KOREA ENERGY ECONOMICS INSTITUTE

www.keei.re.kr

(REC)

15-12

:

:

i

1.

2013~2040

37% (IEA, 2014).

,

.

.

.

,

, ,

.

, 2013 18 TOE

(IEA, 2015). ( )

2004 2013 2

45% (IEA, 2015).

2012 (Renewable Portfolio

Standard, RPS) (Renewable

Energy Certificate, REC)

. REC

.

REC

ii

REC . RPS

REC

.

. , RPS

REC , /

.

.

,

.

2.

REC REC

REC .

REC

. REC

REC

.

RPS 80

.

REC 2 .

iii

, . REC

, ,

, , .

.

. 3 . ,

, RPS

. , REC REC 5%

REC REC

. , RPS , REC

. 2014 / REC

, 2016 -

REC

.

REC

.

(Levelized cost

of energy) (experience curve) .

(SMP)

REC, LCOE

. RPS , REC

REC . RPS

1.5

, REC

. Bayus

iv

. () () ,

Bayus .

, 2016 686.6/W 2024

500.5/W, 1,121.7/W 2024 617

/W .

2016 1,808.4/W 2024 1,117.9/W 38%

. LCOE

2016 167.06/kWh 2024 106.39/kWh 36%

.

2016 1,129.9/W 2024

1,054.8/W 6.6% .

1,128

/W . 2016

2,257.8/W 2024 2,182.7/W 3.3%

. LCOE 2016 140.60/kWh 2024

136.83/kWh .

SMP 7

2 .

(S1) 7 , ,

,

. (S2)

, , .

SMP 2016 91/kWh 2024 76.7~84/kWh

.

v

SMP .

LCOE SMP REC

. SMP

6 . SMP 7

100% ,

2 . REC

15%, 20%, 25%

. 7

, REC 2016 71/REC~102/REC

2024 54/REC~84/REC

. LCOE REC

. REC

REC REC

.

,

REC .

7 2

SMP . REC

2016 71/REC~102/REC 2024 49/REC~76

/REC .

SMP 1

, REC .

vi

3.

REC REC

, REC

. RPS

. () RPS

, RPS .

RPS REC

.

SMP+REC

. REC 12

. REC

SMP

.

SMP+REC

.

REC , SMP

PPA

.

,

SMP+REC

. SMP REC

. 2 SMP REC

.

vii

REC

.

. SMP

.

FIT .

REC , ,

,

. ,

. REC

RPS .

REC

.

Abstract i

ABSTRACT

1. Research Background and Purpose

Energy demand has steadily risen worldwide and is expected to

increase by 37% between 2013 and 2040 (IEA, 2014). A regions

energy use is bound to increase as its industries and economy

develop, increasing greenhouse gas emissions. All nations are

attempting to address climate change, with an increasing focus on

seeking new and renewable eco-friendly energy sources instead of

using traditional energy sources. New and renewable energy has

potentially positive environmental and economic effects, such as

reducing greenhouse gases, which help address climate change,

offering alternatives to fossil fuels and driving economic growth

through industrialization. As a result, the supply of new and

renewable energy has steadily increased, with the global production

of new and renewable energy reaching 1.8 billion TOE in 2013

(IEA, 2015). New and renewable energy usage in the electric power

sector (including hydroelectricity) roughly doubled from 2004 to the

end of 2013, and about 45% of the power generation facilities being

built are for new and renewable energy (IEA, 2015).

In Korea, Renewable Energy Certificate (REC) prices have been

attracting interest since the Renewable Portfolio Standard (RPS) was

ii

implemented in 2012. As these prices are related to residents

electricity bills, the publics burden will increase or decrease along

with them. Renewable Energy Certificate prices are also important

because the profits and outcomes of new and renewable energy

projects will depend on their prospects. Therefore, to reduce future

uncertainty regarding RPS policy, Researchers are required to

forecast REC prices.

This study develops a price prediction model for RECs. We

analyze the characteristics of the domestic RPS market, develop a

methodology for predicting REC prices, and apply it to the overall

solar/non-solar market. Finally this study also suggests measures for

stabilizing REC prices; a price stabilization policy is needed because

higher REC price volatility brings higher risk to the government,

new and renewable energy suppliers, and energy generation

companies.

2. Summary

Studies on REC prices have either analyzed the factors in REC

price volatility or predicted future REC prices. Despite the

importance of research predicting REC prices, little research of this

type has been done either in Korea or abroad. By developing an

REC price prediction model and applying it to Koreas REC market,

we seek to contribute to research on new and renewable energy in

Korea and to foreign research as well. Close to 80 national and state

Abstract iii

governments are implementing RPS, and this study should help

further the market and policy research on new and renewable energy

in these countries.

This study proposes two methodologies for predicting REC prices.

The first is a Bayesian multivariate normal model that uses past data

to estimate the models parameters and predict prices. The variables

explaining REC prices include the required an amount of new and

renewable energy, the supply of new and renewable energy, the

system marginal price (SMP), levelized cost, and policy changes. We

used Gibbs sampling to estimate posterior distribution. The

estimation found no statistically significant coefficients for any

variables except for the policy dummy variable. This result had three

main causes. First, insufficient data were available because Koreas

RPS market is new. Second, we could not predict REC prices by

looking only at REC supply because the trading volume of RECs on

the spot market accounts for only 5% of the total REC market.

Third, alterations in RPS policy have led to constant changes in the

REC market mechanism. For example, the former method of

predicting price based on trends in solar and non-solar REC prices is

less effective because the exchange of solar RECs for non-solar

RECs was unofficially permitted in the second half of 2014, and the

solar and non-solar markets will be unified after 2016. Therefore,

prediction methods that use past trends cannot be applied to Korea's

REC market, which should be noted by future researchers.

iv

The second methodology developed by this study is a prediction

model using the levelized cost of energy and experience curves. This

methodology is based on the facts that the profit source of new and

renewable energy is SMP and REC and that their sum exceeds the

Levelized Cost of Energy (LCOE) reflecting the ideal rate of return.

We also considered the characteristics of Koreas RPS system to

derive the standard price for RECs and estimate their maximum

price. When a company charged with RPS fails to reach a target, it

is charged a penalty of the current price 1.5; this value is the

upper limit of REC prices in the spot market. To predict the

levelized cost of energy, we used estimated experience curves and a

Bayes model. We used experience curves for the prices of solar

modules and wind turbines and a Bayes model for non-module

prices.

