For achieving high‐ penetration renewables!...Jun 30 Jul 02 Jul 04 Jul 06 0 5000 10000 15000...
Transcript of For achieving high‐ penetration renewables!...Jun 30 Jul 02 Jul 04 Jul 06 0 5000 10000 15000...
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Marc PerezClean Power Research
Inexpensive, Firm PV Without Conventional Backup:The Role of Supply Shaping Through Curtailment
This work is based upon work supported by:National Science Foundation GRF Grant No. DGE 1144155
DoE Sunshot Grant No. DE‐EE0007669Columbia University Center for Life Cycle Analysis
Clean Power Research
For achieving high‐penetration renewables!
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Why do we think we need backup to achieve high penetration?
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Jan Mar May Jul Sep Nov Jan
020
040
060
080
010
00
allMISO$datetime
min
ne_d
at.tp
i$G
loba
l
Hourly Interval
W/m
2Intraday Variability
Storage is considered to be the keystone to mitigate variability on all timescalesIt’s because wind + solar are intermittent and what we really need is firm power
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Jan Mar May Jul Sep Nov Jan
020
040
060
080
010
00
allMISO$datetime
min
ne_d
at.tp
i$G
loba
l
Hourly Interval
W/m
2
Fri Sat Sun Mon Tue Wed
020
040
060
080
010
00
allMISO$datetime[1:(24 * 5)]
min
ne_d
at.tp
i$G
loba
l[1:(2
4 * 5
)]Diurnal Variability + clouds
W/m
2
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Jan Mar May Jul Sep Nov Jan
24
68
dd$dates
dd$t
imes
erie
s
Daily Interval
kWh/m2
Interday variability: mesoscale weather
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Jan Mar May Jul Sep Nov Jan
8010
012
014
016
018
020
0
allMISO$datetime
pred
ict(l
oess
(unl
ist(t
s) ~
unl
ist(d
d), d
ata.
fram
e(m
m),
span
= 0
.5),
s
eq(1
, 12,
leng
th.o
ut =
leng
th(a
llMIS
O$d
atet
ime)
))
Monthly Interval
kWh/m2
Seasonal variability: oribital
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Jan Mar May Jul Sep Nov Jan
010
2030
4050
60
allMISO$datetime
prod
uctio
n.ta
rget
.TS/
1000
Say we want to produce baseload with PV
GW
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Jan Mar May Jul Sep Nov Jan
010
2030
4050
60
allMISO$datetime
prod
uctio
n.ta
rget
.TS/
1000
Possible on an energy basis: GW
∫ = ∫
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Jan Mar May Jul Sep Nov Jan
010
2030
4050
60
allMISO$datetime
prod
uctio
n.ta
rget
.TS/
1000
Possible on an energy basis: GW
∫ = ∫
Fri Sat Sun Mon Tue Wed
010
2030
4050
60
allMISO$datetime[1:(24 * 5)]
prod
uctio
n.ta
rget
.TS[
1:(2
4 * 5
)]/10
00But impossible due to diurnal intermittency…
GW
Night ☾
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Jan Mar May Jul Sep Nov Jan
010
2030
4050
60
allMISO$datetime
prod
uctio
n.ta
rget
.TS/
1000
Possible on an energy basis: GW
∫ = ∫
Fri Sat Sun Mon Tue Wed
010
2030
4050
60
allMISO$datetime[1:(24 * 5)]
prod
uctio
n.ta
rget
.TS[
1:(2
4 * 5
)]/10
00Possible with storage…
GW
Shortfall: discharge☾
Surplus: charge ☀
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Jan Mar May Jul Sep Nov Jan
020
0040
0060
0080
0010
000
1200
0
Time
Ener
gyBut how much? Storage State of charge for one year to produce baseload w/PV
Charge in the summer☀
❄ Discharge in the winter
GWh
1 year
Fri Sat Sun Mon Tue Wed
5200
5300
5400
5500
5600
TimeEn
ergy
1 week
Seasonal trend > diurnal trend on an energy basis
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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1 year of storage SoC 1 week of dispatch
GW GWh
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Oversizing Effect on Aggregate LCOE
oversizing factor
LCO
E (c
/kW
h)
1 x 1.2 x 1.7 x 2.5 x 5 x 100 x
01
23
45
67
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PV Load
Storage
PV>Load?
Call on Storage
N
Storage Full ?
