricity discharge of storage type st at local time t in season s innode n [kW]sten, st , s, t : stored electricity of storage type st at local time t in season s in node n[kWh]kstn, st : kW-capacity of storage type st in node n [kW]where n, n2∈ {1:CH-N, 2:CH-NE, 3:JP-H, 4:JP-E, 5:JP-W, 6:KR, 7:RU-FE, 8:RU-SK,
9:RU-SI, 10:GD}s∈ {1:Summer-Peak, 2:Summer-Average, 3:Winter-Average, 4: Winter-Peak, 5:Intermediate }t ∈ {0 , 1, … , 23}p∈ {1:Nuclear, 2:Coal-ﬁred, 3:Gas-ﬁred, 4:Oil-ﬁred, 5:Hydro, 6: Wind, 7:PV, 8:
Additional hydro 1 (Add-Hyd1), 9:Additional hydro 2 (Add-Hyd2)}
st ∈ {Pumped Hydro Storage}l∈ {HV interconnection}

endogenous variables of the model.
A.1.1. Objective function
Eq. (A.1) is the objective function. This model minimizes total
system cost for a single representative year. System cost is composed of annualized initial cost, O&M cost, fuel cost and carbon
cost for the whole of Northeast Asia (NEA). The cost of power
generation includes all four cost components. The cost of storage
and transmission lines consists of initial cost and ﬁxed O&M cost.
Therefore, the model chooses cross-boundary power trade if its
beneﬁt (usually the savings in generation cost) is larger than thetransmission line cost.
Eqs. (A.2)–(A.5) describe each component. In Eq. (A.2), annualized initial cost of generation, storage and transmission facilities are calculated as the product of a capital recovery factor (PA,STA and LA, respectively), unit construction cost and installed capacity. For capital recovery factor calculation, the assumed discount rate is 3% and the lifetime assumptions are as discussed in
Sections 2.3.2 and 2.3.5. Eq. (A.3) describes the O&M cost of generation (ﬁxed and variable O&M cost), storage (ﬁxed O&M) andcross-boundary transmission lines (ﬁxed O&M). Eqs. (A.4) and(A.5) respectively describe fuel cost and carbon cost for directemissions. Time slot length in hours (HW) is calculated in Eq. (A.6).
Assumed season length (SW) in months is in Table A2, and timeslice length (TW) is 1 h.

min. TC = ∑ (CIn + COn + CFn + CCn)n

Table A2
Assumed length of seasons.
SeasonAssumed length [Months]

Summer-peak
Summer-average
Winter-peakWinter-average
Intermediate

0.2
2.8
0.2
2.8
6.0

(A.1)

COn = ∑ (POFn, p⋅kpn, p + ∑ ∑ POVn, p⋅xpn, p, s, t ⋅HWs, t )pst+∑ STOFn, st⋅kstn, st + ∑ ∑ LOFl⋅kln, n2, lstn2 < nl

CFn = ∑ ∑ ∑ PFn, p⋅xpn, p, s, t ⋅HWs, tpstCCn = ∑ ∑ ∑ CTAX⋅Carbonp⋅xpn, p, s, t ⋅HWs, t

(A.3)

(A.4)

t

(A.5)

HWs, t = 8760⋅SWs⋅TWt /12/24

(A.6)

