1. Integrating wind resources: siting decisions in the Midwest Julian Lamy (speaker) Ines Azevedo Paulina 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? 2

3. Annual average capacity factor EWITS (2012), 2006 3

4. Variability is also a big concern, even for the highest capacity resources 4

5. Coefficient of Variation (CoV) in hourly output EWITS (2012), 2006 5

6. Transmission: hard to say… 6 MTEP 2012, pg. 49

7. Data on available existing transmission capacity is limited, what about generation? 7 17% 61% 22% (eGRID, 2012), % of generation in 2009 by area 26%

8. 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 8

9. 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 k b=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 9

10. Market Clearing (wind “must-run”) Annual wind generation target Generator capacity and ramp limits 10 ramp cost for each non- wind generator (i) Capital costs incurred for each wind farm Generation cost for each non-wind generator (i)

11. 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 11

12. Transmission Assumptions $/MW-kmYear $$Source $200-900$2001Fertig and Apt (2011) $100 – 1,300ParameterizedDenholm (2009) $1,200-4,200$2009Hoppock & Patino (2010) Case$/MW-km Base$1,000 High$2,000 Costs Distance required per site 12 To account for additional transmission needed along the grid: 100%, 200%, 300%, 400%

13. MISO LMP map, accessed July 3, 2013 https://www.misoenergy.org/MarketsOperations/RealTimeMarketData/Pages/LMPContourMap.aspx Selection of transmission node clusters 13

14. MISO Delivery $1,000/ MW-km - 200% - $10/MWh 14

15. MISO Delivery $1,000/ MW-km - 200% - $10/MWh ~ 50 km each from node cluster ~ 10 km from node cluster 15 How does the answer change under different ramping cost assumptions?

16. MISO Delivery - $1,000/ MW-km – 200% ~8 GW built in MISO 16 % of total MW built

17. Summary of Scenarios Considered Distance Scenario Transmission needed (% of distance) Transmission Costs ($/MW-km) Ramp Costs ($/MWh) Illinois delivery50%, 100% $1,000, $2,000 $0, $5, $10, $30, $100 MISO delivery 100%, 200%, 300%, 400% 17

18. 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 18

19. 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 19

20. Acknowledgements 20 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.

21. Appendix 21

22. MISO Delivery - $1,000/ MW-km – 100% ~8 GW built in MISO 22

23. MISO Delivery - $1,000/ MW-km – 300% ~8 GW built in MISO 23

24. MISO Delivery - $1,000/ MW-km – 400% ~8 GW built in MISO 24

25. Illinois Delivery - $1,000/ MW-km – 100% ~8 GW built in MISO 25

26. Illinois Delivery - $1,000/ MW-km – 50% ~8 GW built in MISO 26

27. Different sites within Illinois are chosen! Ramping Cost Assumption ($/MWh) Site IDCFCOV051030100 402245%0.68IL 420844%0.72IL 432744%0.70IL 421444%0.73IL 439744%0.71IL 464044%0.71IL 414044%0.72IL 447444%0.69IL 465943%0.72IL 424243%0.71IL 443144%0.71IL 466243%0.72IL 424143%0.72IL 466743%0.70IL 451943%0.69IL 460343%0.70IL 455443%0.71IL 443543%0.73IL 463543%0.70IL 463643%0.72IL 460543%0.73IL 460643%0.73IL 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 < b k <1) 27

28. 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 TypeGW$/MWh v Capacity Factor Ramp limit per hour (% of max MW) Must-runNuclear8.5$1290%- Wind_exist ii 8.1$0‘Varied’- Wind_new iv ‘Varied’$0‘Varied’- Hydro3.5$095%- Other i 7.1$5090%60% Dispatch iii Coal70$2590%60% Gas35$3790%100% Coal + Gas105$3090%100% i: includes residual fuel oil, biomass, and other generation ii: wind data from MISO ( 2012a) iii: total load data is from MISO (201b) iv: not currently included, scenarios to be included in final report v: Computed using $/mmbtu from AEO (2013), and mmbtu/kwh from EGrid (2009) 28

29. 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 29

30. Xcel Energy RFP 30

31. Impact Study Assessment for ND 31

32. Remote ND, SD, MN, NE Local IL, IN, IA, MO Lakes MI, WI Remote ND, SD, MN, NE Local IL, IN, IA, MO Lakes MI, WI Box Plots of CF and COV by region 32

33. StateTWhsPerc. remoteND355% SD122% MN538% NE366% localIL19931% IN12219% IA569% MO9515% lakesWI6310% MI10917% Existing Generators in MISO (eGRID, 2012) 33

34. 34 100% 200% 100% 400% 300% Ramping Cost Assumptions ($/MWh)