1. Guolin Yao Mark D. Staples Robert Malina Wallace E. Tyner Stochastic Techno-Economic Analysis of Alcohol-to-Jet Fuel Production

2. Outline Introduction Pathway and Feedstock Description Methodology Results Conclusions

3. Introduction Aviation biofuels can be used to meet the Renewable Fuel Standard (RFS). US FAA has a short-term goal of 1 billion gallons of alternative aviation fuel consumption by 2018 for military and commercial applications. Unlike ground transportation which can use ethanol, CNG, or electricity, aviation requires the use of energy dense, non-oxygenate, hydro-carbon, liquid fuels. Four major aviation biofuel technologies are currently technically feasible: Fischer-Tropsch (F-T), hydroprocessed renewable esters and fatty acids (HEFA), sugar conversion (fermentation, thermochemical), and direct liquefaction (pyrolysis). More than twenty airlines have already used petroleum-derived jet fuel blended with aviation biofuels on thousands of passenger flights.

4. Introduction Three contributions of this research: –Breakeven price distributions in addition to NPV and IRR –Link technical efficiency with input/output quantities through econometric methods –Time-series price estimation based on historical prices

5. Pathway and Feedstock Description Feedstock Fermentation Ethanol Dehydration Oligomerization Fuels Alcohol to Jet Pathway

6. Pathway and Feedstock Description Alcohol to Jet Pathway Three feedstocks: Corn grain, sugarcane, switchgrass Two procedures: feedstock to ethanol & ethanol to fuels Four basic categories of stochastic variables:  Two conversion factors  Feedstock prices  Natural gas prices  Output fuel prices Other variables are linked to the four basic stochastic variables

7. Methodology Linkage of Stochastic Variables within the Model

8. Methodology Conversion Efficiency Assumptions PERT Distribution:

9. Methodology Capital Costs Consist of –Feedstock Preprocessing & Fermentation A function of feedstock input quantity. Estimated from Staples et al. (2014) on the basis of dollars-per-unit-mass of feedstock processing capacity Feedstock quantity is dependent on two conversion factors which determine overall feedstock to jet conversion efficiency. –Dehydration, Oligomerization and Hydrotreating Fixed, as a function of facility size

10. Methodology Corn and Sugarcane Prices MA2 Time-series estimation effectively captures the uniqueness of the motion processes of each product market, based on historical prices. Corn grain and sugarcane are commodities with mature markets. Annual historical prices from 1980 to 2014 are available from USDA. The second-order moving average process (MA2) results in the best fit of historical data to project future corn grain and sugarcane prices according to the Akaike information criterion (AIC).

11. Methodology Exception: Switchgrass Price CV=SD/mean  CV(mean)=[CV(upland)+CV(lowland)]/2  SD(mean)=mean(mean)*CV(mean) Yield (1000kg/ha): min=mean-2SD=10.8-2*5.08=0.65; mean=10.08; max=mean-2SD=10.8+2*5.08=20.95 Average switchgrass cost=$116.50/1000kg Farmer Payment ($/ha)=Average Switchgrass Cost ($/kg)* Mean Yields (kg/ha)=$1258.2/ha Switchgrass Cost ($/kg) =Farmer Payment ($/ha)/ Yield Distribution (kg/ha)

12. Methodology Natural Gas and Other Input Prices MA1 The operating costs for water, power and other (enzymes, yeast, chemicals) inputs are less than 0.01%, 0.1%, and 1% of the total costs for each feedstock case, respectively, and their variations do not exert significant influence on the calculated NPV and breakeven price distributions. Therefore, we treat their prices as exogenous and deterministic.

