Estimating the cost of novel (pre-commercial) systems for CO2 capture - Ed Rubin, Carnegie-Mellon University

972 views
771 views

Published on

A presentation from the 2013 CCS Costs Workshop.

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
972
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
22
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Estimating the cost of novel (pre-commercial) systems for CO2 capture - Ed Rubin, Carnegie-Mellon University

  1. 1. Estimating the Cost of Novel (Pre-Commercial) Systems for CO2 Capture Edward S. Rubin Department of Engineering and Public Policy Department of Mechanical Engineering Carnegie Mellon University Pittsburgh, Pennsylvania Presentation to the CCS Cost Workshop International Energy Agency (IEA) Paris, France November 6, 2013
  2. 2. Characteristics of Novel (Pre-Commercial) Capture Systems • Includes any technology not yet deployed or available for purchase at a commercial scale – Current stage of development may range from concept to large pilot or demonstration project • • Process design details still preliminary or incomplete • May require new components and/or materials that are not yet manufactured or used commercially Process performance not yet validated at scale, or under a broad range of conditions E.S. Rubin, Carnegie Mellon
  3. 3. Examples of Pre-Commercial Systems: Everything beyond Present Post-combustion (existing, new PC) Pre-combustion (IGCC) Chemical looping Cost Reduction Benefit Oxycombustion (new PC) OTM boiler CO2 compression (all) Amine solvents Physical solvents Cryogenic oxygen Present E.S. Rubin, Carnegie Mellon Advanced physical solvents Advanced chemical solvents Ammonia CO2 compression PBI membranes Solid sorbents Membrane systems ITMs Biomass cofiring Ionic liquids Biological processes Metal organic frameworks CAR process Enzymatic membranes 5+ years 10+ years 15+ years Time to Commercialization 20+ years Source: USDOE, 2010
  4. 4. Capital Cost per Unit of Capacity Capital Cost per Unit of Capacity Typical Cost Trend of a New Technology Research Development Demonstration Deployment Mature Technology Research Development Demonstration Deployment Adspted from EPRI Mature Technology TAG E.S. Rubin, Carnegie Mellon Time or Cumulative Capacity Time or Cumulative Capacity
  5. 5. Historical Trends of FGD and SCR (DeSOx and DeNOx) Costs . . . 8 1982 1976 1980 250 1982 200 1990 1995 150 1975 100 1974 1972 (1000 MW, eff =80-90%) 1968 (200 MW, eff =87%) 50 20 30 40 50 60 70 80 Cumulative World Wet FGD Installed Capacity (GW) Source: Rubin et al. 2007 All costs based on a standardized 500 MW coal-fired power plant (except where noted) SCR Capital Costs ($/kW), 1997$ 120 10 6 5 4 3 2 1980 1990 .. 1976 . display the characteristics seen in the previous1995 slide 1975 1974 1968 1972 (200 MW, eff =87%) (1000 MW, eff =80-90%) 1 0 0 7 O&M Costs (1997$/MWh) Capital Costs ($/kW) in 1997$ 300 1980 ← First Japan commercial installation on a coal-fired power plant 0 1980 10 0 20 30 40 50 60 70 80 90 100 First 1983 ←1983 German commercial installation Cumulative World Wet FGD Installed 90110100 100 Capacity (GW) 1989 1989 90 1979 80 1978 ↓ First US commercial installation 70 60 1993 1977 1993 1995 1995 2000 2000 50 40 -20 -10 0 10 20 30 40 50 Cumulative World SCR Installed Capacity (GW) E.S. Rubin, Carnegie Mellon 60 70 80
  6. 6. Five Approaches to Cost Estimation for Novel Systems • • • • • Conventional engineering-economic estimates Conventional with probabilistic inputs Application of learning (experience) curves Expert judgment /elicitations Don’t ask, don’t tell E.S. Rubin, Carnegie Mellon