Y
Curtail
Y
NCharge
A
Simplified PV/Storage Dispatch Schema
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Optimally oversized PVOptimally oversized PV
A B
Physical Data High resolution (spatial/temporal) time‐synchronous SolarAnywhere® Resource Data: T, Irradiance Firm, Guaranteed Production Profile (time‐synchronous load) NCLD Landcover Tech. specs for PV, storage
Development Data High/Low Technological Development Residential/Community/Commercial/Utility Dev.
• CapEx for PV, Storage ($/kW | $/kWh) • OpEx for PV, Storage ($/kWh | $/kWh)• WACC/discount rate• Spatial Allocation
Modeling Inputs
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Jun 30 Jul 02 Jul 04 Jul 06
050
0010
000
1500
0Hourly Production Target (zoom)
date
MN LoadProduction TargetNormalized Hybrid RE production
0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
p: hourly w: 0 g: 0 s: mid.term_high_utility.led dr: 3
Curtailment %
PV + Storage Meeting 100% of Hourly Load in MN, Utility‐scale‐led, Low Technological Development in 2050, WACC of 3% single point.
PV + Storage Meeting 100% of Hourly Load in MN, Utility‐scale‐led, Low Technological Development in 2050, WACC of 3% single point.
MN LoadNormalized PVMN LoadNormalized PV
MWh
MWh
LCOE ($/kWh)
LCOE ($/kWh)
Oversizing factorOversizing factor
1x 1.2x 1.7x 2.5x 5x 100x1x 1.2x 1.7x 2.5x 5x 100x
18.7 c/kWh18.7 c/kWh
Non‐official results
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PV Spread more evenly (yet stillAvoiding sensitive landcover)PV Allocated just in Minneapolis
Geographic Dispersion Has an Effect on Cost
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Jun 30 Jul 02 Jul 04 Jul 06
050
0010
000
1500
0Hourly Production Target (zoom)
date
MN LoadProduction TargetNormalized Hybrid RE production
0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
p: hourly w: 0 g: 0 s: mid.term_high_utility.led dr: 3
Curtailment %
PV + Storage Meeting 100% of Hourly Load in MN, Utility‐scale‐led, Low Technological Development in 2050, WACC of 3% distributed.
PV + Storage Meeting 100% of Hourly Load in MN, Utility‐scale‐led, Low Technological Development in 2050, WACC of 3% distributed.
12.6 c/kWh12.6 c/kWh
MN LoadNormalized PVMN LoadNormalized PV
MWh
MWh
LCOE ($/kWh)
LCOE ($/kWh)
Oversizing factorOversizing factor
1x 1.2x 1.7x 2.5x 5x 100x1x 1.2x 1.7x 2.5x 5x 100x
Non‐official results
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PV $/WBattery $/kWh
…As the degree of technological development
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Jun 30 Jul 02 Jul 04 Jul 06
050
0010
000
1500
0Hourly Production Target (zoom)
date
MN LoadProduction TargetNormalized Hybrid RE production
0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
p: hourly w: 0 g: 0 s: long.term_high_utility.led dr: 3
Curtailment %
PV + Storage Meeting 100% of Hourly Load in MN, Utility‐scale‐led, High Technological Development in 2050, WACC of 3%
PV + Storage Meeting 100% of Hourly Load in MN, Utility‐scale‐led, High Technological Development in 2050, WACC of 3%
7.04 c/kWh7.04 c/kWh
MN LoadNormalized PVMN LoadNormalized PV
MWh
MWh
LCOE ($/kWh)
LCOE ($/kWh)
Oversizing factorOversizing factor
1x 1.2x 1.7x 2.5x 5x 100x1x 1.2x 1.7x 2.5x 5x 100x
Non‐official results
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PV Load
Storage
PV,wind>Load
Call on Storage
N
Storage Full
Y
Curtail
Y
NCharge
A
Simplified PV/Storage Dispatch Schema
Wind
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Jun 30 Jul 02 Jul 04 Jul 06
050
0010
000
1500
0Hourly Production Target (zoom)
date
MN LoadProduction TargetNormalized Hybrid RE production
0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
: hourly w: 100 g: 0 s: long.term_high_utility.led dr: 3
Curtailment %
Wind + Storage Meeting 100% of Hourly Load in MN, Utility‐scale‐led, High Technological Development in 2050, WACC of 3%
Wind + Storage Meeting 100% of Hourly Load in MN, Utility‐scale‐led, High Technological Development in 2050, WACC of 3%
7.02 c/kWh7.02 c/kWh
