IEEJ：November 2017 © IEEJ2017

Analysis on Cost-optimal Deployment of Variable
Renewables and Grid Interconnection in Northeast Asia
Takashi Otsuki1
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 Japan

Abstract
International grid interconnections have gained attention in Northeast Asia (NEA) as a means to promotevariable renewables (VRE), such as wind power and solar PV. This paper quantitatively investigates thebenefits of international power transmission for VRE deployment, employing a detailed temporal resolution
power system model for NEA. The model determines the optimal hourly dispatch for a single year, whichallows us explicit consideration of the power systems characteristics and intermittency of VRE. The results
suggest that international transmission may significantly affect VRE deployment in NEA, by promoting wind
power in Mongolia and replacing higher-cost solar PV and battery storage otherwise installed inneighboring countries in a “no international transmission” case. However, strict CO2 emissions regulations,such as 80% reductions, are necessary for implementation. This implies that international transmission of
VRE would be an option for long-term deep decarbonization in NEA; the relevant planning organizations
need to consider potential feasibility in the context of long-term CO2 reductions strategies.

Keywords
Northeast Asia; Variable renewables; Super grid; Linear programming.

Email address: takashi.otsuki@aperc.ieej.or.jp. This paper is translated and edited from Journal of Japan Society ofEnergy and Resources, Vol. 38, No. 5 (in Japanese).
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1.

Introduction
International power grid interconnection has gained attention in Northeast Asia (China, Japan, Korea,

Mongolia and Far East region of Russia) over the past two decades. In particular, after the recent air
pollution problems in China and the nuclear accident at Fukushima, long-distance internationaltransmissions are highlighted as a means to accelerate the integration of variable renewables (VRE2)—for
example, to connect abundant solar and wind power in Mongolia to the grid—and decarbonize theelectricity system [1, 2, 3].
Various analyses of grid interconnection in Northeast Asia have been conducted since at least the
1990s [4, 5, 1, 6, 7, 8]. However, few studies so far have examined the cost-optimal deployment of VRE, or
the priority of grid interconnection for the region. For example, Belyaev, et al. [4] and Chung & Kim [5] do
not explicitly address VRE in their analysis. Energy Charter, et al. [1], Otsuki, et al. [6] and Otsuki [8] focus
on the economics of Mongolian renewables, yet, do not consider VRE in neighboring countries; theseanalyses, therefore, could not explore the optimal installation of VRE in Northeast Asia. In addition, theseanalyses employ power generation mix models with a simplified temporal resolution,3 which might haveresulted in underestimating the cost for integration measures.4 Bogdanov & Breyer [7] discussed thefeasibility of a 100% renewable power system, using an optimization model with an hourly temporal
resolution for a year. This analysis implies that international transmission contributes to curbing the cost ofachieving a 100% renewable power supply. However, the priority of international transmission as anelectricity supply option—for example, the condition, such as the level of environmental policies, whereinterconnection should be implemented—was not fully analyzed in that study.
Therefore, the author developed an optimal power generation mix model with an hourly temporal
resolution to analyze the optimal installation of VRE and the priority of grid interconnection in Northeast
Asia. Although there exist uncertainties regarding the degree of future energy cooperation in Northeast
Asia due to geopolitical issues, this paper can contribute to stakeholders’ and policy makers’ discussions
by showing economic implications in a quantitative manner.
This paper proceeds as follows: Section 2 gives an overview of the optimal power generation mix
model; Section 3 presents the simulation results; and Section 4 summarizes major conclusions andimplications, and then proposes a future research agenda.

This paper defines VREs as solar PV and onshore wind.
Energy Charter, et al. [1] does not explicitly consider load curves nor supply-demand balance. Otsuki, et al. [6] andOtsuki [8] only model seasonal load curves (one calendar year is decomposed into 120 time segments = 24h per day ×
1 representative day per season × 5 seasons per year).
4
According to IRENA [38], generation expansion models with limited time slices tend to, for example, underestimate theeconomics of flexible dispatchable generation.
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2.
2.1.

MethodOverview
This paper developed an NEA-wide multi-region optimal power generation mix model, referring toOtsuki [8] and Komiyama, et al. [9] (Fig.1 and Appendix 1 for detailed formulation). This is a linear
programming model, which determines cost-optimal power generating capacity and hourly operation
through minimizing annual total system cost for Northeast Asia. Total system cost in this model includes
capital, operation and maintenance (O&M) and fuel costs for generation, storage and inter-regionaltransmission technologies. This model uses capital recovery factors, assuming a discount rate of 5%, toannualize capital cost for modeled technologies. This model takes into account nine types of generation(solar PV, wind, hydro, nuclear, coal-fired, gas-fired, oil-fired, hydrogen turbine, and fuel cell), three typesof storage (pumped hydro, battery, and compressed hydrogen tank), and one intra-regional transmission
technology (HVDC = High Voltage Direct Current). This paper assumes water electrolysis for hydrogenproduction.
The model divides Northeast Asia into fifteen nodes (Fig.2), represented by eleven city nodes andfour supply nodes. City nodes have electricity demand as well as generation and storage facilities, while
supply nodes have only generation and/or storage facilities to export to neighboring nodes. Hydro plants
are considered in the China Three-Gorges (PRC-TG) node, while solar PV and wind turbine in the otherthree supply nodes. Branches in Fig.2 indicate assumed transmission routes. This study formulates interregional transmission as a transport problem, keeping the optimization problem linear and optimizing grid
extensions, generation expansion as well as their operations simultaneously. Transmission distance isestimated based on airline distance plus 20% for possible route circuity.

• Solar PV• Wind• Coal-fired• Gas-fired• Oil-fired• Hydro• Nuclear
From other nodes

Electricity imports• Fuel cell
• H2 turbine

Suppressed
electricity

H2

H2 tank

Electrolyzer
Electricity• Pumped hydro• Battery

Electricity load
(exogenous)

To other nodes

Electricity exports

Fig.1 Schematic diagram of the optimal power generation mix model for Northeast Asia. Note: H2 indicateshydrogen.

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China-Uyghur(PRC-UG)

Russia-FarEast(RUS-FE)

Mongolia(MN)

China-Northeast(PRC-NE)

China-North(PRC-N)
China-Northwest(PRC-NW)
China-Tibet(PRC-TB)
China-Central(PRC-C)

City node
Supply node

JapanHokkaido(JPN-H)
Korea
(ROK)

China-East(PRC-E)

China-ThreeGorges(PRC-TG)

Japan-East(JPN-E)
Japan-West(JPN-W)

China-South(PRC-S)

Fig.2 Regional division and assumed transmission route

From the viewpoint of grid stability, this model limits the level of system non-synchronous penetration(SNSP) as formulated in Eq. A30. Non-synchronous power in this paper includes VRE, fuel cell, battery
and inter-regionally transmitted power. The assumption of the maximum SNSP in Section 3 is 75%, basedon a study in Ireland5 [10, 11]. However, it is important to note that the maximum limit may depend oneach grid’s individual characteristics. Further studies, together with actual operating experiences, would benecessary to determine the appropriate level in Northeast Asian countries. Future work is necessary to
perform a sensitivity analysis to investigate the effects of the SNSP limit on the generation mix and VRE
deployment in Northeast Asia.

Case setting

2.2.

This paper examines seven cases as summarized in Table 1. The simulated year in this study is2030. The Base case does not allow international grid enhancement or limit CO2 emissions. The Domestic
cases (three cases) do not assume international grid extension, but three levels of CO2 emissions
regulation for the whole region: -25%, -50% and -80% from the Base case. The International cases (threecases) consider international grid extension as well as three levels of emissions regulation.
General assumptions for the modelled technologies are as follows: nuclear, hydro and pumped hydrocapacity are imposed exogenously, and the model determines the capacity of other technologies as well as
hourly operation of all technologies based on cost-minimization. Therefore, as nuclear and hydro capacity
are exogenous variables in this study, the model attempts to satisfy the CO 2 regulation by shifting tocleaner fossil fuels and expanding VRE.

2.3.

Input data assumptions

2.3.1.

Electricity load curves

Hourly load curves for a year in Japan, Korea and Russia were obtained from the governments,
electricity system operators or market operators [12, 13, 14]. These load curves were adjusted by referring

EirGrid, stated-owned transmission system operator in Ireland, has limited the SNSP below 50% since 2011 [10], andincreased the limit to 60% as a trial since November 2016 with an ultimate aim of 75% by 2020 [11].
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to the projected electricity consumption for 2030 [15]. Actual load curves in China are not publicly
available; this study therefore estimated them using available data (Appendix 2).
2.3.2.