  7. 7. Conventional engineeringeconomic costing E.S. Rubin, Carnegie Mellon
  8. 8. Most novel processes use conventional costing methods Many cost elements depend on the technical maturity of the process E.S. Rubin, Carnegie Mellon
  9. 9. Cost estimates for novel processes often ignore “process contingency cost” guidelines • Process Contingency Cost “factor applied to new technology … to quantify the uncertainty in the technical performance and cost of the commercial-scale equipment.” - EPRI TAG Process Contingency Cost Technology Status (% of associated process capital) New concept with limited data 40+ Concept with bench-scale data 30-70 Small pilot plant data 20-35 Full-sized modules have been operated 5-20 Process is used commercially 0-10 E.S. Rubin, Carnegie Mellon Source: EPRI, 1993; AACE, 2011 • Most studies of advanced capture systems for full-scale power plants (~500 MW) assume much smaller values than these guidelines call for, e.g.: • 7% • <20% (EPRI, 2011) • • (IEAGHG, 2011) 18% (USDOE, 2010) 10% (IECR, 2008)
  10. 10. What system are we trying to cost? • • Early commercial deployment? (first of a kind), or Mature widely-deployed system? (Nth of a kind) The major difference is usually in the system design (though some cost factors also may change) Whichever case is chosen, process contingency cost depends on the current state of technical E.S. Rubin, Carnegie Mellon
  11. 11. Cost studies also commonly ignore guidelines for “project contingency cost” • Project Contingency Cost “factor covering the cost of additional equipment or other costs that would result from a more detailed design of a definitive project at an actual site.” - EPRI TAG EPRI Cost Classification Class I (~AACE Class 5/4) Class II (~AACE Class 3) Class III (~ AACE Class 3/2) Class IV (~AACE Class 1) Project Contingency Design Effort (% of total process capital, eng’g &home office fees, and process contingency) Simplified 30–50 Preliminary 15–30 Detailed 10–20 Many Class I-III studies assume ≤10% Conclusion: • The total contingency cost for novel capture processes is grossly under-estimated in most cost estimates • (e.g., totals of ~20-30% rather than ~40-100%) Finalized 5–10 Source: EPRI, 1993 E.S. Rubin, Carnegie Mellon •
  12. 12. Example of Contingency Cost Impact on Total Cost Increase in Capital Cost and COE (based on a 2-stage membrane capture system) Process + Project Contingency Cost for the CO2 capture process alone Parameter 20% 50% 100% Capture System Capital Cost ($/kW) 17% 42% 85% Capture System COE ($/MWh) 8% 20% 40% Total Plant Capital Cost ($/kW) 5% 13% 26% Total Plant COE ($/MWh) 4% 11% 18% E.S. Rubin, Carnegie Mellon
  13. 13. Conventional costing with probabilistic variables E.S. Rubin, Carnegie Mellon
  14. 14. Case Study of PC Plant with a 2-Stage Membrane Capture System Source: IECM v.8.0.2 (2013) Membrane System Parameter Ideal CO2 Permeance (S.T.P.) Ideal CO2/N2 Selectivity (S.T.P.) Feed-side Compressor Efficiency Vacuum Pump Efficiency Expander Efficiency CO2 Compressor Efficiency Cost Parameters Membrane Module Cost Total Indirect Capital Cost CO2 T&S Costs E.S. Rubin, Carnegie Mellon Unit gpu ratio % % % % Nominal Value 1000 50 85 85 85 80 $/sq ft % PFC $/ton 4.645 37 5 Distribution Function triangular (500, 1000,5000) triangular (40, 50, 75) uniform (70, 85) uniform (70, 85) uniform (70, 85) uniform (70, 85) triangular (2.322, 4.645,18.58) uniform (20, 60) uniform (1,10) Probability distributions assigned to 6 performance variables and 3 cost variables