p

s

where CIn: annualized initial cost of generation, storage andtransmission facilities in node n [$/yr]; COn: annual O&M cost ofgeneration, storage and transmission facilities in node n [$/yr];
CFn: annual fuel cost in node n [$/yr]; CCn: annual carbon cost forfuel combustion [$/yr]; PAp: capital recovery factor for generation
type p; PIn,p: initial cost for generation type p in node n [$/kW];
STAst: capital recovery factor for storage type st; STIn,st: initial costfor storage type st in node n [$/kW]; LAl: capital recovery factor fortransmission lines; LIn,n2,l: initial cost of a transmission line between nodes n and n2 [$/kW]; POFn,p: ﬁxed O&M cost for generation type p [$/kW/yr]; POVn,p: variable O&M cost for generation
type p [$/kWh]; STOFn,st: ﬁxed O&M cost for storage type st [$/kW/
yr]; LOFl: ﬁxed O&M cost for a transmission line [$/kW/yr]; PFn,p:fuel cost for generation type p [$/kWh]; Carbonp: carbon intensity
for generation type p [t-CO2/kWh]; CTAX: carbon price [$/t-CO2];
HWs,t: length of time slot at local time t in season s [h]; SWs: lengthof season s [Months] (see Table A2); TWt: length of time slice t [h](¼1 h).
A.1.2. Constraints and equations
A.1.2.1. Power demand and supply balance
Eq. (A.7) ensures that electricity demand must be satisﬁed at all
times in all seasons and in all nodes. The left part indicates thesum of power supply from domestic generators, net power imports and net power discharge of storage facilities. Cross-boundarytransmission losses are considered when transmitted power
reaches the importing node by multiplying exported power bytransmission efﬁciency (xl*LE). Time differences between power
exporting and importing nodes are considered for imported
power. ImT indicates the local time at the origin of electricity
imports deﬁned as below. Note that we number hours of the dayfrom 0 to 23 (Table A1).
The dual solution to this equation indicates the marginal costsof electricity generation. The marginal costs are determined by thevariable cost of generation, storage, and transmission. Losses intransmission and storage indirectly increase marginal costs andtotal system cost, as they lead to higher demand for power generation (Schaber et al., 2012). Eq. (A.8) shows how we calculatecross-boundary transmission efﬁciency.
This model does not explicitly consider modes of operation ofgeneration and storage facilities. In the real world, storage facilities
are either in charging mode (pumping mode for pumped hydrostorage), discharging mode (generation mode) or standby mode at
any given time. In our linear programming model, Eq. (A.7) technically allows both charging and discharging simultaneously.
However, because of the power losses this would involve, as326

T. Otsuki et al. / Energy Policy 89 (2016) 311–329

represented in Eqs. (A.23) and (A.24) below, the model will avoid asolution with simultaneous charging and discharging.

xchn, st, s, t + xdcn, st, s, t ≤ STAVst⋅kstn, st(A.14)∑ xpn, p, s, t + ∑ ∑ (xln2, n, l, s, ImTn2,n,t⋅LEn, n2, l − xln, n2, l, s, t )+sten, st, s, t ≤ STAVst⋅CRTst⋅kstn, st(A.15)

xln2, n, l, s, t + xln, n2, l, s, t ≤ LAVl⋅kln, n2, l(A.16)pn2

l∑ (xdcn, st , s, t − xchn, st , s, t ) = ELDn, s, t
st

(A.7)

DISn, n2LEn, n2, l = (1 − LOSl ) 1000

(A.8)

where ELDn,s,t: electric load at local time t in season s in node n[kW]; LEn,n2,l: cross-boundary transmission efﬁciency for line typel between nodes n and n2; LOSl: transmission loss rate for line typel [per thousand km]; DISn,n2: transmission distance between nodes
n and n2 [km]; ImTn2,n,t: local time at the origin of electricity imports, deﬁned as:

where PAVp : availability factor for generation type p (p ¼1,…,5,8,9); ROFn, p, s, t : output proﬁle of intermittent renewable (windand PV) energy at local time t in season s, in node n (p ¼6, 7);
STAVst : availability factor for storage type st; CRTst : maximum ratioof kWh to kW for storage type st; LAVl : availability factor fortransmission line type l.
A.1.2.4. Ramping constraint for thermal generation (nuclear andfossil fuel-ﬁred)
The model considers technology-speciﬁc ramping constraints
for nuclear and fossil fuel-ﬁred plants. For technical reasons, eachtechnology has its own controllability, with output of these generators changeable within their ramping capabilities. Ramping up
and ramping down limits are modeled as follows in this study:

t + TimDn2, n + 24 for t + TimDn2, n < 0;t + TimDn2, n for 0 ≤ t + TimDn2, n < 24;
t + TimDn2, n − 24 for t + TimDn2, n ≥ 24;
and TimDn2,n: time difference between nodes (e.g., TimDCH-N,KR¼  1)).