Our estimates indicate that the price of solar modules will fall

from 686.6 KRW/W in 2016 to 500.5 KRW/W in 2024 and that

prices in the non-module sector will fall from 1,121.7 KRW/W in

2016 to 617 KRW/W in 2024. Therefore, we expect the price of

solar systems to fall by about 38%, from 1,808.4 KRW/W in 2016

to 1,117.9 KRW/W in 2024. We thus expect solar LCOE to fall by

about 36%, from 167.06 KRW/kWh in 2016 to 106.39 KRW/kWh in

2024.

For wind energy, we predict that turbine prices will fall by about

6.6%, from 1,129.9 KRW/W in 2016 to 1,054.8 KRW/W in 2024.

Abstract v

As non-turbine prices consist mostly of construction costs, prices will

definitely not fall; they will stay at 1,128 KRW/W. Therefore, we

expect the price of wind systems to fall by about 3.3%, from 2,257.8

KRW/W in 2016 to 2,182.7 KRW/W in 2024. As a result, we

predict wind LCOE to fall slightly, from 140.60 KRW/kWh in 2016

to 136.83 KRW/kWh in 2024.

We estimated SMP using a model of the electricity market

assuming two different scenarios to account for uncertainty in the

Seventh Master Plan for Electricity in Korea. The first scenario (S1)

assumed that the Plan would achieve its goals in areas such as

demand prediction, demand management, construction of generators

and power lines, the generation of new and renewable energy, and

the diversification of energy sources. The second scenario (S2)

assumed that the construction of atomic and coal generators would

be delayed due to uncertainty in supply and that new and renewable

energy generation goals would not be reached. The estimate

indicated that SMP will fall from 91 KRW/kWh in 2016 to 76.7-84

KRW/kWh in 2024. We inferred that SMP would continue to fall

through the addition of mainstream generation facilities for sources

such as atomic and coal energy.

We predicted REC prices based on the estimated LCOE and SMP

values for solar and wind energy. We created six scenarios to reflect

uncertainty in the SMP predictions and the share of solar energy. For

the SMP, we assumed that the Seventh Basic Plan for Electricity

vi

Supply and Demand would be fully implemented or that atomic

energy and thermal energy construction would be delayed by two

years. We assumed that the share of solar energy out of the total

supply of REC would be 15%, 20%, or 25%. Our estimates found

that, when mainstream generation facilities were built according to

the Seventh Basic Plan, REC prices in the spot market would fall

from between 71 and 102 thousand KRW/REC in 2016 to between

54 and 84 thousand KRW/REC in 2024. This would happen because

REC prices would fall when new and renewable energy LCOE fell.

If the REC supply is far below the required amount (as it is at

present), we predict that the REC spot price will rise and reach the

maximum level for RECs. However, if more facilities for new and

renewable energy are built and supply becomes sufficient, the price

will rest between the maximum and minimum levels; if the supply

suddenly exceeds the required amount, REC prices may fall below

the minimum level.

If construction of the Seventh Basic Plan's mainstream generation

facilities is delayed by two years, SMP will rise. In this case, the

REC spot market price will fall from between 71 and 102 thousand

KRW/REC in 2016 to between 49 and 76 thousand KRW/REC in

2024. If the generation facilities are completed later than planned,

SMP will be higher than in scenario 1, and REC prices will fall.

Abstract vii

3. Policy Suggestions

Renewable Energy Certificate price stabilization policies are needed

to stabilize the market because REC prices will be volatile due to

factors such as uncertain REC supply. The first proposal is to

stimulate energy sale competition and place RPS obligations on

energy sellers. In other large countries, companies selling energy

(energy suppliers) carry out RPS duties, whereas Korean generation

companies carry out RPS duties. As generation companies must

generate new and renewable energy and carry out RPS duties in

Korea, there are no forces causing REC prices to fall.

The second proposal is to guarantee a fixed SMP+REC price

when contracts are being signed. For small-scale solar energy RECs,

a 12-year contract is signed, which should reduce price volatility.

However, because most new and renewable energy RECs and SMPs

change with time, this is an uncertainty factor for new and

renewable energy companies. To ease this uncertainty, the

government should approve the setting of long-term contract prices at

a level where SMP+REC revenue is reasonable. A similar proposal

is to fix REC prices in a long-term contract similar to the existing

method and hedge SMP volatility with a PPA or contract for

differences with the Korea Electric Power Corporation.

The third proposal is for financial institutions such as insurance

companies, reinsurance companies, and banks to create a financial

product that guarantees generation companies a fixed return from

viii

SMP+REC. The companies would transfer the profit or loss from

SMP or REC price fluctuations to the financial institutions and pay

fees for this alternative. Financial institutions could use the secondary

market to hedge SMP and REC price fluctuations.

The fourth proposal is to introduce REC futures products similar

to those in Australia. If futures products might be used, volatility

risk on spot market price could be hedged. Another proposal is for

the government to suggest long-term SMPs to eliminate future

uncertainty. There is also a way to apply FIT to small businesses, as

happens in England and Australia. A

We hope that by predicting REC prices this study helps reduce

uncertainty about the future for governments, companies obligated to

supply new and renewable energy, new and renewable energy

generation companies, and consumers. Estimating future costs due to

the distribution of new and renewable energy will allow governments

and consumers to prepare more thoroughly. Companies obligated to

supply new and renewable energy can use REC price prediction data

to estimate potential RPS penalties and costs. Companies generating

new and renewable energy can use REC price prediction data to

examine the feasibility of future projects.

