Cost Assessment of Cellulosic Ethanol Production and Distribution in the US

1. Cost Assessment of Cellulosic Ethanol Production and Distribution in the US William R Morrow W. Michael Griffin H. Scott Matthews

2. Introduction • Part I – Optimization Modeling • Modeling • Estimation of Parameters • Part II – Optimization Solutions • Scenarios • Data Trends • Part III – Monetizing the Solutions • Freight Rate Calculation • Transportation Cost Estimations • Part IV – A Quick Comparison to Petroleum • Economics • Transportation • Part V – Global Biomass resources • Part VI – Conclusions

3. Part I – Optimization Modeling (Modeling) Modeling Goals • Estimate an Extended Corn Based Ethanol Scenario • Model domestic switchgrass energy crop (published data) as the feedstock for cellulosic ethanol production • Estimate transportation costs as domestic cellulosic ethanol production increases • Identify any capacity limitations for a switchgrass based cellulosic ethanol fuel economy

4. Part I – Optimization Modeling (Modeling) Our Model • Distributes ethanol to MSAs • Capable of large blend ratios • Expands corn production as far as believable & makes up remaining required ethanol with switchgrass based cellulosic ethanol • Only considers truck and rail and transport • Uses freight rates derived from US Economic Input Output data, and Commodity Flow Survey

5. Part I – Optimization Modeling (Parameter Estimation) Gasoline ConsumptionTop 271 Consuming MSA’s (76% of US Gasoline Consumption)

6. Part I – Optimization Modeling (Parameter Estimation) Gasoline To Ethanol Consumption Current (1997 Modeled year) Gasoline Consumption: →130 Billion Gallons per yr Fuel Energy Content: Gasoline: → 120,000 BTU/Gal Ethanol: → 86,100 BTU/Gal

7. Part I – Optimization Modeling (Parameter Estimation) Expanded Corn Ethanol Plants Current Corn Ethanol Production: → 3 Billion Gallons Expanded Corn Ethanol Production → 5 Billion Gallons

8. Part I – Optimization Modeling (Parameter Estimation) Ethanol by Feedstock

9. Part I – Optimization Modeling (Parameter Estimation) Switchgrass Availability Modeling using ORNL POLYSIS Model (published Data) • Based on ORECCL – Oak Ridge Energy Crop County Level Database • Energy Crop Availability & Yield • Production Costs & Land Rents • Projects Energy Crop Farmgate Prices • Comprised of 305 “Regions” (Similar to ASD’s) • Several counties grouped together (Total of 2,787 Counties) • Similar Soil type, moisture, sunlight, terrain, etc. • Estimates Switchgrass: • Tons/per year for each region • Based on $/ton farmgate prices (e.g. 30$/ton, 35$/ton, etc.)

10. Part I – Optimization Modeling (Parameter Estimation) Switchgrass availability(Acreage as a function of $/ton) • Estimated Range: • Upper Bound: • 5 Tons / Acre • 85 Gallons / Ton • Lower Bound: • 10 Tons / Acre • 100 Gallons / Ton

11. Part I – Optimization Modeling (Parameter Estimation) Transforming Switchgrass into Ethanol Gallons • Minimum plant size of 2,200 Ton SWG/day • based on the work of Wooley et al. (1999, 1999a) • 85 Gallons / Ton SWG (from range of 68 ~ 100 Gallons / Ton SWG) • based on the work of Wooley et al. (1999, 1999a) • Question: Can a POLYSIS Region produce enough SWG to support the minimum plant requirement? At what price ($ / Ton SWG)?

12. Part I – Optimization Modeling (Parameter Estimation) Plant Size as a Function of Cost(For Corn Stover) Source: Lignocellulosic Biomass to Ethanol Process Design and Economics Utilizing Co-Current Dilute Acid Prehydrolysis and Enzymatic Hydrolysis for Corn Stover – Aden et. al. 2002

13. Part I – Optimization Modeling (Parameter Estimation) % Usable Switchgrass(as a function of $/ton)

14. Part I – Optimization Modeling (Parameter Estimation) Switchgrass Availability (50 $/Ton SWG)

15. Part II – Optimization Solutions (Scenarios) Linear Optimization Scenarios • E5 Scenario – 5.2 Billion Gallon Ethanol • Expanded corn-based ethanol production – 5.2 BGY • No switchgrass-based cellulosic ethanol production – 0 BGY • E10 Scenario – 10.6 Billion Gallon Ethanol • Expanded corn-based ethanol production – 5.2 BGY • Switchgrass-based cellulosic ethanol production – 5.4 BGY (30$/ton SWG) • E20 Scenario – 22.1 Billion Gallon Ethanol • Expanded corn-based ethanol production – 5.2 BGY • Switchgrass-based cellulosic ethanol production – 16.9 BGY (50$/ton SWG)