  15. 15. Case Study: SCPC-CCS (550 MWnet) w/ 2-Stage Membrane Capture System 100% Cumulative Probability 95% CONFIDENCE INTERVALS= 80% COE: DV +31/ -6 MWh (+51%/ -10%) Deterministic Value (DV) 60% CO2 Avoidance Cost: DV +49/ -8 $/ton= (+62%/ -10%) Mean = $72 /MWh 2.5% = $55 /MWh 97.5% = $97 /MWh 40% 100% 0% 80% 0 30 60 90 120 Added Cost for CCS (Constant 2011 $/MWh) Conclusion: • Explicit characterization of uncertainties can improve cost estimates by revealing risks as well as opportunities E.S. Rubin, Carnegie Mellon Cumulative Probability 20% Deterministic Value (DV) 150 60% Mean = $94 /ton 2.5% = $71 /ton 97.5% = $128 /ton 40% 20% 0% 0 30 60 90 120 150 Cost of CO2 Avoided (Constant 2011 $/ton) 180
  16. 16. Cost estimates based on learning curves E.S. Rubin, Carnegie Mellon
  17. 17. One-Factor Learning (Experience) Curves are the Most Prevalent Model equation: Ci = a xi –b where, Ci = cost to produce the i th unit xi = cumulative capacity thru period i b = learning rate exponent a = coefficient (constant) Fractional cost reduction for a doubling of cumulative capacity (or production) is defined as the learning rate: LR = 1 – 2b 20000 1983 1981 Photovoltaics 10000 5000 USA Japan 2000 1992 1995 1982 Windmills (USA) 1000 500 1987 1963 Gas turbines (USA) 200 100 10 1000 10000 100 Cumulative MW installed Source: IIASA, 1996 • Most appropriate for projecting future cost of a current technology already commercially deployed • Application to novel (pre-commercial) processes requires careful consideration of the “starting point” (cost and experience base) for future cost reductions E.S. Rubin, Carnegie Mellon RD&D Commercialization 1980 100000
  18. 18. Reported Learning Rates for Power Plant Technologies Vary a Lot Histogram of Learning Rates in the Literature 12 3 Mean = 14% Median = 13% Std Dev = 13% Natural Gas-Fired Plants On-Shore Wind Plants 10 Mean = 16% Median = 12% Std Dev = 14% 8 Frequency Count 2 6 4 1 2 0 -5% to -1% 0 -15% to -10% to - -5% to -9% 4% 1% 0% to 4% 5% to 10% to 15% to 20% to 25% to 9% 14% 19% 24% 29% Learning Rate Dependent Variable = $/kW (n=9) Dependent Variable = $/kWh (n=2) 30% to 34% 0% to 4% 5% to 9% 10% to 15% to 14% 19% Learning Rate 20% to 24% 24% to 29% 30% to 34% Dependent Variable for electric powerDependent Variable = $/kW (n=23) Table ES-1: Summary of studies characterizing historical learning rates = $/kWh (n=12) generation technologies. Technology Number of studies reviewed Number of studies with one factor Number of studies with two factors Range of learning rates for “learning by doing” (LBD) 2 1 8 4 35 24 2 1 6 4 29 22 0 0 2 0 6 2 5.6% to 12% 2.5% to 7.6% -11% to 34% <0 to 6% -3% to 32% 10% to 53% 4 7 3 3 4 7 0 0 0 0 0 2 Range of rates for “learning by researching” (LBR) 12% to 45% 0% to 24% Years covered across all studies Coal* PC IGCC Natural Gas* Nuclear Wind (on-shore) Solar PV BioPower Biomass production Power generation** Geothermal power Hydropower 0.48% to 11.4% 10% to 26.8% 10% to 18% 1902-2006 Projections 1980-1998 1975-1993 1980-2010 1959-2001 2.63% to 20.6% 1971-2006 1976-2005 1980-2005 1980-2001 2.38% to 17.7% *Does not include plants with CCS. **Includes combined heat and power (CHP) and biodigesters. E.S. Rubin, Carnegie Mellon Source: Azevedo et al,2013
  19. 19. Effect of Learning Curve Uncertainties for Power Plants w/ CCS % COE REDUCTION AFTER 100 GW EXPERIENCE • Projected reduction in cost of electricity (COE) for each plant type varies by a factor of ~2 to 4 Nonetheless, the judicious use of learning curves can suggest a pathway from FOAK to NOAK costs for novel technologies Source: Rubin et al., 2007 Percent Reduction in COE • 30 25 20 15 10 5 0 NGCC PC IGCC Oxyfuel E.S. Rubin, Carnegie Mellon