i

1 1

2 3

1. RPS REC 3

2. REC 5

3 RPS 91. RPS 9

2. RPS 21

4 29

1. 1: 29

2. 2: (Levelized cost of energy) 32

5 45

1. 45

2. (Levelized cost of energy) 52

ii

6 71

1. 71

2. REC 79

3. REC 90

7 97

103

109

iii

RPS REC 8

2014 9

11

RPS 15

FIT 17

RPS 19

FIT RPS 21

25

26

26

27

28

LCOE 34

SMP 40

2016 41

46

( REC) 47

( REC) 50

( REC) 52

( REC) 52

53

Bayus 53

iv

54

55

56

6 vs. 7 ( ) 58

RPS 59

59

60

61

2016 REC 62

RPS 73

MPR: RPS (CEC) 81

REC 87

REC ( 1) 109

REC ( 2) 110

REC ( 3) 110

REC ( 4) 111

REC ( 5) 111

REC ( 6) 112

v

[ 3-1] RPS 12

[ 3-2] Bundled REC 13

[ 3-3] Unbundled REC 13

[ 3-4] New Jersey Maryland SRECs 14

[ 3-5] 22

[ 3-6] 23

[ 3-7] 23

[ 3-8] 24

[ 4-1] 36

[ 4-2] M-Core 40

[ 5-1] Quartiles

( REC) 48

[ 5-2] Kernel density

( REC) 49

[ 5-3] Quartiles

( REC) 50

[ 5-4] Kernel density

( REC) 51

[ 5-5] LCOE 55

[ 5-6] LCOE 57

[ 5-7] REC ( 1) 63

[ 5-8] REC ( 2) 64

vi

[ 5-9] REC ( 3) 65

[ 5-10] REC ( 4) 66

[ 5-11] REC ( 5) 68

[ 5-12] REC ( 6) 69

[ 6-1] 74

[ 6-2] 75

[ 6-3] 76

[ 6-4] 77

[ 6-5] 78

[ 6-6] RES () 80

[ 6-7] Rate Cap 2% Rule 82

[ 6-8] PV FIT 84

[ 6-9] FIT-CfD 86

[ 6-10] REC 89

[ 6-11] SMP+REC 92

[ 6-12] REC + SMP 92

[ 6-13] 93

[ 6-14] (Futures) 94

1 1

1

2013 2040

37% (IEA, 2014).

, .

,

. ,

, ,

.

, 2013

18 TOE (IEA, 2015). ( )

2004 2013 2

45%

(IEA, 2015).

2012 (Renewable Portfolio

Standard, RPS) (Renewable

Energy Certificate, REC) . REC

.

REC

REC . RPS

2

REC

.

. , RPS

REC /

.

.

,

.

. 2

. RPS

REC , RPS

. 3

. RPS

2012 RPS REC

. 4

5

. 6 REC .

RPS REC

,

REC .

2 3

2

RPS REC

. RPS REC

.

REC

.

.

, REC .

, , CO

.

REC

, .

1. RPS REC

,

.

,

.

Eirik et al.(2006) (system of

banking or storage) . (rational

expectations simulation model) (Green Certificates,

4

GCs)

. GCs

,

.

(intertemporal)

. , GCs

2 ( 1.23/ 0.50) .

, GC

(0.72), GC (0.32).

RPS (Renewable Obligation, RO)

ROC(Renewable Obligation Certificate)

(buy-out)

. Jeff et al.(2013) (ROC

) (expected share)

.

ROC(-) ROC

, ROC ROC

.

ROC , ,

.

, Jacob(2003) TGC(Tradable Green Certificats)

-

.

2 5

2. REC

RPS REC

. REC

.

(2012) 5

REC REC .

(Levelized Generation Cost)

, REC

. ( RPS

) 13 96.3% 2293.6%

. REC 1 REC

13 289 18 247, 22 216.8

. 2015 9 1 REC

92.639

.

(2014) Eco-System:

REC . 1) RPS

.

(fuzzy logic) REC . REC

(Fuzzy-based REC Price Prediction, FRPP),

REC(AVG-based REC Price Prediction, ARPP),

1) IT

6

REC (Trend-based REC Price Prediction, TRPP)

REC FRPP 94.2%

ARPP 2.2%, TRPP 11.9% . REC

14(12 12~143)

FRPP ARPP 80, TRPP 127

.

30 RPS 10

REC SRECs(Solar Renewable Energy Credits)

. Dawei et al.(2012) SRECs

4 (SRECs , SRECs , , )

. -

(linear optimization)

SRECs . PJM2) SRECs

SREC

, .

SREC

. SREC

, 2028 4.1%

. SREC $700/MWh

. REC 2011

520MW, 2025 7,400MW

.

2) DC, 13

2 7

Michael et al.(2013) SMART-SREC()

. SREC ,

. SREC

.

(, , )

,

.

. ,

SREC ,

.

, Christoph et al.(2011) TGC (Tradable

Green Certificats)

.

(Cash-flow model) TGC 2014(

3 ) 68.34(PLN240)

. TGC 2009 69 2028 3

.

8

RPS REC

Eirik et al.(2006)* - 1.23, 0.50- 0.72, 0.32

Jeff et al.(2013)- ROC(-)

- ROC

Jacob(2003)- TGC(Tradable Green Certificats) , -

REC

(2012)

- RPS : 13(96.3%)22(93.6%)

- 1 REC : 13(289)18(247)22(216.8)

(2014) - REC REC 94.2%

Dawei et al.(2012)- SREC , ,

Michael et al.(2013) - SREC

Christoph et al.(2011)

- TGC 2014( 3 ) 68.34(PLN240)

- TGC 2009 69 2028 3

RPS REC

3 RPS 9

3 RPS

1. RPS

FIT RPS

.

(RPS)

. FIT

( , ) 2004 2013 10 3

RPS ( , ) 2004 10

7 (REN21, 2015, pp.9). RPS

RES(Renewable Energy Standards)

, , , , , , .

2004

2013 2014

48 144 164

RPS/ // 11 99 98

FIT // 34 106 108

(Tendering)// n/a 55 60

/ 10 63 64

2014

: REN21, 2015, p.9

10

.

1)

RPS

. 2008 27 RPS

RPS

5 (, 2010).

RPS , 2015 3

29 DC 2 RPS .

8 2 RPG(Renewable porfolio

goal) .

RPS 1.0 .

.

Colorado, Nevada, Washington

1.2~2.45 .

, , 15 3) . ,

Arizona, New Mexico (Set-aside)

.

3) : Connecticut, California, Iowa, Hawaii, Illinois, Massachusetts, Maine, Montana, Minnesota, New Hampshire, New York, New Jersey, Ohio, North Carolina, Pennsylvania, Oregon, Rhode Island, Wisconsin

3 RPS 11

(state)

Colorado

1.25 2015 ( DG )

1.5

2.02014 12 31 30MW

3.0

; 2015 7 1

2016 12 31 .

Nevada2.0

2.4

Washington1.2

2.0 apprenticeship 2005

: DSIRE, 2015: Nevada PECs(portfolio energy

credits)

2)

California 2020

33%, Colorado 2020 30%, Minnesota 2025

26.5% . 2020

. New York 2015 29%,

Maine 2017 40%

[ 3-1].

12

[ 3-1] RPS

: DSIRE, 2015

California CPUC(California Public

Utilities Commission) REC

Bundled REC Unbundled REC

.

Bundled REC

REC (

3-2 ). Unbundled REC

REC

( 3-3 )(Polsinelli, 2013a).

