Applications of the Biomass Scenario Model Brian W Bush Presentation at EIA’s “ Biofuels in AEO2013 ” Workshop 20 March 2013 NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
Focus of Biomass Supply-Chain Analysis Deployment Analysis: exploring how rapidly biofuel technologies might be deployed to make a significant contribution to the country’s transportation energy – Generate plausible scenarios – Understand the transition dynamics – Investigate potential market Government Policies Marketplace Structure penetration scenarios Analysis Producer/Consumer exchanges Implications – Analyze prospective Investment Inclusion decisions /scope Evolution of Financial decisions policies and incentives Supply Chain for Biofuels – Identify high-impact drivers and bottlenecks – Study competition for biomass resources Input Scenarios – Assess R&D and deployment strategies Feedstock demand Oil prices – Enable and facilitate focused Learning curves discussion among stakeholders 4
Example of Influences/Feedbacks Financial incentives for Financial attractiveness Land allocated to cellulosics growing cellulosics of growing cellulosics - Feedstock Cellulosic crop Financial incentives for price production ethanol conversion plants - Financial attractiveness of Feedstock biofuel conversion plants demand Biofuel conversion Biofuel capacity supply Biofuel + conversion costs Financial incentives for stations owners to supply KEY Technology maturity ethanol “ “ - = negative ( balancing / - counteracting ) loop “ ” + = positive Biofuel price relative ( reinforcing ) loop to petroleum fuels (for ethanol) station Gas tax + - availability Biofuel demand Biofuel consumers 5
Key Characteristics of BSM Modules SUPPLY CHAIN Feedstock Feedstock Biofuels Biofuels Biofuels End Production Logistics Production Distribution Use Feedstock Logistics Module Vehicle Scenario Module Conversion Module q Multiple logistics stages q Cars and Light Trucks q 15 conversion platforms q Cost breakdowns q Multiple (9 +scenario) vehicle q 4 development stages q Transportation distance technologies q 6 learning attributes q Land eligibility q Fleet vintaging q Cascading learning curves q Vehicle choice scenarios q Project economics q E10/E20/E85 potential q Industry growth and investment dynamics Fuel Use Module Feedstock Supply Module q Non-, occasional, and q 6 Feedstock types frequent users q 10 geographic regions q Relative price/fuel choice q 10+ land uses Distribution Logistics Module dynamics q Farmer decision logic q Distribution terminal focus q Land allocation dynamics q Differential cost structure, based on Dispensing Station Module q New agriculture practices infrastructure (storage and intra/inter- q Markets and prices q Fueling-station economics region transport costs) q Tankage and equipment investment decision q Distribution-coverage effects DYNAMIC MODELS OF SUPPLY INFRASTRUCTURE, PHYSICAL CONSTRAINTS, MARKETS, AND DECISION MAKING POLICIES INCENTIVES EXTERNALITIES 6
BSM Regionalization 7
Categorization of Cropland With residue collection Corn With secondary crop Soy With residue collection Wheat Active Cropland Available Annuals With secondary crop for With residue collection Other Grains Traditional With secondary crop and With residue collection Celluosic Cotton Crops With secondary crop Immature Herbaceous All Cropland Perennial Mature Energy Immature Crops Woody Mature Hay Used as Forage Pasture Growing as Pasture Harvested for Cellulose Immature Planted with Energy Crops Mature Harvested for Cellulose CRP Unharvested Excluded from FSM High value cash crops, etc. 8
Biofuel Pathways in the BSM Biomass Biorefinery Finished Petrochemical Blending at Feedstocks Processing Fuels Refining Refinery Ethanol and Catalytic synthesis (TC) Mixed Alcohols Lignocellulosic Gasification Methanol Synthesis, Syn Gas Biomass Methanol -to-Gasoline Gasoline Fischer -Tropsch synthesis Energy crops Gasoline (herbaceous and Pyrolysis woody) Hydro-processing Diesel Bio-Oils AqueousPhase Jet Reforming Pretreatment & Hydrolysis Residues Fermentation (BC) (herbaceous, Sugars Ethanol woody, urban) Fermentation Butanol Hydrolysis Fermentation Sugars Ethanol Corn Natural Oils Extraction Hydrodeoxygenation (Oilseeds Oils Diesel and Jet and Algae) “Drop In” points for infrastructure - compatible fuels: Processing at biorefinery Optional processing 9