Generation and storage technologies

Nuclear, hydro and pumped hydro capacity are given, referring to the projected capacity for 20306[15], while capacity of other technologies, such as VRE and fossil fuel plants, are determined by themodel. Initial values are based on the 2030 capacity for VRE [15] and on the actual capacity in 2010 forother technologies7.
Economic and technical assumptions were obtained from Komiyama, et al. [16] for the hydrogensystems (electrolyzer, compressed hydrogen tank and hydrogen turbine), METI [17] for pumped hydro andbattery, and IEA [18] and Komiyama & Fujii [19] for other technologies (see A1.3 for detailed assumptions).
Table 2 shows capital cost assumptions for VRE technologies, as an example; this study assumed
400 USD/kWh for battery cost. VREs’ hourly output profiles in each node were estimated based on themethods presented in Komiyama, et al. [9]. This study used the meteorological data in NREL [20], JMA[21] and KMA [22] for China, Japan and Korea, respectively. Solar radiation data for Mongolia is from
NREL [23]; but, as for wind speed, due to data availability, this paper used the data from the Inner
Mongolia region in China [20] as a proxy. Meteorological data for the Russia Far East region is also limited;
thus, this study referred to data from Chinese observation points near the China-Russia border [20].
Estimated capacity factors are described in Table 2; the factor varies by node and this table shows therange in each country. Assumed installation potential for VRE are based on [24, 25] for China, MOE [26]for Japan, KOPIA [27] and Kim, et al. [28] for Korea, and Charter, et al. [1] and Elliott, et al. [29] for
Mongolia. Assumed upper limits for solar PV are 39,400 GW in China, 339 GW in Japan, 25 GW in Korea
and 1,500 GW in Mongolia, and for wind power 1,800 GW, 286 GW, 41 GW and 1,100 GW, respectively.
2.3.3.

Inter-regional transmission technology

This study estimated the cost for transmission, assuming HVDC overhead line for overlandtransmission and HVDC cable for undersea transmission. AC-DC conversion stations were installed at theeach end of the interconnection. A cost of 4.2 million USD/km (M USD/km) was assumed 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. Assumed lifetime, transmission losses, AC-DC
conversion losses and annual fixed O&M cost were 40 years, 5%/1000 km, 1.5%/station and 0.3% in aratio to initial cost for all line types, respectively. Initial values for transmission capacity are based on the2010 actual capacity; for example, Chen, et al. [30] for domestic transmission capacity in China.

This paper refers to the BAU Scenario in APERC [15], which includes current policies and trends.
In summary, this paper uses the 2010 actual capacity as Initial values for all generation, storage and transmission
technologies except for VRE, nuclear, hydro and pumped hydro.
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Table 1 Case setting
Base case

Domestic cases

International cases(3 cases)(3 cases)

Domestic grid

Extension allowed for all cases

International grid

Extension not allowed

CO2 emissions regulation

No limit

Allowed-25%, -50% and -80% from the Base case

Table 2 Assumptions for capital cost (top, in USD/kW) and capacity factor (bottom, in %) of solar PV andwind power
China

Japan

Korea

Mongolia

RussiaFarEastSolar PV

Wind turbines

3.
3.1.

1380

2400

2400

1430

1400

12-18%

11-16%

12%

15%

13%

1150

2000

2000

1150

1930

10-29%

20-22%

19%

24%

20%

Results and discussion
Generation mix
International interconnection would significantly affect cost-optimal VRE deployment in Northeast Asia

under strict CO2 emissions regulation, such as 50% and 80% reductions (Fig.3a). In the Domestic cases,
wind power shows a saturating trend after 25% CO2 reduction, as it reaches a techno-economic
installation limit. Thus, the region achieves further emissions reduction by expanding solar PV. On theother hand, in the International cases, wind power grows even under the 50% and 80% CO2 reduction. For
example, under 80% reduction, the share of wind doubles from 16% in the Domestic to 33% in theInternational case. Mongolia (MN) expands wind power for electricity exports, largely affecting thegeneration mix in neighboring countries, in particular, China because of its large market size (Fig.3b). In
the International (50% reduction) case, China imports wind power from Mongolia to reduce emissions,rather than shifting fossil fuels from coal to gas (this is why gas and coal show a negative and positive
value, respectively, under the 50% CO2 reduction in Fig.3b). Under 80% reduction, wind power in Mongolia(total 1,100 GW) replaces solar PV in China, especially PRC-N (China-North).
In contrast, the results also imply modest impacts of international interconnection under 25%
reduction (Fig.3b). This is because the region can meet the emissions reduction mostly by domestic VRE.
As mentioned in the previous paragraph, international interconnection has significant effects on VRE
deployment and the generation mix in Northeast Asia, through providing access to wind power in
Mongolia; yet, strict emissions reduction policy would be necessary for implementation.

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TWh

TWh

10,000
Coal

0

Wind

Wind

PV

PV

Gas

Hydro
NuclearBattery (in)ImportBase

Domestic

CO2 -80%

CO2 -50%

CO2 -25%

CO2 -80%

CO2 -50%

CO2 -25%

CO2 NoReg.

-10,000

International

3,000

Wind（Mongolia）
PV（China-North）
PV（Other China）
VRE（Japan）
VRE（Korea）
Coal (China)
Gas (China)
Other

1,500
0
-1,500-3,000CO2 -80%

Battery (out)Nuclear
Hydro
Coal
Gas
Oil
PV
Suppressed PV
WindSuppressed wind
Pumped (in)Pumped (out)Battery (in)Battery (out)Electrolyzer
Hydrogen
Fuel cell
Imports
Exports

CO2 -50%

ExportCO2 -25%

20,000Changes due to international interconnection(a) Generation mix(b) Changes due to international interconnection
Fig.3 Generation mix in Northeast Asia

Fig.4 illustrates the hourly supply-demand profile in January in PRC-N (China-North), whereinternational interconnection would affect the generation mix significantly (as shown in Fig.3b). Coal-fired
generation accounts for the major share in the Base case; ramping operation of fossil-fuel plants
contributes to integrating VRE. Fig.4b shows the massive growth of solar PV in the Domestic (80%reduction) case. In order to manage excess generation during daytime, various measures, including theramping operation of power plants, charging into battery, transmission to PRC-E (China-East), as well as
suppression control, are dynamically combined. Whereas, the International (80% reduction) case shows
large-scale imports from MN and exports to PRC-E (Fig.4c). Electricity imports replace solar PV, reducing
daytime excess generation and lowering the need for storage. Note that the node operates gas-fired
generation even in the 80% reduction cases to satisfy the SNSP constraint (see Section 2.1 and Eq. A30).
Battery becomes the prevailing storage technology in the Domestic cases, especially under 80%
reduction regulation, to store excess generation from solar PV (Fig.5). In China, battery power-capacityand energy-capacity reach 1,540 GW and 9,230 GWh, respectively. Cycle efficiency of battery
technologies is, in general, superior to other storage technologies, such as hydrogen storage, and thus
suitable for frequent daily cyclic operation for solar PV. In the International (80% reduction) case, Mongolia
installs battery as well as hydrogen storage to manage wind’s intermittency. Fig.6a-b illustrate that battery
is operated for diurnal storage, while hydrogen storage is for longer-term, like weekly, storage since acompressed hydrogen tank has lower storage losses (as also pointed out in Komiyama, et al. [9]). Thisimplies that the choice of optimal storage technology depends on technical characteristics as well as
operation patterns.

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GW
500

Wind

400

500

400
300

300
200

Coal

100

200
100
0

0

-100

Jan. 31

Jan. 30

Jan. 29

Jan. 28

Jan. 27

Jan. 26

Jan. 25

Jan. 24

Jan. 23

Jan. 22Jan. 21Jan. 20

Jan. 19

Jan. 18

Jan. 17

Jan. 16Jan. 15

Jan. 14Jan. 13Jan. 12Jan. 11

Jan. 10Jan. 9Jan. 8Jan. 7Jan. 6

Jan. 5Jan. 4Jan. 3Jan. 2

Jan. 1-100

Nuclear
Hydro
Coal
Gas
Oil
PV
Wind
Suppressed PV
Suppressed wind
Hydrogen
Fuel cell
Trade_MN
Trade_PRC-E
Trade_OtherNodes
PumpedBattery
Electrolyzer
Load(a) Base case
GW
1200

PV

Wind

800
400
0
-400

Jan. 31

Jan. 30

Jan. 29

BatteryJan. 28

Jan. 27

Jan. 26

Jan. 25

Trade(PRC-E)
Jan. 24

Jan. 23

Jan. 21Jan. 20

Jan. 19

Jan. 18

Jan. 17

Jan. 16Jan. 15

Jan. 14Jan. 13Jan. 12Jan. 11

Jan. 10Jan. 9Jan. 8Jan. 7Jan. 6

Jan. 5Jan. 4Jan. 3Jan. 2

Jan. 1

Jan. 22

Gas-800

Nuclear
Hydro
1200 Coal
Gas
800 Oil
PV
Wind
400 Suppressed PV
Suppressed wind0
Hydrogen
Fuel cell-400 Trade_MN
Trade_PRC-E
Trade_OtherNodes-800 Pumped
Battery
Electrolyzer
Load(b) Domestic case (80% CO2 reduction from the Base)
GW
1200