3 RPS 13

[ 3-2] Bundled REC

[ 3-3] Unbundled REC

Bundled REC

REC .

1

REC

Bundled . ,

REC

Unbundled (Polsinelli, 2013a).

New Jersey Maryland unbundled ,

Flett Exchange . 2014 7

14

2015 9 New Jersey Maryland SRECs

. [ 3-4]

Maryland SRECs

.

[ 3-4] New Jersey Maryland SRECs

: Flettexchange , 2015.11.2.

.

1)

.

2012 80.8%

.

3 RPS 15

6

FIT . , ,

0.8 ,

, //(70km) /

1.8 (, 2014).

0.8 , ,

0.9

1.0 (200kW ),

1.3 ,

1.5

1.8 , , //(70km) /

RPS

: , 2014, pp.10

2)

2010 10.1%,

2020 17%. (27) 2010 12.5%,

2020 20%

34%, 31% (Kotra(a),

2014). 2020 26% 2020

32~35% (

, 2012).

RPS REC

16

(, 2009). 2016

FIT4) (Norton Rose Fulbright ).

.

1)

(2001 12)

. FIT

.

RPS 2003 4

.

(, 2010).

2011

. 2012

92.9% 4%(KOTRA(b), 2014).

. 2020 30,000MW

20% (, 2012).

2003 FIT RPS

2012 FIT .

4) FIT

3 RPS 17

2)

2012 FIT

. RPS 2010

1.35%, 2014 1.63%

(, 2011). RPS

FIT FIT .

FIT .

: 10kW

(): 10kW

() 100% (Surplus electricity)

40/kWh+

(534/kWh)

42/kWh(564/kWh)

( ) 20 10

325,000/kW(436 /kW)

466,000/kW(625 /kW)

10,000/kW(134,168/kW)

4,700/kW(63,059/kW)

(IRR) 6% 3.2%

FIT

: , 2014

FIT

FIT (, , ,

, ) . (Residential)

18

-(Non-Residential) (

) 10kW, 10kW~500kW

500kW .

FIT 6

(, 2014).

2013 13.6GW (REN21, 2014, pp.64).

.

1)

2010 MRET(Mandatory Renewable Energy Target,

) 2011 RET(Renewable

Energy Target, ) . RET 2015

6 23

33,000GWh( 2 ) .

2014 45.9% , 30.9% , 15.3%

, 7.6% .

76.8% .

2013 2014 88%

16%

(Clean Energy Council ).

ARENA(Australian Renewable Energy Agency)

3 RPS 19

. ARENA 25 2022

.

.

.

2)

2011 RET

. RET ,

.

50% .

MRET RET

Renewable Energy(Electricity) 2000 Renewable Energy Amendment Bill 2009 2000 6 2009 6 9

() 2002 1 1 2009 6 9 2001~2010(2020) 2010~2020(2030)

()

2001: 400GWh

2010: 9,500GWh2010: 12,500GWh

2020: 45,000GWh

, , , , , , , ,

, , , MSW(Solar Credit)

(Solar, ),

1MWh 1REC 1MWh 5REC 1REC

MWh 40$ MWh 65$Banking ()

RPS

20

RET SRES(Small-scale Renewable Energy Scheme )

LRET(Large-scale Renewable Energy Target) . SRES

. LRET

.

LRET LGCs(Large-scale Generation Certificates)

,

. , 2020

33,000GWh .

LRET SRES

STCs

. STCs

.

.

.

(Australian Government ).

$140

.

.

3 RPS 21

2. RPS

. FIT RPS

01

11 FIT . 12

RPS . RPS

(, 2014).

RPS FIT .

2015 5 RPS 11,825 FIT

6. FIT 2011 10 RPS

3 5 RPS

. RPS FIT

4 .

RPS 185 FIT(94) 3

FIT 4.5 2,199MW

. .

FIT(2002~2011) RPS(2012~2015 5)

(MW) () (MW) () 497 1,978 1877 11,825

489 94 2199 185 986 2,072 4076 12,010

FIT RPS

:

22

[ 3-5] [ 3-8]

/ (MW)

.

RPS

.

. 45% .

RPS FIT 3 .

FIT 0 RPS

. FIT

RPS 63 , 47,

40 .

[ 3-5]

(: )

: , 2014

3 RPS 23

[ 3-6]

(: MW)

: , 2014

[ 3-7] RPS

. 2

3~4 .

[ 3-7]

(: )

: , 2014

24

[ 3-8]

. 105

RPS .

54, 30 .

[ 3-8]

(: MW)

: , 2014

RPS /

.

.

RPS

. (2012) 2.0%, 2024

10% . 2012

276GWh 15 1,971GWh

.

3 RPS 25

(%)12 2.013 2.514 3.015 3.016 3.517 4.018 4.519 5.020 6.021 7.022 8.023 9.0

24 10.0

: , 2014

RPS 2014 14 2015 17

3 . 2015 ,

, , , ,

, , , , , SK E&S, GS

EPS, GS , , ,

.

.

0.7 1.5

0.25 5.5

( ).

26

0.25 IGCC,

0.5 ,

1.0 , , , RDF , , ( )

1.5 , ( 5km )

2.0 ,

2.0 ( 5km), , ( )

1.0~2.5

5.5

ESS( )

15

5.0 16

4.2 17

: , 2014

1.2

100kw

1.0 100kw

0.7 3000kw

1.5

3000kw

1.0 3000kw

1.5

: , 2014

3 RPS 27

.

2016

- .

.

2016 . 2014 /

.

2015 / REC 9

/REC .

2012 2013 2014 2015

167,218 186,476 106,571 90,793

64,762 144,338 100,303 92,634

(: / MWh)

: , 2015

.

.

, 3MW , 100kW

60% .

.

28

2016~2017 2018~2019

200MW 250MW

300MW 350MW

: , 2015

2015

1.5GW .

.

.

REC

.

12 .

(, , )

.

/ REC

.

20% REC 25~30%

.

/

.

.

.

4 29

4

REC 2

. .

, .

.

(Levelized cost of energy) (experience curve)

.

1. 1:

REC

(), (), (),

() . .

(1)

REC

.

.

.

(2)

30

4 ,

, .

. (likelihood

function) .

(3)

. ,

(conjugate prior) .

,

. ,

(4)

,

.

(Gibbs sampler) .

. ,

4 31

.

(Markov

chain) .

( 2)

,

.

(5)

,

. , n

.

(6)

,

k

,

. .