Appropriate Uses of the BSM • The BSM is an excellent tool for generating and evaluating scenarios and relative impacts of cost targets, policy drivers, tipping points, etc. High-level system models such as the BSM cannot provide absolutes to a high degree of precision. Designed to . . . Not Designed to . . . Generate scenarios to explore future Generate x gallons in y years with z biofuel landscapes. dollars investment. Identify areas of potential high Identify specific numerical values of leverage. particular investments. Assess relative merits of technologies Make fine distinctions between potential and logistics in a gross sense, given of technologies. solid technological assumptions. Explore the potential for tipping-point Predict tipping points precisely and pin and lock-in/lock-out dynamics. them to specific times. Build intuition, insight, and knowledge Represent a definitive embodiment of around the supply chain. knowledge. Think through the relative efficacy of Determine recommended policies in different policy prescriptions. isolation. 10
Scenario Analyses Completed Coordinating policies Individual Pricing policies Policy mixes with high benefits for low cost Synergies between volumetric Effects of phasing out supportive and capital-oriented policies policies Influence of Coupling of ethanol tariffs petroleum and Price-stabilizing biofuels prices influence of forest Tradeoffs between grants Most effective points for and crop residues and loan guarantees volumetric subsidies Nature of price Sensitivity of feedstock fluctuations in various Effects of reverse-auctions for Methods for reducing bottlenecks and ethanol production elements of the supply volumetric credits from lack of distribution or dispensing to plant-gate feedstock chain infrastructure prices Conditions for achieving RFS or other targets Extent to which policy Impacts of petroleum exacerbates instabilities price scenarios and Effect of Biomass Crop price shocks Assistance Program Likelihood of boom/bust cycles Conditions under which Differential conversion technologies compete investment in competing conversion System Effects of industrial technologies learning rates characteristics Competing technologies 11
Insights along the Cellulosic Ethanol* Supply Chain * Most of these insights hold for other biofuels in addition to cellulosic ethanol. 12
Policies Implemented in Isolation Are Not as Effective as Certain Policies Implemented in Coordination 15% increase due } to policy synergy Dynamic Interaction: the point-of-use subsidy decreases financial risk for gas station owners, causing more E85 tankage to be installed. The resulting increase in ethanol demand, in conjunction with the point-of-production subsidy, decreases the risk for those wanting to invest in biorefineries. This increased confidence results in more biorefineries being built and increased cellulosic ethanol production. 13
Key Insights from Biofuels Supply-Chain Analyses Four keys to industry development: 1. Profitability at point of production 2. High rates of industry learning 3. An aggressive start in building pilot, demo, and pioneer-scale plants 4. For ethanol, a high level of infrastructure investment to sustain low enough point-of-use prices The “take off” is likely to be wild and wooly: 1. Unstable, higher than anticipated, feedstock prices 2. Boom/bust development of production capacity 3. Potential for biofuel price instability Significant production volumes are feasible. 1. RFS2 volumes are achievable in 2030 with heavy startup subsidies. 2. When subsidies are limited to promoting the Caveat: The results depend on details of the policy, incentive, and subsidy parameters for the scenarios and on a variety of most economically attractive pathway, state-of-technology assumptions; this chart just presents a few production levels can be greater than RFS2 of the many potential scenarios. levels. 3. Technologies with favorable long-term economic cost structures can succeed if subsidies are deliberately designed to overcome initial maturity deficiencies. 14
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