Integrating wind resources: siting decisions in the MidwestJulian Lamy (speaker)Ines AzevedoPaulina Jaramillo

2

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

5

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)