  20. 20. Cost estimates based on expert elicitations E.S. Rubin, Carnegie Mellon
  21. 21. Common Applications of Expert Judgment • Estimating the future value of a parameter used in a model or cost calculation • Direct estimates of the future value of a cost or other characteristic of interest E.S. Rubin, Carnegie Mellon
  22. 22. Example of Expert Elicitations (1) One of four amine system parameters estimated by experts and used to calculate expected cost reductions from advanced solvents (below) E.S. Rubin, Carnegie Mellon
  23. 23. Example of Expert Elicitations (2) Elicitations by Nemet et al. of future energy penalty and avoidance cost for seven capture technologies as a function of three policy scenarios Scen.3: Minimum cost of CO2 avoided ($/tCO2) across 7 technologies 0.08 Absorption 0.07 Abs=0.20 Ads=0.03 Mem=0.02 0.06 Oth=0.04 Pre=0.30 density 0.05 Oxy=0.24 Precombustion CLC=0.17 0.04 2025 0.03 Oxyfuel 0.02 0.01 Chemical Looping 0 30 40 50 60 70 80 90 100 110 $/tCO2 Source: Jenni, K.E., Baker, E.D., Nemet, G.F. (2013). IJGGC, 12, 136-145. E.S. Rubin, Carnegie Mellon Source: Nemet, G.F., Baker, E., Jenni, K.E. (2013). Energy, 56, 218–228. 120
  24. 24. How does expert judgment compare to costs projected using learning rates ? Nuclear NGCC w/o CCS 5,400 1,200 5,300 5,200 Original EPRI EXPERTS 1,100 Base Case ALT 1 - CES 1,050 1,000 950 ALT 2 - CTAX20 Cost ($/kW) Cost ($/kW) 1,150 Original EPRI EXPERTS 5,100 Base Case 5,000 ALT 1 - CES ALT 2 - CTAX20 4,800 ALT Base Case 4,900 ALT Base Case Conclusion: 4,700 4,600 • 2050 Expert judgment is2015 2020 2025 2030 2035 2040 2045 2050 often 2015 2020 2025 2030 2035 2040 2045 inconsistent with learning On Shore Wind curve projections Solar PV (Rooftop) 2,500 4,000 3,500 3,000 EXPERTS Original EPRI 1,500 Base Case ALT 1 - CES 1,000 ALT 2 - CTAX20 ALT Base Case 500 Cost ($/kW) Cost ($/kW) 2,000 Original EPRI EXPERTS 2,500 Base Case 2,000 ALT 1 - CES 1,500 ALT 2 - CTAX20 1,000 ALT Base Case 500 0 0 2015 2020 2025 2030 2035 2040 2045 2050 E.S. Rubin, Carnegie Mellon 2015 2020 2025 2030 2035 2040 2045 2050 Source: Azevedo et al,2013
  25. 25. Don’t ask, don’t tell E.S. Rubin, Carnegie Mellon
  26. 26. Avoid Cost Estimates for Processes at Early Stages of Development • This approach favors the use of performance metrics, rather that cost estimates, to evaluate and screen novel components or process designs in the early stages of development (e.g., TRL levels 1-3 or higher) • For novel CO2 capture processes, projected reductions in the power plant energy penalty is a favored metric • No guarantee, however, that improvements in key performance metrics will necessarily result in lower overall cost (e.g., COE) for the novel system E.S. Rubin, Carnegie Mellon
  27. 27. So where does this leave us ? E.S. Rubin, Carnegie Mellon
  28. 28. Conclusions and Recommendations Conclusions • A variety of methods are available to estimate the future cost of new, pre-commercial capture systems; but … • Some methods are not commonly used; others are often used inappropriately; no clear consistency across method Recommendation • Establish a task force to develop improved guidelines, emphasizing needs for transparency and incorporation of uncertainties in cost estimates for novel processes E.S. Rubin, Carnegie Mellon
  29. 29. A Final Word of Wisdom “It’s tough to make predictions, especially about the future” - Yogi Berra Future CCS Costs E.S. Rubin, Carnegie Mellon
  30. 30. Thank You rubin@cmu.edu E.S. Rubin, Carnegie Mellon

×