13. Methodology Output Prices ARMA11

14. Methodology Breakeven Jet Price Distribution  All sets of simulated values for all the uncertain variables are saved.  1000 sets of randomly simulated values are plugged in the model, respectively, to generate 1000 corresponding breakeven prices.  The resulting distribution of breakeven jet prices are derived. Probability and cumulative density distributions are generated and fitted to the closest standard distributions.  The breakeven price at each percentile is reported.  All sets of simulated values for all the uncertain variables are saved.  1000 sets of randomly simulated values are plugged in the model, respectively, to generate 1000 corresponding breakeven prices.  The resulting distribution of breakeven jet prices are derived. Probability and cumulative density distributions are generated and fitted to the closest standard distributions.  The breakeven price at each percentile is reported.

15. Methodology Other Basic Assumptions  20% equity and 80% of capital investment, financed through loans at a 5.5% interest rate for 10 years, based on the original assumption by Staples et al. (2014).  Capital investment has a fixed component and a stochastic component. The fixed capital investment is 99.83 for all feedstocks. The mode of the total capital costs are $300, $347, and $697 million for corn grain, sugarcane, and switchgrass respectively.  Working capital is calculated as 20% of first year (4 th project year) operating costs.

16. Results Net Present Value (NPV)

17. Results Net Present Value (NPV)

18. Results Net Present Value (NPV) Sugarcane FSD (SSD) Corn Grain FSD (SSD) Switchgrass

19. Results Breakeven Price Distributions Swtichgrass FSD (SSD) Corn Grain and Sugarcane Corn Grain SSD Sugarcane but not FSD Sugarcane Corn Grain is profitable at higher feedstock prices due to higher DDGS revenue

20. Results Sensitivity Analysis Price uncertainty is not included here because there is a stochastic price variable each year for each price, which cannot be simply aggregated to a single range. Technical uncertainties insert greater impacts on NPV variations. Corn grain and sugarcane ATJ is more sensitive to ethanol-to-fuel efficiency. Switchgrass ATJ is more sensitive to feedstock-to-ethanol efficiency.

21. Conclusions Sugarcane ATJ is not only the least expensive pathway of the three considered, but also the least risky. Even for sugarcane ATJ, we find that there is an 88% probability that investors will not break even without price supports. ATJ fuel can qualify under the US Renewable Fuels Standard, and thus it would qualify for RINs. RIN values are approximately $0.20/liter ($0.75/gallon) of fuel, thereby reducing the required mean breakeven price of sugarcane, corn grain and switchgrass ATJ fuel to around $0.77/liter ($2.90/gallon), $0.82/liter ($3.09/gallon) and $1.18/liter ($4.46/gallon), respectively (OPIS Ethanol & Biodiesel Information Service, 2015). Technical efficiency is a major contributor to uncertainty within the ATJ pathway, and highly impacts expected NPV and required breakeven prices. It suggests that investment to achieve higher conversion efficiencies could significantly increase the economic viability of the ATJ pathway.

22. Conclusions In this research, we use the three methodological innovations to calculate probabilistic distributions of NPV and breakeven prices. The NPV results show that the ATJ processes considered are not economic under the projected economic and technical conditions. The cumulative breakeven price distributions provide a breakeven price for each probability level, and the distribution of breakeven prices provides useful information for public and private investors irrespective of their specific risk-profile. From a policy-perspective, risk profiles as those developed in this paper can also be used to assess the impact of alternative policies such as loan guarantees, tax credits, crop insurance, end user off-take agreements, reverse auction based on off-take contract and capital subsidy on reducing project risk (Tyner and Van Fossen, 2014)

23. Acknowledgement We are pleased to acknowledge funding support from the Federal Aviation Administration for this research. This work was partially funded by the US Federal Aviation Administration (FAA) Office of Environment and Energy as a part of ASCENT Project 107208 under FAA Award Number 13-C-AJFE-PU. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA or other ASCENT Sponsors.

25. Appendix

27. Welch’s t test Corn Grain> Sugarcane Corn Grain< Switchgrass Sugarcane<Switchgrass Switchgrass> Corn Grain> Sugarcane

28. Appendix Stochastic Dominance