wind and solar power - university blog servicesites.utexas.edu/energyinstitute/education/ut... ·...
TRANSCRIPT
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What the Characteristics of Wind and Solar Electric Power Production Mean for Their Future
Jay Apt
Tepper School of Business and Department of Engineering & Public Policy
Carnegie Mellon University
March 1, 2012
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First, some background
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Why de-carbonize electric power?
Source: U.S. EIA 2011
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Why de-carbonize electric power?
Source: U.S. EIA 2011
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Source: U.S. EIA 2011
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45% Demand Growth by 2030?
(or more, with plug-in hybrid electric vehicles)
Source: U.S. EIA 2011
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30% low-CO2 in 2010
Source: U.S. EIA 2011
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Source: U.S. EIA 2011
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What percent of US electricity is now generated by renewables?
10.3 %
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Source: U.S. EIA 2011
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Source: U.S. EIA 2011
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Operating Wind Farms February 22, 2012 Wind farms > 5 MW
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Source: U.S. EIA 2011
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Land use can be benign
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Or, Not so Benign
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Occasionally, the failures are dramatic
Vermont is one of the few US states that requires owners to put aside money
for decommissioning old wind farms.
Source: http://www.youtube.com/watch?v=QL-cRuYAxg0
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Where the wind is
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Data are good!
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20 Source: ERCOT
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Source: ERCOT data processed by J. Apt
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Source: ERCOT data processed by J. Apt
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Monthly electricity demand and wind generation capacity factors in the Mid-Atlantic Highlands. Source: National Research Council
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Wind sometimes fails for many days
5 10 15 20 25 30Date in January 2009
250
500
750
1000
1250
1500
WM
BPA Balancing Authority Total WindGeneration
Sum of ~1000 turbines
Source: BPA
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15 Days of 10-Second Time Resolution Data
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What is the character of the fluctuations?
What frequencies are present, and at what amplitudes?
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2.6 Days
30 Seconds
Fourier Transform to get the Power Spectrum
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2.6 Days
30 Seconds
Frequency - 5/3
Sensor Noise Floor
Turbine inertia (low-pass filter)
Log (Frequency)
Log
(kW
)
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Frequency -5/3
• Turbulent eddies in fluids were predicted in 1941 by Kolmogorov on theoretical grounds to have just such a spectrum.
• First confirmed in 1962 by Grant, Stewart and Moilliet in a tidal channel near Vancouver, over a factor of 100 in frequency.
• Later observed in the atmosphere in 1967 and 1973.
• Not white noise! Very important implications.
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We can learn some important things from the power spectrum
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10-7 10-6 10-5 10-4 10-310-4
10-3
10-2
10-1
100
101
102
103
Frequency (Hz)
Pow
er S
pect
ral D
ensi
ty
1 Wind Plant24 Hours
Smoothing by Adding Wind Farms
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10-7 10-6 10-5 10-4 10-310-4
10-3
10-2
10-1
100
101
102
103
Frequency (Hz)
Pow
er S
pect
ral D
ensi
ty
1 Wind Plant4 Wind Plants24 Hours
Smoothing by Adding Wind Farms
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10-7 10-6 10-5 10-4 10-310-4
10-3
10-2
10-1
100
101
102
103
Frequency (Hz)
Pow
er S
pect
ral D
ensi
ty
1 Wind Plant4 Wind Plants20 Wind Plants
24 Hours
Smoothing by Adding Wind Farms
Source: Katzenstein, W., E. Fertig, and J. Apt, The Variability of Interconnected Wind Plants. Energy Policy, 2010. 38(8): 4400-4410.