Trade(MN)
800
400
0
-400

Gas

Trade(PRC-E)

Trade(other nodes)(c) International case (80% CO2 reduction from the Base)Fig.4 Hourly electricity supply-demand profile in January, China-North (PRC-N)

Fig.5 Battery and H2 storage capacity(Domestic and International cases under 80% CO2 reduction from the Base)

8

Jan. 31

Jan. 30

Jan. 29

Jan. 28

Jan. 27

Jan. 26

Jan. 25

Jan. 24

Jan. 23

Jan. 22Jan. 21Jan. 20

Jan. 19

Jan. 18

Jan. 17

Jan. 16Jan. 15

Jan. 14Jan. 13Jan. 12Jan. 11

Jan. 10Jan. 9Jan. 8Jan. 7Jan. 6

Jan. 5Jan. 4Jan. 3Jan. 2

Jan. 1-800

Nuclear
Hydro
1200 Coal
Gas
800 Oil
PV
Wind
400 Suppressed PV
Suppressed wind0
Hydrogen
Fuel cell-400 Trade_MN
Trade_PRC-E
Trade_OtherNodes-800 Pumped
Battery
Electrolyzer
LoadIEEJ：November 2017 © IEEJ2017

GW
1200

Wind

Hydrogen turbine

800
400
0
-400

Jan. 31

Jan. 30

Jan. 29

Jan. 28

BatteryJan. 27

Jan. 26

Jan. 24

Jan. 23

Jan. 22Jan. 21Jan. 20

Jan. 19

Jan. 18

Jan. 17

Jan. 16Jan. 15

Jan. 14Jan. 13Jan. 12Jan. 11

Jan. 10Jan. 9Jan. 8Jan. 7Jan. 6

Jan. 5Jan. 4Jan. 3Jan. 2

Jan. 1

Jan. 25

Exports

Electrolyzer-800

Nuclear
Hydro
1200 Coal
Gas
800 Oil
PV
Wind
400 Suppressed PV
Suppressed wind0
Hydrogen
Fuel cell-400 Exports
Trade_PRC-E
Trade_OtherNodes-800 Pumped
Battery
Electrolyzer
Load(a) Electricity supply-demand
GWh

4000

Battery
H2 storage

3000

2000
1000

Jan. 31

Jan. 30

Jan. 29

Jan. 28

Jan. 27

Jan. 26

Jan. 25

Jan. 24

Jan. 23

Jan. 22Jan. 21Jan. 20

Jan. 19

Jan. 18

Jan. 17

Jan. 16Jan. 15

Jan. 14Jan. 13Jan. 12Jan. 11

Jan. 10Jan. 9Jan. 8Jan. 7Jan. 6

Jan. 5Jan. 4Jan. 3Jan. 2

Jan. 10(b) Stored electricity in battery and H2 storage system
Fig.6 Hourly operational profile in the International case under 80% reduction, January, Mongolia

3.2.

Inter-regional transmission
Inter-regional transmission is relatively modest in the Base case, except for the following routes in

China: from PRC-NE (China-Northeast) to PRC-N (China-North) and from PRC-TG (China-Three Gorges)
to PRC-E (China-East) to transmit wind and hydro power, respectively (Fig.7a).
Transmission capacity grows to access VRE resources under stricter CO2 regulation (Fig.7b-c). In theDomestic (80% reduction) case, China enhances its transmission network from wind-rich nodes, such as
PRC-N and PRC-NW (China-Northwest), to large demand centers, including PRC-E and PRC-C. The
result also implies the need for nationwide transmission infrastructure for Japan to utilize abundant windresources in JPN-H (Japan-Hokkaido). Wind power capacity in JPN-H reaches 109 GW, equivalent to 70%
of wind power potential in Hokkaido [26]. The assumed peak load in JPN-H is 7 GW; that level of wind
installation would bring significant changes in the node.
In the International (80% reduction) case, inter-regional transmission notably expands from MN to
PRC-N, from PRC-N to PRC-E and from PRC-N to ROK (Fig.7c); net transmitted electricity on theseroutes reaches a significant8 level: 860 TWh, 840 TWh/yr and 220 TWh/yr, respectively. Transmission also
increases between Japan and Korea (68 TWh/yr from ROK to JPN-W, and 10 TWh/yr in the otherdirection), although its scale is modest compared with Mongolia-China and China-Korea, implying larger
opportunities for China, Korea and Mongolia.

8

Assumed electricity demand in PRC-N, PRC-E and ROK is 2,470TWh/yr, 2,590TWh/yr and 660TWh/yr, respectively.

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Capacity [GW]

Flow [TWh/yr]

0-10
10-50
50-100100-500

0-30
30-100100-500
500-2000(a) Base case
Capacity [GW]

Flow [TWh/yr]

0-10
10-50
50-100100-500

0-30
30-100100-500
500-2000(b) Domestic case (80% CO2 reduction from the Base)
Capacity [GW]

Flow [TWh/yr]

0-10
10-50
50-100100-500

0-30
30-100100-500
500-2000(c) International case (80% CO2 reduction from the Base)Fig.7 Inter-regional transmission capacity and flow

3.3.

Total system cost and marginal abatement cost
International transmission of VRE contributes to curbing the cost for emissions reduction, in particular,

under regulation stricter than 50% reduction (Fig.8). The economic benefits due to reduced total cost are
relatively modest—savings of 0.3% and 1.6%—from the Domestic to the International cases under 25%
and 50% emissions constraints, respectively, and expand to 11% under 80% CO2 reduction. The costsaving under 80% reduction is mainly due to curbing capital cost for VRE and battery; even though theregion needs to invest in inter-regional transmission facilities, benefits from access to cost-competitive

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VRE in neighboring countries as well as less need for battery capacity are estimated to exceed the cost fortransmission. These results also suggest that cost-competitiveness of international transmission would beenhanced by the lower cost of VRE in neighboring countries and transmission facilities, whereas
undermined by improved economics of domestic VRE, especially solar PV, and battery storage.
The economic benefit implied by the model under 50% reduction, a saving of 1.6%, was relatively
modest, although wind power is largely installed in Mongolia (Fig.3b). This trend is also illustrated in Fig.9;
CO2 marginal abatement cost is also notably curbed under the 80% reduction regulation, yet not under
50%. Strong emission policies, such as 80% reduction, would be necessary for international transmissionof VRE to be attractive from an economic viewpoint.

USD/MWh

50

0

0

Base

Domestic

400
200
0

International

Fig.8 Annual total system cost and average generation cost

4.

International

600

CO2 -80%

500

Domestic

CO2 -50%

100

USD/tCO2
800

Fixed cost (VRE)
Fixed cost (transmission)
Fixed cost (battery)
Fixed cost (other)
Fuel cost
Other variable cost
Average cost (right axis)

CO2 -80%

1,000

CO2 -50%150

CO2 -25%1,500

CO2 -80%200

CO2 -50%

2,000

CO2 -25%250

CO2 NoReg.

2,500

CO2 -25%

Billion USD/yr

Fig.9 CO2 marginal abatement cost

Conclusion and future work
This paper discusses the impacts of international interconnection on cost-optimal deployment of VRE

in Northeast Asia, employing a multi-region optimal power generation mix with an hourly temporal
resolution. The results suggest that international interconnection would significantly affect VRE deployment
in Northeast Asia, by promoting wind power in Mongolia and replacing higher-cost solar PV and battery
storage otherwise installed in the Domestic cases. However, strict CO2 emissions regulations, such as
80% reductions from the Base, are necessary for implementation. This implies that internationaltransmissions of VRE would be an option for long-term deep decarbonization in NEA; the relevant
planning organizations need to consider potential feasibility in the context of long-term CO2 reductions
strategies.
Future work should include modeling and analysis on energy security perspectives. This paper
assumes that the Northeast Asia countries fully cooperate for regional optimization in order to quantify the
cost-optimal deployment of VRE. Therefore, emergency situations, such as the disruption of electricity
trade due to technical or political issues, were outside of the scope of this research. Incorporating energy
security aspects into the model, for example, by using stochastic programming techniques, would be an

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important research contribution to comprehensively understand the opportunities and barriers for gridinterconnection.

Acknowledgement
The author would like to sincerely thank his colleagues at the Asia Pacific Energy Research Centre(APERC), in particular, Mr. Choong Jong OH and Mr. Alexey KABALINSKIY for their assistance with datacollection and Mr. James KENDELL and Ms. Kirsten SMITH for their help with proofreading. The research
reported in this paper was generously supported by APERC. The views expressed, however, are those of
the author and not necessarily those of APERC.

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Appendix 1.