16. Part II – Optimization Solutions (Scenarios) Forecasted E20 Scenario(50 $/ Ton SWG)

17. Part I – Optimization Modeling (Modeling) Linear Optimization Equations Variables: Constraints: Economic Eq.:

18. Part II – Optimization Solutions (Scenarios) Optimization Solutions Scatter PlotE20 Scenario

19. Part II – Optimization Solutions (Trends) Optimization SolutionsHistograms Trend toward shorter shipments as production expands

20. Part III – Monetizing the Solutions (Freight Rate Estimation) Freight Rate Dilemma • Problem: Freight industry does not publishes freight rates directly • Solution: Use US Government data sources and extrapolate freight rates • Data sources: • US Department of Commerce; Bureau of Economic Analysis – Input ~ Output Accounts • US Department of Transportation; Commodity Flow Survey

21. Part III – Monetizing the Solutions (Freight Rate Estimation) Freight Rate Estimation Method • EIO Accounts: • Use of Commodities by Industry 1997 – Total Commodity Output. • IO Code 482000 – Truck Transportation • IO Code 244000 – Rail Transportation • CFS Database: • Shipment by Destination and Mode of Transport 1997 • Truck • Rail • US State to State Distance matrix

22. Part III – Monetizing the Solutions (Freight Rate Estimation) Freight Rate Equations & Data Let: i = Origin State; j = Destination State

23. Part III – Monetizing the Solutions (Freight Rate Estimation) Freight Rate: f (Distance) Average Freight Rate per Ton-Mile: US DOTME Truck – 26.6 ¢/Ton-Mile (2001) 21.5 ¢/Ton-Mile Class I Rail – 02.2 ¢/Ton-Mile (2001) 07.2 ¢/Ton-Mile

24. Part III – Monetizing the Solutions (Trans. Cost Estimations) Monetized Optimization Solutions Legend Truck Freight Rates Rail Freight Rates

25. Part IV – Quick Comparison to Gasoline Economics Source: Aden et. al. 2002 Based on Energy Equivalency

26. Part IV – Quick Comparison to Gasoline Transportation By Mode Petroleum Ethanol Truck Rail

27. Part IV – Quick Comparison to Gasoline Petroleum Plant LocationsGeographical Dispersion

28. Part IV – Quick Comparison to Gasoline Petroleum Pipeline LocationsGeographical Dispersion

29. Part IV – Quick Comparison to Gasoline Petroleum & E20 Ethanol LocationsGeographical Dispersion

30. Part IV – Quick Comparison to Gasoline Ethanol Pipeline Challenges • Can not ship ethanol in petroleum pipelines • Location of ethanol production is more widely distributed than refineries locations • Ethanol produced at an ethanol plants is small when compared to gasoline production at refineries • CONCLUSION: Ethanol will require its own pipeline infrastructure • Dual fuel economy • Build ethanol pipelines for E5, E10, E20, E85, E100?

31. Part V – Global Biomass Production Estimates from IPCC 3rd Assessment Report • Raw Biomass Energy Potential • Year 2050 → 440 joules 18 per year • Year 2100 → 310 joules 18 per year • Converted to Liquid biofuels (@ 35% efficiency – EIA) • Year 2050 → 154 joules 18 per year • Year 2100 → 109 joules 18 per year • Converted to Gallons of Gasoline Equivilent • Year 2050 → 785 Gallons 9 per year • Year 2100 → 555 Gallons 9 per year • Gasoline Consumption (OECD Countries) - EIA • ~ 300 Gallons 9 per year

32. Part VI – Conclusions • Higher production – higher plant dispersion – shorter distance – lower transport cost • Comparison to gasoline costs • Ethanol Not likely be cheaper to transport in Short Term • Domestic Switchgrass Ethanol Limitations • E20 our upper bound for modeling • Oak Ridge Data (only goes to 50$/ton) • Displaces approximately 20% of existing agricultural products • Additional Biomass is available in the US & Internationally