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Two implications from this
1. When we talk about smoothing, the time scale is very important: 12 hours has much less smoothing than 1 hour.
2. The point of diminishing returns from connecting wind plants together is quickly reached.
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10-7 10-6 10-5 10-4 10-310-4
10-3
10-2
10-1
100
101
102
103
Frequency (Hz)
Pow
er S
pect
ral D
ensi
ty
1 Wind Plant4 Wind Plants20 Wind Plants
24 Hours
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Smoothing by adding wind farms in ERCOT … has diminishing returns
Source: Katzenstein, W., E. Fertig, and J. Apt, The Variability of Interconnected Wind Plants. Energy Policy, 2010. 38(8): 4400-4410.
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Larger areas
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BPA
CAISO
ERCOT
MISO
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2009 PSDs for all 4 regions
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Does interconnecting regions provide additional smoothing?
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Less Smoothing
More Smoothing
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Correlation coefficients 2009 and (2008)
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Interconnection does reduce hour-to-hour step changes
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Estimating the Cost of Wind Power Variability
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Time Scale
10 Minutes – 1 Hour > 1 Hour < 10 Minutes
Forecast Error
Load Following Regulation
Variability Costs
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Load Following Component
Regulation
Component
Hourly
Ene
rgy
Compo
nent
Wind Energy
0 15 30 45 60 min
t
Each hour decompose wind energy into the following components
Up Energy
Down Energy
↓ Capacity
↑ Capacity
← qH
← qH
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qForecasted
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2008
0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.53
4
5
6
7
8
9
10
11
12
Capacity Factor
Varia
bilit
y C
ost (
$/M
Wh)
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2008 and 2009
0.2 0.25 0.3 0.35 0.4 0.45 0.53
4
5
6
7
8
9
10
11
12
Capacity Factor
Varia
bilit
y C
ost (
$/M
Wh)
20082009
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Hydroelectric Power has Droughts
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Does wind power have droughts?
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Wind Probably Does Too
Source: Katzenstein, W., E. Fertig, and J. Apt, The Variability of Interconnected Wind Plants.
Energy Policy, 2010. 38(8): 4400-4410.
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Capacity Factor
The percent of nameplate energy (MWh) that is actually produced during a year.
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2008 29.2%
2009 24.4%
2010 30.3%
2011 33.7%
ERCOT’s Wind Capacity Factor Varies Yearly
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Operating Solar PV February 22, 2012 Units > 5 MW
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Solar
• The Sun deposits on US land 4,000 times the US net electricity generation
• At 7% efficiency, solar cells (not including packaging) would cover 0.5% of US land area, as compared to 27% cropland.
• Capacity factor: 19% in Arizona, 14% in Ohio, 11% for the PV on the DOE HQ in DC, 15% for USA produced solar as a whole in 2009.
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Solar Photovoltaic Unsubsidized cost is ~ 25 cents per kWh, 4 times the cost of electricity produced by a conventional coal-fired power plant. (32 in PA)
• Price of solar cells has not been decreasing much.
• Solar cells make up only 50-60% of the system price.
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Even though it is much more expensive, solar must be better, right?
Work with Dr. Aimee Curtright of CMU
(now at RAND)
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Solar
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0
1000
2000
3000
4000
1400000 1450000 1500000 1550000
Seconds since 00:00:00 Jan 1, 2007kW
0
1000
2000
3000
250 750 1250
kW
(b)
Comparison of Wind with Solar PV 4.6 MW TEP Solar Array (Arizona)
Minutes
kW
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Nameplate capacity Capacity Factor: 19%
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Comparison of wind and solar PV
Solar PV
Wind
Source: CEIC Working Paper CEIC-07-12, available at www.cmu.edu/electricity
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What about solar thermal?