Model formulation and assumptions

This section describes the equations of the model in detail in order to provide a detailed
understanding of this study. The model is formulated as a linear programming problem that aims tominimize annual total system cost for Northeast Asia. Table A1 shows the endogenous variables of themodel.

Table A1 Endogenous variables of the multi-region power generation mix modelz

Total annual cost [USD/yr]

cfn

Annual fixed cost at node n [USD/yr]

cvn

Annual variable cost at node n [USD/yr]

kgn,iCapacity of generation type i at node n [kW]akgn,i,dAvailable capacity of generation type i in day d at node n [kW]mkgn,i,m

Capacity of generation type i maintained under schedule m [kW]ks1n,skW-capacity of storage type s at node n [kW]ks2n,skWh-capacity of storage type s at node n [kWh]kln,n2,lCapacity of transmission type l between nodes n and n2 [kW]ken

Capacity of electrolyzer at node n [kW]xgn,i,d,t

Output of generation type i at local time t in day d at node n [kW]mxgn,i,dMaximum output level of generation type i in days d and d+1 at node n [kW]dgn,i,d,tSuppressed output of generation type i (i=solar PV or wind) at local time t in day d at node n [kW]schn,s,d,tElectricity charge of storage type s at local time t in day d at node n [kW]sdcn,s,d,tElectricity discharge of storage type s at local time t in day d at node n [kW]xssn,s,d,tStored electricity type s at local time t in day d at node n [kWh]xln,n2,l,d,tTransmitted power from nodes n to n2 via transmission type l at time t in day d (node n time) [kW]xen,d,tOutput of electrolyzer (hydrogen production) at local time t in day d at node n [kW]where:
𝑛, 𝑛2: 𝑛𝑜𝑑𝑒 𝑖𝑛𝑑𝑒𝑥 (1: 𝑃𝑅𝐶 − 𝑁𝐸, 2: 𝑃𝑅𝐶 − 𝑁, 3: 𝑃𝑅𝐶 − 𝐸, 4: 𝑃𝑅𝐶 − 𝐶, 5: 𝑃𝑅𝐶 − 𝑁𝑊, 6: 𝑃𝑅𝐶 − 𝑆, 7: 𝑃𝑅𝐶 − 𝑇𝐺,
8: 𝑃𝑅𝐶 − 𝑈𝐺, 9: 𝑃𝑅𝐶 − 𝑇𝐵, 10: 𝐽𝑃𝑁 − 𝐻, 11: 𝐽𝑃𝑁 − 𝐸, 12: 𝐽𝑃𝑁 − 𝑊, 13: 𝑅𝑂𝐾, 14: 𝑅𝑈𝑆 − 𝐹𝐸, 15: 𝑀𝑁)
𝑑: 𝑑𝑎𝑦 𝑖𝑛𝑑𝑒𝑥 (0, 1, … , 364 or 365) , 𝑡: 𝑡𝑖𝑚𝑒 𝑖𝑛𝑑𝑒𝑥 (0,1, … , 23), 𝑚: 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒 𝑖𝑛𝑑𝑒𝑥 (0,1,2,3)
𝑖: 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 𝑖𝑛𝑑𝑒𝑥 (1: 𝑆𝑜𝑙𝑎𝑟 𝑃𝑉, 2: 𝑊𝑖𝑛𝑑, 3: 𝐻𝑦𝑑𝑟𝑜, 4: 𝑁𝑢𝑐𝑙𝑒𝑎𝑟, 5: 𝐶𝑜𝑎𝑙 − 𝑓𝑖𝑟𝑒𝑑, 6: 𝐺𝑎𝑠 − 𝑓𝑖𝑟𝑒𝑑,
7: 𝑂𝑖𝑙 − 𝑓𝑖𝑟𝑒𝑑, 8: 𝐻𝑦𝑑𝑟𝑜𝑔𝑒𝑛 𝑡𝑢𝑟𝑏𝑖𝑛𝑒, 9: 𝐹𝑢𝑒𝑙 𝑐𝑒𝑙𝑙)𝑠: 𝑠𝑡𝑜𝑟𝑎𝑔𝑒 𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 𝑖𝑛𝑑𝑒𝑥 (1: 𝑃𝑢𝑚𝑝𝑒𝑑 ℎ𝑦𝑑𝑟𝑜, 2: 𝐵𝑎𝑡𝑡𝑒𝑟𝑦, 3: 𝐶𝑜𝑚𝑝𝑟𝑒𝑠𝑠𝑒𝑑 ℎ𝑦𝑑𝑟𝑜𝑔𝑒𝑛 𝑡𝑎𝑛𝑘)𝑙: 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 𝑖𝑛𝑑𝑒𝑥 (1: 𝐻𝑉𝐷𝐶 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛)

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Objective function

A1.1.

The objective function—annual total system cost for Northeast Asia—is formulated as Eq. A1-Eq. A3.
Total system cost consists of fixed cost and variable cost. Fixed cost includes capital cost as well as O&M
cost for generation, storage, transmission and electrolyzer. Variable cost is modelled as the following two
components: fuel cost for generation and cost for consumable material in battery technologies.

𝑚𝑖𝑛. 𝑧 = ∑(𝑐𝑓𝑛 + 𝑐𝑣𝑛 )Eq. A1𝑛

𝑐𝑓𝑛 = ∑ 𝐴𝐺𝑖 ∙ 𝐶𝐺𝑛,𝑖 ∙ 𝑘𝑔𝑛,𝑖 + ∑ 𝐴𝑆𝑠 ∙ (𝐶𝑆1𝑛,𝑠 ∙ 𝑘𝑠1𝑛,𝑠 + 𝐶𝑆2𝑛,𝑠 ∙ 𝑘𝑠2𝑛,𝑠 )𝑖𝑠Eq. A2+ ∑ ∑ 𝐴𝐿𝑙 ∙ 𝐶𝐿𝑛,𝑛2,𝑙 ∙ 𝑘𝑙𝑛,𝑛2,𝑙 + 𝐴𝐸 ∙ 𝐶𝐸 ∙ 𝑘𝑒𝑛
𝑛2>𝑛 𝑙𝑐𝑣𝑛 = ∑ (𝐹𝐺𝑛,𝑖 ∙ ∑ ∑𝑖𝑑𝑡

𝑥𝑔𝑛,𝑖,𝑑,𝑡 ∙ 𝐻𝑊
) + ∑ (𝑉𝑆𝑛,𝑠 ∙ ∑ ∑ 𝑠𝑐ℎ𝑛,𝑠,𝑑,𝑡 ∙ 𝐻𝑊 )𝐸𝑓𝑓𝐺𝑛,𝑖𝑠𝑑Eq. A3𝑡where: AGi: annual fixed charge rate (calculated from capital recovery factor and annual O&M cost rate) for
generation type i at node n; CGn,i: capital cost for generation type i at node n [USD/kW]; ASs: annual fixedcharge rate for storage type s; CS1n,s: capital cost for kW-capacity of storage type s at node n [USD/kW];
CS2n,s: capital cost for kWh-capacity of storage type s at node n [USD/kWh]; ALl: annual fixed charge rate
for transmission type l; CLn,n2,l: capital cost for transmission type l between node n and node n2 [USD/kW];
AE: annual fixed charge rate for electrolyzer; CE: capital cost for electrolyzer [USD/kW]; FGn,i: fuel cost for
generation type i at node n [USD/kWh]; FffGn,i: conversion efficiency of generation type i at node n[USD/kWh]; VSn,s: Consumable material (electrode, electrolyte and separator) cost for battery [USD/kWh];
HW: time slot length (HW=1 hour in this study).

A1.2.
A1.2.1.

Constraints
Power demand and supply balance

Eq. A4 ensures that electricity demand must be satisfied at all times in all days and at all nodes. The
left part indicates the sum of power supply from generators, electricity consumption for water elecctrolysis,
net power imports and net power discharge of storage technologies. Time differences between power
exporting and importing nodes are considered as DA and TA in Eq. A4.

∑ 𝑥𝑔𝑛,𝑖,𝑑,𝑡 −𝑖𝑥𝑒𝑛,𝑑,𝑡+ ∑ ∑(𝑥𝑙𝑛2,𝑛,𝑙,𝐷𝐴𝑛2,𝑛,𝑑,𝑡,𝑇𝐴𝑛2,𝑛,𝑡 ∙ 𝐸𝑓𝑓𝐿𝑛2,𝑛,𝑙 − 𝑥𝑙𝑛,𝑛2,𝑙,𝑑,𝑡 )
𝐸𝑓𝑓𝐸𝑛
𝑛2

𝑙

2

Eq. A4+ ∑(𝑥𝑑𝑐𝑛,𝑠,𝑑,𝑡 − 𝑥𝑐ℎ𝑛,𝑠,𝑑,𝑡 ) = 𝐿𝑛,𝑑,𝑡
𝑠=1

𝐸𝑓𝑓𝐿𝑛,𝑛2,𝑙 = (1 − 𝐿𝐿𝑙 )𝐷𝐼𝑆𝑛,𝑛2

Eq. A514

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where: Ln,d,t: electric load at time t in day d at node n [kW]; EffEn: conversion efficiency of electrolyzer at
node n; EffLn,n2,l: transmission efficiency for transmission type l between nodes n and n2; LLl: transmission
loss for transmission type l between node n and n2 [per thousand km]; DISn,n2: transmission distance
between nodes n and n2 [thousand km]; DAn2,n,d,t and TAn2,n,t: local time (day and time, respectively) at theorigin of electricity imports.