xpn, p, s, t ≤ xpn, p, s, t − 1 + LFR p⋅kpn, p (t ≠ 0)

(A.17)

xpn, p, s,0 ≤ xpn, p, s,23 + LFR p⋅kpn, p(A.18)

xpn, p, s, t ≥ xpn, p, s, t − 1 − LFR p⋅kpn, p (t ≠ 0)

(A.19)

xpn, p, s,0 ≥ xpn, p, s,23 − LFR p⋅kpn, p(A.20)

A.1.2.2. Installable capacity constraint
Installable capacity of each technology is constrained by its
minimum and maximum deployable limits (see Sections 2.3.2 and2.3.5 for initial capacity and upper limit assumptions).

PMIn, p ≤ kpn, p ≤ PMA n, p

(A.9)

where LFRp : maximum load following rate for generation type
p [/h].

SMIn, st ≤ kstn, st ≤ SMA n, st(A.10)

A.1.2.5. Daily minimum output constraint for thermal plants

LMIn, n2, l ≤ kln, n2, l ≤ LMA n, n2, l(A.11)

Eq. (A.21) requires that thermal plants generate electricity at noless than their minimum output threshold. The model calculates
the minimum output threshold by multiplying minimum output
rate (MOL) and daily maximum output (mp) as described in Eq.
(A.21). Daily maximum output for each plant type in each season isdetermined by Eq. (A.22).

where PMIn, p : initial capacity for generation type p in node n[kW]; PMAn, p : capacity upper limit for generation type p in node n[kW]; SMIn, st : initial capacity for storage type st in node n [kW];SMAn, st : capacity upper limit for storage type st in node n [kW];LMIn, n2, l : initial capacity for transmission line type l between noden and n2 [kW]; LMAn, n2, l : capacity upper limit for transmission line
type l between node n and n2 [kW].
A.1.2.3. Output constraint

xpn, p, s, t + den, p, s, t = ROFn, p, s, t⋅kpn, p (p = 6, 7)(A.21)

mpn, p, s ≥ xpn, p, s, t

(A.22)

where MOL p : minimum output rate of operation for generation
type p.

Eqs. (A.12)–(A.16) constrain output of generation, storage andtransmission facilities. The output of power generation technologies, except wind power and PV, are constrained to their available
capacity (Eq. (A.12)). For wind and PV, the seasonal hourly availability proﬁles (ROF) are exogenously given in Eq. (A.13). These
proﬁles for Gobi Desert (GD) are estimated in Section 2.3 based on
observation data (Elliott et al., 2001). The left part of Eq. (A.13)
indicates two destinations for output power from wind and PV:
power supplied to the grid (xp) or suppressed (de). Eq. (A.14)
constrains the charge to or discharge from storage facilities to theiravailable power capacity (kW-capacity). Eq. (A.15) constrains
stored electricity to the energy capacity (kWh-capacity) of the
facility, i.e., reservoir capacity for pumped hydro.

xpn, p, s, t ≤ PAVp⋅kpn, p (p = 1, …, 5, 8, 9)

xpn, p, s, t ≥ mpn, p, s ⋅MOL p(A.12)(A.13)

A.1.2.6. Stored energy balance
Eqs. (A.23) and (A.24) relates power charge (xch), power discharge (xdc) and the level of stored electricity (ste). Self-discharge
loss and charge/discharge efﬁciency are considered in this equation.

sten, st, s, t = (1 − SDRst )⋅sten, st, s, t − 1+( CEF ⋅xchstn, st, s, t − xdcn, st, s, t /)CEFst ⋅TWt (t ≠ 0)

(A.23)

sten, st, s,0 = (1 − SDRst )⋅sten, st, s,23
+ ( CEFst ⋅xchn, st, s,0 − xdcn, st, s,0/ CEFst )⋅TWt(A.24)

where SDRst : self-discharge rate for storage type st; CEFst : cycle
efﬁciency for storage type st; and TWt : length of time slice t [h](¼1 h).