10,000 1,000

. REC

32

.

2. 2: (Levelized cost of energy)

(levelized cost of energy,

LCOE) (Systmem marginal

price, SMP) REC .

SMP REC LCOE

.

(7)

t REC LCOE SMP

. REC

, REC

REC

.

(8)

t REC LCOE SMP

.

4 33

.

( LCOE) (kWh)

. LCOE

,

.

LCOE

. t LCOE .

(9)

t

. , ,

, (Balance of plant, BOP), (Engineering

procurement and construction, EPC), .

, ,

. r (discount rate), d (degradation factor),

(capacity factor),

. T . LCOE

.

34

() () 1 MW 20 kW 1 MW

CAPEX 18/MW( 15) 20/MW 25/MW

(%) 14.75% 14.75% 23%

(Degradation Rate) 0.8% 0.8% 0.3%

O&M 1,600/ 352,000/ 3,000/

1,400/ 308,000/ 1,750/WACC 7% 7% 7% 70% 70% 70%

5%/ 5%/ 5%/ 22% 11% 22%

LCOE

.

15.5% 3

14.75% . 0.8%,

5%, 22% .

11% . WACC(Weighted Average Cost

of Capital, )

7% .

LCOE LCOE

. (9)

4 35

.

. , ,

EPC, . ,

.

. , , ,

.

Arrow(1962)

(experience curve) .

(IEA, 2000).

, , ,

(Bhandari and Stadler, 2009; Nemet, 2006). ,

, . 1976

.

.

.

.

36

[ 4-1]

: EPI (2013), Mints (2013)

j (10) .

(10)

t j ,

. t j

. (10)

.

(10) (ordinary least square)

.

4 37

ln lnln

(11)

(, 2013).

(learning rate)

. 2

. LR .

(12)

. () ()

.

, .

Bayus (1993)

. Bayus (1993) t , ,

, (13)

.

exp

t j .

38

.

, j

.

. Bayus

Cho and Koo (2012), Lee et al.

(2006) .

. (SMP)

SMP .

( , 2012).

(13)

.

(14)

4 39

,

: Lagrangian multiplier

: Lagrangian multiplier

F:

t:

T:

i:

N:

c:

M:

:

P: t i

: t c

U: t i

: t c

: t

: t

: i

: c .

SUDP(Single Unit Dynamic

Programming) . SUDP

40

LR(Lagrangian Relaxation) DP(Dynamic Programming)

, LR

, DP

. SMP

.

[ 4-2] M-Core

:

(/) ,

GT/ST

(// ) ( ) , HVDC

, , (* ) , , HVDC SMP (, , , ) // (CP,

SEP, CON, COFF)

SMP

:

4 41

. REC

LCOE SMP .

REC

. 2016 REC

.

(+)

Q0 QR QS

P0 PR PS

min(Px,Py,P0) min(Px,Py,PM,P0,PR) (12)

2016

(: /REC)

: (Px,Py) 2

REC , ,

, .

.

2(, 1)

.

(12 ) .

,

.

42

REC 85% ,

. REC

.

REC RPS 1.5

, REC

.

REC

. REC

. -

REC .

REC 1.5 .

RPS REC 1.5

REC .

REC

REC .

REC 12

REC .

REC

. , REC LCOE SMP .

(15)

LCOE SMP REC

.

4 43

REC .

/

.

/ .

REC .

,

.

/ .

5 45

5

1.

.

, REC , SMP, LCOE,

, REC .

RPS 2002 40

.

REC

,

5) . REC SMP

.

RPS

. LCOE BNEF(2015) . 2013

REC

. REC

RPS REC .

REC ,

. .

5) : http://rec.kpx.info/index.jsp

46

Y - REC

CONS. -

DEMAND

SUPPLY REC

SMP

LCOE BNEF (2015)

DUMMY

.

WinBugs .

10,000 , 1,000 .

.

.

RPS REC

(DEMAND-SUPPLY)

.

ARIMA, VAR

.

5 47

2.50% 97.50%

CONS. 254.8 1811 -1700 2183

DEMAND -0.04554 0.1305 -0.3043 0.2082

SUPPLY -0.04611 0.4529 -0.9476 0.832

SMP 23.57 927.3 -1822 1868

LCOE -2.506 999.6 -1945 1969

DUMMY 6.22 1112 -1955 1961

( REC)

3 .

. RPS

RPS , LCOE

.

.

, REC REC 5%

. REC ,

, . REC

REC REC

.

, REC .

. , REC

, 2014

. REC

REC . RPS

48

REC

. 2016

- REC

.

,

REC .

.

(Quartile)

. REC , , SMP, LCOE

.

[ 5-1] Quartiles ( REC)

5 49

(kernel density)

. , 0

.

[ 5-2] Kernel density ( REC)

. .

. .

50

2.50% 97.50% Cons. 298.7 994.4 -1666 2238

Demand 8.78E-04 0.008807 -0.0166 0.01818Supply 0.3151 0.8045 -1.272 1.899SMP 36.35 801.9 -1548 1631

LCOE -145.1 997.2 -2104 1812Dummy 34.93 1004 -1927 1993

( REC)

Quartile

. 0 REC

.

[ 5-3] Quartiles ( REC)

5 51

(kernel density)

. 0

.

[ 5-4] Kernel density ( REC)

.

.

. ,

REC

.

52

P-

Cons. -240667.809 890200.664 0.7886

Demand 0.035 0.177 0.8433

Supply -0.040 0.068 0.5532

SMP 307.964 349.864 0.3853

LCOE 1255.689 2938.594 0.6720

Dummy* 49431.854 10727.882 0.0001

( REC)

*: 5%

P-

Cons. 471192.364 445913.372 0.2983

Demand* 0.006 0.003 0.0227

Supply 0.103 0.221 0.6438

SMP 22.102 375.732 0.9534

LCOE -3189.912 3064.873 0.3055

Dummy* 102116.722 16425.304 0.0000

( REC)

*: 5%

2. (Levelized cost of energy)

.

.

10 . 0.3223

, 1% .

() 20% .

5 53

2 20%

.

p-value

** 0.3223 0.0040 3.52E-44

0.9942

**: 1%

(, , EPC ) Bayus

.

,

. 1%

.

p-value

** 12.2203 0.5938 0.0000

** 0.1207 0.0179 0.0003

0.8661

Bayus

**: 1%

Bayus

,

. 2016 686.6/W

54

2024 500.5/W 27% .

2016 1,121.7/W 2024 617/W 45%

. 2016 1,808.4/W

2024 1,117.9/W 38% .

