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Modeling Energy Security and Economic Sustainability Issues of the U.S. Biofuel Industry

Modeling Energy Security and Economic Sustainability Issues of the U.S. Biofuel Industry. Rocío Uría-Martínez Paul N. Leiby Gbadebo Oladosu 30 th USAEE Conference Washington, DC. October 10, 2011. Research sponsored by the Laboratory Directed Research and Development Program

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Modeling Energy Security and Economic Sustainability Issues of the U.S. Biofuel Industry

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  1. Modeling Energy Security and Economic Sustainability Issues of the U.S. Biofuel Industry RocíoUría-Martínez Paul N. Leiby Gbadebo Oladosu 30th USAEE Conference Washington, DC. October 10, 2011 Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, for the U.S. Department of Energy

  2. OBJECTIVES How could future boom and bust cycles in biofuel infrastructure be avoided/mitigated? Abiding national objective Central motivation for 2007 EISA legislation Exploring energy security and economic sustainability implications of U.S. biofuel industry configurations and policies using system analysis tools Combinations of -feedstocks -logistics designs -conversion technologies -biofuel types -… Where in the supply chain is support most needed? Should it be taxes, subsidies, mandates, loan guarantees? Long-run optimization & Short-run simulations

  3. ENERGY SECURITY It is not just about displacing gasoline but also about creating reliable supply chains that are resilient to market shocks How correlated are ethanol and gasoline prices? For parity pricing on a gge basis, Pethanol = 0.67* Pgasoline WHOLESALE GASOLINE AND ETHANOL PRICES CHARLOTTE (NC) Biofuels link agricultural and energy commodities Correlation coefficient = 71% Data: IMF/IFS database, Commodity Prices & Indices,

  4. ECONOMIC SUSTAINABILITY U.S. Grain Ethanol Capacity vs. Gasoline Price Capacity Installed Sources: Renewable Fuels Association, Official Nebraska Government Website Market volatility has been very challenging for the developing biofuels industry Gasoline Price Capacity Under Construction Ethanol Plant Operating Margins Volatile, and Collapsed to Near Minimum Sustainable Capacity Idled Ethanol price Line indicates minimum sustainable returns (“margin”) to Ethanol Production Plant. Actual returns highly variable. Other Operating Costs Net corn cost Plant “margin”

  5. SYSTEM CONFIGURATION MATTERS FOR ENERGY SECURITY AND ECONOMIC SUSTAINABILITY Long Run Vehicle Choice Conventional Vehicles FFVs Short Run FFV Fuel Choice E10 E85 Diesel, middle & heavy cuts, chemicals Co-products Blending & Retail Imported Ethanol Gasoline Ethanol Imported Gasoline Petroleum Refineries Biorefineries Inventories Inventories uniform format bales Imported Crude Oil Cellulosic feedstocks Light Heavy Corn Domestic Crude Oil

  6. INVENTORIES BioTrans model accounts for two types of inventories: Speculative inventories- held only when the market signals arbitrage opportunities Working inventories- held for operational reasons (typical stock-to-use ratio is 15%) Net marginal cost of storage = marginal cost – convenience yield Convenience yield is the benefit from holding a physical commodity • Will biomass/ethanol speculative inventories keep probability of stockout sufficiently low? • Is 15% a reasonable stock-to-use ratio for biomass feedstocks and/or ethanol?

  7. U.S. Petroleum Stock Variation 2005-2009 (Including SPR): - Typical within-year stock variation: 6% (9% excl SPR). - Max variation over 5 years: 22%.- Stocks are ~16% of annual demand (~25% incl SPR) These 5-year peak levels set in 2006 & 2007 1,800,000 5-year Ave. 5-year Min. 1,500,000 Source: IAF Advisors, Feb. 4, 2009

  8. Corn production, and stocks fluctuate widely, (seasonally, and year to year), far more than oil- Typical within-year stock variation of 4X (400%).- Year-to-year peak variation over 5 years: 33%.- Stocks are ~80% of annual demand Source: USDA, ERS, Feedgrains database