T. Otsuki et al. / Energy Policy 89 (2016) 311–329

327

A.1.2.7. Capacity reserve margin constraint
Constraint (A.25) ensures a certain level of capacity reserve
margin in each node in order to maintain a secure and reliable
power supply system. There should be enough excess generationand storage capacity at each node to cover demand plus the reserve margin.

∑ kpn, p⋅PCCp + ∑ kstn, st⋅STCCst ≥ (1 + RVMn)⋅ELDn, s, tstp

(A.25)

where RVMn : operating reserve margin in node n; PCCp : capacity credit for generation type p; STCCst : capacity credit forstorage type st.
A.1.2.8. Upper constraints on net transmission inﬂows for city nodes
In general, each power service area needs to be prepared fortransmission interruptions. Thus, in the scenarios in this study, thenet transmission inﬂows at each city node (left part of the equation) are limited to be less than the reserve margin level(NIS ¼RVM).

∑ (xln2, n, l, s, t ⋅LEn, n2, l − xln, n2, l, s, t ) ≤ NISn⋅ELDn, s, t(for n = 1, …, 7)l, n2(A.26)

where NISn : maximum share of net transmission inﬂows in
node n (we assume this is equal to the operating reserve margin
for the scenarios in this study).
A.1.2.9. Additional hydro power export constraints
As explained in the RuHyd scenario in Section 2.2, this study
considers additional hydro developments in Russia Far East (RUFE) and Russia Siberia (RU-SI) for exporting to foreign nodes. Thisequation ensures that power exports from these nodes should begreater than or equal to the output power of additional hydro(AddHyd-1 and AddHyd-2).
9∑xln, n2, l, s, t ≥ ∑ xpn, p, s, t

l, n2 ∉ ENn

p=8(A.27)

A.1.2.10. Transmission line capacity constraints for supply nodes
Eq. (A.28) requires each supply node (GD, RU-SI, and RU-FE) tohave enough transmission capacity to deliver the output of theinstalled generation capacity in the node. This constraint may be
active especially in the Gobitec and Gobitec þ RuHyd scenarios,
where there are minimum constraints on generation capacity in
the Gobi Desert (GD) node. This constraint forces the total GD
transmission capacity to at least match the total GD generation
capacity.

∑ kln, n2, l ≥ ∑ kpn, p (for n = 8, 9, 10)
l, n2

p

Fig. A1. Comparison of modeled generated electricity and historical data inNortheast Asia.

margin for Japan and Korea in 2010 based on available historical
data (TEPCO, 2013; MKE, 2013). No carbon tax is assumed andother parameters are the same as shown in Tables 2–6 (Section
2.3).
Fig. A1 compares modeled electricity generation and historical
data (JEPIC, 2014). So-called base load and middle load plants(nuclear, coal, gas and hydro) show relatively good ﬁt. By contrast,
the model tends to underestimate generation from peak load
plants (oil). As reported in Schaber et al. (2012), this is probably
because of the deterministic nature of the optimization model.
Probabilistic aspects, such as unforeseen forecast errors or generation outages, are not considered in the model, yet peak load
plants are often operated to balance electricity demand and supply
in those events.
The model reproduces similar trends of cross-boundary electricity trade as shown in Fig. A2. The observed differences might
be because the actual power trade is not always scheduled basedon cost-optimization, as well as because the model simpliﬁes
several aspects of the real world, especially in terms of simpliﬁed
modeling of electricity ﬂow (see paragraph 7 in Section 2.1) andtemporal resolution.
Fig. A3 compares historical average retail electricity prices withmodeled average marginal costs of power generation. Unfortunately, we know of no source of historical data on marginal
generation costs for the NEA economies. Historical average retailelectricity prices were obtained from JEPIC (2012) for China, METI(2014) for Japan, KESIS (2015) for Korea and RAO Energy System ofEast (2013) for the Russia Far East area. The computed marginal
generating costs tend to be lower than the historical retail prices(i.e. 4 c/kWh in Japan and 1 c/kWh in Russia Far East). This is partly
because of the additional costs, taxes or subsidies not explicitly(A.28)