Year (A) (B) (A+B)

2016 686.6 1,121.7 1,808.4 2017 668.8 1,037.3 1,706.1 2018 649.2 973.9 1,623.1 2019 625.5 929.0 1,554.4 2020 595.8 900.6 1,496.4 2021 567.9 836.8 1,404.7 2022 542.8 786.0 1,328.7 2023 520.3 696.6 1,216.9 2024 500.5 617.4 1,117.9

(: /W)

LCOE . LCOE

2016 167.06/kWh 2024 106.39/kWh

. 36% ,

5.8% . LCOE

95.26/kWh 463.71/kWh .

157.15/kWh (BNEF, 2015).

LCOE .

,

5 55

LCOE .

[ 5-5] LCOE

.

0.1221 , 1%

. () 8.11

. 2 8.11%

. 20%

,

.

p-value

** 0.1221 0.0056 9.26E-21

0.9368

**: 1%

56

1,128/W .

. 2016 1,129.9/W 2024

1,054.8/W 6.6% .

2016 2,257.8/W 2024 2,182.7/W 3.3%

.

.

Year

2016 1,129.9 2,257.8 2017 1,111.4 2,239.3 2018 1,099.7 2,227.7 2019 1,089.0 2,216.9 2020 1,079.0 2,206.9 2021 1,072.4 2,200.3 2022 1,067.2 2,195.1 2023 1,061.2 2,189.2 2024 1,054.8 2,182.7

(: /W)

LCOE . LCOE 2016 140.60

/kWh 2024 136.83/kWh

. 2.7% .

LCOE 49.03/kWh 307.45

/kWh . 90.16/kWh

5 57

(BNEF, 2015). LCOE

50% .

,

.

LCOE

.

[ 5-6] LCOE

. SMP

7 ,

2016~2024 SMP .6) 7

6) SUDP M-Core( ) .

58

2

. (S1) 7 , ,

,

. (S2)

, ,

.

2014 2027 , 6 7

2,276MW 2,236MW

.

7 2020

2 .

(6) (7) (6) (7) 2014 24,516 20,716 3,800 25,149 25,149 02015 24,516 21,716 2,800 27,169 26,169 1,0002016 24,516 23,116 1,400 34,929 33,873 1,0562017 25,916 25,329 587 35,929 34,873 1,0562018 27,316 26,729 587 38,299 34,873 3,4262019 28,716 26,729 1,987 43,669 35,873 7,7962020 30,116 26,729 3,387 43,669 36,913 6,7562021 31,516 28,129 3,387 44,669 42,713 1,9562022 32,916 30,929 1,987 44,669 43,293 1,3762023 34,416 32,329 2,087 44,669 43,293 1,3762024 35,916 32,329 3,587 44,669 43,293 1,376 2,327 2.470

6 vs. 7 ( )

(: MW)

:

5 59

RPS 2012 64.7%

2013 67.2%, 2014 78.1% .

, 7

RPS 78.1% .

2012 2013 2014

(REC) 6,420 10,897 12,905

(REC) 4,154 7,324 10,078

(%) 64.7 67.2 78.1

RPS

:

SMP .

S1 7

S2

2 (#5, 6, 7, 8, #3, 4, #1, 2)

2 (#1, 2, NSP#1, 2, #1, G#1, 2, #1, 2)

RPS 78.1%

7

SMP 2016 90.6/kWh 2024 76.7/kWh

15.3% . 7

60

( ) SMP

.

SMP 2016 91.5/kWh 2024 84.0/kWh

8.2%

.

.

S1 S2

2016 90.6 91.5

2017 88.1 88.9

2018 86.9 88.3

2019 87.7 90.1

2020 88.3 91.4

2021 85.3 91.1

2022 79.7 91.9

2023 77.7 89.7

2024 76.7 84.0

(: /kWh)

. REC

REC .

5 61

SMP - REC

REC .

SMP

. SMP 7

100% , 2

. REC

15%, 20%, 25% .

. REC

REC .

SMP ( 100% ) SMP ( 2 )

(15%)

(20%)

(25%)

(15%)

(20%)

(25%)

1 2 3 4 5 6

REC .

LCOE SMP

. REC

REC

. REC

. REC

1.5 . REC

, , ,

62

. REC

. REC

/ .

(+)

(REC) 921 887 963 159 3,681 6,537 13,390

7% 7% 7% 1% 27% 49% 100%

(/REC) 91.58 70.70 107.00 107.00 49.06 49.06 57.38

() 86.08

() 70.7

2016 REC

REC

[ 5-7] [ 5-12] . 1

7 , SMP

, 15% .

1 REC 2016 71/REC

2024 54/REC .

LCOE REC

. 2021 51/REC

SMP LCOE

REC .

5 63

REC 2016 86/REC 2024 82/REC

.

REC

. REC

REC

REC .

.

REC

.

[ 5-7] REC ( 1)

:

64

2 7 ,

SMP , 5% 20%

. 2 REC

2016 71/REC 2024 54/REC 1

. SMP

REC , REC

. REC /

REC SMP

.

[ 5-8] REC ( 2)

:

5 65

REC 2016 94/REC 2024 83/REC

1 .

, REC

.

,

. 1 REC

REC REC

.

[ 5-9] REC ( 3)

:

66

3 7 ,

10% 25% . 3

REC 2 3

. REC 2016 102/REC 2024 84/REC

1 2 .

.

4 7 2

SMP , 15%

. 4 REC

2016 71/REC 2024 49/REC 1

[ 5-10] REC ( 4)

:

5 67

.

SMP 1

, REC . REC

SMP , SMP

REC , SMP REC

.

REC 2016 89/REC 2024 75

/REC 1 .

5 7 2

, 5% 20%

. 5 REC 2016 71

/REC 2024 49/REC 4

. 2 SMP REC

.

REC 2016 94/REC 2024 75/REC

4 . SMP

, REC . 2

,

.

68

[ 5-11] REC ( 5)

:

6 7 2

, 10% 25%

. 6 5 REC

2016 71/REC 2024 49/REC

.

REC 2016 102/REC 2024 76/REC

5 .

REC .

5 69

[ 5-12] REC ( 6)

:

6 71

6

RPS

SMP REC .

REC SMP

. 2014 12 SMP

143.7/kW 2015 9 SMP 85.7/kW

40% .

. LCOE=SMP+REC

(LCOE)

SMP REC .

SMP REC

.

.

RPS REC

, .

1.

.

.

