Integrating wind resources: siting decisions in the Midwest
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Integrating wind resources: siting decisions in the MidwestJulian Lamy (speaker)Ines AzevedoPaulina Jaramillo
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The Midwest has ambitious renewable targets• Illinois RPS: 25% by 2025, 60 to 75% from wind
• 30 TWh (10 GW) of wind needed for this target • RPSs in MN, MO, WI, and MI add another 30
TWh (10 GW)• Currently MISO has about 10 GW
• Research question: in an ideal world, if we could choose to build the farms anywhere in MISO, where would we build them?
• What metrics to consider?
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Cap
acity
Fac
tor
30%
32%
34%
36%
38%
40%
42%
44%
46%
Annual average capacity factor
EWITS (2012), 2006
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Variability is also a big concern, even for the highest capacity resources
1 8 15 22 29 36 43 50 57 64 71 78 85 92 990%
10%20%30%40%50%60%70%80%90%
100%
First 100 hours of 2006
Hou
rly
Cap
acity
Fac
tor
Illinois farm (293 MW)CF 44%COV: 0.52
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Coe
ffici
ent o
f var
iatio
n (C
OV
)
0.65
0.7
0.75
0.8
0.85
0.9
Coefficient of Variation (CoV) in hourly output
EWITS (2012), 2006
𝐶𝑂𝑉=𝜎𝜇
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Transmission: hard to say…
MTEP 2012, pg. 49
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Data on available existing transmission capacity is limited, what about generation?
17%
61%
22%
(eGRID, 2012), % of generation in 2009 by area
26%
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Past research suggests that building around Illinois is best
• Hoppock and Patiño-Echeverri (2010) • Evaluated wind farms using capacity factors for
hypothetical sites using EWITS data (2008)• Remote wind farms were required to build transmission
lines for delivery to Illinois (with sensitivities)
• This paper add to the literature by:
1. In addition to capacity factors, we include a metric to account for the temporal variability of each farm using a simple dispatch model
2. Delivery must be to some node cluster within MISO, not necessarily to Illinois
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Generation cost for each non-wind generator (i)
ramp cost for each non-wind generator (i)
Capital costs incurred for each wind farm
marginal gen cost for non-wind generator i
Generation in hour t by non-wind generator i
Change in generation from hour (t-1) to t
Binary variable : b=1: build farm kb=0: don’t build farm k
Annualized wind capital cost + annualized transmission capital cost
Ramp cost ($/MWh) incurred by non-wind generator i
Modeling Approach
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Modeling Approach
Market Clearing (wind “must-run”)
Annual wind generation target
Generator capacity and ramp limits
ramp cost for each non-wind generator (i)
Capital costs incurred for each wind farm
Generation cost for each non-wind generator (i)
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Assumptions: Ramping Cost• DeCarolis and Keith (2006)
• Increasing wind power to serve 50% of demand adds about $10-20/MWh due to intermittency + transmission costs
• Lueken et al. (2012) • Analyzed the variability of 20 wind farms in ERCOT over one year and
concluded that costs due to variability are on average $4/MWh • Hirst (2001)
• 100 MW wind farm in MN for delivery to PJM• Intra-hour balance cost: $7 to 28/MWh • regulation costs: $5 to $30/MWh
• Very uncertain so we used a parametric analysis and tested the sensitivity to the results:
• $0, $5, $10, $30, and $100/MWh • Incurred during hourly changes in dispatchable generation
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Transmission Assumptions
$/MW-km Year $$ Source
$200-900 $2001 Fertig and Apt (2011)
$100 – 1,300 Parameterized Denholm (2009)
$1,200-4,200 $2009 Hoppock & Patino (2010)
Case $/MW-km
Base $1,000
High $2,000
Costs
Distance required per site
x
To account for additional transmission needed along the grid:100%, 200%, 300%, 400%
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MISO LMP map, accessed July 3, 2013https://www.misoenergy.org/MarketsOperations/RealTimeMarketData/Pages/LMPContourMap.aspx
Selection of transmission node clusters
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MISO Delivery $1,000/ MW-km - 200% - $10/MWh
Cap
acity
Fac
tor
44%
44%
45%
45%
46%
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Cap
acity
Fac
tor
44%
44%
45%
45%
46%
MISO Delivery $1,000/ MW-km - 200% - $10/MWh
~ 50 km each from node cluster
~ 10 km from node cluster
How does the answer change under different ramping cost assumptions?
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0 5 10 30 1000%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
82% 76% 72%56%
45%
18% 24%24%
29%
29%
4%15%
26%
Ramping Cost Assumption ($/MWh)
MISO Delivery - $1,000/ MW-km – 200%
~8 GW built in MISO
% of totalMW built
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Summary of Scenarios Considered
Distance Scenario Transmission needed (% of distance)
Transmission Costs ($/MW-km)
Ramp Costs($/MWh)
Illinois delivery 50%, 100%$1,000, $2,000
$0, $5, $10, $30, $100MISO delivery
100%, 200%, 300%, 400%
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Conclusions• In most scenarios, remote wind is optimal
even when not accounting for variability ($0/MWh)
• When ramping costs ≥$10/MWh, the optimal portfolio of wind farm locations changes
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Next Steps• Refine scenario to better represent the necessary
transmission capacity to connect farms to MISO’s grid• MISO’s historical Impact Studies• Find someone with a detailed dispatch/ power flow model
…unlikely but I’m hopeful…• Other ideas??
• Better represent transmission capacity needs within Illinois. Currently, assume that 0 km need to be built
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Acknowledgements
This work was supported by the center for Climate and Energy Decision Making (SES-
0949710), through a cooperative agreement between the National Science Foundation and
Carnegie Mellon University, and by the RenewElec project.