  9. BIOMASS FEEDSTOCK LOGISTICS SYSTEM DESIGN Uniform format Design Conventional Design Baled switchgrass Baled switchgrass • Uniform format biomass • as a way of reducing risk • for biorefineries: • by broadening feedstock base • by offering homogeneous quality Long Term Storage Bales stacked along edge of field $4.7/dry ton Long Term Storage Bales stacked along edge of field $5.36/dry ton FARM FARM Average distance = 10 miles Transport Bales on flatbed truck $4.61/dry ton; $0.12/dry ton-mile Transport Bales on flatbed truck $4.61/dry ton; $0.12/dry ton-mile Average distance = 71 miles Preprocessing Single grind $13.03/dry ton Short Term Queue Bales on asphalt pad Densification Pelleting $22.37/dry ton DEPOT Preprocessing Single grind $13.03/dry ton Short Term Queue Pellets in bins Handling Conveyors, dust control $2.38/dry ton BIOREFINERY (optimal size = 0.69 M dry tons) Average distance = 205 miles Transport Pellets in train $5.23/dry ton, $0.027/dry ton-mile Short Term Queue Pellets in bins BIOREFINERY (optimal size = 6.38 M dry tons) Handling Conveyors, dust control $0.21/dry ton

  10. FLEXIBLE BIOREFINERIES Feedstocks Ethanol conversion processes Co-products corn dry milling DDGs carbon fiber biochemical stover electricity multifeedstock biochemical switchgrass multifeedstock thermochemical higher alcohols forest residues i= feasible feedstock set J=feasible output set s=feedstock i fraction g=output j fraction Z= total input (dry tons) Q=total output (gallons) Biorefinery feedstock costs Biorefinery revenue

  11. FLEXIBLE BIOREFINERIES Brazilian sugarcane mills allow for changes in biorefinery product mix in response to relative product value

  12. FLEXIBLE FUEL VEHICLES Needed to increase consumption of ethanol beyond what can be absorbed in E10 blend • How much retail capacity is needed if RFS-2 advanced cellulosic biofuel objective • Is attained entirely/partially with ethanol? • How much would it cost to build that capacity?

  13. APPROACH: BioTrans System Design is Novel, While Building on Existing Capabilities BLM Modeling framework developed at INL to simulate bioenergy feedstock supply logistics from the field to biorefinery TAFV, HyTrans ORNL Dynamic market optimization to balance motor fuel supply to demand POLYSYS Simulate bioenergy crop production given changes in policy, economic, or resource conditions BILT Biofuelsupply chain transportation and optimization model developed at ORNL • BioTrans-Long-Run Model • Integrates summary representations from each of above • - Dynamic Optimization by GAMS • - Annual, 20 years • 9 Census Divisions • Multiple sectors • Balances markets and determines fixed capital (biorefineries, retail capacity) Electric Sector External runs for demand (NEMS, ORCED) Petroleum Sector Simple supply, Refineries (ORNL-RYM, NEMS runs) BioTransStochastic Short-Run Simulations Simulate monthly over 1 year (Python) -Shocks from oil producer behavior, disruptions and accidents -Shocks to yields from weather events: droughts, floods, pests -Infrastructure reliability Other Measures of Sustainability - Long run economic costs - GHG Emission Coefficients (GREET) - Water Use Coefficients Oil Security Metrics Model Provide framework for quantifying and measuring energy and economic security impacts

  14. BASECASE RESULTS: FEEDSTOCK SUPPLY MIX & COST FEEDSTOCK VOLUMES USED IN ETHANOL PRODUCTION Balanced set of cellulosic feedstocks is optimal at the national level although there is regional specialization COST PER GALLON OF ETHANOL Cellulosic ethanol cost is expected to be below that of grain ethanol for most pathways

  15. BASECASE RESULTS: FEEDSTOCK LOGISTICS DESIGN COSTS The tradeoff between transportation costs and capital costs does not provide justification to adopt uniform format design for biomass feedstocks Number of new cellulosic biorefineries (2010-2030) uniform format design: 203 conventional design: 260 Net present value of costs associated to production of grain and cellulosic ethanol (2010-2030)