REC

72

RPS .

.

REC

. REC RPS

REC

.

RPS .

REC

. RPS

.

(Cap) REC

.

16

RPS

. REC

REC .

-

RPS .

REC .

, ,

. REC ,

REC .

6 73

(CA) (NY) (NJ)

CPUC PSC ORER NFPAS

RAM - - - -

RFP RFP

(Flett Exchange)

(LGC Market)

(e-ROC)

20MW - - -

1,000MW (SBC)

- - -

(, )

(PG&E, SEC, SD&E)

NYSERDA(

)

(, )

()

Pay as Bid Pay as Bid

()

RenewableAttribute

SREC( )

LGC ROC

Bundle Unbundle Unbundle Unbundle Unbundle

10 10 2() 2015 2037

RPS

: , 2011

74

6( )

6(GS Power, , , SK ENS, MPC

, ) .

.

[ 6-1]

: , (2005)

() (CP: Capacity Payment,

) (SMP, System Margun Price, ) .

(Capability)

.

( )

(SMP) . SMP

SMP

. SMP .

50 () . 2010 9

6 75

,

16 7)

(Vertically Integrated

Utilities) .

, (Deregulated)

ISO-NE (NH) .

.

.

[ 6-2]

: Energy in New Hanpshire

1998 , 2001

7) , , , , , , , , , , , , , , , D.C 16

76

(Black-out) .

IOU(Investor Owned Utility,

) 3 60%

. ISO

CAISO (Deregulation) ,

. REC

ACP(Alternative Compliance Payment)8) RPS

(Climate Policy Initiative, 2012).

[ 6-3]

: Electric Load Serving Entities in CA, CEC ENERGY ALMANAC

. IOU 2

8) ACP RPS

6 77

60%

. (Vertically Integrated)

.

(Retail Price Cap)

REC (Climate

Policy Initiative, 2012).

[ 6-4]

: EIA(2010.9 )

1990

.

.

(, 2013).

38 , 9 , 8

78

.

[ 6-5]

: KOTRA, 2013

1990

,

. 4

, 2/3

. , VICTORIA

SOUTH AUSTRALIA

AGL ENERGY, TRUENERGY . , QUEENSLAND STANWELL

CS ENERGY, 66% . TASMANIA

HYDRO TASAMANIA .(AER, 2014)

6 79

2. REC

.

1)

CEC(California Energy Committee) MPR(Marginal Price Referent)

, RPS REC ,

9) . MPR

CCGT( ) ,

NPV(Net Present Value)

. CEC MPR

, O&M ,

, .10)

(EPA) MPR

2010 RES

.

9) REC (, 2013)

10) (SB 1078, 107) MPR 500MW CCGT(Combined Cycle Gas Turbine) ,

80

[ 6-6] RES ()

: EPA, 2010

RPS

, MPR TOD factor, Location, Lost Salary, Lost Tax

Rev. REC . TOD factor

Location , Lost Salary

, Lost Tax (Tax Revenue) .

CEC MPR

RPS

.

6 81

2011 Market Price Referent() -

10 15 20 25

2012 0.07688 0.08352 0.08956 0.09274

2013 0.08103 0.08755 0.09375 0.09695

2014 0.08454 0.09151 0.09756 0.10081

2015 0.08804 0.09520 0.10132 0.10464

2016 0.09156 0.09883 0.10509 0.10848

2017 0.09488 0.10223 0.10859 0.11206

2018 0.09831 0.10570 0.11218 0.11572

2019 0.10186 0.10928 0.11587 0.11946

2020 0.10550 0.11296 0.11965 0.12326

2021 0.10916 0.11675 0.12354 0.12712

2022 0.11299 0.12067 0.12752 0.13105

2023 0.11691 0.12469 0.13160 0.13504

MPR: RPS (CEC)

: CA PUC Energy Division, Resolution E-4442(2011)

REC RPS

1REC USD 50 (Cap)

(Climate Policy Initiative, 2012). REC

(Polsinelli, 2013b).

82

2)

RPS RPS

REC Rate Cap

. Rate Cap REC

. , ,

(Polsinelli, 2013b).

[ 6-7] Rate Cap 2% Rule

: RM Group, 2013

Rate Cap 2% Rule .

RES RES 2%

, . REC

Cap RES

6 83

RES .

3) REC

: (Contract Price Cap) RPS REC Bundled

REC (Cap) .

15%

.

: RPS (Auction) RPS RPS

. RPS

REC .

: (Sales Revenue Cap) RPS .

REC

REC .

, , , (Climate Policy

Initiative, 2012).

84

.

1) FIT rate

FIT

, RPS(RO, Renewable Obligation) FIT

. FIT

. Ofgem(The office of gas and electricity markets)

(PV) (Non-PV) FIT rate

, FIT

. FIT

.(Ofgem e-serve, 2014)

[ 6-8] PV FIT

: FIT Quarterly Statistics Ofgem data : 50kW 100kW PV, middle rate

6 85

, lower rate

, middle rate

FIT 25 .

lower rate middle rate higher rate

. stand alone

. FIT

.

2) FIT with CfD(Contract for Difference)

2012 FIT (with CfD) . FIT

with CfD

.

.

.

2012 11.3% 2020 30%

(, 2013).

FIT RO

. RO 2017 FIT

with CfD 2017

.

86

FIT with CfD (Strike price)

(Reference Price) .

.

,

.

,

.

11),

(

, 2013). , ,

2019 (, 2013).

[ 6-8] FIT-CfD

: , , 2013

11) 2013 (/MWh): : 155, : 100, : 125, : 95, : 105

6 87

.

1,000 (REC)

(large scale generation certificates)

1 5

EO

REC ,

AUD $0.05 AUD $50.00 ( 1,000 )

3

15

9:00 am~4:00 pm

Deliverable

Designated Registry , (CER)

REC

:

ASX REC REC

. REC

,

Exchange For Physical(EFP)

88

. ,

, ASX , , ,

.

,

(ASX, 2011).

REC

. REC

, REC

. REC

((Large-Scale

Generation Certificates, LGC) ,

(Small-Scale Technology Certificates, STC) .

REC , 15

REC .

3 ,

. , REC

delivery ASX

, REC delivery ASX .

REC .(ASX, 2011).

6 89

[ 6-10] REC

: ASX, 2011

90

3. REC

. REC

, RPS

. RPS

REC .

REC .

RPS REC

.

REC .

SMP

.

.

SMP REC

(Risk Premium) .

RPS REC

.