A.2. Model validation
In order to validate the model’s ability to properly assess thereal-world power system, we performed a simulation of the NEA
power system for the year 2010, the most recent year before theGreat East Japan Earthquake. The installed capacity of generation,
storage and cross-boundary transmission facilities is calibrated tohistorical data (JEPIC, 2012). Costs and fuel prices for 2010 are
based on IEA (2010), IEA (2014b) and Energy and Environment
Council (2011). Historical average efﬁciencies of fossil fuel-ﬁred
plants are estimated using IEA (2014c). We adjusted the reserve

Fig. A2. Modeled net imports and historical data .

328

T. Otsuki et al. / Energy Policy 89 (2016) 311–329

Fig. A3. Comparison of the average retail electricity prices with the modeled
average marginal costs of power generation .

included in our model. The historical retail electricity price in
Korea is lower than the computed average marginal costs. This isprobably because Korea government regulation holds electricity
price at a level lower than the actual generating costs. In fact, from2008 to 2012, Korea Electric Power Corporation was in a chronic
state of deﬁcit (KEPCO, 2015).
The generated electricity and cross-boundary trades in thebenchmark results are insensitive to fuel price changes: a considerable price increase (90% for coal and 20% for gas) does nothave major impacts on the results.

Referencess
Adiyabat, A., et al., 2006. Evaluation of solar energy potential and PV module performance in the Gobi Desert of Mongolia. Prog. Photovolt.: Res. Appl. 14,
553–566.
APERC, 2013. APEC Energy Demand and Supply Outlook, 5th edition. Asia Paciﬁc
Energy Research Centre (APERC), Tokyo.
Bahrman, M., Johnson, B., 2007. The ABCs of HVDC transmission technologies. IEEE
Power Energy Mag. 5 (2).
Battushig, M., et al., 2003. Performance monitoring of PV modules for VLS-PV
systems in Gobi Desert of Mongolia. In: Proceedings of 3rd World Conference
on Photovoltaic Energy Convension, May 11–18, 2003, Osaka, Japan.
Bowen, B.H., Sparrow, F., Yu, Z., 1999. Modeling electricity trade policy for thetweleve nations of the Southern African Power Pool (SAPP). Util. Policy 8,
183–197.
Chang, Y., Li, Y., 2013. Power generation and cross-border grid planning for the
integrated ASEAN electricity market: a dynamic linear programming model.
Energy Strategy Rev. 2, 153–160.
Choi, J., et al., 2006. Probablistic reliability based tie line capacity for interconnecting power systems of South Korea, North Korea and Far East Russia. In:
Proceedings of 9th International Conference on Probabilistic Methods Applied
to Power Systems, KTH, Sweden, June 11–15.
Chung, K.-H., Kim, B.H., 2007. Economic feasibility on the interconnected electric
power systems in North-East Asia. J. Electr. Eng. Technol. 2 (4), 452–460.
EDMC, 2014. Handbook of Energy & Economic Statistics. The Energy Conservation
Center, Tokyo, Japan.
EIA, 2013. Updated Capital Cost Estimates for Utility Scale Electricity GEnerating
Plants. Energy Information Administration (US DOE), Washington.
Electric utility course, 2008. Electric Power Industry Encyclopedia. Energy Forum,
Tokyo (in Japanese).
Elliott, D., et al., 2001. Wind Energy Resource Atlas of Mongolia. NREL/TP-50028972. Colorado.
Energy and Environment Council, 2011. Report of cost validation committee. Energy
and Environment Council (Prime Minister's Ofﬁce, Japan), Tokyo (in Japanese).
Energy Charter, et al., 2014. Gobitec and Asian Super Grid for Renewable Energies inNortheast Asia. Energy Charter Secretariat.
Eurus Energy, 2012. Solar Power – Requirements. [Online] Available at: 〈http://eurus-energy.com/en/solar_power/condition.html〉 (accessed 03.08.15).
FEPC, 2010. Statistics of 60-year Power Industry History in Japan. [Online] Available
at: 〈http://www.fepc.or.jp/library/data/60tokei/〉 (accessed 27.11.14) (inJapanese).
Google, 2015. Google Map. [Online] Available at: 〈https://www.google.co.jp/〉 (accessed 18.08.15).
Government of Japan, 2014. Portal Site about Energy Saving. [Online] Available at:〈http://setsuden.go.jp/〉 (accessed 13.03.15) (in Japanese).
Graaf, T.V. d, Sovacool, B.K., 2014. Thinking big: politics, progress and security in the
management of Asian and European energy megaprojects. Energy Policy 74,