  16. BASECASE RESULTS: STOCKS AND BIOREFINERY TYPES Flexible biorefineries help minimize total system costs over the planning period even though they are 20% more expensive to build BIOREFINERY CAPITAL STOCK Supply availability for multiple feedstocks changes over time and a flexible biorefinery can adjust to those changes CELLULOSIC BIOMASS INVENTORIES On the other hand, the model only chooses to keep speculative stocks. Working stock costs do not meet a counterbalancing benefit under perfect foresight conditions

  17. BASECASE RESULTS: E85 RETAIL CAPACITY Optimal station and pump share vary significantly from region to region E85 STATION SHARE E85 PUMP SHARE E85 RETAIL LOAD FACTOR Cost of E85 retail infrastructure is heavily dependent on load factor 10-15 c/gallon for utilization factors comparable to those of E10 pumps Over $1/gallon for low utilization factors

  18. SCENARIO: FEEDSTOCK PRICE SHOCK Doubling the cost of corn and corn stover in years 2024 and 2025 FB1: flexible biochemical available FB0: flexible biochemical not available SU1: stock-to-use ratio >=15% SU0: unconstrained stocks DRY MILL ACTIVITY LEVEL (Census Division 3) 2024 BIOFUEL PRODUCTION RELATIVE TO BASELINE

  19. SCENARIO: FEEDSTOCK PRICE SHOCK Feedstock price shock propagates to ethanol but not to the pump ETHANOL PRICE AT BIOREFINERY GATE_ROC A 100% increase in supply costs for corn and stover leads to: 51% increase in Pethanol (FB1SU1) 58% increase in Pethanol (FB0SU0) 2.8% increase in PE10 (FB1SU1) 3.1% increase in PE10 (FB0SU0) E10 PRICE AT THE PUMP_ROC

  20. SCENARIO: FEEDSTOCK PRICE SHOCK Total NPV of costs and savings from flexible biochemical biorefineries (FB) and 15% stock-to-use ratio (SU) (2010-2030) Cost of adding FB and SU to the system: $14.2 billion Cost of coping with disruption with FB and SU: $7.1 billion Cost of coping with disruption without FB or SU: $13.4 billion Net savings from FB and SU: $6.3 billion We would need 2.2 shocks of this magnitude over a 20-year period to make the flexibility investment worthwhile Even though the system as a whole experiences savings, some supply chain participants (dry mill owners) are actually made worse off by the extra flexibility

  21. FINAL REMARKS • Biofuels are an important piece of the puzzle in the quest for alternative fuels • that would reduce U.S. dependence on petroleum • However, we should think more rigorously about how energy security is obtained, • and how the biofuel supply chain itself can improve resilience. • Demand flexibility is currently limited by the “blend wall”: E10 blends cannot absorb • ethanol volumes much beyond current production levels • With diverse feedstocks and technologies, a major supply/price shock for a single • feedstock may have only a modest effect on retail prices of fuel blends, but could have • pronounced effects on the profitability of biorefineries • Flexibility elements (inventories, FFVs, flexible biorefineries, biomass preprocessing) • reduce price variance but increase average price

  22. SUPPLEMENTARY SLIDES

  23. Approach Effectively Aggregating and Disaggregating Across Different Scales (E.g. for Feedstock Supply Data) POLYSYS DATA, e.g. Census Division 4 FITTED CURVE FOR D4SB MODEL Aggregated to fitted continuous supply curve Model Equilibrium on Fitted Curve • Identify price level corresponding to cumulative production in the original data • Identify counties producing under at model equilibrium for results display and sustainability analysis Disaggregated model solution

  24. Progress: State of development of LR model V0.9 Implementation Complete analytical specification of LR Model, Multi-stage pathways, field to gas-tank Long-run, nonlinear dynamic model, 2010-2030 Depicts 7 stages in the feedstock’s path from farm to biorefinery Census Division based (testing with regions 3 and 4, Rest-of-Country) Four feedstocks (corn, stover, perennial grasses; forest), twenty annual periods (2010-2030) and five conversion processes “Working” stocks representation Biorefinery technology choice Biochemical vs. thermochemical pathways Initial representation flexible biorefineries (flex thermo, flex or dedbiochem) Co-products Allows tracking economic sustainability (based on ethanol price, and co-products and key input prices) and environmental sustainability (e.g., GHG and water footprint) issues. Coupled to basic model of demand markets, vehicle and fuel choice