MN LoadNormalized WindMN LoadNormalized Wind
MWh
MWh
LCOE ($/kWh)
LCOE ($/kWh)
Oversizing factorOversizing factor
1x 1.2x 1.7x 2.5x 5x 100x1x 1.2x 1.7x 2.5x 5x 100x
Non‐official results
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Costs can be reduced further by blending the wind and solar resources
…where anticorrelated
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Jun 30 Jul 02 Jul 04 Jul 06
050
0010
000
1500
0Hourly Production Target (zoom)
date
MN LoadProduction TargetNormalized Hybrid RE production
0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
p: hourly w: 62 g: 0 s: long.term_high_utility.led dr: 3
Curtailment %
Optimal Wind/PV + Storage Meeting 100% of Hourly Load in MN, Utility‐scale‐led, High Technological Development in 2050, WACC of 3%
Optimal Wind/PV + Storage Meeting 100% of Hourly Load in MN, Utility‐scale‐led, High Technological Development in 2050, WACC of 3%
5.3 c/kWh5.3
c/kWh
MN LoadNormalized RenewablesMN LoadNormalized Renewables
MWh
MWh
LCOE ($/kWh)
LCOE ($/kWh)
Oversizing factorOversizing factor
1x 1.2x 1.7x 2.5x 5x 100x1x 1.2x 1.7x 2.5x 5x 100x
Non‐official results
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Adding a small amount of conventional gas e.g. from stranded assets
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0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
p: hourly w: 62 g: 5 s: long.term_high_utility.led dr: 3
Curtailment %
Jun 30 Jul 02 Jul 04 Jul 06
050
0010
000
1500
0Hourly Production Target (zoom)
date
MN LoadProduction TargetNormalized Hybrid RE production
Optimal Wind/PV + Storage Meeting 95% Hourly Load in MN, 5% met by gas Utility‐scale‐led, High Technological Development in 2050, WACC of 3%
Optimal Wind/PV + Storage Meeting 95% Hourly Load in MN, 5% met by gas Utility‐scale‐led, High Technological Development in 2050, WACC of 3%
3.6 c/kWh3.6
c/kWh
MN LoadNormalized RenewablesMN LoadNormalized Renewables
MWh
MWh
LCOE ($/kWh)
LCOE ($/kWh)
Oversizing factorOversizing factor
1x 1.2x 1.7x 2.5x 5x 100x1x 1.2x 1.7x 2.5x 5x 100x
Non‐official results
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0
4
8
10
12
Current MN Load+ 5 million EVsw/load shifting
One Day
EV Modeling in MN
DSM Can shift load to surplus hours, but unless V2G is only intraday
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0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
p: hourly w: 62 g: 5 s: long.term_high_utility.led dr: 3
Curtailment %
Jun 30 Jul 02 Jul 04 Jul 06
050
0010
000
1500
0Hourly Production Target (zoom)
date
MN LoadProduction TargetNormalized Hybrid RE production
Optimal Wind/PV + Storage Meeting 95% Hourly Load in MN, 5% met by gas Utility‐scale‐led, High Technological Development in 2050, WACC of 3% +EV electrification & load‐shifting
Optimal Wind/PV + Storage Meeting 95% Hourly Load in MN, 5% met by gas Utility‐scale‐led, High Technological Development in 2050, WACC of 3% +EV electrification & load‐shifting
3.4 c/kWh3.4
c/kWh
MN LoadNormalized RenewablesMN LoadNormalized Renewables
MWh
MWh
LCOE ($/kWh)
LCOE ($/kWh)
Oversizing factorOversizing factor
1x 1.2x 1.7x 2.5x 5x 100x1x 1.2x 1.7x 2.5x 5x 100x
Non‐official results
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Key Takeaways
55% PV, 40% wind, 5% gas
3.6 c/kWh
• Supply shaping via oversizing + curtailment has significant value for minimizing cost at high penetration
• Major cost reduction by optimizing relative penetration of PV + wind• Major cost reduction by including minor amounts of gas backup at key
times to minimize need for storage
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Thanks!
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Marc PerezClean Power Research
Inexpensive, Firm PV Without Conventional Backup:The Role of Supply Shaping Through Curtailment
This work is based upon work supported by:National Science Foundation GRF Grant No. DGE 1144155
DoE Sunshot Grant No. DE‐EE0007669Columbia University Center for Life Cycle Analysis
Clean Power Research
For achieving high‐penetration renewables!