A1.2.2.

Hydrogen energy balance

Eq. A6 is to balance hydrogen production and consumption. The left part indicates the sum ofhydrogen production in electrolyzer and net discharge of hydrogen tank, while the right part describes
consumption for hydrogen turbine or fuel cell.
9

𝑥𝑒𝑛,𝑑,𝑡 + (𝑥𝑑𝑐𝑛,3,𝑑,𝑡 − 𝑥𝑐ℎ𝑛,3,𝑑,𝑡 ) = ∑𝑖=8A1.2.3.

𝑥𝑔𝑛,𝑖,𝑑,𝑡
𝐸𝑓𝑓𝐺𝑛,𝑖Eq. A6Stored energy balance

Eq. A7 relates power charge (xch), power discharge (xdc) and the level of stored electricity (xss).
Self-discharge loss and charge/discharge efficiency are considered in this equation.
𝑥𝑠𝑠𝑛,𝑠,𝑑,𝑡+1 = 𝑥𝑠𝑠𝑛,𝑠,𝑑,𝑡 ∙ (1 − 𝐿𝑆𝑠 ) + (√𝐸𝑓𝑓𝑆𝑠 ∙ 𝑥𝑐ℎ𝑛,𝑠,𝑑,𝑡 −𝑥𝑑𝑐𝑛,𝑠,𝑑,𝑡√𝐸𝑓𝑓𝑆𝑠) ∙ 𝐻𝑊

Eq. A7

where: LSs: self-discharge rate for storage type s; EffSs: cycle efficiency of storage type s.

A1.2.4.

Installable capacity constraint

Installable capacity of each technology is constrained by its minimum and maximum deployable
limits.
𝑀𝑖𝑛𝐾𝐺𝑛,𝑖 ≤ 𝑘𝑔𝑛,𝑖 ≤ 𝑀𝑎𝑥𝐾𝐺𝑛,𝑖

Eq. A8𝑀𝑖𝑛𝐾𝑆1𝑛,𝑠 ≤ 𝑘𝑠1𝑛,𝑠 ≤ 𝑀𝑎𝑥𝐾𝑆1𝑛,𝑠

Eq. A9𝑘𝑠2𝑛,𝑠 = 𝑅𝑆𝑠 ∙ 𝑘𝑠1𝑛,𝑠Eq. A10(𝑠 = 1, 2)

𝑀𝑖𝑛𝐾𝐿𝑛,𝑛2,𝑙 ≤ 𝑘𝑙𝑛,𝑛2,𝑙 ≤ 𝑀𝑎𝑥𝐾𝐿𝑛,𝑛2,𝑙Eq. A11

𝑀𝑖𝑛𝐾𝐸𝑛 ≤ 𝑘𝑒𝑛 ≤ 𝑀𝑎𝑥𝐾𝐸𝑛

Eq. A12

where: MinKGn,i: initial capacity for generation type i at node n [kW]; MaxKGn,i: capacity upper limit for
generation type i at node n [kW]; MinKS1n,s: initial kW-capacity for storage type s at node n [kW]; MaxKS1n,s:kW-capacity upper limit for storage type s at node n [kW]; RSs: kWh-capacity ratio to kW-capacity (pumped
hydro and battery); MinKLn,n2,l: initial transmission capacity for transmission type l between nodes n and n2[kW]; MaxKLn,n2,l: capacity upper limit for transmission type l between nodes n and n2 [kW]; MinKEn: initial
capacity for electrolyzer at node n [kW]; MaxKEn: capacity upper limit for electrolyzer at node n [kW].

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A1.2.5.

Output constraint

Eq. A13-Eq. A19 constrain output of generation, storage, transmission and electrolyzer. For solar PV
and wind, the hourly output availability profiles (UG1) are exogenously given in Eq. A13. The left part of
Eq. A13 indicates two destination for output power from wind and solar PV: power supplied to the grid (xg)or suppressed (dg). Eq. A14 models the output of hydro power and fuel cell. The other types of power
generation technologies are constrained to their available capacity (Eq. A15). Eq. A16 constrains the
charge to or discharge from storage facilities to their available power capacity (kW-capacity). Eq. A17
constrains stored electricity to the energy capacity (kWh-capacity) of the facility, i.e., reservoir capacity forpumped hydro. Eq. A18 and Eq. A19 are for the output of transmission facilities and electrolyzer,
respectively.
𝑥𝑔𝑛,𝑖,𝑑,𝑡 + 𝑑𝑔𝑛,𝑖,𝑑,𝑡 = 𝑈𝐺1𝑛,𝑖,𝑑,𝑡 ∙ 𝑘𝑔𝑛,𝑖 (𝑖 = 1, 2)

Eq. A13𝑥𝑔𝑛,𝑖,𝑑,𝑡 ≤ 𝑈𝐺2𝑛,𝑖 ∙ 𝑘𝑔𝑛,𝑖(𝑖 = 3, 9)

Eq. A14(𝑖 = 4, 5, … , 8)

Eq. A15𝑥𝑐ℎ𝑛,𝑠,𝑑,𝑡 + 𝑥𝑑𝑐𝑛,𝑠,𝑑,𝑡 ≤ 𝑈𝑆𝑠 ∙ 𝑘𝑠1𝑛,𝑠Eq. A16

𝑥𝑠𝑠𝑛,𝑠,𝑑,𝑡 ≤ 𝑈𝑆𝑠 ∙ 𝑘𝑠2𝑛,𝑠Eq. A17

𝑥𝑙𝑛,𝑛2,𝑙,𝑑,𝑡 + 𝑥𝑙𝑛2,𝑛,𝑙,𝑑,𝑡 ≤ 𝑈𝐿𝑙 ∙ 𝑘𝑙𝑛,𝑛2,𝑙Eq. A18

𝑥𝑒𝑛,𝑑,𝑡 ≤ 𝑈𝐸 ∙ 𝑘𝑒𝑛

Eq. A19

𝑥𝑔𝑛,𝑖,𝑑,𝑡 ≤ 𝑎𝑘𝑔𝑛,𝑖,𝑑where: UG1n,i,d,t: output profile of variable renewable (solar PV and wind) energy at local time t in day d atnode n; UG2n,i: availability factor of hydro power and fuel cell at node n; USs: availability factor of storage
type s; ULl: availability factor of transmission type l; UE: availability factor of electrolyzer.

A1.2.6.

Ramping constraint for thermal generation

The model considers technology-specific ramping constraints for thermal plants (nuclear, coal-fired,gas-fired, oil-fired, and hydrogen turbine). For technical reasons, each technology 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:
𝑥𝑔𝑛,𝑖,𝑑,𝑡+1 ≤ 𝑥𝑔𝑛,𝑖,𝑑,𝑡 + 𝑅𝑎𝑚𝑝𝑈𝑝𝑖 ∙ 𝑎𝑘𝑔𝑛,𝑖,𝑑(𝑖 = 4, 5, … , 8)

Eq. A20

𝑥𝑔𝑛,𝑖,𝑑,𝑡+1 ≥ 𝑥𝑔𝑛,𝑖,𝑑,𝑡 − 𝑅𝑎𝑚𝑝𝐷𝑛𝑖 ∙ 𝑎𝑘𝑔𝑛,𝑖,𝑑(𝑖 = 4, 5, … , 8)

Eq. A21

where: RampUpi: maximum ramp up rate per unit of time for generation type i; RampDni: maximum ramp
down rate per unit of time for generation type i.

A1.2.7.

Minimum output constraint for thermal generation

Eq. A22 requires that thermal plants, excluding the plants served as DSS (Daily Start and Stop)
generators (DssG), generate electricity at no less than their minimum output threshold. The right-hand sidevalue of Eq. A22, which is a multiplication of available plant's capacity without DSS mode (mxg-DssG*akg)
and a ratio of minimum output level (MolG), corresponds to the minimum output level of each generation

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type. Maximum output level (mxg) is estimated through Eq. A23 and Eq. A24.
𝑥𝑔𝑛,𝑖,𝑑,𝑡 ≥ (𝑚𝑥𝑔𝑛,𝑖,𝑑 − 𝐷𝑠𝑠𝐺𝑖 ∙ 𝑎𝑘𝑔𝑛,𝑖,𝑑 ) ∙ 𝑀𝑜𝑙𝐺𝑖
𝑚𝑥𝑔𝑛,𝑖,𝑑 ≥ 𝑥𝑔𝑛,𝑖,𝑑,𝑡

Eq. A22(𝑖 = 4, 5, … , 8)

Eq. A23(𝑖 = 4, 5, … , 8)𝑚𝑥𝑔𝑛,𝑖,𝑑 ≥ 𝑥𝑔𝑛,𝑖,𝑑+1,𝑡

Eq. A24(𝑖 = 4, 5, … , 8)

where: DssGi: share of daily start and stop operation (DSS) of generation type i; MolGi: minimum output rateof operation for generation type i.