RPS MPR

, RPS (Cap)

. FIT FIT

. REC REC

.

6 91

. REC

1) RPS

() RPS

, RPS .

RPS

REC .

RPS .

.

RPS

REC .

2) SMP+REC

REC 12

REC

. SMP

. SMP+REC

. SMP+REC

SMP

.

92

[ 6-11] SMP+REC

3) REC() + SMP()

REC

. PPA

SMP .

[ 6-12] REC + SMP

SMP

.

RPS MPR

6 93

MPR .

RPS SMP

RPS

.

4)

SMP+REC

,

SMP+REC .

SMP REC

. 2

SMP REC

.12)

. [ 6-13]

: The Transfer of the Weather risk faced with the challenges of the future, SCOR Focus publication, 2012

12) SCOR Focus publication(2012) The Transfer of the Weather risk faced with the challenges of the future

94

REC (Futures Market)

REC .

()

. (Forward) (Clearing

House) RPS REC

.

[ 6-14] (Futures)

. (

REC) .

6 95

SMP

REC

.

.

REC

.

FIT

RPS

.

FIT .

7 97

7

2012 RPS REC

REC .

REC

REC . RPS

RPS , REC

. REC

REC

. RPS

REC .

REC REC

REC .

REC

. REC

REC

.

RPS 80

.

REC 2 .

98

, . REC

, ,

, , .

.

. 3 . ,

, RPS .

, REC REC 5%

REC REC . ,

RPS REC .

2014 / REC

, 2016 -

REC

.

REC

.

(Levelized cost

of energy) (experience curve) .

SMP REC,

LCOE .

RPS , REC

REC . RPS

1.5 ,

REC .

Bayus . ()

7 99

() , Bayus

.

, 2016 686.6/W 2024

500.5/W, 1,121.7/W 2024 617

/W .

2016 1,808.4/W 2024 1,117.9/W 38%

. LCOE

2016 167.06/kWh 2024 106.39/kWh 36%

.

2016 1,129.9/W 2024 1,054.8

/W 6.6% .

1,128/W

. 2016 2,257.8

/W 2024 2,182.7/W 3.3% .

LCOE 2016 140.60/kWh 2024 136.83

/kWh .

SMP 7

2 .

7 , , ,

. (S2) ,

, .

SMP 2016 91/kWh 2024 76.7~84/kWh

.

SMP .

100

LCOE SMP REC

. SMP

6 . SMP 7

100% ,

2 . SMP

REC 15%,

20%, 25% . 7

, REC 2016 71

/REC~102/REC 2024 54/REC~84/REC

. LCOE

REC .

REC REC

REC .

,

REC

.

7 2

SMP . REC

2016 71/REC~102/REC 2024 49/REC~76

/REC .

SMP 1

, REC .

REC

, REC .

7 101

RPS

. () RPS ,

RPS .

RPS

REC .

SMP+REC

. REC 12

. REC

SMP

.

SMP+REC

.

REC , SMP

PPA

.

,

SMP+REC

. SMP REC

. 2 SMP REC

.

REC

.

. SMP

.

102

FIT .

.

REC , ,

,

. ,

. REC

RPS .

REC

.

103

, , 2011, RPS ,

22 1, pp.143-165

, 2004, ,

, , , , , , 2012, SUDP

SMP , 2012

, pp.424-425.

, 2013, , ,

pp.43-52.

, 2014a, 2014

, 2014b, 7

, 2014, ,

, 2013, .

13-13.

, 2013,

, ,

, 2013

.

, 2002, .

, 2015, LNG ,

104

, 2014, RPS , Current

Photovoltaic Research 2 4, pp.182-188.

, 2010, (RPS)

, 59(12): pp.22-27

, 2014, M-Core()

, 2007,

__________, 2010, (RPS)

, 2010.

__________, 2011, RPS REC , 2011.

__________, 2013, CAISO, .

, 2005,

__________, 2010, ,

, 2009,

__________, 2012,

, 2012, ,

,

, 2013,

,

, 2014, Eco-System: REC

, 23(4): pp.1-8

, 2013, 6

, 2012, .

, 2015, -

, 2014, 14

105

, pp.9-10

, 2005,

, 2013,

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109

1. REC

2016 70.7 86.1

2017 70.0 85.5

2018 63.9 84.6

2019 57.1 83.4

2020 51.4 82.2

2021 50.5 81.3

2022 51.7 81.9

2023 52.7 82.1

2024 53.7 82.1

REC ( 1)

(: /REC)

:

110

2016 70.7 94.1

2017 70.0 89.8

2018 63.9 88.1

2019 57.1 86.6

2020 51.4 85.1

2021 50.4 83.6

2022 51.6 83.6

2023 52.7 83.3

2024 53.7 82.9

REC ( 2)

(: /REC)

:

2016 70.7 102.1

2017 70.0 94.0

2018 63.9 91.5

2019 57.1 89.7

2020 51.4 87.9

2021 50.4 85.9

2022 51.5 85.4

2023 52.6 84.6

2024 53.6 83.7

REC ( 3)

(: /REC)

:

111

2016 70.7 89.0

2017 69.1 86.5

2018 62.5 85.0

2019 54.7 83.3

2020 49.2 81.6

2021 48.8 79.1

2022 48.3 76.7

2023 48.2 74.8

2024 48.8 74.6

REC ( 4)

(: /REC)

:

2016 70.7 94.1

2017 69.1 89.5

2018 62.5 87.4

2019 54.7 85.5

2020 49.2 83.5

2021 48.8 80.7

2022 48.2 77.9

2023 48.1 75.8

2024 48.8 75.0

REC ( 5)

(: /REC)

:

112

2016 70.7 102.1

2017 69.1 93.8

2018 62.5 90.9

2019 54.7 88.7

2020 49.1 86.4

2021 48.7 83.0

2022 48.2 79.7

2023 48.1 77.1

2024 48.7 75.8

REC ( 6)

(: /REC)

:

(PA) , , 2014.

Forecasting Demand for a Newly Introduced Product Using Reservation Price Data and Bayesian Updating, Technological Forecasting and Social Change, 2012, 79 (7), 1280-1291.

Diffusion of Renewable Energy Technologies in South Korea on Incorporating Their Competitive Interrelationships, Energy Policy, 69, 248-257

2015-12

(REC)

2015 12 30 2015 12 31

44543, 405-11 : (052)714-2114() : (052)422-2028

1992 12 7 7 (02)2273-1775

2015 ISBN 978-89-5504-549-9 93320

* .

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