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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,
(I),
, 2013,
, p.7
, , , , , 2012,
(RPS) REC ,
, 2012 , pp.19-20
Advisory Committee, 2013, Overview of Colorados Renewable
Energy Standard(CRS 40-2-124).
AER, 2014, State of the Energy Market.
ASX, 2011, Renewable Energy Certificate (REC) Futures,
.
Australian Gov, 2014, Renewable Energy Target Review, Climate
Change Authority
Bhandari, R. and Stadler, I. 2009, Grid Parity Analysis of Solar
Photovoltaic Systems in Germany Using Experience Curve,
Solar Energy 83: pp.1634-1644
BNEF, 2015, H1 2015 Global levelized cost of electricity update
Cho, Y., Koo, Y., 2012. Investigation of the effect of secondary
market on the diffusion of innovation. Technological
Forecasting and Social Change, 79, pp.1362-1371
Climate Policy Initiative, 2012, Limiting the Cost of Renewables:
Lesson for California; CPI Working Paper
106
Christoph H., Thomas W., 2011, Economic functioning and
politically pragmatic justification of tradable green certificates
in Poland, Environmental Economics and Policy Studies 13(2):
pp.157-175
Dawei Z., Daniel R. B., Navigant, Inc., Solar renewable energy
credits(SRECs) price forecast
DOER. 2011. Renewable Energy Portfolio Standard Guideline
_____. 2014. Massachusetts RPS & APS Annual Compliance Report
for 2012
DORA PUC. 2013. What does the Renewable Energy Standard(RES)
require?
DSIRE, 2015, Renewable Portfolio Standard Policies.
Eirik S. Amundsen, Fridrik M. Baldursson and Jorgen B. M., 2006,
Price Volatility and Banking in Green Certificate Markets,
Environmental and Resource Economics 35(4): pp.259-287
Farrokh Albuyeh et al. Implementation of the California
Independent System Operator. IEEE: 2
Gov. Energy Office. 2010. Colorados 30% Renewable Energy
Standard: Policy Design and New Markets
IEA, 2000, Experience Curves for Energy Technology Policy
____, 2014, World Energy Outlook
____, 2015, Medium-Term Market Report 2015
Jacob L., 2003, Financial risks for green electricity investors,
Denmark Energy Policy 31(1): pp.21-32
Jeff Bryan et al., Estimating the Pricce of ROCs, 2013, Stirling
107
Economics Discussion Paper, University of Stirling, Stirling
Management School
KOTRA, 2014a 1
_____, 2014b,
Lee, J., Cho, Y., Lee, J.-D., Lee, C.-Y., 2006. Forecasting future
demand for large-screen television sets using conjoint analysis
with diffusion model. Technological Forecasting and Social
Change 73: pp.362-376.
Michael C., Javad K., and W.B. Piwell., 2015, Smart-srec: A
Stochastic Model of the New Jersey solar Renewable Energy
Certificate market, Journal of Environmental Economics and
Management 73: pp.13-31
Navigant Consulting, 2010. 2010 Colorado Utility Report: 4
Nemet, G. 2006, Beyond the learnig curve: Factors influencing cost
reductions in photovoltaics, Energy Policy 34: pp.3218-3232
Ofgem e-serve 2014, Feed-in-Tariff: Annual Report(2013-14)
Polsinelli Law Firm. 2013a. All RECs are mot created equal:
Bundling and Geographic Sourcing; Renewable Energy Las
Insider
___________________. 2013b. All RECs are mot created equal:
Rate Caps and Shelf-Life, Renewable Energy Law Insider
REN21, 2014, Renewables 2015 Global Status Report: p.64.
______, 2015, Renewables 2015 Global Status Report: p.9
RM Group, LLC. 2013. Analysis of the Rate Impact of Colorados
108
Renewable Energy Standard
Scor. 2012. The transfer of weather risk faced with the challenges
of the future. Technical Newsletter pp.1-8SEIA, RPS Solar Carve Out Colorado
http://www.cpuc.ca.gov/PUC/energy/Renewables/mpr
http://www.shaktifoundation.in
http://www.ferc.gov/industries/electric/indus-act/rto.asp
http://www.energyalmanac.ca.gov/electricity
http://www.energy.ca.gov
http://www.gov.uk/government/organisations/department-of-energy-clima
te-change
http://www.asx.com.au/products/energy-derivatives/renewable-energy-cer
tificates.htm
http://www.nortonrosefulbright.com/knowledge/publications/66177/europ
ean-renewable-energy-incentive-guide-italy(Norton Rosefulbright
)
http://rec.kpx.info/index( )
http://markets.flettexchange.com/new-jersey-class-i-rec/(
flettexchange )
http://www.knrec.or.kr( )
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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