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Appendix
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MISO Delivery - $1,000/ MW-km – 100%
0 5 10 30 1000%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
32% 32% 31% 25% 19%
55% 55% 55%55%
54%
13% 13% 14% 20%22%
6%
Ramping Cost Assumption ($/MWh)~8 GW built in MISO
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MISO Delivery - $1,000/ MW-km – 300%
0 5 10 30 1000%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
100% 99% 99%
75%
53%
1% 1%17% 22%
8%
24%
Ramping Cost Assumption ($/MWh)~8 GW built in MISO
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MISO Delivery - $1,000/ MW-km – 400%
0 5 10 30 1000%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
100% 100% 100%84%
61%
16%23%
14%
2%
Ramping Cost Assumption ($/MWh)~8 GW built in MISO
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Illinois Delivery - $1,000/ MW-km – 100%
~8 GW built in MISO
0 5 10 30 1000%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
100% 100% 100% 100% 100%
Ramping Cost Assumption ($/MWh)
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Illinois Delivery - $1,000/ MW-km – 50%
~8 GW built in MISO
0 5 10 30 1000%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
99% 99% 99% 92%76%
1% 1% 1% 4%20%
4% 4%
Ramping Cost Assumption ($/MWh)
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Different sites within Illinois are chosen!Ramping Cost Assumption ($/MWh)
Site ID CF COV 0 5 10 30 1004022 45% 0.68 IL IL IL IL IL4208 44% 0.72 IL IL IL IL IL4327 44% 0.70 IL IL IL IL IL4214 44% 0.73 IL IL IL IL IL4397 44% 0.71 IL IL IL IL IL4640 44% 0.71 IL IL IL IL IL4140 44% 0.72 IL IL IL IL IL4474 44% 0.69 IL IL IL IL IL4659 43% 0.72 IL IL IL IL IL4242 43% 0.71 IL IL IL IL IL4431 44% 0.71 IL IL IL IL IL4662 43% 0.72 IL IL IL IL IL4241 43% 0.72 IL IL IL IL IL4667 43% 0.70 IL IL IL IL4519 43% 0.69 IL IL IL IL4603 43% 0.70 IL IL IL IL4554 43% 0.71 IL IL4435 43% 0.73 IL IL4635 43% 0.70 IL4636 43% 0.72 IL4605 43% 0.73 IL4606 43% 0.73 IL
Strange pattern likely because of optimal “grouping” of farms to decrease variability
Red represent < 100% capacity of the wind farm was built (i.e., 0 < bk <1)
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Assumptions: Dispatchable Generators• Nuclear, hydro, and existing wind are “must-run”• Gas + Coal are aggregated into one representative
dispatchable unit• Model has to dispatch 1 generator to support the new wind
Tech Type GW $/MWhv Capacity Factor Ramp limit per hour(% of max MW)
Must-run Nuclear 8.5 $12 90% -Wind_existii 8.1 $0 ‘Varied’ -
Wind_newiv ‘Varied’ $0 ‘Varied’ -Hydro 3.5 $0 95% -Otheri 7.1 $50 90% 60%
Dispatchiii Coal 70 $25 90% 60%Gas 35 $37 90% 100%
Coal + Gas 105 $30 90% 100%
i: includes residual fuel oil, biomass, and other generationii: wind data from MISO ( 2012a)iii: total load data is from MISO (201b)
iv: not currently included, scenarios to be included in final reportv: Computed using $/mmbtu from AEO (2013), and mmbtu/kwh from EGrid (2009)
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Conclusions• MISO delivery scenarios
• In most scenarios, remote wind is optimal even when not accounting for variability ($0/MWh)
• When ramping costs ≥$10/MWh, the optimal portfolio of wind farm locations changes
• Illinois delivery scenarios• Probably too pessimistic for remote wind• For 100% transmission case, Illinois is always optimal• For the 50% transmission case, adjoining states such as MO
and IA are competitive when ramping costs ≥ $30/MWh• Even with Illinois only wind development, accounting for
ramping costs ≥ $30/MWh affects siting within Illinois
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Xcel Energy RFP
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Impact Study Assessment for ND
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30%
32%
34%
36%
38%
40%
42%
44%
46%
remote local + adj lakes
capa
city
fact
or
0.65
0.7
0.75
0.8
0.85
0.9
remote local + adj lakesco
effic
ient
of v
aria
tion
RemoteND, SD, MN, NE
LocalIL, IN, IA,
MO
LakesMI, WI
RemoteND, SD, MN, NE
LocalIL, IN, IA,
MO
LakesMI, WI
Box Plots of CF and COV by region
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MW
h
0
2
4
6
8
10
12
14
16
18
x 106
State TWhs Perc.remote ND 35 5% SD 12 2% MN 53 8% NE 36 6%local IL 199 31% IN 122 19% IA 56 9% MO 95 15%lakes WI 63 10% MI 109 17%
Existing Generators in MISO (eGRID, 2012)
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100% 99% 99%
75%
53%
8%
24%
1% 1%17% 22%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 30 100Ramping Cost Assumption ($/MWh)
32% 32% 31% 25% 19%
55% 55% 55%55%
54%
13% 13% 14% 20%22%
6%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 30 100Ramping Cost Assumption ($/MWh)
100%
200%
100%
400%
300%
82% 76% 72%56%
45%
18% 24%24%
29%
29%
4%15%
26%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 30 100Ramping Cost Assumption ($/MWh)
100% 100% 100%84%
61%
14%
16%23%
2%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 30 100Ramping Cost Assumption ($/MWh)Ramping Cost Assumptions ($/MWh)