Available capacity and maintenance constraint for thermal generation

A1.2.8.

Available capacities for thermal generation (akg) are calculated by excluding capacities under
maintenance from total capacities (Eq. A25). The maintenance schedule is exogenously given as theparameter UM, which indicates the rate of plant shutdown under each maintenance schedule. This study
assumes four profiles as illustrated in Fig. A1. The model determines the capacity maintained under eachschedule (mkg).
𝑎𝑘𝑔𝑛,𝑖,𝑑 + ∑ 𝑈𝑀𝑚,𝑑 ∙ 𝑚𝑘𝑔𝑛,𝑖,𝑚 = 𝑘𝑔𝑛,𝑖(𝑖 = 4, 5, … , 8)

Eq. A25𝑚∑ 𝑈𝑀𝑚,𝑑 ∙ 𝑚𝑘𝑔𝑛,𝑖,𝑚 ≥ (1 − 𝑀𝑎𝑥𝐴𝐺𝑖 ) ∙ 𝑘𝑔𝑛,𝑖(𝑖 = 4, 5, … , 8)

Eq. A26𝑚∑ 𝐴𝑣𝑒𝑈𝑀𝑚 ∙ 𝑚𝑘𝑔𝑛,𝑖,𝑚 = (1 − 𝐴𝑣𝑒𝐴𝐺𝑛,𝑖 ) ∙ 𝑘𝑔𝑛,𝑖(𝑖 = 4, 5, … , 8)

Eq. A27𝑚

𝐴𝑣𝑒𝑈𝑀𝑚 =1∑ 𝑈𝑀𝑚,𝑑𝐷

Eq. A28𝑚where: UMm,d: rate of plant shutdown under maintenance schedule m (Fig. A1); AveUMm: average rate ofplant shutdown under maintenance schedule m; MaxAGi: seasonal peak availability of generation type i;
AveAGn,i: annual average availability of generation type i at node n.

UM (m=0)
UM (m=1)
UM (m=2)
UM (m=3)

1

0.8
0.6
0.4
0.2
0

0

100

200

300

Day

Fig. A1 Rate of plant shutdown under each maintenance schedule

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CO2 emissions constraint

A1.2.9.

Eq. A29 constrains the total CO2 emissions in Northeast Asia.
∑ ∑ (𝐶𝑎𝑟𝑏𝑜𝑛𝑖 ∙ ∑ ∑
𝑛

𝑖

𝑑

𝑡

𝑥𝑔𝑛,𝑖,𝑑,𝑡 ∙ 𝐻𝑊
) ≤ 𝑀𝑎𝑥𝐶𝑂2
𝐸𝑓𝑓𝐺𝑛,𝑖Eq. A29

where: Carboni: carbon content of the fuel for generation type i [gCO2 per kWh]; MaxCO2i: carbon emissions
regulation for Northeast Asia [gCO2].

Constraint on the maximum SNSP

A1.2.10.

As mentioned in Section 2.1, this study limits SNSP in each city node from the viewpoint of gridstability. The left part of Eq. A30 indicates the total output from non-synchronous technologies (solar PV,
wind, fuel cell, discharge from battery storage and HVDC electricity imports). The right part multiplies the
maximum SNSP and the sum of load, charge to pumped hydro and battery, electricity consumption forelectrolyzer and electricity exports.
∑ 𝑥𝑔𝑛,𝑖,𝑑,𝑡 + 𝑥𝑑𝑐𝑛,2,𝑑,𝑡 + ∑ ∑ 𝑥𝑙𝑛2,𝑛,𝑙,𝐷𝐴𝑛2,𝑛,𝑑,𝑡,𝑇𝐴𝑛2,𝑛,𝑡
𝑖∈𝑁𝑆𝐺

𝑛2

𝑙

Eq. A302

𝑥𝑒𝑛,𝑑,𝑡≤ 𝑀𝑎𝑥𝑆𝑁𝑆𝑃 ∙ (𝐿𝑛,𝑑,𝑡 + ∑ 𝑥𝑐ℎ𝑛,𝑠,𝑑,𝑡 ++ ∑ 𝑥𝑙𝑛,𝑛2,𝑑,𝑡 )
𝐸𝑓𝑓𝐸𝑛
𝑠=1

𝑛2

where: MaxSNSP: the maximum SNSP (75%), NSG: set of non-synchronous technologies (solar PV, windand fuel cell).

A1.2.11.

Transmission constraint for supply nodes

Eq. A31 and Eq. A32 requires supply nodes to have enough transmission and/or storage facilities todeliver or store the output of the installed generation capacity in the node. Eq. A31 is for the China-Three
Gorges (PRC-TG) node, and Eq. A32 for the China-Uyghur (PRC-UG), the China-Tibet (PRC-TB) and theMongolia (MN) node. Eq. A33 limits transmission inflow into the supply nodes.
∑ 𝑘𝑔𝑛,𝑖 ≤ ∑ 𝑘𝑙𝑛,𝑛2,𝑙(𝑛 = 7)

Eq. A31𝑖2

2∑ 𝑘𝑔𝑛,𝑖 ≤ ∑ 𝑘𝑙𝑛,𝑛2,𝑙 + ∑ 𝑘𝑠𝑛,𝑠 + 𝑘𝑒𝑛
𝑖=1

𝑛2,𝑙

𝑥𝑙𝑛2,𝑛,𝑙,𝐷𝐴𝑛2,𝑛,𝑑,𝑡,𝑇𝐴𝑛2,𝑛,𝑡 = 0

A1.3.

(𝑛 = 8, 9, 15)

Eq. A32

𝑠=1

Eq. A33(𝑛 = 7, 8, 9, 15)

Detailed assumptions for generation and storage technologies

Fig. A2 depicts initial capacity settings for the generation and storage technologies, and Table A2-Table
A8 summarize the economic and technical assumptions. Main sources are as follows: Komiyama, et al. [16]for hydrogen system, METI [17] for pumped hydro and battery, IEA [18] and Komiyama & Fujii [19] for capital

18

IEEJ：November 2017 © IEEJ2017

cost for other technologies, and Komiyama & Fujii [19] for technical assumptions of thermal generation (suchas ramping capability, share of DSS and minimum output rate). As for capital cost for solar PV in the Mongolia
node, this study considers the cost of cleaning sand dust, which was estimated from the cleaning costs in adesert area [31]. Note that “--” in Table A2-Table A8 indicates non-applicable for that technology. Availability
factor of hydro and annual average availability of several technologies vary by node and these tables show
the range in the country. Assumptions of capital and fuel cost in the Korea node are from those in the Japannodes in this study.

GW
400
300

200
100

MN

RUS_FE

ROK

JPN_W

JPN_E

JPN_H

PRC_TB

PRC_UG

PRC_TG

PRC_S

PRC_NW

PRC_C

PRC_E

PRC_N

PRC_NE

0

Solar PV
Wind
Hydro
NuclearCoal-fired
Gas-firedOil-firedPumpedBattery
Electrolyzer
H2 storageH2 turbine
Fuel cellFig. A2 Initial capacity settings

Table A2 Assumptions for generation technologies (for all nodes)

Annual fixed charge rate[%]
Carbon content of fuel[MtCO2/Mtoe]
Seasonal peak availability(thermal plants)
Maximum ramp-up rate
(thermal plants) [%/h]Maximum ramp-down rate
(thermal plants) [%/h]Share of DSS operation(thermal plants) [%]Minimum output level(thermal plants) [%]

Solar
PV
9.5

Wind

Hydro

Nuclear8.5

Coalfired8

Gasfired8

Oilfired8

H2
turbine
8

Fuel
cell
9

11

6.5

0

0

0

0

3.9

2.4

3.0

0

0

--

--

--

95

95

95

95

95

--

--

--

--

0

26

44

44

44

--

--

--

--

0

31

31

31

31

--

--

--

--

0

0

40

70

40

--

--

--

--

80

30

30

30

30

--

19

IEEJ：November 2017 © IEEJ2017

Table A3 Assumptions for generation technologies in the China nodes

Capital cost [USD/kW]
Fuel cost [USD/MWh]
Availability factor(hydro and fuel cell) [%]Annual average availability(thermal plants) [%]Conversion efficiency [%]