16–27.
HEPCO, 2014. Press Release: Started Construction of Additional Transmission Line
Between Hokkaido and Honshu-island. [Online] Available at: 〈http://www.
hepco.co.jp/info/2014/1189511_1635.html〉 (accessed 30.06.15) (in Japanese).
Hippel, D. v., 2001. Estimated Costs and Beneﬁts of Power Grid Interconnections inNortheast Asia. Prepared for the Northeast Asia Grid Interconnection Workshop, Beijing, China, May 14–16, 2001.
Hippel, D. v, Gulidov, R., Kalashnikov, V., Hayes, P., 2011. Northeast Asia regionalenergy infrastructure proposals. Energy Policy 39, 6855–6866.
IEA, 2003. Renewables in Russia From Opportunity to Reality. International Energy
Agency, Paris.
IEA, 2010. Projected Cost of Generating Electricity, 2010 Edition International Energy Agency, Paris.
IEA, 2013. World Energy Outlook 2013. International Energy Agency, Paris.
IEA, 2014a. The Power of Transformation – Wind, Sun and The Economics of
Flexible Power Systems. International Energy Agency, Paris.
IEA, 2014b. World Energy Outlook Investment Costs. [Online] Available at: 〈http://
www.worldenergyoutlook.org/weomodel/investmentcosts/〉 (accessed
13.03.15).
IEA, 2014c. World Energy Statistics, Retrieved from OECD/IEA CD-ROM service.
IES, 2004. The Long Run Marginal Cost of Electricity Generation in New South
Wales. Intelligent Energy Systems, Sydney.
Inter RAO, 2013. Bridge of Strategic Importance. Energy Without Borders 4, 16–17.
JAIF, 2013. Electricity situation in Russia. [Online] Available at: 〈http://www.jaif.or.
jp/ja/news/2013/russia_nuclear_development.pdf〉 (accessed 13.03.15) (inJapanese).
JAIF, 2014. Nuclear Development in China. [Online] Available at: 〈http://www.jaif.or.
jp/ja/asia/china/china_data.pdf〉 (accessed 13.03.15) (in Japanese).
JEPIC, 2006. Overseas Electric Power Industry Statistics 2006. Japan Electric Power
Information Center Inc., Tokyo (in Japanese).
JEPIC, 2012. Overseas Electric Power Industry Statistics 2012. Japan Electric Power
Information Center Inc., Tokyo (in Japanese).
JEPIC, 2013. Overseas Electric Power Industry Statistics 2013. Japan Electric Power
Information Center Inc., Tokyo (in Japanese).
JEPIC, 2014. Overseas Electric Power Industry Statistics 2014. Japan Electric Power
Information Center Inc., Tokyo (in Japanese).
KEPCO, 2014. KEPCO's future plans of Northeast Asia supergrid. Presentation material at "Roadmap to Asian Super Grid", January 2014.
KEPCO, 2015. KEPCO-Comprehensive Income. [Online] Available at: 〈http://home.
kepco.co.kr/kepco/EN/C/htmlView/ENCBHP00501.do?menuCd ¼ EN030206〉(accessed 30.06.15).
KESIS, 2015. Korea Energy Statistics Information System. [Online] Available at:〈http://www.kesis.net/〉 (accessed 13.03.15).
KESIS, 2015. Korea Energy Statistics Information System. [Online] Available at:〈http://www.kesis.net/ﬂexapp/KesisFlexApp.jsp?
