Optimal allocation of willingness to pay for renewable electricity generationand grid transmission in transition to a low-carbon society in JapanLu GAO*, Yuki HIRUTA and Shuichi ASHINA, National Institute for Environmental Studies, Japan

4. Methodology

1. Introduction► Japan decided a net zero GHG

4.1 Estimation of WTP

reduction target by 2050.
► To achieve this target, electricity
production by renewable energy
resources expect to play a key role
► Another challenge istransmitting
electricity generated in remote areas.
► Although the higher capital cost for
renewable energy and transmitting,there is a movement to defray theadditional cost, impacting theirfurther diffusion► We have developed series ofmodels to simulate, How well theWTP will impact on the renewableenergy and transmitting?

 Regression was used to forecast
Where Gender is the percentage of female
share within total population (%), Income is theannual average household income (JPY),= exp − exp
α=ln −

ln%−,

Y

Xbase

Xt,50%

864
20-2

1 2 3 4 5 6 7 8 9 101112131415161718192021222324(b) Load factor of electricity from WT
HourR015Load Factor (100%)

R02
R03

4

R05

3R06
2

R07
R08

1

Economiccondition

R09
R101820222050( ): Installed capacity: RE potential

Cc, Co: Capital cost ,operation and maintenancecost

Unit: Billion JPY/Year
250
6040
20105

•

F(X):Acceptability function

Installed renewable energy capacity under different scenario in 2050•

R1:Hokkaido

R6:Kansai

30

R2:Tohoku
R3:Tokyo
R4:Hokuriku

R7:Chugoku
R8:Shikoku

Ref

Unit: Billion JPY/Year250
6040
20105Unit: Billion JPY/Year250
6040
20105S1S240 Unit: GW40 Unit: GW

303030

R6:Kansai

202020101010000Ref

S1S2S3Ref

S1

S2S3Ref

S1S240

S330

45040
609
2240000

5000
10000

Chub
u

2520
0000

2000

Chubu

1500
0Hokurik
u

8100

10000

16

2.

MEJ, 2010. Study of Potential for the introduction of Renewable
Energy. Ministry of the Environment.

3.

Shiraki, H., Ashina, S., Kameyama, Y., Moriguchi, Y., Hashimoto,
S.,
2011.
Simulation
analysis
ofrenewableenergyinstallationscenariosin Japan’s electricitysectorin 2020usingamultiregional optimalgeneration planning model. Journal of Japan
Society of Energy and Resources 33, 1-10.

160000190170160000

24000
0Tokyo320000
4000
00

KansaiKansaiC

80000

19Reference
Ashina, S., Fujino, J., 2007. Simulation analysis of CO2 reduction
scenarios in Japan’s electricity sector using multi-regional optimalgeneration planning model. Journal of Japan Society of Energyand Resources 29, 1-7.

Hokkaido
u ushuKy
To
ikok
houShku
ko
gu
h00
00
741
602
2500
00
402400u
rikku 2431oH
347
80

80000

16000016000024000
80000000
To00
hoku

8000
01983
13

32003050
12103827
6 45948560

00
090
4589
500

Chubu

•

0500

(d) S3 (RE+Transmission)
394
4480000
3200
00

15000

10000

0100
4280
21Tokyo

8000

6000

Chugoku

19981

43152070
00
24u ushukHokkaido
koKy
uShi
koug
Ch
475
67

80000

iKansa5000

2000(c) S2 (Transmission)
6509
53 60 44478
118000

6833
5000

4000

03
1520
2500

6000

00
59u 15000
gok
Chu 20000

30000

6000

00
040
901
14

8000

1000
000
40

Acknowledgment
This research was supported byEnvironment
ResearchandTechnology Development Fund (21711, 1-2003) of Environmental
Restoration and Conservation
Agency and Ministry of theEnvironment Japan.

160000u
rik 3ku 3936
oH 34392

Chubu

㻠㻜
Transmission
capacity㻠㻜 in S2and S3 㻞㻜→ increase㻞㻜 9-18fold than㻜 Ref
㻜㻜Specific 㻜 from㻞㻜
Tohoku to Tokyo,
and Chubu toTokyo→ great protentional
increase transmission
capacity

S3Unit: GW

R2:Tohoku

10Ref

S1

S2

S30

Ref

S1S2

• R3 is higher thanotherarea,
which
iscontributed
bythehigher
income

S340 Unit: GW30

R10:Okinawa

Unit: GW

40 Unit: GW

40 Unit: GW

R8:Shikoku3030

R3:Tokyo

R5:Chubu

20

20

101010Ref

S1

S20S3Ref

S1

S210S30Ref

S1

S20RefS1

S2S3S3

㻞㻚㻝RE generation in S3RE generation in S1

㻤㻜㻞㻜

S220

㻝㻜㻜•5000

iku
Hokur

4000

0200
1000
0KansaiToh
Hokkaido
oku
shu
Kyu

000
100200

oToky

4000

12000kukoi
Sh

500
0 8638S1

5.4 Sensitive Analysis
Percentage (%)

45
53

6000

0 891418
1171000okyTo1218040Houkkaido T00
Kyush
ohoku00
75ku
koiSh0020050030200㻢㻜