Solar
PV
1380
---

Wind

Hydro

Nuclear

1150
---

3450
-22-72

2400
15
--

--

--

--

60

--

--

--

100

Coalfired
750
12.9
--

Gasfired
550
55.9
--

Oilfired
700
62.3
--

H2
turbine
550
---

Fuel
cell
400
-80

6075
35

90

75

80

--

45

39

55

50

Table A4 Assumptions for generation technologies in the Japan nodes

Capital cost [USD/kW]
Fuel cost [USD/MWh]
Availability factor(for hydro and fuel cell) [%]Annual average availability(thermal plants) [%]Conversion efficiency [%]

Solar
PV
2400
---

Wind

Hydro

Nuclear

2000
---

5200
-35

4000
15
--

--

--

--

65-90

--

--

--

100

Coalfired
2500
17.2
--

Gasfired
1100
55.9
--

Oilfired
1900
62.3
--

H2
turbine
1100
---

Fuel
cell
400
-80

6085
41

7585
50

75

80

--

39

55

50

Table A5 Assumptions for generation technologies in the Korea node

Capital cost [USD/kW]
Fuel cost [USD/MWh]
Availability factor(for hydro and fuel cell) [%]Annual average availability(thermal plants) [%]Conversion efficiency [%]

Solar
PV
2400
---

Wind

Hydro

Nuclear4000
15--

Coalfired
2500
17.2
--

Gasfired
1100
55.9
--

Oilfired
1900
62.3
--

H2
turbine
1100
---

Fuel
cell
400
-80

2000
---

5200
-40

--

--

--

95

90

90

95

80

--

--

--

--

100

37

51

39

55

50

Table A6 Assumptions for generation technologies in the Russia Far East node

Capital cost [USD/kW]
Fuel cost [USD/MWh]
Availability factor(hydro and fuel cell) [%]Annual average availability(thermal plants) [%]Conversion efficiency [%]

Solar
PV
1400
---

Wind

Hydro

Nuclear

1930
---

4550
-33

--

--

--

--

20

3800
15
--

Coalfired
2100
12.9
--

Gasfired
800
38.7
--

Oilfired
1900
62.3
--

H2
turbine
800
---

Fuel
cell
400
-80

--

80

75

75

75

80

--

--

100

35

35

39

55

50

IEEJ：November 2017 © IEEJ2017

Table A7 Assumptions for generation technologies in the Mongolia node
Capital cost [USD/kW]
Availability factor(fuel cell) [%]Annual average availability(hydrogen turbine) [%]Conversion efficiency [%]Solar PV
1430
--

Wind
1150
--

H2 turbine
800--

Fuel cell
400
80

--

--

80

--

--

--

55

50

Table A8 Assumptions for storage technologies and electrolyzer (for all nodes)
Capital cost for kW-capacity[USD/kW]
Capital cost for kWh-capacity(storage) [USD/kWh]Annual fixed charge rate [%]Availability factor [%]Cycle efficiency (storage) [%]Self-discharge loss (storage)[%/h]Non-durable material cost(storage)
kWh-capacity ratio to kWcapacity (pumped and battery)
Conversion efficiency(electrolyzer) [%]Appendix 2.

Pumped hydro--Battery--

Compressed H2 tank
700

Electrolyzer
400

230

400

15

--

6.5
80
70
0.01

9
80
85
0.1

9
80
90
0.01

9
80
---

0

35

0

--

6

6

--

--

--

--

--

70

Assumed electricity load curves for China

Historic load curves for a year in China are not publicly available. Therefore, this paper estimatedhourly load curve for each Chinese city node in the following three steps. First, daily electricity
consumption Dd [GWh] was estimated at each node, using actual temperature data in major cities [21] andrelationships between temperature and daily electricity consumption [32, 33]. Second, a reference load
pattern was estimated for each day RLd,t by using a weighted average of available seasonal load curves[34, 35, 36]. Note that RLd,t indicates the share of electricity consumption in each time slot of the day.
Then, this paper developed a linear programming model, as summarized in Eq. A34-Eq. A39 to estimate
the hourly load curve for a year by adjusting a reference load curve (Dd×RLd,t). The model aims tominimize adjustment penalties under various constraints, including a daily consumption constraint (Eq.
A36), annual load factor constraint (Eq. A37-Eq. A38) and continuity constraint (Eq. A39).

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IEEJ：November 2017 © IEEJ2017

Table A9 Endogenous variables of the load estimation modelz

Sum of adjustment penalties

Loadd,tAdjusted load curve [GW]peak

Annual peak load of the adjusted load curve [GW]

dud,t,sp, dld,i,sp

Variables to adjust the reference load curve (Dd×RLd,t) upward and downward, respectively(0≦dud,t,sp≦0.025, 0≦dld,t,sp≦0.025)

where:
𝑑: 𝑑𝑎𝑦 𝑖𝑛𝑑𝑒𝑥 (0, 1, … , 364 or 365), 𝑡: 𝑡𝑖𝑚𝑒 𝑖𝑛𝑑𝑒𝑥 (0, 1, … , 23),
𝑠𝑝: 𝑎𝑑𝑗𝑢𝑠𝑡𝑚𝑒𝑛𝑡 𝑠𝑡𝑒𝑝 𝑖𝑛𝑑𝑒𝑥 (0, 1, … , 19 in this study)

Objective function

A2.1.

This model aims to minimize the sum of adjustment penalties.
𝑚𝑖𝑛. 𝑧 = ∑ ∑ ∑ 𝑃𝑒𝑠𝑝 ∙ (𝑑𝑢𝑑,𝑡,𝑠𝑝 + 𝑑𝑙𝑑,𝑡,𝑠𝑝 )𝑑𝑡Eq. A34

𝑠𝑝

where: Pesp: adjustment penalties [/GW]. This study assumes that Pesp=(SP+1)2 (Pe0=1, Pe1=4, … ,Pe19=400).

Constraints

A2.2.
A2.2.1.

Load adjustment equation

This equation is to adjust the reference load curve. dud,t,sp and dld,t,sp in the right side are the variables
for adjustment.
Eq. A35

𝑙𝑜𝑎𝑑𝑑,𝑡 = 𝐷𝑑 ∙ 𝑅𝐿𝑑,𝑡 ∙ {1 + ∑(𝑑𝑢𝑑,𝑡,𝑠𝑝 − 𝑑𝑙𝑑,𝑡,𝑠𝑝 )}𝑠𝑝where: Dd: Daily electricity consumption [GWh]; RLd,t: reference load pattern (in a ratio to the daily electricity
consumption).

A2.2.2.

Daily consumption constraint

This constraint ensures that the daily summation of adjusted load (Loadd,t) is equal to the estimateddaily consumption (Dd).
𝐷𝑑 = ∑ 𝑙𝑜𝑎𝑑𝑑,𝑡

Eq. A36𝑡

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IEEJ：November 2017 © IEEJ2017

Annual load factor constraint

A2.2.3.

This constraint limits the annual load factor, the ratio between average load and peak load, within thespecified range.
Eq. A37

𝑙𝑜𝑎𝑑𝑑,𝑡 ≤ 𝑝𝑒𝑎𝑘
𝑀𝑖𝑛𝐿𝐹 ≤ ∑ ∑𝑑𝑡𝑙𝑜𝑎𝑑𝑑,𝑡≤ 𝑀𝑎𝑥𝐿𝐹
𝑁𝐷 ∙ 𝑁𝑇 ∙ 𝑝𝑒𝑎𝑘

Eq. A38

where: MinLF: Lower bound for adjusted load factor; MaxLF: Upper bound for adjusted load factor; ND: The
number of day (ND = 365 or 366); NT: The number of time slices in the day (NT = 24).

A2.2.4.

Continuity constraint

This constraint is for smoothing the adjusted load across the day.
Eq. A39

𝑙𝑜𝑎𝑑𝑑,23 ∙ (1 − 𝐶𝐿) ≤ 𝑙𝑜𝑎𝑑𝑑+1,0 ≤ 𝑙𝑜𝑎𝑑𝑑,23 ∙ (1 + 𝐶𝐿)
where: CL: Continuity coefficientThis paper validated the estimated load curves using the best available data (Fig. A3-Fig. A5). Fig. A3compares quarterly power generation in China [37] with estimated consumption calculated by loadd,t.
Generation and estimated consumption are expressed in a ratio to the annual total. Note that, in a strict
sense, generation and consumption are not comparable; yet, due to data availability, generation data wasused as a proxy in this validation. Fig. A3 implies that seasonality, such as increasing generation in summer(July-September), is well captured in the estimated consumption.
Load duration curves in several areas or provinces are available in China; therefore, this study also
validated the estimated load curves on a duration-curve basis. Fig. A4 compares the estimated curve for
China-North with the actual load duration curves in the Beijing-Tianjin-Tangshan area. Fig. A5 illustrates
China-South and actual data in Yunnan province. These figures also imply that the estimated curves well

Ratio to the annual total

reproduce the actual consumption trends.