menuId ¼Q0109&reportId¼ &chk ¼Y〉 (accessed 01.01.15).
Komiyama, R., Fujii, Y., 2014. Assessment of massive integration of photovoltaic
system considering rechargeable battery in Japan with high time-resolution
optimal power generation mix model. Energy Policy 66, 73–89.
Komiyama, R., Otsuki, T., Fujii, Y., 2015. Energy modeling and analysis for optimal
grid integration of large-scale variable renewables using hydrogen storage inJapan. Energy 87, 537–555.
KPX, 2015. Acutal value of peak load over past 5 years. [Online] Available at: 〈http://power.kpx.or.kr/powerinfo_en.php〉 (accessed 04.08.15).
Kunstýř, J., Mano, S., 2013. Energy Security and Cross-Border Electricity Trade: Can
the Asian Super Grid project pose security risks for Japan? Can the electricity
imports be used as an extortion weapon? [Online] Available at: 〈http://jref.or.
jp/images/pdf/20140130/Energy_weapon_ﬁnal.pdf〉 (accessed 18.02.15).
Lee, S.-S., Park, J.-K., Moon, S.-I., Yoon, Y.-T., 2005. Seasonal load patterns and power
ﬂow analysis for interconnection in Northeast Asia. In: IEEE Power Engineering
Society General Meeting.
Lilliestam, J., Ellenbeck, S., 2011. Energy security and renewable electricity trade –Will Desertec make Europe vulnerable to the "energy weapon"? Energy Policy
39, 3380–3391.
Ling, S.Y., 2013. China gas price deregulating the market. Platts insight October.
Mano, S., 2014. Asia Super Grid Opportunities and Risks. [Online] Available at:〈http://jref.or.jp/images/pdf/20140128/JREF_Roadmap_to_ASG_Session1_Mano.
pdf〉 (accessed 10.07.15).
Matsuo, Y., et al., 2015. Quantitative Analysis of Effects of International Power Grid
Interconnection in ASEAN Region. [Online] Available at: 〈https://eneken.ieej.or.
jp/data/5909.pdf〉 (accessed 30.06.15).
McElroy, M.B., Lu, X., Nielsen, C.P., Wang, Y., 2009. Potential for wind-generated
electricity in China. Science 325 (5946), 1378–1380.
METI, 2014. Energy White Paper 2014. [Online] Available at: 〈http://www.enecho.
meti.go.jp/about/whitepaper/2014html/〉 (accessed 21.01.15).
METI, 2015. Report of Power Generating Costs Working Group. [Online] Available
at: 〈http://www.enecho.meti.go.jp/committee/council/basic_policy_sub
committee/mitoshi/cost_wg/pdf/cost_wg_03.pdf〉 (accessed 30.05.15).
MKE, 2013. The 6th Basic Plan for Long-term Electricity Supply and Demand [2013–2027]. Ministry of Knowledge Economy, Korea.
Morita, K., 2013. IEEJ Coal Trend No. 10. [Online] Available at: 〈https://eneken.ieej.
or.jp/data/4939.pdf〉 (accessed 13.03.15).
MOTIE, 2014. Korea Energy Master Plan Outlook and Policies to 2035. Ministry ofTrade, Industry and Energy, Korea.
MUFJ, 2013. Coal Market Situation and Outlook in China. [Online] Available at:〈https://reports.btmuc.com/ﬁleroot_sh/FILE/information/130313_01.pdf〉