35000

052000402000Ref

20㻢㻜

120040105.3 Power transmission capacity
20003080

R4:Hokuriku

Tokyo㻤㻜(b) S1 (RE)

0R9:Kyushu

㻝㻜㻜(a) Reference

10200• In
S1,theelectricity,
generation is
expected to be
17-fold
thanRef

R1:Hokkaido

2040 Unit: GW
30

Unit: GW

S340 Unit: GW

R7:Chugoku

30

R9:Kyushu
R10:Okinawa

R5:Chubu

TWTP is higher in Tokyo→ a higher income

4060

㻤㻜

(MEJ, 2010)

TRout: Energy supply
quantity to other prefecture

5.2 Influence of WTP on diffusion of RE0(c) Wind power potential in Japan

Unit: MW30000
6000
4000
2000
50050

TRin: Energy supply quantity
from other prefecture

F(X): Acceptability
function

HH: number of household

Total WTP increase from500 to 1,400 Billion JPYHour(d) Solar power potential in Japan

FIX: Nuclear & hydro: RE potential undernatural condition, estimatedby Shiraki et al., 2011

Japan

24(MEJ, 2010)

STR: PumpHydro &Second battery

Xt䠄WTP䠅㻝㻜㻜

Unit: MW
30000
6000
4000
2000
500
50

O: Electricity generation

Naturalcondition

5. Result and Discussion

01.

DMND: Electricity demand×90 Unit: GWR0416( ))×㻠㻜㻠㻜

CO2 emission (Gt-C)

10≤(−−Exogenousձ

2015

Ref (WTP = 0)

1214+: Renewable energy cost
TL: cost for transmission lines

ղ× .
+)+( )<= ( )×5.1 Prediction of the Total WTP

SSP1

Load Factor (100%)

14

12=(F(X)

2. Data 3. Methodology

10isin( )RE capacity= ( )×

(Xbase,Y50% (Xt,50%,Y50%))

Y50%

RE Potential%

Y:Acceptability (%)

transmitting (50% WTP for each item)

8+Where, F(X) is the acceptability function, Y50%
acceptability
rates
in50%,XisWTP
JPY/(household·month), t is the year

9 Scenario 3 : WTP used for RE and6

Efficiency: Energy
conversion efficiency

RE cost−)(Nuclear Oil
Hydro Coal
Gas NGCC×≥% =transmitting

16== Supply O(t)TWTP: Total WTP; X:WTP

Min Intermediate
Mid Intermediate
Max Intermediate
Min Summer
Mid Summer
Max Summer
Min Winter
Mid Winter
Max Winter+Cc: capital cost, Co: O&M cost, Cf: fuel cost, Ct: transportcost, CCO2 : carbon cost, RE cost: cost for RE

Exogenous−= ex p( + ln − ln

9 Scenario 2: WTP only used for18+

O: Electricity demandղ Shift in acceptability curve

9 Scenario 1 : WTP only used for RE

20+

Where Fbase (X) is the base acceptability function, X is WTP
in JPY/(household·month). a and b are assumed to 6.505
and 1.065.

Fbase(X)(a) Load factor of electricity from PV in
Tokyo area (Shiraki et al., 2011)+(Ashina et al., 2007)

Demandձ Baseline (Weibull distribution)= exp − exp(×( + ))×+( + ) −Subject to4.2 Acceptability rate

9 Ref : WTP = 04

Total cost
Minimization

WTPmed = f (Gender, Income)3. Future Scenario

2

4.3 Energy using model

RE capacity in S3
RE capacity in S1㻢㻜㻠㻜㻞㻜㻢㻜㻤㻜㻝㻜㻜㻢㻜㻤㻜㻝㻜㻜

S1 (RE)S3 (RE + Transmission line)

㻞㻚㻜㻡

㻞

㻝㻚㻥㻡

㻝㻚㻥㻝㻚㻤㻡㻜
㻜㻞㻜㻠㻜㻢㻜

㻤㻜

㻝㻜㻜㻜

㻠㻜

㻢㻜

㻤㻜

㻝㻜㻜

Share of WTP for RE (%)

Share of WTP for RE (%)• Transmission → improve RE generation
under similarly capacity1㻞㻜• Transmission + RE
→ future increase the CO2 emission

0000

6. Conclusion and future work
• From 2015 – 2050, total WTP→ increase from 500 to 1400 Billion JPY• Electricity generation from renewable energy and transmission line
→ consider the WTP is expected to be 17-fold and 18-fold than Ref• Carbon emission→ Both consider the RE and transmission line can future increase
the carbon emission reduction

 Future work
9 Increasing second battery + RE by WTP
9 Demand management (Peak shifting)ZeroGHG