40%
30%

Estimated demand
Actual generation (2016)
Actual generation (2015)

20%
10%
0%
Jan-MarApr-Jun

Jul-Sep

Oct-Dec

Fig. A3 Comparison between quarterly generation and estimated consumption in China

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IEEJ：November 2017 © IEEJ2017

100%
80%
60%
40%

PRC-N (estimated)
Beijing-Tianjin京津唐地域 (2000, 2007,
Tangshan area (2000,
2010 and 2014)
2007, 2010, 2014)

20%
0%
1

2001

4001

6001

8001

Hour

Fig. A4 Comparison between historic load duration curves in Beijing-Tianjin-Tangshan areaand estimated curve for the China-North node100%

PRC-S (estimated)
Yunnan (actual, 2012)

75%
50%
25%
0%

1

2001

4001

6001

8001

Hour

Fig. A5 Comparison between a historic load duration curve in Yunnan provinceand estimated curve for the China-South node24

IEEJ：November 2017 © IEEJ2017

References[1] E. Charter, KEEI, E. RAS, M. o. Mongolia and JREF, "Gobitec and Asian Super Grid for
Renewable Energies in Northeast Asia," Energy Charter Secretariat, ISBN 978-905948143-5, 2014.
[2] KEPCO, "KEPCO's Future Plans of Northeast Asia Supergrid," 2014. [Online]. Available:
http://www.energycharter.org/fileadmin/DocumentsMedia/Forums/ECF_Ulaanbaatar_2014_
S2_Kwang_Hee.pdf. [Accessed 17 Oct 2016].
[3] SGCC, "Connotation and Outlook of Global Energy Interconnection," 2016. [Online].
Available: https://www.renewableei.org/images/pdf/20160525/Special_address2_Wan_Haibin.pdf. [Accessed 17 Oct 2016].
[4] L. Belyaev, N. Voropai, S. Podkovalnikov and G. Shutov, "Problems of power grid formationin Northeast Asia," Elektrichestvo, vol. 2, pp. 15-27, 1998.
[5] K. H. Chung and B. H. Kim, "Economic Feasibility on the Interconnected Electric Power
Systems in North-East Asia," Journal of Electrical Engineering & Technology, vol. 2, no. 4,
pp. 452-460, 2007.
[6] T. Otsuki, A. B. Mohd Isa and R. Samuelson, "Electric power grid interconnections inNortheast Asia: A quantitative analysis of opportunities and challenges," Energy Policy, vol.
89, pp. 311-329, 2016.
[7] D. Bogdanov and C. Breyer, "North-East Asian Super Grid for Grid for 100% renewableenergy supply: Optimal mix of energy technologies for electricity, gas and heat supply
options," Energy Conversion and Management, vol. 112, pp. 176-190, 2016.
[8] T. Otsuki, "Costs and benefits of large-scale deployment of wind turbines and solar PV in
Mongolia for international poewr exports," Renewable Energy, vol. 108, pp. 321-335, 2017.
[9] R. Komiyama, T. Otsuki and Y. Fujii, "Energy modeling and analysis for optimal grid
integration of large-scale variable renewables using hydrogen storage in Japan," Energy,
vol. 81, pp. 537-555, 2015.
[10] EirGrid, "Ensuring a Secure Reliable and Efficient Power System in a Changing
Environment," EirGrid, 2011.
[11] EirGrid, "DS3 Programme Operational Capability Outlook 2016," EirGrid, 2016.
[12] GOJ, "Setsuden.go.jp," 2015. [Online]. Available: http://setsuden.go.jp/. [Accessed 13 Mar2015].
[13] S. UPS, "Generation and consumption," 2016. [Online]. Available: http://www.so-cdu.ru/.
[Accessed 17 Oct 2016].

25

IEEJ：November 2017 © IEEJ2017[14] KPX, "Power system operation information," 2016. [Online]. Available:
http://www.kpx.or.kr/www/contents.do?key=20. [Accessed 17 Oct 2016].
[15] APERC, "APEC Energy Demand and Supply Outlook 6th Edition," APEC#216-RE-01.8,
ISBN 978-981-09-8921-7, 2016.
[16] R. Komiyama, T. Otsuki and Y. Fujii, "Optimal Power Generation Mix considering Hydrogen
Storage of Variable Renewable Power Generation," IEEJ Transactions on Power andEnergy, vol. 134, no. 10, pp. 885-895 (in Japanese), 2014.
[17] METI, "Battery strategies," 2012. [Online]. Available:
http://www.enecho.meti.go.jp/committee/council/basic_problem_committee/028/pdf/28sankou2-2.pdf. [Accessed 17 Oct 2016].
[18] IEA, "WEO - Investment Costs," 2016. [Online]. Available:
http://www.worldenergyoutlook.org/weomodel/investmentcosts/. [Accessed 17 Oct 2016].
[19] R. Komiyama and Y. Fujii, "Assessment of post-Fukushima renewable energy policy inJapan's nation-wide power grid," Energy Policy, vol. 101, pp. 594-611, 2017.
[20] NREL, "Meteorology: typical meteorological year data for selected stations in China from
NREL," 2016. [Online]. Available: https://catalog.data.gov/dataset/meteorology-typicalmeteorological-year-data-for-selected-stations-in-china-from-nrel-af0da. [Accessed 17 Oct
2016].
[21] JMA, "Automated Meteorological Data Acquisition System," Japan Meteorological Agency(in Japanese), 2015.
[22] KMA, "Current Weather," 2016. [Online]. Available:
http://www.kma.go.kr/weather/observation/currentweather.jsp. [Accessed 10 Jul 2016].
[23] NREL, "PV Watts Calculator," 2017. [Online]. Available: http://pvwatts.nrel.gov/. [Accessed
7 Mar 2017].
[24] G. He and D. M. Kammen, "Where, when and how much wind is available? A provincialscale wind resource assessment for China," Energy Policy, vol. 74, pp. 116-122, 2014.
[25] G. He and D. M. Kammen, "Where, when and how much solar is available? A provincialscale solar resource assessment for China," Renewable Energy, vol. 85, pp. 74-82, 2016.
[26] MOE, "Study on Basic Zoning Information Concerning Renewable Energies (FY2015)," (inJapanese, "平成 27 年度再生可能エネルギーに関するゾーニング基礎情報整備告書"),2016.
[27] KOPIA, "Study on potential solar power deployment capacity," (in Korean), 2011.
[28] H.-G. Kim, Y.-H. Kang, H.-J. Hwang and C.-J. Yun, "Evaluation of Inland Wind Resource
Potential of South Korea According to Environmental Conservation Value Assessment,"

26

IEEJ：November 2017 © IEEJ2017

Energy Procedia, vol. 577, pp. 773-781, 2014.
[29] D. Elliott, M. Schwartz, G. Scott, S. Haymes, D. Heimiller and R. George, "Wind Energy
Resource Atlas of Mongolia," NREL/TP-500-28972, 2001.
[30] Q. Chen, C. Kang, H. Ming, Z. Wang, Q. Xia and G. Xu, "Assessing the low-carbon effects
of inter-regional energy delivery in China's electricity sector," Renewable and Sustainable
Energy Reviews, vol. 32, pp. 671-683, 2014.
[31] SASIA, "Desert Protocol Tests to be Required for PV in Saudi Arabia," 2016. [Online].
Available: http://saudi-sia.com/desert-protocol-tests-to-be-required-for-pv-in-saudi-arabia/.
[Accessed 25 Jun 2016].
[32] Z. Y. Zhang, D. Y. Gong and J. J. Ma, "A study on the electric power load of Beijing and its
relationships with meteorological factors during summer annd winter," MeteorologicalApplications, vol. 21, pp. 141-148, 2014.
[33] Y.-L. Hou, H.-Z. Mu, G.-T. Dong and J. Shi, "Influences of Urban Temperature on theElectricity Consumption of Shanghai," Advances in Climate Change Research, vol. 5, no.
2, pp. 74-80, 2014.
[34] JEPIC, "Overseas electric power industry statistics 2006," 2006.
[35] C. Cheng, "Electricity Demand-Side Management for an Energy Efficient Future in China:
Technology Options and Policy Priorities," Ph.D Dissertation. Massachusetts Institute ofTechnology, 2005.
[36] P. Duan, K. Xie, T. Guo and X. Huang, "Short-Term Load Forecasting for Electric Power
Systems Using the PSO-SVR and FCM Clustering Techniques," Energies, vol. 4, pp. 173184, 2011.
[37] NBS, "National Data - Monthly - Energy - Output of Electricity," 2017. [Online]. Available:
http://data.stats.gov.cn/english/index.htm. [Accessed 12 Jun 2017].
[38] IRENA, "Planning for the renewable future," ISBN 978-92-95111-06-6, 2017.

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