  25. Issue: Reliability - Variability of Biofuels Supply and Price Gasoline/diesel and biofuels are subject to different long-run forces, and different supply/demand shocks; but are also linked Q: How do gasoline and ethanol prices move in relations to one another, at different points in supply chain (plant-gate to retail)? over the long run and short run? Q: What does this imply for diversification benefits of alternative fuels? Data: IMF/IFS database, Commodity Prices & Indices, Monthly, 1970 to Dec 2008.

  26. U.S. Census Regions and Divisions 1 9 8 4 3 2 5 6 7 ECONOMIC SUSTAINABILITY ECONOMIC SUSTAINABILITY Source: http://www.eia.doe.gov/emeu/reps/maps/us_census.html

  27. E85 Retail Capacity Evolution number of retail stations number of pumps per station estimated maximum throughput per pump Fixed parameters Options to increase E85 throughput increase number of retail stations offering E85 increase number of E85 pumps in stations offering E85 increase utilization factor of existing E85 pumps Annualized cost of new E85 retail infrastructure Annualized cost of new E85 retail infrastructure Costs: $102,000/underground storage tank $15,000/dispenser for pump share=0.12 for 100% load factor

  28. INITIAL ILLUSTRATIVE D4SB MODEL RESULTS: Biorefinery Flexibility Reduces Response Cost To Supply Shocks Scenario: Doubling in the cost of stover in 2020 and 2021 FLEXIBLE BIOCHEMICAL AND THERMOCHEMICAL CONVERSION PROCESSES: STOVER-DEDICATED BIOCHEMICAL CONVERSION PROCESSES:

  29. INITIAL ILLUSTRATIVE MODEL RESULTS: Biorefinery Flexibility Reduces Response Cost To Supply Shocks Scenario: Doubling in the cost of stover in 2020 and 2021 Scenario: Doubling in the cost of stover in 2020 and 2021 Census Division 4. Production from Biochemical and Therrmochemical pathways, stover & perennial grass No feedstock flexibility for Biochemical Complete feedstock flexibility for Biochemical

  30. MATERIAL BALANCE IN REGION R, PERIOD T Q – quantity QIN – flows into storage QOUT – flows out of storage X – exports M – imports YAB - yield of A per unit of B FARM Q biomass,field,r,t ≥ Q biomass,collection,r,t = Q biomass,transport,r,t QIN biomass,storage,r,t DEPOT QOUT biomass,storage,r,t Q biomass,storage,r,t Q biomass,preprocessing,r,t Q biomass,other,r,t BIOREFINERY Q formatted,preprocessing,r,t Q co-product,refining,r,t Q formatted,transport,r,t = Q formatted,refining,r,t Q biofuel,refining,r,t From regions R’ STORAGE/BLENDING TERMINAL M biofuel,transport,r,t Q biofuel,transport,r,t X biofuel,transport,r,t From petroleum sector QIN biofuel,storage,r,t = Q gasoline,refined,r,t Q biofuel,blending,r,t QOUTbiofuel,storage,r,t Q biofuel,storage,r,t = PUMP Q blend,blending,r,t Q blend,distribution,r,t = Q blend,consumption,r,t Q blend,retail,r,t

  31. BASECASE RESULTS: FEEDSTOCK SUPPLY

  32. LOGISTIC DESIGN UNIFORM LOGISTIC DESIGN as a way of reducing risk for biorefineries: by broadening feedstock base so that a given biorefinery will not be captive of local supply by offering a more homogeneous quality that minimizes process adjustment costs Optimal biorefinery size (Thermochemical. Rest of the country. 2020) Conventional logistic design  2.25 million dry tons Pioneer logistic design  20 million dry tons

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