T. Otsuki et al. / Energy Policy 89 (2016) 311–329(accessed 13.03.15).
Nagayama, H., 2014. An analysis of management efﬁciency at Japanese and Korean
electric companies and the effects of business integration. Dev. Eng. 20, 1–15.
NHK, 2014. "Re-raise" the Electricity Price and its Wide Impacts. [Online] Available
at: 〈http://www.nhk.or.jp/ohayou/marugoto/2014/09/0916.html〉 (accessed
15.02.15).
Peterson, E.W., Hennessey Jr, J.P., 1979. On the use of power laws for estimates ofwind power potential. J. Appl. Meteol. 17, 390–394.
Podkovalnikov, S., 2002. Study for Russia, Democratic People Republic of Korea,
Republic of Korea and China Power Interconnection: Analysis of Current Status.
Energy Systems Institute, Siberian Branch, Russian Academy of Sciences.
Popel, O., 2012. The state and problems of renewable energy development in theworld and in Russia. Yaroslavl, Russian tytle: "СОСТОЯНИЕ ИПРОБЛЕМЫ РАЗВИТИЯ ВОЗОБНОВЛЯЕМОЙ ЭНЕРГЕТИКИВ МИРЕ И В РОССИИ" (in Russian).

RAO Energy System of East, 2013. Annual Report 2013: Description of Tariff Policy,
Changes in 2013. [Online] Available at: 〈http://raoesv13en.dtdemo.ru/#/en/
1824〉 (accessed 18.03.15).
Schaber, K., Steinke, F., Hamache, T., 2012. Transmission grid extensions for the
integration of variable renewable energies in Europe: who beneﬁts where?
Energy Policy 43, 123–135.
SGCC, 2014. Ofﬁcial. [Online] Available at: 〈http://www.sgcc.com.cn/xwzx/gsxw/
2014/09/309866.shtml〉 (accessed 30.06.15).
Shinoda, K., 2013. Coal Information in China (28 March 2013). [Online] Available at:〈http://coal.jogmec.go.jp/info/docs/report_130328_17.pdf〉 (accessed 13.03.15)(in Japanese).
Shiraki, H., Kameyama, Y., Moriguchi, Y., Hashimoto, S., 2011. Simulation analysis ofrenewable energy installation scenarios in Japan's Electricity Sector in 2020
using a multi-regional optimal generation planning model. J. Jpn. Soc. Energy

329

Resour. 33, 1 (in Japanese).
Skoltech, 2015. The Study of Northeast Asia Supergrid and Exchange of Electricity
Intensive Services in the Region. Presentation material at the second international workshop on the Asian energy supergrid project, Moscow, 3 April, 2015.
〈http://crei.skoltech.ru/energysystems/news-2/〉).
Smirnov, V.V., 2012. The perspectives of Expanding Cooperation between Russia
and China in the Electric Power Industry, Irkutsk: Presentation Material at AEC2012, Russia.
SO UPS, 2014b. 2014 Annual report of SO-UPS. [Online] Available at: 〈http://www.
so-ups.ru/ﬁleadmin/ﬁles/company/reports/disclosure/2015/ups_rep2014.pdf〉(accessed 04.08.15) (in Russian).
SO UPS, 2014. Generation and consumption (per hour) - SO-UPS website. [Online]
Available at: 〈http://www.so-cdu.ru/〉 (accessed 30.11.14) (in Russian).
Streets, D., 2003. Environmental beneﬁts of electricity grid interconnections inNortheast Asia. Energy 28, 789–807.
TEPCO, 2013. Electricity Demand and Supply in 2013 Summer. Tokyo Electric Power
Company, Tokyo.
TEPCO, 2014. Tokyo Electric Power Company: Action plan for FY 2014. [Online]
Available at: 〈http://www.tepco.co.jp/ir/tekiji/pdf/140331-1.pdf〉 (accessed
30.06.15) (in Japanese).
Yun, W.-C., Zhang, Z.X., 2006. Electic power grid interconnection in Northeast Asia.
Energy Policy 34, 2298–2309.
Yoon, J., 2007. Possible Interconnection Scenarios and impacts on composite system
reliability between ROK, DPRK and RF. Presentation Material at IEEE RES 2007
General Meeting.
Zhao, M., Zhizhang, L., Qingzhu, Z., 2009. Testing and analyzing of solar energy
resource assessment in Inner Mongolia. In: Proceedings of IEEE ICIEA 2009,
Xi'an.