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Source for images: Acconia-na.com
New work with Colleen Horin Lueken and Gilbert Cohen
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58 Source: CEIC Working Paper CEIC-11-04
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We have estimated variability costs, using CAISO prices
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One ERCOT wind farm
Solar thermal (NSO)
Solar PV (Springerville, AZ)
Solar PV (20 MW+ class)
Average capacity factor
34% 23% 19% 25%
Cost of variability per MWh
$4.3 $5.2 $11.0 $7.9
Variability cost as a percent of total cost of power (@$42/MWh)
10.2% 11.9% 26.5% 18.9%
Source: CEIC Working Paper CEIC-11-04
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NOx and CO2 Emissions from Gas Turbines Paired with Wind or Solar for Firm Power
Work with Warren Katzenstein
GE LM6000 sealegacy.com
Siemens-Westinghouse 501FD summitvineyardllc.com
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Approach
+
+
+
1
2
n
=
Firm Power Variable Power Compensating Power
Time
Power
Gas
Wind
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Model with GE LM6000
0 0.5 1 1.5 2 2.50
10
20
30
40
Time (hours)
Pow
er L
evel
s (M
W)
Wind + CT Operating Parameters
Ideal Fill PowerWind PowerActual Fill Power
0 0.5 1 1.5 2 2.5-20
-10
0
10
20
Time (hours)
Ram
p R
ate
(MW
/min
)
Ramp Rates
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Gas Turbine Data Obtained
• NOx emissions & heat rate – 1 minute resolution – 11 days (from 2 501FDs: 200
MW, DLN, SCR) – 145 days (from 3 LM6000s: 50
MW, steam NOx control) – Data:
• Gas flow • Load (MW) • NOx ppm and pounds • NOx ppm corrected to 15% O2 • O2 % • Heat rate (mBtu/hour)
– From operating gas turbines in a US power company
90 95 100 105 110 115 1200
5
10
15
20
25
30
35
40
45
50
Time (hours)
Pow
er (M
W)
Data Slice of Power Output of LM6000 Data Obtained
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Siemens-Westinghouse 501FD Regression Analysis
0 20 40 60 80 100 120 140 160 180 2000
0.5
1
1.5
2
2.5
3
3.5
Power (MW)
NO
x E
mitt
ed (
lb/m
in)
GE Document GER-3568G
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Data Set Power Plant Type
Data Set Resolution
Data Set Length
Eastern Wind 1 second 240 hours
Northern Great Plains Wind 10 second 15 hours
Central Great Plains Wind 10 second 83 hours
Southern Great Plains Wind 10 second 222 hours
Southwest Solar 1 minute 732 days
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Results
•Penetration P of renewables from 0 to 100% •Emissions factor (kg of CO2 or NOx per MWh) •Expected reductions vs. our model's predictions: If the actual system emissions are Mgas+renewable then the fraction of expected emissions reductions that are achieved is (Mgas - Mgas+renewable) / (Mgas * P)
Emissions Factor
Penetration
Expected
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Emissions Factors LM6000 Steam, no SCR
501FD DLN, SCR
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.05
0.1
0.15
0.2
0.25
0.3
0.35
α (Penetration Factor)
CO
2 Em
issi
ons
(tonn
es/M
Wh)
Expected
Predicted
(a) LM6000
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
α (Penetration Factor)
NO
x Em
issi
ons
(kg/
MW
h)
Expected
Predicted
(b) LM6000
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.05
0.1
0.15
0.2
0.25
0.3
0.35
α (Penetration Factor)
CO
2 Em
issi
ons
(tonn
es/M
Wh)
Expected
Predicted
(c) 501FD
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
α (Penetration Factor)
NO
x Em
issi
ons
(kg/
MW
h)
Expected
Predicted
(d) 501FD
Source: Katzenstein, W. and J. Apt, Air Emissions Due To Wind And Solar Power. Environmental Science & Technology, 2009. 43(2): 253-258.
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Final Comments • Smart integration of large-scale wind or solar will use
a portfolio of fill-in power (some with high ramp rates, much more with slow ramp rates).
• The amount of smoothing by interconnection is very dependent on the time scale considered.
• Large area connections don’t substantially change the variability.
• There are likely to be years of good wind, and not such good wind.
• Although solar thermal capital costs are currently higher than PV, variability costs are about half that of PV, comparable to wind.